Refined_version_Metropolitan_Museum_Art_EDA.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mihaitudor9/met_collection_cleaning/blob/main/Metropolitan_Museum_Art_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "**The point of this project is to retrieve a \"dirty\" dataset available online and come up with a few research objectives that could be achieved after the EDA procedures. The task also requires comparing two studied algorithms (e.g., regression, classification, DR, etc.) employing the retrieved dataset.**"
      ],
      "metadata": {
        "id": "UyvEcziO12Ty"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le_g4Hiqj3JU"
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      "source": [
        "# Findings ways of improvements of the collection of paintings and drawings in the Metropolitan Museum Of Art\n",
        "\n",
        "Natalia Guseva\n",
        "\n",
        "Paula Gitu\n",
        "\n",
        "Mihai Tudor\n",
        "\n",
        "\n",
        "Maastricht University 2021\n",
        "\n",
        "Data Analysis(KEN 3450)\n",
        "\n",
        "2021"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqs27Q-6XztO"
      },
      "source": [
        "# Ethical Considerations\n",
        "\n",
        "1. **Source of the dataset:** The dataset has been downloaded from Github: https://github.com/metmuseum/openaccess\n",
        "\n",
        "2. **How the dataset was created ?** The dataset looks like the result of scraping. A short look at https://www.metmuseum.org/art/collection/search/436532 shall prove that the info in each field in of .csv file originates from the metmuseum.org website.\n",
        "3.  There are plenty of meaningful insights from this analysis we performed. However, it was almost impossible to think of a practical yet quality prediction model. Moreover, as stated in due time, we might have encountered specific problems precisely answering the research question we mentioned in the project proposal. Still, given our updated knowledge about the dataset, we quickly reoriented and found other aspects to focus on. \n",
        "\n",
        "4. **Did you make decisions that affect the analysis?** Certainly, plenty of items had missing or unusable essential fields (i.e., dimensions, year of completion, or country). Therefore, several items had to be deleted, or specific analyses such as geographical ones had to be conducted on very tiny subsets of the complete data.\n",
        "\n",
        "\n",
        "5. Due to an extreme variety in text fields, a complete clean-up, and analysis of all object types were impossible to perform on a dataset containing all 100K items. Therefore, our analysis focused only on the drawings and paintings within the dataset.\n",
        "\n",
        "6. **The main stakeholder of our project is the museum itself**. As evoked in the project proposal, a museum as famous as Met Museum in NY shall have detailed statistics of all stored items. \n",
        "\n",
        "  \n",
        "  6.1. They should be completely aware of where most of their items originate from or, in contrast, which country or region is missing items from. \n",
        "  \n",
        "  6.2. Likewise, a museum should have competent statistics over how diverse the nationalities of the artists being displayed there are. \n",
        "  \n",
        "  6.3. Last but not least, such a famous museum shall perform concrete statistics on the available market before deciding on extending an existing wing of the museum or building an additional one to incorporate the artwork to arrive in the coming years.\n",
        "\n",
        "7. **Who can benefit from this project?** Artists from countries that are not very well represented in the collection or even items completed in a specific year where very few other artworks were completed.\n",
        "The possible visitors might benefit from this project as well. Such statistics could be released to the public such that any potential visitor is thoroughly informed of the overall source of articles. Certain visitors might be more fascinated by the artwork being inspired and created in the early 17th century or the 1920s. The satistical results, showing that those periods might be well represented in the Met Collection, can influence the visitors to visit as soon as possible."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QR0KvmtfNHos"
      },
      "source": [
        "import matplotlib\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import statsmodels.formula.api as sm\n",
        "import re\n",
        "from numpy import arange\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "sns.set_theme(style=\"darkgrid\")\n",
        "pd.set_option('mode.chained_assignment', None)\n",
        "pd.set_option('display.max_columns', None)\n",
        "\n",
        "# Displaying all values of the dataframe if necessary\n",
        "#pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVhLNevDNHox"
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      "source": [
        "# Due to mixed values, we will be treating most fields as a text for now and see if there\n",
        "# is anything to be done such that numerical values could be extracted out of these.\n",
        "# There are, however, a few Boolean and Numerical types exceptions.\n",
        "\n",
        "df = pd.read_csv(\"MetObjects.txt\", dtype={\n",
        "    'Gallery Number': str, 'Period': str,\n",
        "    'Portfolio': str,\n",
        "    'Dimensions': str\n",
        "    }, low_memory=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2Jj8u4sHvBG"
      },
      "source": [
        "For the first part of our analysis, we would like to focus on the **Paintings** and **Drawings**. Indeed, there are many similarities between these two types of artworks, and we will study these throughout the notebook and the differences."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNPoqHItvvnP"
      },
      "source": [
        "# For this 1st part of the analysis, we are only interested in the\n",
        "# paintings and drawings\n",
        "df = df.loc[df['Classification'].isin(['Paintings', 'Drawings'])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6r5V9PLJYbnb"
      },
      "source": [
        "While the complete list of columns is much larger, there was plenty of redundant information within the columns. (e.g., two columns were representing the ID)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rA8-JvuPNHoy"
      },
      "source": [
        "# Re-order the columns such that <Object ID> is the first column.\n",
        "# Other reorders should follow\n",
        "df = df[\n",
        "    ['Object ID', 'Department', 'AccessionYear', 'Object Name', 'Title', 'Culture',\n",
        "     'Reign', 'Artist Display Name', 'Artist Nationality', 'Artist Begin Date', 'Artist End Date',\n",
        "     'Artist Wikidata URL',\n",
        "     'Object Begin Date', 'Object End Date', 'Medium', 'Dimensions',\n",
        "     'City', 'Country', 'Classification', 'Object Wikidata URL', 'Tags']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HME4padewUVK"
      },
      "source": [
        "As much as we enjoyed performing geographical analysis on the data, the **\"Country\"** variable is missing for 98% of the items. Therefore, answering questions such as \"Do we need items from a certain Country or Region to have a complete and submerging experience in our museum\" remains relatively unanswered. However, we will perform some analysis on that 2% just to get an idea of where most items could be originating from, without stating that such an analysis is pertinent. \n",
        "\n",
        "Even though the **\"Artist Nationality\"** is present in 85% of the items, we will see that it is almost impossible to extract any relevant geographical info from that feature.\n",
