DataVis_Day1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2t6QvIhTJaT6"
   },
   "source": [
    "# Working with google Colab\n",
    "\n",
    "During our praticals, we will work in `google colab`, it pretty much works like a `jupyter notebook` but with the advantage that we don´t need to install `python` or `python packages` on our local machine and can start right away! \n",
    "\n",
    "\n",
    "If you want to read more about Colab, you can start here:\n",
    "- [Colab FAQ](https://research.google.com/colaboratory/faq.html)\n",
    "\n",
    "\n",
    "------ -\n",
    "\n",
    "### Awesome! Let´s get started\n",
    "![](https://media0.giphy.com/media/unQ3IJU2RG7DO/200w.webp?cid=ecf05e47f6mswynnu54ew49x7ciyvu7on88fjiseqmbkcw89&rid=200w.webp&ct=g)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Introduction\n",
    "\n",
    "Before we start getting into data visualization, we will briefly introduce some core concepts and how-to`s \n",
    "of python programming. \n",
    "\n",
    "This introduction is far from complete and is rather meant to be a primer. \n",
    "\n",
    "If you want to know more, I suggest the excellent course from my former colleague Peer Herholz [here](!https://peerherholz.github.io/Python_for_Psychologists_Winter2021/index.html).\n",
    "\n",
    "\n",
    "If you ever got stuck with a specific problem, using [stackoverflow](!http://stackoverflow.com) or `LLMs` with the right query might help you. \n",
    "\n",
    "Besides that `google colab / jupyter notebooks` offer tab completion and additional help functionality by e.g., \n",
    "using `pd.read_csv?`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cell Types\n",
    "\n",
    "There are generellay two types of cells in `jupyter notebooks' - either code or markdown cells. This explanation is\n",
    "currently written in a markdown cell. Code is usually written and executed in code cells.\n",
    "\n",
    "We can transform a code cell into a markdown cell via ```esc + m``` and a markdown cell into a code cell via ```esc + y```. \n",
    "In code cells, we usually add comments with `# some comment`.\n",
    "\n",
    "Btw: \n",
    "- `shift+enter` executes a cell \n",
    "- `esc + b` inserts a cell below\n",
    "- `esc + a` inserts a cell above\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modules\n",
    "\n",
    "Most functionality in python is derived from modules. To make modules available in your python programme, we need to import it first. \n",
    "\n",
    "This is as easy as:\n",
    "\n",
    "```import math ``` \n",
    "\n",
    "```import numpy as np``` (note, `as` indicates an access-name, this could be anything!)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Types and Variable Assignments \n",
    "\n",
    "Python supports a variety of data types and structures:\n",
    "\n",
    "- numbers (integers, floats)\n",
    "- strings\n",
    "- booleans (True/False)\n",
    "- lists\n",
    "- dictionaries \n",
    "... \n",
    "\n",
    "A variable can potentially store any data type or structure. For that, we need to assign a value to a variable. \n",
    "This can be done by `my_variable = value`. Note, that we dont need to specify the data type, as this is done dynamically.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_variable = 5\n",
    "float_variable = 5.1\n",
    "string_variable = \"data visialization is fun!\"\n",
    "bool_variable = True  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 <class 'int'>\n",
      "data visialization is fun! <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(number_variable, type(number_variable))\n",
    "\n",
    "print(string_variable, type(string_variable))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### converting data types \n",
    "\n",
    "Data Types can be changed with the following functions:\n",
    "\n",
    "- `int()` converts a variables data type into an integer \n",
    "- `float()` ... \n",
    "- `bool()` ... \n",
    "- `str()` ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(float_variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Structures\n",
    "\n",
    "For now we will exclusively focus on `lists` as they might a bit more common for most usecases. However, bear in mind \n",
    "that are other structures, such as `dictionaries`,  `tuples` ....\n",
    "\n",
    "A list can store elements of different types, e.g., 'strings', 'integers' etc ...\n",
    "\n",
    "The common syntax for a list is:\n",
    "\n",
    "```my_list = [\"hello\", \"world\", 1,2, 4.5]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 1, 2, 4.5]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [\"hello\", \"world\", 1,2, 4.5]\n",
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling lists\n",
    "\n",
    "Lists are very common and offer a good showcase for some core concepts of python programming, such as\n",
    "\n",
    "- indexing (note: indexing in python starts with 0)\n",
    "- loops\n",
    "\n",
    "and the usage of in-built functions:\n",
    "- sort\n",
    "- remove\n",
    "- append\n",
    "- extend\n",
    "Note: built-in functions are part of `lists` and thus the syntax follows the logic `my_list.sort()` etc ..\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[0] #returns the first element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[-1] #returns the last element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['world', 1]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[1:3] # slicing, select element, note: python excluded the stop index! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 1, 2, 4.5, 'otter', 'otter']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### built in functionality \n",
    "my_list.append(\"otter\")\n",
    "\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hello', 'world', 1, 2, 4.5, 'otter']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list.remove(\"otter\")\n",
    "my_list\n",
    "# execute cell twice "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/s1/tcr0fxpj0x7dym6zmzp38gm40000gn/T/ipykernel_13598/1879591915.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'int' and 'str'"
     ]
    }
   ],
   "source": [
    "my_list.sort()\n",
