Recommendation_System_SlopeOne.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from surprise import Reader, Dataset\n",
    "from surprise import SlopeOne\n",
    "os.chdir('/Recommendation system of films/Data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('ratings.csv', delimiter=',')\n",
    "df = df.drop(['timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>326.127564</td>\n",
       "      <td>19435.295718</td>\n",
       "      <td>3.501557</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>2991.000000</td>\n",
       "      <td>3.500000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>610.000000</td>\n",
       "      <td>193609.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              userId        movieId         rating\n",
       "count  100836.000000  100836.000000  100836.000000\n",
       "mean      326.127564   19435.295718       3.501557\n",
       "std       182.618491   35530.987199       1.042529\n",
       "min         1.000000       1.000000       0.500000\n",
       "25%       177.000000    1199.000000       3.000000\n",
       "50%       325.000000    2991.000000       3.500000\n",
       "75%       477.000000    8122.000000       4.000000\n",
       "max       610.000000  193609.000000       5.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100836 entries, 0 to 100835\n",
      "Data columns (total 3 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",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimav\\.conda\\envs\\my-rdkit-env\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rating', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(df['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=userId<br>rating=%{x}<br>value=%{y}<extra></extra>",
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          "text": "value"
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     "metadata": {},
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   ],
   "source": [
    "real=df.groupby(['userId'])['rating'].count()\n",
    "real=pd.DataFrame(real)\n",
    "fig = px.line(real)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['userId'] = df['userId'].astype(str)\n",
    "df['movieId'] = df['movieId'].astype(str)\n",
    "df['userId'] = 'person_'+df['userId'].astype(str)\n",
    "df['movieId'] = 'movie_'+df['movieId'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new=df.copy()\n",
    "df=df.rename(columns={\"movieId\": \"userId/movieId\"})\n",
    "df = df.pivot_table(index=['userId'], columns='userId/movieId', values='rating', aggfunc=np.sum).reset_index()\n",
    "df.index=df['userId'].values\n",
    "df=df.drop(['userId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 5.0\n"
     ]
    }
   ],
   "source": [
    "copy = df_new.copy()\n",
    "df_new = df.copy()\n",
    "df = copy.copy()\n",
    "minimum_rating = min(df['rating'].values)\n",
    "maximum_rating = max(df['rating'].values)\n",
    "print(minimum_rating,maximum_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader(rating_scale=(minimum_rating,maximum_rating))\n",
    "data = Dataset.load_from_df(df[['userId', 'movieId', 'rating']], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dimav\\AppData\\Local\\Temp/ipykernel_10656/1063358168.py:2: DeprecationWarning:\n",
      "\n",
      "`np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.slope_one.SlopeOne at 0x2292a20f760>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope = SlopeOne()\n",
    "slope.fit(data.build_full_trainset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.9882</td>\n",
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       "      <td>3.3459</td>\n",
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       "      <td>2.7754</td>\n",
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       "      <td>3.4677</td>\n",
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       "      <td>4.1899</td>\n",
       "      <td>3.4677</td>\n",
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       "      <td>4.0240</td>\n",
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       "      <td>3.0982</td>\n",
       "      <td>3.8718</td>\n",
       "      <td>3.7142</td>\n",
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       "      <td>3.4242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_99</th>\n",
