#1.(연습)MovieLens_데이터를_통한_간단한_평점_데이터_탐색.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
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    "colab": {
      "name": "(연습)MovieLens 데이터를 통한 간단한 평점 데이터 분석.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/moch1996/Vaycold/blob/main/Recommend_Algorithm/(%EC%97%B0%EC%8A%B5)MovieLens_%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%A5%BC_%ED%86%B5%ED%95%9C_%EA%B0%84%EB%8B%A8%ED%95%9C_%ED%8F%89%EC%A0%90_%EB%8D%B0%EC%9D%B4%ED%84%B0_%EB%B6%84%EC%84%9D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufIA86zmMUFE"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VR18Kh5Qazbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7eef7720-7faa-48aa-c1ec-a2e382eb6eb6"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDeQV6hAbXrd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32424dda-189d-400d-84d4-a8b0f181b6e7"
      },
      "source": [
        "os.listdir('drive/MyDrive/recommend/movielens/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ratings.csv', 'tags.csv', 'movies.csv', 'README.txt', 'links.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BWGYMfqMUFJ"
      },
      "source": [
        "# Google colab: 데이터 경로 설정\n",
        "path = '../data/ml-latest-small/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaZXEZGzbJoD"
      },
      "source": [
        "path = '/content/drive/MyDrive/recommend/movielens/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2OpVGSSMUFK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d780109d-8d40-456e-85ee-214063a5693b"
      },
      "source": [
        "os.listdir(path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ratings.csv', 'tags.csv', 'movies.csv', 'README.txt', 'links.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7ZX1H3kMUFK"
      },
      "source": [
        "#data셋을 불러오는 과정\n",
        "ratings_df = pd.read_csv(os.path.join(path + 'ratings.csv'), encoding='utf-8')\n",
        "tags_df = pd.read_csv(os.path.join(path + 'tags.csv'), encoding='utf-8')\n",
        "movies_df = pd.read_csv(os.path.join(path + 'movies.csv'), index_col='movieId', encoding='utf-8')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-cIXIaOMUFK"
      },
      "source": [
        "* timestamp는 영화에 대한 평점을 기록한 시점. 영화가 처음 출시한 시점이 아니다"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUcPrEr_MUFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67979ebd-1da2-47f9-8b28-008774f2f419"
      },
      "source": [
        "print(ratings_df.shape)\n",
        "print(ratings_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100836, 4)\n",
            "   userId  movieId  rating  timestamp\n",
            "0       1        1     4.0  964982703\n",
            "1       1        3     4.0  964981247\n",
            "2       1        6     4.0  964982224\n",
            "3       1       47     5.0  964983815\n",
            "4       1       50     5.0  964982931\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrQ18sVaMUFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2150d428-3f21-4807-c43e-bef456b0103e"
      },
      "source": [
        "print(tags_df.shape)\n",
        "print(tags_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3683, 4)\n",
            "   userId  movieId              tag   timestamp\n",
            "0       2    60756            funny  1445714994\n",
            "1       2    60756  Highly quotable  1445714996\n",
            "2       2    60756     will ferrell  1445714992\n",
            "3       2    89774     Boxing story  1445715207\n",
            "4       2    89774              MMA  1445715200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hnd1-q6MUFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74f7c272-1fe0-41a9-ba10-622bf0654b93"
      },
      "source": [
        "print(movies_df.shape)\n",
        "print(movies_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9742, 2)\n",
            "                                      title                                       genres\n",
            "movieId                                                                                 \n",
            "1                          Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy\n",
            "2                            Jumanji (1995)                   Adventure|Children|Fantasy\n",
            "3                   Grumpier Old Men (1995)                               Comedy|Romance\n",
            "4                  Waiting to Exhale (1995)                         Comedy|Drama|Romance\n",
            "5        Father of the Bride Part II (1995)                                       Comedy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHhO01w6MUFL"
      },
      "source": [
        "### 평점 데이터의 기초 통계량"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4qZAdhJMUFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "244ce1b5-34c8-49fe-e529-154a9de6b5d9"
      },
      "source": [
        "#몇명의 유저가 평점을 내렸는 지??\n",
        "n_unique_users = len(ratings_df['userId'].unique())\n",
        "print(n_unique_users)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "610\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "da3OSx9wMUFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8423065f-f3bc-4728-9a58-b496fcad407d"
      },
      "source": [
        "#몇개의 영화가 평가받았는 지??\n",
        "n_unique_movies = len(ratings_df['movieId'].unique())\n",
        "print(n_unique_movies)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gzr3f1xKMUFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ae89cfb-efe2-46ff-dbb7-40ae2bdd76b7"
      },
