no4klbd.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMzprPpmFHAk2P+zDUrgz/V",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/syambahardi/7a3514617eefb263e5b92b886115086c/no4klbd.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYZbJFm0RUis",
        "outputId": "858ad22c-28e4-4db5-d239-0729400ac152"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-03 06:18:35--  https://github.com/nzhinusoftcm/review-on-collaborative-filtering/raw/master/recsys.zip\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/nzhinusoftcm/review-on-collaborative-filtering/master/recsys.zip [following]\n",
            "--2023-01-03 06:18:35--  https://raw.githubusercontent.com/nzhinusoftcm/review-on-collaborative-filtering/master/recsys.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 15312323 (15M) [application/zip]\n",
            "Saving to: ‘recsys.zip’\n",
            "\n",
            "recsys.zip          100%[===================>]  14.60M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2023-01-03 06:18:36 (187 MB/s) - ‘recsys.zip’ saved [15312323/15312323]\n",
            "\n",
            "Archive:  recsys.zip\n",
            "   creating: recsys/\n",
            "  inflating: recsys/datasets.py      \n",
            "  inflating: recsys/preprocessing.py  \n",
            "  inflating: recsys/utils.py         \n",
            "  inflating: recsys/requirements.txt  \n",
            "   creating: recsys/.vscode/\n",
            "  inflating: recsys/.vscode/settings.json  \n",
            "   creating: recsys/__pycache__/\n",
            "  inflating: recsys/__pycache__/datasets.cpython-36.pyc  \n",
            "  inflating: recsys/__pycache__/datasets.cpython-37.pyc  \n",
            "  inflating: recsys/__pycache__/utils.cpython-36.pyc  \n",
            "  inflating: recsys/__pycache__/preprocessing.cpython-37.pyc  \n",
            "  inflating: recsys/__pycache__/datasets.cpython-38.pyc  \n",
            "  inflating: recsys/__pycache__/preprocessing.cpython-36.pyc  \n",
            "  inflating: recsys/__pycache__/preprocessing.cpython-38.pyc  \n",
            "   creating: recsys/memories/\n",
            "  inflating: recsys/memories/ItemToItem.py  \n",
            "  inflating: recsys/memories/UserToUser.py  \n",
            "   creating: recsys/memories/__pycache__/\n",
            "  inflating: recsys/memories/__pycache__/UserToUser.cpython-36.pyc  \n",
            "  inflating: recsys/memories/__pycache__/UserToUser.cpython-37.pyc  \n",
            "  inflating: recsys/memories/__pycache__/ItemToItem.cpython-37.pyc  \n",
            "  inflating: recsys/memories/__pycache__/user2user.cpython-36.pyc  \n",
            "  inflating: recsys/memories/__pycache__/ItemToItem.cpython-36.pyc  \n",
            "   creating: recsys/models/\n",
            "  inflating: recsys/models/SVD.py    \n",
            "  inflating: recsys/models/MatrixFactorization.py  \n",
            "  inflating: recsys/models/ExplainableMF.py  \n",
            "  inflating: recsys/models/NonnegativeMF.py  \n",
            "   creating: recsys/models/__pycache__/\n",
            "  inflating: recsys/models/__pycache__/SVD.cpython-36.pyc  \n",
            "  inflating: recsys/models/__pycache__/MatrixFactorization.cpython-37.pyc  \n",
            "  inflating: recsys/models/__pycache__/ExplainableMF.cpython-36.pyc  \n",
            "  inflating: recsys/models/__pycache__/ExplainableMF.cpython-37.pyc  \n",
            "  inflating: recsys/models/__pycache__/MatrixFactorization.cpython-36.pyc  \n",
            "   creating: recsys/metrics/\n",
            "  inflating: recsys/metrics/EvaluationMetrics.py  \n",
            "   creating: recsys/img/\n",
            "  inflating: recsys/img/MF-and-NNMF.png  \n",
            "  inflating: recsys/img/svd.png      \n",
            "  inflating: recsys/img/MF.png       \n",
            "   creating: recsys/predictions/\n",
            "   creating: recsys/predictions/item2item/\n",
            "   creating: recsys/weights/\n",
            "   creating: recsys/weights/item2item/\n",
            "   creating: recsys/weights/item2item/ml1m/\n",
            "  inflating: recsys/weights/item2item/ml1m/similarities.npy  \n",
            "  inflating: recsys/weights/item2item/ml1m/neighbors.npy  \n",
            "   creating: recsys/weights/item2item/ml100k/\n",
            "  inflating: recsys/weights/item2item/ml100k/similarities.npy  \n",
            "  inflating: recsys/weights/item2item/ml100k/neighbors.npy  \n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "if not (os.path.exists(\"recsys.zip\") or os.path.exists(\"recsys\")):\n",
        "    !wget https://github.com/nzhinusoftcm/review-on-collaborative-filtering/raw/master/recsys.zip    \n",
