03-Working with Series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Series Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('iris')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2, 'setosa'],\n",
       "       [4.9, 3.0, 1.4, 0.2, 'setosa'],\n",
       "       [4.7, 3.2, 1.3, 0.2, 'setosa'],\n",
       "       [4.6, 3.1, 1.5, 0.2, 'setosa'],\n",
       "       [5.0, 3.6, 1.4, 0.2, 'setosa'],\n",
       "       [5.4, 3.9, 1.7, 0.4, 'setosa'],\n",
       "       [4.6, 3.4, 1.4, 0.3, 'setosa'],\n",
       "       [5.0, 3.4, 1.5, 0.2, 'setosa'],\n",
       "       [4.4, 2.9, 1.4, 0.2, 'setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.1, 'setosa'],\n",
       "       [5.4, 3.7, 1.5, 0.2, 'setosa'],\n",
       "       [4.8, 3.4, 1.6, 0.2, 'setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.1, 'setosa'],\n",
       "       [4.3, 3.0, 1.1, 0.1, 'setosa'],\n",
       "       [5.8, 4.0, 1.2, 0.2, 'setosa'],\n",
       "       [5.7, 4.4, 1.5, 0.4, 'setosa'],\n",
       "       [5.4, 3.9, 1.3, 0.4, 'setosa'],\n",
       "       [5.1, 3.5, 1.4, 0.3, 'setosa'],\n",
       "       [5.7, 3.8, 1.7, 0.3, 'setosa'],\n",
       "       [5.1, 3.8, 1.5, 0.3, 'setosa'],\n",
       "       [5.4, 3.4, 1.7, 0.2, 'setosa'],\n",
       "       [5.1, 3.7, 1.5, 0.4, 'setosa'],\n",
       "       [4.6, 3.6, 1.0, 0.2, 'setosa'],\n",
       "       [5.1, 3.3, 1.7, 0.5, 'setosa'],\n",
       "       [4.8, 3.4, 1.9, 0.2, 'setosa'],\n",
       "       [5.0, 3.0, 1.6, 0.2, 'setosa'],\n",
       "       [5.0, 3.4, 1.6, 0.4, 'setosa'],\n",
       "       [5.2, 3.5, 1.5, 0.2, 'setosa'],\n",
       "       [5.2, 3.4, 1.4, 0.2, 'setosa'],\n",
       "       [4.7, 3.2, 1.6, 0.2, 'setosa'],\n",
       "       [4.8, 3.1, 1.6, 0.2, 'setosa'],\n",
       "       [5.4, 3.4, 1.5, 0.4, 'setosa'],\n",
       "       [5.2, 4.1, 1.5, 0.1, 'setosa'],\n",
       "       [5.5, 4.2, 1.4, 0.2, 'setosa'],\n",
       "       [4.9, 3.1, 1.5, 0.2, 'setosa'],\n",
       "       [5.0, 3.2, 1.2, 0.2, 'setosa'],\n",
       "       [5.5, 3.5, 1.3, 0.2, 'setosa'],\n",
       "       [4.9, 3.6, 1.4, 0.1, 'setosa'],\n",
       "       [4.4, 3.0, 1.3, 0.2, 'setosa'],\n",
       "       [5.1, 3.4, 1.5, 0.2, 'setosa'],\n",
       "       [5.0, 3.5, 1.3, 0.3, 'setosa'],\n",
       "       [4.5, 2.3, 1.3, 0.3, 'setosa'],\n",
       "       [4.4, 3.2, 1.3, 0.2, 'setosa'],\n",
       "       [5.0, 3.5, 1.6, 0.6, 'setosa'],\n",
       "       [5.1, 3.8, 1.9, 0.4, 'setosa'],\n",
       "       [4.8, 3.0, 1.4, 0.3, 'setosa'],\n",
       "       [5.1, 3.8, 1.6, 0.2, 'setosa'],\n",
       "       [4.6, 3.2, 1.4, 0.2, 'setosa'],\n",
       "       [5.3, 3.7, 1.5, 0.2, 'setosa'],\n",
       "       [5.0, 3.3, 1.4, 0.2, 'setosa'],\n",
       "       [7.0, 3.2, 4.7, 1.4, 'versicolor'],\n",
       "       [6.4, 3.2, 4.5, 1.5, 'versicolor'],\n",
       "       [6.9, 3.1, 4.9, 1.5, 'versicolor'],\n",
       "       [5.5, 2.3, 4.0, 1.3, 'versicolor'],\n",
       "       [6.5, 2.8, 4.6, 1.5, 'versicolor'],\n",
       "       [5.7, 2.8, 4.5, 1.3, 'versicolor'],\n",
       "       [6.3, 3.3, 4.7, 1.6, 'versicolor'],\n",
       "       [4.9, 2.4, 3.3, 1.0, 'versicolor'],\n",
       "       [6.6, 2.9, 4.6, 1.3, 'versicolor'],\n",
       "       [5.2, 2.7, 3.9, 1.4, 'versicolor'],\n",
       "       [5.0, 2.0, 3.5, 1.0, 'versicolor'],\n",
       "       [5.9, 3.0, 4.2, 1.5, 'versicolor'],\n",
       "       [6.0, 2.2, 4.0, 1.0, 'versicolor'],\n",
       "       [6.1, 2.9, 4.7, 1.4, 'versicolor'],\n",
       "       [5.6, 2.9, 3.6, 1.3, 'versicolor'],\n",
       "       [6.7, 3.1, 4.4, 1.4, 'versicolor'],\n",
       "       [5.6, 3.0, 4.5, 1.5, 'versicolor'],\n",
       "       [5.8, 2.7, 4.1, 1.0, 'versicolor'],\n",
       "       [6.2, 2.2, 4.5, 1.5, 'versicolor'],\n",
       "       [5.6, 2.5, 3.9, 1.1, 'versicolor'],\n",
       "       [5.9, 3.2, 4.8, 1.8, 'versicolor'],\n",
       "       [6.1, 2.8, 4.0, 1.3, 'versicolor'],\n",
       "       [6.3, 2.5, 4.9, 1.5, 'versicolor'],\n",
       "       [6.1, 2.8, 4.7, 1.2, 'versicolor'],\n",
       "       [6.4, 2.9, 4.3, 1.3, 'versicolor'],\n",
       "       [6.6, 3.0, 4.4, 1.4, 'versicolor'],\n",
