notebook6a21d4412b.ipynb

{
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python",
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      "mimetype": "text/x-python",
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        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "nvidiaTeslaT4",
      "dataSources": [
        {
          "sourceId": 9778699,
          "sourceType": "datasetVersion",
          "datasetId": 5990534
        },
        {
          "sourceId": 9779308,
          "sourceType": "datasetVersion",
          "datasetId": 5990924
        }
      ],
      "dockerImageVersionId": 30787,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": true
    },
    "colab": {
      "name": "notebook6a21d4412b",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ramyrahmeni/DeepLearning/blob/main/notebook6a21d4412b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "# IMPORTANT: SOME KAGGLE DATA SOURCES ARE PRIVATE\n",
        "# RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES.\n",
        "import kagglehub\n",
        "kagglehub.login()\n"
      ],
      "metadata": {
        "id": "wEC_nQ5uTSAV"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "source": [
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "ramyrahmeni_basedata_path = kagglehub.dataset_download('ramyrahmeni/basedata')\n",
        "ramyrahmeni_u1test_path = kagglehub.dataset_download('ramyrahmeni/u1test')\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "4VyZigzrTSAf"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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        "trusted": true,
        "execution": {
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          "iopub.execute_input": "2024-11-01T08:56:04.381624Z",
          "iopub.status.idle": "2024-11-01T08:56:05.393672Z",
          "shell.execute_reply.started": "2024-11-01T08:56:04.381572Z",
          "shell.execute_reply": "2024-11-01T08:56:05.392594Z"
        },
        "id": "5mtQr2BTTSAh",
        "outputId": "9200c74f-1ae6-492a-c376-f791aa1b81f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "/kaggle/input/basedata/u1.base\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Charger le fichier de données\n",
        "column_names = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
        "train_data = pd.read_csv('/kaggle/input/basedata/u1.base', sep='\\t', names=column_names)\n",
        "\n",
        "# Afficher un aperçu des données\n",
        "print(train_data.head())\n",
        "\n",
        "\n",
        "# Display the first few rows of the training data\n",
        "train_data.head()\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T08:56:07.750718Z",
          "iopub.execute_input": "2024-11-01T08:56:07.751825Z",
          "iopub.status.idle": "2024-11-01T08:56:07.846384Z",
          "shell.execute_reply.started": "2024-11-01T08:56:07.751748Z",
          "shell.execute_reply": "2024-11-01T08:56:07.845316Z"
        },
        "id": "-54pHdc7TSAk",
        "outputId": "9c47051b-d80e-4874-8f29-9eed2ef2cb3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "   user_id  movie_id  rating  timestamp\n0        1         1       5  874965758\n1        1         2       3  876893171\n2        1         3       4  878542960\n3        1         4       3  876893119\n4        1         5       3  889751712\n",
          "output_type": "stream"
        },
        {
          "execution_count": 2,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   user_id  movie_id  rating  timestamp\n0        1         1       5  874965758\n1        1         2       3  876893171\n2        1         3       4  878542960\n3        1         4       3  876893119\n4        1         5       3  889751712",
            "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>user_id</th>\n      <th>movie_id</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>5</td>\n      <td>874965758</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>876893171</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>878542960</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>4</td>\n      <td>3</td>\n      <td>876893119</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>5</td>\n      <td>3</td>\n      <td>889751712</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "vote_matrix = train_data.pivot(index='user_id', columns='movie_id', values='rating')\n",
        "vote_matrix"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T09:39:50.403659Z",
          "iopub.execute_input": "2024-11-01T09:39:50.404359Z",
          "iopub.status.idle": "2024-11-01T09:39:50.451931Z",
          "shell.execute_reply.started": "2024-11-01T09:39:50.40432Z",
          "shell.execute_reply": "2024-11-01T09:39:50.450935Z"
        },
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        "outputId": "9d9de790-0538-445b-f7a6-98c8e9f59f14"
