tools_pandas.ipynb

{
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      "source": [
        "**Tools - pandas**\n",
        "\n",
        "*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.*\n",
        "\n",
        "Prerequisites:\n",
        "* NumPy – if you are not familiar with NumPy, we recommend that you go through the [NumPy tutorial](tools_numpy.ipynb) now."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY4jDNGWNwGs"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BO5v-xwNNwGu"
      },
      "source": [
        "First, let's import `pandas`. People usually import it as `pd`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obBBWaMBNwGw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvCEphIgNwG0"
      },
      "source": [
        "# `Series` objects\n",
        "The `pandas` library contains these useful data structures:\n",
        "* `Series` objects, that we will discuss now. A `Series` object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).\n",
        "* `DataFrame` objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).\n",
        "* `Panel` objects. You can see a `Panel` as a dictionary of `DataFrame`s. These are less used, so we will not discuss them here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tM_SG2tNwG3"
      },
      "source": [
        "## Creating a `Series`\n",
        "Let's start by creating our first `Series` object!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x6Ku69NvNwG6",
        "outputId": "39f091f2-9d8f-4e56-fb58-66a9c2e4e313"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    2\n",
              "1   -1\n",
              "2    3\n",
              "3    5\n",
              "dtype: int64"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s = pd.Series([2,-1,3,5])\n",
        "s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2sYnnFDNwHA"
      },
      "source": [
        "## Similar to a 1D `ndarray`\n",
        "`Series` objects behave much like one-dimensional NumPy `ndarray`s, and you can often pass them as parameters to NumPy functions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Nt-9YLaNwHC",
        "outputId": "4804694d-a181-4d24-8bfd-0f8eec1d0e52"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      7.389056\n",
              "1      0.367879\n",
              "2     20.085537\n",
              "3    148.413159\n",
              "dtype: float64"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "np.exp(s)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-vEbU_CNwHD"
      },
      "source": [
        "Arithmetic operations on `Series` are also possible, and they apply *elementwise*, just like for `ndarray`s:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pRhy0gLJNwHF",
        "outputId": "168c2865-1837-4754-db27-0a0c44dea4aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    1002\n",
              "1    1999\n",
              "2    3003\n",
              "3    4005\n",
              "dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s + [1000,2000,3000,4000]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ilSxWA23NwHG"
      },
      "source": [
        "Similar to NumPy, if you add a single number to a `Series`, that number is added to all items in the `Series`. This is called * broadcasting*:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UTukHc4hNwHI",
        "outputId": "8ba4e27a-95b8-417f-9bf3-caa16209a4dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    1002\n",
              "1     999\n",
              "2    1003\n",
              "3    1005\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s + 1000"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cj44iso-NwHK"
      },
      "source": [
        "The same is true for all binary operations such as `*` or `/`, and even conditional operations:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u4GlV-WDNwHL",
        "outputId": "3d43dcc0-58dd-4c02-ffc5-9444740bd094"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    False\n",
              "1     True\n",
              "2    False\n",
              "3    False\n",
              "dtype: bool"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s < 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3K3tD7aANwHN"
      },
      "source": [
        "## Index labels\n",
        "Each item in a `Series` object has a unique identifier called the *index label*. By default, it is simply the rank of the item in the `Series` (starting at `0`) but you can also set the index labels manually:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pgat8UhmNwHP",
        "outputId": "eecef87c-b9ec-41aa-d2a3-017f4b1d36fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice       68\n",
              "bob         83\n",
              "charles    112\n",
              "darwin      68\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
        "s2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oTwaD0ONwHQ"
      },
      "source": [
        "You can then use the `Series` just like a `dict`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4axuRzr2NwHR",
        "outputId": "50c8af11-3f6d-458b-ba6a-59d9c135e607"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s2[\"bob\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7-nHYx_NwHS"
      },
      "source": [
        "You can still access the items by integer location, like in a regular array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "do3sYVWlNwHT",
        "outputId": "c7532589-2f3f-4e37-fdaa-0973c1784ec7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s2[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGKms6ZqNwHU"
      },
      "source": [
        "To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Denx4gfANwHW",
        "outputId": "a0f665f3-360f-49f0-bb96-d7ef0ac231a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s2.loc[\"bob\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPWfH5EzNwHX",
        "outputId": "36db175c-a094-4143-cf9f-8dda1639ee7f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "83"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s2.iloc[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6IrsQsuNwHY"
      },
      "source": [
        "Slicing a `Series` also slices the index labels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "udd7QDuuNwHZ",
        "outputId": "4312435a-b084-499b-f307-59b80fd25251"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "bob         83\n",
              "charles    112\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s2.iloc[1:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8X6gM4ATNwHa"
      },
      "source": [
        "This can lead to unexpected results when using the default numeric labels, so be careful:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VdDh62FbNwHb",
        "outputId": "28411eb6-8432-4d74-b60f-33b96b4d8066"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    1000\n",
              "1    1001\n",
              "2    1002\n",
              "3    1003\n",
              "dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
        "surprise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ogMIGWcNwHe",
        "outputId": "55a58e25-6630-4a4c-9b24-eeba1ecaa736"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2    1002\n",
              "3    1003\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "surprise_slice = surprise[2:]\n",
        "surprise_slice"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWaNGga3NwHf"
      },
      "source": [
        "Oh look! The first element has index label `2`. The element with index label `0` is absent from the slice:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DRTRgDmaNwHg",
        "outputId": "acf78c98-2f31-40ca-e2d8-2af5647e2788"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key error: 0\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    surprise_slice[0]\n",
        "except KeyError as e:\n",
        "    print(\"Key error:\", e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRzfTOEFNwHh"
      },
      "source": [
        "But remember that you can access elements by integer location using the `iloc` attribute. This illustrates another reason why it's always better to use `loc` and `iloc` to access `Series` objects:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ojK2CK9TNwHi",
        "outputId": "d3bdf689-71e3-40bd-88d3-a098223dfdb3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1002"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "surprise_slice.iloc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04oJ1yk4NwHj"
      },
      "source": [
        "## Init from `dict`\n",
        "You can create a `Series` object from a `dict`. The keys will be used as index labels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UIa3jeZYNwHk",
        "outputId": "9e73966a-0b10-4574-8462-205d975a0450"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice     68\n",
              "bob       83\n",
              "colin     86\n",
              "darwin    68\n",
              "dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
        "s3 = pd.Series(weights)\n",
        "s3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cg7M2co3NwHl"
      },
      "source": [
        "You can control which elements you want to include in the `Series` and in what order by explicitly specifying the desired `index`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wn5nJAy1NwHm",
        "outputId": "deb1dc2d-a03d-4eb8-c81d-219a9d0c08ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "colin    86\n",
              "alice    68\n",
              "dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
        "s4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sD_UE7TMNwHn"
      },
      "source": [
        "## Automatic alignment\n",
        "When an operation involves multiple `Series` objects, `pandas` automatically aligns items by matching index labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_GtBNXi3NwHn",
        "outputId": "1d5729eb-70b0-4a90-a0cf-e2d5886e0b8b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
            "Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "alice      136.0\n",
              "bob        166.0\n",
              "charles      NaN\n",
              "colin        NaN\n",
              "darwin     136.0\n",
              "dtype: float64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(s2.keys())\n",
        "print(s3.keys())\n",
        "\n",
        "s2 + s3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_cDJZbONwHo"
      },
      "source": [
        "The resulting `Series` contains the union of index labels from `s2` and `s3`. Since `\"colin\"` is missing from `s2` and `\"charles\"` is missing from `s3`, these items have a `NaN` result value. (ie. Not-a-Number means *missing*).\n",
        "\n",
        "Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xLk-o8PcNwHp",
        "outputId": "f3a2417d-aee9-4f94-df22-4575f3a6316e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "s2 = [ 68  83 112  68]\n",
            "s5 = [1000 1000 1000 1000]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "alice     NaN\n",
              "bob       NaN\n",
              "charles   NaN\n",
              "darwin    NaN\n",
              "0         NaN\n",
              "1         NaN\n",
              "2         NaN\n",
              "3         NaN\n",
              "dtype: float64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s5 = pd.Series([1000,1000,1000,1000])\n",
        "print(\"s2 =\", s2.values)\n",
        "print(\"s5 =\", s5.values)\n",
        "\n",
        "s2 + s5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCL_3FH_NwHq"
      },
      "source": [
        "Pandas could not align the `Series`, since their labels do not match at all, hence the full `NaN` result."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciefFTvNNwHr"
      },
      "source": [
        "## Init with a scalar\n",
        "You can also initialize a `Series` object using a scalar and a list of index labels: all items will be set to the scalar."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JD32-j8JNwHs",
        "outputId": "336caba9-1360-418e-e79f-7a1cd08e9442"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "life          42\n",
              "universe      42\n",
              "everything    42\n",
              "dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
        "meaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LspHGkefNwHt"
      },
      "source": [
        "## `Series` name\n",
        "A `Series` can have a `name`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n14PQgwsNwHu",
        "outputId": "8befca5a-86d5-411a-8f9c-263252f61a44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "bob      83\n",
              "alice    68\n",
              "Name: weights, dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
        "s6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZaIFnXlNwHv"
      },
      "source": [
        "## Plotting a `Series`\n",
        "Pandas makes it easy to plot `Series` data using matplotlib (for more details on matplotlib, check out the [matplotlib tutorial](tools_matplotlib.ipynb)). Just import matplotlib and call the `plot()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "KB3bhUcNNwHw",
        "outputId": "78d34108-2dd0-45a7-c337-a7f13eb18ac2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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g6qdrvY4jYUjlvp9VRaXMzS/hwlHd8GseGfHYdcf3YtyQjtzzzgo+WFbkdRwJMyr3/cyY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\ni4hEIJW7iEgEUrmLiEQglbuISAT6X/s00irHqiZcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10b5bbe80>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
        "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
        "s7.plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5cSoRIVNwHx"
      },
      "source": [
        "There are *many* options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent [Visualization](http://pandas.pydata.org/pandas-docs/stable/visualization.html) section of pandas' documentation, and look at the example code."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fxehrgWONwHx"
      },
      "source": [
        "# Handling time\n",
        "Many datasets have timestamps, and pandas is awesome at manipulating such data:\n",
        "* it can represent periods (such as 2016Q3) and frequencies (such as \"monthly\"),\n",
        "* it can convert periods to actual timestamps, and *vice versa*,\n",
        "* it can resample data and aggregate values any way you like,\n",
        "* it can handle timezones.\n",
        "\n",
        "## Time range\n",
        "Let's start by creating a time series using `pd.date_range()`. This returns a `DatetimeIndex` containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YcA3b3NZNwHy",
        "outputId": "ec77bccb-83af-412b-e80e-0d171944d907"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
              "               '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
              "               '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
              "               '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
              "               '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
              "               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
              "              dtype='datetime64[ns]', freq='H')"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n",
        "dates"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jm_4aWfANwHz"
      },
      "source": [
        "This `DatetimeIndex` may be used as an index in a `Series`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7VFHeipZNwH0",
        "outputId": "50855c02-f6a5-46ba-efb6-05072347fc63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00    4.4\n",
              "2016-10-29 18:30:00    5.1\n",
              "2016-10-29 19:30:00    6.1\n",
              "2016-10-29 20:30:00    6.2\n",
              "2016-10-29 21:30:00    6.1\n",
              "2016-10-29 22:30:00    6.1\n",
              "2016-10-29 23:30:00    5.7\n",
              "2016-10-30 00:30:00    5.2\n",
              "2016-10-30 01:30:00    4.7\n",
              "2016-10-30 02:30:00    4.1\n",
              "2016-10-30 03:30:00    3.9\n",
              "2016-10-30 04:30:00    3.5\n",
              "Freq: H, dtype: float64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series = pd.Series(temperatures, dates)\n",
        "temp_series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADy71KY5NwH1"
      },
      "source": [
        "Let's plot this series:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "olsb_-KXNwH2",
        "outputId": "5db76913-acbe-4c79-c66b-c59b730b01c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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D0mURcU/FOT0/1ZZU+afaZWaS2sqsVteXOi/1eJa1vZXiVdsAEMCdwH8Ap9XwfDgA+CDw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\njBu3mVlm3LjNzDLz/wEJ5lbocofr5wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10b68fda0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "temp_series.plot(kind=\"bar\")\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCQqHXJBNwH3"
      },
      "source": [
        "## Resampling\n",
        "Pandas lets us resample a time series very simply. Just call the `resample()` method and specify a new frequency:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9N7v5SCiNwH3",
        "outputId": "26444385-4906-4594-deb0-0ec552be5e00"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatetimeIndexResampler [freq=<2 * Hours>, axis=0, closed=left, label=left, convention=start, base=0]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_freq_2H = temp_series.resample(\"2H\")\n",
        "temp_series_freq_2H"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVKaWc9ONwH5"
      },
      "source": [
        "The resampling operation is actually a deferred operation, which is why we did not get a `Series` object, but a `DatetimeIndexResampler` object instead. To actually perform the resampling operation, we can simply call the `mean()` method: Pandas will compute the mean of every pair of consecutive hours:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J9XEhCoCNwH6"
      },
      "outputs": [],
      "source": [
        "temp_series_freq_2H = temp_series_freq_2H.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIAPRBtBNwH7"
      },
      "source": [
        "Let's plot the result:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zoC3YmPtNwH7",
        "outputId": "ef7d6cba-9f94-4c47-8e0e-08c5d9a3b486"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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f9P8vzwD2Ah4xWqrhqufP+BlX5fzNZ2/25KSks4HX275kwbI1dE/v/g+2dxkt\n3ACS9gFu84LriBese7ztfxgh1mAzkD/jZ0SV81fI3nJxN7/xol0ZPzHLmi3uiIhYWsvHuCMiYgkp\n7oiIYlLcERHFpLgjIor5/4Xj0noYGwK1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10b739208>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "temp_series_freq_2H.plot(kind=\"bar\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMGWP_pxNwH8"
      },
      "source": [
        "Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of `5.1` at 6:30pm, and `6.1` at 7:30pm. After resampling, we just have one value of `5.6`, which is the mean of `5.1` and `6.1`. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2e5kxb1UNwH9",
        "outputId": "0872b776-1eb3-46ce-a4e2-9404984780ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 16:00:00    4.4\n",
              "2016-10-29 18:00:00    5.1\n",
              "2016-10-29 20:00:00    6.1\n",
              "2016-10-29 22:00:00    5.7\n",
              "2016-10-30 00:00:00    4.7\n",
              "2016-10-30 02:00:00    3.9\n",
              "2016-10-30 04:00:00    3.5\n",
              "Freq: 2H, dtype: float64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_freq_2H = temp_series.resample(\"2H\").min()\n",
        "temp_series_freq_2H"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7VyXuM1NwH-"
      },
      "source": [
        "Or, equivalently, we could use the `apply()` method instead:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FV4OaCC6NwH-",
        "outputId": "aa3892c5-1acc-45f2-9d74-2176f736ccaf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 16:00:00    4.4\n",
              "2016-10-29 18:00:00    5.1\n",
              "2016-10-29 20:00:00    6.1\n",
              "2016-10-29 22:00:00    5.7\n",
              "2016-10-30 00:00:00    4.7\n",
              "2016-10-30 02:00:00    3.9\n",
              "2016-10-30 04:00:00    3.5\n",
              "Freq: 2H, dtype: float64"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n",
        "temp_series_freq_2H"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOYR6zu1NwH_"
      },
      "source": [
        "## Upsampling and interpolation\n",
        "This was an example of downsampling. We can also upsample (ie. increase the frequency), but this creates holes in our data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t8W8R9w-NwH_",
        "outputId": "dc1b772a-0009-493c-be0a-96a45762256d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00    4.4\n",
              "2016-10-29 17:45:00    NaN\n",
              "2016-10-29 18:00:00    NaN\n",
              "2016-10-29 18:15:00    NaN\n",
              "2016-10-29 18:30:00    5.1\n",
              "2016-10-29 18:45:00    NaN\n",
              "2016-10-29 19:00:00    NaN\n",
              "2016-10-29 19:15:00    NaN\n",
              "2016-10-29 19:30:00    6.1\n",
              "2016-10-29 19:45:00    NaN\n",
              "Freq: 15T, dtype: float64"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
        "temp_series_freq_15min.head(n=10) # `head` displays the top n values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPK0h9etNwIA"
      },
      "source": [
        "One solution is to fill the gaps by interpolating. We just call the `interpolate()` method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Hed51DBvNwIB",
        "outputId": "b2622ae2-e202-4c57-bc0f-eb3888082866"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00    4.400000\n",