      "source": [
        "print('평점의 평균: ', ratings_df['rating'].mean())\n",
        "print('평점의 표준편차: ', ratings_df['rating'].std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "평점의 평균:  3.501556983616962\n",
            "평점의 표준편차:  1.0425292390605359\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tdj02TNIMUFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba6da105-b762-4803-80db-9ccba4e18c97"
      },
      "source": [
        "ratings_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100836 entries, 0 to 100835\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   userId     100836 non-null  int64  \n",
            " 1   movieId    100836 non-null  int64  \n",
            " 2   rating     100836 non-null  float64\n",
            " 3   timestamp  100836 non-null  int64  \n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 3.1 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozqiewBxMUFN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "66118a32-79f8-4291-9f76-89710f5fb7a1"
      },
      "source": [
        "ratings_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100836.000000</td>\n",
              "      <td>100836.000000</td>\n",
              "      <td>100836.000000</td>\n",
              "      <td>1.008360e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>326.127564</td>\n",
              "      <td>19435.295718</td>\n",
              "      <td>3.501557</td>\n",
              "      <td>1.205946e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>182.618491</td>\n",
              "      <td>35530.987199</td>\n",
              "      <td>1.042529</td>\n",
              "      <td>2.162610e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.281246e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>177.000000</td>\n",
              "      <td>1199.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.019124e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>325.000000</td>\n",
              "      <td>2991.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.186087e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>477.000000</td>\n",
              "      <td>8122.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.435994e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>610.000000</td>\n",
              "      <td>193609.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.537799e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              userId        movieId         rating     timestamp\n",
              "count  100836.000000  100836.000000  100836.000000  1.008360e+05\n",
              "mean      326.127564   19435.295718       3.501557  1.205946e+09\n",
              "std       182.618491   35530.987199       1.042529  2.162610e+08\n",
              "min         1.000000       1.000000       0.500000  8.281246e+08\n",
              "25%       177.000000    1199.000000       3.000000  1.019124e+09\n",
              "50%       325.000000    2991.000000       3.500000  1.186087e+09\n",
              "75%       477.000000    8122.000000       4.000000  1.435994e+09\n",
              "max       610.000000  193609.000000       5.000000  1.537799e+09"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Paw5q2tcMUFN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "612ad1a2-c6b5-4777-9bcd-5d809395b76c"
      },
      "source": [
        "# nan 값이 있는지 확인한다\n",
        "ratings_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId       0\n",
              "movieId      0\n",
              "rating       0\n",
              "timestamp    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6f9F2j1MUFN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "4f764da3-631a-4ba9-e93f-5a043c6cc4ff"
      },
      "source": [
        "ratings_df[['userId','movieId','rating']].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1b349ecf50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1b375a62d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1b34988910>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1b34940f90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc5UiBEMd7v_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "b73bf3f3-6d67-4cf7-ca5e-dae5af5871c3"
      },
      "source": [
        "ratings_df[['rating']].hist(bins=10)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1b3495ab90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46svrtymMUFN"
      },
      "source": [
        "### Pandas의 GroupBy 사용하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psxaJFqpiqwU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "466c0f8c-1897-4e72-a4ef-04455c1493e9"
      },
      "source": [
        "ratings_df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964981247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964983815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964982931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>70</td>\n",
              "      <td>3.0</td>\n",
              "      <td>964982400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>101</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964980868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>110</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>151</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964984041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>157</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964984100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
              "3       1       47     5.0  964983815\n",
              "4       1       50     5.0  964982931\n",