        "\n",
        "The **\"Reign\"** variable seems missing from all items. Therefore, that column will get deleted. Similarly, there are only many items containing the **\"City\"** field."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ya_XuvwENHoy",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da053423-f6cf-494c-804b-f0ba50509870"
      },
      "source": [
        "# Number of rows that have a value NaN\n",
        "for column in df:\n",
        "    print(column, len(df[pd.isnull(df[column])]), \"/\", len(df['Object ID']))\n",
        "\n",
        "del df['Reign']\n",
        "del df['City']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Object ID 0 / 36280\n",
            "Department 0 / 36280\n",
            "AccessionYear 90 / 36280\n",
            "Object Name 25 / 36280\n",
            "Title 2220 / 36280\n",
            "Culture 26912 / 36280\n",
            "Reign 36280 / 36280\n",
            "Artist Display Name 1273 / 36280\n",
            "Artist Nationality 5793 / 36280\n",
            "Artist Begin Date 4879 / 36280\n",
            "Artist End Date 4865 / 36280\n",
            "Artist Wikidata URL 8727 / 36280\n",
            "Object Begin Date 0 / 36280\n",
            "Object End Date 0 / 36280\n",
            "Medium 301 / 36280\n",
            "Dimensions 706 / 36280\n",
            "City 36188 / 36280\n",
            "Country 35597 / 36280\n",
            "Classification 0 / 36280\n",
            "Object Wikidata URL 25603 / 36280\n",
            "Tags 6957 / 36280\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBxsviAVNHo0"
      },
      "source": [
        "# Clean up of the Culture column\n",
        "df['Culture'] = df['Culture'].str.replace('for export', '')\n",
        "df['Culture'] = df['Culture'].str.replace(', for Swedish market', ' (Swedish market)')\n",
        "df['Culture'] = df['Culture'].str.replace(', for American market', ' (American market)')\n",
        "df['Culture'] = df['Culture'].str.replace('or', ',')\n",
        "df['Culture'] = df['Culture'].str.replace(', probably', '')\n",
        "df['Culture'] = df['Culture'].str.replace(', possibly', '')\n",
        "df['Culture'] = df['Culture'].str.replace('probably', '')\n",
        "\n",
        "# There is some evident redundancy in the Culture and Country fields as well as some typos\n",
        "df['Culture'] = df['Culture'].str.replace('(Veneto)', 'Veneto')\n",
        "df['Culture'] = df['Culture'].str.replace('K,ea', 'K,ea, Japan')\n",
        "df['Culture'] = df['Culture'].str.replace('Fl,ence', 'Florence')\n",
        "\n",
        "# USA -> United States\n",
        "# US -> United States\n",
        "# The United States -> United States\n",
        "df['Country'] = df['Country'].str.replace('USA', 'United States')\n",
        "df['Country'] = df['Country'].str.replace('US', 'United States')\n",
        "df['Country'] = df['Country'].str.replace('The United States', 'United States')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23OIcFL3NHo1"
      },
      "source": [
        "df.loc[:, 'Covered Area'] = -1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1jmESeN4UHD"
      },
      "source": [
        "Apart from missing values in the **'Dimensions'** field, we notice other types of inadequately inserted dimensions.\n",
        "\n",
        "A big part of our analysis focused on the dimensions. As a result of this, we will be cleaning the items with unworkable dimensions such as:\n",
        "unavailable, irregular, trimmed, with base, N.A, trimmed to a plate or other types from which we could not extract usable dimensions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzTcAMESNHo2"
      },
      "source": [
        "df = df[df['Dimensions'].notna()]\n",
        "# Originally, the dimensions of each object were stored both in inches and centimeters\n",
        "# Should only keep the centimeters dimensions written within the () brackets\n",
        "# i.e. 15 3/4 x 11 3/4 in. (40 x 29.8 cm) - > 40 x 29.8\n",
        "# keeping in mind that the resulting column will denote the dimensions in CM\n",
        "df['Dimensions'] = df['Dimensions'].apply(lambda st: st[st.find(\"(\") + 1:st.find(\")\")])\n",
        "df['Dimensions'] = (df['Dimensions'].str.replace(\"cm\", \"\"))\n",
        "df['Dimensions'] = (df['Dimensions'].str.replace(\" \", \"\"))\n",
        "df['Dimensions'] = (df['Dimensions'].str.replace(\"×\", \"x\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WsVDn9PNHo1"
      },
      "source": [
        "# below, we look for each object's dimensions, and if it does not contain a digit, we delete it\n",
        "for index, dimensions in enumerate(df['Dimensions']):\n",
        "    string = df.iloc[index]['Dimensions']\n",
        "    nr_d = 0\n",
        "    for c in string:\n",
        "        if c.isdigit():\n",
        "            nr_d += 1\n",
        "    if (nr_d == 0): # if the dimensions contain no digit\n",
        "        df.at[index, 'Dimensions'] = 'DELETE' # mark the item\n",
        "\n",
        "df = df[~df['Dimensions'].str.contains(\"DELETE\", na=False)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "foKm3wVJ5H5c"
      },
      "source": [
        "Plenty of the columns contains multiple variables. Still, it is rather confusing that these variables are separated differently depending on the column. Surely, we would like a standardized version in order to reduce the confusion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5f_UCZ8vNHo2"
      },
      "source": [
        "# Standardizing the multiple column columns such that the variables look like 'Tags' instead\n",
        "df['Medium'] = df['Medium'].str.lower()\n",
        "df['Medium'] = (df['Medium'].str.replace(\", \", \"|\"))\n",
        "df['Artist Nationality'] = (df['Artist Nationality'].str.replace(\", \", \"|\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h98jNVUENHo3"
      },
      "source": [
        "# It might not be the best idea to split the <Dimensions> column into <Width> and <Length> features.\n",
        "# As expected, the dataset contains both 2-dimensional and 3-dimensional items\n",
        "# We already mentioned that our analysis would focus on the Drawings and Paintings within the dataset,\n",
        "# hence we will be interested in 2-dimensional items.\n",
        "\n",
        "# A simple count of the 'x' symbol will tell us whether the item is 2-dimensional or not.\n",
        "# (e.g. 1 x 5.3 VS 2 x 4 x 8)\n",
        "df['Dimensionality'] = df['Dimensions'].apply(lambda st: str.count(st, \"x\") + 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3R0doKy76YmX"
      },
      "source": [
        "There are quite a lot of items like **Coins** being listed by their diameter instead of the Width and Length.\n",
        "We can quickly fix these by duplicating the diameter value such that the **'Covered Area'** field will be correctly computed.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLo2Z5nJNHo4"
      },
      "source": [
        "for index, (dimensionality, dimensions) in enumerate(zip(df['Dimensionality'], df['Dimensions'])):\n",
        "    if (df.iloc[index]['Dimensionality']) == 1:\n",
        "        df.at[index, 'Dimensionality'] = 2\n",
        "        df.at[index, 'Dimensions'] = df.iloc[index]['Dimensions'] + \"x\" + df.iloc[index]['Dimensions']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXZsxXwBNHo6"
      },
      "source": [
        "# count the total covered area of each 2-dimensional item in the remaining subset\n",
        "for index_item, item in df.iterrows():\n",
        "\n",
        "    if (df['Dimensionality'][index_item] == 2):\n",
        "        number_dimensions = []\n",
        "        text_dimension = df['Dimensions'][index_item]\n",