        "    !unzip recsys.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from recsys.datasets import mlLatestSmall, ml100k, ml1m\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os"
      ],
      "metadata": {
        "id": "6RwnCYWcSEHZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings, movies = mlLatestSmall.load()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKPqrpNQSMlP",
        "outputId": "9d7e2224-366a-432a-fc79-e1836a25fcb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Download data 100.5%\n",
            "Successfully downloaded ml-latest-small.zip 978202 bytes.\n",
            "Unzipping the ml-latest-small.zip zip file ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(ratings.userid, ratings.itemid, ratings.rating, aggfunc=sum)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "XtyWHvAESRR7",
        "outputId": "c4dc8188-6f1a-4396-8531-70302d1313c5"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>610 rows × 9724 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-297c668d-b440-46ef-b2db-ed174e0c1607')\"\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get user's mean rating\n",
        "umean = ratings.groupby(by='userid')['rating'].mean()"
      ],
      "metadata": {
        "id": "K4GzwcfPSVS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rating_matrix(ratings):\n",
        "    \"\"\"\n",
        "    1. Fill NaN values with item's average ratings\n",
        "    2. Normalize ratings by subtracting user's mean ratings\n",
        "    \n",
        "    :param ratings : DataFrame of ratings data\n",
        "    :return\n",
        "        - R : Numpy array of normalized ratings\n",
        "        - df : DataFrame of normalized ratings\n",
        "    \"\"\"\n",
        "    \n",
        "    # fill missing values with item's average ratings\n",
        "    df = pd.crosstab(ratings.userid, ratings.itemid, ratings.rating, aggfunc=sum)\n",
        "    df = df.fillna(df.mean(axis=0))\n",
        "    \n",
        "    # subtract user's mean ratings to normalize data\n",
        "    df = df.subtract(umean, axis=0)\n",
        "    \n",
        "    # convert our dataframe to numpy array\n",
        "    R = df.to_numpy()\n",
        "    \n",
        "    return R, df\n",
        "\n",
        "# generate rating matrix by calling function rating_matrix\n",
        "R, df = rating_matrix(ratings)"
      ],
      "metadata": {
        "id": "Y7K7fbJlSYnn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "FyvcWqLlScx-",
        "outputId": "650cd5ca-0917-451b-e81b-fd8758ba5fd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "itemid    1         2         3         4         5         6         7       \\\n",
              "userid                                                                         \n",
              "1      -0.366379 -0.934561 -0.366379 -2.009236 -1.294951 -0.366379 -1.181194   \n",
              "2      -0.027346 -0.516458 -0.688660 -1.591133 -0.876847 -0.002197 -0.763091   \n",
              "3       1.485033  0.995921  0.823718 -0.078755  0.635531  1.510181  0.749288   \n",
              "4       0.365375 -0.123737 -0.295940 -1.198413 -0.484127  0.390523 -0.370370   \n",
              "5       0.363636 -0.204545 -0.376748 -1.279221 -0.564935  0.309715 -0.451178   \n",
              "\n",
              "itemid    8         9         10      ...    193565    193567    193571  \\\n",
              "userid                                ...                                 \n",
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              "4      -0.680556 -0.430556 -0.059343  ... -0.055556 -0.555556  0.444444   \n",
              "5      -0.761364 -0.511364 -0.140152  ... -0.136364 -0.636364  0.363636   \n",
              "\n",
              "itemid    193573    193579    193581    193583    193585    193587    193609  \n",
              "userid                                                                        \n",
              "1      -0.366379 -0.866379 -0.366379 -0.866379 -0.866379 -0.866379 -0.366379  \n",
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              "3       1.564103  1.064103  1.564103  1.064103  1.064103  1.064103  1.564103  \n",
              "4       0.444444 -0.055556  0.444444 -0.055556 -0.055556 -0.055556  0.444444  \n",
              "5       0.363636 -0.136364  0.363636 -0.136364 -0.136364 -0.136364  0.363636  \n",
              "\n",
              "[5 rows x 9724 columns]"
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              "      <td>-0.366379</td>\n",
              "      <td>-0.366379</td>\n",
              "      <td>-0.866379</td>\n",
              "      <td>-0.366379</td>\n",
              "      <td>-0.866379</td>\n",
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              "      <td>0.051724</td>\n",
              "      <td>0.051724</td>\n",
              "      <td>-0.448276</td>\n",