              "5       1       70     3.0  964982400\n",
              "6       1      101     5.0  964980868\n",
              "7       1      110     4.0  964982176\n",
              "8       1      151     5.0  964984041\n",
              "9       1      157     5.0  964984100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXW5O63-MUFN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f20492d0-47ec-409e-cc64-e5dbfa0292cd"
      },
      "source": [
        "#grouoby를 활용하여 userid와 rating을 묶어봄\r\n",
        "ratings_df.groupby(['userId', 'rating']).size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId  rating\n",
              "1       1.0         1\n",
              "        2.0         5\n",
              "        3.0        26\n",
              "        4.0        76\n",
              "        5.0       124\n",
              "                 ... \n",
              "610     3.0       230\n",
              "        3.5       315\n",
              "        4.0       286\n",
              "        4.5       148\n",
              "        5.0       180\n",
              "Length: 3976, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gmfg_-SsMUFO"
      },
      "source": [
        "* userId와 rating을 기준으로 기초 통계량"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfLK5nCYMUFO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eef155ed-5f43-41c9-acac-6a4d83695b7c"
      },
      "source": [
        "ratings_df.groupby(['userId']).mean()['rating']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId\n",
              "1      4.366379\n",
              "2      3.948276\n",
              "3      2.435897\n",
              "4      3.555556\n",
              "5      3.636364\n",
              "         ...   \n",
              "606    3.657399\n",
              "607    3.786096\n",
              "608    3.134176\n",
              "609    3.270270\n",
              "610    3.688556\n",
              "Name: rating, Length: 610, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZcdTl9bMUFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "3a45bb84-3848-45b3-b7a7-1682bb5854ee"
      },
      "source": [
        "userid_rating_df = pd.DataFrame({'count': ratings_df.groupby(['userId', 'rating']).size()})\n",
        "userid_rating_df = userid_rating_df.reset_index()\n",
        "userid_rating_df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>rating</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  rating  count\n",
              "0       1     1.0      1\n",
              "1       1     2.0      5\n",
              "2       1     3.0     26\n",
              "3       1     4.0     76\n",
              "4       1     5.0    124\n",
              "5       2     2.0      1\n",
              "6       2     2.5      1\n",
              "7       2     3.0      4\n",
              "8       2     3.5      4\n",
              "9       2     4.0      9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsGzApmQMUFO"
      },
      "source": [
        "user_info = ratings_df.groupby('userId')['movieId'].count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJr-2ZxQihB6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "934e8114-e541-457e-d361-edf8df14ff6b"
      },
      "source": [
        "user_info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userId\n",
              "1       232\n",
              "2        29\n",
              "3        39\n",
              "4       216\n",
              "5        44\n",
              "       ... \n",
              "606    1115\n",
              "607     187\n",
              "608     831\n",
              "609      37\n",
              "610    1302\n",
              "Name: movieId, Length: 610, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqEttHMkMUFO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a27f06a3-a0dd-4d5d-ddd0-8529cbe9e2a0"
      },
      "source": [
        "user_info.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     610.000000\n",
              "mean      165.304918\n",
              "std       269.480584\n",
              "min        20.000000\n",
              "25%        35.000000\n",
              "50%        70.500000\n",
              "75%       168.000000\n",
              "max      2698.000000\n",
              "Name: movieId, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aw7F9_SrMUFO"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJPBLfmoMUFP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "d1f5749d-da62-4ed5-dbc9-bfd48a272916"
      },
      "source": [
        "# user가 몇개의 영화에 대해서 rating을 했는지를 볼 수 있는 분포(그래프)\n",
        "sns.distplot(user_info.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b25880790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AofT5zIMUFP"
      },
      "source": [
        "### user가 평균적으로 준 평점과 평점을 준 영화의 수"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xzu5HLQWMUFP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb43adf8-aa59-422e-97ec-d7ec7dce7af4"
      },
      "source": [
        "stats_df = pd.DataFrame({\n",
        "    'movie_count': ratings_df.groupby('userId')['movieId'].count(),\n",
        "    'rating_avg': ratings_df.groupby('userId')['rating'].mean(), \n",
        "'rating_std': ratings_df.groupby('userId')['rating'].std()})\n",
        "\n",
        "print(stats_df.shape)\n",
        "print(stats_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(610, 3)\n",
            "        movie_count  rating_avg  rating_std\n",
            "userId                                     \n",
            "1               232    4.366379    0.800048\n",
