ht_pandas_2.ipynb

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      "name": "ht_pandas_2.ipynb",
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      "collapsed_sections": []
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Задание 1**\n",
        "Напишите функцию, которая классифицирует фильмы из материалов занятия по следующим правилам:\n",
        "- оценка 2 и меньше - низкий рейтинг\n",
        "- оценка 4 и меньше - средний рейтинг\n",
        "- оценка 4.5 и 5 - высокий рейтинг\n",
        "\n",
        "Результат классификации запишите в столбец class"
      ],
      "metadata": {
        "id": "--yYELAeQCF2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## скачаем и распакуем данные"
      ],
      "metadata": {
        "id": "g8W-hcNz9tmf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COFqTYxlPSe_",
        "outputId": "4848cd4b-3505-4d2c-9291-b402eddb4498"
      },
      "outputs": [
        {
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          "text": [
            "--2022-01-06 21:22:08--  https://u.netology.ngcdn.ru/backend/uploads/lms/attachments/files/data/15257/ml-latest-small.zip\n",
            "Resolving u.netology.ngcdn.ru (u.netology.ngcdn.ru)... 104.22.40.115, 104.22.41.115, 172.67.26.246, ...\n",
            "Connecting to u.netology.ngcdn.ru (u.netology.ngcdn.ru)|104.22.40.115|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2149503 (2.0M) [application/x-zip-compressed]\n",
            "Saving to: ‘ml-latest-small.zip.1’\n",
            "\n",
            "ml-latest-small.zip 100%[===================>]   2.05M  --.-KB/s    in 0.06s   \n",
            "\n",
            "2022-01-06 21:22:09 (33.6 MB/s) - ‘ml-latest-small.zip.1’ saved [2149503/2149503]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://u.netology.ngcdn.ru/backend/uploads/lms/attachments/files/data/15257/ml-latest-small.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/ml-latest-small.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fh9rQPaOPiiO",
        "outputId": "9c541fb4-016f-494d-b7ef-bc8e4748d0b0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/ml-latest-small.zip\n",
            "replace ml-latest-small/ad_campaigns.xlsx? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            "  inflating: ml-latest-small/ad_campaigns.xlsx  \n",
            "  inflating: ml-latest-small/keywords.csv  \n",
            "  inflating: ml-latest-small/links.csv  \n",
            "  inflating: ml-latest-small/movies.csv  \n",
            "  inflating: ml-latest-small/ratings.csv  \n",
            "  inflating: ml-latest-small/README.txt  \n",
            "  inflating: ml-latest-small/tags.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Расчет"
      ],
      "metadata": {
        "id": "K66bcXm-96HK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "giTGrZvDP23U"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rating_df = pd.read_csv('/content/ml-latest-small/ratings.csv')\n",
        "rating_df.tail()"
      ],
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              "      <th>userId</th>\n",
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              "      <th>100003</th>\n",
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              "        userId  movieId  rating   timestamp\n",
              "99999      671     6268     2.5  1065579370\n",
              "100000     671     6269     4.0  1065149201\n",
              "100001     671     6365     4.0  1070940363\n",
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "def assign_rating(rating):\n",
        "  '''\n",
        "    функция классифицирует фильмы на три категории \n",
        "    Low, Average и High в зависимости от значения rating\n",
        "  '''\n",
        "  if rating <= 2:\n",
        "    return 'Low rating'\n",
        "  elif  rating <= 4:\n",
        "    return 'Average rating'\n",
        "  else:\n",
        "    return 'High rating'"
      ],
      "metadata": {
        "id": "C_41RDWHRCjf"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rating_df['rating_level'] = rating_df['rating'].apply(assign_rating)\n",
        "rating_df['rating_level'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeylhTFHQPte",
        "outputId": "eab21496-301a-444e-a368-a933ee045710"
      },
      "execution_count": 6,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Average rating    63801\n",
              "High rating       22818\n",
              "Low rating        13385\n",
              "Name: rating_level, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Задание 2\n",
        "Используем файл keywords.csv.\n",
        "\n",
        "Необходимо написать гео-классификатор, который каждой строке сможет выставить географическую принадлежность определенному региону. Т. е. если поисковый запрос содержит название города региона, то в столбце ‘region’ пишется название этого региона. Если поисковый запрос не содержит названия города, то ставим ‘undefined’.\n",
        "\n",
        "\n",
        "Правила распределения по регионам Центр, Северо-Запад и Дальний Восток:\n",
        "\n",
        "geo_data = {\n",
        "\n",
        "'Центр': ['москва', 'тула', 'ярославль'],\n",
        "\n",
        "'Северо-Запад': ['петербург', 'псков', 'мурманск'],\n",
        "\n",
        "'Дальний Восток': ['владивосток', 'сахалин', 'хабаровск']\n",
        "}\n",
        "\n",
        "Результат классификации запишите в отдельный столбец region."