              "      <td>0.051724</td>\n",
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              "      <td>-0.448276</td>\n",
              "      <td>-0.448276</td>\n",
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              "      <td>-0.055556</td>\n",
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              "      <td>-1.279221</td>\n",
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              "      <td>0.363636</td>\n",
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              "      <td>0.363636</td>\n",
              "      <td>-0.136364</td>\n",
              "      <td>-0.136364</td>\n",
              "      <td>-0.136364</td>\n",
              "      <td>0.363636</td>\n",
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              "<p>5 rows × 9724 columns</p>\n",
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              "          const element = document.querySelector('#df-da9b02b6-87ab-4cb8-9e78-4dd16af07430');\n",
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              "            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",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "users = sorted(ratings['userid'].unique())\n",
        "items = sorted(ratings['itemid'].unique())\n",
        "\n",
        "# create our id encoders\n",
        "uencoder = LabelEncoder()\n",
        "iencoder = LabelEncoder()\n",
        "\n",
        "# fit our label encoder\n",
        "uencoder.fit(users)\n",
        "iencoder.fit(items)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vfvntuaSiFe",
        "outputId": "16b54a07-7638-41ed-b5c1-f368ea01061e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class SVD:\n",
        "    \n",
        "    def __init__(self, umeam):\n",
        "        \"\"\"\n",
        "        :param\n",
        "            - umean : mean ratings of users\n",
        "        \"\"\"\n",
        "        self.umean = umean.to_numpy()\n",
        "        \n",
        "        # init svd resultant matrices\n",
        "        self.P = np.array([])\n",
        "        self.S = np.array([])\n",
        "        self.Qh = np.array([])\n",
        "        \n",
        "        # init users and items latent factors\n",
        "        self.u_factors = np.array([])\n",
        "        self.i_factors = np.array([])\n",
        "    \n",
        "    def fit(self, R):\n",
        "        \"\"\"\n",
        "        Fit the SVD model with rating matrix R\n",
        "        \"\"\"\n",
        "        P, s, Qh = np.linalg.svd(R, full_matrices=False)\n",
        "        \n",
        "        self.P = P\n",
        "        self.S = np.diag(s)\n",
        "        self.Qh = Qh\n",
        "        \n",
        "        # latent factors of users (u_factors) and items (i_factors)\n",
        "        self.u_factors = np.dot(self.P, np.sqrt(self.S))\n",
        "        self.i_factors = np.dot(np.sqrt(self.S), self.Qh)\n",
        "    \n",
        "    def predict(self, userid, itemid):\n",
        "        \"\"\"\n",
        "        Make rating prediction for a given user on an item\n",
        "        \n",
        "        :param\n",
        "            - userid : user's id\n",
        "            - itemid : item's id\n",
        "            \n",
        "        :return\n",
        "            - r_hat : predicted rating\n",
        "        \"\"\"\n",
        "        # encode user and item ids\n",
        "        u = uencoder.transform([userid])[0]\n",
        "        i = iencoder.transform([itemid])[0]\n",
        "        \n",
        "        # the predicted rating is the dot product between the uth row \n",
        "        # of u_factors and the ith column of i_factors\n",
        "        r_hat = np.dot(self.u_factors[u,:], self.i_factors[:,i])\n",
        "        \n",
        "        # add the mean rating of user u to the predicted value\n",
        "        r_hat += self.umean[u]\n",
        "        \n",
        "        return r_hat\n",
        "        \n",
        "    \n",
        "    def recommend(self, userid):\n",
        "        \"\"\"\n",
        "        :param\n",
        "            - userid : user's id\n",
        "        \"\"\"\n",
        "        # encode user\n",
        "        u = uencoder.transform([userid])[0]\n",
        "        \n",
        "        # the dot product between the uth row of u_factors and i_factors returns\n",
        "        # the predicted value for user u on all items        \n",
        "        predictions = np.dot(self.u_factors[u,:], self.i_factors) + self.umean[u]\n",
        "        \n",
        "        # sort item ids in decreasing order of predictions\n",
        "        top_idx = np.flip(np.argsort(predictions))\n",
        "\n",
        "        # decode indices to get their corresponding itemids\n",