    "# understanding errors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m \u001b[0mmy_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Remove first occurrence of value.\n",
      "\n",
      "Raises ValueError if the value is not present.\n",
      "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
     ]
    }
   ],
   "source": [
    "my_list.remove?\n",
    "# getting help "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control Flow Operators \n",
    "\n",
    "Python has several features that allow us to control the code execution:\n",
    "\n",
    "- iterations (e.g, for loops)\n",
    "- conditionals (e.g., if else)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n",
      "world\n",
      "1\n",
      "2\n",
      "4.5\n",
      "otter\n"
     ]
    }
   ],
   "source": [
    "for element in my_list:\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'int'>\n",
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "for element in my_list:\n",
    "    print(type(element))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "hello\n",
      "1\n",
      "world\n",
      "2\n",
      "1\n",
      "3\n",
      "2\n",
      "4\n",
      "4.5\n",
      "5\n",
      "otter\n"
     ]
    }
   ],
   "source": [
    "for ix, element in enumerate(my_list):\n",
    "    print(ix)\n",
    "    # element in my_list is: \".format(ix))\n",
    "    print(element)\n",
    "    \n",
    "## sometimes it is useful to have access to the indices of list elements "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HELLO\n",
      "WORLD\n",
      "1\n",
      "2\n",
      "4.5\n",
      "OTTER\n"
     ]
    }
   ],
   "source": [
    "for element in my_list:\n",
    "    if type(element) == str:\n",
    "        print(element.upper())\n",
    "    else:\n",
    "        print(element)\n",
    "        \n",
    "#print(\"hello\") #showcase for intendation level!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data visualization is awesome! \n",
      "data visualization is awesome! \n",
      "data visualization is awesome! \n",
      "data visualization is awesome! \n",
      "data visualization is awesome! \n"
     ]
    }
   ],
   "source": [
    "for x in range(5):\n",
    "    print(\"data visualization is awesome! \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "4\n",
      "9\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "for x in range(5):\n",
    "    print(x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the last code could be re-written using a \"list comprehension\"\n",
    "# which follows the syntax \n",
    "# do something for each element in an iterable \n",
    "[x**2 for x in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['HELLO', 'WORLD', 'OTTER']\n",
      "['HELLO', 'WORLD', 1, 2, 4.5, 'OTTER']\n"
     ]
    }
   ],
   "source": [
    "#list comprehensions also allow for if and else statements\n",
    "\n",
    "\n",
    "print([x.upper() for x in my_list if type(x)== str]) #only prints elements of type(str)\n",
    "\n",
    "\n",
    "print([x.upper() if type(x)== str else x for x in my_list])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RO8ZHdDKQ3x9"
   },
   "source": [
    "# Introduction Data Visualization\n",
    "\n",
    "Python offers multiple modules for **data visualization**. \n",
    "\n",
    "For this course, we will focus on the most common and general ones:\n",
    "\n",
    "- [matplotlib](!https://matplotlib.org/) \n",
    "- [seaborn](!https://seaborn.pydata.org/) \n",
    "\n",
    "\n",
    "# matplotlib \n",
    "\n",
    "- designed to provide complete control over your plots\n",
    "\n",
    "Matplotlib (and all hig level APIs that build upon it) builds upon a specifc anatomy of a figure:\n",
    "\n",
    "![](https://matplotlib.org/stable/_images/sphx_glr_anatomy_001_2_0x.png)\n",
    "\n",
    "\n",
    "Thats a lot of information, but let´s boil it down: \n",
    "\n",
    "- **figures**: the entire graphic on which axes, titles, legends etc. are drawn.\n",
    "- **axes**: an area in which you can locate points in terms of x-y coordinates\n",
    "- **axis**: providing scale for data (ticks, tick labels)\n",
    "- **artist**: everything that you can see on the figure (e.g. text, lines)\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "avkaWJjoaPDZ"
   },
   "source": [
    "## Getting started with `Matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hBx7r6UV3WpY"
   },
   "outputs": [],
   "source": [
    "# importing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "# make sure to run the second line to embedd your graphics directly in your jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q_WGL6sXa53w"
   },
   "outputs": [],
   "source": [
    "x =  np.linspace(0,20,10)# generates 20 numbers between 0 and 10 \n",
    "y =  x**2             # x square "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "id": "CPB8lcS2biwf",
    "outputId": "e20861fe-8be9-41e2-d466-8290858a5422"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f94c2f76f90>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "plt.plot(x,y) # creates a figure with a specific size on which we can draw\n",
    " # lineplot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2wWzoim_cUWt"
   },
   "source": [
    "This was super easy! However, the plot misses some important to make it meaningful, such as `axes label` and a `title`. This can be easily added with matplotlibs `plt` interface.\n",
    "\n",
    "*Note*: As stated above, `matplotlib` allows you full control over your plots, which (often) means more line of code! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "NwLjtBECc1u_",