        "        number_dimensions = [float(s) for s in re.findall(r'-?\\d+\\.?\\d*', text_dimension)]\n",
        "\n",
        "        covered_area = np.prod(np.array(number_dimensions))\n",
        "        df['Covered Area'][index_item] = covered_area"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYw_sqb5NHo7"
      },
      "source": [
        "# convert columns to numeric values\n",
        "df['AccessionYear'] = df['AccessionYear'].astype('float')\n",
        "df['AccessionYear'] = df['AccessionYear'].astype('Int32')\n",
        "\n",
        "df['Object ID'] = df['Object ID'].astype('Int64')\n",
        "df['Dimensionality'] = df['Dimensionality'].fillna(0).astype('Int32')\n",
        "\n",
        "df['Object Begin Date'] = pd.to_numeric(df['Object Begin Date'], errors='coerce')\n",
        "df['Object End Date'] = pd.to_numeric(df['Object End Date'], errors='coerce')\n",
        "\n",
        "df['Artist Begin Date'] = pd.to_numeric(df['Artist Begin Date'], errors='coerce')\n",
        "df['Artist End Date'] = pd.to_numeric(df['Artist End Date'], errors='coerce')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zoQPmA2NHo9"
      },
      "source": [
        "# count the total covered area of each item by iterating over all items, and \n",
        "# for each one of them, extracting the numerical values from within the\n",
        "# Dimensions field into a list.\n",
        "for index_item, item in df.iterrows():\n",
        "\n",
        "    if (df['Dimensionality'][index_item] == 2):\n",
        "        number_dimensions = []\n",
        "        text_dimension = df['Dimensions'][index_item]\n",
        "        number_dimensions = [float(s) for s in re.findall(r'-?\\d+\\.?\\d*', text_dimension)]\n",
        "\n",
        "        covered_area = np.prod(np.array(number_dimensions))\n",
        "        df['Covered Area'][index_item] = covered_area"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OI6vSA6YNHo-"
      },
      "source": [
        "# Unfortunately, there are a couple of dimensions that could not have been extracted\n",
        "# given a terrible input when the item was introduced into the DB by the employees\n",
        "# Since part of our analysis needs the area covered by each painting/drawing, we will only keep\n",
        "# the ones with a suitable input\n",
        "df = df[df['Dimensionality'] == 2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UjRnnnuNHpA"
      },
      "source": [
        "# remove items that have begin or end date 0\n",
        "df = df[df['Object Begin Date'] > 0]\n",
        "df = df[df['Object End Date'] > 0]\n",
        "\n",
        "# calculate the total time of working on each item\n",
        "df['years_worked_item'] = df['Object End Date'] - df['Object Begin Date']\n",
        "df['years_worked_item'] = pd.to_numeric(df['years_worked_item'], errors='coerce')\n",
        "\n",
        "# calculate how long each artis lived\n",
        "df['years_lived_artist'] = df['Artist End Date'] - df['Artist Begin Date']\n",
        "df['years_lived_artist'] = pd.to_numeric(df['years_lived_artist'], errors='coerce')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lt1ZktyvNHpA"
      },
      "source": [
        "# There are a couple of outliers that have to be ignored throughout the plotting\n",
        "# poorly inserted data.\n",
        "# Otherwise, a few 6000 values would show up and some artwork created by toddlers.\n",
        "\n",
        "# While for the latter, it is not necessarily a bug, it might be possible that either drawing \n",
        "# or paintings created by the children of certain high profile celebrities will be shown \n",
        "# into a museum, we would like to get rid of these.\n",
        "df = df[df['years_lived_artist'] < 120]\n",
        "df = df[df['years_lived_artist'] > 5]\n",
        "# Assuming we are not interested in the extremely tiny ones, deleting items under\n",
        "# 10 square CM\n",
        "df = df[df['Covered Area'] > 10]\n",
        "\n",
        "# There is only one extreme outlier that we would like to remove. Otherwise, the plot \n",
        "# will be highly skewed. If it were more than 1 of these enormous \n",
        "# paintings/drawings, we would have kept them instead, but it is hard to learn \n",
        "# anything if only one exists in the entire museum.\n",
        "df = df[df['Covered Area'] < 200000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sniboJmi8Dpw"
      },
      "source": [
        "# 1.1. What cultures/subcultures are present in the paintings/drawings at Met Collection, and are there any noticeable differences in their dimensions?\n",
        "\n",
        "While the immense variety in either European or Asian subcultures might make the following plot a bit harder to study, there are, however, some insights to be learned from it:\n",
        "\n",
        "1. Asian subcultures tend to be more diverse in dimension than their European counterparts.\n",
        "2.   Without deep knowledge of the domain, it is probably impossible to clean this subset any further. For example. There are some items from Punjab Hills - Kingdom of Guler, and then there are some other paintings from the same region listed in a different kingdom. We cannot just clean the data any further and remove the whole part about the kingdom, as the region's adherence to a kingdom or another might have influenced the culture in that area.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AbauSwlNHpE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e012987d-5c2f-415c-88e5-2eda3da0798a"
      },
      "source": [
        "sns.lmplot('Artist End Date', 'Covered Area', df, hue='Culture', fit_reg=False)\n",
        "plt.title(\"Year when the artist finished the item VS Area covered by item\")\n",
        "plt.xlabel(\"Year of finishing item\")\n",
        "plt.ylabel(\"Covered area (CM^2)\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2Fcc8-CCkBJ"
      },
      "source": [
        "Even though we might have specific preconceived ideas about how quickly drawings are being completed, there is no explicit dependency between the dimension of a painting/drawings and the time spent finishing the artwork."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyqqHjF3NHpF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "3db5d97d-c43e-4589-85ea-0e5d68b2b273"
      },
      "source": [
        "sns.lmplot('years_worked_item', 'Covered Area', df, hue='Classification', fit_reg=False)\n",
        "plt.title(\"Years worked on the item vs Covered Area (CM^2)\")\n",
        "plt.xlabel(\"Years worked\")\n",
        "plt.ylabel(\"CM^2\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umW_hC5psW3a"
      },
      "source": [
        "# 1.2. **What materials are usually being used ?**\n",
        "\n",
        "Our project proposal mentioned that we would talk the material into account when performing any analysis. However, this has to be the most diverse column in the entire dataset, and any cleaning procedure is most likely impossible. \n",
        "\n",
        "It should be mentioned that these are some of the materials composing the paintings and drawings. For either classification of items, the list is even absurder than this.\n",
        "\n",
        "Apart from this, we can also mention that there are a lot of multiple values in plenty of the data, which leads to questions that cannot be answered, such as \"What percentage of the item is being used by Material X ?\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fPl_6hAEQYs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "outputId": "9b7af29e-63e1-4675-b28b-935a36605dad"
      },
      "source": [
        "materials_count = df['Medium'].value_counts()\n",
        "print(materials_count[0:10])\n",
        "\n",
        "labels = df['Medium'].value_counts().index\n",