              "2016-10-29 17:45:00    4.452911\n",
              "2016-10-29 18:00:00    4.605113\n",
              "2016-10-29 18:15:00    4.829758\n",
              "2016-10-29 18:30:00    5.100000\n",
              "2016-10-29 18:45:00    5.388992\n",
              "2016-10-29 19:00:00    5.669887\n",
              "2016-10-29 19:15:00    5.915839\n",
              "2016-10-29 19:30:00    6.100000\n",
              "2016-10-29 19:45:00    6.203621\n",
              "Freq: 15T, dtype: float64"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
        "temp_series_freq_15min.head(n=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sTTlIioiNwIE",
        "outputId": "efd08d2e-9a9f-4a14-cbe8-fdb02f90d762"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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+Z0zfAPQ0xux2Pt8GtDHGHCpgWUOBoQDVqlVr+ddffxUqJ6WUOq8j\nO2Hlu7B6OmSmQeUWpDS5g3dTmvJh4n7SMrJpWKkUg9vH0jeuMsEBfnZnfEEutE+/UEVfRPoAVxhj\nRohIZ1ws+vnpgVylVLE4eRzWfggrJkLKHxBRiewWt/OVX1cmrk5n0/5jlAkNYFDratzUtjpVIkPs\nzvicirrojwFuBnKAYKAU8Jkx5qZ883wDPG2MWS4i/sB+INqcJ6AWfaVUscrLg23fw68TYNsPAJiK\nTdkTfSmzUuvz7p+R5OGge8OK3No+lrY1y3rkWT9FWvTPCNSZglv6I4EmxphhIjIIGGCMufZ8y9Oi\nr5SyTfIW2DQfti6CXSvA5JEbUpaNoa2ZmVqfrzMaUrliJW5tH0v/uCqEBHpO148tRV9EngUSjDFf\niEgwMBNoDqQCg4wxf55veVr0lVIeIT3VavlvXQRbv4WMVPJwsMmvLotONmS1fwsaXtKZG9vVomrZ\nULuzLb6i725a9JVSHicvF/YkwtZFmG0/wJ7VCIajJpTleY04WKEDDTr2p2WzONu6frToK6VUUUlP\nhe0/kb7xW3K2fEeprAMA7PSrRtn2gwlvfRNEVCjWlLToK6VUcTCGkwc2s2HJXPhtLi1lM0b8kDrd\nofmNUKcH+AcWeRpa9JVSqpht2JPGM9Pm0T3re24JXU5QxkEILQdNB1kbgAqNiiy2Fn2llLLB/rRM\n7pi+ii37DjOxXRrdMr+FTQsgLxvqXQG9XoTIam6PWywDrimllPqniqWD+eSudnSqX4k7finLMyEP\nk/vAJuj6BPy5GMa3gaWvQk6WLflp0VdKKTcLC/LnnZtbcUfHGry3bAdD52znRJv/g5EroVZX+O5p\neCcediwt9ty06CulVBHwcwhP9GnIf/s35sfNB7lm4nL2SRQMmgXXfwzZ6TCtN8wdBseTiy0vLfpK\nKVWEbm5bnamDL2Fnajr9xy9jw540qNcTRqyAjg/A+jnwVitInA7FcIxVi75SShWxzvXKM2d4O/wd\nDq6ZuJxvfz8AgaFw2VMwfJk19POX91rdPkVc+LXoK6VUMahfsRRzR7anboVwhs5MYPLPf1rj9kfX\ng1u+gFa3w7LXYOGjRVr4tegrpVQxKR8RzEdD29GzUUWe+2ojT3y+gZzcPOsOX71fgTbDYcXb8NUD\n1uifRcC/SJaqlFKqQCGBfoy/oQX/+2YzE3/axs7UDMbf0JyI4ADoOQb8g6wWf24WXPkGONw7kqe2\n9JVSqpg5HMIjveozdkATfvnjEFe/vZzdh9NBBC57Gjo9Amvet87syc1xb2y3Lk0ppdQFG9S6GtNu\na83etAz6j/+FpF1HrMLf5VHo9iSs/wQ+vR1ys90WU4u+UkrZqGOdKOaOaE9IoINBk5bz9fp91oT4\nB6HHC/D75/DJLZBz0i3xtOgrpZTNapePYO6IDjSsVIrhs1Yz8adt1pk97UbCFS/B5gVWV48bzurR\noq+UUh4gKjyID+5sy5XNKjP260088ul6snPzoPWd1rg9v30G62e7HEeLvlJKeYjgAD9evy6Oe7rW\n5uOEXdw6dSVp6dnQ8X6o2ga+GgVpe1yKoUVfKaU8iMMhPNi9Hi9d04xVO1IZ8PYydh4+CVdNhLwc\n+HyES+fwa9FXSikPdHXLGGbe0YZDx7PoP2EZicciocdz1vDMCVMKvVwt+kop5aHa1izH3BHtKRXs\nz/XvruAL/x5Q+zJY9AQc2lqoZWrRV0opD1YzOpy5IzoQFxPJvR8lMbXsg5iAYJh7V6Eu3NKir5RS\nHq5MWCAzh7RmQPMqPLvkMDPK3gt7Eq07cF0kHXtHKaW8QJC/Hy9f24zYqDCe+hZqRXamw09jkTqX\nQ+W4C15OoVv6IhIsIitFZK2I/CYizxQwz2ARSRaRJOdjSGHjKaWUrxMR7u1Wh9cHxXH/sZs4ZEqR\nNedOyM684GW40r1zEuhqjGkGxAE9RaRtAfN9bIyJcz4muxBPKaUU0C+uCm/f2Y2nZQSBqVvYN/c/\nF/zeQhd9YznufBrgfBT9vb6UUkrRKrYsD40cwecBvajw29QLfp9LB3JFxE9EkoCDwLfGmBUFzDZQ\nRNaJyBwRqXqW5QwVkQQRSUhOLr4bBCullDerXi6MziMnklD2igt+jxg3DOAjIpHAXOAeY8yGfK+X\nA44bY06KyF3AdcaYrudaVqtWrUxCQoLLOSmllC8RkURjTKvzzeeWUzaNMUeAH4GeZ7yeYow5NR7o\nZKClO+IppZQqHFfO3ol2tvARkRDgcmDTGfNUyve0L7CxsPGUUkq5zpXz9CsB00XED2vj8YkxZr6I\nPAskGGO+AO4Vkb5ADpAKDHY1YaWUUoXnlj59d9I+faWUunjF2qevlFLKO2jRV0opH6JFXymlfIjH\n9emLSDLwVyHfHgUccmM6nh7Xzti6zr4R29fi2hnb1bjVjTHR55vJ44q+K0Qk4UIOZJSUuHbG1nX2\njdi+FtfO2MUVV7t3lFLKh2jRV0opH1LSiv4kH4trZ2xdZ9+I7Wtx7YxdLHFLVJ++UkqpcytpLX2l\nlFLnoEVfKaV8iFcVfRGJEZHSNsU+7/mvJSyuK4PxuRq7nE1xy9sR1xk7xMbYttQBEQmyKW6YHXGd\nsaOdP8WuHLyi6ItIqIi8DHyDNbLnzc7Xi/yDc8Z+FZgvIo+ISFfn635FHDdYRN4GfhSRZ/PFLdK/\nmYiEi8g7wJDiLkTO2K8CX4nIcyLSpRjjvgwsEJFXRKSX8/Xi+H6Fi8hbwGQR6VlcjRoRiRCRF0Wk\nrDEmrzgLf76/83gRuUJEShVj3NeAqSIysLg38iIyDFgnIk2MMcauja1XFH3gCSDaGNMImAHcCdZ9\neosh9n+ASKwbxKwHZopIkDEmt4jj3g6UBzoB27G+qMHGmLyiCigiZYBXsNa1BdC4qGIVELsu1t3X\ncrHWPRnrsy+OuJ9gDTN+FbADGArF9v16DQgEPgOuBx4p6oAi0gxYCNyPdXOjYiMi3YHlQCawFBgC\n9CqGuH2AZUA28CFwF8V0U6d8jYdg4DDwGEBR/i+fi0cXfRHxF5FgIASY53y5ArDw1A1aimpr6Ywd\n6ow33hhz2BjzFdaXdVxRxBaRwDNeWu68+9h7WP8oLzjnc2sLNF/ck8BbQFMgHYgv6q6WfLFPAJOM\nMaOMMb8DC4B9IhJTxHFTgP8zxtxnjNkFlMLauwpyzuf279epv5+IRAGVgQeMMZ9ibXAricid7o6Z\nPy5wFHjeGBMIdBSRDs7WfpHtveaLfQx4yRjzqDFmGrAZqHfGPO6Me+rvtx24wxgz2hgzD2u4g6Pu\njncWDudnWwYYDpQRkRuc+RVpj0GByRR3wPMRkXoi8j8AY0yOMSYTa+t4hYgsB0YDZYGVzt2kPHd9\nWQqInY61db5WREqLSDWs1slVIhLrrtgiUkdEpgIvi0gb58tBWGNxnDLaGbeWc9ewKOJmGWPWGWPS\nsDayzYA4V+NcYOy9wLx86xUK1DfG7C7iuMeMMVucu/5PA8OAhs5cqrj5+1VfRCZi3VyolDHmEJCH\nc88V685zc4E+IlLWHTHPEnc71u1NAcYAbwMUxd5rAbGXAx+KSIBzlk1AOWd8t+1ZFRD3N2NMglh3\n/PsaaOucdq2IhLsrbv7YInKfiEQYY3Kdn204VqNiAjBMRGKxGrTFyqOKvoj0xtrNHSUi+XdzXwCe\nAnYDTY0xo4CpwEvgni/LOWI/hnWXsHeAr7F2h2dj7Za6HFtEhgOfA4nAAeAeEWnujNFbRBo54+x2\nzvefIoo7EuhwaroxZjGwC+ji7tb2WWJfaozJzrdeZbFagUUdt51z8gngS2NMjDFmmDP2RHDb96sG\n8D6wDWtj+raIxGHtNfYQkTLO+0mvw2qVtnA15lnijheRNsaYDABjzKtAoLO/2a0KiP2WM3aWMSbb\nOVtHYEsRxm16Kq5zcirwgTGmJjAFaA/0L6LYpz7vU9+xQOBHY8znWHt4SUDD4u7b96iij/WPeCNQ\nF3hYRCKcr+fw9wh06c7XJgI54r4j8QXGNsbsBG7D2uh0Msb8DOwDNoBbdkkPAPcZY8Zjtbr8gVrO\nIv+lM5cKznkXUvgRSM8XNwir0OY/c2cWEA00FpF7RaRpMcQ+tbvbEPjN+doNYvW9uztu4Km4xpKY\nb95PgV3ivrOY6gOHjDHjsPqTN2MVm0xgLfCoM4/tQCzWRqgo4m7FakzE5pvnPuBxABG5NN/3zd2x\n/8gf29naL4Nzr0NE2ojzvttFFLeWs9U9E8AYswjreN0xN8Q8V+w+Yh003g98KiLrsfZwdgOJxd23\n71FF3xiTAGwyxvyBVeBO7XYarAJwKVZLeADwEbDKGOOWf46zxRYRf2NMDrDFGHPIudXui3Wg0R2t\nwC+BxSIS6NwFPIh1ABfgSawDm0+JyBDgRayWijucGffAqbjO9cUYsxFrI/QRcCuQVQyxT3UxdASi\nRWQu1sY4u8AluRY3/2d9moi0wtq7XH/qs3CDDUCmiNR3tnK/xurCqot1+X1/ERkgIm2xNrTu6t8+\nM+4CZ9z4UzMYY74BjopIFvB/WF1ORRn7Uuf0UxvUliKyCKtxVZRx2+efydmIqYF7h1EuKHYQ0AdI\nw9rA3GGM6YPVVTzajbEvjDGm2B9YW3dHvudy5u9YfV9HgEvyTeuEdSbPYuC6Yo4djLXl3gLc6M64\nZ8z3PdYexannFbE2Mh8Vc1wBegB7gBvc/VmfJ3YwVus3Ebi2GONGAW8WNq5zGaXPeH7qO1Uba+9i\nSL5p9wNPOX/vD4zFOkPspiKO+3/AE87fw7C6MLe78D9V2NgdsTYw3wODiiMuVkO3BtYxq18LE7cQ\nsR/AKu5nvie8MLFdfRR/QOsLtgl4GRh+lnn8nD+fwOoDA+s0wgAbY/sBlYswrgPr2MFXzlgOoI0r\n61zIuOKM6+fKl9KV2M5pV9kUt70L6/wMVvfJC6eKCeCfb/oQrONQ7ZzP2wIbXPlOuxB3Xb7pHYs5\n9nrn72HAg8W9zlgHTwfb+Hn7ufo3d+n7UqzB4Fqs3Z3SWFvbzUB8AfPlb33nYO0SvYLVAiyw1VbE\nsV8FgooyrnPe+lh9yTcAq7HO2Q4ozDq7GPdRXNvYuBL7scL+U7gY9z/5/3ELEbsP8ANQBauRsB/r\n2Aw49zqAalgtvgVYZ3IMwjqYGGpTXJdami7GDvO2uHb+nd35KPoA+b5YwL1Y5ySfev4BsASoVMD7\nooB3gTVAB2+K7ULcYVi7ux9jnc3iFXF9eJ0j8/1+FTAu3/MXgNkFvEewztiZh9X/29pb4uo6F3/s\nongU3YKtc2/HY7Wmrsc6kHITMA3rIFIQ8DqQgHNXnn/2w/pT+GJvS2w3xK0LDPWWuD68zmWdsb8G\n7sA6GNzXGTvwVCyskw96nvpO5Xu/YF1h7hVxdZ2LP3ZRPorqatbLsU7DOoB12l83YDBWy2sb1gHR\nX7EOWr2F1erC5Dt1yVgXRy3zlthuirvFGHNRN1KwK66dsW1e5x5YZ3cdxmrJdQMuxyoMTYCu+WK9\nifPsDJPvLCBjSfaGuLrOxR+7qBXVSIqHgJeNMdMBRKQ6EGOsqxufw2qlBRtjdjtPj0t0zucwrp+z\naldsX4trZ2w713k71kHA353LvAVINsZkizWI2CgR+c1YQzr8BNR3niLq6umudsW1M7YvrnORc7no\ni4gYY+3LnGKMWSMiW/L9kyXjvJzfGGNEJNX5D9oQq09soXPaRf1D2hXb1+LqOv8j9hbntGisazla\nYl3R2tAY85rz3O+nRGQV1gG81RdbCOyKq+tc/LFtYVzoG8LZr5Xv+dnOh/4f+U7NwuoHuxTYSL4D\nb94Q29fi6joXHBvrat4rnb83xjrwfzvW2VadgZnAvd4SV9e5+GPb9Sj8G+EerLEjngX6nvrA+OfB\nslPnQE8D2jpfuxzr0udACn8apC2xfS2urvPZYxfwnheBm/M9P+u8nhZX17n4Y9v5KNSBXBHphHWm\nxO3A78ATYg3PaoxzF1pE6hnrUvcArMvK40TkW6xzqcVYgy6d9JbYvhZX1/mcseue8Z5LsPYs9px6\nzVx8V5Itce2M7Yvr7BEuZgvB3xcfDASeyff6cP6+2q0K1pABn2IdUIvDOh96EYW8ytLO2L4WV9f5\ngmNHYQ2M9g3wC9Dfm+LqOhd/bE95XMiH5A+MAqrme+1qnEMU5HttLdbVje2Bx86Ydl8h/0C2xPa1\nuLrOLsUuzJgxtsTVdS7+2J74ON+H1QTrEvUDwIdnTNvEP/u2+gDzz5gnsNCJ2RTb1+LqOhc6dqGG\nqbArrq5z8cf21Mf5+vQPAW9gjVMSK9b9LU95AHhOrNsZgnXno80iEiAiDudpUK6cwmRXbF+La2ds\nb17nwg71bFdcO2P74jp7rFPDgZ59BpEQY0yGiNwFXG+M6Zxv2jSs+6p+B1wDpBlj3HZ/T7ti+1pc\nO2PrOus6l9R19lgXsZsUgnWRy735XisN9AbmAP8tqt0Ru2L7WlxdZ11nXeeiie1Jj4v90HoAK5y/\nN8E5mBAu9K16emxfi6vrrOus61yyHxd1nr6xbqt2WEROYt0dxuF8vcgvP7Yrtq/FtTO2rrOuc1HG\ntTu2x7iILaQDeA7rxtx3FueWya7YvhZX11nXuaTGtTu2Jz3OeyA3PxHpBfxgCnGlo6vsiu1rce2M\nretcvHSdfdNFFX2llFLerUhuoqKUUsozadFXSikfokVfKaV8iBZ9pZTyIVr0lVLKh2jRV0opH6JF\nXymlfMj/A19hNXiBHe8QAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10b8b8710>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "temp_series.plot(label=\"Period: 1 hour\")\n",
        "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwmnGgamNwIF"
      },
      "source": [
        "## Timezones\n",
        "By default datetimes are *naive*: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone *aware* by calling the `tz_localize()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92ikriq6NwIF",
        "outputId": "901be525-1282-4f62-a3e2-0ff35c19bcbc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 17:30:00-04:00    4.4\n",
              "2016-10-29 18:30:00-04:00    5.1\n",
              "2016-10-29 19:30:00-04:00    6.1\n",
              "2016-10-29 20:30:00-04:00    6.2\n",
              "2016-10-29 21:30:00-04:00    6.1\n",
              "2016-10-29 22:30:00-04:00    6.1\n",
              "2016-10-29 23:30:00-04:00    5.7\n",
              "2016-10-30 00:30:00-04:00    5.2\n",
              "2016-10-30 01:30:00-04:00    4.7\n",
              "2016-10-30 02:30:00-04:00    4.1\n",
              "2016-10-30 03:30:00-04:00    3.9\n",
              "2016-10-30 04:30:00-04:00    3.5\n",
              "Freq: H, dtype: float64"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
        "temp_series_ny"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPoPiNRFNwIG"
      },
      "source": [
        "Note that `-04:00` is now appended to all the datetimes. This means that these datetimes refer to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) - 4 hours.\n",
        "\n",
        "We can convert these datetimes to Paris time like this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mFYwbiScNwIG",
        "outputId": "414d82f7-416d-412b-ea08-05b82678d2da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 23:30:00+02:00    4.4\n",
              "2016-10-30 00:30:00+02:00    5.1\n",
              "2016-10-30 01:30:00+02:00    6.1\n",
              "2016-10-30 02:30:00+02:00    6.2\n",
              "2016-10-30 02:30:00+01:00    6.1\n",
              "2016-10-30 03:30:00+01:00    6.1\n",
              "2016-10-30 04:30:00+01:00    5.7\n",
              "2016-10-30 05:30:00+01:00    5.2\n",
              "2016-10-30 06:30:00+01:00    4.7\n",
              "2016-10-30 07:30:00+01:00    4.1\n",
              "2016-10-30 08:30:00+01:00    3.9\n",
              "2016-10-30 09:30:00+01:00    3.5\n",
              "Freq: H, dtype: float64"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
        "temp_series_paris"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qyt0muVKNwIH"
      },
      "source": [
        "You may have noticed that the UTC offset changes from `+02:00` to `+01:00`: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dmcL0lVkNwII",
        "outputId": "28caa356-bffc-44f9-ecb4-3b3aec44f67d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 23:30:00    4.4\n",
              "2016-10-30 00:30:00    5.1\n",
              "2016-10-30 01:30:00    6.1\n",
              "2016-10-30 02:30:00    6.2\n",
              "2016-10-30 02:30:00    6.1\n",
              "2016-10-30 03:30:00    6.1\n",
              "2016-10-30 04:30:00    5.7\n",
              "2016-10-30 05:30:00    5.2\n",
              "2016-10-30 06:30:00    4.7\n",
              "2016-10-30 07:30:00    4.1\n",
              "2016-10-30 08:30:00    3.9\n",
              "2016-10-30 09:30:00    3.5\n",
              "Freq: H, dtype: float64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
        "temp_series_paris_naive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7TFV2O7aNwII"
      },
      "source": [
        "Now `02:30` is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9GzG0RSdNwIJ",
        "outputId": "d0e72d71-e9c5-4e6a-f762-f7e95662aba2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pytz.exceptions.AmbiguousTimeError'>\n",
            "Cannot infer dst time from Timestamp('2016-10-30 02:30:00'), try using the 'ambiguous' argument\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
        "except Exception as e:\n",
        "    print(type(e))\n",
        "    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKqsPrnoNwIK"
      },
      "source": [
        "Fortunately using the `ambiguous` argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hbobXTYDNwIL",
        "outputId": "0a688b91-718e-4b56-b56c-01d308b9394f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-10-29 23:30:00+02:00    4.4\n",
              "2016-10-30 00:30:00+02:00    5.1\n",
              "2016-10-30 01:30:00+02:00    6.1\n",
              "2016-10-30 02:30:00+02:00    6.2\n",
              "2016-10-30 02:30:00+01:00    6.1\n",
              "2016-10-30 03:30:00+01:00    6.1\n",
              "2016-10-30 04:30:00+01:00    5.7\n",
              "2016-10-30 05:30:00+01:00    5.2\n",
              "2016-10-30 06:30:00+01:00    4.7\n",
              "2016-10-30 07:30:00+01:00    4.1\n",
              "2016-10-30 08:30:00+01:00    3.9\n",
              "2016-10-30 09:30:00+01:00    3.5\n",
              "Freq: H, dtype: float64"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mNzBQKuNwIN"
      },
      "source": [
        "## Periods\n",
        "The `pd.period_range()` function returns a `PeriodIndex` instead of a `DatetimeIndex`. For example, let's get all quarters in 2016 and 2017:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IlDqSHTVNwIO",
        "outputId": "459b6cd4-f1ea-4ab5-b5c9-5f22a762137f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
              "             '2017Q3', '2017Q4'],\n",
              "            dtype='period[Q-DEC]', freq='Q-DEC')"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
        "quarters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UZm40vixNwIP"
      },
      "source": [
        "Adding a number `N` to a `PeriodIndex` shifts the periods by `N` times the `PeriodIndex`'s frequency:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hfzN1ZyNNwIQ",
        "outputId": "3ff11952-2461-4d9d-f31e-bc54cb3e4f5f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
              "             '2018Q2', '2018Q3'],\n",
              "            dtype='period[Q-DEC]', freq='Q-DEC')"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quarters + 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVzPc9ZxNwIR"
      },
      "source": [
        "The `asfreq()` method lets us change the frequency of the `PeriodIndex`. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q6XZJO4ONwIR",
        "outputId": "2c35f246-9480-43bc-acdc-31adb56844db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
              "             '2017-09', '2017-12'],\n",
              "            dtype='period[M]', freq='M')"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quarters.asfreq(\"M\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-5nTqjmNwIS"
      },
      "source": [
        "By default, the `asfreq` zooms on the end of each period. We can tell it to zoom on the start of each period instead:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ph-hkAuUNwIS",
        "outputId": "83f5ff56-4609-46c4-8717-e1a32197bf86"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
              "             '2017-07', '2017-10'],\n",
              "            dtype='period[M]', freq='M')"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quarters.asfreq(\"M\", how=\"start\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82NMdV3FNwIT"
      },
      "source": [
        "And we can zoom out:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dFoHBhURNwIU",
        "outputId": "5c8820aa-2f44-4a4d-c85e-732923727cc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quarters.asfreq(\"A\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysE4g06ANwIU"
      },
      "source": [
        "Of course we can create a `Series` with a `PeriodIndex`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L72VobADNwIV",
        "outputId": "e07452b6-30ea-42be-d8db-5f7db60b3bf4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016Q1    300\n",
              "2016Q2    320\n",
              "2016Q3    290\n",
              "2016Q4    390\n",
              "2017Q1    320\n",
              "2017Q2    360\n",
              "2017Q3    310\n",
              "2017Q4    410\n",
              "Freq: Q-DEC, dtype: int64"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)\n",
        "quarterly_revenue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ul4LjACrNwIV",
        "outputId": "4a5ee2dc-b773-4bb0-8f92-4d24b3d61f0f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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H6qLJI0lNjAnoRd4ydwM/fa2EOWPS+NqCMwK2X38REX584Xia2zr57btOq8M5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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10d315e80>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "quarterly_revenue.plot(kind=\"line\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DcB4bAjJNwIW"
      },
      "source": [
        "We can convert periods to timestamps by calling `to_timestamp`. By default this will give us the first day of each period, but by setting `how` and `freq`, we can get the last hour of each period:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rlK4YKhcNwIW",
        "outputId": "3fd586d0-f069-4b93-ae6b-2d83751ea895"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016-03-31 23:00:00    300\n",
              "2016-06-30 23:00:00    320\n",
              "2016-09-30 23:00:00    290\n",
              "2016-12-31 23:00:00    390\n",
              "2017-03-31 23:00:00    320\n",
              "2017-06-30 23:00:00    360\n",
              "2017-09-30 23:00:00    310\n",
              "2017-12-31 23:00:00    410\n",
              "Freq: Q-DEC, dtype: int64"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n",
        "last_hours"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiRvtIHnNwIX"
      },
      "source": [
        "And back to periods by calling `to_period`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LF0CmpVJNwIY",
        "outputId": "f108600d-a6a0-4342-e21b-d47c99f9b30c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2016Q1    300\n",
              "2016Q2    320\n",
              "2016Q3    290\n",
              "2016Q4    390\n",
              "2017Q1    320\n",
              "2017Q2    360\n",
              "2017Q3    310\n",
              "2017Q4    410\n",
              "Freq: Q-DEC, dtype: int64"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "last_hours.to_period()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnoSNi07NwIY"
      },
      "source": [
        "Pandas also provides many other time-related functions that we recommend you check out in the [documentation](http://pandas.pydata.org/pandas-docs/stable/timeseries.html). To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0YIqvqvENwIZ",
        "outputId": "d5bdea18-b78a-41c6-defa-007d2a27cc3a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
              "             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
              "             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
              "             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
              "            dtype='period[H]', freq='H')"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
        "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
        "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
        "last_bdays.to_period(\"H\") + 9"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOMUxXZiNwIZ"
      },
      "source": [
        "# `DataFrame` objects\n",
        "A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see `DataFrame`s as dictionaries of `Series`.\n",
        "\n",
        "## Creating a `DataFrame`\n",
        "You can create a DataFrame by passing a dictionary of `Series` objects:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QoOVm70NwIa",
        "outputId": "af01b6a3-48f2-4eb9-8cbf-5b820d34657e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "alice         1985       NaN   Biking      68\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people_dict = {\n",
        "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
        "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
        "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
        "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
        "}\n",
        "people = pd.DataFrame(people_dict)\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BuoWQ34ZNwIe"
      },
      "source": [
        "A few things to note:\n",
        "* the `Series` were automatically aligned based on their index,\n",
        "* missing values are represented as `NaN`,\n",
        "* `Series` names are ignored (the name `\"year\"` was dropped),\n",
        "* `DataFrame`s are displayed nicely in Jupyter notebooks, woohoo!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJYRxw7UNwIe"
      },
      "source": [
        "You can access columns pretty much as you would expect. They are returned as `Series` objects:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kxm0goSWNwIf",
        "outputId": "14464719-95b5-43d5-82e4-8dab8069da55"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice      1985\n",
              "bob        1984\n",
              "charles    1992\n",
              "Name: birthyear, dtype: int64"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people[\"birthyear\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfqpBuSwNwIg"
      },
      "source": [
        "You can also get multiple columns at once:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LImshE6_NwIg",
        "outputId": "1c4ffde2-84bd-4e84-8895-73d5adc42a56"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear    hobby\n",
              "alice         1985   Biking\n",
              "bob           1984  Dancing\n",
              "charles       1992      NaN"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people[[\"birthyear\", \"hobby\"]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6RdSmD1NwIh"
      },
      "source": [
        "If you pass a list of columns and/or index row labels to the `DataFrame` constructor, it will guarantee that these columns and/or rows will exist, in that order, and no other column/row will exist. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p6-rsblJNwIh",