    "outputId": "8d06c5de-e2b2-4b90-f8a9-038143d1dd55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'some title')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rjF9sTTvCK19v49JT23B6GFzMWJ5A9UVKUdUV7v1snPl425zgJSOBmaqar6o7gK2UMvOdMaFEVXn447XUi6rNgxeEx8WM5Qn4dzBuG9hTgSXuovEislpEXheRxu6yH/siuXx7Jvmuy/oimZAxO2kf32/P4N7h3WnWMNrrcgIioAEjIg2AD4A7VTULeAXohNNONgV4tjLrs75IJlRk5hbyl/+sJ7FdI64e2N7rcgImkJ0do3DC5W1V/RBAVfe7DdlKgNf4aTfox75ILt+eScaEnL/N38jBnAKeuLQXtcLoYsbyBOookuC0Itmgqn/3We7bi+FSYK17fw4wSkSiRaQD0AX4IRC1GlPTVu46xIwfdnH96R3o2Tq8LmYsT6COIg0BrgXWuP2pAR4ErhKRRECBZOAWAFVdJyLvAutxjkCNsyNIJhQdLSjm7vdW0aJhTEh2ZqyuQPVF+hYobbvw0xO85gngCb8VZUwAPPHperal5/D2TYNoEB150y/ZmbzG+Mnn6/fz1uJdjD2rY1hM4F0VFjDG+EFadh73frCaHq1iw3Kel4qygDGmhqkq97y3mpz8Il64KjHsLwc4EQsYY2rY9O+S+XpzOg9d1IPOzRt6XY6nLGCMqUGbUrP569yNnNu9Ob8bFDkn1JXFAsaYGpJXWMwdM1cSGxPFU5efEraTSFVG5B03M8ZP/jZvExtTs/nXDQOIbxAZ1xqVx7ZgjKkBX29O5/X/7uD60xM4u1tzr8sJGhYwxlRTxpF87n5vFV1bNOD+Ed29Lieo2C6SMdWgqtz3wRoycwt548aBxERF7iHp0tgWjDHVMOOHXXy+YT/3jejOL1rFel1O0LGAMaaKtqYd4fFP1nNml3huOD3B63KCkgWMMVVQUFTCHTNXUi+qNs/+tk9EzfFSGfYdjDFV8OyCTazbl8Xka/vRPDbG63KClm3BGFNJ3209wORF27l6UHvO6xmeLV9rigWMMZVwOLeACe+uokN8fR66MDI6A1SH143XmojIAhHZ4t42dpeLiLzgNl5bLSJ9A1GnMSeiqjzw4RoycvJ5YdSpnFTXvmEoT4UDRkQmudNbVsWxxms9gMHAOLe52v3AF6raBfjCfQwwAmce3i7AWJzuA8Z46r3le5i7NpW7zutGrzaRNbduVVVmC6Y2MF9E1orIfSLStqIvPEHjtZHAdHfYdOAS9/5I4A11LAYaHTdBuDEBlXwgh8fmrOO0jk0Ze2Z495OuSRUOGFW9HaeN6/04fYw2iMjnIjLa7XdUIcc1XmuhqinuU6lAC/e+NV4zQaOwuIQ7ZiURVbsWz15hh6Qro1Lfwbg9jD5R1atwdnWaAdOAVBGZIiInagdbWuM133UrTneBytRjjdeM373wxRZW7T7MXy/tTetG9bwuJ6RUKmBEJFZExojIQmARzlbImcAvgCPA3BO89meN14D9x3Z93Ns0d7k1XjNBYWnyQV5auJXL+7XlwlNsL72yKvMl7/s4f+SXAa8CrVV1rKr+V1V3AxOADmW8ttTGazgN1q5z718HzPZZPto9mjQYyPTZlTImILLyCrlzZhJtG5/EYxf39LqckFSZ42yLgfGqmlrak6paIiItSnuOshuvTQTeFZExwE7gCve5T4ELgK1ALnBDJeo0pkY88vFaUrPyeO/W0yKyp1FNqPB/NVV9pgJjcstYXlbjNYBzSxmvwLiK1mZMTft45V4+TtrHhGFd6du+sdflhCw7k9eY4+w+mMvDH6+l/8mN+f3QTl6XE9IsYIzxUVRcwh9nOXvxk65MpE5t+xOpDtuxNMbHK19tY9nOQzx3ZSLtmpzkdTkhz+LZGNfKXYd47ostjExszSWnnvCULlNBFjDG4B6SnpVEy9gY/jyyl9flhA3bRTIRr6i4hHFvr2DvoaO8M3YwcfWivC4pbFjAmIj3+Cfr+WbLAZ76TW8GJDTxupywYrtIJqK9+X0y07/fyc1nduDKAdZLuqZZwJiI9c2WdB7793rO7d6c+0fY7HT+YAFjItLWtCP8/u0VdGnegOevOpXaNgWDX1jAmIhzKKeAMdOXEl2nFlOu62/XGfmR/Zc1EaWgqIRb3lpOSmYeM8cOpm1jO5nOn2wLxkQMVeWhj9fww46DPH35KXYRYwBYwJiI8do323l32R5uP6czIxPtTN1AsIAxEWHB+v08OXcjF/ZuxZ2/6up1ORHDAsaEvfX7srhj5kp6t4njGesjHVCBarz2uoikichan2WPicheEUlyfy7wee4Bt+naJhE5PxA1mvCUlp3HTdOXEhsTxZTR/alXt7bXJUWUQG3BTAOGl7J8kqomuj+fArgN2UYBPd3XvCwi9q/CVFpeYTFj31jOodxCplzX35rUeyAgAaOqi4CDFRw+EpipqvmqugNnXt6BfivOhCVV5d73V5O0+zCTrky0Towe8fo7mPFu7+nXj/WlpoJN18Aar5myvfDFVuas2se9w7sxvFdLr8uJWF4GzCtAJ5wukSnAs5VdgTVeM6X5ZPU+Jn2+mcv6tuG2X9qcul7yLGBUdb/bKbIEeI2fdoOs6ZqpsqTdh7nr3VUMSGjMk5f1xmnJZbziWcAc18z+UuDYEaY5wCgRiRaRDkAX4IdA12dCz77DR7n5jWU0j43m1d/1I7qOHRvwWkCuRRKRd4ChQLyI7AEeBYaKSCJOP+pk4BYAVV0nIu8C64EiYJyqFgeiThO6cvKLuGn6Mo4WFPP2TYNo2iDa65IMAQoYVb2qlMVTTzD+CeAJ/1VkwklJifLHWUlsTM1i6vUD6NqiodclGZfXR5GMqba/zd/EZ+v389CFPTi7W3OvyzE+LGBMSHtv2W5e/XobVw9qzw1DErwuxxzHAsaErB92HOTBj9YwpHNT/u/innbEKAhZwJiQtCsjl1veXEa7xifx8tX9iLIWr0HJPhUTcrLyCrlx+lJKFKZeP4C4k6yPUbCygDEhpai4hPEzVpJ8IIdXfteXDvH1vS7JnIDNyWtCRkmJ8vDsdSzanM6Tl/Xm9E7xXpdkymEBY0JCSYnywIdrmLVsN78f2omrBlqTtFBgAWOCXnGJcs/7q/hwxV7+cE5nJgyzKS9DhQWMCWqFxSVMeHcV/161jwnDunL7uV28LslUggWMCVoFRSXc/s5K5q1L5f4R3bnVpl4IORYwJijlFxUz7u0VfL4hjYcv6sGYMzp4XZKpAgsYE3TyCou55c3lfL05ncdH9uTa0xK8LslUkQWMCSq5BUXc/MYyvtuWwcTLejPKjhaFNAsYEzSO5Bdx47SlLEs+yDOX9+E3/dp6XZKpJi/7IjURkQUissW9bewuFxF5we2LtFpE+gaiRuOtrLxCRk9dwvKdh3hu1KkWLmHCy75I9wNfqGoX4Av3McAInGkyuwBjcSYHN2EsM7eQa6csYfWeTF686lQu7tPa65JMDfGyL9JIYLp7fzpwic/yN9SxGGh03Py9JowczCng6imL2ZCSzau/68eI3vZRhxMvL3Zsoaop7v1UoIV73/oiRYgDR/K5+rXFbEk7wuTR/fhVjxblv8iElKC4mlpVFWfy78q+zvoihai0rDxGTV5MckYO/7p+AENtqsuw5GXA7D+26+PeprnLrS9SmEvJPMqVkxez7/BRpt0wkCGd7arocOVlwMwBrnPvXwfM9lk+2j2aNBjI9NmVMiFuz6FcrvznYg5k5/PmmIEM7tjU65KMH3nZF2ki8K6IjAF2Ale4wz8FLsBpep8L3BCIGo3/7czI4erXlpCdV8hbNw2iT7tGXpdk/MzLvkgA55YyVoFx/q3IBNr29CNc/doS8ouKmXHzYHq1ifO6JBMAdiav8bst+7O5esoSSkqUd8YOpnvLWK9LMgESFEeRTPjakJLFqMmLAZhp4RJxLGCM36zdm8lVry0mqnYtZo0dTBdr6RpxbBfJ+EXS7sOMnrqEhjFRvHPzYNo3PcnrkowHLGBMjft8/X7+OCuJxvXrMuPmQbRtbOESqSxgTI0pLC7hmfmb+Oei7fRsHcuU6/rTKq6e12UZD1nAmBqRknmUP8xYybKdh/jd4PY8dGEPYqJqe12W8ZgFjKm2RZvTuXNWEnmFxTw/KpGRiaVem2oikAWMqbLiEuX5L7bwjy+30LV5Q166pi+dmzfwuiwTRCxgTJWkZ+dzx8yVfLctg9/2a8ufR/aiXl3bJTL/ywLGVNr32zK4feZKsvMK+dvlp3BF/3blv8hEJAsYU2ElJcorX2/j2c82kRBfnzfHDLQzc80JWcCYCjmYU8AfZyXx9eZ0ft2nNU9e1psG0fbPx5yY/Qsx5Vq+8yDjZ6wk40gBf7mkF9cMao+IeF2WCQEWMKZMqsrUb3cwce5GWjeqx4e/P92mWTCVYgFjSpWZW8jd769iwfr9nN+zBX+7vA9x9aK8LsuEGM8DRkSSgWygGChS1f4i0gSYBSQAycAVqnrIqxojzeo9hxk3YwUph/N4+KIe3DgkwXaJTJUEy3QNZ6tqoqr2dx+X1ZTN+JGq8sb3yVz+yvcUFyvv3noaY87oYOFiqszzLZgyjMSZwxecpmxfAfd5VUwkOJJfxP0frOaT1Smc3a0Zf78ikcb163pdlglxwRAwCnwmIgr8U1UnU3ZTtv8hImNx2svSvn37QNQaljakZDHu7RXsPJjLfcO7c8tZHalVy7ZaTPUFQ8Ccoap7RaQ5sEBENvo+qarqhs/PuGE0GaB///6VbtwW6VSV95bt4eHZa4mrF8WMmwYxyNqImBrkecCo6l73Nk1EPgIG4jZlU9WU45qymRqy+2AuE+dt5D+rUxjSuSnPXXkqzRpGe12WCTOeBoyI1AdqqWq2e/884M/81JRtIv/blM1U08GcAv7x5RbeWryTWiJMGNaVcWd3prbtEhk/8HoLpgXwkXuUog4wQ1XnichSSm/KZqoot6CI17/dwT+/3k5OQRFX9G/Hnb/qSsu4GK9LM2HM04BR1e1An1KWZ1BKUzZTeUXFJby3fA+TFmwmLTufYT1acO/53WyGfxMQXm/BGD9RVT5bv5+/zdvItvQc+p3cmJeu6cuAhCZel2YiiAVMGFqWfJAn525k+c5DdGpWn39e24/zerSwE+ZMwFnAhJEt+7N5at4mPt+wn+YNo3nyst78tl9b6tQOlhO2TaSxgAkDqZl5TFqwmfeW76Z+3Trcc343bhiSwEl17eM13rJ/gSEs82gh//x6G6//dwfFJcr1p3dg/DmdaWKn+JsgYQETgvKLinnz+528uHArh3MLuSSxNXed1412TayDogkuFjAhpKREmb1qL8/M38zew0c5s0s89w3vbpNAmaBlARMCVJVFWw4wce5GNqRk0atNLE/95hTO6BLvdWnGnJAFTBDLyS9i4aY0ZizZxXfbMmjXpB7Pj0rk16e0tqudTUiwgAkymUcL+XLjfj5dk8qizenkF5XQrGE0j/66B9cMOpm6deyQswkdFjBB4GBOAQvWpzJ3bSr/3XqAwmKlZWwMVw1sz/BeLRmQ0MQuRjQhyQLGI2lZecxf54TKkh0HKS5R2jWpxw1DOjC8V0sS2zay3SAT8ixgAmjv4aPMW5vKvLUpLNt5CFXo2Kw+t/6yIyN6taJn61g7nd+EFQsYP0s+kMNcN1RW7ckEoHvLhtx5bldG9G5Jl+YNLFRM2LKA8YMt+7OZu9bZ/dmQkgVAn7Zx3De8O8N7taRDfH2PKzQmMCxgqim/qJj9mfnsyzzKt1sOMHdtCtvScxCBfu0b89CFv2B4r5a0bWxn2ZrIE9QBIyLDgeeB2sAUVZ0YyN9/JL+I1Mw8UjPzSMk8yv6sPFLcx6lZzm1GTsGP42sJDO7YlOtPT+D8ni1pHmuzxZnIFrQBIyK1gZeAYcAeYKmIzFHV9dVdt6pyOLfQCYuso6Rm5pOaedR9nPdjqGTnF/3stY1PiqJlXD1axcVwSttGtIqLoWVcDC1jY+jZOpamDWzibGOOCdqAwekusNWdVhMRmYnTkK3KATM7aS9/X7CZ1Mw88otK/uc5EWjeMJqWcfXo2Kw+QzrH/xgcLeNiaBUXQ4vYGGKialfnPRkTUYI5YNoAu30e7wEG+Q6obOO1pvWjOaVtI87v6YTFsa2PVnExNGsQbRMzGVPDgjlgylXZxmtndIm3CwSNCeLFi7QAAAPjSURBVKBg/l/2XqCdz+O27jJjTIgI5oBZCnQRkQ4iUhcYhdOQzRgTIoJ2F0lVi0RkPDAf5zD166q6zuOyjDGVELQBA6CqnwKfel2HMaZqgnkXyRgT4ixgjDF+YwFjjPEbCxhjjN+Iarnnp4UEEUkHdlZgaDxwwM/lBIq9l+AULu+lou/jZFVtVtoTYRMwFSUiy1S1v9d11AR7L8EpXN5LTbwP20UyxviNBYwxxm8iMWAme11ADbL3EpzC5b1U+31E3HcwxpjAicQtGGNMgFjAGGP8JqICRkSGi8gmEdkqIvd7XU91iEiyiKwRkSQRWeZ1PZUhIq+LSJqIrPVZ1kREFojIFve2sZc1VkQZ7+MxEdnrfi5JInKBlzVWlIi0E5GFIrJeRNaJyB3u8mp9LhETMD6TiI8AegBXiUgPb6uqtrNVNTEEz7mYBgw/btn9wBeq2gX4wn0c7Kbx8/cBMMn9XBLdGQFCQRFwl6r2AAYD49y/j2p9LhETMPhMIq6qBcCxScRNgKnqIuDgcYtHAtPd+9OBSwJaVBWU8T5CkqqmqOoK9342sAFnXuxqfS6RFDClTSLexqNaaoICn4nIcnfy81DXQlVT3PupQAsvi6mm8SKy2t2FCvpdveOJSAJwKrCEan4ukRQw4eYMVe2Ls8s3TkTO8rqgmqLOuROhev7EK0AnIBFIAZ71tpzKEZEGwAfAnaqa5ftcVT6XSAqYsJpEXFX3urdpwEc4u4ChbL+ItAJwb9M8rqdKVHW/qharagnwGiH0uYhIFE64vK2qH7qLq/W5RFLAhM0k4iJSX0QaHrsPnAesPfGrgt4c4Dr3/nXAbA9rqbJjf4yuSwmRz0VEBJgKbFDVv/s8Va3PJaLO5HUPGT7HT5OIP+FxSVUiIh1xtlrAmVd5Rii9FxF5BxiKMx3AfuBR4GPgXaA9zrQbV6hqUH+BWsb7GIqze6RAMnCLz3cYQUtEzgC+AdYAx9qePojzPUyVP5eIChhjTGBF0i6SMSbALGCMMX5jAWOM8RsLGGOM31jAGGP8xgLGGOM3FjDGGL+xgDHG+I0FjPGMiHQSkYMi0td93FpE0kVkqMelmRpiZ/IaT4nIzcAfgf44lz+sUdW7va3K1BQLGOM5EZkDdMC5fmeAquZ7XJKpIbaLZILBa0Av4B8WLuHFtmCMp9wJjlYBC3Emz+od7FdRm4qzgDGeEpGpQANVvVJEJgONVPUKr+syNcN2kYxnRGQkzqz8t7mLJgB9ReQa76oyNcm2YIwxfmNbMMYYv7GAMcb4jQWMMcZvLGCMMX5jAWOM8RsLGGOM31jAGGP8xgLGGOM3/w9Yefw5YrxnxAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4)) \n",