        "values_count = df['Medium'].value_counts().values\n",
        "\n",
        "plt.figure(figsize = (15, 8))\n",
        "ax = sns.barplot(x=labels[:6], y=materials_count[:6])\n",
        "for i, p in enumerate(ax.patches):\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x()+p.get_width()/2., height + 0.05, materials_count[i],ha=\"center\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "oil on canvas                             3112\n",
            "graphite                                   794\n",
            "graphite on paper                          658\n",
            "graphite on off-white wove paper           507\n",
            "oil on wood                                483\n",
            "watercolor on ivory                        480\n",
            "ink on paper                               425\n",
            "pen and brown ink                          349\n",
            "watercolor                                 334\n",
            "hanging scroll; ink and color on paper     314\n",
            "Name: Medium, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnohdknWNHpG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "6d0f90b9-0f08-457a-d3a7-52323c58d0f4"
      },
      "source": [
        "df[\"Object End Date\"].plot(kind=\"hist\")\n",
        "plt.title(\"Plot of the years when items stored at Met were completed\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Number of items\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SXpgBVJ86G_"
      },
      "source": [
        "#1.3. **Is a country too poorly represented when referring to the Met Collection? Are artists from a specific country or area underrepresented? Where do most items come from ?**\n",
        "\n",
        "\n",
        "A kind reminder that only 2% of the items have a \"Country\" variable. However, in that small dataset, things are clear: American items are by far the most numerous, which is not necessarily a surprising aspect. We are talking about a museum in America, after all. However, the disproportion between American and non-American items does seem too high for such an important museum. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOPrGluxNHpG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "f406cb71-7971-4bf2-ea87-42c31fe728ec"
      },
      "source": [
        "#Creating a dataframe that would contain all values from the df dataframe, however, would exclude the rows that contain NaN value for a country.\n",
        "df1 = df[df['Country'].notna()]\n",
        "\n",
        "df1['Country'].value_counts().plot(kind='bar')\n",
        "plt.title(\"Number of items from each country\")\n",
        "plt.xlabel(\"Country\")\n",
        "plt.ylabel(\"Number of items from country\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnaNxZmZ6KI4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "ff62de20-a45b-4a21-8960-75bcd47e6458"
      },
      "source": [
        "df1['Department'].value_counts().plot(kind='bar')\n",
        "plt.title(\"Number of items from each department\")\n",
        "plt.xlabel(\"Department\")\n",
        "plt.ylabel(\"Number of items from department\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMW2AnIlA_Tn"
      },
      "source": [
        "As we can observe from the plots above, the values are primarily contained in only one department ( The American Wing). This, of course, leads us to only two conclusions : \n",
        "1.   The dataset requires identifications of countries for paintings that are stored in other departments\n",
        "2.   The collection can be improved by obtaining items from other departments (The American Wing, since it contains the majority of artworks) \n",
        "\n",
        "As American artists represent the majority of artworks, we can only conclude that the collection can be improved by importing artworks from other countries. A possible suggestion from those plots would be to expand the collection with artworks from European countries ( France, Germany, England, and Italy) and possibly add some artworks from Asian countries ( China or Japan), as those countries were not presented in the list of countries. However, it could be an interesting additional point to the collection as some artists in the dataset originate from those countries. In our opinion, an immense diversity in artwork origins can provide a broader and better experience for a museum customer, as it shows a complete spectrum over different cultures.\n",
        "\n",
        "Even though 98% of the items do not have a **\"Country\"** label, 85% of them do have the artist's nationality. So, we should assume that more valuable geographical information can still be extracted from that field. \n",
        "\n",
        "Once we analyze the nationalities of the artists displayed at Met, things look way more balanced than in the previous plot. First, however, we should mention incredible diversity in the **Nationality** field. Most artists have been born in a place and then lived elsewhere (mainly in the U.S.). \n",
        "i.e., American|born X\n",
        "\n",
        "# **One of the research questions in our proposal was: Are we missing items from a particular country or region ?**\n",
        "\n",
        "While the analysis of the nationalities certainly helps, it is still not a definitive answer to our question. For example, an artist might have very well emigrated to Paris, New York, or London long before he had started working on the item being displayed at Met Museum. Therefore, the nationality alone does not show us precisely which country/region Met museum should focus on to complete its collection."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiuryDU2ElbO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9671466-aaeb-4861-d213-fd32e01d0ccc"
      },
      "source": [
        "# e.g. multiple nationalities\n",
        "labels_count = df['Artist Nationality'].value_counts()\n",
        "print(labels_count[22:30])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "German|born Switzerland    87\n",
            "Belgian                    81\n",
            "American|born Greece       63\n",
            "British|Scottish           54\n",
            "Swedish                    32\n",
            "Indian                     32\n",
            "American|born Canada       31\n",
            "American|born Poland       31\n",
            "Name: Artist Nationality, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pX15JBZ-_nZG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "60d78077-f5c9-4521-844a-f3aef742bee0"
      },
      "source": [
        "labels = df['Artist Nationality'].value_counts().index\n",
        "values = df['Artist Nationality'].value_counts().values\n",
        "\n",
        "plt.figure(figsize = (15, 8))\n",
        "ax = sns.barplot(x=labels[:9], y=values[:9])\n",
        "\n",
        "plt.title(\"Frequency of usage within drawings and paintings\")\n",
        "plt.xlabel(\"Material\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "fig = plt.gcf()\n",
        "\n",
        "for i, p in enumerate(ax.patches):\n",
        "    height = p.get_height()\n",
        "    ax.text(p.get_x()+p.get_width()/2., height + 0.05, values[i],ha=\"center\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGNndnFkAVRD"
      },
      "source": [
        "Regarding the proportion between paintings and drawings, we can easily notice there's almost 3x more drawings than there are paintings. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1T7pwWeNHpH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "65defdb7-6a34-44f7-dd19-86f53f85f0fc"
      },
      "source": [
        "df['Classification'].value_counts().plot(kind='bar')\n",
        "plt.title(\"Counts for artists from each classification of items\")\n",
