{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e2faa7b",
   "metadata": {},
   "source": [
    "# Lenta Uplift Modeling Dataset\n",
    "\n",
    "Lenta is a russian food retailer.\n",
    "\n",
    "Lenta dataset for uplift modeling contains data about Lenta's customers grociery shopping and related marketing campaigns. \n",
    "\n",
    "The dataset was originally released for the [BIGTARGET Hackathon by LENTA and Microsoft](https://bigtarget.online/) and is accessible from `sklift.datasets` module using `fetch_lenta` function.\n",
    "\n",
    "Read more about dataset <a href=\"https://www.uplift-modeling.com/en/latest/api/datasets/fetch_lenta.html\">in the api docs</a>.\n",
    "\n",
    "## Load Lenta dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66c2a06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "# install uplift library scikit-uplift and other libraries \n",
    "!{sys.executable} -m pip install scikit-uplift catboost scikit-learn seaborn matplotlib pandas numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a954bd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklift.datasets import fetch_lenta\n",
    "from sklift.models import ClassTransformation\n",
    "from sklift.metrics import uplift_at_k\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from catboost import CatBoostClassifier\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 20)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbd80380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset type: <class 'sklearn.utils.Bunch'>\n",
      "\n",
      "Dataset features shape: (687029, 193)\n",
      "Dataset target shape: (687029,)\n",
      "Dataset treatment shape: (687029,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'treatment', 'DESCR', 'feature_names', 'target_name', 'treatment_name'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns sklearn Bunch object\n",
    "# with data, target, treatment keys\n",
    "# data features (pd.DataFrame), target (pd.Series), treatment (pd.Series) values \n",
    "dataset = fetch_lenta()\n",
    "\n",
    "print(f\"Dataset type: {type(dataset)}\\n\")\n",
    "print(f\"Dataset features shape: {dataset.data.shape}\")\n",
    "print(f\"Dataset target shape: {dataset.target.shape}\")\n",
    "print(f\"Dataset treatment shape: {dataset.treatment.shape}\")\n",
    "\n",
    "dataset.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed0b1b2",
   "metadata": {},
   "source": [
    "Dataset is a dictionary-like object with the following attributes:\n",
    "* ``data`` (DataFrame object): Dataset without target and treatment.\n",
    "* ``target`` (Series object): Column target by values.\n",
    "* ``treatment`` (Series object): Column treatment by values.\n",
    "* ``DESCR`` (str): Description of the Lenta dataset.\n",
    "* ``feature_names`` (list): Names of the features.\n",
    "* ``target_name`` (str): Name of the target.\n",
    "* ``treatment_name`` (str): Name of the treatment.\n",
    "                \n",
    "**Major columns:**\n",
    "- treatment `group` (str): test/control group flag\n",
    "- target `response_att` (binary): target\n",
    "- data `gender` (str): customer gender\n",
    "- data `age` (float): customer age\n",
    "- data `main_format` (int): store type (1 - grociery store, 0 - superstore)\n",
    "\n",
    "Detailed feature description could be found [here](https://www.uplift-modeling.com/en/latest/api/datasets/fetch_lenta.html#lenta)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee135f97",
   "metadata": {},
   "source": [
    "We can specify the path to the destination folder and the name of the folder where the dataset should be stored with `data_home` and `dest_subdir` parameters. By default the path is `/`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f2757a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_home, dest_subdir = \"/etc\", \"data\"\n",
    "# dataset = fetch_lenta(data_home=data_home, dest_subdir=dest_subdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0bca29",
   "metadata": {},
   "source": [
    "We can load and return data, target, and treatment with setting the `return_X_y_t` parameter to `True`. By default `return_X_y_t=False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47e69732",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data, target, treatment = fetch_lenta(return_X_y_t=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69461abb",
   "metadata": {},
   "source": [