       "       [6.8, 2.8, 4.8, 1.4, 'versicolor'],\n",
       "       [6.7, 3.0, 5.0, 1.7, 'versicolor'],\n",
       "       [6.0, 2.9, 4.5, 1.5, 'versicolor'],\n",
       "       [5.7, 2.6, 3.5, 1.0, 'versicolor'],\n",
       "       [5.5, 2.4, 3.8, 1.1, 'versicolor'],\n",
       "       [5.5, 2.4, 3.7, 1.0, 'versicolor'],\n",
       "       [5.8, 2.7, 3.9, 1.2, 'versicolor'],\n",
       "       [6.0, 2.7, 5.1, 1.6, 'versicolor'],\n",
       "       [5.4, 3.0, 4.5, 1.5, 'versicolor'],\n",
       "       [6.0, 3.4, 4.5, 1.6, 'versicolor'],\n",
       "       [6.7, 3.1, 4.7, 1.5, 'versicolor'],\n",
       "       [6.3, 2.3, 4.4, 1.3, 'versicolor'],\n",
       "       [5.6, 3.0, 4.1, 1.3, 'versicolor'],\n",
       "       [5.5, 2.5, 4.0, 1.3, 'versicolor'],\n",
       "       [5.5, 2.6, 4.4, 1.2, 'versicolor'],\n",
       "       [6.1, 3.0, 4.6, 1.4, 'versicolor'],\n",
       "       [5.8, 2.6, 4.0, 1.2, 'versicolor'],\n",
       "       [5.0, 2.3, 3.3, 1.0, 'versicolor'],\n",
       "       [5.6, 2.7, 4.2, 1.3, 'versicolor'],\n",
       "       [5.7, 3.0, 4.2, 1.2, 'versicolor'],\n",
       "       [5.7, 2.9, 4.2, 1.3, 'versicolor'],\n",
       "       [6.2, 2.9, 4.3, 1.3, 'versicolor'],\n",
       "       [5.1, 2.5, 3.0, 1.1, 'versicolor'],\n",
       "       [5.7, 2.8, 4.1, 1.3, 'versicolor'],\n",
       "       [6.3, 3.3, 6.0, 2.5, 'virginica'],\n",
       "       [5.8, 2.7, 5.1, 1.9, 'virginica'],\n",
       "       [7.1, 3.0, 5.9, 2.1, 'virginica'],\n",
       "       [6.3, 2.9, 5.6, 1.8, 'virginica'],\n",
       "       [6.5, 3.0, 5.8, 2.2, 'virginica'],\n",
       "       [7.6, 3.0, 6.6, 2.1, 'virginica'],\n",
       "       [4.9, 2.5, 4.5, 1.7, 'virginica'],\n",
       "       [7.3, 2.9, 6.3, 1.8, 'virginica'],\n",
       "       [6.7, 2.5, 5.8, 1.8, 'virginica'],\n",
       "       [7.2, 3.6, 6.1, 2.5, 'virginica'],\n",
       "       [6.5, 3.2, 5.1, 2.0, 'virginica'],\n",
       "       [6.4, 2.7, 5.3, 1.9, 'virginica'],\n",
       "       [6.8, 3.0, 5.5, 2.1, 'virginica'],\n",
       "       [5.7, 2.5, 5.0, 2.0, 'virginica'],\n",
       "       [5.8, 2.8, 5.1, 2.4, 'virginica'],\n",
       "       [6.4, 3.2, 5.3, 2.3, 'virginica'],\n",
       "       [6.5, 3.0, 5.5, 1.8, 'virginica'],\n",
       "       [7.7, 3.8, 6.7, 2.2, 'virginica'],\n",
       "       [7.7, 2.6, 6.9, 2.3, 'virginica'],\n",
       "       [6.0, 2.2, 5.0, 1.5, 'virginica'],\n",
       "       [6.9, 3.2, 5.7, 2.3, 'virginica'],\n",
       "       [5.6, 2.8, 4.9, 2.0, 'virginica'],\n",
       "       [7.7, 2.8, 6.7, 2.0, 'virginica'],\n",
       "       [6.3, 2.7, 4.9, 1.8, 'virginica'],\n",
       "       [6.7, 3.3, 5.7, 2.1, 'virginica'],\n",
       "       [7.2, 3.2, 6.0, 1.8, 'virginica'],\n",
       "       [6.2, 2.8, 4.8, 1.8, 'virginica'],\n",
       "       [6.1, 3.0, 4.9, 1.8, 'virginica'],\n",
       "       [6.4, 2.8, 5.6, 2.1, 'virginica'],\n",
       "       [7.2, 3.0, 5.8, 1.6, 'virginica'],\n",
       "       [7.4, 2.8, 6.1, 1.9, 'virginica'],\n",
       "       [7.9, 3.8, 6.4, 2.0, 'virginica'],\n",
       "       [6.4, 2.8, 5.6, 2.2, 'virginica'],\n",
       "       [6.3, 2.8, 5.1, 1.5, 'virginica'],\n",
       "       [6.1, 2.6, 5.6, 1.4, 'virginica'],\n",
       "       [7.7, 3.0, 6.1, 2.3, 'virginica'],\n",
       "       [6.3, 3.4, 5.6, 2.4, 'virginica'],\n",
       "       [6.4, 3.1, 5.5, 1.8, 'virginica'],\n",
       "       [6.0, 3.0, 4.8, 1.8, 'virginica'],\n",
       "       [6.9, 3.1, 5.4, 2.1, 'virginica'],\n",
       "       [6.7, 3.1, 5.6, 2.4, 'virginica'],\n",
       "       [6.9, 3.1, 5.1, 2.3, 'virginica'],\n",
       "       [5.8, 2.7, 5.1, 1.9, 'virginica'],\n",
       "       [6.8, 3.2, 5.9, 2.3, 'virginica'],\n",
       "       [6.7, 3.3, 5.7, 2.5, 'virginica'],\n",
       "       [6.7, 3.0, 5.2, 2.3, 'virginica'],\n",
       "       [6.3, 2.5, 5.0, 1.9, 'virginica'],\n",
       "       [6.5, 3.0, 5.2, 2.0, 'virginica'],\n",
       "       [6.2, 3.4, 5.4, 2.3, 'virginica'],\n",
       "       [5.9, 3.0, 5.1, 1.8, 'virginica']], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         setosa\n",
       "1         setosa\n",
       "2         setosa\n",