        "plt.xlabel(\"Classification\")\n",
        "plt.ylabel(\"Number of items from class\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-LtvHM5NHpH"
      },
      "source": [
        "# We have to convert them back to int since they have been converted\n",
        "# to float throughout the previous operations.\n",
        "df['Object Begin Date'] = df['Object Begin Date'].astype('Int32')\n",
        "df['Object End Date'] = df['Object End Date'].astype('Int32')\n",
        "\n",
        "df['Artist Begin Date'] = df['Artist Begin Date'].astype('Int32')\n",
        "df['Artist End Date'] = df['Artist End Date'].astype('Int32')\n",
        "\n",
        "df['years_lived_artist'] = df['years_lived_artist'].astype('Int32')\n",
        "\n",
        "# del df['years_lived_artist']\n",
        "# del df['years_worked_item']\n",
        "df['Artist Begin Date'] = df['Artist Begin Date'].map(int)\n",
        "df['years_lived_artist'] = df['years_lived_artist'].map(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbzFSuB4-2gO"
      },
      "source": [
        "It might not necessarily surprise that the dimensions of **drawings** are way too diverse. For the second part of this analysis, we would like to look at the **paintings** alone and hope to see better-structured data and certain tendencies in our plots. As seen in the previous plot, we continue the following analysis with 7500 (representing the **paintings**) out of the 100.000 items we started with."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vu9J8PJKNHpI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "577e4e1a-f607-4b57-c042-d81415311bbd"
      },
      "source": [
        "dfp = df.loc[df['Classification'] == 'Paintings']\n",
        "dfp.head(n = 3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-744fa9dc-14cf-4eb7-9e79-4db8fab5d2fd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Object ID</th>\n",
              "      <th>Department</th>\n",
              "      <th>AccessionYear</th>\n",
              "      <th>Object Name</th>\n",
              "      <th>Title</th>\n",
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              "      <th>Artist Nationality</th>\n",
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              "      <th>Artist End Date</th>\n",
              "      <th>Artist Wikidata URL</th>\n",
              "      <th>Object Begin Date</th>\n",
              "      <th>Object End Date</th>\n",
              "      <th>Medium</th>\n",
              "      <th>Dimensions</th>\n",
              "      <th>Country</th>\n",
              "      <th>Classification</th>\n",
              "      <th>Object Wikidata URL</th>\n",
              "      <th>Tags</th>\n",
              "      <th>Covered Area</th>\n",
              "      <th>Dimensionality</th>\n",
              "      <th>years_worked_item</th>\n",
              "      <th>years_lived_artist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>471</td>\n",
              "      <td>The American Wing</td>\n",
              "      <td>1883</td>\n",
              "      <td>Painting</td>\n",
              "      <td>Plaque Portrait of Benjamin Franklin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jean-Baptiste Nini</td>\n",
              "      <td>Italian</td>\n",
              "      <td>1717</td>\n",
              "      <td>1786</td>\n",
              "      <td>https://www.wikidata.org/wiki/Q3164318</td>\n",
              "      <td>1776</td>\n",
              "      <td>1883</td>\n",
              "      <td>ivory</td>\n",
              "      <td>6.7x5.1</td>\n",
              "      <td>France</td>\n",
              "      <td>Paintings</td>\n",
              "      <td>https://www.wikidata.org/wiki/Q79024309</td>\n",
              "      <td>Benjamin Franklin|Men|Portraits</td>\n",
              "      <td>34.17</td>\n",
              "      <td>2</td>\n",
              "      <td>107.0</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>428</th>\n",
              "      <td>485</td>\n",
              "      <td>The American Wing</td>\n",
              "      <td>1883</td>\n",
              "      <td>Painting</td>\n",
              "      <td>Portrait of Benjamin Franklin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>H. Sanford</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1783</td>\n",
              "      <td>1822</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1776</td>\n",
              "      <td>1883</td>\n",
              "      <td>panel</td>\n",
              "      <td>13.7x10.8</td>\n",
              "      <td>France</td>\n",
              "      <td>Paintings</td>\n",
              "      <td>https://www.wikidata.org/wiki/Q79023347</td>\n",
              "      <td>Benjamin Franklin|Men|Portraits</td>\n",
              "      <td>147.96</td>\n",
              "      <td>2</td>\n",
              "      <td>107.0</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3674</th>\n",
              "      <td>3926</td>\n",
              "      <td>The American Wing</td>\n",
              "      <td>1883</td>\n",
              "      <td>Painting</td>\n",
              "      <td>Panel of George Washington</td>\n",
              "      <td>NaN</td>\n",
              "      <td>William P. Babcock</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1826</td>\n",
              "      <td>1899</td>\n",
              "      <td>https://www.wikidata.org/wiki/Q18508740</td>\n",
              "      <td>1846</td>\n",
              "      <td>1883</td>\n",
              "      <td>panel</td>\n",
              "      <td>14x11.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Paintings</td>\n",
              "      <td>https://www.wikidata.org/wiki/Q79022461</td>\n",
              "      <td>George Washington|Portraits|Men</td>\n",
              "      <td>159.60</td>\n",
              "      <td>2</td>\n",
              "      <td>37.0</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-744fa9dc-14cf-4eb7-9e79-4db8fab5d2fd button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-744fa9dc-14cf-4eb7-9e79-4db8fab5d2fd');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Object ID         Department  AccessionYear Object Name  \\\n",
              "414         471  The American Wing           1883    Painting   \n",
              "428         485  The American Wing           1883    Painting   \n",
              "3674       3926  The American Wing           1883    Painting   \n",
              "\n",
              "                                     Title Culture Artist Display Name  \\\n",
              "414   Plaque Portrait of Benjamin Franklin     NaN  Jean-Baptiste Nini   \n",
              "428          Portrait of Benjamin Franklin     NaN          H. Sanford   \n",
              "3674            Panel of George Washington     NaN  William P. Babcock   \n",
              "\n",
              "     Artist Nationality  Artist Begin Date  Artist End Date  \\\n",
              "414             Italian               1717             1786   \n",
              "428                 NaN               1783             1822   \n",
              "3674                NaN               1826             1899   \n",
              "\n",
              "                          Artist Wikidata URL  Object Begin Date  \\\n",
              "414    https://www.wikidata.org/wiki/Q3164318               1776   \n",
              "428                                       NaN               1776   \n",
              "3674  https://www.wikidata.org/wiki/Q18508740               1846   \n",
              "\n",
              "      Object End Date Medium Dimensions Country Classification  \\\n",
              "414              1883  ivory    6.7x5.1  France      Paintings   \n",
