R5_Internal_Lab_PCA_CBRS_PBRS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0zAbYvv-WCve"
   },
   "source": [
    "# Principal Component Analysis with Cancer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1waXlt6iWCvh"
   },
   "outputs": [],
   "source": [
    "#Import all the necessary modules\n",
    "#Import all the necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import zscore\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6FZ90t1vWCvq"
   },
   "source": [
    "# Q1. Load the Data file into Python DataFrame and view top 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>1017122</td>\n",
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       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1018099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <th>7</th>\n",
       "      <td>1018561</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1033078</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "        ID  ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0  1000025               5          1           1                  1   \n",
       "1  1002945               5          4           4                  5   \n",
       "2  1015425               3          1           1                  1   \n",
       "3  1016277               6          8           8                  1   \n",
       "4  1017023               4          1           1                  3   \n",
       "5  1017122               8         10          10                  8   \n",
       "6  1018099               1          1           1                  1   \n",
       "7  1018561               2          1           2                  1   \n",
       "8  1033078               2          1           1                  1   \n",
       "9  1033078               4          2           1                  1   \n",
       "\n",
       "   Single Epithelial Cell Size Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "0                            2           1                3                1   \n",
       "1                            7          10                3                2   \n",
       "2                            2           2                3                1   \n",
       "3                            3           4                3                7   \n",
       "4                            2           1                3                1   \n",
       "5                            7          10                9                7   \n",
       "6                            2          10                3                1   \n",
       "7                            2           1                3                1   \n",
       "8                            2           1                1                1   \n",
       "9                            2           1                2                1   \n",
       "\n",
       "   Mitoses  Class  \n",
       "0        1      2  \n",
       "1        1      2  \n",
       "2        1      2  \n",
       "3        1      2  \n",
       "4        1      2  \n",
       "5        1      4  \n",
       "6        1      2  \n",
       "7        1      2  \n",
       "8        5      2  \n",
       "9        1      2  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df = pd.read_csv(\"breast-cancer-wisconsin-data.csv\")\n",
    "cancer_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xusO2ruNWCv4"
   },
   "outputs": [],
   "source": [
    "  # Id columns is to identify rows hence can be skipped in analysis\n",
    "  # All columns have numerical values\n",
    "  # Class would be the target variable. Should be removed when PCA is done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KYTFwE0iWCv_"
   },
   "source": [
    "# Q2 Print the datatypes of each column and the shape of the dataset. Perform descriptive analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      "ID                             699 non-null int64\n",
      "ClumpThickness                 699 non-null int64\n",
      "Cell Size                      699 non-null int64\n",
      "Cell Shape                     699 non-null int64\n",
      "Marginal Adhesion              699 non-null int64\n",
      "Single Epithelial Cell Size    699 non-null int64\n",
      "Bare Nuclei                    699 non-null object\n",
      "Normal Nucleoli                699 non-null int64\n",
      "Bland Chromatin                699 non-null int64\n",
      "Mitoses                        699 non-null int64\n",
      "Class                          699 non-null int64\n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 60.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cancer_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>699.0</td>\n",
       "      <td>1.071704e+06</td>\n",
       "      <td>617095.729819</td>\n",
       "      <td>61634.0</td>\n",
       "      <td>870688.5</td>\n",
       "      <td>1171710.0</td>\n",
       "      <td>1238298.0</td>\n",
       "      <td>13454352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClumpThickness</th>\n",
       "      <td>699.0</td>\n",
       "      <td>4.417740e+00</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Size</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.134478e+00</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Shape</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.207439e+00</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.806867e+00</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.216023e+00</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.437768e+00</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.866953e+00</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>699.0</td>\n",
       "      <td>1.589413e+00</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.689557e+00</td>\n",
       "      <td>0.951273</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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      ],
      "text/plain": [
       "                             count          mean            std      min  \\\n",
       "ID                           699.0  1.071704e+06  617095.729819  61634.0   \n",
       "ClumpThickness               699.0  4.417740e+00       2.815741      1.0   \n",
       "Cell Size                    699.0  3.134478e+00       3.051459      1.0   \n",
       "Cell Shape                   699.0  3.207439e+00       2.971913      1.0   \n",
       "Marginal Adhesion            699.0  2.806867e+00       2.855379      1.0   \n",
       "Single Epithelial Cell Size  699.0  3.216023e+00       2.214300      1.0   \n",
       "Normal Nucleoli              699.0  3.437768e+00       2.438364      1.0   \n",
       "Bland Chromatin              699.0  2.866953e+00       3.053634      1.0   \n",
       "Mitoses                      699.0  1.589413e+00       1.715078      1.0   \n",
       "Class                        699.0  2.689557e+00       0.951273      2.0   \n",
       "\n",
       "                                  25%        50%        75%         max  \n",
       "ID                           870688.5  1171710.0  1238298.0  13454352.0  \n",
       "ClumpThickness                    2.0        4.0        6.0        10.0  \n",
       "Cell Size                         1.0        1.0        5.0        10.0  \n",
       "Cell Shape                        1.0        1.0        5.0        10.0  \n",
       "Marginal Adhesion                 1.0        1.0        4.0        10.0  \n",
       "Single Epithelial Cell Size       2.0        2.0        4.0        10.0  \n",
       "Normal Nucleoli                   2.0        3.0        5.0        10.0  \n",
       "Bland Chromatin                   1.0        1.0        4.0        10.0  \n",
       "Mitoses                           1.0        1.0        1.0        10.0  \n",
       "Class                             2.0        2.0        4.0         4.0  "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The means are slightly greater than median values, hence we understand that there is a right tail created because of Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box and whisker plots\n",