        "        top_items = iencoder.inverse_transform(top_idx)\n",
        "        \n",
        "        # sorted predictions\n",
        "        preds = predictions[top_idx]\n",
        "        \n",
        "        return top_items, preds"
      ],
      "metadata": {
        "id": "pg-jj8M9Sk0b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create our svd model\n",
        "svd = SVD(umean)\n",
        "\n",
        "# fit our model with normalized ratings\n",
        "svd.fit(R)"
      ],
      "metadata": {
        "id": "gj35spniSpKD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "EAuDLs-rSs2l",
        "outputId": "96febfd6-b75b-4114-a4ec-3df69d6acfcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userid  itemid  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"
            ],
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# user for which we make predictions\n",
        "userid = 1\n",
        "\n",
        "# list of items for which we are making predictions for user 1\n",
        "items = [1,3,6,47,50,70,101,110,151,157]\n",
        "\n",
        "# predictions\n",
        "for itemid in items:\n",
        "    r = svd.predict(userid=userid, itemid=itemid)\n",
        "    print('prediction for userid={} and itemid={} : {}'.format(userid, itemid, r))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afkeajSSSvzk",
        "outputId": "d38ebf89-03ba-49bb-ddd7-be999a6be942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prediction for userid=1 and itemid=1 : 3.9999999999999996\n",
            "prediction for userid=1 and itemid=3 : 4.0000000000000036\n",
            "prediction for userid=1 and itemid=6 : 3.9999999999999867\n",
            "prediction for userid=1 and itemid=47 : 5.0\n",
            "prediction for userid=1 and itemid=50 : 4.9999999999999964\n",
            "prediction for userid=1 and itemid=70 : 2.999999999999981\n",
            "prediction for userid=1 and itemid=101 : 5.000000000000006\n",
            "prediction for userid=1 and itemid=110 : 3.9999999999999862\n",
            "prediction for userid=1 and itemid=151 : 5.0000000000000115\n",
            "prediction for userid=1 and itemid=157 : 5.000000000000028\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "userid = 1\n",
        "\n",
        "# items sorted in decreasing order of predictions for user 1\n",
        "sorted_items, preds = svd.recommend(userid=userid)\n",
        "\n",
        "##\n",
        "# Now let's exclud from that sorted list items already purchased by the user\n",
        "##\n",
        "\n",
        "# list of items rated by the user\n",
        "uitems = ratings.loc[ratings.userid == userid].itemid.to_list()\n",
        "\n",
        "# remove from sorted_items items already in uitems and pick the top 30 ones\n",
        "# as recommendation list\n",
        "top30 = np.setdiff1d(sorted_items, uitems, assume_unique=True)[:30]\n",
        "\n",
        "# get corresponding predictions from the top30 items\n",
        "top30_idx = list(np.where(sorted_items == idx)[0][0] for idx in top30)\n",
        "top30_predictions = preds[top30_idx]\n",
        "\n",
        "# find corresponding movie titles\n",
        "zipped_top30 = list(zip(top30,top30_predictions))\n",
        "top30 = pd.DataFrame(zipped_top30, columns=['itemid','predictions'])\n",
        "List = pd.merge(top30, movies, on='itemid', how='inner')\n",
        "\n",
        "# show the list\n",
        "List"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "4mx3HvgsSy9s",
        "outputId": "c711937b-080d-4db4-a565-3bb45eee8472"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    itemid  predictions                                              title  \\\n",
              "0      148          5.0                   Awfully Big Adventure, An (1995)   \n",
              "1     6086          5.0                                 I, the Jury (1982)   \n",
              "2   136445          5.0                 George Carlin: Back in Town (1996)   \n",
              "3     6201          5.0                                   Lady Jane (1986)   \n",
              "4     2075          5.0                                    Mephisto (1981)   \n",
              "5     6192          5.0          Open Hearts (Elsker dig for evigt) (2002)   \n",
              "6   117531          5.0                                   Watermark (2014)   \n",
              "7   158398          5.0                              World of Glory (1991)   \n",
              "8     6021          5.0  American Friend, The (Amerikanische Freund, De...   \n",
              "9   136556          5.0       Kung Fu Panda: Secrets of the Masters (2011)   \n",
              "10  136447          5.0         George Carlin: You Are All Diseased (1999)   \n",