    "## Target share for `treatment / control` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0710201e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d634c2400>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, sharey=True, figsize=(15,4))\n",
    "\n",
    "treatment = dataset[\"treatment\"]\n",
    "target = dataset[\"target\"]\n",
    "\n",
    "sns.countplot(x=treatment, ax=ax[0])\n",
    "sns.countplot(x=target, ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584754cb",
   "metadata": {},
   "source": [
    "The current sample is unbalanced in terms of both treatment and target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe2cfacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def crosstab_plot(treatment, target):\n",
    "    ct = pd.crosstab(treatment, target, normalize='index')\n",
    "    \n",
    "    sns.heatmap(ct, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r')\n",
    "    plt.ylabel('Treatment')\n",
    "    plt.xlabel('Target')\n",
    "    plt.title(\"Treatment - Target\", size = 15)\n",
    "    \n",
    "crosstab_plot(dataset.treatment, dataset.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e215f4",
   "metadata": {},
   "source": [
    "## Distributions of some features by treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52214108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9d399d1100>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(15,4))\n",
    "\n",
    "test_index = dataset.treatment[dataset.treatment == 'test'].index\n",
    "control_index = dataset.treatment[dataset.treatment == 'control'].index\n",
    "\n",
    "sns.distplot(dataset.data.loc[test_index, 'response_sms'], label='test', ax=ax[0])\n",
    "sns.distplot(dataset.data.loc[control_index, 'response_sms'], label='control', ax=ax[0])\n",
    "ax[0].title.set_text('Test & Control response SMS Distribution')\n",
    "ax[0].legend()\n",
    "\n",
    "sns.distplot(dataset.data.loc[test_index, 'age'], label='test', ax=ax[1])\n",
    "sns.distplot(dataset.data.loc[control_index, 'age'], label='control', ax=ax[1])\n",
    "ax[1].title.set_text('Test & Control age Distribution')\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0cb340",
   "metadata": {},
   "source": [
    "Clients from the test treatment group tend to respond to sms with a slightly greater probability than clients from the control group. The behavior in the test and control groups does not differ depending on the clients age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b13eef",
   "metadata": {},
   "source": [
    "## Data analysys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3385fcf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 687029 entries, 0 to 687028\n",
      "Columns: 193 entries, age to stdev_discount_depth_1m\n",
      "dtypes: float64(191), int64(1), object(1)\n",
      "memory usage: 1011.6+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70f59789",
   "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>age</th>\n",
       "      <th>cheque_count_12m_g20</th>\n",
       "      <th>cheque_count_12m_g21</th>\n",
       "      <th>cheque_count_12m_g25</th>\n",
       "      <th>cheque_count_12m_g32</th>\n",
       "      <th>cheque_count_12m_g33</th>\n",
       "      <th>cheque_count_12m_g38</th>\n",
       "      <th>cheque_count_12m_g39</th>\n",
       "      <th>cheque_count_12m_g41</th>\n",
       "      <th>cheque_count_12m_g42</th>\n",
       "      <th>cheque_count_12m_g45</th>\n",
       "      <th>cheque_count_12m_g46</th>\n",
       "      <th>cheque_count_12m_g48</th>\n",
       "      <th>cheque_count_12m_g52</th>\n",
       "      <th>cheque_count_12m_g56</th>\n",
       "      <th>cheque_count_12m_g57</th>\n",
       "      <th>cheque_count_12m_g58</th>\n",
       "      <th>cheque_count_12m_g79</th>\n",
       "      <th>cheque_count_3m_g20</th>\n",
       "      <th>cheque_count_3m_g21</th>\n",
       "      <th>cheque_count_3m_g25</th>\n",
       "      <th>cheque_count_3m_g42</th>\n",
       "      <th>cheque_count_3m_g45</th>\n",
       "      <th>cheque_count_3m_g52</th>\n",
       "      <th>cheque_count_3m_g56</th>\n",
       "      <th>cheque_count_3m_g57</th>\n",
       "      <th>cheque_count_3m_g79</th>\n",
       "      <th>cheque_count_6m_g20</th>\n",
       "      <th>cheque_count_6m_g21</th>\n",
       "      <th>cheque_count_6m_g25</th>\n",
       "      <th>cheque_count_6m_g32</th>\n",
       "      <th>cheque_count_6m_g33</th>\n",
       "      <th>cheque_count_6m_g38</th>\n",
       "      <th>cheque_count_6m_g39</th>\n",
       "      <th>cheque_count_6m_g40</th>\n",
       "      <th>cheque_count_6m_g41</th>\n",
       "      <th>cheque_count_6m_g42</th>\n",