        "outputId": "b2720201-b06c-42ae-98c0-56c941a361dd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>weight</th>\n",
              "      <th>height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>eugene</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        birthyear  weight height\n",
              "bob        1984.0    83.0    NaN\n",
              "alice      1985.0    68.0    NaN\n",
              "eugene        NaN     NaN    NaN"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d2 = pd.DataFrame(\n",
        "        people_dict,\n",
        "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
        "        index=[\"bob\", \"alice\", \"eugene\"]\n",
        "     )\n",
        "d2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3wRDJbiNwIi"
      },
      "source": [
        "Another convenient way to create a `DataFrame` is to pass all the values to the constructor as an `ndarray`, or a list of lists, and specify the column names and row index labels separately:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "paVGaXaxNwIj",
        "outputId": "63f907d0-fdab-46b3-aebb-a5c93ac1cb50"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "alice         1985       NaN   Biking      68\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "values = [\n",
        "            [1985, np.nan, \"Biking\",   68],\n",
        "            [1984, 3,      \"Dancing\",  83],\n",
        "            [1992, 0,      np.nan,    112]\n",
        "         ]\n",
        "d3 = pd.DataFrame(\n",
        "        values,\n",
        "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
        "        index=[\"alice\", \"bob\", \"charles\"]\n",
        "     )\n",
        "d3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqVP6IwZNwIk"
      },
      "source": [
        "To specify missing values, you can either use `np.nan` or NumPy's masked arrays:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0Te35d6NwIk",
        "outputId": "4ddd8ac4-a63e-468e-df3a-3964459cbe3e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        birthyear children    hobby weight\n",
              "alice        1985      NaN   Biking     68\n",
              "bob          1984        3  Dancing     83\n",
              "charles      1992        0      NaN    112"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "masked_array = np.ma.asarray(values, dtype=np.object)\n",
        "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
        "d3 = pd.DataFrame(\n",
        "        masked_array,\n",
        "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
        "        index=[\"alice\", \"bob\", \"charles\"]\n",
        "     )\n",
        "d3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MllzT8U7NwIl"
      },
      "source": [
        "Instead of an `ndarray`, you can also pass a `DataFrame` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z4cNs8QHNwIm",
        "outputId": "97f49cfc-7f1e-4608-e0bf-48a143b71086"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         hobby children\n",
              "alice   Biking      NaN\n",
              "bob    Dancing        3"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d4 = pd.DataFrame(\n",
        "         d3,\n",
        "         columns=[\"hobby\", \"children\"],\n",
        "         index=[\"alice\", \"bob\"]\n",
        "     )\n",
        "d4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6YELb5UNwIn"
      },
      "source": [
        "It is also possible to create a `DataFrame` with a dictionary (or list) of dictionaries (or list):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "07yTdEJeNwIn",
        "outputId": "22e53e49-e8ee-4ab5-c536-358b0e77b6a1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "alice         1985       NaN   Biking      68\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people = pd.DataFrame({\n",
        "    \"birthyear\": {\"alice\":1985, \"bob\": 1984, \"charles\": 1992},\n",
        "    \"hobby\": {\"alice\":\"Biking\", \"bob\": \"Dancing\"},\n",
        "    \"weight\": {\"alice\":68, \"bob\": 83, \"charles\": 112},\n",
        "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
        "})\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yO5FRUTaNwIo"
      },
      "source": [
        "## Multi-indexing\n",
        "If all columns are tuples of the same size, then they are understood as a multi-index. The same goes for row index labels. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YS7xhBN1NwIo",
        "outputId": "56cd7f18-b380-4805-df6c-9d6912bc7933"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">private</th>\n",
              "      <th colspan=\"2\" halign=\"left\">public</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>children</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>112</td>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>3.0</td>\n",
              "      <td>83</td>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                private           public         \n",
              "               children weight birthyear    hobby\n",
              "London charles      0.0    112      1992      NaN\n",
              "Paris  alice        NaN     68      1985   Biking\n",
              "       bob          3.0     83      1984  Dancing"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d5 = pd.DataFrame(\n",
        "  {\n",
        "    (\"public\", \"birthyear\"):\n",
        "        {(\"Paris\",\"alice\"):1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
        "    (\"public\", \"hobby\"):\n",
        "        {(\"Paris\",\"alice\"):\"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
        "    (\"private\", \"weight\"):\n",
        "        {(\"Paris\",\"alice\"):68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
        "    (\"private\", \"children\"):\n",
        "        {(\"Paris\", \"alice\"):np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
        "  }\n",
        ")\n",
        "d5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4v5H7GaoNwIp"
      },
      "source": [
        "You can now get a `DataFrame` containing all the `\"public\"` columns very simply:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t1nr3aSSNwIq",
        "outputId": "6cc387bc-2c0b-4035-d86c-5b4c892b2eea"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                birthyear    hobby\n",
              "London charles       1992      NaN\n",
              "Paris  alice         1985   Biking\n",
              "       bob           1984  Dancing"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d5[\"public\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fS79ILitNwIr",
        "outputId": "68601eba-f4b4-4ea7-bc55-f12ad0eec75e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "London  charles        NaN\n",
              "Paris   alice       Biking\n",
              "        bob        Dancing\n",
              "Name: (public, hobby), dtype: object"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d5[\"public\", \"hobby\"]  # Same result as d5[\"public\"][\"hobby\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcxlkzVxNwIs"
      },
      "source": [
        "## Dropping a level\n",
        "Let's look at `d5` again:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uSXiaBP8NwIu",
        "outputId": "cc1561ca-a30e-4eba-ba6a-4190677c76d9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">private</th>\n",
              "      <th colspan=\"2\" halign=\"left\">public</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>children</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>112</td>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>3.0</td>\n",
              "      <td>83</td>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                private           public         \n",
              "               children weight birthyear    hobby\n",
              "London charles      0.0    112      1992      NaN\n",
              "Paris  alice        NaN     68      1985   Biking\n",
              "       bob          3.0     83      1984  Dancing"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZxMwL3SGNwIv"
      },
      "source": [
        "There are two levels of columns, and two levels of indices. We can drop a column level by calling `droplevel()` (the same goes for indices):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iRt_2GhFNwIx",
        "outputId": "1a58185f-5dd3-44ab-c914-47278bace56b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>children</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>London</th>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>112</td>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "      <td>1985</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>3.0</td>\n",
              "      <td>83</td>\n",
              "      <td>1984</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                children  weight  birthyear    hobby\n",
              "London charles       0.0     112       1992      NaN\n",
              "Paris  alice         NaN      68       1985   Biking\n",
              "       bob           3.0      83       1984  Dancing"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d5.columns = d5.columns.droplevel(level = 0)\n",
        "d5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JlbhNEsHNwIy"
      },
      "source": [
        "## Transposing\n",
        "You can swap columns and indices using the `T` attribute:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kXKGFeSNwIy",
        "outputId": "4159f12f-28f6-4683-ebde-f9211095b142"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>London</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Paris</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>charles</th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>112</td>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>birthyear</th>\n",
              "      <td>1992</td>\n",
              "      <td>1985</td>\n",
              "      <td>1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           London   Paris         \n",
              "          charles   alice      bob\n",
              "children        0     NaN        3\n",
              "weight        112      68       83\n",
              "birthyear    1992    1985     1984\n",
              "hobby         NaN  Biking  Dancing"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d6 = d5.T\n",
        "d6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KmfsM9ENwIz"
      },
      "source": [
        "## Stacking and unstacking levels\n",
        "Calling the `stack()` method will push the lowest column level after the lowest index:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "359jFbB-NwI0",
        "outputId": "ec039319-b5d9-431f-cdff-9f88a5bfa805"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>London</th>\n",
              "      <th>Paris</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">children</th>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">weight</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>112</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">birthyear</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">hobby</th>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  London    Paris\n",
              "children  bob        NaN        3\n",
              "          charles      0      NaN\n",
              "weight    alice      NaN       68\n",
              "          bob        NaN       83\n",
              "          charles    112      NaN\n",
              "birthyear alice      NaN     1985\n",
              "          bob        NaN     1984\n",
              "          charles   1992      NaN\n",
              "hobby     alice      NaN   Biking\n",
              "          bob        NaN  Dancing"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d7 = d6.stack()\n",
        "d7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDDOLv3jNwI1"
      },
      "source": [
        "Note that many `NaN` values appeared. This makes sense because many new combinations did not exist before (eg. there was no `bob` in `London`).\n",
        "\n",
        "Calling `unstack()` will do the reverse, once again creating many `NaN` values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nZh35yqdNwI2",
        "outputId": "d4240ce2-b868-4197-d601-7a8a3b17987b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">London</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>None</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>birthyear</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1992</td>\n",
              "      <td>1985</td>\n",
              "      <td>1984</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>Biking</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          London                Paris                 \n",
              "           alice  bob charles   alice      bob charles\n",
              "children    None  NaN       0    None        3     NaN\n",
              "weight       NaN  NaN     112      68       83     NaN\n",
              "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
              "hobby        NaN  NaN    None  Biking  Dancing    None"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d8 = d7.unstack()\n",
        "d8"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAVKNn60NwI3"
      },
      "source": [
        "If we call `unstack` again, we end up with a `Series` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fz4TWv5CNwI3",
        "outputId": "ab9eb814-6e8f-4739-bb7b-1cd6740d59a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "London  alice    children        None\n",
              "                 weight           NaN\n",
              "                 birthyear        NaN\n",
              "                 hobby            NaN\n",
              "        bob      children         NaN\n",
              "                 weight           NaN\n",
              "                 birthyear        NaN\n",
              "                 hobby            NaN\n",
              "        charles  children           0\n",
              "                 weight           112\n",
              "                 birthyear       1992\n",
              "                 hobby           None\n",
              "Paris   alice    children        None\n",
              "                 weight            68\n",
              "                 birthyear       1985\n",
              "                 hobby         Biking\n",
              "        bob      children           3\n",
              "                 weight            83\n",
              "                 birthyear       1984\n",
              "                 hobby        Dancing\n",
              "        charles  children         NaN\n",
              "                 weight           NaN\n",
              "                 birthyear        NaN\n",
              "                 hobby           None\n",
              "dtype: object"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d9 = d8.unstack()\n",
        "d9"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSxK16IiNwI4"
      },
      "source": [
        "The `stack()` and `unstack()` methods let you select the `level` to stack/unstack. You can even stack/unstack multiple levels at once:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "e7OPTY8jNwI4",
        "outputId": "4cab7c71-5efe-4460-9fe6-92985390bba7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">London</th>\n",
              "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>alice</th>\n",
              "      <th>bob</th>\n",
              "      <th>charles</th>\n",
              "      <th>alice</th>\n",
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              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>None</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weight</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>birthyear</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1992</td>\n",
              "      <td>1985</td>\n",
              "      <td>1984</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>Biking</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          London                Paris                 \n",
              "           alice  bob charles   alice      bob charles\n",
              "children    None  NaN       0    None        3     NaN\n",
              "weight       NaN  NaN     112      68       83     NaN\n",
              "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
              "hobby        NaN  NaN    None  Biking  Dancing    None"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d10 = d9.unstack(level = (0,1))\n",
        "d10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uEWp1KmNwI5"
      },
      "source": [
        "## Most methods return modified copies\n",
        "As you may have noticed, the `stack()` and `unstack()` methods do not modify the object they apply to. Instead, they work on a copy and return that copy. This is true of most methods in pandas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsdOTWLYNwI9"
      },
      "source": [
        "## Accessing rows\n",
        "Let's go back to the `people` `DataFrame`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oxq_whrpNwI-",
        "outputId": "5ba79664-3c48-40f9-e449-f92dc3fba65a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "        text-align: left;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "alice         1985       NaN   Biking      68\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tA0TOV6KNwI_"
      },
      "source": [
        "The `loc` attribute lets you access rows instead of columns. The result is a `Series` object in which the `DataFrame`'s column names are mapped to row index labels:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PpJtSwERNwI_",
        "outputId": "a81cfe09-3162-430f-cda2-22de954f34f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "birthyear    1992\n",
              "children        0\n",
              "hobby         NaN\n",
              "weight        112\n",
              "Name: charles, dtype: object"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.loc[\"charles\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mj9lFKFKNwJA"
      },
      "source": [
        "You can also access rows by integer location using the `iloc` attribute:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "csaBQsL_NwJA",
        "outputId": "d65c2a83-191c-49e6-d33c-3d39a4927d94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "birthyear    1992\n",
              "children        0\n",
              "hobby         NaN\n",
              "weight        112\n",
              "Name: charles, dtype: object"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.iloc[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9J4hhm9NwJB"
      },
      "source": [
        "You can also get a slice of rows, and this returns a `DataFrame` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yzlLWCSeNwJC",
        "outputId": "709e9810-6d63-4b93-cbda-5bd4fbd265b1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "execution_count": 71,
          "metadata": {},
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      ],
      "source": [
        "people.iloc[1:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uW_aeLzBNwJC"
      },
      "source": [
        "Finally, you can pass a boolean array to get the matching rows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nm9YEYhlNwJD",
        "outputId": "5b11d0f7-2461-4a64-d6a0-795541d8cdc4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children   hobby  weight\n",
              "alice         1985       NaN  Biking      68\n",
              "charles       1992       0.0     NaN     112"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people[np.array([True, False, True])]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Dhj-tnUNwJE"
      },
      "source": [
        "This is most useful when combined with boolean expressions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEGGRE9RNwJE",
        "outputId": "29587aa2-cbd5-4723-e67d-6c3a6f74a802"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       birthyear  children    hobby  weight\n",
              "alice       1985       NaN   Biking      68\n",
              "bob         1984       3.0  Dancing      83"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people[people[\"birthyear\"] < 1990]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecyYM6aRNwJF"
      },
      "source": [
        "## Adding and removing columns\n",
        "You can generally treat `DataFrame` objects like dictionaries of `Series`, so the following work fine:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B8QMNM9VNwJF",
        "outputId": "af0d5b1b-34fc-44cf-b7ac-5ebff73c8a9c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>birthyear</th>\n",
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              "      <th>hobby</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>1992</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         birthyear  children    hobby  weight\n",
              "alice         1985       NaN   Biking      68\n",
              "bob           1984       3.0  Dancing      83\n",
              "charles       1992       0.0      NaN     112"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bWWwyTs3NwJG",
        "outputId": "8157ca90-84d7-4e46-c159-8aa1e61df349"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  weight  age  over 30\n",
              "alice     Biking      68   33     True\n",
              "bob      Dancing      83   34     True\n",
              "charles      NaN     112   26    False"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people[\"age\"] = 2018 - people[\"birthyear\"]  # adds a new column \"age\"\n",
        "people[\"over 30\"] = people[\"age\"] > 30      # adds another column \"over 30\"\n",
        "birthyears = people.pop(\"birthyear\")\n",
        "del people[\"children\"]\n",
        "\n",
        "people"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PHRfcGDkNwJH",