      ],
      "metadata": {
        "id": "XdbtyF0TS6dF"
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    {
      "cell_type": "code",
      "source": [
        "keyword_df = pd.read_csv('/content/ml-latest-small/keywords.csv')\n",
        "keyword_df.head()"
      ],
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              "      <th></th>\n",
              "      <th>keyword</th>\n",
              "      <th>shows</th>\n",
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              "      <th>0</th>\n",
              "      <td>вк</td>\n",
              "      <td>64292779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>одноклассники</td>\n",
              "      <td>63810309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>порно</td>\n",
              "      <td>41747114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ютуб</td>\n",
              "      <td>39995567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>вконтакте</td>\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "         keyword     shows\n",
              "0             вк  64292779\n",
              "1  одноклассники  63810309\n",
              "2          порно  41747114\n",
              "3           ютуб  39995567\n",
              "4      вконтакте  21014195"
            ]
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      "source": [
        "center = ['москва', 'тула', 'ярославль']\n",
        "north_west = ['петербург', 'псков', 'мурманск']\n",
        "far_east = ['владивосток', 'сахалин', 'хабаровск']"
      ],
      "metadata": {
        "id": "G1qj_MJXT1eG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def assign_region(keyword):\n",
        "   keywords = keyword.lower().split()\n",
        "   for key in keywords:\n",
        "      if key in center:\n",
        "          return 'Центр'\n",
        "      elif key in north_west:\n",
        "          return 'Северо-Запад'\n",
        "      elif key in far_east:\n",
        "          return 'Дальний Восток'\n",
        "      else:\n",
        "          return 'undefined'"
      ],
      "metadata": {
        "id": "wnG1vPgSWkjL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keyword_df['region'] = keyword_df['keyword'].apply(assign_region)\n",
        "keyword_df['region'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkVUVR8oV51N",
        "outputId": "02fc04e9-bcbe-418e-c9b5-32e0ce7b0ea7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "undefined         99956\n",
              "Центр                34\n",
              "Северо-Запад          5\n",
              "Дальний Восток        5\n",
              "Name: region, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Мини-проверка"
      ],
      "metadata": {
        "id": "vhc8Vl7kDh9k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "keyword_df[keyword_df['region'] == 'Центр']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KNrlI1JYChfZ",
        "outputId": "cf1a2338-97a4-4ca0-b2a3-fb67f20e9362"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>keyword</th>\n",
              "      <th>shows</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1259</th>\n",
              "      <td>москва</td>\n",
              "      <td>153050</td>\n",
              "      <td>Центр</td>\n",
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              "      <th>5983</th>\n",
              "      <td>москва третий рим</td>\n",
              "      <td>43182</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6075</th>\n",
              "      <td>ярославль</td>\n",
              "      <td>41720</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6999</th>\n",
              "      <td>москва 24</td>\n",
              "      <td>37459</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7103</th>\n",
              "      <td>москва сити</td>\n",
              "      <td>37022</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7339</th>\n",
              "      <td>москва слезам не верит</td>\n",
              "      <td>35857</td>\n",
              "      <td>Центр</td>\n",
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              "    <tr>\n",
              "      <th>8132</th>\n",
              "      <td>москва погода</td>\n",
              "      <td>151701</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11785</th>\n",
              "      <td>тула</td>\n",
              "      <td>23898</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16175</th>\n",
              "      <td>москва слезам не верит смотреть в хорошем каче...</td>\n",
              "      <td>18407</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18843</th>\n",
              "      <td>москва фм</td>\n",
              "      <td>16400</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39920</th>\n",
              "      <td>москва карта</td>\n",
              "      <td>15769</td>\n",
              "      <td>Центр</td>\n",
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              "    <tr>\n",
              "      <th>40095</th>\n",
              "      <td>москва время</td>\n",
              "      <td>39941</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>44863</th>\n",
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              "      <td>16043</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45309</th>\n",
