notebooks/matplotlib/pandas-matplot-lib-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Pandas`  + `Matplotlib` Library Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Real World Examples\n",
    "### Line Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "gas = pd.read_csv('../../data/gas_prices.csv')\n",
    "print(type(gas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Canada</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>South Korea</th>\n",
       "      <th>UK</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3.63</td>\n",
       "      <td>2.65</td>\n",
       "      <td>4.59</td>\n",
       "      <td>3.16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.90</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2.49</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.73</td>\n",
       "      <td>3.56</td>\n",
       "      <td>3.27</td>\n",
       "      <td>4.53</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.06</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.68</td>\n",
       "      <td>4.16</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.45</td>\n",
       "      <td>3.59</td>\n",
       "      <td>3.52</td>\n",
       "      <td>3.70</td>\n",
       "      <td>4.36</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.99</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.96</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.43</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2.94</td>\n",
       "      <td>3.21</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>2.12</td>\n",
       "      <td>1.61</td>\n",
       "      <td>4.41</td>\n",
       "      <td>3.94</td>\n",
       "      <td>4.39</td>\n",
       "      <td>3.64</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.62</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3.53</td>\n",
       "      <td>4.07</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1.47</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.83</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1.49</td>\n",
       "      <td>3.04</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1999</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.52</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.42</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.27</td>\n",
       "      <td>1.79</td>\n",
       "      <td>3.80</td>\n",
       "      <td>4.29</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.86</td>\n",
       "      <td>3.80</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.77</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.01</td>\n",
       "      <td>4.18</td>\n",
       "      <td>4.58</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2001</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3.51</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.57</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.76</td>\n",
       "      <td>4.13</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2002</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3.15</td>\n",
       "      <td>2.24</td>\n",
       "      <td>3.84</td>\n",
       "      <td>4.16</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.59</td>\n",
       "      <td>4.53</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.04</td>\n",
       "      <td>4.11</td>\n",
       "      <td>4.70</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2004</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.37</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5.24</td>\n",
       "      <td>5.29</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.03</td>\n",
       "      <td>4.51</td>\n",
       "      <td>5.56</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>3.23</td>\n",
       "      <td>2.89</td>\n",
       "      <td>5.46</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.74</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.22</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.97</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.26</td>\n",
       "      <td>5.88</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.10</td>\n",
       "      <td>4.47</td>\n",
       "      <td>2.31</td>\n",