        "outputId": "cb84b6b1-a112-47fe-d429-8be619b7d9fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice      1985\n",
              "bob        1984\n",
              "charles    1992\n",
              "Name: birthyear, dtype: int64"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "birthyears"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHhbGsiBNwJI"
      },
      "source": [
        "When you add a new colum, it must have the same number of rows. Missing rows are filled with NaN, and extra rows are ignored:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bM6351uoNwJI",
        "outputId": "9e50254e-de14-40f0-9862-5903f3514bae"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  weight  age  over 30  pets\n",
              "alice     Biking      68   33     True   NaN\n",
              "bob      Dancing      83   34     True   0.0\n",
              "charles      NaN     112   26    False   5.0"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\":1})  # alice is missing, eugene is ignored\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krphE-DYNwJI"
      },
      "source": [
        "When adding a new column, it is added at the end (on the right) by default. You can also insert a column anywhere else using the `insert()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QdsSomxXNwJJ",
        "outputId": "3290a21e-9556-4af2-99aa-79d3d911ddf6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets\n",
              "alice     Biking     172      68   33     True   NaN\n",
              "bob      Dancing     181      83   34     True   0.0\n",
              "charles      NaN     185     112   26    False   5.0"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.insert(1, \"height\", [172, 181, 185])\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onc2_fqwNwJJ"
      },
      "source": [
        "## Assigning new columns\n",
        "You can also create new columns by calling the `assign()` method. Note that this returns a new `DataFrame` object, the original is not modified:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uelv1dSoNwJK",
        "outputId": "8eb2584e-66a5-4416-fe91-1288102e7168"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>has_pets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
              "\n",
              "         has_pets  \n",
              "alice       False  \n",
              "bob         False  \n",
              "charles      True  "
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.assign(\n",
        "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
        "    has_pets = people[\"pets\"] > 0\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SfAGSy4NwJK"
      },
      "source": [
        "Note that you cannot access columns created within the same assignment:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pHAmihogNwJK",
        "outputId": "e5e2e9f6-eab3-409f-a3d4-852356d4e320"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key error: 'body_mass_index'\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    people.assign(\n",
        "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
        "        overweight = people[\"body_mass_index\"] > 25\n",
        "    )\n",
        "except KeyError as e:\n",
        "    print(\"Key error:\", e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E11yxeUvNwJL"
      },
      "source": [
        "The solution is to split this assignment in two consecutive assignments:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UOdcLd4MNwJM",
        "outputId": "62bbe7b5-f4f6-4bff-8eb6-b8846833fbcb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
              "\n",
              "         overweight  \n",
              "alice         False  \n",
              "bob            True  \n",
              "charles        True  "
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
        "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNeil6sPNwJN"
      },
      "source": [
        "Having to create a temporary variable `d6` is not very convenient. You may want to just chain the assigment calls, but it does not work because the `people` object is not actually modified by the first assignment:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZYply7qBNwJO",
        "outputId": "e23d9dba-cf34-42f2-aa5b-82d411535a0d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key error: 'body_mass_index'\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    (people\n",
        "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
        "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
        "    )\n",
        "except KeyError as e:\n",
        "    print(\"Key error:\", e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJwp9mZ5NwJO"
      },
      "source": [
        "But fear not, there is a simple solution. You can pass a function to the `assign()` method (typically a `lambda` function), and this function will be called with the `DataFrame` as a parameter:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CcTEvZOvNwJP",
        "outputId": "22270a8b-0f28-498a-fc4e-07168b491d8d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
              "\n",
              "         overweight  \n",
              "alice         False  \n",
              "bob            True  \n",
              "charles        True  "
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(people\n",
        "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
        "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4ir-mmSNwJR"
      },
      "source": [
        "Problem solved!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYFkH5jKNwJT"
      },
      "source": [
        "## Evaluating an expression\n",
        "A great feature supported by pandas is expression evaluation. This relies on the `numexpr` library which must be installed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jxyw7KMwNwJU",
        "outputId": "0c182d93-599c-492c-e03e-b6414780eefe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice      False\n",
              "bob         True\n",
              "charles     True\n",
              "dtype: bool"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.eval(\"weight / (height/100) ** 2 > 25\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umb9Qm8YNwJU"
      },
      "source": [
        "Assignment expressions are also supported. Let's set `inplace=True` to directly modify the `DataFrame` rather than getting a modified copy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GoPhAppGNwJV",
        "outputId": "4146e5e6-d84c-41e7-b7b5-79784c4d983e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index\n",
              "alice     Biking     172      68   33     True   NaN        22.985398\n",
              "bob      Dancing     181      83   34     True   0.0        25.335002\n",
              "charles      NaN     185     112   26    False   5.0        32.724617"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9Md9ouHNwJW"
      },
      "source": [
        "You can use a local or global variable in an expression by prefixing it with `'@'`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "poZqHbHeNwJW",
        "outputId": "5cd7c36e-bc5f-4354-c3e6-cdeb0fa12c14"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
              "      <td>5.0</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
              "\n",
              "         overweight  \n",
              "alice         False  \n",
              "bob           False  \n",
              "charles        True  "
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "overweight_threshold = 30\n",
        "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsTRc7M3NwJb"
      },
      "source": [
        "## Querying a `DataFrame`\n",
        "The `query()` method lets you filter a `DataFrame` based on a query expression:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "97qkWmWpNwJb",
        "outputId": "ac7154a5-1a1f-4142-a8f5-f9cceca0c696"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       hobby  height  weight  age  over 30  pets  body_mass_index  overweight\n",
              "bob  Dancing     181      83   34     True   0.0        25.335002       False"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.query(\"age > 30 and pets == 0\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvFBAuxWNwJc"
      },
      "source": [
        "## Sorting a `DataFrame`\n",
        "You can sort a `DataFrame` by calling its `sort_index` method. By default it sorts the rows by their index label, in ascending order, but let's reverse the order:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "poTX08ZpNwJf",
        "outputId": "c30c256f-3da5-43c1-e901-6eca4664458a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>age</th>\n",
              "      <th>over 30</th>\n",
              "      <th>pets</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>overweight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>185</td>\n",
              "      <td>112</td>\n",
              "      <td>26</td>\n",
              "      <td>False</td>\n",
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              "      <td>32.724617</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>181</td>\n",
              "      <td>83</td>\n",
              "      <td>34</td>\n",
              "      <td>True</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>172</td>\n",
              "      <td>68</td>\n",
              "      <td>33</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
              "charles      NaN     185     112   26    False   5.0        32.724617   \n",
              "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
              "alice     Biking     172      68   33     True   NaN        22.985398   \n",
              "\n",
              "         overweight  \n",
              "charles        True  \n",
              "bob           False  \n",
              "alice         False  "
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.sort_index(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N74uVXReNwJf"
      },
      "source": [
        "Note that `sort_index` returned a sorted *copy* of the `DataFrame`. To modify `people` directly, we can set the `inplace` argument to `True`. Also, we can sort the columns instead of the rows by setting `axis=1`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D1MFRb4oNwJg",
        "outputId": "e7695a73-e399-4107-8d02-ed3c9d056abe"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>height</th>\n",
              "      <th>hobby</th>\n",
              "      <th>over 30</th>\n",
              "      <th>overweight</th>\n",
              "      <th>pets</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>33</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>172</td>\n",
              "      <td>Biking</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>34</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>181</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>26</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>5.0</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
              "alice     33        22.985398     172   Biking     True       False   NaN   \n",
              "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
              "charles   26        32.724617     185      NaN    False        True   5.0   \n",
              "\n",
              "         weight  \n",
              "alice        68  \n",
              "bob          83  \n",
              "charles     112  "
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.sort_index(axis=1, inplace=True)\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7IFesFNNwJh"
      },
      "source": [
        "To sort the `DataFrame` by the values instead of the labels, we can use `sort_values` and specify the column to sort by:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "50U8NtlMNwJi",
        "outputId": "c26719fc-4a4b-4e58-dc03-4de3c6c39971"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>body_mass_index</th>\n",
              "      <th>height</th>\n",
              "      <th>hobby</th>\n",
              "      <th>over 30</th>\n",
              "      <th>overweight</th>\n",
              "      <th>pets</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>26</td>\n",
              "      <td>32.724617</td>\n",
              "      <td>185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>5.0</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>33</td>\n",
              "      <td>22.985398</td>\n",
              "      <td>172</td>\n",
              "      <td>Biking</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>34</td>\n",
              "      <td>25.335002</td>\n",
              "      <td>181</td>\n",
              "      <td>Dancing</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>0.0</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
              "charles   26        32.724617     185      NaN    False        True   5.0   \n",
              "alice     33        22.985398     172   Biking     True       False   NaN   \n",
              "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
              "\n",
              "         weight  \n",
              "charles     112  \n",
              "alice        68  \n",
              "bob          83  "
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "people.sort_values(by=\"age\", inplace=True)\n",
        "people"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uueT6n3SNwJj"
      },
      "source": [
        "## Plotting a `DataFrame`\n",
        "Just like for `Series`, pandas makes it easy to draw nice graphs based on a `DataFrame`.\n",
        "\n",
        "For example, it is trivial to create a line plot from a `DataFrame`'s data by calling its `plot` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z_j8w9mWNwJk",
        "outputId": "0cd2b2b2-6be2-49bc-c371-dcdf65caaad5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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fYuHChdxyyy1Js12KiPTF0Ej0GXL88cezYsUKdu7cSWFhIccddxw1NTU8//zz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\nYojcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x10b89bf28>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "people.plot(kind = \"line\", x = \"body_mass_index\", y = [\"height\", \"weight\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oORpFdlcNwJl"
      },
      "source": [
        "You can pass extra arguments supported by matplotlib's functions. For example, we can create scatterplot and pass it a list of sizes using the `s` argument of matplotlib's `scatter()` function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "joDRQIZaNwJm",
        "outputId": "87d41e00-7d51-45d4-c603-9aee829202a7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x10b89b7f0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "people.plot(kind = \"scatter\", x = \"height\", y = \"weight\", s=[40, 120, 200])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lfQ-IhxANwJm"
      },
      "source": [
        "Again, there are way too many options to list here: the best option is to scroll through the [Visualization](http://pandas.pydata.org/pandas-docs/stable/visualization.html) page in pandas' documentation, find the plot you are interested in and look at the example code."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9IVeJ_cNwJn"
      },
      "source": [
        "## Operations on `DataFrame`s\n",
        "Although `DataFrame`s do not try to mimick NumPy arrays, there are a few similarities. Let's create a `DataFrame` to demonstrate this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C438QxsXNwJo",
        "outputId": "1d7ebc4c-b6f8-4e47-bd9b-127838237a19"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov\n",
              "alice      8    8    9\n",
              "bob       10    9    9\n",
              "charles    4    8    2\n",
              "darwin     9   10   10"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades_array = np.array([[8,8,9],[10,9,9],[4, 8, 2], [9, 10, 10]])\n",
        "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\",\"bob\",\"charles\",\"darwin\"])\n",
        "grades"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvWoesyDNwJo"
      },
      "source": [
        "You can apply NumPy mathematical functions on a `DataFrame`: the function is applied to all values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S0E8htTqNwJp",
        "outputId": "19b1004c-95dc-4f73-9efa-d3adbc3dc23b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>2.828427</td>\n",
              "      <td>2.828427</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>3.162278</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.828427</td>\n",
              "      <td>1.414214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.162278</td>\n",
              "      <td>3.162278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              sep       oct       nov\n",
              "alice    2.828427  2.828427  3.000000\n",
              "bob      3.162278  3.000000  3.000000\n",
              "charles  2.000000  2.828427  1.414214\n",
              "darwin   3.000000  3.162278  3.162278"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.sqrt(grades)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MIT_b5BNwJq"
      },
      "source": [
        "Similarly, adding a single value to a `DataFrame` will add that value to all elements in the `DataFrame`. This is called *broadcasting*:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "adv0PkhKNwJq",
        "outputId": "7300a3b8-23d5-469f-a557-6bd3dc15b8a4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov\n",
              "alice      9    9   10\n",
              "bob       11   10   10\n",
              "charles    5    9    3\n",
              "darwin    10   11   11"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades + 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OctZsyV2NwJr"
      },
      "source": [
        "Of course, the same is true for all other binary operations, including arithmetic (`*`,`/`,`**`...) and conditional (`>`, `==`...) operations:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MODCeL76NwJr",
        "outputId": "dbb983a0-e988-4315-955b-9e32ea229529"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           sep   oct    nov\n",
              "alice     True  True   True\n",
              "bob       True  True   True\n",
              "charles  False  True  False\n",
              "darwin    True  True   True"
            ]
          },
          "execution_count": 96,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades >= 5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSSPmc4TNwJs"
      },
      "source": [
        "Aggregation operations, such as computing the `max`, the `sum` or the `mean` of a `DataFrame`, apply to each column, and you get back a `Series` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ftt7mVn5NwJt",
        "outputId": "a5683398-6d7a-4b7c-c4d6-59361eb0dc98"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sep    7.75\n",
              "oct    8.75\n",
              "nov    7.50\n",
              "dtype: float64"
            ]
          },
          "execution_count": 97,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qruPj6mNwJt"
      },
      "source": [
        "The `all` method is also an aggregation operation: it checks whether all values are `True` or not. Let's see during which months all students got a grade greater than `5`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ugFp3qIiNwJu",
        "outputId": "175db299-4983-424a-bf1c-cb29cd6d3482"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "sep    False\n",
              "oct     True\n",
              "nov    False\n",
              "dtype: bool"
            ]
          },
          "execution_count": 98,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(grades > 5).all()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlZvhx-TNwJw"
      },
      "source": [
        "Most of these functions take an optional `axis` parameter which lets you specify along which axis of the `DataFrame` you want the operation executed. The default is `axis=0`, meaning that the operation is executed vertically (on each column). You can set `axis=1` to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than `5`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IX4AiSxdNwJx",
        "outputId": "dd69af81-b8ff-45e4-8936-8883e220a336"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice       True\n",
              "bob         True\n",
              "charles    False\n",
              "darwin      True\n",
              "dtype: bool"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(grades > 5).all(axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MobRNwlsNwJz"
      },
      "source": [
        "The `any` method returns `True` if any value is True. Let's see who got at least one grade 10:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8UgMIXHsNwJ0",
        "outputId": "ed1a25e7-a21f-401e-e200-73c71b0171c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alice      False\n",
              "bob         True\n",
              "charles    False\n",
              "darwin      True\n",
              "dtype: bool"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(grades == 10).any(axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_keVa75JNwJ1"
      },
      "source": [
        "If you add a `Series` object to a `DataFrame` (or execute any other binary operation), pandas attempts to broadcast the operation to all *rows* in the `DataFrame`. This only works if the `Series` has the same size as the `DataFrame`s rows. For example, let's subtract the `mean` of the `DataFrame` (a `Series` object) from the `DataFrame`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2qHBagzuNwJ8",
        "outputId": "6095c539-12c7-4137-b02f-253194575a8e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.25</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>2.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>-3.75</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>1.25</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct  nov\n",
