{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hNYAM1jr8P8v",
    "outputId": "318f51a0-e7a8-4d69-86f3-3412ca800459"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: 1: wget: not found\r\n"
     ]
    }
   ],
   "source": [
    "!wget -c https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l6wJKCw7iqQ8",
    "outputId": "91fcfedc-4cdf-4208-9a9e-a02b56b6db83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (1.2.3)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from pandas) (2021.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /snap/jupyter/6/lib/python3.7/site-packages (from pandas) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from pandas) (1.20.1)\n",
      "Requirement already satisfied: six>=1.5 in /snap/jupyter/6/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas) (1.12.0)\n",
      "Requirement already satisfied: scikit-learn in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (0.24.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from scikit-learn) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from scikit-learn) (2.1.0)\n",
      "Requirement already satisfied: numpy>=1.13.3 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from scikit-learn) (1.20.1)\n",
      "Requirement already satisfied: scipy>=0.19.1 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from scikit-learn) (1.6.1)\n",
      "Requirement already satisfied: matplotlib in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (3.3.4)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib) (8.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /snap/jupyter/6/lib/python3.7/site-packages (from matplotlib) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib) (1.20.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: six>=1.5 in /snap/jupyter/6/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib) (1.12.0)\n",
      "Collecting seaborn\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/68/ad/6c2406ae175f59ec616714e408979b674fe27b9587f79d59a528ddfbcd5b/seaborn-0.11.1-py3-none-any.whl (285kB)\n",
      "\u001b[K     |████████████████████████████████| 286kB 1.1MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.0 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from seaborn) (1.6.1)\n",
      "Requirement already satisfied: matplotlib>=2.2 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from seaborn) (3.3.4)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from seaborn) (1.20.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from seaborn) (1.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /snap/jupyter/6/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.8.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (8.1.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
      "Requirement already satisfied: pytz>=2017.3 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2021.1)\n",
      "Requirement already satisfied: six>=1.5 in /snap/jupyter/6/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.12.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.11.1\n"
     ]
    }
   ],
   "source": [
    "!pip install --user pandas\n",
    "!pip install --user scikit-learn\n",
    "!pip install --user matplotlib\n",
    "!pip install --user seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "id": "uXRk5Z4tixiJ",
    "outputId": "ed788406-9f0f-418c-93ef-54398dc4613d"
   },
   "outputs": [
    {
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       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
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       "      <td>11.01</td>\n",
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       "      <td>2.96</td>\n",
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       "      <th>16593</th>\n",
       "      <td>16596</td>\n",
       "      <td>Woody Woodpecker in Crazy Castle 5</td>\n",
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       "      <td>Platform</td>\n",
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       "      <td>Shooter</td>\n",
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       "      <td>0.00</td>\n",
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       "        Rank                                              Name Platform  \\\n",
       "0          1                                        Wii Sports      Wii   \n",
       "1          2                                 Super Mario Bros.      NES   \n",
       "2          3                                    Mario Kart Wii      Wii   \n",
       "3          4                                 Wii Sports Resort      Wii   \n",
       "4          5                          Pokemon Red/Pokemon Blue       GB   \n",
       "...      ...                                               ...      ...   \n",
       "16593  16596                Woody Woodpecker in Crazy Castle 5      GBA   \n",
       "16594  16597                     Men in Black II: Alien Escape       GC   \n",
       "16595  16598  SCORE International Baja 1000: The Official Game      PS2   \n",