       "      <td>5.92</td>\n",
       "      <td>6.36</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2007</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.59</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.88</td>\n",
       "      <td>6.73</td>\n",
       "      <td>4.49</td>\n",
       "      <td>2.40</td>\n",
       "      <td>6.21</td>\n",
       "      <td>7.13</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.08</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.63</td>\n",
       "      <td>5.74</td>\n",
       "      <td>2.45</td>\n",
       "      <td>5.83</td>\n",
       "      <td>7.42</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Australia  Canada  France  Germany  Italy  Japan  Mexico  \\\n",
       "0   1990        NaN    1.87    3.63     2.65   4.59   3.16    1.00   \n",
       "1   1991       1.96    1.92    3.45     2.90   4.50   3.46    1.30   \n",
       "2   1992       1.89    1.73    3.56     3.27   4.53   3.58    1.50   \n",
       "3   1993       1.73    1.57    3.41     3.07   3.68   4.16    1.56   \n",
       "4   1994       1.84    1.45    3.59     3.52   3.70   4.36    1.48   \n",
       "5   1995       1.95    1.53    4.26     3.96   4.00   4.43    1.11   \n",
       "6   1996       2.12    1.61    4.41     3.94   4.39   3.64    1.25   \n",
       "7   1997       2.05    1.62    4.00     3.53   4.07   3.26    1.47   \n",
       "8   1998       1.63    1.38    3.87     3.34   3.84   2.82    1.49   \n",
       "9   1999       1.72    1.52    3.85     3.42   3.87   3.27    1.79   \n",
       "10  2000       1.94    1.86    3.80     3.45   3.77   3.65    2.01   \n",
       "11  2001       1.71    1.72    3.51     3.40   3.57   3.27    2.20   \n",
       "12  2002       1.76    1.69    3.62     3.67   3.74   3.15    2.24   \n",
       "13  2003       2.19    1.99    4.35     4.59   4.53   3.47    2.04   \n",
       "14  2004       2.72    2.37    4.99     5.24   5.29   3.93    2.03   \n",
       "15  2005       3.23    2.89    5.46     5.66   5.74   4.28    2.22   \n",
       "16  2006       3.54    3.26    5.88     6.03   6.10   4.47    2.31   \n",
       "17  2007       3.85    3.59    6.60     6.88   6.73   4.49    2.40   \n",
       "18  2008       4.45    4.08    7.51     7.75   7.63   5.74    2.45   \n",
       "\n",
       "    South Korea    UK   USA  \n",
       "0          2.05  2.82  1.16  \n",
       "1          2.49  3.01  1.14  \n",
       "2          2.65  3.06  1.13  \n",
       "3          2.88  2.84  1.11  \n",
       "4          2.87  2.99  1.11  \n",
       "5          2.94  3.21  1.15  \n",
       "6          3.18  3.34  1.23  \n",
       "7          3.34  3.83  1.23  \n",
       "8          3.04  4.06  1.06  \n",
       "9          3.80  4.29  1.17  \n",
       "10         4.18  4.58  1.51  \n",
       "11         3.76  4.13  1.46  \n",
       "12         3.84  4.16  1.36  \n",
       "13         4.11  4.70  1.59  \n",
       "14         4.51  5.56  1.88  \n",
       "15         5.28  5.97  2.30  \n",
       "16         5.92  6.36  2.59  \n",
       "17         6.21  7.13  2.80  \n",
       "18         5.83  7.42  3.27  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1990\n",
      "1     1991\n",
      "2     1992\n",
      "3     1993\n",
      "4     1994\n",
      "5     1995\n",
      "6     1996\n",
      "7     1997\n",
      "8     1998\n",
      "9     1999\n",
      "10    2000\n",
      "11    2001\n",
      "12    2002\n",
      "13    2003\n",
      "14    2004\n",
      "15    2005\n",
      "16    2006\n",
      "17    2007\n",
      "18    2008\n",
      "Name: Year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# We can see individual columns using [column_name]\n",
    "print(gas['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.16\n",
       "1     1.14\n",
       "2     1.13\n",
       "3     1.11\n",
       "4     1.11\n",
       "5     1.15\n",
       "6     1.23\n",
       "7     1.23\n",
       "8     1.06\n",
       "9     1.17\n",
       "10    1.51\n",
       "11    1.46\n",
       "12    1.36\n",
       "13    1.59\n",
       "14    1.88\n",
       "15    2.30\n",
       "16    2.59\n",
       "17    2.80\n",