              "alice    0.25 -0.75  1.5\n",
              "bob      2.25  0.25  1.5\n",
              "charles -3.75 -0.75 -5.5\n",
              "darwin   1.25  1.25  2.5"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHQ_0tkBNwJ9"
      },
      "source": [
        "We subtracted `7.75` from all September grades, `8.75` from October grades and `7.50` from November grades. It is equivalent to subtracting this `DataFrame`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bz5OUQL1NwKA",
        "outputId": "9b28bd5e-6e38-46ea-fc0f-56985592ef86"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>7.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct  nov\n",
              "alice    7.75  8.75  7.5\n",
              "bob      7.75  8.75  7.5\n",
              "charles  7.75  8.75  7.5\n",
              "darwin   7.75  8.75  7.5"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIOAdsJhNwKB"
      },
      "source": [
        "If you want to subtract the global mean from every grade, here is one way to do it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "anbZAHXLNwKB",
        "outputId": "a4c1706f-3a01-4662-a456-f2e60a0c59aa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>-4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov\n",
              "alice    0.0  0.0  1.0\n",
              "bob      2.0  1.0  1.0\n",
              "charles -4.0  0.0 -6.0\n",
              "darwin   1.0  2.0  2.0"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades - grades.values.mean() # subtracts the global mean (8.00) from all grades"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EcwNeFqNwKD"
      },
      "source": [
        "## Automatic alignment\n",
        "Similar to `Series`, when operating on multiple `DataFrame`s, pandas automatically aligns them by row index label, but also by column names. Let's create a `DataFrame` with bonus points for each person from October to December:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FqwLoUhNwKD",
        "outputId": "715a9dd9-0aeb-4412-b46d-227de86065da"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  NaN  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bonus_array = np.array([[0,np.nan,2],[np.nan,1,0],[0, 1, 0], [3, 3, 0]])\n",
        "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\",\"colin\", \"darwin\", \"charles\"])\n",
        "bonus_points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "yH8FlOc0NwKJ",
        "outputId": "f2422898-b5fe-47f7-c8fc-a5ed649c318e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct  sep\n",
              "alice    NaN   NaN   NaN  NaN\n",
              "bob      NaN   NaN   9.0  NaN\n",
              "charles  NaN   5.0  11.0  NaN\n",
              "colin    NaN   NaN   NaN  NaN\n",
              "darwin   NaN  11.0  10.0  NaN"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades + bonus_points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taqQDKeZNwKL"
      },
      "source": [
        "Looks like the addition worked in some cases but way too many elements are now empty. That's because when aligning the `DataFrame`s, some columns and rows were only present on one side, and thus they were considered missing on the other side (`NaN`). Then adding `NaN` to a number results in `NaN`, hence the result.\n",
        "\n",
        "## Handling missing data\n",
        "Dealing with missing data is a frequent task when working with real life data. Pandas offers a few tools to handle missing data.\n",
        " \n",
        "Let's try to fix the problem above. For example, we can decide that missing data should result in a zero, instead of `NaN`. We can replace all `NaN` values by a any value using the `fillna()` method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Z-me96k0NwKL",
        "outputId": "145648be-f198-4748-b330-3a0907a783ab"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct  sep\n",
              "alice    0.0   0.0   0.0  0.0\n",
              "bob      0.0   0.0   9.0  0.0\n",
              "charles  0.0   5.0  11.0  0.0\n",
              "colin    0.0   0.0   0.0  0.0\n",
              "darwin   0.0  11.0  10.0  0.0"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(grades + bonus_points).fillna(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBAiHlj8NwKM"
      },
      "source": [
        "It's a bit unfair that we're setting grades to zero in September, though. Perhaps we should decide that missing grades are missing grades, but missing bonus points should be replaced by zeros:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cZBwUM9qNwKN",
        "outputId": "681a5ed9-a1fd-4a43-a410-bca889cad7eb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct   sep\n",
              "alice    NaN   9.0   8.0   8.0\n",
              "bob      NaN   9.0   9.0  10.0\n",
              "charles  NaN   5.0  11.0   4.0\n",
              "colin    NaN   NaN   NaN   NaN\n",
              "darwin   NaN  11.0  10.0   9.0"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fixed_bonus_points = bonus_points.fillna(0)\n",
        "fixed_bonus_points.insert(0, \"sep\", 0)\n",
        "fixed_bonus_points.loc[\"alice\"] = 0\n",
        "grades + fixed_bonus_points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aexA7JAANwKP"
      },
      "source": [
        "That's much better: although we made up some data, we have not been too unfair.\n",
        "\n",
        "Another way to handle missing data is to interpolate. Let's look at the `bonus_points` `DataFrame` again:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CkhttXpfNwKQ",
        "outputId": "a57d6e43-4ccf-4076-f0aa-8ed3c7c3cf5f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  NaN  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bonus_points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFJXzy5dNwKQ"
      },
      "source": [
        "Now let's call the `interpolate` method. By default, it interpolates vertically (`axis=0`), so let's tell it to interpolate horizontally (`axis=1`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e3FJCFc4NwKb",
        "outputId": "2f4a0ce1-8eb8-467e-f4ee-64d7fe18d1d5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  1.0  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "execution_count": 109,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bonus_points.interpolate(axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FKwFeOSNwKc"
      },
      "source": [
        "Bob had 0 bonus points in October, and 2 in December. When we interpolate for November, we get the mean: 1 bonus point. Colin had 1 bonus point in November, but we do not know how many bonus points he had in September, so we cannot interpolate, this is why there is still a missing value in October after interpolation. To fix this, we can set the September bonus points to 0 before interpolation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kz89NxrsNwKd",
        "outputId": "952b5908-75ce-4321-a89e-53b8ea3acfbc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sep  oct  nov  dec\n",
              "bob      0.0  0.0  1.0  2.0\n",
              "colin    0.0  0.5  1.0  0.0\n",
              "darwin   0.0  0.0  1.0  0.0\n",
              "charles  0.0  3.0  3.0  0.0\n",
              "alice    0.0  0.0  0.0  0.0"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "better_bonus_points = bonus_points.copy()\n",
        "better_bonus_points.insert(0, \"sep\", 0)\n",
        "better_bonus_points.loc[\"alice\"] = 0\n",
        "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
        "better_bonus_points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WAA1Y6oNwKe"
      },
      "source": [
        "Great, now we have reasonable bonus points everywhere. Let's find out the final grades:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lQ6c-FgfNwKe",
        "outputId": "34e866bb-c328-407b-a685-bc5494f79a88"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dec</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         dec   nov   oct   sep\n",
              "alice    NaN   9.0   8.0   8.0\n",
              "bob      NaN  10.0   9.0  10.0\n",
              "charles  NaN   5.0  11.0   4.0\n",
              "colin    NaN   NaN   NaN   NaN\n",
              "darwin   NaN  11.0  10.0   9.0"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades + better_bonus_points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-e5dOC-1NwKf"
      },
      "source": [
        "It is slightly annoying that the September column ends up on the right. This is because the `DataFrame`s we are adding do not have the exact same columns (the `grades` `DataFrame` is missing the `\"dec\"` column), so to make things predictable, pandas orders the final columns alphabetically. To fix this, we can simply add the missing column before adding:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "2UV2VyctNwKm",
        "outputId": "689f6c45-cabb-4b57-e0e1-7160417b403f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov  dec\n",
              "alice     8.0   8.0   9.0  NaN\n",
              "bob      10.0   9.0  10.0  NaN\n",
              "charles   4.0  11.0   5.0  NaN\n",
              "colin     NaN   NaN   NaN  NaN\n",
              "darwin    9.0  10.0  11.0  NaN"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grades[\"dec\"] = np.nan\n",
        "final_grades = grades + better_bonus_points\n",
        "final_grades"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ogKiIwfXNwKn"
      },
      "source": [
        "There's not much we can do about December and Colin: it's bad enough that we are making up bonus points, but we can't reasonably make up grades (well I guess some teachers probably do). So let's call the `dropna()` method to get rid of rows that are full of `NaN`s:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EMuUT12fNwKo",
        "outputId": "a8812ac1-5dfa-4c56-b3f0-e62d1a30c086"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov  dec\n",
              "alice     8.0   8.0   9.0  NaN\n",
              "bob      10.0   9.0  10.0  NaN\n",
              "charles   4.0  11.0   5.0  NaN\n",
              "darwin    9.0  10.0  11.0  NaN"
            ]
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_grades_clean = final_grades.dropna(how=\"all\")\n",
        "final_grades_clean"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJW0sqLQNwKp"
      },
      "source": [
        "Now let's remove columns that are full of `NaN`s by setting the `axis` argument to `1`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KqcgOExCNwKp",
        "outputId": "898585a0-14d1-4b50-f0b7-732791295152"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov\n",
              "alice     8.0   8.0   9.0\n",
              "bob      10.0   9.0  10.0\n",
              "charles   4.0  11.0   5.0\n",
              "darwin    9.0  10.0  11.0"
            ]
          },
          "execution_count": 114,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
        "final_grades_clean"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1tycES9NwKq"
      },
      "source": [
        "## Aggregating with `groupby`\n",
        "Similar to the SQL language, pandas allows grouping your data into groups to run calculations over each group.\n",
        "\n",
        "First, let's add some extra data about each person so we can group them, and let's go back to the `final_grades` `DataFrame` so we can see how `NaN` values are handled:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "JUk2mHmFNwKq",
        "outputId": "d9e881ee-e521-4ca8-c86b-cbd17efb0151"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "      <th>hobby</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dancing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Biking</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep   oct   nov  dec    hobby\n",
              "alice     8.0   8.0   9.0  NaN   Biking\n",
              "bob      10.0   9.0  10.0  NaN  Dancing\n",
              "charles   4.0  11.0   5.0  NaN      NaN\n",
              "colin     NaN   NaN   NaN  NaN  Dancing\n",
              "darwin    9.0  10.0  11.0  NaN   Biking"
            ]
          },
          "execution_count": 115,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
        "final_grades"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIZoZrRANwKr"
      },
      "source": [
        "Now let's group data in this `DataFrame` by hobby:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FlUbrw3GNwKr",
        "outputId": "060021f3-ff80-4c25-c35e-d3255a16fda8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<pandas.core.groupby.DataFrameGroupBy object at 0x10b680e10>"
            ]
          },
          "execution_count": 116,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grouped_grades = final_grades.groupby(\"hobby\")\n",
        "grouped_grades"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uK7RxIozNwKs"
      },
      "source": [
        "We are ready to compute the average grade per hobby:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NyL4dNetNwKt",
        "outputId": "5501fc79-2243-4f47-f733-3a318fd0ac25"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sep</th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hobby</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Biking</th>\n",
              "      <td>8.5</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dancing</th>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          sep  oct   nov  dec\n",
              "hobby                        \n",
              "Biking    8.5  9.0  10.0  NaN\n",
              "Dancing  10.0  9.0  10.0  NaN"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grouped_grades.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9snbue-oNwKt"
      },
      "source": [
        "That was easy! Note that the `NaN` values have simply been skipped when computing the means."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpgqvyXbNwKu"
      },
      "source": [
        "## Pivot tables\n",
        "Pandas supports spreadsheet-like [pivot tables](https://en.wikipedia.org/wiki/Pivot_table) that allow quick data summarization. To illustrate this, let's create a simple `DataFrame`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u5q44IGUNwKu",
        "outputId": "96b92091-421b-4822-bcb2-a1bb5b084576"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>oct</th>\n",
              "      <th>nov</th>\n",
              "      <th>dec</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colin</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         oct  nov  dec\n",
              "bob      0.0  NaN  2.0\n",
              "colin    NaN  1.0  0.0\n",
              "darwin   0.0  1.0  0.0\n",
              "charles  3.0  3.0  0.0"
            ]
          },
          "execution_count": 118,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bonus_points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tf6LtwseNwKv",
        "outputId": "3701b747-e609-40ea-c738-86b71fdefce6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>month</th>\n",
              "      <th>grade</th>\n",
              "      <th>bonus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alice</td>\n",
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              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>alice</td>\n",
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              "      <td>8.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alice</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>bob</td>\n",
              "      <td>sep</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bob</td>\n",
              "      <td>oct</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bob</td>\n",
              "      <td>nov</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>charles</td>\n",
              "      <td>sep</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>charles</td>\n",
              "      <td>oct</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>charles</td>\n",
              "      <td>nov</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>darwin</td>\n",
              "      <td>sep</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>darwin</td>\n",
              "      <td>oct</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>darwin</td>\n",
              "      <td>nov</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       name month  grade  bonus\n",
              "0     alice   sep    8.0    NaN\n",
              "1     alice   oct    8.0    NaN\n",
              "2     alice   nov    9.0    NaN\n",
              "3       bob   sep   10.0    0.0\n",
              "4       bob   oct    9.0    NaN\n",
              "5       bob   nov   10.0    2.0\n",
              "6   charles   sep    4.0    3.0\n",
              "7   charles   oct   11.0    3.0\n",
              "8   charles   nov    5.0    0.0\n",
              "9    darwin   sep    9.0    0.0\n",
              "10   darwin   oct   10.0    1.0\n",
              "11   darwin   nov   11.0    0.0"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "more_grades = final_grades_clean.stack().reset_index()\n",
        "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
        "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
        "more_grades"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6D1b_M6gNwKw"
      },
      "source": [
        "Now we can call the `pd.pivot_table()` function for this `DataFrame`, asking to group by the `name` column. By default, `pivot_table()` computes the mean of each numeric column:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jCE-eQ3tNwKw",
        "outputId": "827bcf16-1226-42a8-c783-20f7148f1e8b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bonus</th>\n",
              "      <th>grade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            bonus      grade\n",
              "name                        \n",
              "alice         NaN   8.333333\n",
              "bob      1.000000   9.666667\n",
              "charles  2.000000   6.666667\n",
              "darwin   0.333333  10.000000"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.pivot_table(more_grades, index=\"name\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPXD52ddNwKy"
      },
      "source": [
        "We can change the aggregation function by setting the `aggfunc` argument, and we can also specify the list of columns whose values will be aggregated:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GHkxVEAKNwKy",
        "outputId": "4c89e58d-8f6e-4796-ce6d-a3eef746d7eb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bonus</th>\n",
              "      <th>grade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>3.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         bonus  grade\n",
              "name                 \n",
              "alice      NaN    9.0\n",
              "bob        2.0   10.0\n",
              "charles    3.0   11.0\n",
              "darwin     1.0   11.0"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\",\"bonus\"], aggfunc=np.max)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGQ9mwztNwK0"
      },
      "source": [
        "We can also specify the `columns` to aggregate over horizontally, and request the grand totals for each row and column by setting `margins=True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3epnZPoLNwK1",
        "outputId": "d438a32e-1127-47d8-ffeb-76ea20fa442d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>month</th>\n",
              "      <th>nov</th>\n",
              "      <th>oct</th>\n",
              "      <th>sep</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>9.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>10.00</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>9.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charles</th>\n",
              "      <td>5.00</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darwin</th>\n",
              "      <td>11.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.00</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>8.75</td>\n",
              "      <td>9.5</td>\n",
              "      <td>7.75</td>\n",
              "      <td>8.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "month      nov   oct    sep        All\n",
              "name                                  \n",
              "alice     9.00   8.0   8.00   8.333333\n",
              "bob      10.00   9.0  10.00   9.666667\n",
              "charles   5.00  11.0   4.00   6.666667\n",
              "darwin   11.00  10.0   9.00  10.000000\n",
              "All       8.75   9.5   7.75   8.666667"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAdNBMQzNwK8"
      },
      "source": [
        "Finally, we can specify multiple index or column names, and pandas will create multi-level indices:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qX8DswfxNwK8",
        "outputId": "16df929f-b5c8-4553-88ab-4935bc276f2d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>bonus</th>\n",
              "      <th>grade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">alice</th>\n",
              "      <th>nov</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">bob</th>\n",
              "      <th>nov</th>\n",
              "      <td>2.000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>0.000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">charles</th>\n",
              "      <th>nov</th>\n",
              "      <td>0.000</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>3.000</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>3.000</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">darwin</th>\n",
              "      <th>nov</th>\n",
              "      <td>0.000</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oct</th>\n",
              "      <td>1.000</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sep</th>\n",
              "      <td>0.000</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <th></th>\n",
              "      <td>1.125</td>\n",