    "plt.plot(x,y) #lineplot\n",
    "plt.xlabel(\"x\", fontsize=12)\n",
    "plt.ylabel(\"y\", fontsize=12)\n",
    "plt.title(\"some title\", fontsize=16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lSFQRQnvwr9-"
   },
   "source": [
    "`Matplotlib` also allows to easily draw multiple `subplots` within one `plot` by using the `subplots` function. Graphics are can than be added via the `axes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "Qby-7PSDurFR",
    "outputId": "1e9cb0fb-05d8-4260-8b6b-256ee6d3d961"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6,3))# create subplot with 1 row and 2 cols\n",
    "\n",
    " axes[0].plot(x,y, linestyle=\"--\", color=\"orange\")#lineplot x,y\n",
    " axes[1].plot(y,x, linewidth=10, color=\"red\")# lineplot y,x\n",
    "\n",
    " plt.tight_layout()#adjusts spacing between plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dCXHo15pZh12"
   },
   "source": [
    "Of course `matplotlib` comes with support for basically all *standard plots* such as `boxplots`,`barplots`, `histograms`, `scatterplots` ... Make sure to check [their great overview](https://matplotlib.org/stable/gallery/index.html) of whats possible! \n",
    "\n",
    "--------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B93l5M0FHthu"
   },
   "source": [
    "# Real Data \n",
    "\n",
    "\n",
    "First we will make use of datasets provided by the seaborn package. For handling the data, we will rely on `pandas`.\n",
    "\n",
    "![](https://media.giphy.com/media/EatwJZRUIv41G/giphy.gif) \n",
    "\n",
    "Oh no, not on that fluffy fellow but a python module of the same name!\n",
    "\n",
    "\n",
    "If you want to learn more about pandas, check:\n",
    "- [10 minutes to pandas](https://pandas.pydata.org/docs/user_guide/10min.html) \n",
    "- [pandas getting started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html)\n",
    "\n",
    "\n",
    "\n",
    "We will cover some very basic dataframe handling in the next few lines, i.e.,\n",
    "- loading data from `sns.load_datasets()`\n",
    "- inspecting the dataframe with `.head()` and `.describe()`\n",
    "- checking for missing values and excluding them\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "phoOj8GMJVhx"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8p7eUerA51kZ",
    "outputId": "51d537ff-78fd-4460-a49f-4e157fc7b464"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(344, 7)\n"
     ]
    }
   ],
   "source": [
    "df = sns.load_dataset(\"penguins\")\n",
    "print(df.shape) #observations x variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yMFAce9vJxVk"
   },
   "source": [
    "### Understanding the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "l-ZqNvFE6DLE",
    "outputId": "1e46f205-d191-4a53-b7a9-c07b944b0ab9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-373a6629-3433-4d2e-958f-5cbb5f02866e\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
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       "          const element = document.querySelector('#df-373a6629-3433-4d2e-958f-5cbb5f02866e');\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",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    Male  \n",
       "1       3800.0  Female  \n",
       "2       3250.0  Female  \n",
       "3          NaN     NaN  \n",
       "4       3450.0  Female  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #depicts the first 5 rows of our dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "KBTGuM2n7pIt",
    "outputId": "3ae64a8e-58c5-44f7-b394-934c73d549ef"
   },
   "outputs": [
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       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
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       "      <th>body_mass_g</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.921930</td>\n",
       "      <td>17.151170</td>\n",
       "      <td>200.915205</td>\n",
       "      <td>4201.754386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.459584</td>\n",
       "      <td>1.974793</td>\n",
       "      <td>14.061714</td>\n",
       "      <td>801.954536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.100000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <td>190.000000</td>\n",
       "      <td>3550.000000</td>\n",
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       "      <th>50%</th>\n",
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       "      <th>75%</th>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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      "text/plain": [
       "       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "count      342.000000     342.000000         342.000000   342.000000\n",
       "mean        43.921930      17.151170         200.915205  4201.754386\n",
       "std          5.459584       1.974793          14.061714   801.954536\n",
       "min         32.100000      13.100000         172.000000  2700.000000\n",
       "25%         39.225000      15.600000         190.000000  3550.000000\n",
       "50%         44.450000      17.300000         197.000000  4050.000000\n",
       "75%         48.500000      18.700000         213.000000  4750.000000\n",
       "max         59.600000      21.500000         231.000000  6300.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() #summarizes all numeric columns (central tendency, distribution, dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vzPMeYX0kFtB",