       "      <td>4.7059</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>3.6469</td>\n",
       "      <td>4.3679</td>\n",
       "      <td>3.8263</td>\n",
       "      <td>3.1115</td>\n",
       "      <td>3.3679</td>\n",
       "      <td>3.8679</td>\n",
       "      <td>2.9426</td>\n",
       "      <td>3.8679</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2846</td>\n",
       "      <td>2.5346</td>\n",
       "      <td>3.1580</td>\n",
       "      <td>4.6638</td>\n",
       "      <td>3.6861</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>3.7320</td>\n",
       "      <td>3.0636</td>\n",
       "      <td>4.3798</td>\n",
       "      <td>3.6861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 9724 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId/movieId  movie_1  movie_10  movie_100  movie_100044  movie_100068  \\\n",
       "person_1         4.0000    4.2665     3.5419        4.7950        4.0298   \n",
       "person_10        3.7932    3.4834     2.3127        3.8600        2.5331   \n",
       "person_100       4.2751    3.7771     3.1128        4.6126        3.7897   \n",
       "person_101       3.9197    3.5873     2.8862        4.0240        3.1343   \n",
       "person_102       3.9090    3.3154     2.8391        3.7460        3.2009   \n",
       "...                 ...       ...        ...           ...           ...   \n",
       "person_95        4.3654    4.0207     3.2481        4.7292        3.6030   \n",
       "person_96        5.0000    3.5517     2.8445        4.5513        3.5151   \n",
       "person_97        4.6108    4.3289     3.7668        4.8373        4.1528   \n",
       "person_98        4.5000    3.8381     2.8867        4.3184        2.9130   \n",
       "person_99        4.7059    4.0000     3.6469        4.3679        3.8263   \n",
       "\n",
       "userId/movieId  movie_100083  movie_100106  movie_100159  movie_100163  \\\n",
       "person_1              3.5708        3.7950        4.2070        3.1950   \n",
       "person_10             2.6999        2.8600        3.4484        2.3497   \n",
       "person_100            3.1162        3.6126        4.0494        2.9882   \n",
       "person_101            2.9484        3.0240        3.7927        2.4842   \n",
       "person_102            2.3268        2.7460        3.1071        2.4033   \n",
       "...                      ...           ...           ...           ...   \n",
       "person_95             3.3097        3.7292        3.9739        2.7553   \n",
       "person_96             2.9065        3.5513        3.5096        2.6335   \n",
       "person_97             3.8254        3.8373        4.6759        3.2735   \n",
       "person_98             3.4561        3.3184        3.9539        2.8841   \n",
       "person_99             3.1115        3.3679        3.8679        2.9426   \n",
       "\n",
       "userId/movieId  movie_100194  ...  movie_99750  movie_99764  movie_998  \\\n",
       "person_1              5.0000  ...       3.5027       4.0773     3.1887   \n",
       "person_10             4.2435  ...       2.3952       4.1566     3.6883   \n",
       "person_100            4.6732  ...       3.3459       3.2107     2.7754   \n",
       "person_101            4.0018  ...       2.9922       3.0574     2.9397   \n",
       "person_102            4.1071  ...       2.5571       2.8571     2.5524   \n",
       "...                      ...  ...          ...          ...        ...   \n",
       "person_95             5.0000  ...       3.3089       4.1339     2.9411   \n",
       "person_96             5.0000  ...       3.0939       3.8156     2.3312   \n",
       "person_97             5.0000  ...       3.4444       4.1111     3.5126   \n",
       "person_98             5.0000  ...       3.0510       5.0000     3.6422   \n",
       "person_99             3.8679  ...       3.2846       2.5346     3.1580   \n",
       "\n",
       "userId/movieId  movie_99813  movie_99846  movie_99853  movie_999  movie_99910  \\\n",
       "person_1             4.7391       3.7333       5.0000     4.0398       3.3216   \n",
       "person_10            4.1068       3.1692       5.0000     3.1029       2.3889   \n",
       "person_100           4.6255       3.4677       5.0000     3.6379       3.2844   \n",