    "cancer_df.plot(kind='box', subplots=True, layout=(4,4), fontsize=8, figsize=(14,14))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the box plot its evident that we have outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5a4a2080>"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = cancer_df.corr()\n",
    "plt.subplots(figsize = (10,10))\n",
    "sns.heatmap(corrmat, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting. We can see that many of the attributes have a strong correlation:\n",
    "Example: > 0:50\n",
    "\n",
    "Except Mitoses, all the attributes are highly co related with output variable \"Class\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ecghJZJIWCwW"
   },
   "source": [
    "# Q3 Check for missing value check, incorrect data, duplicate data and perform imputation with mean, median, mode as necessary.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             False\n",
       "ClumpThickness                 False\n",
       "Cell Size                      False\n",
       "Cell Shape                     False\n",
       "Marginal Adhesion              False\n",
       "Single Epithelial Cell Size    False\n",
       "Bare Nuclei                    False\n",
       "Normal Nucleoli                False\n",
       "Bland Chromatin                False\n",
       "Mitoses                        False\n",
       "Class                          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             False\n",
       "ClumpThickness                 False\n",
       "Cell Size                      False\n",
       "Cell Shape                     False\n",
       "Marginal Adhesion              False\n",
       "Single Epithelial Cell Size    False\n",
       "Bare Nuclei                    False\n",
       "Normal Nucleoli                False\n",
       "Bland Chromatin                False\n",
       "Mitoses                        False\n",
       "Class                          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is no null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                              0\n",
       "ClumpThickness                  0\n",
       "Cell Size                       0\n",
       "Cell Shape                      0\n",
       "Marginal Adhesion               0\n",
       "Single Epithelial Cell Size     0\n",
       "Bare Nuclei                    16\n",
       "Normal Nucleoli                 0\n",
       "Bland Chromatin                 0\n",
       "Mitoses                         0\n",
       "Class                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df[cancer_df == \"?\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are ? in Bare Nuclei column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "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>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1017122</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1018099</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1018561</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1033078</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1033078</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1035283</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1036172</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1041801</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1043999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1044572</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1047630</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1048672</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1049815</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1050670</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1050718</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1054590</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1054593</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1056784</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1057013</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1059552</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1065726</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1066373</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1066979</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1067444</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1070935</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>1350423</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>1352848</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>1353092</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>1354840</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>1354840</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>1355260</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>1365075</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>1365328</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>1368267</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>1368273</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>1368882</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>1369821</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>1371026</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>1371920</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>466906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>466906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>534555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>536708</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>566346</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>603148</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>654546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>654546</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>695091</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>714039</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>763235</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>776715</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>841769</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>888820</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>897471</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>897471</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  ClumpThickness  Cell Size  Cell Shape  Marginal Adhesion  \\\n",
       "0    1000025               5          1           1                  1   \n",
       "1    1002945               5          4           4                  5   \n",
       "2    1015425               3          1           1                  1   \n",
       "3    1016277               6          8           8                  1   \n",
       "4    1017023               4          1           1                  3   \n",
       "5    1017122               8         10          10                  8   \n",
       "6    1018099               1          1           1                  1   \n",
       "7    1018561               2          1           2                  1   \n",
       "8    1033078               2          1           1                  1   \n",
       "9    1033078               4          2           1                  1   \n",
       "10   1035283               1          1           1                  1   \n",
       "11   1036172               2          1           1                  1   \n",
       "12   1041801               5          3           3                  3   \n",
       "13   1043999               1          1           1                  1   \n",
       "14   1044572               8          7           5                 10   \n",
       "15   1047630               7          4           6                  4   \n",
       "16   1048672               4          1           1                  1   \n",
       "17   1049815               4          1           1                  1   \n",
       "18   1050670              10          7           7                  6   \n",