    "outputId": "d8145612-cfb9-4bf1-e042-d354c27bfc1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species               0\n",
       "island                0\n",
       "bill_length_mm        2\n",
       "bill_depth_mm         2\n",
       "flipper_length_mm     2\n",
       "body_mass_g           2\n",
       "sex                  11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=0) # checking for missing values in columns (ie variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OqQv7-xZlIyg"
   },
   "source": [
    "Usually it´s up to you as a researcher how to handle missing data. The most common things to do with missing data is `imputation` or `dropping NaNs`. For now, we will just drop every row that has at least one missing data point (ie `NaN`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ditmI2BpnEgx",
    "outputId": "afac9505-ac74-4fb4-d0e9-b1aaaa23789e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species              0\n",
       "island               0\n",
       "bill_length_mm       0\n",
       "bill_depth_mm        0\n",
       "flipper_length_mm    0\n",
       "body_mass_g          0\n",
       "sex                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = df.dropna(axis=\"index\", how=\"any\", inplace=False) #dropping rows that contain >= 1 missing value \n",
    "df_clean.isnull().sum() #sanity check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o42gfFV2pqVr"
   },
   "source": [
    "Lets investigate whether there might be any interesting relationships between our variables. To get a first (non-visual) impression of the relatedness of our variables, we can compute pairwise correlations between columns using `df.corr()`. Note: This might not be suitable with larger dataframes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "id": "rRxHEWTXnFxQ",
    "outputId": "de512d2d-fe34-4cd3-a1ec-e438a64249a2"
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-79302d0c-3166-4444-83f1-072cabf8290b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-79302d0c-3166-4444-83f1-072cabf8290b');\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",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                   bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "bill_length_mm           1.000000      -0.228626           0.653096   \n",
       "bill_depth_mm           -0.228626       1.000000          -0.577792   \n",
       "flipper_length_mm        0.653096      -0.577792           1.000000   \n",
       "body_mass_g              0.589451      -0.472016           0.872979   \n",
       "\n",
       "                   body_mass_g  \n",
       "bill_length_mm        0.589451  \n",
       "bill_depth_mm        -0.472016  \n",
       "flipper_length_mm     0.872979  \n",
       "body_mass_g           1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6zbujUe2ZWVH"
   },
   "source": [
    "Now that we have our data at hand, lets try to plot the relationship between\n",
    "`body_mass` and `flipper_length` as a scatterplot using `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "abSOblTMqxjS",
    "outputId": "fcee957c-3a69-4fbb-f810-afae2c142b6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'flipper_length')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XQafnLwo5TtET5CEOFREd0+nonE5H/HR6/qLQC/Ciq4hLbdZqbZux3eM898LROe1DlUEuWDYy513NuJpD9fZdsGxk1kvwSXY1Oodm0rp1uuZPp+cvCjlO0TWkiQ5qJrIlKmUawMIFFd786lPmOD6IjiqKsu+WXeNccu7oTDRQs9E5zUZGdTrip9PzF4Uih0TXUFRqs2bSstUIRxUVmXqtH9O6dRqllRM9QVFCQyvjhqODihRCelVk6VYkDomuoSihoZnUazXC0UFFCiG9KrJ0K3KcomtoJbVZUvRK1tRrYebPs5kxL1g2UljqNaV1Kxd6VBddQ7NCQyNhJW1atvAa40CQH25y6tjMmFmEoHaduygGiUOi5ylCWJFY0/sorZzoa3oxIkd0FjlO0fP0YkSO6CyFPqqb2WLg88DLqSaGudbd/8zM3gZcBfwscJ677wwdcyXwXmAa+IC735E0hx7Vy0M4siWp7ENUdE1tXXCoMsDk0WO4R0fnJEXPxM0/vKDCT58/OisJSK2ExWjd/I1sDb+QvuHL9zE1HZ3hXeuP3U/H6qqb2SnAKe7+XTN7CbALWEv17+wx4H8BH6o5TjM7G7gROA84Ffg68Cp3n44aH+Q4y0Jc9E2Nocogm9dVHWBSvyjeef5pXL12eeQcacetDBonzp/HxOTUnLo/WWwN79tw831zMjLV95Pz7F469gK8uz8JPBlsP2tmDwGj7n5nYFj9IRcDX3T3F4AfmNk+qk70niLtFK0TlT4sTDiVWBanCXDjjgNcvXZ5wxRlSeNOTTsnnjCPE0+YlxgJ1GjM8L44pxnuJ8fZm7TtdSQzWwKsAHYkdBsFtoe+Px601Y91OXA5wGmnnZabjaJ50ogizQonteicVgWZPFLMFTWf6C7aIg6Z2YuBW4DfdfeftDqeu1/r7ivdfeXISHwZAtE+0ogiaSJxoqhF57QS4ZN1/qS+WeYTvUnhjtPMKlSd5g3u/pUG3ceBcIGXVwRtouQ0irBJG4kTRa3mT6MIn/qaQ83OXxmw2DFrdYw2rFlKZaDxfGWnF+sBtYNCH9Wtuoj5aeAhd/94ikNuBb5gZh+nKg6dBXynQBNFTtRHtiSp6uF+WVT1RmnX4hSfKJW7NsZJQxWefeHo7JIZ9UWCiPke4ze7RVVvNlWdKF5V/3ngW8Aeqio6wIeBE4A/B0aACeBed18THPMR4D3AUaqP9l9LmkOquoDmI3maSSnXaF+3RA4p+imZTqrq/0js/8v8dcwxHwM+VphRoidpVjhq5rg8hKMyoOin5lHkkOgJmo3kaUYAarSvW+iFc+gUyo4kcqOZmjhZx9g0tocbdxxg2h0zGJo3wOTUMYYXVKgM2JzooPGJSVZv2RZpy9jucQ4fia4zVBN2kurllLWWTtrfoVfrAbUDOU6RC3kIDY3G2DS2Z6bOD4A7HA7Suj19eIrKoDE8VJkTHRRlS1yk0/BQhaveck6skJUkcpVBEMryOyhVXfMorZzIhTyEhkZjnHnl7bNKVUSRVrjpVWGkV8+rE6jmkCicPISGRmM0cpqN5gvv61VhpFfPq2ykFofMbNTM/q2Z/ULtU6RhorvIQ2hoNMbg3NwGkX3T2NKrwkivnlfZSOU4zey/AXcDm4ANwedDBdoluoyoiJywOJMmIqVRXZ1aBFEcSdFB9aJHWWr45B25U5bz6nXSPqqvBZYGWYuEmENYaBifmGwozjQaI0qsqEUQRanqWYWbMggjRUTulOG8+oFU4pCZfQ14m7v/tHiTsiFxqHxIoEiHrlO5aVocMrM/p3rjcBi418y+Aczcdbr7B/I0VPQGEijSoevUvTR6VK/dyu2imoAjTHe/xyQK49Thocg7KQkUs9F16l4SxSF3/5y7fw4Yrm2H2ha2x0TRLdSEjtoaZxgJFHORkNO9pH0d6d0Rbf8xRztEl1MTOmp3UM7x7C6jw0OqvxPB2hWjbF63nNHhIQxdp26i0RrneuDXgDPMLPyo/hLgUJGGie4iqh5QrYqkhI541q4YlaPsQhqtcX6barG1lwF/Emp/Fri/KKNE9yGhQ/QTiY7T3fcD+4HXt8cc0a1I6BD9RKoX4M3sWeaq6M9QVd1/390fzdsw0V20K0VZs6nrwsc1KushRCPSRg79D6qler9Adc3/UuBM4LvAdcAbijBOdA/tiFhpNtKm/rinD0/N7FOdHdEMaSOH7nP319S13evur43a104UOdQ/5F1XKMsYov9IihxK+zrSYTN7u5kNBJ+3A88H+/QivGgLedcVytpHiBppHedlwLuAp4AfB9vvNLMh4LcLsk2IWeRdVyhrHyFqpHKc7v6ou/+qu7/M3UeC7X3uPhlUshSicJqNtIk6LusYQoRJq6qPAO8DloSPcff3FGOWEHNpVoCqP06qumiVtOLQt4FvUU32MfO+ibvfUpxp6ZA4JIQogjxqDi1w9z/I0SYhhOha0opDXzWziwq1RAghuoS0d5y/A3zYzI4AR6i+BO/u/tLCLBM9xaaxPTMlLwYMTpg3wPMxJS86QVJEUrPRSqJ3SeU43f0lRRsiepdNY3u4fvtjM9+POUxOHQPKEbmTFJEE5F4XSHQ/aatcmpm908z+S/B9sZmdV6xpole4cceBxP2TU9NsvWNvm6yZS1RKvJpNSftE/5J2jfMvqGZI+rXg+0+B/1mIRaLnmE7x5kYnI3eSIpKULk9EkdZxrnL3KwjCLN39aWB+YVaJnmLQ6gtpzKWTkTtJEUnNRiuJ3iatODRlZoMEcenBC/HHCrNKxJJFqMhD1IgbI8vY61ctnrXGWU+nI3capcRrR7o80V2kdZzXAH8N/IyZfQx4K7CpMKtEJFnSqjWbgi3NGDv3H+KWXeOpx7567XKA0qrqaSKSpKqLMKkihwDMbBnwRqqvIn3D3R8q0rC09