       "      <th>cheque_count_6m_g45</th>\n",
       "      <th>cheque_count_6m_g46</th>\n",
       "      <th>cheque_count_6m_g48</th>\n",
       "      <th>cheque_count_6m_g52</th>\n",
       "      <th>cheque_count_6m_g56</th>\n",
       "      <th>cheque_count_6m_g57</th>\n",
       "      <th>cheque_count_6m_g58</th>\n",
       "      <th>cheque_count_6m_g79</th>\n",
       "      <th>children</th>\n",
       "      <th>crazy_purchases_cheque_count_12m</th>\n",
       "      <th>crazy_purchases_cheque_count_1m</th>\n",
       "      <th>crazy_purchases_cheque_count_3m</th>\n",
       "      <th>crazy_purchases_cheque_count_6m</th>\n",
       "      <th>crazy_purchases_goods_count_12m</th>\n",
       "      <th>crazy_purchases_goods_count_6m</th>\n",
       "      <th>disc_sum_6m_g34</th>\n",
       "      <th>food_share_15d</th>\n",
       "      <th>food_share_1m</th>\n",
       "      <th>gender</th>\n",
       "      <th>k_var_cheque_15d</th>\n",
       "      <th>k_var_cheque_3m</th>\n",
       "      <th>k_var_cheque_category_width_15d</th>\n",
       "      <th>k_var_cheque_group_width_15d</th>\n",
       "      <th>k_var_count_per_cheque_15d_g24</th>\n",
       "      <th>k_var_count_per_cheque_15d_g34</th>\n",
       "      <th>k_var_count_per_cheque_1m_g24</th>\n",
       "      <th>k_var_count_per_cheque_1m_g27</th>\n",
       "      <th>k_var_count_per_cheque_1m_g34</th>\n",
       "      <th>k_var_count_per_cheque_1m_g44</th>\n",
       "      <th>k_var_count_per_cheque_1m_g49</th>\n",
       "      <th>k_var_count_per_cheque_3m_g24</th>\n",
       "      <th>k_var_count_per_cheque_3m_g27</th>\n",
       "      <th>k_var_count_per_cheque_3m_g32</th>\n",
       "      <th>k_var_count_per_cheque_3m_g34</th>\n",
       "      <th>k_var_count_per_cheque_3m_g41</th>\n",
       "      <th>k_var_count_per_cheque_3m_g44</th>\n",
       "      <th>k_var_count_per_cheque_6m_g24</th>\n",
       "      <th>k_var_count_per_cheque_6m_g27</th>\n",
       "      <th>k_var_count_per_cheque_6m_g32</th>\n",
       "      <th>k_var_count_per_cheque_6m_g44</th>\n",
       "      <th>k_var_days_between_visits_15d</th>\n",
       "      <th>k_var_days_between_visits_1m</th>\n",
       "      <th>k_var_days_between_visits_3m</th>\n",
       "      <th>k_var_disc_per_cheque_15d</th>\n",
       "      <th>k_var_disc_share_12m_g32</th>\n",
       "      <th>k_var_disc_share_15d_g24</th>\n",
       "      <th>k_var_disc_share_15d_g34</th>\n",
       "      <th>k_var_disc_share_15d_g49</th>\n",
       "      <th>k_var_disc_share_1m_g24</th>\n",
       "      <th>k_var_disc_share_1m_g27</th>\n",
       "      <th>k_var_disc_share_1m_g34</th>\n",
       "      <th>k_var_disc_share_1m_g40</th>\n",
       "      <th>k_var_disc_share_1m_g44</th>\n",
       "      <th>k_var_disc_share_1m_g49</th>\n",
       "      <th>k_var_disc_share_1m_g54</th>\n",
       "      <th>k_var_disc_share_3m_g24</th>\n",
       "      <th>k_var_disc_share_3m_g26</th>\n",
       "      <th>k_var_disc_share_3m_g27</th>\n",
       "      <th>k_var_disc_share_3m_g32</th>\n",
       "      <th>k_var_disc_share_3m_g33</th>\n",
       "      <th>k_var_disc_share_3m_g34</th>\n",
       "      <th>k_var_disc_share_3m_g38</th>\n",
       "      <th>k_var_disc_share_3m_g40</th>\n",
       "      <th>k_var_disc_share_3m_g41</th>\n",
       "      <th>k_var_disc_share_3m_g44</th>\n",
       "      <th>k_var_disc_share_3m_g46</th>\n",
       "      <th>k_var_disc_share_3m_g48</th>\n",
       "      <th>k_var_disc_share_3m_g49</th>\n",
       "      <th>k_var_disc_share_3m_g54</th>\n",
       "      <th>k_var_disc_share_6m_g24</th>\n",
       "      <th>k_var_disc_share_6m_g27</th>\n",
       "      <th>k_var_disc_share_6m_g32</th>\n",
       "      <th>k_var_disc_share_6m_g34</th>\n",
       "      <th>k_var_disc_share_6m_g44</th>\n",
       "      <th>k_var_disc_share_6m_g46</th>\n",
       "      <th>k_var_disc_share_6m_g49</th>\n",
       "      <th>k_var_disc_share_6m_g54</th>\n",
       "      <th>k_var_discount_depth_15d</th>\n",
       "      <th>k_var_discount_depth_1m</th>\n",
       "      <th>k_var_sku_per_cheque_15d</th>\n",
       "      <th>k_var_sku_price_12m_g32</th>\n",
       "      <th>k_var_sku_price_15d_g34</th>\n",
       "      <th>k_var_sku_price_15d_g49</th>\n",
       "      <th>k_var_sku_price_1m_g24</th>\n",
       "      <th>k_var_sku_price_1m_g26</th>\n",
       "      <th>k_var_sku_price_1m_g27</th>\n",
       "      <th>k_var_sku_price_1m_g34</th>\n",
       "      <th>k_var_sku_price_1m_g40</th>\n",
       "      <th>k_var_sku_price_1m_g44</th>\n",