       "19   1050718               6          1           1                  1   \n",
       "20   1054590               7          3           2                 10   \n",
       "21   1054593              10          5           5                  3   \n",
       "22   1056784               3          1           1                  1   \n",
       "23   1057013               8          4           5                  1   \n",
       "24   1059552               1          1           1                  1   \n",
       "25   1065726               5          2           3                  4   \n",
       "26   1066373               3          2           1                  1   \n",
       "27   1066979               5          1           1                  1   \n",
       "28   1067444               2          1           1                  1   \n",
       "29   1070935               1          1           3                  1   \n",
       "..       ...             ...        ...         ...                ...   \n",
       "669  1350423               5         10          10                  8   \n",
       "670  1352848               3         10           7                  8   \n",
       "671  1353092               3          2           1                  2   \n",
       "672  1354840               2          1           1                  1   \n",
       "673  1354840               5          3           2                  1   \n",
       "674  1355260               1          1           1                  1   \n",
       "675  1365075               4          1           4                  1   \n",
       "676  1365328               1          1           2                  1   \n",
       "677  1368267               5          1           1                  1   \n",
       "678  1368273               1          1           1                  1   \n",
       "679  1368882               2          1           1                  1   \n",
       "680  1369821              10         10          10                 10   \n",
       "681  1371026               5         10          10                 10   \n",
       "682  1371920               5          1           1                  1   \n",
       "683   466906               1          1           1                  1   \n",
       "684   466906               1          1           1                  1   \n",
       "685   534555               1          1           1                  1   \n",
       "686   536708               1          1           1                  1   \n",
       "687   566346               3          1           1                  1   \n",
       "688   603148               4          1           1                  1   \n",
       "689   654546               1          1           1                  1   \n",
       "690   654546               1          1           1                  3   \n",
       "691   695091               5         10          10                  5   \n",
       "692   714039               3          1           1                  1   \n",
       "693   763235               3          1           1                  1   \n",
       "694   776715               3          1           1                  1   \n",
       "695   841769               2          1           1                  1   \n",
       "696   888820               5         10          10                  3   \n",
       "697   897471               4          8           6                  4   \n",
       "698   897471               4          8           8                  5   \n",
       "\n",
       "     Single Epithelial Cell Size Bare Nuclei  Normal Nucleoli  \\\n",
       "0                              2           1                3   \n",
       "1                              7          10                3   \n",
       "2                              2           2                3   \n",
       "3                              3           4                3   \n",
       "4                              2           1                3   \n",
       "5                              7          10                9   \n",
       "6                              2          10                3   \n",
       "7                              2           1                3   \n",
       "8                              2           1                1   \n",
       "9                              2           1                2   \n",
       "10                             1           1                3   \n",
       "11                             2           1                2   \n",
       "12                             2           3                4   \n",
       "13                             2           3                3   \n",
       "14                             7           9                5   \n",
       "15                             6           1                4   \n",
       "16                             2           1                2   \n",
       "17                             2           1                3   \n",
       "18                             4          10                4   \n",
       "19                             2           1                3   \n",
       "20                             5          10                5   \n",
       "21                             6           7                7   \n",
       "22                             2           1                2   \n",
       "23                             2         NaN                7   \n",
       "24                             2           1                3   \n",
       "25                             2           7                3   \n",
       "26                             1           1                2   \n",
       "27                             2           1                2   \n",
       "28                             2           1                2   \n",
       "29                             2           1                1   \n",
       "..                           ...         ...              ...   \n",
       "669                            5           5                7   \n",
       "670                            5           8                7   \n",
       "671                            2           1                3   \n",
       "672                            2           1                3   \n",
       "673                            3           1                1   \n",
       "674                            2           1                2   \n",
       "675                            2           1                1   \n",
       "676                            2           1                2   \n",
       "677                            2           1                1   \n",
       "678                            2           1                1   \n",
       "679                            2           1                1   \n",
       "680                            5          10               10   \n",
       "681                            4          10                5   \n",
       "682                            2           1                3   \n",
       "683                            2           1                1   \n",
       "684                            2           1                1   \n",
       "685                            2           1                1   \n",
       "686                            2           1                1   \n",
       "687                            2           1                2   \n",
       "688                            2           1                1   \n",
       "689                            2           1                1   \n",
       "690                            2           1                1   \n",
       "691                            4           5                4   \n",
       "692                            2           1                1   \n",
       "693                            2           1                2   \n",