       "3         setosa\n",
       "4         setosa\n",
       "         ...    \n",
       "145    virginica\n",
       "146    virginica\n",
       "147    virginica\n",
       "148    virginica\n",
       "149    virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species = df[\"species\"]\n",
    "species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "setosa        50\n",
       "versicolor    50\n",
       "virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akila\\AppData\\Local\\Temp\\ipykernel_14148\\3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sepal_length    5.843333\n",
       "sepal_width     3.057333\n",
       "petal_length    3.758000\n",
       "petal_width     1.199333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "games=pd.read_csv(\"Data/vgsalesGlobale.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.49</td>\n",
       "      <td>29.02</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.46</td>\n",
       "      <td>82.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.88</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.31</td>\n",
       "      <td>35.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.75</td>\n",
       "      <td>11.01</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.96</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                      Name Platform    Year         Genre Publisher  \\\n",
       "0     1                Wii Sports      Wii  2006.0        Sports  Nintendo   \n",
       "1     2         Super Mario Bros.      NES  1985.0      Platform  Nintendo   \n",
       "2     3            Mario Kart Wii      Wii  2008.0        Racing  Nintendo   \n",
       "3     4         Wii Sports Resort      Wii  2009.0        Sports  Nintendo   \n",
       "4     5  Pokemon Red/Pokemon Blue       GB  1996.0  Role-Playing  Nintendo   \n",
       "\n",
       "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \n",
       "0     41.49     29.02      3.77         8.46         82.74  \n",
       "1     29.08      3.58      6.81         0.77         40.24  \n",
       "2     15.85     12.88      3.79         3.31         35.82  \n",
       "3     15.75     11.01      3.28         2.96         33.00  \n",
       "4     11.27      8.89     10.22         1.00         31.37  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank              int64\n",
       "Name             object\n",
       "Platform         object\n",
       "Year            float64\n",
       "Genre            object\n",
       "Publisher        object\n",
       "NA_Sales        float64\n",
       "EU_Sales        float64\n",
       "JP_Sales        float64\n",
       "Other_Sales     float64\n",
       "Global_Sales    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      16598\n",
       "unique        12\n",
       "top       Action\n",
       "freq        3316\n",
       "Name: Genre, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action          3316\n",
       "Sports          2346\n",
       "Misc            1739\n",
       "Role-Playing    1488\n",
       "Shooter         1310\n",
       "Adventure       1286\n",
       "Racing          1249\n",
       "Platform         886\n",
       "Simulation       867\n",
       "Fighting         848\n",
       "Strategy         681\n",
       "Puzzle           582\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action          0.199783\n",
       "Sports          0.141342\n",
       "Misc            0.104772\n",
       "Role-Playing    0.089649\n",
       "Shooter         0.078925\n",
       "Adventure       0.077479\n",
       "Racing          0.075250\n",
       "Platform        0.053380\n",
       "Simulation      0.052235\n",
       "Fighting        0.051090\n",
       "Strategy        0.041029\n",
       "Puzzle          0.035064\n",
       "Name: Genre, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.value_counts(normalize=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(games.Genre.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action          3316\n",
       "Sports          2346\n",
       "Misc            1739\n",