            "2                29    3.948276    0.805615\n",
            "3                39    2.435897    2.090642\n",
            "4               216    3.555556    1.314204\n",
            "5                44    3.636364    0.990441\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJOMXq1FMUFP"
      },
      "source": [
        "### rating이 많은 영화(다시 말해, 사람들이 관심 많은 영화)\n",
        "\n",
        "* 많이 본 영화일수록 평점이 좋다? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cbf_3tzTMUFP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fe19631-8bc6-4fda-e1f6-d137dd7c6f4e"
      },
      "source": [
        "movieid_user_df = pd.DataFrame({\n",
        "    'num_users_watch': ratings_df.groupby('movieId')['userId'].count(),\n",
        "    'avg_ratings': ratings_df.groupby('movieId')['rating'].mean(),\n",
        "    'std_ratings': ratings_df.groupby('movieId')['rating'].std()\n",
        "})\n",
        "movieid_user_df = movieid_user_df.reset_index()\n",
        "print(movieid_user_df.shape)\n",
        "print(movieid_user_df.head(10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9724, 4)\n",
            "   movieId  num_users_watch  avg_ratings  std_ratings\n",
            "0        1              215     3.920930     0.834859\n",
            "1        2              110     3.431818     0.881713\n",
            "2        3               52     3.259615     1.054823\n",
            "3        4                7     2.357143     0.852168\n",
            "4        5               49     3.071429     0.907148\n",
            "5        6              102     3.946078     0.817224\n",
            "6        7               54     3.185185     0.977561\n",
            "7        8                8     2.875000     1.125992\n",
            "8        9               16     3.125000     0.974679\n",
            "9       10              132     3.496212     0.859381\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDVxZOiWMUFQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "7c8ae621-b178-4c2d-d555-2905b5090408"
      },
      "source": [
        "# 영화를 많이 본 순서대로 분류하기\r\n",
        "movieid_user_df.sort_values(by='num_users_watch', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>num_users_watch</th>\n",
              "      <th>avg_ratings</th>\n",
              "      <th>std_ratings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>356</td>\n",
              "      <td>329</td>\n",
              "      <td>4.164134</td>\n",
              "      <td>0.831244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>318</td>\n",
              "      <td>317</td>\n",
              "      <td>4.429022</td>\n",
              "      <td>0.713019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>296</td>\n",
              "      <td>307</td>\n",
              "      <td>4.197068</td>\n",
              "      <td>0.951997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>510</th>\n",
              "      <td>593</td>\n",
              "      <td>279</td>\n",
              "      <td>4.161290</td>\n",
              "      <td>0.853983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1938</th>\n",
              "      <td>2571</td>\n",
              "      <td>278</td>\n",
              "      <td>4.192446</td>\n",
              "      <td>0.975243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3053</th>\n",
              "      <td>4093</td>\n",
              "      <td>1</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3049</th>\n",
              "      <td>4089</td>\n",
              "      <td>1</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6687</th>\n",
              "      <td>58351</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3045</th>\n",
              "      <td>4083</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9723</th>\n",
              "      <td>193609</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9724 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieId  num_users_watch  avg_ratings  std_ratings\n",
              "314       356              329     4.164134     0.831244\n",
              "277       318              317     4.429022     0.713019\n",
              "257       296              307     4.197068     0.951997\n",
              "510       593              279     4.161290     0.853983\n",
              "1938     2571              278     4.192446     0.975243\n",
              "...       ...              ...          ...          ...\n",
              "3053     4093                1     1.500000          NaN\n",
              "3049     4089                1     2.000000          NaN\n",
              "6687    58351                1     4.000000          NaN\n",
              "3045     4083                1     4.000000          NaN\n",
              "9723   193609                1     4.000000          NaN\n",
              "\n",
              "[9724 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NL65-j07MUFQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3f720e6f-c71b-4fc4-f399-d2241b3ef9cf"
      },
      "source": [
        "# long tail economy\n",
        "movieid_user_df['num_users_watch'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b347cae90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZytVkUfka1uy",
        "outputId": "5de70a2b-cea3-4d40-ddc6-0518314da0f2"
      },
      "source": [
        "movieid_user_df['movieId']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            1\n",
              "1            2\n",
              "2            3\n",
              "3            4\n",
              "4            5\n",