       "694                            3           2                1   \n",
       "695                            2           1                1   \n",
       "696                            7           3                8   \n",
       "697                            3           4               10   \n",
       "698                            4           5               10   \n",
       "\n",
       "     Bland Chromatin  Mitoses  Class  \n",
       "0                  1        1      2  \n",
       "1                  2        1      2  \n",
       "2                  1        1      2  \n",
       "3                  7        1      2  \n",
       "4                  1        1      2  \n",
       "5                  7        1      4  \n",
       "6                  1        1      2  \n",
       "7                  1        1      2  \n",
       "8                  1        5      2  \n",
       "9                  1        1      2  \n",
       "10                 1        1      2  \n",
       "11                 1        1      2  \n",
       "12                 4        1      4  \n",
       "13                 1        1      2  \n",
       "14                 5        4      4  \n",
       "15                 3        1      4  \n",
       "16                 1        1      2  \n",
       "17                 1        1      2  \n",
       "18                 1        2      4  \n",
       "19                 1        1      2  \n",
       "20                 4        4      4  \n",
       "21                10        1      4  \n",
       "22                 1        1      2  \n",
       "23                 3        1      4  \n",
       "24                 1        1      2  \n",
       "25                 6        1      4  \n",
       "26                 1        1      2  \n",
       "27                 1        1      2  \n",
       "28                 1        1      2  \n",
       "29                 1        1      2  \n",
       "..               ...      ...    ...  \n",
       "669               10        1      4  \n",
       "670                4        1      4  \n",
       "671                1        1      2  \n",
       "672                1        1      2  \n",
       "673                1        1      2  \n",
       "674                1        1      2  \n",
       "675                1        1      2  \n",
       "676                1        1      2  \n",
       "677                1        1      2  \n",
       "678                1        1      2  \n",
       "679                1        1      2  \n",
       "680               10        7      4  \n",
       "681                6        3      4  \n",
       "682                2        1      2  \n",
       "683                1        1      2  \n",
       "684                1        1      2  \n",
       "685                1        1      2  \n",
       "686                1        1      2  \n",
       "687                3        1      2  \n",
       "688                1        1      2  \n",
       "689                1        8      2  \n",
       "690                1        1      2  \n",
       "691                4        1      4  \n",
       "692                1        1      2  \n",
       "693                1        2      2  \n",
       "694                1        1      2  \n",
       "695                1        1      2  \n",
       "696               10        2      4  \n",
       "697                6        1      4  \n",
       "698                4        1      4  \n",
       "\n",
       "[699 rows x 11 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.replace('?',np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_df = cancer_df.apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      "ID                             699 non-null int64\n",
      "ClumpThickness                 699 non-null int64\n",
      "Cell Size                      699 non-null int64\n",
      "Cell Shape                     699 non-null int64\n",
      "Marginal Adhesion              699 non-null int64\n",
      "Single Epithelial Cell Size    699 non-null int64\n",
      "Bare Nuclei                    683 non-null float64\n",
      "Normal Nucleoli                699 non-null int64\n",
      "Bland Chromatin                699 non-null int64\n",
      "Mitoses                        699 non-null int64\n",
      "Class                          699 non-null int64\n",
      "dtypes: float64(1), int64(10)\n",
      "memory usage: 60.1 KB\n"
     ]
    }
   ],
   "source": [
    "cancer_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns are converted to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = cancer_df['Bare Nuclei'].mean()\n",
    "cancer_df['Bare Nuclei'] = cancer_df['Bare Nuclei'].fillna(means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replaced '?' with Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We could see \"?\" values in column, this should be removed from data set\n",
    "\n",
    "# Check for missing value in any other column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No missing values found. So let us try to remove ? from bare nuclei column\n",
    "\n",
    "# Get count of rows having ?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jAmWPaXpWCw3"
   },
   "outputs": [],
   "source": [
    "# 16 values are corrupted. We can either delete them as it forms roughly 2% of data.\n",
    "# Here we would like to impute it with suitable values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have replaced with mean, as most of the time mean represents the population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YAu8NvGaWCxr"
   },
   "source": [
    "# Q4. Perform bi variate analysis including correlation, pairplots and state the inferences. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ClumpThickness</th>\n",
       "      <th>Cell Size</th>\n",
       "      <th>Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055308</td>\n",
       "      <td>-0.041603</td>\n",
       "      <td>-0.041576</td>\n",
       "      <td>-0.064878</td>\n",
       "      <td>-0.045528</td>\n",
       "      <td>-0.098668</td>\n",
       "      <td>-0.060051</td>\n",
       "      <td>-0.052072</td>\n",
       "      <td>-0.034901</td>\n",
       "      <td>-0.080226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClumpThickness</th>\n",
       "      <td>-0.055308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644913</td>\n",
       "      <td>0.654589</td>\n",
       "      <td>0.486356</td>\n",
       "      <td>0.521816</td>\n",
       "      <td>0.587300</td>\n",
       "      <td>0.558428</td>\n",
       "      <td>0.535835</td>\n",
       "      <td>0.350034</td>\n",
       "      <td>0.716001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Size</th>\n",
       "      <td>-0.041603</td>\n",
       "      <td>0.644913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906882</td>\n",
       "      <td>0.705582</td>\n",
       "      <td>0.751799</td>\n",
       "      <td>0.686801</td>\n",
       "      <td>0.755721</td>\n",
       "      <td>0.722865</td>\n",
       "      <td>0.458693</td>\n",
       "      <td>0.817904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell Shape</th>\n",
       "      <td>-0.041576</td>\n",
       "      <td>0.654589</td>\n",
       "      <td>0.906882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.683079</td>\n",
       "      <td>0.719668</td>\n",
       "      <td>0.709606</td>\n",
       "      <td>0.735948</td>\n",
       "      <td>0.719446</td>\n",
       "      <td>0.438911</td>\n",
       "      <td>0.818934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <td>-0.064878</td>\n",
       "      <td>0.486356</td>\n",
       "      <td>0.705582</td>\n",
       "      <td>0.683079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.599599</td>\n",
       "      <td>0.665049</td>\n",
       "      <td>0.666715</td>\n",
       "      <td>0.603352</td>\n",
       "      <td>0.417633</td>\n",