       "person_101           3.9080       3.0018       5.0000     3.2629       2.7717   \n",
       "person_102           3.9716       3.1264       5.0000     3.0771       2.4454   \n",
       "...                     ...          ...          ...        ...          ...   \n",
       "person_95            4.4963       3.7142       5.0000     3.7438       3.0982   \n",
       "person_96            4.0985       3.1488       5.0000     3.4180       2.7664   \n",
       "person_97            4.8876       3.9875       4.6944     4.2156       3.3909   \n",
       "person_98            4.5629       3.4242       5.0000     3.4326       2.8773   \n",
       "person_99            4.6638       3.6861       5.0000     3.7320       3.0636   \n",
       "\n",
       "userId/movieId  movie_99917  movie_99992  \n",
       "person_1             4.2505       3.7333  \n",
       "person_10            3.2690       3.1692  \n",
       "person_100           4.1899       3.4677  \n",
       "person_101           3.4849       3.0018  \n",
       "person_102           3.5500       3.1264  \n",
       "...                     ...          ...  \n",
       "person_95            3.8718       3.7142  \n",
       "person_96            3.5540       3.1488  \n",
       "person_97            4.2449       3.9875  \n",
       "person_98            3.8154       3.4242  \n",
       "person_99            4.3798       3.6861  \n",
       "\n",
       "[610 rows x 9724 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slope_one_new=df_new.copy()\n",
    "for user in df_new.index:\n",
    "    for movie in df_new.columns:\n",
    "        if str(df_slope_one_new.loc[user, movie])=='nan':\n",
    "            df_slope_one_new.at[user, movie] = round(slope.predict(user, movie).est,4)\n",
    "df_slope_one_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.rename(columns={\"movieId\": \"userId/movieId\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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>userId/movieId</th>\n",
       "      <th>movie_1</th>\n",
       "      <th>movie_10</th>\n",
       "      <th>movie_100</th>\n",
       "      <th>movie_100044</th>\n",
       "      <th>movie_100068</th>\n",
       "      <th>movie_100083</th>\n",
       "      <th>movie_100106</th>\n",
       "      <th>movie_100159</th>\n",
       "      <th>movie_100163</th>\n",
       "      <th>movie_100194</th>\n",
       "      <th>...</th>\n",
       "      <th>movie_99750</th>\n",
       "      <th>movie_99764</th>\n",
       "      <th>movie_998</th>\n",
       "      <th>movie_99813</th>\n",
       "      <th>movie_99846</th>\n",
       "      <th>movie_99853</th>\n",
       "      <th>movie_999</th>\n",
       "      <th>movie_99910</th>\n",
       "      <th>movie_99917</th>\n",
       "      <th>movie_99992</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>person_1</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_10</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>person_100</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>person_101</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",
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       "      <th>person_102</th>\n",
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       "      <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",
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       "      <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",
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       "    <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>person_95</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",
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       "      <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>person_96</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>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>person_97</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>person_98</th>\n",
       "      <td>4.5</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>person_99</th>\n",
       "      <td>NaN</td>\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>...</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>"
      ],
      "text/plain": [
       "userId/movieId  movie_1  movie_10  movie_100  movie_100044  movie_100068  \\\n",