       "      <th>k_var_sku_price_1m_g49</th>\n",
       "      <th>k_var_sku_price_1m_g54</th>\n",
       "      <th>k_var_sku_price_3m_g24</th>\n",
       "      <th>k_var_sku_price_3m_g26</th>\n",
       "      <th>k_var_sku_price_3m_g27</th>\n",
       "      <th>k_var_sku_price_3m_g32</th>\n",
       "      <th>k_var_sku_price_3m_g33</th>\n",
       "      <th>k_var_sku_price_3m_g34</th>\n",
       "      <th>k_var_sku_price_3m_g40</th>\n",
       "      <th>k_var_sku_price_3m_g41</th>\n",
       "      <th>k_var_sku_price_3m_g44</th>\n",
       "      <th>k_var_sku_price_3m_g46</th>\n",
       "      <th>k_var_sku_price_3m_g48</th>\n",
       "      <th>k_var_sku_price_3m_g49</th>\n",
       "      <th>k_var_sku_price_3m_g54</th>\n",
       "      <th>k_var_sku_price_6m_g24</th>\n",
       "      <th>k_var_sku_price_6m_g26</th>\n",
       "      <th>k_var_sku_price_6m_g27</th>\n",
       "      <th>k_var_sku_price_6m_g32</th>\n",
       "      <th>k_var_sku_price_6m_g41</th>\n",
       "      <th>k_var_sku_price_6m_g42</th>\n",
       "      <th>k_var_sku_price_6m_g44</th>\n",
       "      <th>k_var_sku_price_6m_g48</th>\n",
       "      <th>k_var_sku_price_6m_g49</th>\n",
       "      <th>main_format</th>\n",
       "      <th>mean_discount_depth_15d</th>\n",
       "      <th>months_from_register</th>\n",
       "      <th>perdelta_days_between_visits_15_30d</th>\n",
       "      <th>promo_share_15d</th>\n",
       "      <th>response_sms</th>\n",
       "      <th>response_viber</th>\n",
       "      <th>sale_count_12m_g32</th>\n",
       "      <th>sale_count_12m_g33</th>\n",
       "      <th>sale_count_12m_g49</th>\n",
       "      <th>sale_count_12m_g54</th>\n",
       "      <th>sale_count_12m_g57</th>\n",
       "      <th>sale_count_3m_g24</th>\n",
       "      <th>sale_count_3m_g33</th>\n",
       "      <th>sale_count_3m_g57</th>\n",
       "      <th>sale_count_6m_g24</th>\n",
       "      <th>sale_count_6m_g25</th>\n",
       "      <th>sale_count_6m_g32</th>\n",
       "      <th>sale_count_6m_g33</th>\n",
       "      <th>sale_count_6m_g44</th>\n",
       "      <th>sale_count_6m_g54</th>\n",
       "      <th>sale_count_6m_g57</th>\n",
       "      <th>sale_sum_12m_g24</th>\n",
       "      <th>sale_sum_12m_g25</th>\n",
       "      <th>sale_sum_12m_g26</th>\n",
       "      <th>sale_sum_12m_g27</th>\n",
       "      <th>sale_sum_12m_g32</th>\n",
       "      <th>sale_sum_12m_g44</th>\n",
       "      <th>sale_sum_12m_g54</th>\n",
       "      <th>sale_sum_3m_g24</th>\n",
       "      <th>sale_sum_3m_g26</th>\n",
       "      <th>sale_sum_3m_g32</th>\n",
       "      <th>sale_sum_3m_g33</th>\n",
       "      <th>sale_sum_6m_g24</th>\n",
       "      <th>sale_sum_6m_g25</th>\n",
       "      <th>sale_sum_6m_g26</th>\n",
       "      <th>sale_sum_6m_g32</th>\n",
       "      <th>sale_sum_6m_g33</th>\n",
       "      <th>sale_sum_6m_g44</th>\n",
       "      <th>sale_sum_6m_g54</th>\n",
       "      <th>stdev_days_between_visits_15d</th>\n",
       "      <th>stdev_discount_depth_15d</th>\n",
       "      <th>stdev_discount_depth_1m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>153.09</td>\n",
       "      <td>0.6488</td>\n",
       "      <td>0.3254</td>\n",
       "      <td>Ж</td>\n",
       "      <td>0.7288</td>\n",
       "      <td>1.8741</td>\n",
       "      <td>0.5263</td>\n",
       "      <td>0.7692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6682</td>\n",
       "      <td>0.5592</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.5871</td>\n",
       "      <td>0.4654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.6183</td>\n",
       "      <td>0.4845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5471</td>\n",
       "      <td>0.4554</td>\n",
       "      <td>0.6479</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>1.4055</td>\n",
       "      <td>1.4080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1559</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8300</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.3846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7418</td>\n",
       "      <td>0.5004</td>\n",
       "      <td>1.2014</td>\n",
       "      <td>1.3485</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.2304</td>\n",
       "      <td>0.7229</td>\n",
       "      <td>0.5943</td>\n",
       "      <td>1.5156</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.8036</td>\n",
       "      <td>0.6366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7793</td>\n",
       "      <td>1.2143</td>\n",