              "      <td>8.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               bonus  grade\n",
              "name    month              \n",
              "alice   nov      NaN   9.00\n",
              "        oct      NaN   8.00\n",
              "        sep      NaN   8.00\n",
              "bob     nov    2.000  10.00\n",
              "        oct      NaN   9.00\n",
              "        sep    0.000  10.00\n",
              "charles nov    0.000   5.00\n",
              "        oct    3.000  11.00\n",
              "        sep    3.000   4.00\n",
              "darwin  nov    0.000  11.00\n",
              "        oct    1.000  10.00\n",
              "        sep    0.000   9.00\n",
              "All            1.125   8.75"
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-AQX7LFNwK9"
      },
      "source": [
        "## Overview functions\n",
        "When dealing with large `DataFrames`, it is useful to get a quick overview of its content. Pandas offers a few functions for this. First, let's create a large `DataFrame` with a mix of numeric values, missing values and text values. Notice how Jupyter displays only the corners of the `DataFrame`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1qWi1VKPNwK-",
        "outputId": "55df9555-7f1c-46ed-e2bf-16cd2ae4f138"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
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              "      <th>C</th>\n",
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              "      <th>2</th>\n",
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              "      <td>33.0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>55.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>55.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>154.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>66.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>66.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>165.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>77.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>77.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>...</td>\n",
              "      <td>88.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>...</td>\n",
              "      <td>99.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>...</td>\n",
              "      <td>110.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>...</td>\n",
              "      <td>121.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>...</td>\n",
              "      <td>132.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>...</td>\n",
              "      <td>143.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>143.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>55.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>...</td>\n",
              "      <td>154.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>99.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>154.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>66.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>...</td>\n",
              "      <td>165.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>110.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>77.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>...</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>55.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>55.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>154.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>66.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>66.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>165.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>77.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>...</td>\n",
              "      <td>88.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>...</td>\n",
              "      <td>99.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>...</td>\n",
              "      <td>110.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>...</td>\n",
              "      <td>121.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>...</td>\n",
              "      <td>132.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>...</td>\n",
              "      <td>143.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9970</th>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>...</td>\n",
              "      <td>99.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9971</th>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>...</td>\n",
              "      <td>110.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9972</th>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>...</td>\n",
              "      <td>121.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9973</th>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>...</td>\n",
              "      <td>132.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9974</th>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>...</td>\n",
              "      <td>143.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9975</th>\n",
              "      <td>143.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>55.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>...</td>\n",
              "      <td>154.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>99.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9976</th>\n",
              "      <td>154.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>66.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>...</td>\n",
              "      <td>165.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>110.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9977</th>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>77.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9978</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9979</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9980</th>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9981</th>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>...</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9982</th>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9983</th>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>55.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9984</th>\n",
              "      <td>55.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>154.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>...</td>\n",
              "      <td>66.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9985</th>\n",
              "      <td>66.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>165.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>...</td>\n",
              "      <td>77.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9986</th>\n",
              "      <td>77.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>...</td>\n",
              "      <td>88.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>66.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9987</th>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>...</td>\n",
              "      <td>99.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9988</th>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>...</td>\n",
              "      <td>110.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>121.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9989</th>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>...</td>\n",
              "      <td>121.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9990</th>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>...</td>\n",
              "      <td>132.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9991</th>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>...</td>\n",
              "      <td>143.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>88.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9992</th>\n",
              "      <td>143.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>55.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>...</td>\n",
              "      <td>154.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>99.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9993</th>\n",
              "      <td>154.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>66.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>...</td>\n",
              "      <td>165.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>132.0</td>\n",
              "      <td>110.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9994</th>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>77.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143.0</td>\n",
              "      <td>121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>132.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>99.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>...</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          A      B      C some_text      D      E      F      G      H      I  \\\n",
              "0       NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
              "1      11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
              "2      22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
              "3      33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
              "4      44.0   55.0   88.0    Blabla  143.0   33.0  132.0   66.0   22.0    NaN   \n",
              "5      55.0   66.0   99.0    Blabla  154.0   44.0  143.0   77.0   33.0   11.0   \n",
              "6      66.0   77.0  110.0    Blabla  165.0   55.0  154.0   88.0   44.0   22.0   \n",
              "7      77.0   88.0  121.0    Blabla    NaN   66.0  165.0   99.0   55.0   33.0   \n",
              "8      88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
              "9      99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
              "10    110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
              "11    121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
              "12    132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
              "13    143.0  154.0    NaN    Blabla   55.0  132.0   44.0  165.0  121.0   99.0   \n",
              "14    154.0  165.0   11.0    Blabla   66.0  143.0   55.0    NaN  132.0  110.0   \n",
              "15    165.0    NaN   22.0    Blabla   77.0  154.0   66.0    NaN  143.0  121.0   \n",
              "16      NaN    NaN   33.0    Blabla   88.0  165.0   77.0   11.0  154.0  132.0   \n",
              "17      NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
              "18     11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
              "19     22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
              "20     33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
              "21     44.0   55.0   88.0    Blabla  143.0   33.0  132.0   66.0   22.0    NaN   \n",
              "22     55.0   66.0   99.0    Blabla  154.0   44.0  143.0   77.0   33.0   11.0   \n",
              "23     66.0   77.0  110.0    Blabla  165.0   55.0  154.0   88.0   44.0   22.0   \n",
              "24     77.0   88.0  121.0    Blabla    NaN   66.0  165.0   99.0   55.0   33.0   \n",
              "25     88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
              "26     99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
              "27    110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
              "28    121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
              "29    132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
              "...     ...    ...    ...       ...    ...    ...    ...    ...    ...    ...   \n",
              "9970   88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
              "9971   99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
              "9972  110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
              "9973  121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
              "9974  132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
              "9975  143.0  154.0    NaN    Blabla   55.0  132.0   44.0  165.0  121.0   99.0   \n",
              "9976  154.0  165.0   11.0    Blabla   66.0  143.0   55.0    NaN  132.0  110.0   \n",
              "9977  165.0    NaN   22.0    Blabla   77.0  154.0   66.0    NaN  143.0  121.0   \n",
              "9978    NaN    NaN   33.0    Blabla   88.0  165.0   77.0   11.0  154.0  132.0   \n",
              "9979    NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
              "9980   11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
              "9981   22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
              "9982   33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
              "9983   44.0   55.0   88.0    Blabla  143.0   33.0  132.0   66.0   22.0    NaN   \n",
              "9984   55.0   66.0   99.0    Blabla  154.0   44.0  143.0   77.0   33.0   11.0   \n",
              "9985   66.0   77.0  110.0    Blabla  165.0   55.0  154.0   88.0   44.0   22.0   \n",
              "9986   77.0   88.0  121.0    Blabla    NaN   66.0  165.0   99.0   55.0   33.0   \n",
              "9987   88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
              "9988   99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
              "9989  110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
              "9990  121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
              "9991  132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
              "9992  143.0  154.0    NaN    Blabla   55.0  132.0   44.0  165.0  121.0   99.0   \n",
              "9993  154.0  165.0   11.0    Blabla   66.0  143.0   55.0    NaN  132.0  110.0   \n",
              "9994  165.0    NaN   22.0    Blabla   77.0  154.0   66.0    NaN  143.0  121.0   \n",
              "9995    NaN    NaN   33.0    Blabla   88.0  165.0   77.0   11.0  154.0  132.0   \n",
              "9996    NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
              "9997   11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
              "9998   22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
              "9999   33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
              "\n",
              "      ...        Q      R      S      T      U      V      W      X      Y  \\\n",
              "0     ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
              "1     ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
              "2     ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
              "3     ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
              "4     ...     55.0   44.0   55.0   88.0  143.0   33.0  132.0   66.0   22.0   \n",
              "5     ...     66.0   55.0   66.0   99.0  154.0   44.0  143.0   77.0   33.0   \n",
              "6     ...     77.0   66.0   77.0  110.0  165.0   55.0  154.0   88.0   44.0   \n",
              "7     ...     88.0   77.0   88.0  121.0    NaN   66.0  165.0   99.0   55.0   \n",
              "8     ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
              "9     ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
              "10    ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
              "11    ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
              "12    ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
              "13    ...    154.0  143.0  154.0    NaN   55.0  132.0   44.0  165.0  121.0   \n",
              "14    ...    165.0  154.0  165.0   11.0   66.0  143.0   55.0    NaN  132.0   \n",
              "15    ...      NaN  165.0    NaN   22.0   77.0  154.0   66.0    NaN  143.0   \n",
              "16    ...      NaN    NaN    NaN   33.0   88.0  165.0   77.0   11.0  154.0   \n",
              "17    ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
              "18    ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
              "19    ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
              "20    ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
              "21    ...     55.0   44.0   55.0   88.0  143.0   33.0  132.0   66.0   22.0   \n",
              "22    ...     66.0   55.0   66.0   99.0  154.0   44.0  143.0   77.0   33.0   \n",
              "23    ...     77.0   66.0   77.0  110.0  165.0   55.0  154.0   88.0   44.0   \n",
              "24    ...     88.0   77.0   88.0  121.0    NaN   66.0  165.0   99.0   55.0   \n",
              "25    ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
              "26    ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
              "27    ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
              "28    ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
              "29    ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
              "...   ...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "9970  ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
              "9971  ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
              "9972  ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
              "9973  ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
              "9974  ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
              "9975  ...    154.0  143.0  154.0    NaN   55.0  132.0   44.0  165.0  121.0   \n",
              "9976  ...    165.0  154.0  165.0   11.0   66.0  143.0   55.0    NaN  132.0   \n",
              "9977  ...      NaN  165.0    NaN   22.0   77.0  154.0   66.0    NaN  143.0   \n",
              "9978  ...      NaN    NaN    NaN   33.0   88.0  165.0   77.0   11.0  154.0   \n",
              "9979  ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
              "9980  ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
              "9981  ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
              "9982  ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
              "9983  ...     55.0   44.0   55.0   88.0  143.0   33.0  132.0   66.0   22.0   \n",
              "9984  ...     66.0   55.0   66.0   99.0  154.0   44.0  143.0   77.0   33.0   \n",
              "9985  ...     77.0   66.0   77.0  110.0  165.0   55.0  154.0   88.0   44.0   \n",
              "9986  ...     88.0   77.0   88.0  121.0    NaN   66.0  165.0   99.0   55.0   \n",
              "9987  ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
              "9988  ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
              "9989  ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
              "9990  ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
              "9991  ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
              "9992  ...    154.0  143.0  154.0    NaN   55.0  132.0   44.0  165.0  121.0   \n",
              "9993  ...    165.0  154.0  165.0   11.0   66.0  143.0   55.0    NaN  132.0   \n",
              "9994  ...      NaN  165.0    NaN   22.0   77.0  154.0   66.0    NaN  143.0   \n",
              "9995  ...      NaN    NaN    NaN   33.0   88.0  165.0   77.0   11.0  154.0   \n",
              "9996  ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
              "9997  ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
              "9998  ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
              "9999  ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
              "\n",
              "          Z  \n",
              "0     143.0  \n",
              "1     154.0  \n",
              "2     165.0  \n",
              "3       NaN  \n",
              "4       NaN  \n",
              "5      11.0  \n",
              "6      22.0  \n",
              "7      33.0  \n",
              "8      44.0  \n",
              "9      55.0  \n",
              "10     66.0  \n",
              "11     77.0  \n",
              "12     88.0  \n",
              "13     99.0  \n",
              "14    110.0  \n",
              "15    121.0  \n",
              "16    132.0  \n",
              "17    143.0  \n",
              "18    154.0  \n",
              "19    165.0  \n",
              "20      NaN  \n",
              "21      NaN  \n",
              "22     11.0  \n",
              "23     22.0  \n",
              "24     33.0  \n",
              "25     44.0  \n",
              "26     55.0  \n",
              "27     66.0  \n",
              "28     77.0  \n",
              "29     88.0  \n",
              "...     ...  \n",
              "9970   44.0  \n",
              "9971   55.0  \n",
              "9972   66.0  \n",
              "9973   77.0  \n",
              "9974   88.0  \n",
              "9975   99.0  \n",
              "9976  110.0  \n",
              "9977  121.0  \n",
              "9978  132.0  \n",
              "9979  143.0  \n",
              "9980  154.0  \n",
              "9981  165.0  \n",
              "9982    NaN  \n",
              "9983    NaN  \n",
              "9984   11.0  \n",
              "9985   22.0  \n",
              "9986   33.0  \n",
              "9987   44.0  \n",
              "9988   55.0  \n",
              "9989   66.0  \n",
              "9990   77.0  \n",
              "9991   88.0  \n",
              "9992   99.0  \n",
              "9993  110.0  \n",
              "9994  121.0  \n",
              "9995  132.0  \n",
              "9996  143.0  \n",
              "9997  154.0  \n",
              "9998  165.0  \n",
              "9999    NaN  \n",
              "\n",
              "[10000 rows x 27 columns]"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
        "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
        "large_df[large_df % 16 == 0] = np.nan\n",
        "large_df.insert(3,\"some_text\", \"Blabla\")\n",
        "large_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EF1PQXOjNwK_"
      },
      "source": [
        "The `head()` method returns the top 5 rows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fqAhCe4rNwK_",
        "outputId": "e00ed882-5bb3-46c6-a48e-cc88fd2f9407"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>some_text</th>\n",