              "      <td>москва слезам не верит смотреть онлайн</td>\n",
              "      <td>7976</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47121</th>\n",
              "      <td>москва сочи авиабилеты</td>\n",
              "      <td>13146</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52463</th>\n",
              "      <td>москва санкт петербург</td>\n",
              "      <td>9756</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52727</th>\n",
              "      <td>москва третий рим православный сайт</td>\n",
              "      <td>6630</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55882</th>\n",
              "      <td>ярославль достопримечательности</td>\n",
              "      <td>6306</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57421</th>\n",
              "      <td>москва метро</td>\n",
              "      <td>68024</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59617</th>\n",
              "      <td>москва слезам не верит актеры</td>\n",
              "      <td>6408</td>\n",
              "      <td>Центр</td>\n",
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              "    <tr>\n",
              "      <th>66224</th>\n",
              "      <td>москва сити фото</td>\n",
              "      <td>6086</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68181</th>\n",
              "      <td>москва слезам не верит фильм 1980</td>\n",
              "      <td>5173</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70611</th>\n",
              "      <td>москва 24 онлайн прямой эфир</td>\n",
              "      <td>5055</td>\n",
              "      <td>Центр</td>\n",
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              "    <tr>\n",
              "      <th>71891</th>\n",
              "      <td>москва слезам не верит смотреть бесплатно в хо...</td>\n",
              "      <td>5167</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74372</th>\n",
              "      <td>москва 3 рим</td>\n",
              "      <td>5057</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74724</th>\n",
              "      <td>москва минск</td>\n",
              "      <td>6023</td>\n",
              "      <td>Центр</td>\n",
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              "    <tr>\n",
              "      <th>81003</th>\n",
              "      <td>москва я терплю тебя фильм 2016</td>\n",
              "      <td>4468</td>\n",
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              "    <tr>\n",
              "      <th>83053</th>\n",
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              "      <td>4320</td>\n",
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              "      <th>85584</th>\n",
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              "      <td>4396</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>87259</th>\n",
              "      <td>москва сочи</td>\n",
              "      <td>4822</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92517</th>\n",
              "      <td>ярославль погода</td>\n",
              "      <td>31930</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93321</th>\n",
              "      <td>москва авито</td>\n",
              "      <td>979292</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93819</th>\n",
              "      <td>москва нижний новгород</td>\n",
              "      <td>6220</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98183</th>\n",
              "      <td>москва октябрьская это какой вокзал</td>\n",
              "      <td>3696</td>\n",
              "      <td>Центр</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f34824a3-3949-482e-b3c2-dd961974a005')\"\n",
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            "text/plain": [
              "                                                 keyword   shows region\n",
              "1259                                              москва  153050  Центр\n",
              "5983                                   москва третий рим   43182  Центр\n",
              "6075                                           ярославль   41720  Центр\n",
              "6999                                           москва 24   37459  Центр\n",
              "7103                                         москва сити   37022  Центр\n",
              "7339                              москва слезам не верит   35857  Центр\n",
              "8132                                       москва погода  151701  Центр\n",
              "11785                                               тула   23898  Центр\n",
              "16175  москва слезам не верит смотреть в хорошем каче...   18407  Центр\n",
              "18843                                          москва фм   16400  Центр\n",
              "39920                                       москва карта   15769  Центр\n",
              "40095                                       москва время   39941  Центр\n",
              "44863                      москва симферополь авиабилеты   16043  Центр\n",
              "45309             москва слезам не верит смотреть онлайн    7976  Центр\n",
              "47121                             москва сочи авиабилеты   13146  Центр\n",
              "52463                             москва санкт петербург    9756  Центр\n",