       "      <td>1.0723</td>\n",
       "      <td>1.3947</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.4621</td>\n",
       "      <td>0.4864</td>\n",
       "      <td>0.7067</td>\n",
       "      <td>0.0589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5946</td>\n",
       "      <td>0.0823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8669</td>\n",
       "      <td>0.3707</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7177</td>\n",
       "      <td>0.0866</td>\n",
       "      <td>1.3485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4640</td>\n",
       "      <td>0.3956</td>\n",
       "      <td>0.1930</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8019</td>\n",
       "      <td>0.1895</td>\n",
       "      <td>0.6128</td>\n",
       "      <td>2.1596</td>\n",
       "      <td>0.6810</td>\n",
       "      <td>0.6546</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>1.2374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8756</td>\n",
       "      <td>0.6718</td>\n",
       "      <td>2.0876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6055</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.3393</td>\n",
       "      <td>0.5821</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>10.0</td>\n",
       "      <td>84.314</td>\n",
       "      <td>98.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>137.282</td>\n",
       "      <td>28.776</td>\n",
       "      <td>6.0</td>\n",
       "      <td>169.658</td>\n",
       "      <td>10.680</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.776</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4469.86</td>\n",
       "      <td>658.85</td>\n",
       "      <td>1286.32</td>\n",
       "      <td>7736.05</td>\n",
       "      <td>418.80</td>\n",
       "      <td>3233.31</td>\n",
       "      <td>811.73</td>\n",
       "      <td>2321.61</td>\n",
       "      <td>182.82</td>\n",
       "      <td>283.84</td>\n",
       "      <td>3648.23</td>\n",
       "      <td>3141.25</td>\n",
       "      <td>356.67</td>\n",
       "      <td>237.25</td>\n",
       "      <td>283.84</td>\n",
       "      <td>3648.23</td>\n",
       "      <td>1195.37</td>\n",
       "      <td>535.42</td>\n",
       "      <td>1.7078</td>\n",
       "      <td>0.2798</td>\n",
       "      <td>0.3008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.99</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Ж</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.9630</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0102</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0027</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1289</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6188</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4981</td>\n",
       "      <td>0.6382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1289</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4981</td>\n",
       "      <td>0.6382</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3993</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6192</td>\n",
       "      <td>0.5405</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6192</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.744</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.39</td>\n",
       "      <td>62.69</td>\n",
       "      <td>58.71</td>\n",
       "      <td>93.35</td>\n",
       "      <td>87.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>122.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>58.71</td>\n",
       "      <td>87.01</td>\n",
       "      <td>179.83</td>\n",
       "      <td>113.39</td>\n",
       "      <td>62.69</td>\n",
       "      <td>58.71</td>\n",
       "      <td>87.01</td>\n",
       "      <td>179.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>122.98</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>290.00</td>\n",
       "      <td>0.3739</td>\n",
       "      <td>0.4768</td>\n",
       "      <td>М</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3295</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4159</td>\n",
       "      <td>0.8485</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6009</td>\n",
       "      <td>0.6205</td>\n",
       "      <td>1.0035</td>\n",
       "      <td>0.5712</td>\n",
       "      <td>0.5762</td>\n",
       "      <td>0.4714</td>\n",
       "      <td>0.9830</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8362</td>\n",
       "      <td>1.3183</td>\n",
       "      <td>0.8560</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6077</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7665</td>\n",