              "428              1883  panel  13.7x10.8  France      Paintings   \n",
              "3674             1883  panel    14x11.4     NaN      Paintings   \n",
              "\n",
              "                          Object Wikidata URL  \\\n",
              "414   https://www.wikidata.org/wiki/Q79024309   \n",
              "428   https://www.wikidata.org/wiki/Q79023347   \n",
              "3674  https://www.wikidata.org/wiki/Q79022461   \n",
              "\n",
              "                                 Tags  Covered Area  Dimensionality  \\\n",
              "414   Benjamin Franklin|Men|Portraits         34.17               2   \n",
              "428   Benjamin Franklin|Men|Portraits        147.96               2   \n",
              "3674  George Washington|Portraits|Men        159.60               2   \n",
              "\n",
              "      years_worked_item  years_lived_artist  \n",
              "414               107.0                  69  \n",
              "428               107.0                  39  \n",
              "3674               37.0                  73  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xh6yonUxNHqK"
      },
      "source": [
        "# create a new data frame where we keep unique artists names\n",
        "artists = dfp.groupby('Artist Display Name', as_index=False).agg(\n",
        "    count=('Object ID', 'count'), # count how many items each arists has at the Met museum\n",
        "    total_covered=('Covered Area', sum), # sum their total covered area in the museum\n",
        "    nationality=('Artist Nationality',\n",
        "                 lambda x: x.iloc[0]), # add their nationality\n",
        "    year_born=('Artist Begin Date',\n",
        "                 lambda x: x.iloc[0]), # the year in which the artist was born\n",
        "    years_lived=('years_lived_artist',\n",
        "                 lambda x: x.iloc[0]) # as well as the already computed value representing\n",
        "                 # the number of years the artist lived for\n",
        ") # total_worked=('years_worked_item', sum)\n",
        "\n",
        "# compute average covered area in the artworks created by each artist\n",
        "artists['Average Covered Area'] = artists.apply(lambda x: x['total_covered'] / x['count'], axis=1)\n",
        "#dffff['Years Worked Average'] = dffff.apply(lambda x: x['total_worked'] / x['count'], axis=1)\n",
        "\n",
        "# keep only the artists with at least 2 paintings at Met Museum\n",
        "artists = artists[artists['count'] > 1]\n",
        "# sort the values to see the artists with most total covered area\n",
        "artists.sort_values('total_covered', ascending=False, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fdp2CWtnNHqL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "04b407f6-33ae-4522-fc19-392a8b0a5a4a"
      },
      "source": [
        "artists.head(n = 3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Artist Display Name</th>\n",
              "      <th>count</th>\n",
              "      <th>total_covered</th>\n",
              "      <th>nationality</th>\n",
              "      <th>year_born</th>\n",
              "      <th>years_lived</th>\n",
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              "  <tbody>\n",
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              "      <th>947</th>\n",
              "      <td>Giovanni Battista Tiepolo</td>\n",
              "      <td>20</td>\n",
              "      <td>730376.04</td>\n",
              "      <td>Italian</td>\n",
              "      <td>1696</td>\n",
              "      <td>74</td>\n",
              "      <td>36518.80200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Andy Warhol</td>\n",
              "      <td>27</td>\n",
              "      <td>568325.96</td>\n",
              "      <td>American</td>\n",
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              "      <td>21049.10963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>Clyfford Still</td>\n",
              "      <td>12</td>\n",
              "      <td>552549.63</td>\n",
              "      <td>American</td>\n",
              "      <td>1904</td>\n",
              "      <td>76</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d3de29ab-700f-4d92-bcf9-f719052416bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "           Artist Display Name  count  total_covered nationality  year_born  \\\n",
              "947  Giovanni Battista Tiepolo     20      730376.04     Italian       1696   \n",
              "144                Andy Warhol     27      568325.96    American       1928   \n",
              "477             Clyfford Still     12      552549.63    American       1904   \n",
              "\n",
              "     years_lived  Average Covered Area  \n",
              "947           74           36518.80200  \n",
              "144           59           21049.10963  \n",
              "477           76           46045.80250  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84mJSvWwTl3l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "outputId": "9aff4b14-26de-40b1-b7c5-f7a6b60fcfa3"
      },
      "source": [
        "sns.lmplot(x=\"year_born\", y=\"years_lived\", data=artists, fit_reg=False)\n",
        "plt.title(\"Year of birth vs life expectancy of the artists\")\n",
        "plt.xlabel(\"Year of birth\")\n",
        "plt.ylabel(\"Life expectancy\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4zTwFWCzxBa"
      },
      "source": [
        "Indeed, this plot is not telling us anything new. The life expectancies of all humankind have been in a steady increase throughout the last 1000 years. However, I think it would be interesting to create two separate prediction models: trained on our artist data from Met Collection and trained on general data we can find on the internet. By plotting these two prediction lines together, we could conclude matters such as :\n",
        "\"Starting from 1860, American artists have a higher life expectancy than the common American.\"\n",
        "\n",
        "Therefore, we will build an OLS model trained on our subset's \"year_born\" feature of all American painters. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gf6VKvERjKtf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "c1937215-9e1e-4676-e084-e095b0c79fbb"
      },
      "source": [
        "# For the following comparison, we shall focus only on the American painters and their life expectancies\n",
        "american_artists = artists[artists['nationality'] == 'American']\n",
        "\n",
        "life_exp = sm.ols(formula=\"years_lived ~ year_born\", data=american_artists).fit()\n",
        "sns.regplot(x = 'year_born', y = 'years_lived', data=american_artists)\n",
        "life_exp.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>       <td>years_lived</td>   <th>  R-squared:         </th> <td>   0.118</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.115</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   37.31</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 25 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>3.38e-09</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>14:54:00</td>     <th>  Log-Likelihood:    </th> <td> -1132.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   281</td>      <th>  AIC:               </th> <td>   2269.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   279</td>      <th>  BIC:               </th> <td>   2277.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>  -96.8134</td> <td>   28.228</td> <td>   -3.430</td> <td> 0.001</td> <td> -152.381</td> <td>  -41.246</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>year_born</th> <td>    0.0921</td> <td>    0.015</td> <td>    6.108</td> <td> 0.000</td> <td>    0.062</td> <td>    0.122</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>15.616</td> <th>  Durbin-Watson:     </th> <td>   1.926</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  16.780</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.592</td> <th>  Prob(JB):          </th> <td>0.000227</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.170</td> <th>  Cond. No.          </th> <td>6.48e+04</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.48e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:            years_lived   R-squared:                       0.118\n",
