Matplotlib Tutorial (Part-2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the necessarry Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real World Examples\n",
    "\n",
    "#### Line Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1990\n",
      "3     1993\n",
      "6     1996\n",
      "9     1999\n",
      "12    2002\n",
      "15    2005\n",
      "18    2008\n",
      "Name: Year, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Reading the data from the CSV file:\n",
    "url = 'https://raw.githubusercontent.com/chiragrathi24/Matplotlib_Tutorial/main/gas_prices.csv' \n",
    "gas = pd.read_csv(url)\n",
    "\n",
    "#Defining the size of the graph:\n",
    "plt.figure(figsize = (8,5))\n",
    "\n",
    "#Giving the title of the graph:\n",
    "plt.title('Gas price over the Time (in USD)', fontdict={'fontweight': 'bold', 'fontsize' : 18})\n",
    "\n",
    "#Plotting the selected countries in the graph:\n",
    "plt.plot(gas.Year, gas.USA,'b.-', label = 'USA')\n",
    "plt.plot(gas.Year, gas.Canada, 'y.-', label = 'Canada')\n",
    "plt.plot(gas.Year, gas.Australia, 'r.-', label = 'Australia')\n",
    "plt.plot(gas.Year, gas.UK, 'g.-', label = 'UK')\n",
    "\n",
    "#Another way to plot many values from the CSV file:\n",
    "# countries_we_want_to_plot = ['USA', 'Canada', 'Australia', 'UK']\n",
    "# for country in gas:\n",
    "#     if country in countries_we_want_to_plot:\n",
    "#         plt.plot(gas.Year, gas[country], marker = '.')\n",
    "\n",
    "#Plotting all the countries in the CSV file:\n",
    "# for country in gas:\n",
    "#     if country != 'Year':\n",
    "#         plt.plot(gas.Year, gas[country], marker = '.')\n",
    "\n",
    "#Displaying the years in the intervals of three:\n",
    "print(gas.Year[::3])\n",
    "\n",
    "#Defining the intervals of the ticks:\n",
    "plt.xticks(gas.Year[::3].tolist() + [2011])\n",
    "\n",
    "#Labelling the axes:\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Prices in USD')\n",
    "\n",
    "#Displaying the legend:\n",
    "plt.legend()\n",
    "\n",
    "#Saving the graph in the image format:\n",
    "plt.savefig('Gas_price_figure.png', dpi = 300)\n",
    "\n",
    "#Displaying the graph:\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Fifa Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/chiragrathi24/Matplotlib_Tutorial/main/fifa_data.csv'\n",
    "fifa = pd.read_csv(url)\n",
    "fifa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Defining the bins for the axes:\n",
    "bins = [40,50,60,70,80,90,100]\n",
    "\n",
    "#Plotting the Histogram and defining its properties:\n",
    "plt.hist(fifa.Overall, bins = bins, color = '#336fe8')\n",
    "\n",
    "#Defining the ticks for the X-Axis:\n",
    "plt.xticks(bins)\n",
    "\n",
    "#Giving the title for the graph and labelling the axes:\n",
    "plt.title('Distribution of the Payers Skills in the FIFA 2018')\n",
    "plt.xlabel('Skills Level')\n",
    "plt.ylabel('Number of the Players')\n",
    "\n",
    "#Displaying the Histogram:\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left Foot:  4211\n",
      "Right Foot : 13948\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding the number of players who use their left foot or the right foot for the shooting:\n",
    "left = fifa.loc[fifa['Preferred Foot'] == 'Left'].count()[0]\n",
    "right = fifa.loc[fifa['Preferred Foot'] == 'Right'].count()[0]\n",
    "print('Left Foot: ',left)\n",
    "print('Right Foot :', right)\n",
    "\n",
    "#Labelling and assigning the colours to the sectors:\n",
    "labels = ['Left', 'Right']\n",
    "colors = ['#336fe8', '#33e878']\n",
    "\n",
    "#Plotting the Pie chart:\n",
    "plt.pie([left,right], labels = labels, colors = colors, autopct = '%.2f%%')\n",
    "\n",
    "#Giving the title to the chart and then displaying it:\n",
    "plt.title('Foot preferenec of the FIFA Players:')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a more advanced Pie chart:\n",
    "#Finding the weight of the players and then changing them from the string values to the integer values:\n",
    "fifa.Weight = [int(x.strip('lbs')) if type(x) == str else x for x in fifa.Weight]\n",
    "\n",
    "#Defining the style of the Pie chart:\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "#Classifying the weights of the players into the different categories:\n",
    "light =  fifa.loc[fifa.Weight < 125].count()[0]\n",
    "light_medium = fifa.loc[(fifa.Weight >= 125) & (fifa.Weight < 150)].count()[0]\n",
    "medium = fifa[(fifa.Weight >= 150) & (fifa.Weight < 175)].count()[0]\n",
    "medium_heavy = fifa[(fifa.Weight >= 175) & (fifa.Weight < 200)].count()[0]\n",
    "heavy = fifa[(fifa.Weight >= 200)].count()[0]\n",
    "\n",
    "#Storing the weights categories into the list, labelling the different sectors and then seprating the closely plotted sectors:\n",
    "weights = [light, light_medium, medium, medium_heavy, heavy]\n",
    "labels = ['Under 125', '125-150', '150-175', '175-200', 'Over 200']\n",
    "explode = (.4,.2,0,0,.4)\n",
    "\n",
    "#Giving the title to the Pie chart:\n",
    "plt.title(\" Weight distributions of the FIFA Players (in lbs)\")\n",
    "\n",
    "#Plotting the Pie chart and then displaying it:\n",
    "plt.pie(weights, labels = labels, autopct = '%.2f &&', pctdistance = 0.8, explode = explode)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box and Whisker plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Selecting the style of the plot:\n",
    "plt.style.use('default')\n",
    "\n",
    "#Defining the size of the Whisker plot:\n",
    "plt.figure(figsize = (5,8))\n",
    "\n",
    "#Finding the overall score of the FC Barcelona and Real Madrid:\n",
    "barcelona = fifa.loc[fifa.Club == 'FC Barcelona']['Overall']\n",
    "madrid = fifa.loc[fifa.Club == 'Real Madrid']['Overall']\n",
    "\n",
    "#Labelling the names of the teams:\n",
    "labels = ['FC Barcelona', 'Real Madrid']\n",
    "\n",
    "#Plotting the Box and the Whisker plot:\n",
    "boxes = plt.boxplot([barcelona, madrid], labels = labels, patch_artist = True, medianprops = {'linewidth':2})\n",
    "#Changing the colours:\n",
    "for box in boxes['boxes']:\n",
    "    #Set the edge colour:\n",
    "    box.set(color = '#4287f5', linewidth = 2)\n",
    "    #For changing the fill colours:\n",
    "    box.set(facecolor = '#bcb5e8')\n",
    "\n",
    "#Giving the title:\n",
    "plt.title('Professional Soccer Team Comparisons')\n",
    "\n",
    "#Labelling the Y-axis:\n",
    "plt.ylabel('FIFA Overall Ratings')\n",
    "\n",
    "#Displaying the plot:\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}