              "52727                москва третий рим православный сайт    6630  Центр\n",
              "55882                    ярославль достопримечательности    6306  Центр\n",
              "57421                                       москва метро   68024  Центр\n",
              "59617                      москва слезам не верит актеры    6408  Центр\n",
              "66224                                   москва сити фото    6086  Центр\n",
              "68181                  москва слезам не верит фильм 1980    5173  Центр\n",
              "70611                       москва 24 онлайн прямой эфир    5055  Центр\n",
              "71891  москва слезам не верит смотреть бесплатно в хо...    5167  Центр\n",
              "74372                                       москва 3 рим    5057  Центр\n",
              "74724                                       москва минск    6023  Центр\n",
              "81003                    москва я терплю тебя фильм 2016    4468  Центр\n",
              "83053           москва слезам не верит фильм 1980 актеры    4320  Центр\n",
              "85584                                    москва панорама    4396  Центр\n",
              "87259                                        москва сочи    4822  Центр\n",
              "92517                                   ярославль погода   31930  Центр\n",
              "93321                                       москва авито  979292  Центр\n",
              "93819                             москва нижний новгород    6220  Центр\n",
              "98183                москва октябрьская это какой вокзал    3696  Центр"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keyword_df[keyword_df['region'] == 'Дальний Восток']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OePaj76kDa0C",
        "outputId": "8445296d-fae0-4516-8ee7-142384b8c7b5"
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      "execution_count": 12,
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        {
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              "      <th>5858</th>\n",
              "      <td>владивосток</td>\n",
              "      <td>43038</td>\n",
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              "      <th>9084</th>\n",
              "      <td>хабаровск</td>\n",
              "      <td>29655</td>\n",
              "      <td>Дальний Восток</td>\n",
              "    </tr>\n",
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              "      <th>21445</th>\n",
              "      <td>сахалин</td>\n",
              "      <td>14494</td>\n",
              "      <td>Дальний Восток</td>\n",
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              "      <th>38863</th>\n",
              "      <td>владивосток время</td>\n",
              "      <td>14794</td>\n",
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              "      <th>46720</th>\n",
              "      <td>хабаровск время</td>\n",
              "      <td>10777</td>\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6a30d50a-19e6-4ed6-bf82-c84f1055d161 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6a30d50a-19e6-4ed6-bf82-c84f1055d161');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 keyword  shows          region\n",
              "5858         владивосток  43038  Дальний Восток\n",
              "9084           хабаровск  29655  Дальний Восток\n",
              "21445            сахалин  14494  Дальний Восток\n",
              "38863  владивосток время  14794  Дальний Восток\n",
              "46720    хабаровск время  10777  Дальний Восток"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Задание 3 (бонусное)\n",
        "\n",
        "Есть мнение, что “раньше снимали настоящее кино, не то что сейчас”. Ваша задача проверить это утверждение, используя файлы с рейтингами фильмов из прошлого домашнего занятия (файл movies.csv и ratings.csv из базы grouplens.org.../movielens). Т. е. проверить верно ли, что с ростом года выпуска фильма его средний рейтинг становится ниже.\n",
        "\n",
        "При этом мы не будем затрагивать субьективные факторы выставления этих рейтингов, а пройдемся по следующему алгоритму:\n",
        "\n",
        "    В переменную years запишите список из всех годов с 1950 по 2010.\n",
        "\n",
        "    Напишите функцию production_year, которая каждой строке из названия фильма выставляет год выпуска. Не все названия фильмов содержат год выпуска в одинаковом формате, поэтому используйте следующий алгоритм:\n",
        "        для каждой строки пройдите по всем годам списка years\n",
        "        если номер года присутствует в названии фильма, то функция возвращает этот год как год выпуска\n",
        "        если ни один из номеров года списка years не встретился в названии фильма, то возвращается 1900 год\n",
        "\n",
        "    Запишите год выпуска фильма по алгоритму пункта 2 в новый столбец ‘year’\n",
        "\n",
        "    Посчитайте средний рейтинг всех фильмов для каждого значения столбца ‘year’ и отсортируйте результат по убыванию рейтинга\n",
        "\n",
        "P/S/ все файлы находятся в архиве «дополнительные файлы к лекции и дз»\n",
        "\n",
        "Важно! При копировании юпитер ноутбука изменяет кавычки ’ на ` , что может способствовать ошибки в коде. В данной ситуации необходимо их просто заменить."