       "      <td>0.696800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <td>-0.045528</td>\n",
       "      <td>0.521816</td>\n",
       "      <td>0.751799</td>\n",
       "      <td>0.719668</td>\n",
       "      <td>0.599599</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.581261</td>\n",
       "      <td>0.616102</td>\n",
       "      <td>0.628881</td>\n",
       "      <td>0.479101</td>\n",
       "      <td>0.682785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <td>-0.098668</td>\n",
       "      <td>0.587300</td>\n",
       "      <td>0.686801</td>\n",
       "      <td>0.709606</td>\n",
       "      <td>0.665049</td>\n",
       "      <td>0.581261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675896</td>\n",
       "      <td>0.577362</td>\n",
       "      <td>0.338740</td>\n",
       "      <td>0.816050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <td>-0.060051</td>\n",
       "      <td>0.558428</td>\n",
       "      <td>0.755721</td>\n",
       "      <td>0.735948</td>\n",
       "      <td>0.666715</td>\n",
       "      <td>0.616102</td>\n",
       "      <td>0.675896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.665878</td>\n",
       "      <td>0.344169</td>\n",
       "      <td>0.756616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <td>-0.052072</td>\n",
       "      <td>0.535835</td>\n",
       "      <td>0.722865</td>\n",
       "      <td>0.719446</td>\n",
       "      <td>0.603352</td>\n",
       "      <td>0.628881</td>\n",
       "      <td>0.577362</td>\n",
       "      <td>0.665878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.712244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>-0.034901</td>\n",
       "      <td>0.350034</td>\n",
       "      <td>0.458693</td>\n",
       "      <td>0.438911</td>\n",
       "      <td>0.417633</td>\n",
       "      <td>0.479101</td>\n",
       "      <td>0.338740</td>\n",
       "      <td>0.344169</td>\n",
       "      <td>0.428336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>-0.080226</td>\n",
       "      <td>0.716001</td>\n",
       "      <td>0.817904</td>\n",
       "      <td>0.818934</td>\n",
       "      <td>0.696800</td>\n",
       "      <td>0.682785</td>\n",
       "      <td>0.816050</td>\n",
       "      <td>0.756616</td>\n",
       "      <td>0.712244</td>\n",
       "      <td>0.423170</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   ID  ClumpThickness  Cell Size  Cell Shape  \\\n",
       "ID                           1.000000       -0.055308  -0.041603   -0.041576   \n",
       "ClumpThickness              -0.055308        1.000000   0.644913    0.654589   \n",
       "Cell Size                   -0.041603        0.644913   1.000000    0.906882   \n",
       "Cell Shape                  -0.041576        0.654589   0.906882    1.000000   \n",
       "Marginal Adhesion           -0.064878        0.486356   0.705582    0.683079   \n",
       "Single Epithelial Cell Size -0.045528        0.521816   0.751799    0.719668   \n",
       "Bare Nuclei                 -0.098668        0.587300   0.686801    0.709606   \n",
       "Normal Nucleoli             -0.060051        0.558428   0.755721    0.735948   \n",
       "Bland Chromatin             -0.052072        0.535835   0.722865    0.719446   \n",
       "Mitoses                     -0.034901        0.350034   0.458693    0.438911   \n",
       "Class                       -0.080226        0.716001   0.817904    0.818934   \n",
       "\n",
       "                             Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "ID                                   -0.064878                    -0.045528   \n",
       "ClumpThickness                        0.486356                     0.521816   \n",
       "Cell Size                             0.705582                     0.751799   \n",
       "Cell Shape                            0.683079                     0.719668   \n",
       "Marginal Adhesion                     1.000000                     0.599599   \n",
       "Single Epithelial Cell Size           0.599599                     1.000000   \n",
       "Bare Nuclei                           0.665049                     0.581261   \n",
       "Normal Nucleoli                       0.666715                     0.616102   \n",
       "Bland Chromatin                       0.603352                     0.628881   \n",
       "Mitoses                               0.417633                     0.479101   \n",
       "Class                                 0.696800                     0.682785   \n",
       "\n",
       "                             Bare Nuclei  Normal Nucleoli  Bland Chromatin  \\\n",
       "ID                             -0.098668        -0.060051        -0.052072   \n",
       "ClumpThickness                  0.587300         0.558428         0.535835   \n",
       "Cell Size                       0.686801         0.755721         0.722865   \n",
       "Cell Shape                      0.709606         0.735948         0.719446   \n",
       "Marginal Adhesion               0.665049         0.666715         0.603352   \n",
       "Single Epithelial Cell Size     0.581261         0.616102         0.628881   \n",
       "Bare Nuclei                     1.000000         0.675896         0.577362   \n",
       "Normal Nucleoli                 0.675896         1.000000         0.665878   \n",
       "Bland Chromatin                 0.577362         0.665878         1.000000   \n",
       "Mitoses                         0.338740         0.344169         0.428336   \n",
       "Class                           0.816050         0.756616         0.712244   \n",
       "\n",
       "                              Mitoses     Class  \n",
       "ID                          -0.034901 -0.080226  \n",
       "ClumpThickness               0.350034  0.716001  \n",
       "Cell Size                    0.458693  0.817904  \n",
       "Cell Shape                   0.438911  0.818934  \n",
       "Marginal Adhesion            0.417633  0.696800  \n",
       "Single Epithelial Cell Size  0.479101  0.682785  \n",
       "Bare Nuclei                  0.338740  0.816050  \n",
       "Normal Nucleoli              0.344169  0.756616  \n",
       "Bland Chromatin              0.428336  0.712244  \n",
       "Mitoses                      1.000000  0.423170  \n",
       "Class                        0.423170  1.000000  "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for correlation of variable\n",
    "cancer_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. ClumpThickness has a good corelation with Cell Size, Cell Shape, Single Epithelial Cell size, normal nucleoli, Bland Chromatin, Class\n",
    "2. Cell Size has a good corelation with Cell Shape, Marginal Adhesion, Single Epithelial Cell size, normal nucleoli, Bland Chromatin, Class\n",
    "3. Cell Shape has a good corelation with Marginal Adhesion, Single Epithelial Cell size, normal nucleoli, Bland Chromatin, Class\n",
    "4. Marginal Adhesion has a good corelation with Single Epithelial Cell size, normal nucleoli, Bland Chromatin, Class\n",
    "5. Single Epithelial Cell size has a good corelation with normal nucleoli, Bland Chromatin, Class\n",
    "6. normal nucleoli Cell size has a good corelation with Bland Chromatin, Class\n",
    "7. Bland Chromatin has a good corelation with Class\n",
    "8. Mitoses have very less corelation with all the other attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ysZh50iDWCxz"
   },
   "outputs": [],
   "source": [
    "# Cell size shows high significance with cell shape,marginal adhesion, single epithelial cell size,bare nuclei, normal nucleoli \n",
    "# and bland chromatin\n",