      },
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "movie_id  1     2     3     4     5     6     7     8     9     10    ...  \\\nuser_id                                                               ...   \n1          5.0   3.0   4.0   3.0   3.0   NaN   4.0   1.0   5.0   NaN  ...   \n2          4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   2.0  ...   \n3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n939        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0   NaN  ...   \n940        NaN   NaN   NaN   2.0   NaN   NaN   4.0   5.0   3.0   NaN  ...   \n941        5.0   NaN   NaN   NaN   NaN   NaN   4.0   NaN   NaN   NaN  ...   \n942        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n943        NaN   5.0   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN  ...   \n\nmovie_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                               \n1          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n939        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n940        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n943        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[943 rows x 1650 columns]",
            "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>movie_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>5.0</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    <tr>\n      <th>2</th>\n      <td>4.0</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>2.0</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    <tr>\n      <th>3</th>\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      <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    <tr>\n      <th>4</th>\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      <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    <tr>\n      <th>5</th>\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      <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    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>939</th>\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>5.0</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    <tr>\n      <th>940</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>3.0</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    <tr>\n      <th>941</th>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</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    <tr>\n      <th>942</th>\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      <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    <tr>\n      <th>943</th>\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>NaN</td>\n      <td>NaN</td>\n      <td>3.0</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>943 rows × 1650 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import cupy as cp\n",
        "from tqdm import tqdm\n",
        "\n",
        "def pearson_similarity(user1, user2, min_common=7):\n",
        "    # Convert numpy arrays to cupy arrays\n",
        "    user1 = cp.asarray(user1)\n",
        "    user2 = cp.asarray(user2)\n",
        "\n",
        "    # Identify common (non-NaN) ratings\n",
        "    common_ratings = ~cp.isnan(user1) & ~cp.isnan(user2)\n",
        "    n_common = common_ratings.sum()\n",
        "\n",
        "    if n_common < min_common:\n",
        "        return cp.nan\n",
        "\n",
        "    # Compute Pearson correlation using common ratings only\n",
        "    return cp.corrcoef(user1[common_ratings], user2[common_ratings])[0, 1]\n",
        "\n",
        "n_users = vote_matrix.shape[0]\n",
        "similarity_matrix = cp.zeros((n_users, n_users))\n",
        "\n",
        "# Wrap the outer loop with tqdm to show progress\n",
        "for i in tqdm(range(n_users), desc=\"Calculating similarity matrix\"):\n",
        "    for j in range(i + 1, n_users):\n",
        "        similarity = pearson_similarity(vote_matrix.iloc[i].values, vote_matrix.iloc[j].values)\n",
        "        similarity_matrix[i, j] = similarity\n",
        "        similarity_matrix[j, i] = similarity\n",
        "\n",
        "similarity_matrix = cp.nan_to_num(similarity_matrix, nan=0)\n",
        "print(cp.asnumpy(similarity_matrix))  # Convert back to numpy if needed for display\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T08:56:12.156176Z",
          "iopub.execute_input": "2024-11-01T08:56:12.156542Z",
          "iopub.status.idle": "2024-11-01T09:03:38.654158Z",
          "shell.execute_reply.started": "2024-11-01T08:56:12.156506Z",
          "shell.execute_reply": "2024-11-01T09:03:38.653167Z"
        },
        "id": "faURbufxTSAn",
        "outputId": "e50750a9-ddc9-4037-e790-e3d0c256ad47"