       "16596  16599                                        Know How 2       DS   \n",
       "16597  16600                                  Spirits & Spells      GBA   \n",
       "\n",
       "         Year         Genre   Publisher  NA_Sales  EU_Sales  JP_Sales  \\\n",
       "0      2006.0        Sports    Nintendo     41.49     29.02      3.77   \n",
       "1      1985.0      Platform    Nintendo     29.08      3.58      6.81   \n",
       "2      2008.0        Racing    Nintendo     15.85     12.88      3.79   \n",
       "3      2009.0        Sports    Nintendo     15.75     11.01      3.28   \n",
       "4      1996.0  Role-Playing    Nintendo     11.27      8.89     10.22   \n",
       "...       ...           ...         ...       ...       ...       ...   \n",
       "16593  2002.0      Platform       Kemco      0.01      0.00      0.00   \n",
       "16594  2003.0       Shooter  Infogrames      0.01      0.00      0.00   \n",
       "16595  2008.0        Racing  Activision      0.00      0.00      0.00   \n",
       "16596  2010.0        Puzzle    7G//AMES      0.00      0.01      0.00   \n",
       "16597  2003.0      Platform     Wanadoo      0.01      0.00      0.00   \n",
       "\n",
       "       Other_Sales  Global_Sales  \n",
       "0             8.46         82.74  \n",
       "1             0.77         40.24  \n",
       "2             3.31         35.82  \n",
       "3             2.96         33.00  \n",
       "4             1.00         31.37  \n",
       "...            ...           ...  \n",
       "16593         0.00          0.01  \n",
       "16594         0.00          0.01  \n",
       "16595         0.00          0.01  \n",
       "16596         0.00          0.01  \n",
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "vgsales = pd.read_csv('vgsales.csv')\n",
    "vgsales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "id": "Ymp2F4Cdj9XP",
    "outputId": "9096c32c-1392-4817-fdd8-035dc4a0176a"
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1980.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4151.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8300.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12449.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.490000</td>\n",
       "      <td>29.020000</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>10.570000</td>\n",
       "      <td>82.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rank                         Name Platform          Year  \\\n",
       "count   16598.000000                        16598    16598  16327.000000   \n",
       "unique           NaN                        11493       31           NaN   \n",
       "top              NaN  Need for Speed: Most Wanted       DS           NaN   \n",
       "freq             NaN                           12     2163           NaN   \n",
       "mean     8300.605254                          NaN      NaN   2006.406443   \n",
       "std      4791.853933                          NaN      NaN      5.828981   \n",
       "min         1.000000                          NaN      NaN   1980.000000   \n",
       "25%      4151.250000                          NaN      NaN   2003.000000   \n",
       "50%      8300.500000                          NaN      NaN   2007.000000   \n",
       "75%     12449.750000                          NaN      NaN   2010.000000   \n",
       "max     16600.000000                          NaN      NaN   2020.000000   \n",
       "\n",
       "         Genre        Publisher      NA_Sales      EU_Sales      JP_Sales  \\\n",
       "count    16598            16540  16598.000000  16598.000000  16598.000000   \n",
       "unique      12              578           NaN           NaN           NaN   \n",
       "top     Action  Electronic Arts           NaN           NaN           NaN   \n",
       "freq      3316             1351           NaN           NaN           NaN   \n",
       "mean       NaN              NaN      0.264667      0.146652      0.077782   \n",
       "std        NaN              NaN      0.816683      0.505351      0.309291   \n",
       "min        NaN              NaN      0.000000      0.000000      0.000000   \n",
       "25%        NaN              NaN      0.000000      0.000000      0.000000   \n",
       "50%        NaN              NaN      0.080000      0.020000      0.000000   \n",
       "75%        NaN              NaN      0.240000      0.110000      0.040000   \n",
       "max        NaN              NaN     41.490000     29.020000     10.220000   \n",
       "\n",
       "         Other_Sales  Global_Sales  \n",
       "count   16598.000000  16598.000000  \n",
       "unique           NaN           NaN  \n",
       "top              NaN           NaN  \n",
       "freq             NaN           NaN  \n",
       "mean        0.048063      0.537441  \n",
       "std         0.188588      1.555028  \n",
       "min         0.000000      0.010000  \n",
       "25%         0.000000      0.060000  \n",
       "50%         0.010000      0.170000  \n",
       "75%         0.040000      0.470000  \n",