       "18    3.27\n",
       "Name: USA, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gas['USA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      NaN\n",
       "1     1.96\n",
       "2     1.89\n",
       "3     1.73\n",
       "4     1.84\n",
       "5     1.95\n",
       "6     2.12\n",
       "7     2.05\n",
       "8     1.63\n",
       "9     1.72\n",
       "10    1.94\n",
       "11    1.71\n",
       "12    1.76\n",
       "13    2.19\n",
       "14    2.72\n",
       "15    3.23\n",
       "16    3.54\n",
       "17    3.85\n",
       "18    4.45\n",
       "Name: Australia, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gas['Australia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can now plot different countries' gas price using a line plot\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(gas['Year'], gas['USA'], '.-', label='USA')\n",
    "plt.plot(gas['Year'], gas['Australia'], '.-', label='Australia')\n",
    "plt.plot(gas['Year'], gas['France'], '.-', label='France')\n",
    "plt.plot(gas['Year'], gas['Germany'], '.-', label='Germany')\n",
    "\n",
    "plt.xticks(gas['Year'][::2])\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Price (USD)')\n",
    "plt.title('Gas Price For Different Countries (USD)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fifa Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Photo</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Flag</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>...</th>\n",
       "      <th>Composure</th>\n",
       "      <th>Marking</th>\n",
       "      <th>StandingTackle</th>\n",
       "      <th>SlidingTackle</th>\n",
       "      <th>GKDiving</th>\n",
       "      <th>GKHandling</th>\n",
       "      <th>GKKicking</th>\n",
       "      <th>GKPositioning</th>\n",
       "      <th>GKReflexes</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/158023.png</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>https://cdn.sofifa.org/flags/52.png</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>€226.5M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/20801.png</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>https://cdn.sofifa.org/flags/38.png</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>€127.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/190871.png</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>https://cdn.sofifa.org/flags/54.png</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>€228.1M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
       "      <td>Spain</td>\n",
       "      <td>https://cdn.sofifa.org/flags/45.png</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>€138.6M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>https://cdn.sofifa.org/players/4/19/192985.png</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>https://cdn.sofifa.org/flags/7.png</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>€196.4M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      ID               Name  Age  \\\n",
       "0           0  158023           L. Messi   31   \n",
       "1           1   20801  Cristiano Ronaldo   33   \n",
       "2           2  190871          Neymar Jr   26   \n",
       "3           3  193080             De Gea   27   \n",
       "4           4  192985       K. De Bruyne   27   \n",
       "\n",
       "                                            Photo Nationality  \\\n",
       "0  https://cdn.sofifa.org/players/4/19/158023.png   Argentina   \n",
       "1   https://cdn.sofifa.org/players/4/19/20801.png    Portugal   \n",
       "2  https://cdn.sofifa.org/players/4/19/190871.png      Brazil   \n",
       "3  https://cdn.sofifa.org/players/4/19/193080.png       Spain   \n",
       "4  https://cdn.sofifa.org/players/4/19/192985.png     Belgium   \n",
       "\n",
       "                                  Flag  Overall  Potential  \\\n",
       "0  https://cdn.sofifa.org/flags/52.png       94         94   \n",
       "1  https://cdn.sofifa.org/flags/38.png       94         94   \n",
       "2  https://cdn.sofifa.org/flags/54.png       92         93   \n",
       "3  https://cdn.sofifa.org/flags/45.png       91         93   \n",