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "Calculating similarity matrix: 100%|██████████| 943/943 [07:23<00:00,  2.13it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "[[ 0.          0.          0.         ...  0.67936622 -0.19348921\n   0.24303845]\n [ 0.          0.          0.         ...  0.          0.25943726\n   0.62749502]\n [ 0.          0.          0.         ...  0.          0.\n   0.        ]\n ...\n [ 0.67936622  0.          0.         ...  0.          0.\n   0.        ]\n [-0.19348921  0.25943726  0.         ...  0.          0.\n   0.27743263]\n [ 0.24303845  0.62749502  0.         ...  0.          0.27743263\n   0.        ]]\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "import cupy as cp\n",
        "from tqdm import tqdm\n",
        "\n",
        "def get_k_nearest_neighbors(user_index, similarity_matrix, k=5):\n",
        "    # Get similarities for the specified user\n",
        "    user_similarities = similarity_matrix[user_index]\n",
        "\n",
        "    # Eliminate negative similarities by setting them to 0\n",
        "    user_similarities[user_similarities < 0] = 0\n",
        "\n",
        "    # Get indices of sorted similarities in descending order\n",
        "    sorted_indices = cp.argsort(user_similarities)[::-1]\n",
        "\n",
        "    # Exclude the user itself and select top k neighbors\n",
        "    nearest_neighbors = [idx for idx in sorted_indices if idx != user_index][:k]\n",
        "\n",
        "    return nearest_neighbors\n",
        "\n",
        "# Example usage with progress bar\n",
        "k = 5  # Set the number of neighbors\n",
        "n_users = similarity_matrix.shape[0]\n",
        "all_neighbors = {}\n",
        "\n",
        "# Wrap the loop with tqdm to show progress\n",
        "for user_index in tqdm(range(n_users), desc=\"Finding k nearest neighbors\"):\n",
        "    all_neighbors[user_index] = get_k_nearest_neighbors(user_index, similarity_matrix, k)\n",
        "\n",
        "print(all_neighbors)  # Contains k-nearest neighbors for each user\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T09:04:53.691069Z",
          "iopub.execute_input": "2024-11-01T09:04:53.691922Z",
          "iopub.status.idle": "2024-11-01T09:05:59.64377Z",
          "shell.execute_reply.started": "2024-11-01T09:04:53.691882Z",
          "shell.execute_reply": "2024-11-01T09:05:59.642906Z"
        },
        "id": "yI0OAzbqTSAp",
        "outputId": "0219c5be-19a0-433d-cdf4-2a300e01b942"
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "Finding k nearest neighbors: 100%|██████████| 943/943 [01:05<00:00, 14.36it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
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array(220), array(191), array(359), array(317)], 917: [array(908), array(906), array(214), array(820), array(252)], 918: [array(161), array(586), array(291), array(375), array(650)], 919: [array(912), array(513), array(275), array(729), array(937)], 920: [array(450), array(347), array(784), array(82), array(512)], 921: [array(50), array(356), array(412), array(899), array(322)], 922: [array(479), array(21), array(266), array(328), array(874)], 923: [array(262), array(140), array(286), array(690), array(784)], 924: [array(615), array(176), array(525), array(373), array(404)], 925: [array(750), array(665), array(592), array(568), array(729)], 926: [array(276), array(347), array(480), array(643), array(611)], 927: [array(710), array(388), array(822), array(302), array(391)], 928: [array(234), array(120), array(421), array(314), array(485)], 929: [array(347), array(643), array(140), array(236), array(717)], 930: [array(160), array(558), array(256), array(889), array(268)], 931: [array(562), array(20), array(180), array(155), array(75)], 932: [array(1), array(226), array(134), array(137), array(580)], 933: [array(936), array(469), array(719), array(488), array(860)], 934: [array(151), array(658), array(387), array(858), array(345)], 935: [array(644), array(535), array(709), array(358), array(775)], 936: [array(59), array(933), array(666), array(6), array(852)], 937: [array(783), array(389), array(7), array(399), array(364)], 938: [array(727), array(487), array(408), array(931), array(878)], 939: [array(352), array(104), array(366), array(509), array(158)], 940: [array(892), array(212), array(755), array(293), array(394)], 941: [array(622), array(555), array(556), array(376), array(187)], 942: [array(52), array(676), array(743), array(799), array(115)]}\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_rating(user_index, item_index, vote_matrix, similarity_matrix, neighbors):\n",
        "    # Filter neighbors who have rated the item\n",
        "    neighbors_who_rated = [n for n in neighbors if not np.isnan(vote_matrix.iloc[n, item_index])]\n",
        "\n",
        "    if len(neighbors_who_rated) == 0:\n",
        "        return np.nan  # Return NaN if no neighbors have rated the item\n",
        "\n",
        "    # Calculate weighted average\n",