              "         ...  \n",
              "9719    193581\n",
              "9720    193583\n",
              "9721    193585\n",
              "9722    193587\n",
              "9723    193609\n",
              "Name: movieId, Length: 9724, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "Ar6GAINFbBUw",
        "outputId": "ba8b47d1-d0b2-4910-ed47-c111083720e5"
      },
      "source": [
        "movieid_user_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>num_users_watch</th>\n",
              "      <th>avg_ratings</th>\n",
              "      <th>std_ratings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>215</td>\n",
              "      <td>3.920930</td>\n",
              "      <td>0.834859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>110</td>\n",
              "      <td>3.431818</td>\n",
              "      <td>0.881713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>52</td>\n",
              "      <td>3.259615</td>\n",
              "      <td>1.054823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>2.357143</td>\n",
              "      <td>0.852168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>49</td>\n",
              "      <td>3.071429</td>\n",
              "      <td>0.907148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9719</th>\n",
              "      <td>193581</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9720</th>\n",
              "      <td>193583</td>\n",
              "      <td>1</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9721</th>\n",
              "      <td>193585</td>\n",
              "      <td>1</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9722</th>\n",
              "      <td>193587</td>\n",
              "      <td>1</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9723</th>\n",
              "      <td>193609</td>\n",
              "      <td>1</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9724 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieId  num_users_watch  avg_ratings  std_ratings\n",
              "0           1              215     3.920930     0.834859\n",
              "1           2              110     3.431818     0.881713\n",
              "2           3               52     3.259615     1.054823\n",
              "3           4                7     2.357143     0.852168\n",
              "4           5               49     3.071429     0.907148\n",
              "...       ...              ...          ...          ...\n",
              "9719   193581                1     4.000000          NaN\n",
              "9720   193583                1     3.500000          NaN\n",
              "9721   193585                1     3.500000          NaN\n",
              "9722   193587                1     3.500000          NaN\n",
              "9723   193609                1     4.000000          NaN\n",
              "\n",
              "[9724 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Fb1VkvJbIVh",
        "outputId": "89e2217c-e4d3-4724-dd23-ab5021fde741"
      },
      "source": [
        "movieid_user_df['num_users_watch']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       215\n",
              "1       110\n",
              "2        52\n",
              "3         7\n",
              "4        49\n",
              "       ... \n",
              "9719      1\n",
              "9720      1\n",
              "9721      1\n",
              "9722      1\n",
              "9723      1\n",
              "Name: num_users_watch, Length: 9724, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlIFYhKRMUFQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d5a4e62-0614-4450-a8c4-d9ac73938325"
      },
      "source": [
        " # 1번 또는 1명만 평점을 준 영화\n",
        "movieid_user_df['movieId'][movieid_user_df.num_users_watch == 1].count()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3446"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3HBLlSGlMUFQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a33e0ac9-d616-49d7-f214-af1286b2b844"
      },
      "source": [
        "# 3번 미만의 영화\n",
        "movieid_user_df['movieId'][movieid_user_df.num_users_watch < 3].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4744"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXVPZvjJMUFR"
      },
      "source": [
        "### 평점이 높은 영화(장르), 평점을 많이 받은 영화(장르)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lY-O-e7AMUFR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "ef550ef5-9bff-42af-f821-48757106bff5"
      },
      "source": [
        "# 영화 정보를 활용\n",
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>genres</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>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      title                                       genres\n",
              "movieId                                                                                 \n",
              "1                          Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy\n",
              "2                            Jumanji (1995)                   Adventure|Children|Fantasy\n",
              "3                   Grumpier Old Men (1995)                               Comedy|Romance\n",
              "4                  Waiting to Exhale (1995)                         Comedy|Drama|Romance\n",
              "5        Father of the Bride Part II (1995)                                       Comedy"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHHyZg9fMUFR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "855d153d-ab7c-4b0b-d54c-17649293b269"
