{
 "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": 8,
   "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: numpy>=1.16.5 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from pandas) (1.20.1)\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: 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: 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: 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: scipy>=0.19.1 in /home/tomasz/snap/jupyter/common/lib/python3.7/site-packages (from scikit-learn) (1.6.1)\n",
      "Collecting matplotlib\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/23/3d/db9a6b3c83c9511301152dbb64a029c3a4313c86eaef12c237b13ecf91d6/matplotlib-3.3.4-cp37-cp37m-manylinux1_x86_64.whl (11.5MB)\n",
      "\u001b[K     |████████████████████████████████| 11.6MB 4.9MB/s eta 0:00:01    |██████████▊                     | 3.9MB 1.7MB/s eta 0:00:05\n",
      "\u001b[?25hCollecting cycler>=0.10 (from matplotlib)\n",
      "  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
      "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d2/46/231de802ade4225b76b96cffe419cf3ce52bbe92e3b092cf12db7d11c207/kiwisolver-1.3.1-cp37-cp37m-manylinux1_x86_64.whl (1.1MB)\n",
      "\u001b[K     |████████████████████████████████| 1.1MB 6.1MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement 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: python-dateutil>=2.1 in /snap/jupyter/6/lib/python3.7/site-packages (from matplotlib) (2.8.0)\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",
      "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 (from matplotlib)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8a/bb/488841f56197b13700afd5658fc279a2025a39e22449b7cf29864669b15d/pyparsing-2.4.7-py2.py3-none-any.whl (67kB)\n",
      "\u001b[K     |████████████████████████████████| 71kB 5.5MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six in /snap/jupyter/6/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
      "Installing collected packages: cycler, kiwisolver, pyparsing, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.3.4 pyparsing-2.4.7\n"
     ]
    }
   ],
   "source": [
    "!pip install --user pandas\n",
    "!pip install --user scikit-learn\n",
    "!pip install --user matplotlib"
   ]
  },
  {
   "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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       "        Rank                                              Name Platform  \\\n",
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       "2          3                                    Mario Kart Wii      Wii   \n",
       "3          4                                 Wii Sports Resort      Wii   \n",
       "4          5                          Pokemon Red/Pokemon Blue       GB   \n",
       "...      ...                                               ...      ...   \n",
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       "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",
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       "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",
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       "16594  2003.0       Shooter  Infogrames      0.01      0.00      0.00   \n",
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       "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",
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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>0.505351</td>\n",
       "      <td>0.309291</td>\n",
       "      <td>0.188588</td>\n",
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       "      <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": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "rjfY8oCdlw19",
    "outputId": "c16b5900-3c45-4ab4-c892-5b0be7bbdd7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgsales[\"Platform\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "FrKOc5OxvicT",
    "outputId": "04d5fe12-92e8-4e72-cb36-adbdbbb230d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f668577e690>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vgsales[[\"Platform\",\"JP_Sales\"]].groupby(\"Platform\").mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 0x7f6676f85790>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 467.6x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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": 10,
   "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": 10,
     "metadata": {
      "tags": []
     },
     "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": 11,
   "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",
       "GBA      485\n",
       "XB       485\n",
       "GC       319\n",
       "3DS      304\n",
       "PSV      247\n",
       "N64      193\n",
       "PS4      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",
       "WS         4\n",
       "NG         4\n",
       "TG16       2\n",
       "3DO        1\n",
       "PCFX       1\n",
       "Name: Platform, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgsales_test[\"Platform\"].value_counts()"
   ]
  }
 ],
 "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
}