        "    num = sum(similarity_matrix[user_index, n] * vote_matrix.iloc[n, item_index] for n in neighbors_who_rated)\n",
        "    den = sum(similarity_matrix[user_index, n] for n in neighbors_who_rated)\n",
        "\n",
        "    return num / den if den != 0 else np.nan\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T09:35:11.085006Z",
          "iopub.execute_input": "2024-11-01T09:35:11.085386Z",
          "iopub.status.idle": "2024-11-01T09:35:11.09203Z",
          "shell.execute_reply.started": "2024-11-01T09:35:11.085352Z",
          "shell.execute_reply": "2024-11-01T09:35:11.091012Z"
        },
        "id": "1gNFW4fzTSAq"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the user index for which to generate predictions\n",
        "user_index = 3  # Replace with the desired user index\n",
        "\n",
        "# Retrieve the k nearest neighbors for the specified user and cast to integers\n",
        "neighbors = [int(n) for n in get_k_nearest_neighbors(user_index, similarity_matrix, k=5)]\n",
        "\n",
        "# Predict ratings for the specified user\n",
        "user_predictions = predict_rating(user_index, ,vote_matrix, similarity_matrix, neighbors)\n",
        "\n",
        "print(user_predictions)  # Contains predicted ratings for this user's unknown items\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T10:24:41.086738Z",
          "iopub.status.idle": "2024-11-01T10:24:41.087148Z",
          "shell.execute_reply.started": "2024-11-01T10:24:41.086956Z",
          "shell.execute_reply": "2024-11-01T10:24:41.086976Z"
        },
        "id": "Ss_j3kd9TSAs"
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "# Define the function to fill NaN values in the vote matrix with progress tracking\n",
        "def fill_missing_ratings(vote_matrix, similarity_matrix, k=5):\n",
        "    filled_matrix = vote_matrix.copy()  # Create a copy to avoid modifying the original matrix\n",
        "\n",
        "    # Progress bar for users\n",
        "    for user_index in tqdm(range(vote_matrix.shape[0]), desc=\"Filling missing ratings by user\"):\n",
        "        # Progress bar for items within each user\n",
        "        for item_index in range(vote_matrix.shape[1]):\n",
        "            # Check if the current value is NaN\n",
        "            if np.isnan(vote_matrix.iloc[user_index, item_index]):\n",
        "                # Get the k nearest neighbors for the specified user\n",
        "                neighbors = [int(n) for n in all_neighbors[user_index]]\n",
        "\n",
        "                # Predict the rating for the missing value\n",
        "                predicted_rating = predict_rating(user_index, item_index, vote_matrix, similarity_matrix, neighbors)\n",
        "\n",
        "                # Update the missing value with the predicted rating if a prediction is available\n",
        "                if not np.isnan(predicted_rating):\n",
        "                    filled_matrix.iat[user_index, item_index] = predicted_rating\n",
        "\n",
        "    return filled_matrix\n",
        "\n",
        "# Use the function to fill NaN values in the vote_matrix with progress tracking\n",
        "filled_vote_matrix = fill_missing_ratings(vote_matrix, similarity_matrix, k=5)\n",
        "\n",
        "print(filled_vote_matrix)  # The vote matrix with NaN values filled by predicted ratings\n"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T10:25:35.572577Z",
          "iopub.execute_input": "2024-11-01T10:25:35.572991Z",
          "iopub.status.idle": "2024-11-01T10:35:34.22391Z",
          "shell.execute_reply.started": "2024-11-01T10:25:35.572953Z",
          "shell.execute_reply": "2024-11-01T10:35:34.22287Z"
        },
        "id": "mbvFU4dTTSAt",
        "outputId": "39037579-a090-44d1-d15d-a9075977b834"
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "Filling missing ratings by user: 100%|██████████| 943/943 [09:58<00:00,  1.58it/s]",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "movie_id      1     2     3         4     5     6         7     8     \\\nuser_id                                                                \n1         5.000000   3.0   4.0  3.000000   3.0   NaN  4.000000   1.0   \n2         4.000000   2.0   NaN  3.517010   3.0   NaN  4.000000   NaN   \n3         3.936395   NaN   4.0       NaN   5.0   NaN  2.996848   NaN   \n4         4.000000   3.0   4.0  2.299524   4.0   NaN  4.320614   5.0   \n5         4.286085   NaN   NaN       NaN   2.0   NaN  4.502677   4.0   \n...            ...   ...   ...       ...   ...   ...       ...   ...   \n939       3.629760   NaN   NaN       NaN   NaN   NaN  4.000000   3.0   \n940            NaN   NaN   NaN  2.000000   4.0   NaN  4.000000   5.0   \n941       5.000000   NaN   1.0       NaN   NaN   NaN  4.000000   4.0   \n942            NaN   NaN   NaN       NaN   4.0   NaN  4.000000   5.0   \n943       4.000000   5.0   NaN       NaN   NaN   NaN  3.483513   NaN   \n\nmovie_id      9     10    ...  