              "Model:                            OLS   Adj. R-squared:                  0.115\n",
              "Method:                 Least Squares   F-statistic:                     37.31\n",
              "Date:                Tue, 25 Jan 2022   Prob (F-statistic):           3.38e-09\n",
              "Time:                        14:54:00   Log-Likelihood:                -1132.7\n",
              "No. Observations:                 281   AIC:                             2269.\n",
              "Df Residuals:                     279   BIC:                             2277.\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept    -96.8134     28.228     -3.430      0.001    -152.381     -41.246\n",
              "year_born      0.0921      0.015      6.108      0.000       0.062       0.122\n",
              "==============================================================================\n",
              "Omnibus:                       15.616   Durbin-Watson:                   1.926\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               16.780\n",
              "Skew:                          -0.592   Prob(JB):                     0.000227\n",
              "Kurtosis:                       3.170   Cond. No.                     6.48e+04\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 6.48e+04. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5u2ozFD43cNz"
      },
      "source": [
        "Therefore, on top of the previous data points and prediction line (Blue), we will be overlapping a second prediction line being trained on the life expectancies of American people starting from the year 1800 (Orange) - information retrieved from an additional dataset found online.\n",
        "\n",
        "Once we have done this overlapping, one straightforward question emerges:\n",
        "#1.4. Are the American painters living longer than the average American citizen?\n",
        "\n",
        "The plot seems more than conclusive. The American painter lived way longer than the average American between 1800 and mid-1900. However, starting from the mid-1900, the life expectancies in the two categories seem to have almost balanced. (It is hard to sure since no American painter born after 1950 has an item displayed at Met Museum)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EqeXc7Rjbi3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "23bed41c-73db-456b-dbf0-0409fe064608"
      },
      "source": [
        "# Extracted the life expectancies of Americans starting from 1800 from a document retrieved  from\n",
        "# https://mapipedia.com/s/u/drdave/average_life_expectancy_by_country_since_1800.html\n",
        "\n",
        "sns.regplot(x = 'year_born', y = 'years_lived', data=american_artists)\n",
        "\n",
        "american_life_expectancy = pd.read_csv(r\"/content/american_life_expectancy.csv\", index_col=0, encoding='latin-1')\n",
        "print(american_life_expectancy.head(n=5))\n",
        "\n",
        "sns.regplot(x = 'Year', y = 'Expectancy', data=american_life_expectancy);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               Year  Expectancy\n",
            "Name                           \n",
            "United States  1800        39.4\n",
            "United States  1801        39.4\n",
            "United States  1802        39.4\n",
            "United States  1803        39.4\n",
            "United States  1804        39.4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdyRNkuzNHqo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "43e7dd40-3ec1-428c-a5ad-1a57751f493e"
      },
      "source": [
        "z=artists.iloc[:10,:] # use the first 10 for the barplot\n",
        "p=sns.barplot(x='Artist Display Name', y='total_covered', hue='nationality', data=z)\n",
        "for item in p.get_xticklabels():\n",
        "    item.set_rotation(90)\n",
        "plt.title(\"Total covered area of the 'hardest working' 10 artists being displayed at Met\")\n",
        "plt.xlabel(\"Artists\")\n",
        "plt.ylabel(\"Total covered area\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMTkYUA6NYLJ"
      },
      "source": [
        "# 2.0. There is one more thing we wish to do with this subset representing the **Paintings** at the Met Museum: To compute the total covered dimension and average covered depending on the **Object End Date** (i.e., the year when the item was completed). \n",
        "\n",
        "We are mainly interested in 2 aspects: \n",
        "\n",
        "1. To see if there is a clear trend in how the dimensions of paintings are changing (i.e., is the **average covered area** increasing over the years ?). A prediction model shall be learned to predict this trend for the years to come.\n",
        "\n",
        "2. Which years are the best represented in the Met Collection? (* assuming for this sake that a bigger painting represents better year X than a smaller painting represents year Y)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jee7LkyWNHqr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "fa109e17-129c-4222-c37e-1c0d36d1eeab"
      },
      "source": [
        "# create another dataframe where keeping each year and its statistics\n",
        "df_years = dfp.groupby('Object End Date', as_index=False).agg(\n",
        "    count=('Object ID', 'count'), # how many objects were created that year\n",
        "    total_covered=('Covered Area', sum)) # total covered area of the objects created that year\n",
        "df_years['Average Covered Area'] = df_years.apply(lambda x: x['total_covered'] / x['count'], axis=1)\n",
        "\n",
        "#df_years['Years'] = df_years.index\n",
        "df_years.sort_values('total_covered', ascending=False, inplace=True)\n",
        "df_years.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Object End Date</th>\n",
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              "      <th>Average Covered Area</th>\n",
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              "      <td>1968</td>\n",
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            ],
            "text/plain": [
              "     Object End Date  count  total_covered  Average Covered Area\n",
              "551             1911    140      977007.27           6978.623357\n",
              "624             1984     23      946641.47          41158.324783\n",
              "622             1982     27      840832.17          31141.932222\n",
              "623             1983     30      737892.76          24596.425333\n",
              "608             1968     19      710496.44          37394.549474"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdrXPxx-SLZj"
      },
      "source": [
        "# 2.1 Is there a clear trend in how the average dimension changes over the years?\n",
        "\n",
        "As seen in the following plot, there is a slight increasing trend in the average dimensions of paintings. Therefore, in theory, such a model could be helpful to the museum if it wishes to extend an existing wing or even build a new one to make sure there is enough room for the artwork to be collected in the coming years. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MTrlNjiJLc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "outputId": "54421024-63a4-4e74-fb27-c3ee3a93f734"