FPkUJa0as2mYEszxqBZ5LqlUrOJXqLpyCEzWxT6+hRwY3ifu6tgWxvJIlTkIWrE9Y0TeySYiH6h0aP6LmZXeq39i7Fg+5UF2SUiyFLXJ48aQHFjxN1xSjAR/UKjYm1npBnEzM5x9wfyMUnEkaWuTx41gOLGuOTc0VlrnLX2C5aNsHrLtsgMREtOHmL7o08z7c6gGetXLZ5Z+8xCWlFK0T6iSNKKQ43438DrchpLxJAlrVoeNYCSxlh5+qJZ7RcsG5nlTOvr+oTvXKfdZ1T2LM4zreCVhzAmRBKpxaHEQcx2u/uKHOzJTD+JQ2UmTV2fegbNeGRz+twxaQWvPIQxIfKoOdQI1R3qc5oRhtJEFKWZo75d0T6iaPJynKLPaUYYShNRlGaO+nZF+4iiaeg4gwQfixt0OxJz7GIzu8vMHjSzB8zsd4L2RWZ2p5l9P/hzYWiua8xsn5ndb2ZaN61jbPc4q7ds44yNt7F6yzbGdo+XYr5GdX2iWL+q0V+rxnNECV7N1iYSIi0NHadXF0Fvb9Dn/JhdR4Hfd/ezgfOBK8zsbGAj1ZfozwK+EXwH+GXgrOBzOfDJNCfRL9REj/GJSZzjokdRzjPLfGtXjLJ53XIWLqikGnsAWHn6oob9ouYYHR7CqK5Zbl63fI7gk7afEM2StubQ54BPuPs/tTSZ2d8Anwg+b3D3J83sFOD/uPtSM/tfwfaNQf+9tX5xY/aTONRu0aOZ+bKIRBJrRJnJo+bQKuAyM9sPPMfxDPCvzmDEEmAFsAN4ecgZ/gh4ebA9CoSTNz4etM1ynGZ2OdU7Uk477bS0JnQ97RY9mpkvj8gkIcpOWse5ppVJzOzFwC3A77r7TywkCri7m1kmedXdrwWuheodZyu2dRN5RAMVPV/cMXF9hehG0pbO2G9mPw+c5e6fCdLKvTjNsWZWoeo0b3D3rwTNPzazU0KP6k8F7eNAWDF4RdDWl9RHv9S/ZA7FiB61eaMcYHi+qOicqGgjqK5pHosZp0gUQSSKIG3pjD8E/gC4MmiqANenOM6ATwMPufvHQ7tuBd4dbL8b+JtQ+68H6vr5wDNJ65u9TJQwc8uucS45d7RQ0SM8bz0GXHLu6Ew+zijhiKBP/YtGg4PG8FClrWJNu8U00T+kfVT/D1TXJ78L4O5PmFmaAm6rgXcBe8zs3qDtw8AW4Etm9l5gP/D2YN/twEXAPuAw8Bsp7es54mrd3PXwwUIFlah5azhw18MHE+2r1eKpXz+ZmnZOPGEe9/7hL+VtcixJNuquU7RCWsd5JLwWaWYnpjnI3f8R5tx81HhjRH8HrkhpU0/TqeiXRuPX9hctHOWBIohEUaSNHPpS8KrQsJm9D/g68KnizBKdin5pNH5tfzfU6SmLHaL3SCsO/Xcz+3+AnwCvAj7q7ncWalmf00xauKxCSFgEquXYXLigQmXAmDoW/bLC+MQkq7dsixSqKoPGcy8cZWJyaiZha1rb8yB8/icNVThydO6SgyKIRB5kSSu3Bxii+u9hTzHmiBpZ08JlTaVW37+WcOPpw1NUBo2hygCTU9H1+MJC1V0PH5zJv/nT56tOE45nv3aqYlDRanb9+dTsCLNwQYU//NVztL4pWiaV4zSz/wR8FNhG9d/Dn5vZf3X364o0rt9Zu2I09T/yrEJIkgg0Ne0ca1DDtF6oWr1l26wcnHDcabYjOijpfGosmD9PTlPkQto7zg3ACnf/FwAzOxn4NiDHWRKyCiGNBJI0Kd/CY3RaiEkzj0QhkRdpxaF/AZ4NfX82aBMlIasQ0kggSZPyLTxGp4WYNPNIFBJ5kfaOcx+wI0jS4cDFwP1m9nsAdS+3ixbJIvIkRflUBixWCImL8KnR6I6zXmRppcbRprE9MzXX4+oRNbomjc6nJlydsfG2liOIFI0k0jrOR4JPjVqkT5qX4EUGsog89X3nkHDTGBafxicmGTCIEdIBOHH+IJXBgZnia/XOotkaR5vG9szUH4LoekRprkn9/CcNVTCDicNTc4SrVmoQqZ6RgIw1h8zspVTfU3+2Yec20Wtp5bKkckuTwi2tONNorKJEnjOvvD3y7jZcj6jVdHp5puNTPaP+oeWaQ2a20sz2APdTDZ+8z8zOzdNIUSWLyJKnIJI2Yihv4pYEwu2tCk95CledFsFEOUgrDl0H/Ja7L3H3JVTDIj9TmFV9TBaRJU9BJG3EUN7EiVDh9laFpzyFq06LYKIcpF3jnHb3b9W+uPs/mtnRgmzqa7KILI0EEYAnnplkycbbIkWXsMgxnBAxFDd/MyJJ/THnv3Ihdz9yaE6/cD2iuGtywbIRVm/ZNivtXu2F/LA9rQhXnUrtJ8pNWsf5D0Gs+o1UVfV3AP+nVkzN3b9bkH19RxaRpV7giaL2xFsvutSLHLWIoeGhChOTUzMhmHFRP82IJFHHPPXs83P61dcjirom9Q5sfGJylsgUZU8zTr7e3vqIKanq/UnamkN3Jex2d+/YqniviUPNkrbWT0106YTgkmc9orRjtSLaSAjqb1quOeTuF+RrksibtOJETXTphOCSZz2ivESvZo6VECQSHaeZvdPdr6+96F6PXnwvD2lr/dREl1brF3W6HlHasVoRbdpd40l0D43uOGsJi/Wie8HUp0Qzq6471tYa49Ycw1E3aZh2Z/WWbSw5eYgngpISNdKIHImRSoPHI5XS1iOqDBrT0z6rHlE44ilKnLnr4YOMT0zOSV1XzwA0LdqM7R7nuRfm6p8SggRkfAG+jPTCGmfDCKA6hiqDbF63nJ37D80SRMKMDg+x5OQhtj/6dCqnasBl5582J9Qxi52VAWPr214DEKlib15XHbveEd70TweYmj5uY2XQ2PrW6HGi7HZgQWWAwxFp8N7Z4JyynKfS0vUXSWuciY7TzK5JGtjdP9CibS3TC44zi2hSY3R4iB8983zDqJss4+chyIwGj7GtRj8ljRPVN+21SINEIQGtiUPrgI8AC4Gn8zZMVGk2giXuv7x6B9LO6KGs4lAeAkyWa5F2vFZtEr1No8ihnwB3Uq02eSvwt3UfkQPNRrCkibrJMn4e0UNZaw41U7soqm/aa5F2vCztov9o5Dj/EvgGsAzYBewMPrVtkQMb1ixlqDKYun9NoAhH14Spb08zfhrRo9E4tTGi+iVFP8X1zWJ32muRhiz2i/4k8VHd3a8BrjGzT7r7+9tkU98RlxKtkapeO64+l+XK0+oJ2UoAABgPSURBVBfNrNPVjhseqvCiygATh6cSwxMhuohbbd7N65bPCtN0JzbVXNbop/CYH7zpXk4dHpoTpRNnd9y1yCoMRdmk6CBRj1T1HiNJ+a4p240qX7ZyfCtEzV30nELE0XJaOdE9JBUtqxVvK/L4VkgqOCdEmZDj7DFaVcY7lZczaWyp2aJsyHH2GK0q453Ky5k0ttRsUTbSppUT5Fukq9mx4kIQa6LSkaPJUTbjE5Os3rItMXflhpvvi8zLCbBgfnH/10bNPQAcPpJPkTUh8kKOMyV5Fulqdqyo48Ihl7ViZGHMqjk5w3HdSbkrqwfF2/79p57jsk/dww3ve32Ds2ySurmPUX27AFQYTZQHPaqnJE/hotmxkoSbOE49aYjR4aHEZBjh+bfesXdW3HgUURnb8yDN3BKLRBnQHWdKylDwK8+58ho/T9qRY1OIPNAdZ0rKUPCr2bmyhC52UojJKzRUiKLpqzvOVsSdtAXD0ozZbPGwNMXZwoTzWjY6rnYuX73vyYbjrj5zUWR70vWNyjdai2JKKqoWZadCH0Wn6RvH2aq4k7ZgWJoxmw3pi7Ohlth3Dtb4uKTqjVGsPnNRpDCUdH1htuMOi1iNiqolhYYK0Sn6JuSyiByLZcrb2Inia2mPh8Z5NZXrUpSNlou19QJFRKWUKdKlE8XX2nG8EGWkb8ShIqJSyhTp0qotRR6fNo+nEN1C39xxNivIZB0zHJ0TtR4XJ6CM7R7nj/72gZmXvYeHKlz1lnNm9jUjrKQ9v8s+dU/ko3Qj8Sts1/CCCpUBmxX1E54/sVbRoPHcC52LDsozIkz0B33jOIvIsRges77qYpToESeg7Nx/aE7BsonJKTbcfB879x+aJdpkEVbSnN9ln7on8oX2+YPGJeeOxopfMNsZPn14isqgMTxUaZifM+z8hxdU+OnzR2fOq93RQXlGhIn+oW/EoaJJI67E9aklC44iaV/UHFlZsvG2xHGzCj5Zbem0wNbp+UV5kTjUBtKII3F9khxjmmJjRQkrzQg+WW3ptMDW6flFd1KoOGRm15nZU2b2vVDba8zsHjPbY2Z/a2YvDe270sz2mdleM1tTpG15k0ZcieuTVFAsTbGxooSVZgSfrLZ0WmDr9PyiOyn6jvOzwCeAz4fa/gr4kLv/g5m9B9gA/BczOxu4FDgHOBX4upm9yt2zZbXoEBvWLGXDl++btU5ZGbRZ4kycgHPJuaNz1jih+r/aiyoDPHck+RKMT0zOPHLXv6Aerh9UY9CM81+5kB/+S/xd1fxB44JlI9z0nQOzBJ9wNNIHb7p3TvKQrCJPI1Fr09ieXOoIJc3f6HcTop5C7zjd/ZtAvfLwKuCbwfadwCXB9sXAF939BXf/AbAPOK9I+3Kn3ovUfV+7YpTN65YzOjyEUV1H27xuOVevXc7Wt76GhQsqM32HKgMMDlpDp1nP3Y8c4rJP3QMcFz7q1/Cm3bn7kUOJL6UfmXZu+qcDc5cKghvgm3c+FplxaWJyCue4yDK2ezzR3rhrsnbFKJvG9nD99sdmbJh25/rtj7FpbE/imJlp8LsJUU/h4pCZLQG+6u4/F3z/NvDH7j5mZr8H/JG7v8TMPgFsd/frg36fBr7m7l9OGr+bxKE8xkvLD7e8ueUx4ogTjeL6NiuynHnl7ZFrvINmPLL5oqbGrEfikIijbMXa3gP8lpntAl4CHMk6gJldbmY7zWznwYMHczewGfIWGfIQJ9otGuVtQ5wwlkYwS4vEIdEMbXec7v6wu/+Su58L3Ag8EuwaBxaHur4iaIsa41p3X+nuK0dGRoo1OCV5iwx5iBPtFo3ytiFOGEsjmKVF4pBohrY7TjP7meDPAWAT8JfBrluBS83sBDM7AzgL+E677WuWDWuWMlQZnNWWFLkztnuc1Vu2ccbG21i9ZductcANa5ZSGWzeQdTqCtXblIXKoDFgc9s2rFkam1puVt+B1kSW9asWZ2pvhqy/mxBQ/OtINwL3AEvN7HEzey+w3sz+GXgYeAL4DIC7PwB8CXgQ+Dvgim5R1CFZ5KgnLNokCikxT6Q1XzY6PMRZP3NiZJ/xiUlu2TXOJeeOzrywHj6+3iHWMzo8xDv+zeK5d3eBTTe87/WNnWeLN4ZXr13OO88/bcaGQTPeef5puarqWX43IWoocqgDtBJlFNU37ZhZ+xYxphDdQtnEob6nlSijuP1ZRI60fYsYU4heQI6zA7QSZRS3P4vIkbZvEWMK0QvIcaagkZCTtV8jQWJs9zjPvXA01p4o8SJqTIDDR45GCk9pBJEswkmWvmmvUxStHCtEXijJRwPSph3Lkp4sKQVc/Tg1BgyOeXXNMCqUsfb9qlsfmJV67unDU3PsSJuCLkuqurR9W0njphRwoixIHGpAEUJKHvMVdXzRtGJf2c9N9BYSh1qgCCElj/mKOr5oWrGv7Ocm+gc5zgYUIaTkMV9RxxdNK/aV/dxE/6A1zgZsWLOU3/vSvYQyqzFgRAopSenR6uvaLDl5iO2PPj2TLq2W5i3u3c0nn6mmjqulVlt5+qLIUhQnDVWoDNqcFHXPvVAVieLqBbkTWfIi77RurdRGKqJulBDNIMfZgJ37D81ymlAVaXbuPzRLkMgi+IxPzHaQtTRvSdRsqKVW+8L2xzgW7AuLQROTU1QGjBPnD85KSTcxOZVYL6hGWHDZuf8Q129/bJadte/NOs9Waj8VUTdKiGaQONSAPFKbFZXeLYm4WkVJ9YLq+/3omecLT+smRFlRzaEWyCO1WSfEizj7sohMcWeYZ1o3IboRiUMNyCO1WSfEizj7kuoF1fdrR1o3IboR3XE2YP2qxbPW+cLt9dQLQBcsG+Guhw/OqbmeBwMws8YZxfx5xpFpmA7XCwrV0ol6yb5GTXCpX+OskWdat6Ko/y20FiryRHecDUib2iwqVdz12x+bWUt0ZqeDW33molljrj5z0Uxqs4ULKgwPVWbSnNX3fef5p/Hxd7x2pv/wUIUT588Od5ycOjbLac4YwdxUavXz1dKqrTx90Zy/IAPAytMb5+LsJKnT9gnRJBKHciKtAFRUlEsR83