1673  1674  1675  1676  1677  1678  1679  1680  \\\nuser_id                   ...                                                   \n1         5.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n2         3.000000   2.0  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n3         2.666115   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n4         4.659302   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n5         5.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n...            ...   ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   \n939       5.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n940       3.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n941       2.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n942            NaN   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n943       3.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n\nmovie_id  1681  1682  \nuser_id               \n1          NaN   NaN  \n2          NaN   NaN  \n3          NaN   NaN  \n4          NaN   NaN  \n5          NaN   NaN  \n...        ...   ...  \n939        NaN   NaN  \n940        NaN   NaN  \n941        NaN   NaN  \n942        NaN   NaN  \n943        NaN   NaN  \n\n[943 rows x 1650 columns]\n",
          "output_type": "stream"
        },
        {
          "name": "stderr",
          "text": "\n",
          "output_type": "stream"
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "filled_vote_matrix"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T10:36:00.299588Z",
          "iopub.execute_input": "2024-11-01T10:36:00.30069Z",
          "iopub.status.idle": "2024-11-01T10:36:00.334937Z",
          "shell.execute_reply.started": "2024-11-01T10:36:00.300645Z",
          "shell.execute_reply": "2024-11-01T10:36:00.334021Z"
        },
        "id": "pOaeuujYTSAv",
        "outputId": "d382ae8f-d6b7-4491-db8c-715d476f2a62"
      },
      "outputs": [
        {
          "execution_count": 39,
          "output_type": "execute_result",
          "data": {
            "text/plain": "movie_id      1     2     3         4     5     6         7     8     \\\nuser_id                                                                \n1         5.000000   3.0   4.0  3.000000   3.0   NaN  4.000000   1.0   \n2         4.000000   2.0   NaN  3.517010   3.0   NaN  4.000000   NaN   \n3         3.936395   NaN   4.0       NaN   5.0   NaN  2.996848   NaN   \n4         4.000000   3.0   4.0  2.299524   4.0   NaN  4.320614   5.0   \n5         4.286085   NaN   NaN       NaN   2.0   NaN  4.502677   4.0   \n...            ...   ...   ...       ...   ...   ...       ...   ...   \n939       3.629760   NaN   NaN       NaN   NaN   NaN  4.000000   3.0   \n940            NaN   NaN   NaN  2.000000   4.0   NaN  4.000000   5.0   \n941       5.000000   NaN   1.0       NaN   NaN   NaN  4.000000   4.0   \n942            NaN   NaN   NaN       NaN   4.0   NaN  4.000000   5.0   \n943       4.000000   5.0   NaN       NaN   NaN   NaN  3.483513   NaN   \n\nmovie_id      9     10    ...  1673  1674  1675  1676  1677  1678  1679  1680  \\\nuser_id                   ...                                                   \n1         5.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n2         3.000000   2.0  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n3         2.666115   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n4         4.659302   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n5         5.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n...            ...   ...  ...   ...   ...   ...   ...   ...   ...   ...   ...   \n939       5.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n940       3.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n941       2.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n942            NaN   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n943       3.000000   NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n\nmovie_id  1681  1682  \nuser_id               \n1          NaN   NaN  \n2          NaN   NaN  \n3          NaN   NaN  \n4          NaN   NaN  \n5          NaN   NaN  \n...        ...   ...  \n939        NaN   NaN  \n940        NaN   NaN  \n941        NaN   NaN  \n942        NaN   NaN  \n943        NaN   NaN  \n\n[943 rows x 1650 columns]",