              "11  136503          5.0           Tom and Jerry: Shiver Me Whiskers (2006)   \n",
              "12  134095          5.0                                     My Love (2006)   \n",
              "13    3851          5.0                     I'm the One That I Want (2000)   \n",
              "14  136469          5.0           Larry David: Curb Your Enthusiasm (1999)   \n",
              "15  158882          5.0                                   All Yours (2016)   \n",
              "16  134004          5.0                                What Love Is (2007)   \n",
              "17   67618          5.0                             Strictly Sexual (2008)   \n",
              "18    3567          5.0                                  Bossa Nova (2000)   \n",
              "19  158027          5.0                  SORI: Voice from the Heart (2016)   \n",
              "20   59814          5.0                                  Ex Drummer (2007)   \n",
              "21    5745          5.0                           Four Seasons, The (1981)   \n",
              "22  118894          5.0                 Scooby-Doo! Abracadabra-Doo (2010)   \n",
              "23    5746          5.0                    Galaxy of Terror (Quest) (1981)   \n",
              "24  118834          5.0                  National Lampoon's Bag Boy (2007)   \n",
              "25    3940          5.0                  Slumber Party Massacre III (1990)   \n",
              "26   95311          5.0                                      Presto (2008)   \n",
              "27    3496          5.0                            Madame Sousatzka (1988)   \n",
              "28  156025          5.0              Ice Age: The Great Egg-Scapade (2016)   \n",
              "29    2196          5.0                                   Knock Off (1998)   \n",
              "\n",
              "                                 genres  \n",
              "0                                 Drama  \n",
              "1                  Crime|Drama|Thriller  \n",
              "2                                Comedy  \n",
              "3                         Drama|Romance  \n",
              "4                             Drama|War  \n",
              "5                               Romance  \n",
              "6                           Documentary  \n",
              "7                                Comedy  \n",
              "8          Crime|Drama|Mystery|Thriller  \n",
              "9                    Animation|Children  \n",
              "10                               Comedy  \n",
              "11            Animation|Children|Comedy  \n",
              "12                      Animation|Drama  \n",
              "13                               Comedy  \n",
              "14                               Comedy  \n",
              "15                 Comedy|Drama|Romance  \n",
              "16                       Comedy|Romance  \n",
              "17                 Comedy|Drama|Romance  \n",
              "18                 Comedy|Drama|Romance  \n",
              "19                         Drama|Sci-Fi  \n",
              "20            Comedy|Crime|Drama|Horror  \n",
              "21                         Comedy|Drama  \n",
              "22           Animation|Children|Mystery  \n",
              "23         Action|Horror|Mystery|Sci-Fi  \n",
              "24                               Comedy  \n",
              "25                               Horror  \n",
              "26    Animation|Children|Comedy|Fantasy  \n",
              "27                                Drama  \n",
              "28  Adventure|Animation|Children|Comedy  \n",
              "29                               Action  "
            ],
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              "      <th></th>\n",
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              "      <td>6086</td>\n",
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              "      <td>I, the Jury (1982)</td>\n",
              "      <td>Crime|Drama|Thriller</td>\n",
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              "      <th>2</th>\n",
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              "      <td>George Carlin: Back in Town (1996)</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6201</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Lady Jane (1986)</td>\n",
              "      <td>Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2075</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Mephisto (1981)</td>\n",
              "      <td>Drama|War</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6192</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Open Hearts (Elsker dig for evigt) (2002)</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>117531</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Watermark (2014)</td>\n",