      ],
      "metadata": {
        "id": "mfcxIxyTW-SN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Загрузка данных "
      ],
      "metadata": {
        "id": "uZZzOClaFUbH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rating_df = pd.read_csv('/content/ml-latest-small/ratings.csv')\n",
        "rating_df.drop([\"userId\",\"timestamp\"], axis=1, inplace=True)\n",
        "rating_df.tail()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mwWNXIbjPIok",
        "outputId": "e264cc7e-6b34-4cc3-98e3-e424f75b831e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8e19827d-2c48-4714-aa1a-139aff8b4e75\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\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>movieId</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>6268</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100000</th>\n",
              "      <td>6269</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100001</th>\n",
              "      <td>6365</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100002</th>\n",
              "      <td>6385</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100003</th>\n",
              "      <td>6565</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e19827d-2c48-4714-aa1a-139aff8b4e75')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8e19827d-2c48-4714-aa1a-139aff8b4e75 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e19827d-2c48-4714-aa1a-139aff8b4e75');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        movieId  rating\n",
              "99999      6268     2.5\n",
              "100000     6269     4.0\n",
              "100001     6365     4.0\n",
              "100002     6385     2.5\n",
              "100003     6565     3.5"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies_df = pd.read_csv('/content/ml-latest-small/movies.csv')\n",
        "movies_df.drop([\"genres\"], axis=1, inplace=True) \n",
        "movies_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ccHr_A_WP1il",
        "outputId": "71074f5b-3192-4bfe-91c6-249ed2f12cfa"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-88b52318-93ed-4a96-8b29-98a031f4784b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>movieId</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9120</th>\n",
              "      <td>162672</td>\n",
              "      <td>Mohenjo Daro (2016)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9121</th>\n",
              "      <td>163056</td>\n",
              "      <td>Shin Godzilla (2016)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9122</th>\n",
              "      <td>163949</td>\n",
              "      <td>The Beatles: Eight Days a Week - The Touring Y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9123</th>\n",
              "      <td>164977</td>\n",
              "      <td>The Gay Desperado (1936)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9124</th>\n",
              "      <td>164979</td>\n",
              "      <td>Women of '69, Unboxed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88b52318-93ed-4a96-8b29-98a031f4784b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-88b52318-93ed-4a96-8b29-98a031f4784b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-88b52318-93ed-4a96-8b29-98a031f4784b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      movieId                                              title\n",
              "9120   162672                                Mohenjo Daro (2016)\n",
              "9121   163056                               Shin Godzilla (2016)\n",
              "9122   163949  The Beatles: Eight Days a Week - The Touring Y...\n",
              "9123   164977                           The Gay Desperado (1936)\n",
              "9124   164979                              Women of '69, Unboxed"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Агрегируем rating_df по movieId и находим среднее значение столбца rating  "
      ],
      "metadata": {
        "id": "UVn9d7nLFaZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "aggregated_mean_rating = rating_df.groupby(by='movieId').mean()\n",
        "aggregated_mean_rating.columns = ['mean_rating']\n",
        "aggregated_mean_rating.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "iFx9CjkhRsVv",
        "outputId": "21174080-ba41-4fad-8b03-1e6eb444fb42"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6b9f91a3-80ba-4fd7-9a89-32914ddd2a5f\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.872470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.401869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.161017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.267857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.283019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3.450820</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b9f91a3-80ba-4fd7-9a89-32914ddd2a5f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6b9f91a3-80ba-4fd7-9a89-32914ddd2a5f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6b9f91a3-80ba-4fd7-9a89-32914ddd2a5f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         mean_rating\n",
              "movieId             \n",
              "1           3.872470\n",
              "2           3.401869\n",
              "3           3.161017\n",
              "4           2.384615\n",
              "5           3.267857\n",
              "6           3.884615\n",
              "7           3.283019\n",
              "8           3.800000\n",
              "9           3.150000\n",
              "10          3.450820"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## объеденям две таблицы "
      ],
      "metadata": {
        "id": "139KVIKlI6K5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "joined_df = aggregated_mean_rating.join(movies_df.set_index('movieId'), on='movieId', how='left')\n",
        "joined_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "0ed2VuJYRJ3u",