       "      <td>1.2056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0002</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>0.8461</td>\n",
       "      <td>0.5812</td>\n",
       "      <td>0.6945</td>\n",
       "      <td>1.7252</td>\n",
       "      <td>0.7579</td>\n",
       "      <td>0.6608</td>\n",
       "      <td>0.8560</td>\n",
       "      <td>0.9266</td>\n",
       "      <td>1.1554</td>\n",
       "      <td>0.7782</td>\n",
       "      <td>0.7471</td>\n",
       "      <td>2.0674</td>\n",
       "      <td>0.8871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4668</td>\n",
       "      <td>1.3422</td>\n",
       "      <td>0.3536</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0457</td>\n",
       "      <td>0.2615</td>\n",
       "      <td>0.5856</td>\n",
       "      <td>0.2870</td>\n",
       "      <td>0.5238</td>\n",
       "      <td>0.2017</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>1.8758</td>\n",
       "      <td>0.6338</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4481</td>\n",
       "      <td>0.7673</td>\n",
       "      <td>0.2393</td>\n",
       "      <td>0.2851</td>\n",
       "      <td>0.5170</td>\n",
       "      <td>0.2407</td>\n",
       "      <td>2.5227</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7256</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.102</td>\n",
       "      <td>50.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.294</td>\n",
       "      <td>11.084</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.158</td>\n",
       "      <td>31.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1564.91</td>\n",
       "      <td>971.09</td>\n",
       "      <td>177.93</td>\n",
       "      <td>3257.49</td>\n",
       "      <td>975.21</td>\n",
       "      <td>2555.27</td>\n",
       "      <td>6351.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>783.87</td>\n",
       "      <td>1239.19</td>\n",
       "      <td>533.46</td>\n",
       "      <td>83.37</td>\n",
       "      <td>593.13</td>\n",
       "      <td>1217.43</td>\n",
       "      <td>1336.83</td>\n",
       "      <td>3709.82</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Ж</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.4933</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3295</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7432</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7904</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.5362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5780</td>\n",
       "      <td>0.1315</td>\n",
       "      <td>0.3219</td>\n",
       "      <td>1.1290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7975</td>\n",
       "      <td>1.2530</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1655</td>\n",
       "      <td>0.6560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1326</td>\n",
       "      <td>0.1477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7469</td>\n",
       "      <td>0.2352</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>0.6846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2671</td>\n",
       "      <td>0.1028</td>\n",
       "      <td>3.0736</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.544</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000</td>\n",
       "      <td>34.212</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.778</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>358.22</td>\n",
       "      <td>3798.18</td>\n",
       "      <td>680.93</td>\n",
       "      <td>1425.07</td>\n",
       "      <td>175.73</td>\n",
       "      <td>602.81</td>\n",
       "      <td>3544.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>119.99</td>\n",
       "      <td>73.24</td>\n",
       "      <td>346.74</td>\n",
       "      <td>139.68</td>\n",
       "      <td>1849.91</td>\n",
       "      <td>360.40</td>\n",
       "      <td>175.73</td>\n",
       "      <td>496.73</td>\n",
       "      <td>172.58</td>\n",
       "      <td>1246.21</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>161.12</td>\n",
       "      <td>0.2882</td>\n",
       "      <td>0.2882</td>\n",
       "      <td>Ж</td>\n",
       "      <td>0.9301</td>\n",
       "      <td>0.9014</td>\n",
       "      <td>0.8165</td>\n",
       "      <td>0.7542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7781</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4826</td>\n",
       "      <td>0.7526</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4714</td>\n",