       "Role-Playing    1488\n",
       "Shooter         1310\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sports', 'Platform', 'Racing', 'Role-Playing', 'Puzzle', 'Misc',\n",
       "       'Shooter', 'Simulation', 'Action', 'Fighting', 'Adventure',\n",
       "       'Strategy'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
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       "    .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>Year</th>\n",
       "      <th>1980.0</th>\n",
       "      <th>1981.0</th>\n",
       "      <th>1982.0</th>\n",
       "      <th>1983.0</th>\n",
       "      <th>1984.0</th>\n",
       "      <th>1985.0</th>\n",
       "      <th>1986.0</th>\n",
       "      <th>1987.0</th>\n",
       "      <th>1988.0</th>\n",
       "      <th>1989.0</th>\n",
       "      <th>...</th>\n",
       "      <th>2009.0</th>\n",
       "      <th>2010.0</th>\n",
       "      <th>2011.0</th>\n",
       "      <th>2012.0</th>\n",
       "      <th>2013.0</th>\n",
       "      <th>2014.0</th>\n",
       "      <th>2015.0</th>\n",
       "      <th>2016.0</th>\n",
       "      <th>2017.0</th>\n",
       "      <th>2020.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>226</td>\n",
       "      <td>239</td>\n",
       "      <td>266</td>\n",
       "      <td>148</td>\n",
       "      <td>186</td>\n",
       "      <td>255</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>154</td>\n",
       "      <td>108</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>54</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fighting</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>207</td>\n",
       "      <td>201</td>\n",
       "      <td>184</td>\n",
       "      <td>38</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puzzle</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>79</td>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Racing</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>57</td>\n",
       "      <td>65</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role-Playing</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>95</td>\n",
       "      <td>78</td>\n",
       "      <td>71</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shooter</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>91</td>\n",
       "      <td>81</td>\n",
       "      <td>94</td>\n",
       "      <td>48</td>\n",
       "      <td>59</td>\n",
       "      <td>47</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simulation</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>82</td>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sports</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>184</td>\n",
       "      <td>186</td>\n",
       "      <td>122</td>\n",
       "      <td>54</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>62</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "      <td>46</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Year          1980.0  1981.0  1982.0  1983.0  1984.0  1985.0  1986.0  1987.0  \\\n",
       "Genre                                                                          \n",
       "Action             1      25      18       7       1       2       6       2   \n",
       "Adventure          0       0       0       1       0       0       0       1   \n",
       "Fighting           1       0       0       0       0       1       0       2   \n",
       "Misc               4       0       1       1       1       0       0       0   \n",
       "Platform           0       3       5       5       1       4       6       2   \n",
       "Puzzle             0       2       3       1       3       4       0       0   \n",
       "Racing             0       1       2       0       3       0       1       0   \n",
       "Role-Playing       0       0       0       0       0       0       1       3   \n",
       "Shooter            2      10       5       1       3       1       4       2   \n",
       "Simulation         0       1       0       0       0       1       0       0   \n",
       "Sports             1       4       2       1       2       1       3       4   \n",