        "outputId": "c24ecc87-14d1-42ba-969d-0fcf9ce0e021"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a507d7be-72d0-4e6d-a391-b43ca9e37611\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>mean_rating</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.872470</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.401869</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.161017</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.384615</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.267857</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161944</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>The Last Brickmaker in America (2001)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162376</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>Stranger Things</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162542</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>Rustom (2016)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162672</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>Mohenjo Daro (2016)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163949</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>The Beatles: Eight Days a Week - The Touring Y...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9066 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a507d7be-72d0-4e6d-a391-b43ca9e37611')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a507d7be-72d0-4e6d-a391-b43ca9e37611 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a507d7be-72d0-4e6d-a391-b43ca9e37611');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         mean_rating                                              title\n",
              "movieId                                                                \n",
              "1           3.872470                                   Toy Story (1995)\n",
              "2           3.401869                                     Jumanji (1995)\n",
              "3           3.161017                            Grumpier Old Men (1995)\n",
              "4           2.384615                           Waiting to Exhale (1995)\n",
              "5           3.267857                 Father of the Bride Part II (1995)\n",
              "...              ...                                                ...\n",
              "161944      5.000000              The Last Brickmaker in America (2001)\n",
              "162376      4.500000                                    Stranger Things\n",
              "162542      5.000000                                      Rustom (2016)\n",
              "162672      3.000000                                Mohenjo Daro (2016)\n",
              "163949      5.000000  The Beatles: Eight Days a Week - The Touring Y...\n",
              "\n",
              "[9066 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## функция для выделения даты в строке и ее проверка "
      ],
      "metadata": {
        "id": "VnMSAeJkJUxL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "# years_list = np.arange(1950,2011,1)\n",
        "# Если смотреть с 1900 года кажется более наглядным график\n",
        "years_list = np.arange(1900,2011,1)\n",
        "import re \n",
        "def production_year(title):\n",
        "  year = re.findall(r'\\(\\d{4}-?\\d*\\)',title)\n",
        "  if year:\n",
        "    if int(year[0][1:5]) in years_list:\n",
        "      return int(year[0][1:5])\n",
        "  return 1900\n",
        "  "
      ],
      "metadata": {
        "id": "RAApBidUUWrf"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text ='Fawlty Towers (1975-1979)'\n",
        "production_year(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWxcLgbciJPT",
        "outputId": "c77c3e33-09f2-4eed-d558-f3a539fbfb50"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1975"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text ='the house of 1000 corpses (2003)'\n",
        "production_year(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ohZ7bstkmeR",
        "outputId": "51d59e76-d551-439c-885a-75ba3afd13f5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2003"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text ='(6000 the (100) house 4000) of 1000 corpses (2003)'\n",
        "production_year(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0Ds5EfRktQU",
        "outputId": "cec0ecba-b457-4186-c5ee-7d5b839f7ffa"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2003"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text ='(6000 the (100) house 4000) of 1000 corpses (1949)'\n",
        "production_year(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEwgSE8kQhZ3",
        "outputId": "c2a73f49-6137-4bba-bd5b-8412872dc635"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1949"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Добавили колонку year в объедененный joined_df "
      ],
      "metadata": {
        "id": "UPPPm66GJ2Jc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "joined_df['year'] = joined_df['title'].apply(production_year)\n",
        "joined_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "n-R0jB4vaPMP",
        "outputId": "daa9131d-a80d-433b-ac78-7c9d92c1e8f1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-94b2aff4-4bcf-4a01-952f-385091d08545\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>mean_rating</th>\n",
              "      <th>title</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.872470</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.401869</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.161017</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.384615</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.267857</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94b2aff4-4bcf-4a01-952f-385091d08545')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-94b2aff4-4bcf-4a01-952f-385091d08545 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-94b2aff4-4bcf-4a01-952f-385091d08545');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         mean_rating                               title  year\n",
              "movieId                                                       \n",
              "1           3.872470                    Toy Story (1995)  1995\n",
              "2           3.401869                      Jumanji (1995)  1995\n",
              "3           3.161017             Grumpier Old Men (1995)  1995\n",
              "4           2.384615            Waiting to Exhale (1995)  1995\n",
              "5           3.267857  Father of the Bride Part II (1995)  1995"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## удаление ненужной для анализа колонки"