       "4   https://cdn.sofifa.org/flags/7.png       91         92   \n",
       "\n",
       "                  Club  ... Composure Marking StandingTackle  SlidingTackle  \\\n",
       "0         FC Barcelona  ...      96.0    33.0           28.0           26.0   \n",
       "1             Juventus  ...      95.0    28.0           31.0           23.0   \n",
       "2  Paris Saint-Germain  ...      94.0    27.0           24.0           33.0   \n",
       "3    Manchester United  ...      68.0    15.0           21.0           13.0   \n",
       "4      Manchester City  ...      88.0    68.0           58.0           51.0   \n",
       "\n",
       "  GKDiving  GKHandling  GKKicking  GKPositioning GKReflexes Release Clause  \n",
       "0      6.0        11.0       15.0           14.0        8.0        €226.5M  \n",
       "1      7.0        11.0       15.0           14.0       11.0        €127.1M  \n",
       "2      9.0         9.0       15.0           15.0       11.0        €228.1M  \n",
       "3     90.0        85.0       87.0           88.0       94.0        €138.6M  \n",
       "4     15.0        13.0        5.0           10.0       13.0        €196.4M  \n",
       "\n",
       "[5 rows x 89 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa = pd.read_csv('../../data/fifa_data.csv')\n",
    "fifa.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Players By Skill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        94\n",
       "1        94\n",
       "2        92\n",
       "3        91\n",
       "4        91\n",
       "         ..\n",
       "18202    47\n",
       "18203    47\n",
       "18204    47\n",
       "18205    47\n",
       "18206    46\n",
       "Name: Overall, Length: 18207, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# skills of players overall\n",
    "fifa['Overall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the bins for histogram\n",
    "bins = [x for x in range(0, 101, 10)]\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.hist(fifa['Overall'], bins, rwidth=.8)\n",
    "\n",
    "plt.xlabel('Skill Level')\n",
    "plt.ylabel('Number Of Players')\n",
    "plt.title('Player Skills FIFA 2018')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Left\n",
       "1        Right\n",
       "2        Right\n",
       "3        Right\n",
       "4        Right\n",
       "         ...  \n",
       "18202    Right\n",
       "18203    Right\n",
       "18204    Right\n",
       "18205    Right\n",
       "18206    Right\n",
       "Name: Preferred Foot, Length: 18207, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will find the foot preferences of all the players\n",
    "fifa['Preferred Foot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4211"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_foot_users = (fifa['Preferred Foot'] == 'Left').sum()\n",
    "left_foot_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13948"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_foot_users = (fifa['Preferred Foot'] == 'Right').sum()\n",
    "right_foot_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "label = ['Left Foot', 'Right Foot']\n",
    "plt.pie([left_foot_users, right_foot_users], labels=label, autopct='%.2f%%')\n",
    "plt.title('FIFA Players Foot Preference')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight of Professional Soccer Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        159lbs\n",
       "1        183lbs\n",
       "2        150lbs\n",
       "3        168lbs\n",
       "4        154lbs\n",
       "          ...  \n",
       "18202    134lbs\n",
       "18203    170lbs\n",
       "18204    148lbs\n",
       "18205    154lbs\n",
       "18206    176lbs\n",
       "Name: Weight, Length: 18207, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        159.0\n",
       "1        183.0\n",
       "2        150.0\n",
       "3        168.0\n",
       "4        154.0\n",
       "         ...  \n",
       "18202    134.0\n",
       "18203    170.0\n",
       "18204    148.0\n",
       "18205    154.0\n",
       "18206    176.0\n",
       "Name: Weight, Length: 18207, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa['Weight'] = [int(x.strip('lbs')) if type(x) == str else x for x in fifa['Weight']]\n",