       "max        10.570000     82.740000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgsales.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U9B1rGuPkXYe",
    "outputId": "36c46aa5-b84d-49ba-f00b-bbcdae4d5efb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electronic Arts                 1351\n",
       "Activision                       975\n",
       "Namco Bandai Games               932\n",
       "Ubisoft                          921\n",
       "Konami Digital Entertainment     832\n",
       "                                ... \n",
       "Phantagram                         1\n",
       "989 Sports                         1\n",
       "Illusion Softworks                 1\n",
       "TYO                                1\n",
       "General Entertainment              1\n",
       "Name: Publisher, Length: 578, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgsales[\"Publisher\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fOODoGBDuNVN",
    "outputId": "88220e61-99a8-4d7a-fc84-91601c4844e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DS      2163\n",
       "PS2     2161\n",
       "PS3     1329\n",
       "Wii     1325\n",
       "X360    1265\n",
       "PSP     1213\n",
       "PS      1196\n",
       "PC       960\n",
       "XB       824\n",
       "GBA      822\n",
       "GC       556\n",
       "3DS      509\n",
       "PSV      413\n",
       "PS4      336\n",
       "N64      319\n",
       "SNES     239\n",
       "XOne     213\n",
       "SAT      173\n",
       "WiiU     143\n",
       "2600     133\n",
       "NES       98\n",
       "GB        98\n",
       "DC        52\n",
       "GEN       27\n",
       "NG        12\n",
       "SCD        6\n",
       "WS         6\n",
       "3DO        3\n",
       "TG16       2\n",
       "GG         1\n",
       "PCFX       1\n",
       "Name: Platform, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgsales[\"Platform\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "rjfY8oCdlw19",
    "outputId": "c16b5900-3c45-4ab4-c892-5b0be7bbdd7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "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": [
    "vgsales[\"Platform\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "FrKOc5OxvicT",
    "outputId": "04d5fe12-92e8-4e72-cb36-adbdbbb230d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Platform'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "vgsales[[\"Platform\",\"JP_Sales\"]].groupby(\"Platform\").mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "id": "t-3fmcjiv9Cd",
    "outputId": "ab2be9c6-2cab-4e9c-d2c5-60e672137d92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f432bf95e80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 467.6x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "sns.relplot(data=vgsales, x=\"JP_Sales\", y=\"NA_Sales\", hue=\"Genre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3dKPNi2loZvE",
    "outputId": "ef08ce5e-9c4c-49b0-90ff-7bf74f578339"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PS2     873\n",
       "DS      829\n",
       "Wii     530\n",
       "X360    507\n",
       "PSP     503\n",
       "PS3     488\n",
       "PS      471\n",
       "PC      396\n",
       "XB      339\n",
       "GBA     337\n",
       "GC      237\n",
       "3DS     205\n",
       "PSV     166\n",
       "PS4     143\n",
       "N64     126\n",
       "XOne     95\n",
       "SNES     95\n",
       "SAT      65\n",
       "WiiU     55\n",
       "2600     49\n",
       "NES      43\n",
       "GB       38\n",
       "DC       25\n",
       "GEN      10\n",
       "NG        8\n",
       "3DO       2\n",
       "WS        2\n",
       "GG        1\n",
       "SCD       1\n",
       "Name: Platform, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "vgsales_train, vgsales_test = train_test_split(vgsales, test_size = 0.6, random_state = 1)\n",
    "vgsales_train[\"Platform\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O0aSynxruXwH",
    "outputId": "2512716a-4909-4a49-cf58-c74ae4433f8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DS      1334\n",
       "PS2     1288\n",
       "PS3      841\n",
       "Wii      795\n",
       "X360     758\n",
       "PS       725\n",
       "PSP      710\n",
       "PC       564\n",
       "XB       485\n",
       "GBA      485\n",
       "GC       319\n",
       "3DS      304\n",
       "PSV      247\n",
       "PS4      193\n",
       "N64      193\n",
       "SNES     144\n",
       "XOne     118\n",
       "SAT      108\n",
       "WiiU      88\n",
       "2600      84\n",
       "GB        60\n",
       "NES       55\n",
       "DC        27\n",
       "GEN       17\n",
       "SCD        5\n",
       "NG         4\n",
       "WS         4\n",
       "TG16       2\n",
       "3DO        1\n",
       "PCFX       1\n",
       "Name: Platform, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgsales_test[\"Platform\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Zadanie1.ipynb",
   "provenance": []
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}