    "# Target variable shows high correlation with most of these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us check for pair plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:83: RuntimeWarning: invalid value encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x5a09be48>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2022.38x1980 with 132 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(cancer_df, diag_kind='kde', hue='Class') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the attributes have multiple gausian, hence we dont know if the data comes from same process or they have natural clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OTh11akvWCx8"
   },
   "outputs": [],
   "source": [
    "# Relationship between variables shows come correlation. \n",
    "# Distribution of variables shows most of the values are concentrated on lower side, though range remains same for all that is \n",
    "# Between 1 to 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9hxe7D9BWCx-"
   },
   "source": [
    "# Q5 Remove any unwanted columns or outliers, standardize variables in pre-processing step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ViVkMsQTWCza"
   },
   "outputs": [],
   "source": [
    "# We could see most of the outliers are now removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_df.drop('ID',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 10)"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df = cancer_df[(np.abs(stats.zscore(cancer_df)) <  1.96 ).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(508, 10)"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_df_Standard = sample_df.apply(zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lmMHMAtkWCzm"
   },
   "source": [
    "# Q6 Create a covariance matrix for identifying Principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA\n",
    "# Step 1 - Create covariance matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sample_df.drop('Class',axis=1)\n",
    "y = sample_df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sd = StandardScaler().fit_transform(X_train)\n",
    "X_test_sd = StandardScaler().fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance Matrix \n",
      "%s [[1.00282486 0.48077968 0.48642566 0.42631714 0.40533352 0.39516575\n",
      "  0.35911251 0.4000491  0.29197793]\n",
      " [0.48077968 1.00282486 0.8531616  0.61715331 0.60698667 0.73477826\n",
      "  0.60429829 0.66292193 0.29594359]\n",
      " [0.48642566 0.8531616  1.00282486 0.58476228 0.60237024 0.73797155\n",
      "  0.58766479 0.63489857 0.25436827]\n",
      " [0.42631714 0.61715331 0.58476228 1.00282486 0.5249123  0.67116962\n",
      "  0.50941794 0.57642258 0.27410217]\n",
      " [0.40533352 0.60698667 0.60237024 0.5249123  1.00282486 0.66831063\n",
      "  0.48629122 0.47530362 0.30079807]\n",
      " [0.39516575 0.73477826 0.73797155 0.67116962 0.66831063 1.00282486\n",
      "  0.62051017 0.58360306 0.30702551]\n",
      " [0.35911251 0.60429829 0.58766479 0.50941794 0.48629122 0.62051017\n",
      "  1.00282486 0.55769689 0.33191337]\n",
      " [0.4000491  0.66292193 0.63489857 0.57642258 0.47530362 0.58360306\n",
      "  0.55769689 1.00282486 0.45621686]\n",
      " [0.29197793 0.29594359 0.25436827 0.27410217 0.30079807 0.30702551\n",
      "  0.33191337 0.45621686 1.00282486]]\n"
     ]
    }
   ],
   "source": [
    "cov_matrix = np.cov(X_train_sd.T)\n",
    "print('Covariance Matrix \\n%s', cov_matrix)\n",
    "\n",
    "e_vals, e_vecs = np.linalg.eig(cov_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vbZ-YcsdWCzz"
   },
   "source": [
    "# Q7 Identify eigen values and eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2- Get eigen values and eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors \n",
      "[[ 0.26508375  0.15314693 -0.9144084   0.03338162 -0.14803047  0.05051161\n",
      "   0.09093383 -0.16675815 -0.09207542]\n",
      " [ 0.38696113 -0.17734832  0.00447157  0.69647619  0.33746668 -0.23064408\n",
      "   0.16529114  0.35368584 -0.11922368]\n",
      " [ 0.38034169 -0.22855474 -0.03252295 -0.7041176   0.17095778 -0.26393707\n",
      "   0.15451344  0.38655966 -0.18458462]\n",
      " [ 0.3386026  -0.11061693  0.01101651 -0.07743893  0.30845711 -0.11082156\n",
      "   0.00485891 -0.37766025  0.78543997]\n",
      " [ 0.32994462 -0.12040319 -0.00151312 -0.01624739  0.18827325  0.44683657\n",
      "  -0.7860578   0.0400158  -0.14756517]\n",
      " [ 0.37728253 -0.18948662  0.17565772  0.09972267 -0.78558432 -0.33786707\n",
      "  -0.20779185 -0.06328008  0.04973695]\n",
      " [ 0.32838851  0.01591294  0.28349717 -0.020839    0.12411222  0.0911794\n",
      "   0.27769442 -0.67340964 -0.50674509]\n",
      " [ 0.34564418  0.22281277  0.17864408 -0.00643299 -0.25083009  0.66279224\n",
      "   0.40275732  0.29892078  0.2124926 ]\n",
      " [ 0.20534287  0.88335687  0.13969931 -0.0366602   0.09445502 -0.31762145\n",
      "  -0.20029949  0.08125638 -0.01128975]]\n",
      "\n",
      "Eigenvalues \n",
      "[5.20673582 0.90808557 0.70329801 0.14599246 0.22874063 0.33444615\n",
      " 0.54463207 0.46452238 0.48897064]\n"
     ]
    }
   ],
   "source": [
    "print('Eigenvectors \\n%s' %e_vecs)\n",
    "print('\\nEigenvalues \\n%s' %e_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-2KqQg2KWCz7"
   },
   "source": [
    "# Q8 Find variance and cumulative variance by each eigen vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance: [57.68965505364413, 10.06141757755665, 7.792409830483689, 6.034421041248325, 5.417702869413992, 5.146820785671457, 3.7056005764410274, 2.534403274889124, 1.617568990651593]\n",
      "Cumulative Variance: [ 57.68965505  67.75107263  75.54348246  81.5779035   86.99560637\n",
      "  92.14242716  95.84802773  98.38243101 100.        ]\n"
     ]
    }
   ],
   "source": [
    "tot = sum(e_vals)\n",
    "var_exp = [( i /tot ) * 100 for i in sorted(e_vals, reverse=True)]\n",
    "cum_var_exp = np.cumsum(var_exp)\n",
    "print(\"Variance:\",var_exp)\n",
    "print(\"Cumulative Variance:\", cum_var_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting \n",
    "plt.figure(figsize=(10 , 5))\n",
    "plt.bar(range(1, e_vals.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')\n",
    "plt.step(range(1, e_vals.size + 1), cum_var_exp, where='mid', label = 'Cumulative explained variance')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.xlabel('Principal Components')\n",
    "plt.legend(loc = 'best')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x8YxQPNGWC0e"
   },
   "source": [
    "# Q9 Use PCA command from sklearn and find Principal Components. Transform data to components formed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "scikit_pca = PCA(n_components=7)\n",
    "X_spca = scikit_pca.fit_transform(X_train_sd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8L-IGI0QWC0t"
   },
   "source": [
    "# Q10 Find correlation between components and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "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>0</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-6.461581e-17</td>\n",
       "      <td>1.822465e-16</td>\n",
       "      <td>-1.281330e-16</td>\n",
       "      <td>4.717306e-17</td>\n",
       "      <td>2.903911e-17</td>\n",
       "      <td>7.129882e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-6.461581e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.919758e-16</td>\n",
       "      <td>1.141647e-16</td>\n",
       "      <td>7.530472e-17</td>\n",
       "      <td>-2.704134e-16</td>\n",
       "      <td>2.731629e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.822465e-16</td>\n",
       "      <td>2.919758e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-3.192466e-17</td>\n",
       "      <td>8.931252e-17</td>\n",
       "      <td>-8.765462e-17</td>\n",
       "      <td>3.314119e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.281330e-16</td>\n",
       "      <td>1.141647e-16</td>\n",
       "      <td>-3.192466e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.236463e-16</td>\n",
       "      <td>5.237588e-16</td>\n",
       "      <td>-2.439666e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.717306e-17</td>\n",
       "      <td>7.530472e-17</td>\n",
       "      <td>8.931252e-17</td>\n",