drpE632i3KhSKH2kCrkT1lnL9bI3W61W7RPchx5kSrkT1lnL9bI3W61W7RPUgcapGaCJFGADKq622rt2xrWqyIi+SJiqpJmr8mXD0xMclQZYDJo8dwp+GYjeoolUGQUYSRKBo5zhaojwiqCUBOdT0tTlVvNh3aprE9DSN50qj6NeGqxuGpY6nHjHOGZUr5pggjUTQSh1qg3Snnmo1iaiZyKWt0kAQZ0WtIHCqIdqecazaKqRlRJGt0kAQZ0U/IcbZAu1PONRvJ04wokjU6SIKM6Ce0xhlBWpEjrQjRrFhRb8f5r1wYmUXp/FcuZPWWbZFZmdIKV/WkiQ4KC1UDNjeaqRsFmbIIXKLcyHHWkVftoGb6NbLj0HNHWH3mojl5PL/72DNz7N25/xC37BpvKFwlqepJ1AtVtSClocoAz08d60qnUyaBS5QbiUN1lEXkaFV4Skorl8d55JFur2yU5bcX5UDiUAbKInK0Kjy1mlauEXmk2ysbZfntRfmR46yjLCJHq8JTUlq5POjFlHNl+e1F+ZHjrGPDmqUMVWZnGsoqcoztHmf1lm2csfE2Vm/Z1lRWnrR2xPVbv2oxlcHZTiycVq5V4sSjbkg5F0cev73oDyQO1dFq1EleAkOrwhPATd85MHvQHJ+ia+JRnoXcOo0ijkRaJA7lTFkEhrLYIUS3InGojZRFYCiLHUL0InKcOVMWgaEsdgjRi2iNM2fiooQuWDYyK7on/AJ6M2tpjSJclFpNiOKQ48yZKIHhgmUjs6J46tO6ZRWQ0ghQEjqEKA6JQ20g73pAEn6EKB6JQx0m73pAEn6E6CxynG0g73pAEn6E6Cxa44wg79RiaeoBhYWbIoSfcIq5WgKQ0dDL8lElN7Q2KkQ0WuOso154gapT2rxueUvOY2z3OFfd+gATk1Nz9g0PVbjqLefM5NBMM38W5x41Zo3KgIHB1HT834M8zl+IbiNpjVOOs44ihZc0YxcxfzM1h/KcX4huROJQBooUXtKMXcT8RdouRD9SqOM0s+vM7Ckz+16o7bVmtt3M7jWznWZ2XtBuZnaNme0zs/vN7HVF2hZHkcJLmrGLmL9I24XoR4q+4/ws8Ka6tj8G/sjdXwt8NPgO8MvAWcHncuCTBdsWSZGpxdKMnef8tfR2SY/plQGbk36uHqP6kn2jFHl5pNMTohsoVFV392+a2ZL6ZuClwfZJwBPB9sXA57266LrdzIbN7BR3f7JIG+spMuImzdh5zZ8kCNWoiVL189VU9foib0kRTqrXI/qJwsWhwHF+1d1/Lvj+s8AdVG9kBoB/6+77zeyrwBZ3/8eg3zeAP3D3ROWnGyKHOkEaQaiR4JNFqFI0k+g1yiYOvR/4oLsvBj4IfDrrAGZ2ebA+uvPgwYO5G9gLpBFzGvXJIlQpmkn0E51wnO8GvhJs3wycF2yPA+G6C68I2ubg7te6+0p3XzkyMlKYod1MGjGnUZ8sQpWimUQ/0QnH+QTwi8H2hcD3g+1bgV8P1PXzgWfavb7ZS0SJTGHSCE5ZhCrV6xH9RKHikJndCLwBeJmZPQ78IfA+4M/MbB7wPFUFHeB24CJgH3AY+I0ibet16kWm4QUV3OGZyanUglMWoUpp7EQ/ocghIYSIoGzikBBCdDVynEIIkRGllUtB3mnmotg0tqfrapS347oIUUbkOBvQjoiYTWN7ZtUgmnaf+V5W56lIIdHP6FG9AVvv2DsnbHFyapqtd+zNbY4bdxzI1F4G2nFdhCgrcpwNaEdEzHTMmw1x7WVAkUKin5HjbEA7ImIGLTo7UVx7GVCkkOhn5Dgb0I6ImPWrFmdqLwOKFBL9jMShBrQjIqYmAHWTqq5IIdHPKHJICCEiUOSQEELkiBynEEJkRGucXY6id4RoP3KcXYyid4ToDHpU72IUvSNEZ5Dj7GIUvSNEZ5Dj7GIUvSNEZ5Dj7GIUvSNEZ5A41MUoekeIziDH2eWsXTEqRylEm9GjuhBCZESOUwghMqJH9S5DkUJCdB45zi5CkUJClAM9qncRihQSohzIcXYRihQSohzIcXYRihQSohzIcXYRihQSohxIHOoiFCkkRDmQ4+wyFCkkROfRo7oQQmREjlMIITKiR/U2oYgfIXoHOc42oIgfIXoLPaq3AUX8CNFbyHG2AUX8CNFbyHG2AUX8CNFbyHG2AUX8CNFbSBxqA4r4EaK3kONsE4r4EaJ30KO6EEJkRI5TCCEyUqjjNLPrzOwpM/teqO0mM7s3+PzQzO4N7bvSzPaZ2V4zW1OkbUII0SxFr3F+FvgE8Plag7u/o7ZtZn8CPBNsnw1cCpwDnAp83cxe5e6z3xwXQogOU+gdp7t/EzgUtc/MDHg7cGPQdDHwRXd/wd1/AOwDzivSPiGEaIZOrnH+O+DH7v794PsocCC0//GgbQ5mdrmZ7TSznQcPHizYTCGEmE0nHed6jt9tZsLdr3X3le6+cmRkJGezhBAimY68x2lm84B1wLmh5nFgcej7K4I2IYQoFZ264/z3wMPu/nio7VbgUjM7wczOAM4CvtMR64QQIoGiX0e6EbgHWGpmj5vZe4Ndl1L3mO7uDwBfAh4E/g64Qoq6EKKMmLt32oaWMLODwP5O2wG8DPi/nTYiQLbEUyZ7ZEs0ZbHldHePFFG63nGWBTPb6e4rO20HyJYkymSPbImmTLbEoZBLIYTIiBynEEJkRI4zP67ttAEhZEs8ZbJHtkRTJlsi0RqnEEJkRHecQgiRETnOGMzsRWb2HTO7z8weMLM/CtrPMLMdQfq7m8xsftB+QvB9X7B/SWisltLlJdjyWTP7QShN32uDdjOza4I57zez14XGereZfT/4vLuF6zNoZrvN7Kudui4JtnTyuvzQzPYE8+4M2haZ2Z3B2Hea2cJ22BNjy1VmNh66NheF+kf+Hmb2pqBtn5ltbNKWYTP7spk9bGYPmdnrO3VdcsHd9Yn4AAa8ONiuADuA86m+pH9p0P6XwPuD7d8C/jLYvhS4Kdg+G7gPOAE4A3gEGMzJls8Cb43ofxHwteC484EdQfsi4NHgz4XB9sImr8/vAV8Avhp8b/t1SbClk9flh8DL6tr+GNgYbG8E/ls77Imx5SrgQxF9I3+P4PMI8EpgftDn7CZs+Rzwn4Lt+cBwp65LHh/dccbgVX4afK0EHwcuBL4ctH8OWBtsXxx8J9j/RjMzckiXl2BLHBcDnw+O2w4Mm9kpwBrgTnc/5O5PA3cCb8piC4CZvQJ4M/BXwXejA9clypYGFHpdGsxbuwb116YT9sTZGPV7nAfsc/dH3f0I8MWgb2rM7CTgF4BPA7j7EXefoDuuSyRynAkEj4D3Ak9R/ZEeASbc/WjQJZz6biYtXrD/GeBkMqTLy2KLu+8Idn0seJz5UzM7od6WujlzsQX4H8B/Bo4F30+mQ9clwpYanbguUP0P7e/NbJeZXR60vdzdnwy2fwS8vE32RNkC8NvBtbmu9nhcsC1nAAeBzwRLKn9lZifSuevSMnKcCbj7tLu/lmqmpvOAZWWxxcx+DrgysOnfUH18+YOi7TCzXwGecvddRc/Vgi1tvy4hft7dXwf8MnCFmf1CeKdXnznb9SpLlC2fBM4EXgs8CfxJG+yYB7wO+KS7rwCeo/poPkObr0vLyHGmIHisuAt4PdXHhlo6vnDqu5m0eMH+k4B/Ied0eSFb3uTuTwaPMy8An+H4o27cnHnYshp4i5n9kOpj24XAn9GZ6zLHFjO7vkPXBQB3Hw/+fAr462DuHwePmgR/PtUOe6JscfcfB/8JHwM+RXuuzePA46GnpC9TdaQduS650ImF1W74ACPAcLA9BHwL+BXgZmaLIL8VbF/BbBHkS8H2OcxedH+U7OJQnC2nBG1G9ZF1S/D9zcxeXP+OH19c/wHVhfWFwfaiFq7RGzguyLT9uiTY0pHrApwIvCS0/W2qa3BbmS2C/HHR9iTYckqozweprmvG/h5U7xYfDdpq4tA5TVybbwFLg+2rgmvS9uuS16ejzqnMH+DVwG7gfuB7wEeD9ldSzRO6L3AWJwTtLwq+7wv2vzI01keoro/uBX45R1u2AXuCtus5rrwb8D+DOfcAK0NjvSewcR/wGy1eozdw3Fm1/bok2NKR6xJcg/uCzwPAR4L2k4FvAN8Hvl77x16kPQm2/O9grvup5sANO9LI34Oqyv3Pwb6PNHltXgvsDOYdo+r42n5d8voockgIITKiNU4hhMiIHKcQQmREjlMIITIixymEEBmR4xRCiIzIcQohREbkOEXHMbMlZva9Jo99gwXp5IRoF3KcQgiRETlOURbmmdkNQZLbL5vZAjN7Y5BNZ0+QyecEmEms+7CZfRdYF7QNBMltR0Lf99W+12PVZMefNLPtZvZocOd6XTD/Z0P9PmlmOy2UQDpo32JmDwZZhv570PY2M/ueVRNOf7O4SyU6jRynKAtLgb9w958FfkI1OfFngXe4+3KqMdPvN7MXUU1O8avAucC/AvBq0orrgcuC8f49cJ+7H0yYcyHVxC0fpBp++KdUY7aXW5A1nmqI4UqqYa+/aGavNrOTgf9ANWb71cDVQd+PAmvc/TXAW1q5GKLcyHGKsnDA3e8Otq8H3gj8wN3/OWj7HNVkuMuC9u97NV74+tAY1wG/Hmy/h2pmpCT+NhhjD/Bjd98TOOAHgCVBn7cHd7a7qTrVs6nmFH0e+LSZrQMOB33vBj5rZu+jmiBD9ChynKIs1CdNmMg8gPsBqqnKLqSaLu1rDQ55IfjzWGi79n2emZ0BfAh4Y3BneRvwIq8mZD6Panq0XwH+Lpj/N4FNVFOf7QruTEUPIscpysJpZvb6YPvXqGbSWWJm/zpoexfwD8DDQfuZQfv6unH+iupd6M3uPt2iTS+lmnT3GTN7OdWEwJjZi4GT3P12qo/5rwnaz3T3He7+UaoZzxdHDyu6nXmNuwjRFvZSzVJ+HfAg8AFgO3BzkAD5n6jm9XwhKANxm5kdpprn8SWhcW6l+oje6DG9Ie5+n5ntpuqsD1B9FCeY72+C9Vajuh4LsNXMzgravkE1pZvoQZRWTvQUZrYS+FN3/3edtkX0LrrjFD2DVWt+v5/jyroQhaA7TtHTmNlHgLfVNd/s7h/rhD2iN5DjFEKIjEhVF0KIjMhxCiFERuQ4hRAiI3KcQgiRETlOIYTIyP8P0TIcq9oMaU0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,6))\n",
    "\n",
    "plt.scatter(x=df_clean[\"body_mass_g\"], y=df_clean[\"flipper_length_mm\"])\n",
    "plt.xlabel(\"body_mass\")\n",
    "plt.ylabel(\"flipper_length\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gldk4SmOdrJ5"
   },
   "source": [
    "# seaborn \n",
    "\n",
    "easy generation of plots with less lines of code (vs. `matplotlib`)\n",
    "\n",
    "- various [styles](https://seaborn.pydata.org/tutorial/aesthetics.html) and [color palettes](https://seaborn.pydata.org/tutorial/color_palettes.html)\n",
    "- wide range of supported plots \n",
    "- good starting point for exploring and visualizing your data \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YELrzLqnjHD6"
   },
   "source": [
    "Lets try to recreate the `scatterplot` from above using `seaborn`.\n",
    "\n",
    "Hint: \n",
    "- using e.g. `sns.scatterplot?` in a code cell will show the functions documentation with all parameters etc\n",