              "      <th>D</th>\n",
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              "      <th>...</th>\n",
              "      <th>Q</th>\n",
              "      <th>R</th>\n",
              "      <th>S</th>\n",
              "      <th>T</th>\n",
              "      <th>U</th>\n",
              "      <th>V</th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
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              "  </thead>\n",
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              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
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              "      <td>165.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>11.0</td>\n",
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              "      <td>99.0</td>\n",
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              "      <td>165.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "      <td>...</td>\n",
              "      <td>22.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "      <td>...</td>\n",
              "      <td>33.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>165.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>44.0</td>\n",
              "      <td>33.0</td>\n",
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              "      <td>77.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>Blabla</td>\n",
              "      <td>143.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>55.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      A     B     C some_text      D     E      F     G      H      I  ...    \\\n",
              "0   NaN  11.0  44.0    Blabla   99.0   NaN   88.0  22.0  165.0  143.0  ...     \n",
              "1  11.0  22.0  55.0    Blabla  110.0   NaN   99.0  33.0    NaN  154.0  ...     \n",
              "2  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0    NaN  165.0  ...     \n",
              "3  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0   11.0    NaN  ...     \n",
              "4  44.0  55.0  88.0    Blabla  143.0  33.0  132.0  66.0   22.0    NaN  ...     \n",
              "\n",
              "      Q     R     S     T      U     V      W     X      Y      Z  \n",
              "0  11.0   NaN  11.0  44.0   99.0   NaN   88.0  22.0  165.0  143.0  \n",
              "1  22.0  11.0  22.0  55.0  110.0   NaN   99.0  33.0    NaN  154.0  \n",
              "2  33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0    NaN  165.0  \n",
              "3  44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0   11.0    NaN  \n",
              "4  55.0  44.0  55.0  88.0  143.0  33.0  132.0  66.0   22.0    NaN  \n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "large_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVfBlmrfNwLA"
      },
      "source": [
        "Of course there's also a `tail()` function to view the bottom 5 rows. You can pass the number of rows you want:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7WZhhuYqNwLA",
        "outputId": "3239f83f-97a3-4a58-b534-cf9e873c1db5"
      },
      "outputs": [
        {
          "data": {
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              "      <th>9999</th>\n",
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              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "<p>2 rows × 27 columns</p>\n",
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              "         A     B     C some_text      D     E      F     G     H      I  \\\n",
              "9998  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0   NaN  165.0   \n",
              "9999  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0  11.0    NaN   \n",
              "\n",
              "      ...       Q     R     S     T      U     V      W     X     Y      Z  \n",
              "9998  ...    33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0   NaN  165.0  \n",
              "9999  ...    44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0  11.0    NaN  \n",
              "\n",
              "[2 rows x 27 columns]"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "large_df.tail(n=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b93CdT-YNwLB"
      },
      "source": [
        "The `info()` method prints out a summary of each columns contents:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xYI0stg6NwLC",
        "outputId": "13417258-9a31-4c12-ead4-7a7c9534c25d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 27 columns):\n",
            "A            8823 non-null float64\n",
            "B            8824 non-null float64\n",
            "C            8824 non-null float64\n",
            "some_text    10000 non-null object\n",
            "D            8824 non-null float64\n",
            "E            8822 non-null float64\n",
            "F            8824 non-null float64\n",
            "G            8824 non-null float64\n",
            "H            8822 non-null float64\n",
            "I            8823 non-null float64\n",
            "J            8823 non-null float64\n",
            "K            8822 non-null float64\n",
            "L            8824 non-null float64\n",
            "M            8824 non-null float64\n",
            "N            8822 non-null float64\n",
            "O            8824 non-null float64\n",
            "P            8824 non-null float64\n",
            "Q            8824 non-null float64\n",
            "R            8823 non-null float64\n",
            "S            8824 non-null float64\n",
            "T            8824 non-null float64\n",
            "U            8824 non-null float64\n",
            "V            8822 non-null float64\n",
            "W            8824 non-null float64\n",
            "X            8824 non-null float64\n",
            "Y            8822 non-null float64\n",
            "Z            8823 non-null float64\n",
            "dtypes: float64(26), object(1)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ],
      "source": [
        "large_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YrB9buRCNwLD"
      },
      "source": [
        "Finally, the `describe()` method gives a nice overview of the main aggregated values over each column:\n",
        "* `count`: number of non-null (not NaN) values\n",
        "* `mean`: mean of non-null values\n",
        "* `std`: [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of non-null values\n",
        "* `min`: minimum of non-null values\n",
        "* `25%`, `50%`, `75%`: 25th, 50th and 75th [percentile](https://en.wikipedia.org/wiki/Percentile) of non-null values\n",
        "* `max`: maximum of non-null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wm3ahNTaNwLE",
        "outputId": "220c8548-67b2-4d08-c6ea-f004057ed247"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "        text-align: right;\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>G</th>\n",
              "      <th>H</th>\n",
              "      <th>I</th>\n",
              "      <th>J</th>\n",
              "      <th>...</th>\n",
              "      <th>Q</th>\n",
              "      <th>R</th>\n",
              "      <th>S</th>\n",
              "      <th>T</th>\n",
              "      <th>U</th>\n",
              "      <th>V</th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8824.000000</td>\n",
              "      <td>8822.000000</td>\n",
              "      <td>8823.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>87.977559</td>\n",
              "      <td>87.972575</td>\n",
              "      <td>87.987534</td>\n",
              "      <td>88.012466</td>\n",
              "      <td>87.983791</td>\n",
              "      <td>88.007480</td>\n",
              "      <td>87.977561</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.022441</td>\n",
              "      <td>88.022441</td>\n",
              "      <td>...</td>\n",
              "      <td>87.972575</td>\n",
              "      <td>87.977559</td>\n",
              "      <td>87.972575</td>\n",
              "      <td>87.987534</td>\n",
              "      <td>88.012466</td>\n",
              "      <td>87.983791</td>\n",
              "      <td>88.007480</td>\n",
              "      <td>87.977561</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.022441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.535523</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.535001</td>\n",
              "      <td>47.519371</td>\n",
              "      <td>47.529755</td>\n",
              "      <td>47.536879</td>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.535911</td>\n",
              "      <td>...</td>\n",
              "      <td>47.535523</td>\n",
              "      <td>47.535911</td>\n",
              "      <td>47.535523</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.521679</td>\n",
              "      <td>47.535001</td>\n",
              "      <td>47.519371</td>\n",
              "      <td>47.529755</td>\n",
              "      <td>47.536879</td>\n",
              "      <td>47.535911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>44.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>88.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>132.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>165.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 26 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 A            B            C            D            E  \\\n",
              "count  8823.000000  8824.000000  8824.000000  8824.000000  8822.000000   \n",
              "mean     87.977559    87.972575    87.987534    88.012466    87.983791   \n",
              "std      47.535911    47.535523    47.521679    47.521679    47.535001   \n",
              "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
              "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
              "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
              "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
              "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
              "\n",
              "                 F            G            H            I            J  \\\n",
              "count  8824.000000  8824.000000  8822.000000  8823.000000  8823.000000   \n",
              "mean     88.007480    87.977561    88.000000    88.022441    88.022441   \n",
              "std      47.519371    47.529755    47.536879    47.535911    47.535911   \n",
              "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
              "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
              "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
              "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
              "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
              "\n",
              "          ...                 Q            R            S            T  \\\n",
              "count     ...       8824.000000  8823.000000  8824.000000  8824.000000   \n",
              "mean      ...         87.972575    87.977559    87.972575    87.987534   \n",
              "std       ...         47.535523    47.535911    47.535523    47.521679   \n",
              "min       ...         11.000000    11.000000    11.000000    11.000000   \n",
              "25%       ...         44.000000    44.000000    44.000000    44.000000   \n",
              "50%       ...         88.000000    88.000000    88.000000    88.000000   \n",
              "75%       ...        132.000000   132.000000   132.000000   132.000000   \n",
              "max       ...        165.000000   165.000000   165.000000   165.000000   \n",
              "\n",
              "                 U            V            W            X            Y  \\\n",
              "count  8824.000000  8822.000000  8824.000000  8824.000000  8822.000000   \n",
              "mean     88.012466    87.983791    88.007480    87.977561    88.000000   \n",
              "std      47.521679    47.535001    47.519371    47.529755    47.536879   \n",
              "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
              "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
              "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
              "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
              "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
              "\n",
              "                 Z  \n",
              "count  8823.000000  \n",
              "mean     88.022441  \n",
              "std      47.535911  \n",
              "min      11.000000  \n",
              "25%      44.000000  \n",
              "50%      88.000000  \n",
              "75%     132.000000  \n",
              "max     165.000000  \n",
              "\n",
              "[8 rows x 26 columns]"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "large_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpiMBcDaNwLE"
      },
      "source": [
        "# Saving & loading\n",
        "Pandas can save `DataFrame`s to various backends, including file formats such as CSV, Excel, JSON, HTML and HDF5, or to a SQL database. Let's create a `DataFrame` to demonstrate this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eZg5q7oVNwLF",
        "outputId": "4194f91a-23a0-4675-e633-cc3f36861e79"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68.5</td>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83.1</td>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         hobby  weight  birthyear  children\n",
              "alice   Biking    68.5       1985       NaN\n",
              "bob    Dancing    83.1       1984       3.0"
            ]
          },
          "execution_count": 129,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "my_df = pd.DataFrame(\n",
        "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]], \n",
        "    columns=[\"hobby\",\"weight\",\"birthyear\",\"children\"],\n",
        "    index=[\"alice\", \"bob\"]\n",
        ")\n",
        "my_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFiUak53NwLG"
      },
      "source": [
        "## Saving\n",
        "Let's save it to CSV, HTML and JSON:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nC6WFe-ONwLG"
      },
      "outputs": [],
      "source": [
        "my_df.to_csv(\"my_df.csv\")\n",
        "my_df.to_html(\"my_df.html\")\n",
        "my_df.to_json(\"my_df.json\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQ5P7ZBANwLJ"
      },
      "source": [
        "Done! Let's take a peek at what was saved:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "etNYRSjPNwLJ",
        "outputId": "98a6df13-fa73-4b9b-ab8b-978311973c6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "# my_df.csv\n",
            ",hobby,weight,birthyear,children\n",
            "alice,Biking,68.5,1985,\n",
            "bob,Dancing,83.1,1984,3.0\n",
            "\n",
            "\n",
            "# my_df.html\n",
            "<table border=\"1\" class=\"dataframe\">\n",
            "  <thead>\n",
            "    <tr style=\"text-align: right;\">\n",
            "      <th></th>\n",
            "      <th>hobby</th>\n",
            "      <th>weight</th>\n",
            "      <th>birthyear</th>\n",
            "      <th>children</th>\n",
            "    </tr>\n",
            "  </thead>\n",
            "  <tbody>\n",
            "    <tr>\n",
            "      <th>alice</th>\n",
            "      <td>Biking</td>\n",
            "      <td>68.5</td>\n",
            "      <td>1985</td>\n",
            "      <td>NaN</td>\n",
            "    </tr>\n",
            "    <tr>\n",
            "      <th>bob</th>\n",
            "      <td>Dancing</td>\n",
            "      <td>83.1</td>\n",
            "      <td>1984</td>\n",
            "      <td>3.0</td>\n",
            "    </tr>\n",
            "  </tbody>\n",
            "</table>\n",
            "\n",
            "# my_df.json\n",
            "{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
        "    print(\"#\", filename)\n",
        "    with open(filename, \"rt\") as f:\n",
        "        print(f.read())\n",
        "        print()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkyHANs1NwLK"
      },
      "source": [
        "Note that the index is saved as the first column (with no name) in a CSV file, as `<th>` tags in HTML and as keys in JSON.\n",
        "\n",
        "Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sR_VI_DvNwLK",
        "outputId": "ecce3421-a0c9-40db-ea7e-d71686a1efb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No module named 'openpyxl'\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
        "except ImportError as e:\n",
        "    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dan7Ynk8NwLL"
      },
      "source": [
        "## Loading\n",
        "Now let's load our CSV file back into a `DataFrame`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIDqAE7QNwLL",
        "outputId": "e9302e11-3c19-4ff4-91b3-656606a23b63"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hobby</th>\n",
              "      <th>weight</th>\n",
              "      <th>birthyear</th>\n",
              "      <th>children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alice</th>\n",
              "      <td>Biking</td>\n",
              "      <td>68.5</td>\n",
              "      <td>1985</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bob</th>\n",
              "      <td>Dancing</td>\n",
              "      <td>83.1</td>\n",
              "      <td>1984</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         hobby  weight  birthyear  children\n",
              "alice   Biking    68.5       1985       NaN\n",
              "bob    Dancing    83.1       1984       3.0"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
        "my_df_loaded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXtNEODWNwLM"
      },
      "source": [
        "As you might guess, there are similar `read_json`, `read_html`, `read_excel` functions as well.  We can also read data straight from the Internet. For example, let's load the top 1,000 U.S. cities from github:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xDiUfk0mNwLM",
        "outputId": "c68a85ba-ea56-4add-823b-41d7308bac81"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>Population</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Marysville</th>\n",
              "      <td>Washington</td>\n",
              "      <td>63269</td>\n",
              "      <td>48.051764</td>\n",
              "      <td>-122.177082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perris</th>\n",
              "      <td>California</td>\n",
              "      <td>72326</td>\n",
              "      <td>33.782519</td>\n",
              "      <td>-117.228648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cleveland</th>\n",
              "      <td>Ohio</td>\n",
              "      <td>390113</td>\n",
              "      <td>41.499320</td>\n",
              "      <td>-81.694361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Worcester</th>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>182544</td>\n",
              "      <td>42.262593</td>\n",
              "      <td>-71.802293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Columbia</th>\n",
              "      <td>South Carolina</td>\n",
              "      <td>133358</td>\n",
              "      <td>34.000710</td>\n",
              "      <td>-81.034814</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     State  Population        lat         lon\n",
              "City                                                         \n",
              "Marysville      Washington       63269  48.051764 -122.177082\n",
              "Perris          California       72326  33.782519 -117.228648\n",
              "Cleveland             Ohio      390113  41.499320  -81.694361\n",
              "Worcester    Massachusetts      182544  42.262593  -71.802293\n",
              "Columbia    South Carolina      133358  34.000710  -81.034814"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "us_cities = None\n",
        "try:\n",
        "    csv_url = \"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv\"\n",
        "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
        "    us_cities = us_cities.head()\n",
        "except IOError as e:\n",
        "    print(e)\n",
        "us_cities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjuWPuuKNwLN"
      },
      "source": [
        "There are more options available, in particular regarding datetime format. Check out the [documentation](http://pandas.pydata.org/pandas-docs/stable/io.html) for more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXS9Q85zNwLN"
      },
      "source": [
        "# Combining `DataFrame`s\n",
        "\n",
        "## SQL-like joins\n",
        "One powerful feature of pandas is it's ability to perform SQL-like joins on `DataFrame`s. Various types of joins are supported: inner joins, left/right outer joins and full joins. To illustrate this, let's start by creating a couple simple `DataFrame`s:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vt4iotMqNwLO",
        "outputId": "7c3bc344-7baf-49e7-de4c-3440464683eb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state            city        lat         lng\n",
              "0    CA   San Francisco  37.781334 -122.416728\n",
              "1    NY        New York  40.705649  -74.008344\n",
              "2    FL           Miami  25.791100  -80.320733\n",
              "3    OH       Cleveland  41.473508  -81.739791\n",
              "4    UT  Salt Lake City  40.755851 -111.896657"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_loc = pd.DataFrame(\n",
        "    [\n",
        "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
        "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
        "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
        "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