       "person_1            4.0       NaN        NaN           NaN           NaN   \n",
       "person_10           NaN       NaN        NaN           NaN           NaN   \n",
       "person_100          NaN       NaN        NaN           NaN           NaN   \n",
       "person_101          NaN       NaN        NaN           NaN           NaN   \n",
       "person_102          NaN       NaN        NaN           NaN           NaN   \n",
       "...                 ...       ...        ...           ...           ...   \n",
       "person_95           NaN       NaN        NaN           NaN           NaN   \n",
       "person_96           5.0       NaN        NaN           NaN           NaN   \n",
       "person_97           NaN       NaN        NaN           NaN           NaN   \n",
       "person_98           4.5       NaN        NaN           NaN           NaN   \n",
       "person_99           NaN       4.0        NaN           NaN           NaN   \n",
       "\n",
       "userId/movieId  movie_100083  movie_100106  movie_100159  movie_100163  \\\n",
       "person_1                 NaN           NaN           NaN           NaN   \n",
       "person_10                NaN           NaN           NaN           NaN   \n",
       "person_100               NaN           NaN           NaN           NaN   \n",
       "person_101               NaN           NaN           NaN           NaN   \n",
       "person_102               NaN           NaN           NaN           NaN   \n",
       "...                      ...           ...           ...           ...   \n",
       "person_95                NaN           NaN           NaN           NaN   \n",
       "person_96                NaN           NaN           NaN           NaN   \n",
       "person_97                NaN           NaN           NaN           NaN   \n",
       "person_98                NaN           NaN           NaN           NaN   \n",
       "person_99                NaN           NaN           NaN           NaN   \n",
       "\n",
       "userId/movieId  movie_100194  ...  movie_99750  movie_99764  movie_998  \\\n",
       "person_1                 NaN  ...          NaN          NaN        NaN   \n",
       "person_10                NaN  ...          NaN          NaN        NaN   \n",
       "person_100               NaN  ...          NaN          NaN        NaN   \n",
       "person_101               NaN  ...          NaN          NaN        NaN   \n",
       "person_102               NaN  ...          NaN          NaN        NaN   \n",
       "...                      ...  ...          ...          ...        ...   \n",
       "person_95                NaN  ...          NaN          NaN        NaN   \n",
       "person_96                NaN  ...          NaN          NaN        NaN   \n",
       "person_97                NaN  ...          NaN          NaN        NaN   \n",
       "person_98                NaN  ...          NaN          NaN        NaN   \n",
       "person_99                NaN  ...          NaN          NaN        NaN   \n",
       "\n",
       "userId/movieId  movie_99813  movie_99846  movie_99853  movie_999  movie_99910  \\\n",
       "person_1                NaN          NaN          NaN        NaN          NaN   \n",
       "person_10               NaN          NaN          NaN        NaN          NaN   \n",
       "person_100              NaN          NaN          NaN        NaN          NaN   \n",
       "person_101              NaN          NaN          NaN        NaN          NaN   \n",
       "person_102              NaN          NaN          NaN        NaN          NaN   \n",
       "...                     ...          ...          ...        ...          ...   \n",
       "person_95               NaN          NaN          NaN        NaN          NaN   \n",
       "person_96               NaN          NaN          NaN        NaN          NaN   \n",
       "person_97               NaN          NaN          NaN        NaN          NaN   \n",
       "person_98               NaN          NaN          NaN        NaN          NaN   \n",
       "person_99               NaN          NaN          NaN        NaN          NaN   \n",
       "\n",
       "userId/movieId  movie_99917  movie_99992  \n",
       "person_1                NaN          NaN  \n",
       "person_10               NaN          NaN  \n",