      },
      "source": [
        "s= df_years.sort_values(by=['Average Covered Area'], ascending=False)\n",
        "\n",
        "print(df_years[0:5])\n",
        "\n",
        "# Let us take into account only the years when at least two items were completed\n",
        "df_years_count = df_years[df_years['count'] > 1].count()\n",
        "\n",
        "sns.lmplot(x=\"Object End Date\", y=\"Average Covered Area\", data=df_years, fit_reg=True)\n",
        "plt.title(\"Year when the item was completed VS Dimension\")\n",
        "plt.xlabel(\"Year of completion\")\n",
        "plt.ylabel(\"Dimension (CM^2)\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Object End Date  count  total_covered  Average Covered Area\n",
            "551             1911    140      977007.27           6978.623357\n",
            "624             1984     23      946641.47          41158.324783\n",
            "622             1982     27      840832.17          31141.932222\n",
            "623             1983     30      737892.76          24596.425333\n",
            "608             1968     19      710496.44          37394.549474\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVdSLeTVVr9A"
      },
      "source": [
        "Even though there is a somewhat increasing trend in the average dimension, the \n",
        "\n",
        "Values are especially scattered, making it impossible to build a quality predictive model. \n",
        "\n",
        "The model does not stand any chance of fitting the data very well. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZDz8dnGTypf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95add178-7c68-44db-e023-1e839a75ccde"
      },
      "source": [
        "X = pd.DataFrame(df_years['Object End Date'])\n",
        "Y = df_years['Average Covered Area']\n",
        "\n",
        "model = Ridge(normalize = True)\n",
        "    \n",
        "cross_valid = RepeatedKFold(n_splits = 4, n_repeats = 20, random_state = 1)\n",
        "grid = dict()\n",
        "grid['alpha'] = arange(0, 1, 0.001)\n",
        "\n",
        "search = GridSearchCV(model, grid, scoring = 'neg_mean_absolute_error', n_jobs = -1)\n",
        "model = search.fit(X, Y)\n",
        "\n",
        "print('MAE: %.3f' % model.best_score_)\n",
        "print('Config: %s' % model.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: -6043.310\n",
            "Config: {'alpha': 0.105}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:145: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), Ridge())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * n_samples. \n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ERSL4fuFU7cc"
      },
      "source": [
        "# Slightly regularized Ridge Model vs Baseline Linear Regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CLPdrRLRVT8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0830602c-66e5-428e-dd6c-613235463450"
      },
      "source": [
        "# Ridge Model (alpha = 0.105) VS Linear Regression\n",
        "\n",
        "X_train, X_test , Y_train, Y_test = train_test_split(X, Y, test_size = 0.34, random_state = 2)\n",
        "\n",
        "model_ridge = Ridge(alpha = 0.105, normalize = True)\n",
        "model_ridge.fit(X_train, Y_train)        \n",
        "pred1 = model_ridge.predict(X_test)    \n",
        "pred1Train = model_ridge.predict(X_train)\n",
        "print('Comparison of Ridge model using the optimal alpha previously found VS Simple Linear Regression Model:')\n",
        "print('Validation slightly regularized MSE Ridge: %.3f' % mean_squared_error(Y_test, pred1))   \n",
        "print('Training slighlty regularized MSE Ridge: %.3f' % mean_squared_error(Y_train, pred1Train))   \n",
        "print('---------------------------------------')\n",
        "\n",
        "## Simple Linear Regression model ###############\n",
        "\n",
        "linear_reg_model = LinearRegression()\n",
        "linear_reg_model.fit(X_train, Y_train)\n",
        "linear_model2 = linear_reg_model.predict(X_test)  \n",
        "linear_model_train = linear_reg_model.predict(X_train)  \n",
        "print('Validation Baseline MSE Linear: %.3f' % mean_squared_error(Y_test, linear_model2))   \n",
        "print('Training Baseline MSE Linear: %.3f' % mean_squared_error(Y_train, linear_model_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Comparison of Ridge model using the optimal alpha previously found VS Simple Linear Regression Model:\n",
            "Validation slightly regularized MSE Ridge: 95558735.481\n",
            "Training slighlty regularized MSE Ridge: 70152302.789\n",
            "---------------------------------------\n",
            "Validation Baseline MSE Linear: 95854996.761\n",
            "Training Baseline MSE Linear: 70084129.164\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_base.py:145: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), Ridge())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * n_samples. \n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwgnjctsUU5c"
      },
      "source": [
        "As insignificant as that regularization might have been, the Ridge model slightly outperforms the baseline Linear Regression model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ljx_b9XCQjK6"
      },
      "source": [
        "# 2.2 Which years are the best represented in the Met Collection?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8n1A8axOHx84"
      },
      "source": [
        "According to the Met Collection, 1984 and 1911 were by far the most productive year in terms of the total painted area. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vF-FTo-7NHqt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "68ec2fb6-d47a-43af-e374-285d1733ee1c"
      },
      "source": [
        "df_years=df_years.sort_values(by=['total_covered'], ascending=False)\n",
        "y=df_years.iloc[:20,:] # use the first 20 for the barplot\n",
        "p=sns.barplot(x='Object End Date', y='total_covered', data=y)\n",
        "for item in p.get_xticklabels():\n",
        "    item.set_rotation(90)\n",
        "plt.title(\"The total covered area of the most important 20 years (w.r.t total covered painted area)\")\n",
        "plt.xlabel(\"Years\")\n",
        "plt.ylabel(\"Total covered area in that year\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNaoFoESIxCe"
      },
      "source": [
        "#2.3 How about the most productive years ?\n",
        "\n",
        "On the other hand, there are plenty of years when no painting has been finished, at least not one currently being stored at the Met museum.\n",
        "\n",
        "However, among the years that are present in our remaining subset, the area of paintings completed in the years 1200 and 1341 seems to have been the most modest.\n",
        "\n",
        "Just to get a better idea, there is 4166.6x times more painted area from the most fruitful year (1911) than there is in the least productive one (1341). "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_years=df_years.sort_values(by=['total_covered'], ascending=True)\n",
        "y=df_years.iloc[:20,:] # use the first 20 for the barplot\n",
        "p=sns.barplot(x='Object End Date', y='total_covered', data=y)\n",
        "for item in p.get_xticklabels():\n",
        "    item.set_rotation(90)\n",
        "plt.title(\"The total covered area of the least significant 20 years (w.r.t total covered painted area)\")\n",
        "plt.xlabel(\"Years\")\n",
        "plt.ylabel(\"Total painted area in that year\")\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(15, 10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "n16gicUO02oM",
        "outputId": "94a13df2-855c-43be-c921-1e5fd9626200"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}