        "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
        "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
        "city_loc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KVrKCFGmNwLO",
        "outputId": "e06be6fe-bf01-4c47-9e11-4b60a04b3940"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state\n",
              "3      808976  San Francisco  California\n",
              "4     8363710       New York    New-York\n",
              "5      413201          Miami     Florida\n",
              "6     2242193        Houston       Texas"
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_pop = pd.DataFrame(\n",
        "    [\n",
        "        [808976, \"San Francisco\", \"California\"],\n",
        "        [8363710, \"New York\", \"New-York\"],\n",
        "        [413201, \"Miami\", \"Florida\"],\n",
        "        [2242193, \"Houston\", \"Texas\"]\n",
        "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
        "city_pop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5xHPXwENwLP"
      },
      "source": [
        "Now let's join these `DataFrame`s using the `merge()` function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JUhiFrkNNwLP",
        "outputId": "e88be03b-e9a4-4f01-b8ee-9b673d18b699"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x           city        lat         lng  population     state_y\n",
              "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
              "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
              "2      FL          Miami  25.791100  -80.320733      413201     Florida"
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KoEsvuXVNwLQ"
      },
      "source": [
        "Note that both `DataFrame`s have a column named `state`, so in the result they got renamed to `state_x` and `state_y`.\n",
        "\n",
        "Also, note that Cleveland, Salt Lake City and Houston were dropped because they don't exist in *both* `DataFrame`s. This is the equivalent of a SQL `INNER JOIN`. If you want a `FULL OUTER JOIN`, where no city gets dropped and `NaN` values are added, you must specify `how=\"outer\"`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2EmxkfA8NwLQ",
        "outputId": "4ca3223b-2165-4567-8eab-19f813f07a65"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x            city        lat         lng  population     state_y\n",
              "0      CA   San Francisco  37.781334 -122.416728    808976.0  California\n",
              "1      NY        New York  40.705649  -74.008344   8363710.0    New-York\n",
              "2      FL           Miami  25.791100  -80.320733    413201.0     Florida\n",
              "3      OH       Cleveland  41.473508  -81.739791         NaN         NaN\n",
              "4      UT  Salt Lake City  40.755851 -111.896657         NaN         NaN\n",
              "5     NaN         Houston        NaN         NaN   2242193.0       Texas"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
        "all_cities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RKN-qBNNwLR"
      },
      "source": [
        "Of course `LEFT OUTER JOIN` is also available by setting `how=\"left\"`: only the cities present in the left `DataFrame` end up in the result. Similarly, with `how=\"right\"` only cities in the right `DataFrame` appear in the result. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bZy6oXM5NwLR",
        "outputId": "d7d4a3de-45ea-4ae6-84a5-adb1cd1b89fb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x           city        lat         lng  population     state_y\n",
              "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
              "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
              "2      FL          Miami  25.791100  -80.320733      413201     Florida\n",
              "3     NaN        Houston        NaN         NaN     2242193       Texas"
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tya2Vd62NwLT"
      },
      "source": [
        "If the key to join on is actually in one (or both) `DataFrame`'s index, you must use `left_index=True` and/or `right_index=True`. If the key column names differ, you must use `left_on` and `right_on`. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pmqXJY4rNwLU",
        "outputId": "2268f773-e13c-4353-9267-062e972a76c1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state_x</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>name</th>\n",
              "      <th>state_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state_x           city        lat         lng  population           name  \\\n",
              "0      CA  San Francisco  37.781334 -122.416728      808976  San Francisco   \n",
              "1      NY       New York  40.705649  -74.008344     8363710       New York   \n",
              "2      FL          Miami  25.791100  -80.320733      413201          Miami   \n",
              "\n",
              "      state_y  \n",
              "0  California  \n",
              "1    New-York  \n",
              "2     Florida  "
            ]
          },
          "execution_count": 140,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_pop2 = city_pop.copy()\n",
        "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
        "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YF1LcaGNwLV"
      },
      "source": [
        "## Concatenation\n",
        "Rather than joining `DataFrame`s, we may just want to concatenate them. That's what `concat()` is for:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FroKSkyRNwLW",
        "outputId": "3e03cc17-a244-4c0f-e01b-e2bef52075e0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Miami</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             city        lat         lng  population       state\n",
              "0   San Francisco  37.781334 -122.416728         NaN          CA\n",
              "1        New York  40.705649  -74.008344         NaN          NY\n",
              "2           Miami  25.791100  -80.320733         NaN          FL\n",
              "3       Cleveland  41.473508  -81.739791         NaN          OH\n",
              "4  Salt Lake City  40.755851 -111.896657         NaN          UT\n",
              "3   San Francisco        NaN         NaN    808976.0  California\n",
              "4        New York        NaN         NaN   8363710.0    New-York\n",
              "5           Miami        NaN         NaN    413201.0     Florida\n",
              "6         Houston        NaN         NaN   2242193.0       Texas"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_concat = pd.concat([city_loc, city_pop])\n",
        "result_concat"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RKstyAfNwLW"
      },
      "source": [
        "Note that this operation aligned the data horizontally (by columns) but not vertically (by rows). In this example, we end up with multiple rows having the same index (eg. 3). Pandas handles this rather gracefully:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i9nyA1N2NwLY",
        "outputId": "ff20167d-9ee9-48ad-85b2-b020ec65e379"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            city        lat        lng  population       state\n",
              "3      Cleveland  41.473508 -81.739791         NaN          OH\n",
              "3  San Francisco        NaN        NaN    808976.0  California"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result_concat.loc[3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTk2ACJMNwLY"
      },
      "source": [
        "Or you can tell pandas to just ignore the index:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OJgJlGF-NwLZ",
        "outputId": "e492b5e8-0bbb-4ad7-bbb5-dfef3f98b466"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>New York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Miami</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             city        lat         lng  population       state\n",
              "0   San Francisco  37.781334 -122.416728         NaN          CA\n",
              "1        New York  40.705649  -74.008344         NaN          NY\n",
              "2           Miami  25.791100  -80.320733         NaN          FL\n",
              "3       Cleveland  41.473508  -81.739791         NaN          OH\n",
              "4  Salt Lake City  40.755851 -111.896657         NaN          UT\n",
              "5   San Francisco        NaN         NaN    808976.0  California\n",
              "6        New York        NaN         NaN   8363710.0    New-York\n",
              "7           Miami        NaN         NaN    413201.0     Florida\n",
              "8         Houston        NaN         NaN   2242193.0       Texas"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([city_loc, city_pop], ignore_index=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3EnmjF6NwLZ"
      },
      "source": [
        "Notice that when a column does not exist in a `DataFrame`, it acts as if it was filled with `NaN` values. If we set `join=\"inner\"`, then only columns that exist in *both* `DataFrame`s are returned:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ebMjbjrNwLa",
        "outputId": "151cf628-d01b-440b-b30d-e1154c897cdb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>San Francisco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New-York</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Florida</td>\n",
              "      <td>Miami</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Texas</td>\n",
              "      <td>Houston</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        state            city\n",
              "0          CA   San Francisco\n",
              "1          NY        New York\n",
              "2          FL           Miami\n",
              "3          OH       Cleveland\n",
              "4          UT  Salt Lake City\n",
              "3  California   San Francisco\n",
              "4    New-York        New York\n",
              "5     Florida           Miami\n",
              "6       Texas         Houston"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([city_loc, city_pop], join=\"inner\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "if0UicVANwLa"
      },
      "source": [
        "You can concatenate `DataFrame`s horizontally instead of vertically by setting `axis=1`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "S6XRkf9rNwLb",
        "outputId": "709100d6-b08e-4729-a7ca-07b316fa975c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CA</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NY</td>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FL</td>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OH</td>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>UT</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state            city        lat         lng  population           city  \\\n",
              "0    CA   San Francisco  37.781334 -122.416728         NaN            NaN   \n",
              "1    NY        New York  40.705649  -74.008344         NaN            NaN   \n",
              "2    FL           Miami  25.791100  -80.320733         NaN            NaN   \n",
              "3    OH       Cleveland  41.473508  -81.739791    808976.0  San Francisco   \n",
              "4    UT  Salt Lake City  40.755851 -111.896657   8363710.0       New York   \n",
              "5   NaN             NaN        NaN         NaN    413201.0          Miami   \n",
              "6   NaN             NaN        NaN         NaN   2242193.0        Houston   \n",
              "\n",
              "        state  \n",
              "0         NaN  \n",
              "1         NaN  \n",
              "2         NaN  \n",
              "3  California  \n",
              "4    New-York  \n",
              "5     Florida  \n",
              "6       Texas  "
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([city_loc, city_pop], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rXhijS8NwLc"
      },
      "source": [
        "In this case it really does not make much sense because the indices do not align well (eg. Cleveland and San Francisco end up on the same row, because they shared the index label `3`). So let's reindex the `DataFrame`s by city name before concatenating:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "Zr2YemO1NwLd",
        "outputId": "5190bcdc-2a0c-4526-a90e-18d6fba93fd6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cleveland</th>\n",
              "      <td>OH</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Houston</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miami</th>\n",
              "      <td>FL</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New York</th>\n",
              "      <td>NY</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Salt Lake City</th>\n",
              "      <td>UT</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>San Francisco</th>\n",
              "      <td>CA</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               state        lat         lng  population       state\n",
              "Cleveland         OH  41.473508  -81.739791         NaN         NaN\n",
              "Houston          NaN        NaN         NaN   2242193.0       Texas\n",
              "Miami             FL  25.791100  -80.320733    413201.0     Florida\n",
              "New York          NY  40.705649  -74.008344   8363710.0    New-York\n",
              "Salt Lake City    UT  40.755851 -111.896657         NaN         NaN\n",
              "San Francisco     CA  37.781334 -122.416728    808976.0  California"
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZ409vSSNwLe"
      },
      "source": [
        "This looks a lot like a `FULL OUTER JOIN`, except that the `state` columns were not renamed to `state_x` and `state_y`, and the `city` column is now the index."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kl8DbbjNwLe"
      },
      "source": [
        "The `append()` method is a useful shorthand for concatenating `DataFrame`s vertically:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZpnxJ9qINwLf",
        "outputId": "1142ba01-7b4b-4402-f685-f9fa795f3144"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>population</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>37.781334</td>\n",
              "      <td>-122.416728</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New York</td>\n",
              "      <td>40.705649</td>\n",
              "      <td>-74.008344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Miami</td>\n",
              "      <td>25.791100</td>\n",
              "      <td>-80.320733</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cleveland</td>\n",
              "      <td>41.473508</td>\n",
              "      <td>-81.739791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>40.755851</td>\n",
              "      <td>-111.896657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>San Francisco</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808976.0</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>New York</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8363710.0</td>\n",
              "      <td>New-York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Miami</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>413201.0</td>\n",
              "      <td>Florida</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2242193.0</td>\n",
              "      <td>Texas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             city        lat         lng  population       state\n",
              "0   San Francisco  37.781334 -122.416728         NaN          CA\n",
              "1        New York  40.705649  -74.008344         NaN          NY\n",
              "2           Miami  25.791100  -80.320733         NaN          FL\n",
              "3       Cleveland  41.473508  -81.739791         NaN          OH\n",
              "4  Salt Lake City  40.755851 -111.896657         NaN          UT\n",
              "3   San Francisco        NaN         NaN    808976.0  California\n",
              "4        New York        NaN         NaN   8363710.0    New-York\n",
              "5           Miami        NaN         NaN    413201.0     Florida\n",
              "6         Houston        NaN         NaN   2242193.0       Texas"
            ]
          },
          "execution_count": 147,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_loc.append(city_pop)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVbdv2NxNwLf"
      },
      "source": [
        "As always in pandas, the `append()` method does *not* actually modify `city_loc`: it works on a copy and returns the modified copy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pwwp9kZWNwLg"
      },
      "source": [
        "# Categories\n",
        "It is quite frequent to have values that represent categories, for example `1` for female and `2` for male, or `\"A\"` for Good, `\"B\"` for Average, `\"C\"` for Bad. These categorical values can be hard to read and cumbersome to handle, but fortunately pandas makes it easy. To illustrate this, let's take the `city_pop` `DataFrame` we created earlier, and add a column that represents a category:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C5MWXn1VNwLg",
        "outputId": "8443dabc-dadf-4115-df70-d0e4aa8dde8d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>eco_code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state  eco_code\n",
              "3      808976  San Francisco  California        17\n",
              "4     8363710       New York    New-York        17\n",
              "5      413201          Miami     Florida        34\n",
              "6     2242193        Houston       Texas        20"
            ]
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_eco = city_pop.copy()\n",
        "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
        "city_eco"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hu-uvclTNwLh"
      },
      "source": [
        "Right now the `eco_code` column is full of apparently meaningless codes. Let's fix that. First, we will create a new categorical column based on the `eco_code`s:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZnqM5aTZNwLi",
        "outputId": "d4ed94e8-94b1-47c3-c5b9-0399de8c81db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Int64Index([17, 20, 34], dtype='int64')"
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_eco[\"economy\"] = city_eco[\"eco_code\"].astype('category')\n",
        "city_eco[\"economy\"].cat.categories"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4q17BbdNwLi"
      },
      "source": [
        "Now we can give each category a meaningful name:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UWPwh7xHNwLj",
        "outputId": "6f41f611-cf8a-4fa2-b60b-9e71d6fad1ae"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>eco_code</th>\n",
              "      <th>economy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>34</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>20</td>\n",
              "      <td>Energy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state  eco_code  economy\n",
              "3      808976  San Francisco  California        17  Finance\n",
              "4     8363710       New York    New-York        17  Finance\n",
              "5      413201          Miami     Florida        34  Tourism\n",
              "6     2242193        Houston       Texas        20   Energy"
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_eco[\"economy\"].cat.categories = [\"Finance\", \"Energy\", \"Tourism\"]\n",
        "city_eco"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HUPqyLGNwLk"
      },
      "source": [
        "Note that categorical values are sorted according to their categorical order, *not* their alphabetical order:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y_XP1q_YNwLk",
        "outputId": "57905d2c-aa59-46a7-e2f1-3285e7e0b6fe"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style>\n",
              "    .dataframe thead tr:only-child th {\n",
              "        text-align: right;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>eco_code</th>\n",
              "      <th>economy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>413201</td>\n",
              "      <td>Miami</td>\n",
              "      <td>Florida</td>\n",
              "      <td>34</td>\n",
              "      <td>Tourism</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2242193</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>20</td>\n",
              "      <td>Energy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8363710</td>\n",
              "      <td>New York</td>\n",
              "      <td>New-York</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>808976</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>17</td>\n",
              "      <td>Finance</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   population           city       state  eco_code  economy\n",
              "5      413201          Miami     Florida        34  Tourism\n",
              "6     2242193        Houston       Texas        20   Energy\n",
              "4     8363710       New York    New-York        17  Finance\n",
              "3      808976  San Francisco  California        17  Finance"
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "city_eco.sort_values(by=\"economy\", ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLyqLAX1NwLl"
      },
      "source": [
        "# What next?\n",
        "As you probably noticed by now, pandas is quite a large library with *many* features. Although we went through the most important features, there is still a lot to discover. Probably the best way to learn more is to get your hands dirty with some real-life data. It is also a good idea to go through pandas' excellent [documentation](http://pandas.pydata.org/pandas-docs/stable/index.html), in particular the [Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LrEBAc3XNwLl"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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