       "person_100              NaN          NaN  \n",
       "person_101              NaN          NaN  \n",
       "person_102              NaN          NaN  \n",
       "...                     ...          ...  \n",
       "person_95               NaN          NaN  \n",
       "person_96               NaN          NaN  \n",
       "person_97               NaN          NaN  \n",
       "person_98               NaN          NaN  \n",
       "person_99               NaN          NaN  \n",
       "\n",
       "[610 rows x 9724 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.pivot_table(index=['userId'], columns='userId/movieId', values='rating', aggfunc=np.sum).reset_index()\n",
    "df.index=df['userId'].values\n",
    "df=df.drop(['userId'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def slope_one(target_surname, df):\n",
    "    my_values = df.loc[target_surname,:].copy()\n",
    "\n",
    "    film_nas = my_values[my_values.astype(str)=='nan'].index\n",
    "    film_nonas = my_values[my_values.astype(str)!='nan'].index\n",
    "    my_values_nonas = pd.DataFrame(my_values[my_values.astype(str)!='nan']).reset_index()\n",
    "    my_values_nonas = my_values_nonas.rename(columns={\"userId/movieId\": \"movie_id\", target_surname: \"rating\"})\n",
    "\n",
    "    dfc = pd.DataFrame(df.columns).copy()\n",
    "    i_nas = dfc[dfc['userId/movieId'].isin(film_nas)].index\n",
    "    i_nonas = dfc[dfc['userId/movieId'].isin(film_nonas)].index\n",
    "\n",
    "    df = df.drop([target_surname], axis=0)\n",
    "    my_values = my_values.values.tolist()\n",
    "    l_board=0.5\n",
    "    u_board=5.0\n",
    "\n",
    "    pred_nas=[]\n",
    "\n",
    "    for ina in i_nas:\n",
    "        film_na = df.columns[ina]\n",
    "        indexes = df[df[film_na].astype(str)!='nan'].index\n",
    "\n",
    "        df_copy=df.loc[indexes,:].copy()\n",
    "        df_copy0=pd.DataFrame(df_copy[film_na].copy()).reset_index()\n",
    "        df_copy0=df_copy0.rename(columns={\"index\": \"person_id\", film_na: \"rating_na\"})\n",
    "        df_copy=df_copy[film_nonas].copy().reset_index()\n",
    "        df_copy=df_copy.rename(columns={\"index\": \"person_id\"})\n",
    "\n",
    "        lc = len(df_copy.columns)\n",
    "        df_copy = pd.melt(df_copy, id_vars=['person_id'], value_vars = df_copy.columns[1:lc],\n",
    "                        var_name='movie_id', value_name='rating_nona')\n",
    "        df_copy = df_copy[df_copy['rating_nona'].astype(str)!='nan'].copy()\n",
    "        df_copy.reset_index(drop=True, inplace=True)\n",
    "\n",
    "        df_copy=df_copy.merge(df_copy0, how='left', on=['person_id'])\n",
    "        df_copy['dev']=df_copy['rating_na']-df_copy['rating_nona']\n",
    "        df_copy=df_copy.drop(['rating_na','rating_nona'], axis=1)\n",
    "\n",
    "        df_copy=df_copy.groupby(['movie_id'])['dev'].mean().reset_index()\n",
    "\n",
    "        df_copy=df_copy.merge(my_values_nonas, how='left', on='movie_id')\n",
    "        df_copy['final_rating'] = df_copy['rating'] + df_copy['dev']\n",
    "        df_copy = df_copy.drop(['rating','dev'], axis=1)\n",
    "\n",
    "        pred = round(df_copy['final_rating'].mean(),4)\n",
    "\n",
    "        if str(pred)=='nan':\n",
    "            pred=0.5\n",
    "        elif pred<l_board:\n",
    "            pred=0.5\n",
    "        elif pred>u_board:\n",
    "            pred=5\n",
    "\n",
    "        pred_nas.append(pred)\n",
    "\n",
    "    my_values_pred=my_values.copy()\n",
    "    for i in range(len(pred_nas)):\n",
    "        my_values_pred[i_nas[i]]=pred_nas[i]\n",
    "\n",
    "    df_new = pd.DataFrame([my_values_pred])\n",
    "    df_new.columns = df.columns\n",
    "    df_new.index = [(target_surname+' (slope one)')]\n",
    "    df_new = pd.concat([df, df_new], axis=0)\n",
    "    \n",
    "    return(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration  1  of  5  --> COMPLETE\n",
      "Iteration  2  of  5  --> COMPLETE\n",
      "Iteration  3  of  5  --> COMPLETE\n",
      "Iteration  4  of  5  --> COMPLETE\n",
      "Iteration  5  of  5  --> COMPLETE\n"
     ]
    }
   ],
   "source": [
    "df_slope_one=df.copy()\n",
    "for k, target_surname in enumerate(df.index[0:5]):\n",
    "    df_slope_one=slope_one(target_surname=target_surname, df=df_slope_one)\n",