              "      <td>Documentary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>158398</td>\n",
              "      <td>5.0</td>\n",
              "      <td>World of Glory (1991)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6021</td>\n",
              "      <td>5.0</td>\n",
              "      <td>American Friend, The (Amerikanische Freund, De...</td>\n",
              "      <td>Crime|Drama|Mystery|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>136556</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Kung Fu Panda: Secrets of the Masters (2011)</td>\n",
              "      <td>Animation|Children</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>136447</td>\n",
              "      <td>5.0</td>\n",
              "      <td>George Carlin: You Are All Diseased (1999)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>136503</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Tom and Jerry: Shiver Me Whiskers (2006)</td>\n",
              "      <td>Animation|Children|Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>134095</td>\n",
              "      <td>5.0</td>\n",
              "      <td>My Love (2006)</td>\n",
              "      <td>Animation|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3851</td>\n",
              "      <td>5.0</td>\n",
              "      <td>I'm the One That I Want (2000)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>136469</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Larry David: Curb Your Enthusiasm (1999)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>158882</td>\n",
              "      <td>5.0</td>\n",
              "      <td>All Yours (2016)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>134004</td>\n",
              "      <td>5.0</td>\n",
              "      <td>What Love Is (2007)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>67618</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Strictly Sexual (2008)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3567</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Bossa Nova (2000)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>158027</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SORI: Voice from the Heart (2016)</td>\n",
              "      <td>Drama|Sci-Fi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>59814</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Ex Drummer (2007)</td>\n",
              "      <td>Comedy|Crime|Drama|Horror</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5745</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Four Seasons, The (1981)</td>\n",
              "      <td>Comedy|Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>118894</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Scooby-Doo! Abracadabra-Doo (2010)</td>\n",
              "      <td>Animation|Children|Mystery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5746</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Galaxy of Terror (Quest) (1981)</td>\n",
              "      <td>Action|Horror|Mystery|Sci-Fi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>118834</td>\n",
              "      <td>5.0</td>\n",
              "      <td>National Lampoon's Bag Boy (2007)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>3940</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Slumber Party Massacre III (1990)</td>\n",
              "      <td>Horror</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>95311</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Presto (2008)</td>\n",
              "      <td>Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>3496</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Madame Sousatzka (1988)</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>156025</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Ice Age: The Great Egg-Scapade (2016)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2196</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Knock Off (1998)</td>\n",
              "      <td>Action</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f88aee2-ad57-41d4-8323-a35f0eaa0d92')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\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",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\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",
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              "\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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4f88aee2-ad57-41d4-8323-a35f0eaa0d92 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-4f88aee2-ad57-41d4-8323-a35f0eaa0d92');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gIPQfBk-S3p9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}