            "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>movie_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      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<td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>939</th>\n      <td>3.629760</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>3.0</td>\n      <td>5.000000</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    <tr>\n      <th>940</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.000000</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>5.0</td>\n      <td>3.000000</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    <tr>\n      <th>941</th>\n      <td>5.000000</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>4.0</td>\n      <td>2.000000</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    <tr>\n      <th>942</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>4.000000</td>\n      <td>5.0</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    <tr>\n      <th>943</th>\n      <td>4.000000</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.483513</td>\n      <td>NaN</td>\n      <td>3.000000</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>943 rows × 1650 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [
        "vote_matrix"
      ],
      "metadata": {
        "trusted": true,
        "execution": {
          "iopub.status.busy": "2024-11-01T10:36:07.62057Z",
          "iopub.execute_input": "2024-11-01T10:36:07.621216Z",
          "iopub.status.idle": "2024-11-01T10:36:07.656102Z",
          "shell.execute_reply.started": "2024-11-01T10:36:07.621175Z",
          "shell.execute_reply": "2024-11-01T10:36:07.655188Z"
        },
        "id": "BfDEGcpiTSAw",
        "outputId": "8c55e8e4-3df2-4c29-e794-38ebb5ef12ee"
      },
      "outputs": [
        {
          "execution_count": 40,
          "output_type": "execute_result",
          "data": {
            "text/plain": "movie_id  1     2     3     4     5     6     7     8     9     10    ...  \\\nuser_id                                                               ...   \n1          5.0   3.0   4.0   3.0   3.0   NaN   4.0   1.0   5.0   NaN  ...   \n2          4.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   2.0  ...   \n3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n939        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0   NaN  ...   \n940        NaN   NaN   NaN   2.0   NaN   NaN   4.0   5.0   3.0   NaN  ...   \n941        5.0   NaN   NaN   NaN   NaN   NaN   4.0   NaN   NaN   NaN  ...   \n942        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n943        NaN   5.0   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN  ...   \n\nmovie_id  1673  1674  1675  1676  1677  1678  1679  1680  1681  1682  \nuser_id                                                               \n1          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n939        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n940        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n941        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n942        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n943        NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n\n[943 rows x 1650 columns]",
            "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>movie_id</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>1673</th>\n      <th>1674</th>\n      <th>1675</th>\n      <th>1676</th>\n      <th>1677</th>\n      <th>1678</th>\n      <th>1679</th>\n      <th>1680</th>\n      <th>1681</th>\n      <th>1682</th>\n    </tr>\n    <tr>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>5.0</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    <tr>\n      <th>2</th>\n      <td>4.0</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>2.0</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    <tr>\n      <th>3</th>\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      <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    <tr>\n      <th>4</th>\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      <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    <tr>\n      <th>5</th>\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      <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    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>939</th>\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>5.0</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    <tr>\n      <th>940</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>3.0</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    <tr>\n      <th>941</th>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</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    <tr>\n      <th>942</th>\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      <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    <tr>\n      <th>943</th>\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>NaN</td>\n      <td>NaN</td>\n      <td>3.0</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>943 rows × 1650 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "trusted": true,
        "id": "clqIMX2mTSAx"
      },
      "outputs": [],
      "execution_count": null
    }
  ]
}