    "    print('Iteration ',(k+1),' of ',len(df.index[0:5]),' --> COMPLETE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_slope_one_small = df_slope_one.loc[\n",
    "                        df_slope_one.index[\n",
    "                            (df_slope_one.shape[0]-5):\n",
    "                            df_slope_one.shape[0]\n",
    "                        ],\n",
    "                    ].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>userId/movieId</th>\n",
       "      <th>movie_1</th>\n",
       "      <th>movie_10</th>\n",
       "      <th>movie_100</th>\n",
       "      <th>movie_100044</th>\n",
       "      <th>movie_100068</th>\n",
       "      <th>movie_100083</th>\n",
       "      <th>movie_100106</th>\n",
       "      <th>movie_100159</th>\n",
       "      <th>movie_100163</th>\n",
       "      <th>movie_100194</th>\n",
       "      <th>...</th>\n",
       "      <th>movie_99750</th>\n",
       "      <th>movie_99764</th>\n",
       "      <th>movie_998</th>\n",
       "      <th>movie_99813</th>\n",
       "      <th>movie_99846</th>\n",
       "      <th>movie_99853</th>\n",
       "      <th>movie_999</th>\n",
       "      <th>movie_99910</th>\n",
       "      <th>movie_99917</th>\n",
       "      <th>movie_99992</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>person_1 (slope one)</th>\n",
       "      <td>4.0000</td>\n",
       "      <td>4.3089</td>\n",
       "      <td>3.3861</td>\n",
       "      <td>4.6429</td>\n",
       "      <td>4.2644</td>\n",
       "      <td>3.7036</td>\n",
       "      <td>4.1429</td>\n",
       "      <td>4.6304</td>\n",
       "      <td>3.3701</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.8125</td>\n",
       "      <td>4.4180</td>\n",
       "      <td>3.1623</td>\n",
       "      <td>4.6060</td>\n",
       "      <td>3.7050</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>4.0032</td>\n",
       "      <td>3.4810</td>\n",
       "      <td>4.0299</td>\n",
       "      <td>3.7050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_10 (slope one)</th>\n",
       "      <td>3.6111</td>\n",
       "      <td>3.4040</td>\n",
       "      <td>2.3730</td>\n",
       "      <td>3.6877</td>\n",
       "      <td>3.2415</td>\n",
       "      <td>2.7370</td>\n",
       "      <td>3.1877</td>\n",
       "      <td>3.7164</td>\n",
       "      <td>2.4891</td>\n",
       "      <td>4.1055</td>\n",
       "      <td>...</td>\n",
       "      <td>2.8453</td>\n",
       "      <td>3.6131</td>\n",
       "      <td>2.3079</td>\n",
       "      <td>3.8104</td>\n",
       "      <td>2.7976</td>\n",
       "      <td>4.0942</td>\n",
       "      <td>3.0303</td>\n",
       "      <td>2.5679</td>\n",
       "      <td>3.1365</td>\n",
       "      <td>2.7976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_100 (slope one)</th>\n",
       "      <td>4.2216</td>\n",
       "      <td>3.8416</td>\n",
       "      <td>2.9830</td>\n",
       "      <td>4.2965</td>\n",
       "      <td>3.9074</td>\n",
       "      <td>3.3346</td>\n",
       "      <td>3.7965</td>\n",
       "      <td>4.3099</td>\n",
       "      <td>3.0537</td>\n",
       "      <td>4.6995</td>\n",
       "      <td>...</td>\n",
       "      <td>3.4672</td>\n",
       "      <td>4.1071</td>\n",
       "      <td>2.8331</td>\n",
       "      <td>4.3650</td>\n",
       "      <td>3.3777</td>\n",
       "      <td>4.7402</td>\n",
       "      <td>3.5858</td>\n",
       "      <td>3.1802</td>\n",
       "      <td>3.7527</td>\n",
       "      <td>3.3777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_101 (slope one)</th>\n",
       "      <td>3.8772</td>\n",
       "      <td>3.6287</td>\n",
       "      <td>2.7012</td>\n",
       "      <td>4.0598</td>\n",
       "      <td>3.6306</td>\n",
       "      <td>3.1007</td>\n",
       "      <td>3.5598</td>\n",
       "      <td>4.0687</td>\n",
       "      <td>2.7629</td>\n",
       "      <td>4.4469</td>\n",
       "      <td>...</td>\n",
       "      <td>3.2325</td>\n",
       "      <td>3.8749</td>\n",
       "      <td>2.6458</td>\n",
       "      <td>4.0262</td>\n",
       "      <td>3.1221</td>\n",
       "      <td>4.4809</td>\n",
       "      <td>3.2967</td>\n",
       "      <td>2.9248</td>\n",
       "      <td>3.4391</td>\n",