       "Strategy           0       0       0       0       0       0       0       0   \n",
       "\n",
       "Year          1988.0  1989.0  ...  2009.0  2010.0  2011.0  2012.0  2013.0  \\\n",
       "Genre                         ...                                           \n",
       "Action             2       2  ...     272     226     239     266     148   \n",
       "Adventure          0       0  ...     141     154     108      58      60   \n",
       "Fighting           0       0  ...      53      40      50      29      20   \n",
       "Misc               0       1  ...     207     201     184      38      42   \n",
       "Platform           4       3  ...      29      31      37      12      37   \n",
       "Puzzle             1       5  ...      79      45      43      11       3   \n",
       "Racing             1       0  ...      84      57      65      30      16   \n",
       "Role-Playing       3       2  ...     103     103      95      78      71   \n",
       "Shooter            1       1  ...      91      81      94      48      59   \n",
       "Simulation         1       0  ...     123      82      56      18      18   \n",
       "Sports             2       3  ...     184     186     122      54      53   \n",
       "Strategy           0       0  ...      65      53      46      15      19   \n",
       "\n",
       "Year          2014.0  2015.0  2016.0  2017.0  2020.0  \n",
       "Genre                                                 \n",
       "Action           186     255     119       1       0  \n",
       "Adventure         75      54      34       0       0  \n",
       "Fighting          23      21      14       0       0  \n",
       "Misc              41      39      18       0       0  \n",
       "Platform          10      14      10       0       0  \n",
       "Puzzle             8       6       0       0       0  \n",
       "Racing            27      19      20       0       0  \n",
       "Role-Playing      91      78      40       2       0  \n",
       "Shooter           47      34      32       0       0  \n",
       "Simulation        11      15       9       0       1  \n",
       "Sports            55      62      38       0       0  \n",
       "Strategy           8      17      10       0       0  \n",
       "\n",
       "[12 rows x 39 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(games.Genre, games.Year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16598.000000\n",
       "mean         0.537441\n",
       "std          1.555028\n",
       "min          0.010000\n",
       "25%          0.060000\n",
       "50%          0.170000\n",
       "75%          0.470000\n",
       "max         82.740000\n",
       "Name: Global_Sales, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Global_Sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5374406555006628"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Global_Sales.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02     1071\n",
       "0.03      811\n",
       "0.04      645\n",
       "0.05      632\n",
       "0.01      618\n",
       "         ... \n",
       "9.09        1\n",
       "12.27       1\n",
       "16.38       1\n",
       "20.22       1\n",
       "22.00       1\n",
       "Name: Global_Sales, Length: 623, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Global_Sales.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "games.Year.plot(kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action          3316\n",
       "Sports          2346\n",
       "Misc            1739\n",
       "Role-Playing    1488\n",
       "Shooter         1310\n",
       "Adventure       1286\n",
       "Racing          1249\n",
       "Platform         886\n",
       "Simulation       867\n",
       "Fighting         848\n",
       "Strategy         681\n",
       "Puzzle           582\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games.Genre.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "games.Genre.value_counts().plot(kind=\"bar\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}