       "      <td>-2.236463e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.316598e-16</td>\n",
       "      <td>3.412362e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.903911e-17</td>\n",
       "      <td>-2.704134e-16</td>\n",
       "      <td>-8.765462e-17</td>\n",
       "      <td>5.237588e-16</td>\n",
       "      <td>3.316598e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.289008e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.129882e-17</td>\n",
       "      <td>2.731629e-17</td>\n",
       "      <td>3.314119e-17</td>\n",
       "      <td>-2.439666e-16</td>\n",
       "      <td>3.412362e-16</td>\n",
       "      <td>-1.289008e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0             1             2             3             4  \\\n",
       "0  1.000000e+00 -6.461581e-17  1.822465e-16 -1.281330e-16  4.717306e-17   \n",
       "1 -6.461581e-17  1.000000e+00  2.919758e-16  1.141647e-16  7.530472e-17   \n",
       "2  1.822465e-16  2.919758e-16  1.000000e+00 -3.192466e-17  8.931252e-17   \n",
       "3 -1.281330e-16  1.141647e-16 -3.192466e-17  1.000000e+00 -2.236463e-16   \n",
       "4  4.717306e-17  7.530472e-17  8.931252e-17 -2.236463e-16  1.000000e+00   \n",
       "5  2.903911e-17 -2.704134e-16 -8.765462e-17  5.237588e-16  3.316598e-16   \n",
       "6  7.129882e-17  2.731629e-17  3.314119e-17 -2.439666e-16  3.412362e-16   \n",
       "\n",
       "              5             6  \n",
       "0  2.903911e-17  7.129882e-17  \n",
       "1 -2.704134e-16  2.731629e-17  \n",
       "2 -8.765462e-17  3.314119e-17  \n",
       "3  5.237588e-16 -2.439666e-16  \n",
       "4  3.316598e-16  3.412362e-16  \n",
       "5  1.000000e+00 -1.289008e-16  \n",
       "6 -1.289008e-16  1.000000e+00  "
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_spca).corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content Based Recommendation System - Optional ( Q11 - Q19 will not be graded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q11 Read the Dataset `movies_metadata.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q12 Create a new column with name 'description' combining `'overview' and 'tagline'` columns in the given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q13  Lets drop the null values in `description` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q14 Keep the first occurance and drop duplicates of each title in column `title`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q15   As we might have dropped a few rows with duplicate `title` in above step, just reset the index [make sure you are not adding any new column to the dataframe while doing reset index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q16    Generate tf-idf matrix using the column `description`. Consider till 3-grams, with minimum document frequency as 0.\n",
    "\n",
    "Hint:\n",
    "tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q17  Create cosine similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q18  Write a function with name `recommend` which takes `title` as argument and returns a list of 10 recommended title names in the output based on the above cosine similarities\n",
    "\n",
    "Hint:\n",
    "\n",
    "titles = df['title'] <br>\n",
    "indices = pd.Series(df.index, index=df['title']) <br>\n",
    "\n",
    "def recommend(title): <br>\n",
    "    idx = indices[title] <br>\n",
    "    sim_scores = list(enumerate(cosine_similarities[idx])) <br>\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True) <br>\n",
    "    sim_scores = sim_scores[1:31] <br>\n",
    "    movie_indices = [i[0] for i in sim_scores] <br>\n",
    "    return titles.iloc[movie_indices] <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q19 Give the recommendations from above functions for movies `The Godfather` and `The Dark Knight Rises`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popularity Based Recommendation System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anonymous Ratings on jokes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Ratings are real values ranging from -10.00 to +10.00 (the value \"99\" corresponds to \"null\" = \"not rated\").\n",
    "\n",
    "2. One row per user\n",
    "\n",
    "3. The first column gives the number of jokes rated by that user. The next 100 columns give the ratings for jokes 01 - 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q20 Read the dataset(jokes.csv)\n",
    "\n",
    "Take care about the header in read_csv() as there are no column names given in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumJokes</th>\n",
       "      <th>Joke1</th>\n",
       "      <th>Joke2</th>\n",
       "      <th>Joke3</th>\n",
       "      <th>Joke4</th>\n",
       "      <th>Joke5</th>\n",
       "      <th>Joke6</th>\n",
       "      <th>Joke7</th>\n",
       "      <th>Joke8</th>\n",
       "      <th>Joke9</th>\n",
       "      <th>...</th>\n",
       "      <th>Joke91</th>\n",
       "      <th>Joke92</th>\n",
       "      <th>Joke93</th>\n",
       "      <th>Joke94</th>\n",
       "      <th>Joke95</th>\n",
       "      <th>Joke96</th>\n",
       "      <th>Joke97</th>\n",
       "      <th>Joke98</th>\n",
       "      <th>Joke99</th>\n",
       "      <th>Joke100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74</td>\n",
       "      <td>-7.82</td>\n",
       "      <td>8.79</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-8.16</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>-8.50</td>\n",
       "      <td>-9.85</td>\n",
       "      <td>4.17</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>-5.63</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>4.08</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>6.36</td>\n",
       "      <td>4.37</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>8.88</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>7.86</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.08</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>99.00</td>\n",
       "      <td>8.35</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>8.16</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>6.21</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>8.50</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.04</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.58</td>\n",
       "      <td>4.27</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.73</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.11</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100</td>\n",
       "      <td>-6.17</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-8.50</td>\n",
       "      <td>-7.09</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>-8.69</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>-6.65</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-6.89</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-2.96</td>\n",
       "      <td>-2.18</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-9.08</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>8.59</td>\n",
       "      <td>-9.85</td>\n",
       "      <td>7.72</td>\n",
       "      <td>8.79</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2.33</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>6.84</td>\n",
       "      <td>3.16</td>\n",
       "      <td>9.17</td>\n",
       "      <td>-6.21</td>\n",
       "      <td>-8.16</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>9.27</td>\n",
       "      <td>1.41</td>\n",
       "      <td>-5.19</td>\n",
       "      <td>...</td>\n",
       "      <td>7.23</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>2.14</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-3.54</td>\n",
       "      <td>-9.42</td>\n",
       "      <td>-6.89</td>\n",
       "      <td>-8.74</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-5.29</td>\n",
       "      <td>-8.93</td>\n",
       "      <td>-7.86</td>\n",
       "      <td>...</td>\n",