       "      <td>3.1221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_102 (slope one)</th>\n",
       "      <td>3.8798</td>\n",
       "      <td>3.3626</td>\n",
       "      <td>2.7254</td>\n",
       "      <td>3.9451</td>\n",
       "      <td>3.5542</td>\n",
       "      <td>3.0031</td>\n",
       "      <td>3.4451</td>\n",
       "      <td>3.9679</td>\n",
       "      <td>2.6697</td>\n",
       "      <td>4.3689</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1307</td>\n",
       "      <td>3.7854</td>\n",
       "      <td>2.5475</td>\n",
       "      <td>3.9295</td>\n",
       "      <td>3.0548</td>\n",
       "      <td>4.3936</td>\n",
       "      <td>3.1443</td>\n",
       "      <td>2.7968</td>\n",
       "      <td>3.3690</td>\n",
       "      <td>3.0548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9724 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId/movieId          movie_1  movie_10  movie_100  movie_100044  \\\n",
       "person_1 (slope one)     4.0000    4.3089     3.3861        4.6429   \n",
       "person_10 (slope one)    3.6111    3.4040     2.3730        3.6877   \n",
       "person_100 (slope one)   4.2216    3.8416     2.9830        4.2965   \n",
       "person_101 (slope one)   3.8772    3.6287     2.7012        4.0598   \n",
       "person_102 (slope one)   3.8798    3.3626     2.7254        3.9451   \n",
       "\n",
       "userId/movieId          movie_100068  movie_100083  movie_100106  \\\n",
       "person_1 (slope one)          4.2644        3.7036        4.1429   \n",
       "person_10 (slope one)         3.2415        2.7370        3.1877   \n",
       "person_100 (slope one)        3.9074        3.3346        3.7965   \n",
       "person_101 (slope one)        3.6306        3.1007        3.5598   \n",
       "person_102 (slope one)        3.5542        3.0031        3.4451   \n",
       "\n",
       "userId/movieId          movie_100159  movie_100163  movie_100194  ...  \\\n",
       "person_1 (slope one)          4.6304        3.3701        5.0000  ...   \n",
       "person_10 (slope one)         3.7164        2.4891        4.1055  ...   \n",
       "person_100 (slope one)        4.3099        3.0537        4.6995  ...   \n",
       "person_101 (slope one)        4.0687        2.7629        4.4469  ...   \n",
       "person_102 (slope one)        3.9679        2.6697        4.3689  ...   \n",
       "\n",
       "userId/movieId          movie_99750  movie_99764  movie_998  movie_99813  \\\n",
       "person_1 (slope one)         3.8125       4.4180     3.1623       4.6060   \n",
       "person_10 (slope one)        2.8453       3.6131     2.3079       3.8104   \n",
       "person_100 (slope one)       3.4672       4.1071     2.8331       4.3650   \n",
       "person_101 (slope one)       3.2325       3.8749     2.6458       4.0262   \n",
       "person_102 (slope one)       3.1307       3.7854     2.5475       3.9295   \n",
       "\n",
       "userId/movieId          movie_99846  movie_99853  movie_999  movie_99910  \\\n",
       "person_1 (slope one)         3.7050       5.0000     4.0032       3.4810   \n",
       "person_10 (slope one)        2.7976       4.0942     3.0303       2.5679   \n",
       "person_100 (slope one)       3.3777       4.7402     3.5858       3.1802   \n",
       "person_101 (slope one)       3.1221       4.4809     3.2967       2.9248   \n",
       "person_102 (slope one)       3.0548       4.3936     3.1443       2.7968   \n",
       "\n",
       "userId/movieId          movie_99917  movie_99992  \n",
       "person_1 (slope one)         4.0299       3.7050  \n",
       "person_10 (slope one)        3.1365       2.7976  \n",
       "person_100 (slope one)       3.7527       3.3777  \n",
       "person_101 (slope one)       3.4391       3.1221  \n",
       "person_102 (slope one)       3.3690       3.0548  \n",
       "\n",
       "[5 rows x 9724 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slope_one_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slope_one_small.max().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slope_one_small.min().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_slope_one_small.apply(lambda x: sum(x.isnull()), axis=0).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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