      ],
      "metadata": {
        "id": "S9vKJkE6KdPc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "joined_df.drop([\"title\"], axis=1, inplace=True)\n",
        "joined_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "MjV98p56k6NP",
        "outputId": "7719a148-d204-4f44-8451-03ddfe61946e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3b14d08f-c98e-41ab-bfcf-7d5c8497314e\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_rating</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <td>3.872470</td>\n",
              "      <td>1995</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.401869</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.161017</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.384615</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.267857</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>161944</th>\n",
              "      <td>5.000000</td>\n",
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              "    <tr>\n",
              "      <th>162376</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>1900</td>\n",
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              "    <tr>\n",
              "      <th>162542</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1900</td>\n",
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              "    <tr>\n",
              "      <th>162672</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1900</td>\n",
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              "    <tr>\n",
              "      <th>163949</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9066 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b14d08f-c98e-41ab-bfcf-7d5c8497314e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b14d08f-c98e-41ab-bfcf-7d5c8497314e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b14d08f-c98e-41ab-bfcf-7d5c8497314e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         mean_rating  year\n",
              "movieId                   \n",
              "1           3.872470  1995\n",
              "2           3.401869  1995\n",
              "3           3.161017  1995\n",
              "4           2.384615  1995\n",
              "5           3.267857  1995\n",
              "...              ...   ...\n",
              "161944      5.000000  2001\n",
              "162376      4.500000  1900\n",
              "162542      5.000000  1900\n",
              "162672      3.000000  1900\n",
              "163949      5.000000  1900\n",
              "\n",
              "[9066 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Группируем joined_df по year и посчитаем среднее значение рейтинга фильмов по годам"
      ],
      "metadata": {
        "id": "i9_PASroK_Rt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result_df=joined_df.groupby(by = 'year').mean().reset_index()\n",
        "result_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IpS-sFkVzsfm",
        "outputId": "597c4b92-2889-47a1-8e39-70d44e43104a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-86621060-48a7-4e49-834a-473d0b7d4615\">\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>mean_rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>3.073557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1902</td>\n",
              "      <td>4.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1915</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1916</td>\n",
              "      <td>3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1917</td>\n",
              "      <td>4.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2006</td>\n",
              "      <td>3.265219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2007</td>\n",
              "      <td>3.380247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2008</td>\n",
              "      <td>3.237304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>2009</td>\n",
              "      <td>3.212108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2010</td>\n",
              "      <td>3.201324</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 2 columns</p>\n",
              "</div>\n",
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              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86621060-48a7-4e49-834a-473d0b7d4615');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    year  mean_rating\n",
              "0   1900     3.073557\n",
              "1   1902     4.333333\n",
              "2   1915     3.000000\n",
              "3   1916     3.500000\n",
              "4   1917     4.250000\n",
              "..   ...          ...\n",
              "93  2006     3.265219\n",
              "94  2007     3.380247\n",
              "95  2008     3.237304\n",
              "96  2009     3.212108\n",
              "97  2010     3.201324\n",
              "\n",
              "[98 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## линейная аппроксимация "
      ],
      "metadata": {
        "id": "hqtnPXP8MkkX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "m, c = np.polyfit(result_df.year,result_df.mean_rating,1) \n",
        "\n",
        "def line_oprox(x):\n",
        " return m*x + c\n",
        "\n",
        "y =  line_oprox(result_df.year)\n"
      ],
      "metadata": {
        "id": "NY0YZDvJ3Qnp"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Построим графики \n"
      ],
      "metadata": {
        "id": "JIvwg8c2M7gN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (30,10))\n",
        "plt.plot(result_df.year,result_df.mean_rating, marker='o', label=\"real data\")\n",
        "plt.plot(result_df.year,y, marker='.', label=\"linear approximation\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "r_bM5F2P0KO6",
        "outputId": "a5605f4c-8b26-48c4-8285-c86ce4c26afe"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mean Squared Error"
      ],
      "metadata": {
        "id": "057G22GzN661"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.square(np.subtract(y, result_df.mean_rating)).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRfpl6iDNx6v",
        "outputId": "02b785ef-9191-47de-c631-2901fb2081e5"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07511452599170326"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    }
  ]
}