    "- use [stackoverflow](https://stackoverflow.com/) to find answers to literally all coding-related questions you might have, e.g., if you want to find out, how you could change the size of the markers used in `sns.scatterplot`, [here](https://stackoverflow.com/questions/52785101/seaborn-scatterplot-marker-size-for-all-markers) people already got you covered.\n",
    "- check the [python graph gallery](https://www.python-graph-gallery.com/) to see what kind of plots you could make\n",
    "- check the [seaborn tutorial page](https://seaborn.pydata.org/tutorial.html) to get started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "Sbw06SOlqe--",
    "outputId": "88d891c6-6549-4804-8193-7a3a15e76eac"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f94c5a5d090>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(x=\"body_mass_g\", y=\"flipper_length_mm\", data=df_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nJsuPJJ8rN-i"
   },
   "source": [
    "Now lets try to add `species` as a third varible into our `scatterplot` to visualize whether there might be some *between-species* differences in the association between *body_mass* and *flipper_length*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "8GWHBbjsj49I",
    "outputId": "7b858c22-201d-4dc7-f506-9736aecf5e30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f94c5a749d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(x=\"body_mass_g\", y=\"flipper_length_mm\", hue=\"species\", data=df_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_D0HL4WBkEnh"
   },
   "source": [
    "From this plot we can see that the `Gentoo` penguins appear to have higher values on our variables of interest. However, from the `scatterplot` we can´t really answer the question whether the association between `body_mass` and `flipper_length` varies as a function of `species`. \n",
    "\n",
    "\n",
    "Lets try to use 'sns.lmplot' ([documentation](https://seaborn.pydata.org/generated/seaborn.lmplot.html)) to plot the same association as above, but now for each `specie` and with `sex` overlayed. Additionally try to change the `color` and use different `markers` for male and female penguins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "id": "r6quem6IoJ0T",
    "outputId": "90ed3a65-17bf-4090-8878-feccf54fdaa8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f94c592e890>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152.12x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "sns.lmplot(x=\"body_mass_g\", y=\"flipper_length_mm\", col=\"species\", \n",
    "           hue=\"sex\", data=df_clean, palette=\"viridis\", markers=[\"o\", \"x\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CbXNptZgrGKh"
   },
   "source": [
    "# Uploading file to colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "uXL0lZQzzBvA",
    "outputId": "35c254b1-fd3a-40b5-b31d-2b2ece1e3e14"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-2678f7d3-db3b-4a76-9c23-68eb25b2e79d\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-2678f7d3-db3b-4a76-9c23-68eb25b2e79d\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving mistery.csv to mistery.csv\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mistery.csv': 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      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from google.colab import files\n",
    "files.upload() ## upload the mistery.csv "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8LhDkMl1Z3bT"
   },
   "source": [
    "Reading different datatables with `pandas` can be done by using one of the functions [here](https://pandas.pydata.org/docs/reference/io.html). One of the most common usecases would be `pandas.read_csv()`, see below for some basic functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "0LwD6q0vzEaU",
    "outputId": "94653e36-b52a-4216-e127-5b1db22e8ac6"
   },
   "outputs": [
    {
     "data": {
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       "      <th>subject</th>\n",
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       "      <th>signal</th>\n",
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       "      <td>s13</td>\n",
       "      <td>18</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.017552</td>\n",
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       "      <td>s5</td>\n",
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       "      <td>s11</td>\n",
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       "      <td>s10</td>\n",
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       "                                                     [key], {});\n",
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       "\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",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "  subject  timepoint event    region    signal\n",
       "0     s13         18  stim  parietal -0.017552\n",
       "1      s5         14  stim  parietal -0.080883\n",
       "2     s12         18  stim  parietal -0.081033\n",
       "3     s11         18  stim  parietal -0.046134\n",
       "4     s10         18  stim  parietal -0.037970"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os # import operating system\n",
    "os.listdir() #get list of files in current directory\n",
    "\n",
    "# load the data and read it into a dataframe\n",
    "mistery = pd.read_csv(\"mistery.csv\", sep=\",\", index_col=[0])\n",
    "mistery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 387
    },
    "id": "a9643ewBzLFO",
    "outputId": "480afdc5-4b81-462f-96f1-c5bd13a134f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f94c57688d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 777.625x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try to figure out what kind of data you have \n",
    "# hint: use sns.relplot in a similar way as sns.lmplot before \n",
    "sns.relplot(\n",
    "    data=mistery, kind=\"line\",\n",
    "    x=\"timepoint\", y=\"signal\", col=\"region\",\n",
    "    hue=\"event\")\n",
    "\n",
    "\n",
    "\n",
    "# eigene daten sollten anonymisiert sein \n",
    "# data are uploaded to google - make sure that everything is in accordance with your \n",
    "# protection of data privacy \n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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