      },
      "source": [
        "# 평점을 많이 받은 영화 \n",
        "ratings_count_df = ratings_df.groupby('movieId')['userId'].count()\n",
        "ratings_count_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "1    215\n",
              "2    110\n",
              "3     52\n",
              "4      7\n",
              "5     49\n",
              "Name: userId, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEZM9TxCeYtE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YzbtGqqMUFR"
      },
      "source": [
        "df = pd.DataFrame({\n",
        "    'ratings_count': ratings_df.groupby('movieId')['userId'].count(),\n",
        "})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "SlCFeqWDelSS",
        "outputId": "b9508d0f-ec06-46ef-a5a3-74add5d35d43"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ratings_count</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>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ratings_count\n",
              "movieId               \n",
              "1                  215\n",
              "2                  110\n",
              "3                   52\n",
              "4                    7\n",
              "5                   49"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9K_DnsVCMUFR"
      },
      "source": [
        "df['movie_name'] = df.apply(lambda x: movies_df['title'].loc[x.index])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NPZeK7SMUFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "27383fbb-7719-43e9-b7a2-0f5a1ed28735"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ratings_count</th>\n",
              "      <th>movie_name</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>215</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>110</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>49</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>102</td>\n",
              "      <td>Heat (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>54</td>\n",
              "      <td>Sabrina (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>Tom and Huck (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>16</td>\n",
              "      <td>Sudden Death (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>132</td>\n",
              "      <td>GoldenEye (1995)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ratings_count                          movie_name\n",
              "movieId                                                   \n",
              "1                  215                    Toy Story (1995)\n",
              "2                  110                      Jumanji (1995)\n",
              "3                   52             Grumpier Old Men (1995)\n",
              "4                    7            Waiting to Exhale (1995)\n",
              "5                   49  Father of the Bride Part II (1995)\n",
              "6                  102                         Heat (1995)\n",
              "7                   54                      Sabrina (1995)\n",
              "8                    8                 Tom and Huck (1995)\n",
              "9                   16                 Sudden Death (1995)\n",
              "10                 132                    GoldenEye (1995)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8R8UC0yNMUFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "e652a1f3-e7c2-43c5-ced7-888fbd12f20d"
      },
      "source": [
        "df.sort_values(by='ratings_count', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ratings_count</th>\n",
              "      <th>movie_name</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>356</th>\n",
              "      <td>329</td>\n",
              "      <td>Forrest Gump (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>317</td>\n",
              "      <td>Shawshank Redemption, The (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>307</td>\n",
              "      <td>Pulp Fiction (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>279</td>\n",
              "      <td>Silence of the Lambs, The (1991)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2571</th>\n",
              "      <td>278</td>\n",
              "      <td>Matrix, The (1999)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4093</th>\n",
              "      <td>1</td>\n",
              "      <td>Cop (1988)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4089</th>\n",
              "      <td>1</td>\n",
              "      <td>Born in East L.A. (1987)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58351</th>\n",
              "      <td>1</td>\n",
              "      <td>City of Men (Cidade dos Homens) (2007)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4083</th>\n",
              "      <td>1</td>\n",
              "      <td>Best Seller (1987)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193609</th>\n",
              "      <td>1</td>\n",
              "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9724 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         ratings_count                              movie_name\n",
              "movieId                                                       \n",
              "356                329                     Forrest Gump (1994)\n",
              "318                317        Shawshank Redemption, The (1994)\n",
              "296                307                     Pulp Fiction (1994)\n",
              "593                279        Silence of the Lambs, The (1991)\n",