       "      <td>4.37</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>4.17</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>72</td>\n",
       "      <td>3.01</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.15</td>\n",
       "      <td>3.01</td>\n",
       "      <td>6.41</td>\n",
       "      <td>5.15</td>\n",
       "      <td>8.93</td>\n",
       "      <td>2.52</td>\n",
       "      <td>3.01</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>4.47</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NumJokes  Joke1  Joke2  Joke3  Joke4  Joke5  Joke6  Joke7  Joke8  Joke9  \\\n",
       "0        74  -7.82   8.79  -9.66  -8.16  -7.52  -8.50  -9.85   4.17  -8.98   \n",
       "1       100   4.08  -0.29   6.36   4.37  -2.38  -9.66  -0.73  -5.34   8.88   \n",
       "2        49  99.00  99.00  99.00  99.00   9.03   9.27   9.03   9.27  99.00   \n",
       "3        48  99.00   8.35  99.00  99.00   1.80   8.16  -2.82   6.21  99.00   \n",
       "4        91   8.50   4.61  -4.17  -5.39   1.36   1.60   7.04   4.61  -0.44   \n",
       "5       100  -6.17  -3.54   0.44  -8.50  -7.09  -4.32  -8.69  -0.87  -6.65   \n",
       "6        47  99.00  99.00  99.00  99.00   8.59  -9.85   7.72   8.79  99.00   \n",
       "7       100   6.84   3.16   9.17  -6.21  -8.16  -1.70   9.27   1.41  -5.19   \n",
       "8       100  -3.79  -3.54  -9.42  -6.89  -8.74  -0.29  -5.29  -8.93  -7.86   \n",
       "9        72   3.01   5.15   5.15   3.01   6.41   5.15   8.93   2.52   3.01   \n",
       "\n",
       "    ...     Joke91  Joke92  Joke93  Joke94  Joke95  Joke96  Joke97  Joke98  \\\n",
       "0   ...       2.82   99.00   99.00   99.00   99.00   99.00   -5.63   99.00   \n",
       "1   ...       2.82   -4.95   -0.29    7.86   -0.19   -2.14    3.06    0.34   \n",
       "2   ...      99.00   99.00   99.00    9.08   99.00   99.00   99.00   99.00   \n",
       "3   ...      99.00   99.00   99.00    0.53   99.00   99.00   99.00   99.00   \n",
       "4   ...       5.19    5.58    4.27    5.19    5.73    1.55    3.11    6.55   \n",
       "5   ...      -3.54   -6.89   -0.68   -2.96   -2.18   -3.35    0.05   -9.08   \n",
       "6   ...      99.00   99.00   99.00   99.00   99.00    2.33   99.00   99.00   \n",
       "7   ...       7.23   -1.12   -0.10   -5.68   -3.16   -3.35    2.14   -0.05   \n",
       "8   ...       4.37   -0.29    4.17   -0.29   -0.29   -0.29   -0.29   -0.29   \n",
       "9   ...      99.00    4.47   99.00   99.00   99.00   99.00   99.00   99.00   \n",
       "\n",
       "   Joke99  Joke100  \n",
       "0   99.00    99.00  \n",
       "1   -4.32     1.07  \n",
       "2   99.00    99.00  \n",
       "3   99.00    99.00  \n",
       "4    1.80     1.60  \n",
       "5   -5.05    -3.45  \n",
       "6   99.00    99.00  \n",
       "7    1.31     0.00  \n",
       "8   -3.40    -4.95  \n",
       "9   99.00    99.00  \n",
       "\n",
       "[10 rows x 101 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jokes_df = pd.read_csv(\"jokes.csv\")\n",
    "jokes_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q21 Consider `ratings` named dataframe with only first 200 rows and all columns from 1(first column is 0) of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 101)"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = jokes_df.iloc[0:200,:]\n",
    "ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q22 Change the column indices from 0 to 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumJokes</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74</td>\n",
       "      <td>-7.82</td>\n",
       "      <td>8.79</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-8.16</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>-8.50</td>\n",
       "      <td>-9.85</td>\n",
       "      <td>4.17</td>\n",
       "      <td>-8.98</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>-5.63</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>4.08</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>6.36</td>\n",
       "      <td>4.37</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>8.88</td>\n",
       "      <td>...</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-4.95</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>7.86</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-2.14</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-4.32</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.27</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>9.08</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>99.00</td>\n",
       "      <td>8.35</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>8.16</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>6.21</td>\n",
       "      <td>99.00</td>\n",
       "      <td>...</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.53</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>8.50</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.04</td>\n",
       "      <td>4.61</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>...</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.58</td>\n",
       "      <td>4.27</td>\n",
       "      <td>5.19</td>\n",
       "      <td>5.73</td>\n",
       "      <td>1.55</td>\n",
       "      <td>3.11</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NumJokes      0      1      2      3     4     5     6     7      8  ...    \\\n",
       "0        74  -7.82   8.79  -9.66  -8.16 -7.52 -8.50 -9.85  4.17  -8.98  ...     \n",
       "1       100   4.08  -0.29   6.36   4.37 -2.38 -9.66 -0.73 -5.34   8.88  ...     \n",
       "2        49  99.00  99.00  99.00  99.00  9.03  9.27  9.03  9.27  99.00  ...     \n",
       "3        48  99.00   8.35  99.00  99.00  1.80  8.16 -2.82  6.21  99.00  ...     \n",
       "4        91   8.50   4.61  -4.17  -5.39  1.36  1.60  7.04  4.61  -0.44  ...     \n",
       "\n",
       "      90     91     92     93     94     95     96     97     98     99  \n",
       "0   2.82  99.00  99.00  99.00  99.00  99.00  -5.63  99.00  99.00  99.00  \n",
       "1   2.82  -4.95  -0.29   7.86  -0.19  -2.14   3.06   0.34  -4.32   1.07  \n",
       "2  99.00  99.00  99.00   9.08  99.00  99.00  99.00  99.00  99.00  99.00  \n",
       "3  99.00  99.00  99.00   0.53  99.00  99.00  99.00  99.00  99.00  99.00  \n",
       "4   5.19   5.58   4.27   5.19   5.73   1.55   3.11   6.55   1.80   1.60  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ratings.columns\n",
    "cols = list(cols)\n",
    "temp = cols.pop(0)\n",
    "cols = list(range(0,100))\n",
    "cols.insert(0,temp)\n",
    "ratings.columns = cols\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q23 In the dataset, the null ratings are given as 99.00, so replace all 99.00s with 0\n",
    "Hint: You can use `ratings.replace(<the given value>, <new value you wanted to change with>)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings= ratings.replace(99.00,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q24 Normalize the ratings using StandardScaler and save them in `ratings_diff` variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_diff = StandardScaler().fit_transform(ratings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popularity based recommendation system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q25  Find the mean for each column  in `ratings_diff` i.e, for each joke\n",
    "Consider all the mean ratings and find the jokes with highest mean value and display the top 10 joke IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99     1.840889e-16\n",
       "82     1.243450e-16\n",
       "98     1.198694e-16\n",
       "21     1.065814e-16\n",
       "95     1.054712e-16\n",
       "93     8.770762e-17\n",
       "48     7.771561e-17\n",
       "74     7.743806e-17\n",
       "100    7.549517e-17\n",
       "50     7.494005e-17\n",
       "dtype: float64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df = pd.DataFrame(ratings_diff)\n",
    "sample = ratings_df.mean()\n",
    "sample.sort_values(ascending=False)[0:10]"
   ]
  }
 ],
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