    "fifa['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light = (fifa['Weight'] < 125).sum()\n",
    "light"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2290"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light_medium = ((fifa['Weight'] < 150) & (fifa['Weight'] > 125)).sum()\n",
    "light_medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10876"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medium = ((fifa.Weight >= 150) & (fifa.Weight < 175)).sum()\n",
    "medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4583"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medium_heavy = ((fifa.Weight >= 175) & (fifa.Weight < 200)).sum()\n",
    "medium_heavy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heavy = (fifa.Weight >= 200).sum()\n",
    "heavy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = [light, light_medium, medium, medium_heavy, heavy]\n",
    "labels = ['Under 125', '125-150', '150-175', '175-200', 'over 200']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.pie(weights, labels=labels, pctdistance=0.8, autopct='%.2f%%')\n",
    "plt.title('Weight of Professional Soccer Players (lbs)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        159.0\n",
       "1        183.0\n",
       "2        150.0\n",
       "3        168.0\n",
       "4        154.0\n",
       "         ...  \n",
       "18202    134.0\n",
       "18203    170.0\n",
       "18204    148.0\n",
       "18205    154.0\n",
       "18206    176.0\n",
       "Name: Weight, Length: 18207, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Weight Distributions By Frequnecy\n",
    "weights = fifa['Weight'][:]\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "barcelona = fifa.loc[fifa.Club == \"FC Barcelona\"]['Overall']\n",
    "madrid = fifa.loc[fifa.Club == \"Real Madrid\"]['Overall']\n",
    "revs = fifa.loc[fifa.Club == \"New England Revolution\"]['Overall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        94\n",
       "7        91\n",
       "18       89\n",
       "20       89\n",
       "32       88\n",
       "42       87\n",
       "49       87\n",
       "53       87\n",
       "54       87\n",
       "96       85\n",
       "155      83\n",
       "192      83\n",
       "228      82\n",
       "236      82\n",
       "239      82\n",
       "263      82\n",
       "266      82\n",
       "324      81\n",
       "613      79\n",
       "1007     77\n",
       "1037     77\n",
       "3118     73\n",
       "3684     72\n",
       "6000     69\n",
       "6102     69\n",
       "8284     67\n",
       "8289     67\n",
       "8857     66\n",
       "10526    65\n",
       "10777    65\n",
       "11300    64\n",
       "12502    63\n",
       "14286    61\n",
       "Name: Overall, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barcelona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6        91\n",
       "8        91\n",
       "11       90\n",
       "19       89\n",
       "27       88\n",
       "30       88\n",
       "35       88\n",
       "36       88\n",
       "46       87\n",
       "62       86\n",
       "79       85\n",
       "105      85\n",
       "123      84\n",
       "172      83\n",
       "188      83\n",
       "328      81\n",
       "417      80\n",
       "430      80\n",
       "573      79\n",
       "697      79\n",
       "754      78\n",
       "1143     77\n",
       "2513     74\n",
       "6724     68\n",
       "8732     67\n",
       "9141     66\n",
       "10178    65\n",
       "10269    65\n",
       "11163    64\n",
       "11327    64\n",
       "11877    64\n",
       "12504    63\n",
       "13687    62\n",
       "Name: Overall, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "madrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3008     73\n",
       "4219     71\n",
       "4957     70\n",
       "5070     70\n",
       "5201     70\n",
       "5780     69\n",
       "5823     69\n",
       "6043     69\n",
       "6797     68\n",
       "6858     68\n",
       "7006     68\n",
       "7170     68\n",
       "8365     67\n",
       "8425     67\n",
       "9360     66\n",