              "2571               278                      Matrix, The (1999)\n",
              "...                ...                                     ...\n",
              "4093                 1                              Cop (1988)\n",
              "4089                 1                Born in East L.A. (1987)\n",
              "58351                1  City of Men (Cidade dos Homens) (2007)\n",
              "4083                 1                      Best Seller (1987)\n",
              "193609               1     Andrew Dice Clay: Dice Rules (1991)\n",
              "\n",
              "[9724 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9k28963YMUFS"
      },
      "source": [
        "### 특정 영화의 평점 분포\n",
        "\n",
        "* movieId :  356, Forrest Gump(1994)\n",
        "* movieId : 2571, Matrix, The (1999)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3fG3AM0aMUFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "898c54d6-5761-4658-b344-278f46816c32"
      },
      "source": [
        "ratings_df[ratings_df.movieId == 356]['rating'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b20535ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPIm-Nd7MUFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "be4c94e8-01a1-44dd-e5b2-e4c37acc730d"
      },
      "source": [
        "ratings_df[ratings_df.movieId == 2571]['rating'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b201b0a50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJirl2HiMUFS"
      },
      "source": [
        "### 특정 유저가 주는 평점과 실제 영화에 대한 평점 분포가 비슷할까? \n",
        "\n",
        "* 나는 일반적인 영화 관람객일까? 특이한 취향을 갖고있는 사람일까? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDiOL0P9MUFS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "2e4348da-52f6-41da-8695-99fe2174cbec"
      },
      "source": [
        "ratings_df[(ratings_df.movieId == 356) & (ratings_df.rating < 2)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12274</th>\n",
              "      <td>76</td>\n",
              "      <td>356</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1439165536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13553</th>\n",
              "      <td>89</td>\n",
              "      <td>356</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1520408275</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       userId  movieId  rating   timestamp\n",
              "12274      76      356     1.0  1439165536\n",
              "13553      89      356     0.5  1520408275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHwSuMf5MUFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "6fe29c5f-c5e4-4171-ba8f-58b9fdb9f7e4"
      },
      "source": [
        "ratings_df[(ratings_df.movieId == 2571) & (ratings_df.rating < 2)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>442</th>\n",
              "      <td>4</td>\n",
              "      <td>2571</td>\n",
              "      <td>1.0</td>\n",
              "      <td>945173385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>10</td>\n",
              "      <td>2571</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1455356378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8706</th>\n",
              "      <td>59</td>\n",
              "      <td>2571</td>\n",
              "      <td>1.0</td>\n",
              "      <td>953609098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12239</th>\n",
              "      <td>75</td>\n",
              "      <td>2571</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1158967564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17337</th>\n",
              "      <td>111</td>\n",
              "      <td>2571</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1516140656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22474</th>\n",
              "      <td>153</td>\n",
              "      <td>2571</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1525548255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65107</th>\n",
              "      <td>416</td>\n",
              "      <td>2571</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1187496634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71534</th>\n",
              "      <td>461</td>\n",
              "      <td>2571</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1505935248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71706</th>\n",
              "      <td>462</td>\n",
              "      <td>2571</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1154037742</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       userId  movieId  rating   timestamp\n",
              "442         4     2571     1.0   945173385\n",
              "1130       10     2571     0.5  1455356378\n",
              "8706       59     2571     1.0   953609098\n",
              "12239      75     2571     1.0  1158967564\n",
              "17337     111     2571     0.5  1516140656\n",
              "22474     153     2571     0.5  1525548255\n",
              "65107     416     2571     1.0  1187496634\n",
              "71534     461     2571     0.5  1505935248\n",
              "71706     462     2571     0.5  1154037742"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlTQR9b-MUFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "4d98b892-91e0-40e6-fce1-c8d2b75b04fd"
      },
      "source": [
        "ratings_df[ratings_df.userId == 76]['rating'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b20107110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0NcIHUcMUFT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}