       "9968     65\n",
       "10236    65\n",
       "12372    63\n",
       "12752    63\n",
       "13495    62\n",
       "15436    59\n",
       "15553    59\n",
       "15634    59\n",
       "15909    58\n",
       "16050    58\n",
       "17445    54\n",
       "18170    48\n",
       "Name: Overall, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9TLRH6fUPoF0IXoUrvemMjQ8MovT6B9A+BK+Cld50OscHpqr0+gfQTgSvQpXedGhqmA7qE0CpCF4FKr3p0NQwHdQngJIRvApTetOhqWE6qE8ApSN4FaT0pkNTw3SUXp+cvQtAIngVpfSmQ+jCoEqvT87eBTCG4FWQ0psOoQuDKL0+OXsXQCeCV0FKbzqELkxV6fVJ/QMYj+DVYjQdNFnp9Un9A+iG4NVSNB00Wen1Sf0DmAjBq4VoOmiy0uuT+gfQC8GrZWg6aLqS65P6BzAZgleL0HRQglLrk/oH0A+CV0vQdFCKEuuT+gfQL4JXC9B00GbUP4AmIXiNOJoO2oz6B9A0BK8RRtNBm1H/AJqI4DWiaDpoM+ofQFMRvEYQTQdtRv0DaDKC14ih6aDNqH8ATUfwGiE0HbQZ9Q+gBASvEUHTQZtR/wBKQfAaAaU3nfUrlw99TLRH6fUPoF0IXoUrvemMjQ8MovT6B9A+BK+Cld50OscHpqr0+gfQTgSvQpXedGhqmA7qE0CpCF4FKr3p0NQwHdQngJIRvApTetOhqWE6qE8ApSN4FaT0pkNTw3SUXp+cvQtAIngVpfSmQ+jCoEqvT87eBTCG4FWQ0psOoQuDKL0+OXsXQCeCV0FKbzqELkxV6fVJ/QMYj+DVYjQdNFnp9Un9A+iG4NVSNB00Wen1Sf0DmAjBq4VoOmiy0uuT+gfQC8GrZWg6aLqS65P6BzAZgleL0HRQglLrk/oH0A+CV0vQdFCKEuuT+gfQL4JXC9B00GbUP4AmIXiNOJoO2oz6B9A0BK8RRtNBm1H/AJqI4DWiaDpoM+ofQFMRvEYQTQdtRv0DaDKC14ih6aDNqH8ATUfwGiE0HbQZ9Q+gBASvEUHTQZtR/wBKQfAaAaU3nfUrlw99TLRH6fUPoF0IXoUrvemMjQ8MovT6B9A+s2Z6AujDoi0U795c0ssfvO5Rkt4qSTWFl2zjb17P+BhZhC4AJWKLF4DiELoAlIotXiVYdJtsa95x50gqv+l0G3/FiYcoFg39oTCCSq9/AO3GFq/ClN50aGqYDuoTQOkIXgUpvenQ1DAdpdcnZ+8CkAheRSm96RC6MKjS65OzdwGMIXgVpPSmQ+jCIEqvz87xAYDgVZDSmw6hC1NVen1S/wDGI3i1GE0HTVZ6fVL/ALoheLUUTQdNVnp9Uv8AJkLwaiGaDpqs9Pqk/gH0QvBqGZoOmq7k+qT+AUyG4NUiNB2UoNT6pP4B9IPg1RI0HZSixPqk/gH0i+DVAjQdtBn1D6BJCF4jjqaDNqP+ATQNwWuE0XTQZtQ/gCYieI0omg7ajPoH0FQErxFE00GbUf8AmqzW4GX7DbavsH257VNsb2j7EbYvtv0b26fafnidc2gbmg7ajPoH0HS1BS/bcyW9VtKCiNhT0kMlvVTSiZI+EhG7SVor6Zi65tA2NB20GfUPoASzMoy/ke0/S9pY0mpJB0p6eVq/RNIiSZ+seR4jj6aD4i3aQvHuzXX/r4cpepSkt0rSCcOb07DHf/fmQ5oMgFLVtsUrIlZJ+pCklaoC122SlklaFxH3pJvdIGlut/vbPtb2UttL16xZU9c0R0LpoWv9yuVDHxMAgCaqbYuX7a0kHSbpEZLWSTpd0t/0e/+IWCxpsSQtWLAgapjiSBiF0LXmzJq2UKAsi26Tbc077py+71Ja/a848RDFounPC0C56jy4/mBJv4uINRHxZ0lfl/RUSVvaHgt8O0laVeMcRlppTafX+MBUlV7/ANqpzuC1UtKTbW9s25IOknSlpAskHZ5uc6Sks2qcw8gqvenQ1DAd1CeAUtV5jNfFks6Q9HNJl6XHWizpOElvtP0bSdtI+lxdcxhVpTcdmhqmg/oEULJaz2qMiHdLeve4xddKelKdjzvKSm86NDVMB/UJoHR8cn1BSm86NDVMR+n1ydm7ACSCV1FKbzqELgyq9Prk7F0AYwheBSm96RC6MIjS65OzdwF0IngVpPSmQ+jCVJVen9Q/gPEIXi1G00GTlV6f1D+AbgheLUXTQZOVXp/UP4CJELxaiKaDJiu9Pql/AL0QvFqGpoOmK7k+qX8AkyF4tQhNByUotT6pfwD9IHi1BE0HpSixPql/AP0ieLUATQdtRv0DaBKC14ij6aDNqH8ATUPwGmE0HbQZ9Q+giQheI4qmgzaj/gE0FcFrBNF00GbUP4AmI3iNGJoO2oz6B9B0BK8RQtNBm1H/AEpA8BoRNB20GfUPoBQErxFQetNZv3L50MdEe5Re/wDaheBVuNKbztj4wCBKr38A7UPwKljpTadzfGCqSq9/AO1E8CpU6U2HpobpoD4BlIrgVaDSmw5NDdNBfQIoGcGrMKU3HZoapoP6BFA6gldBSm86NDVMR+n1ydm7ACSCV1FKbzqELgyq9Prk7F0AYwheBSm96RC6MIjS65OzdwF0IngVpPSmQ+jCVJVen9Q/gPEIXi1G00GTlV6f1D+AbgheLUXTQZOVXp/UP4CJELxaiKaDJiu9Pql/AL0QvFqGpoOmK7k+qX8AkyF4tQhNByUotT6pfwD9IHi1BE0HpSixPql/AP0ieLUATQdtRv0DaBKC14ij6aDNqH8ATUPwGmE0HbQZ9Q+giQheI4qmgzaj/gE0FcFrBNF00GbUP4AmI3iNGJoO2oz6B9B0BK8RQtNBm1H/AEpA8BoRNB20GfUPoBQErxFQetNZv3L50MdEe5Re/wDaheBVuNKbztj4wCBKr38A7UPwKljpTadzfGCqSq9/AO1E8CpU6U2HpobpoD4BlGrWZDew/U1JMW7xbZKWSvp0RKyvY2KYWOlNh6aG6aA+AZSsny1e10q6U9Jn0tftku6Q9Kh0HRmV3nRoapgO6hNA6Sbd4iXpKRGxX8f1b9r+WUTsZ/uKuiaGByu96dDUMB2l1ydn7wKQ+tvitantXcaupMubpqt31zIrdFV60yF0YVCl1ydn7wIY00/wepOkH9m+wPYPJP1Q0pttbyJpSZ2TwwOV3nQIXRhE6fXJ2bsAOk26qzEivmV7d0mPTouu7jig/qN1TQwPVnrTIXRhqkqvT+ofwHj9HOMlSftKmp9uv7dtRcQXapsVsqDpoMlKr0/qH0A3/XycxBcl7SrpUkn3psUhieBVMJoOmqz0+qT+AUykny1eCyQ9NiLGf5YXCkXTQZOVXp/UP4Be+jm4/nJJ29c9EeRB00HTlVyf1D+AyfSzxWtbSVfavkTSn8YWRsShtc0KtaDpoASl1if1D6Af/QSvRXVPAvWj6aAUJdYn9Q+gX/18nMSFOSaC+tB00GbUP4AmmTB42f5RRBxg+w498J9kW1JExOa1zw7TRtNBm1H/AJpmwuAVEQek75vlmw6GiaaDNqP+ATTRpGc1ps/xmnQZmoWmgzaj/gE0VT8fJ/G4ziu2Z6n6JHs0FE0HbUb9A2iyCYOX7bel47seb/v29HWHpJsknZVthpgSmg7ajPoH0HQTBq+I+Ld0fNe/R8Tm6WuziNgmIt6WcY7oE00HbUb9AyhBPx8n8TbbW0naXdKGHcsvqnNimBqaDtqM+gdQin7+SfY/SXqdpJ1U/aPsJ0v6iaQDa50Z+lZ601m/cvnQx0R7lF7/ANqln4PrXydpP0krIuKZkp4gaV2dk0L/Sm86Y+MDgyi9/gG0Tz/Ba31ErJck2xtExK8k7VHvtNCP0ptO5/jAVJVe/wDaqZ//1XiD7S0lnSnpXNtrJa2oc1KYXOlNh6aG6aA+AZSqn4Pr/zZdXGT7AklbSPp2rbNCT6U3HZoapoP6RFvZzv6YETH5jTAl/exq/Iv0D7PXS/pWPdPBZEpvOjQ1TAf1iTaLiIG+pntfDFevD1A90Pavbd9p+0u297K9VNK/SfpkviliTOlNh6aG6Si9Pjl7F4DUe4vXhyUdK2kbSWeo+giJkyNi34j4eo7J4YFKbzqELgyq9Prk7F0AY3oFr4iIH0TEnyLiTEmrIuKkTPNCF6U3HUIXBlF6fXL2LoBOvQ6u39L2Cztv23mdrV75ld50CF2YqtLrk/oHMF6v4HWhpBd0XL+o43pIIngVjqaDJiu9Pql/AN1MGLwi4uicE0FeNB00Wen1Sf0DmMiUPk4Co4GmgyYrvT6pfwC9ELxahqaDpiu5Pql/AJMheLUITQclKLU+qX8A/ZjwGK9xZzQ+CGc1loWmg1KUWJ/UP4B+9Tqr8QU91nFWY0FoOmgz6h9Ak3BW44ij6aDNqH8ATdNrV+Mbe90xIv5j+NPBMNF00GbUP4Am6rWrcbNss8DQ0XTQZtQ/gKbqtavxX3NOBMND00GbUf8AmqzXFi9Jku0NJR0j6XGSNhxbHhGvqHFeGBBNB21G/QNoun4+x+uLkraX9BxV/79xJ0l31DkpDIamgzaj/gGUoJ/gtVtEvFPSHyJiiaTnS9q/3mlhqmg6aDPqH0Ap+glef07f19neU9IWkrarb0qYqtKbzvqVy4c+Jtqj9PoH0C6THuMlabHtrSS9Q9LZkjaV9M5aZ4W+ld50xsYHBlF6/QNon55bvGw/RNLtEbE2Ii6KiEdGxHYR8elM80MPpTedzvGBqSq9/gG0U8/gFRH3SXprprlgCkpvOjQ1TAf1CaBU/RzjdZ7tN9ve2fbWY1+1zwwTKr3p0NQwHdQngJL1c4zXS9L313QsC0mPHP50MJnSmw5NDdNBfQIo3aTBKyIekWMimFzpTYemhukovT45exeA1MeuRtsb236H7cXp+u62D6l/ahiv9KZD6MKgSq9Pzt4FMKafY7w+L+luSU9J11dJel9tM8KESm86hC4MovT65OxdAJ36CV67RsQHlT5INSLukuTJ7mR7D9uXdnzdbvv16eD8c21fk75vNc3n0BqlNx1CF6aq9Pqk/gGM10/wutv2RqoOqJftXSX9abI7RcTVEbFPROwjaV9Jd0n6hqTjJZ0fEbtLOj9dxwyg6aDJSq9P6h9AN/0Er0WSviNpZ9tfVhWWpvrZXgdJ+m1ErJB0mKQlafkSSQunOBaGgKaDJiu9Pql/ABPp56zG79leJunJqnYxvi4ibpni47xU0inp8pyIWJ0u3yhpTrc72D5W0rGStMsuu0zx4dALTQdNVnp9Uv8AeunnrMZvSnq2pB9ExDlTDV22Hy7pUEmnj18XEaG0C7PLusURsSAiFsyePXsqD4keaDpoupLrk/oHMJl+djV+SNLTJF1p+wzbh9vecAqP8VxJP4+Im9L1m2zvIEnp+81TmjEGRtNBCUqtT+ofQD8mDV4RcWFE/LOqT6r/tKQjNLWw9DLdv5tRks6WdGS6fKSks6YwFgZE00EpSqxP6h9Av/rZ4qV0VuOLJL1a0n66/+D4ye63iaRnSfp6x+ITJD3L9jWSDk7XUSOaDtqM+gfQJJMeXG/7NEn7S/q2pJMkXRgR9/UzeET8QdI245bdquosR2RA00GbUf8Amqaff5L9OUkvi4h7654Mhoumgzaj/gE0Uc/gZXs7SU+V9ErbknSFpE90HCiPhqLpoM2ofwBNNeExXrafKulnqj7u4QvpS5IuTuvQUDQdtBn1D6DJem3x+rCkhRHxi45lZ9v+hqqzG/evdWYYCE0HbUb9A2i6XsFr83GhS5IUEZfa3qzGOaGL7efurBUnHtL37W865e01zmb4428/d+ehjof2IXShFDvstItuXHV91sdMhwtlsf3cnbX6hpXZHq80vYKXbW8VEWvHLdxafX4MBYYndxHbVvWPBYDmI3ShJDeuul7zjjtnaOM1rf6nspGgjXoFqI9I+p7tp9veLH09Q9XHSnwkx+QAYDJNazpATtR/eSbc4hURi23/XtJ7JT1O1UH2V0p6X0R8M9P8AGBCNB20GfVfpp4fJxER50ga3vZQABgSmg7ajPovF8dqASgOTQdtRv2XjeAFoCg0HbQZ9V++gYKX7TnDnggATIamgzaj/kdD38HL9pa2j7F9vqQHfb4XANSp9KazfuXyoY+J9ii9/nG/yf5X40aSDpP0cklPkLSZpIWSLqp9ZgCQlN50xsYHBlF6/eOBev2vxq9I+rWkZ0n6mKT5ktZGxA8i4r480wPQdqU3nc7xgakqvf7xYL12NT5W0lpJV0m6KiLuVfVZXgCQRelNh6aG6aA+R9OEwSsi9pF0hKrdi+fZ/pGkzTiwHkAOpTcdmhqmg/ocXT0Pro+IX0XEuyPi0ZJeJ2mJpJ/Z/nGW2QFopdKbDk0N00F9jrZex3id3HH5yIhYFhFvljRPEgcrAKhF6U2HpobpKL0+OXt3cr22eO3dcfl1YxeiwlmNAGpRetMhdGFQpdcnZ+/2p1fw4kB6ANmV3nQIXRhE6fXJ2bv96/U5XjvZ/k9J7rj8FxHx2lpnBqCVSm86hC5MVen1Sf1PTa/g9ZaOy0vrnggA1IGmgyYrvT6p/6mbMHhFxJKcEwGAYaPpoMlKr0/qfzATBi/b31SP47wi4tBaZgQAQ0DTQZOVXp/U/+Ac0T1b2X56rztGxIW1zKiLBQsWxNKl7O3MybYmqg2gLrY177hzpj1OU5vOihMP4X3Vdou2mOkZ5LHotpmewYyyvSwiFnRb1+sYr99FxMqa5gQAD7L93J214sRDhjbeTae8fWhjDWP87efuXNNMAJSiV/A6U9ITJcn21yLiRVlmBKC1Vt+Q9289tuwiu0W3DW3L7nhN2dK74sRDFIuG/vAjo9fneLnj8iPrnggAABhMU0IXJtfvB6jyJyEAAA1E6CpLr12Ne9u+XdWWr43SZaXrERGb1z47AAAwIUJXeXp9jtdDc04EAAD0j9BVpl67GgEAQAMRuspF8AIAoCCErrIRvAAAKAShq3wELwAACkDoGg0ELwAAGo7QNToIXgAANBiha7QQvAAAaChC1+gheAEA0ECErtFE8AIAoGEIXaOL4AUAQIMQukYbwQsAgIYoPXStX7l86GOOGoIXAAANMAqha82ZJwx93FFD8AIAYIaNSuiavfD4oY89agheAADMoFEKXRwzNjmCFwAAM6T0UETomjqCFwAAM6D0UEToGgzBCwCAzEoPRYSuwRG8AADIrORQROiaHoIXAACZlRqKCF3TR/ACACCzEkMRoWs4CF4AABSO0FUOghcAAAUjdJWF4AUAQKEIXeUheAEAUCBCV5kIXgAAFIbQVS6CFwAABSF0lY3gBQBAIQhd5SN4AQBQAELXaCB4AQDQcISu0UHwAgCgwQhdo4XgBQBAQxG6Rg/BCwCABiJ0jSaCFwAADUPoGl0ELwAAGoTQNdoIXgAANETpoWv9yuVDH3PUELwAAGiAUQhda848YejjjhqCFwAAM2xUQtfshccPfexRQ/ACAGAGjVLo4pixyRG8AACYIaWHIkLX1BG8AACYAaWHIkLXYAheAABkVnooInQNjuAFAEBmJYciQtf0ELwAAMis1FBE6Jo+ghcAAJmVGIoIXcNB8AIAoHCErnIQvAAAKBihqywELwAACkXoKg/BCwCAAhG6yjRrpicAANNlO/t9I2LgxwSmi9BVLoIXgOIRgtAmhK6ysasRAIBCELrKR/ACAKAAhK7RQPACAKDhCF2jg+AFAECDEbpGC8ELAICGInSNHoIXAAANROgaTQQvAAAahtA1ugheAAA0CKFrtPEBqiOOT/QGgHKUHrrWr1w+9DFHDcFrxBGCAKAMoxC61px5wtDHHTXsagQAYIaNSuiavfD4oY89agheAADMoFEKXRwzNjmCFwAAM6T0UETomjqCFwAAM6D0UEToGoxLOPh6wYIFsXTp0pmeBgAA07bDTrvoxlXXz/Q0arP93J21+oaVMz2NGWV7WUQs6LaOsxoBAMgodyixzRnuDVLrrkbbW9o+w/avbF9l+69sb237XNvXpO9b1TkHAACApqj7GK//K+k7EfFoSXtLukrS8ZLOj4jdJZ2frgMAAIy82oKX7S0k/bWkz0lSRNwdEeskHSZpSbrZEkkL65oDAABAk9S5xesRktZI+rztX9j+rO1NJM2JiNXpNjdKmtPtzraPtb3U9tI1a9bUOE0AAIA86gxesyQ9UdInI+IJkv6gcbsVozrar+sRfxGxOCIWRMSC2bNn1zhNAACAPOoMXjdIuiEiLk7Xz1AVxG6yvYMkpe831zgHAACAxqgteEXEjZKut71HWnSQpCslnS3pyLTsSEln1TUHAACAJqn7c7z+RdKXbT9c0rWSjlYV9k6zfYykFZKOqHkOAAAAjVBr8IqISyV1++TWg+p8XAAAgCbifzUCAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATAheAAAAmRC8AAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATAheAAAAmRC8AAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATAheAAAAmRC8AAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATAheAAAAmRC8AAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQyaw6B7d9naQ7JN0r6Z6IWGB7a0mnSpov6TpJR0TE2jrnAQAA0AQ5tng9MyL2iYgF6frxks6PiN0lnZ+uAwAAjLyZ2NV4mKQl6fISSQtnYA4AAADZ1R28QtL3bC+zfWxaNiciVqfLN0qa0+2Oto+1vdT20jVr1tQ8TQAAgPrVeoyXpAMiYpXt7SSda/tXnSsjImxHtztGxGJJiyVpwYIFXW8DAABQklq3eEXEqvT9ZknfkPQkSTfZ3kGS0veb65wDAABAU9QWvGxvYnuzscuSni3pcklnSzoy3exISWfVNQcAAIAmqXNX4xxJ37A99jhfiYjv2P6ZpNNsHyNphaQjapwDAABAY9QWvCLiWkl7d1l+q6SD6npcAACApuKT6wEAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATGbN9AQAAMDkbGe/b0QM/JjojuAFAEABCEGjgV2NAAAAmRC8AAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJo6ImZ7DpGyvkbRipufRMttKumWmJwHUjDpHG1Dn+c2LiNndVhQRvJCf7aURsWCm5wHUiTpHG1DnzcKuRgAAgEwIXgAAAJkQvDCRxTM9ASAD6hxtQJ03CMd4AQAAZMIWLwAAgEwIXgAAAJkQvBrK9r22L+34mp+WP8n2Rbavtv0L25+1vfG4+z7D9m3pfsttn2d7u5rn+wPbnK6MgXXU/OW2v2l7ywHHOcr2SRMsD9sHdyxbmJYdPoXx59u+fIJ1O9o+Y4J1vEdaJtXWhzuuv9n2onR5ke1V437Pbznu/vNt/zGtu9L2F2w/bMhznLQu0/vksR3X39P5PsLUELya648RsU/H13W250g6XdJxEbFHRDxB0nckbdbl/j9M93u8pJ9Jek2/D2x71lCeATA1YzW/p6T/0RRqdgouk/TSjusvk/TLYQxse1ZE/D4i+g5xGHl/kvRC29tOsP4j437Pr+tym99GxD6S9pK0k6Qj6plqTwsl/SV4RcS7IuK8GZjHSCB4leU1kpZExE/GFkTEGRFx00R3sG1VwWxtuv4k2z9JW8t+bHuPtPwo22fb/r6k821vavvzti9LW81elG737HT/n9s+3famXR7zZel+l9s+sWP5nbbfb/uXtn+agqRsv8D2xWlO540tR6v9RNJcSbK9q+3v2F5m+4e2H52WD1I3P5T0JNsPS7W7m6RLx1bafpftn6XaXZzeP7K9b6rbX6ojEHZ53/xla5jtjWx/1fZVtr8haaOhvDIoyT2qzih8w3QHioh7JV2i+98X+9q+ML0vvmt7B9uPtn3J2H1SPV6WLh+U3iuX2f4v2xuMfwzbd3ZcPtz2ybafIulQSf+etrztmpYf3mtc29fZ/tfUKy4be9+C4NVkG3Vsfv5GWranpGV93v9pti+VtFLSwZL+Ky3/laSnpa1l75L0gY77PFHS4RHxdEnvlHRbROyVtpp9P/3V9g5JB0fEEyUtlfTGzge1vaOkEyUdKGkfSfvZXphWbyLppxGxt6SLJL0yLf+RpCenOX1V0lv7fI4YQbYfKukgSWenRYsl/UtE7CvpzZI+kZYPUjch6TxJz5F0WMdjjDkpIvZLW902knRIWv75NIe9u4zZ+b7p9L8k3RURj5H0bkn79jE/jJ6PS/o721t0WfeGjt/zF/QaxPaGkvaX9J20u/FjqupuX1W/398fEb+S9HDbj0h3e4mkU9N9T5b0kojYS9IsVfU5qYj4sar3yVvSVrnfjptTr3FvSb3ik6reuxDBq8k6dzX+7QD3H9vVuLOqpvHBtHwLSaenv8o/IulxHfc5NyL+J10+WNUvDElSRKyV9GRVm5v/O4W6IyXNG/e4+0n6QUSsiYh7JH1Z0l+ndXdLOiddXiZpfrq8k6Tvpr/M3jJuTmiPjVJd3ShpjqRz01app6iq2UslfVrSDun2g9bNV1XtbnyppFPGrXtm2op2mao/Hh7n6ribLSPionSbL467T+f7ptNfS/qSJEXEcknL+5wfRkhE3C7pC5Je22V1567GZ04wxK6p9m+StDrV0h6q/hA/N617h6r3gySdpipwKX0/Nd3+dxHx67R8ie7/vTwdk4379fS98/d96xG8ynKFBvur+Wzd/2Z4r6QL0l/0L5C0Ycft/jDJOFbVZMZ+UTw2Io6Zwjz+HPd/cNy9qv46kqq/3E5KfzG9atyc0B5/TMeyzFNVa69R9Ttq3bjjYB6Tbj9Q3UTEJaqOl9m2o2GM/fX+CVVbEfaS9Jk+x5zsfQN8VNIxqrb6T9XYMV67StrX9qGq3h9XdLwn9oqIZ6fbnyrpCNuPkhQRcc0UHqvzgz2H8Xv4T+l75+/71iN4leUkSUfa3n9sge0X9nFsywGSxjYPbyFpVbp8VI/7nKsHHsuylaSfSnqq7d3Ssk3Sm7vTJZKebnvbtMvoZZIunGR+nXM6cpLbYsRFxF2qtg68SdJdkn5n+8VSdcyi7bHdfdOpm+MlvX3csrFGc0va0nZ4ms86SetsH5DW/12fj3GRpJenee8p6fFTnCNGRNoiepqq8DXoGLeoqtu3Sbpa0mzbfyVJ6ZjFx6Xb/VZV0HmnqhCmdPv5Y7+7Jf2Duv9evsn2Y2w/RFLnnpY71P0krn7HRQeCV0HSQfQvlfQhVx8ncZWqY1Xu6HLzp6XjBn6p6s3wprT8g5L+zfYv1PsvkPdJ2iodZPxLSc+MiDWqwtoptperOgD6AQdMRsRqVb8cLlB1ttiyiDhrkqe2SNWupGWSbpnktmiBiPiFql1zL1MVdI5JdXiFqmOzpGnUTUR8OyIuGLdsnaqtXJdL+q6qs4HHHC3p42m3jvt8mE9K2jS9T9+j/o/PxGj6sKTxZzd2HuP1l48N6uFMSRurOtbrcEknpvfFpap2yY85VdLfqwp7ioj1qmr49LQb/T5Jn+oy/vGqDgf5saTVHcu/Kukt6SD6XccWTmFcdOBfBgEAAGTCFi8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgk/8PQaFLKKFspFQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "bp = plt.boxplot([barcelona, madrid, revs], labels=['FC Barcelona','Real Madrid','NE Revolution'], \n",
    "                 patch_artist=True, medianprops={'linewidth': 2})\n",
    "\n",
    "plt.title('Professional Soccer Team Comparison')\n",
    "plt.ylabel('FIFA Overall Rating')\n",
    "\n",
    "for box in bp['boxes']:\n",
    "    # change outline color\n",
    "#     box.set(color='#4286f4', linewidth=2)\n",
    "    # change fill color\n",
    "#     box.set(facecolor = '#e0e0e0' )\n",
    "    # change hatch\n",
    "    box.set(hatch = '/') # / *  - (none)\n",
    "    \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}