       "      <td>0.4714</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>1.3497</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.2273</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>0.9172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6549</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6684</td>\n",
       "      <td>0.4566</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>1.2899</td>\n",
       "      <td>1.6070</td>\n",
       "      <td>1.0710</td>\n",
       "      <td>0.9058</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6232</td>\n",
       "      <td>1.3194</td>\n",
       "      <td>0.4903</td>\n",
       "      <td>0.4903</td>\n",
       "      <td>0.9423</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2284</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.2146</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1934</td>\n",
       "      <td>0.5189</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3868</td>\n",
       "      <td>2.0293</td>\n",
       "      <td>0.6860</td>\n",
       "      <td>0.6113</td>\n",
       "      <td>1.0926</td>\n",
       "      <td>0.2211</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.2496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2195</td>\n",
       "      <td>1.4917</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7128</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.454</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.036</td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.454</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226.98</td>\n",
       "      <td>168.05</td>\n",
       "      <td>960.37</td>\n",
       "      <td>1560.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>342.45</td>\n",
       "      <td>1039.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>87.94</td>\n",
       "      <td>226.98</td>\n",
       "      <td>168.05</td>\n",
       "      <td>461.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>237.93</td>\n",
       "      <td>225.51</td>\n",
       "      <td>995.27</td>\n",
       "      <td>1.4142</td>\n",
       "      <td>0.3495</td>\n",
       "      <td>0.3495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687024</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.90</td>\n",
       "      <td>0.6214</td>\n",
       "      <td>0.6924</td>\n",
       "      <td>Ж</td>\n",
       "      <td>1.0931</td>\n",
       "      <td>1.1344</td>\n",
       "      <td>0.8240</td>\n",
       "      <td>0.8281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4714</td>\n",
       "      <td>0.4359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.4359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4714</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6614</td>\n",
       "      <td>0.5162</td>\n",
       "      <td>0.5162</td>\n",
       "      <td>1.0710</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1557</td>\n",
       "      <td>1.1622</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1557</td>\n",
       "      <td>1.0531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3709</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2821</td>\n",
       "      <td>1.1557</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0531</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8648</td>\n",
       "      <td>1.2152</td>\n",
       "      <td>1.3709</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.7422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8648</td>\n",
       "      <td>1.4424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6335</td>\n",
       "      <td>0.6177</td>\n",
       "      <td>0.9331</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>1.3395</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.5187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6498</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0444</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.5187</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4438</td>\n",
       "      <td>1.0347</td>\n",
       "      <td>1.6498</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0347</td>\n",
       "      <td>1.7847</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5756</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.3333</td>\n",
       "      <td>0.4002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.856</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.856</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.09</td>\n",
       "      <td>695.32</td>\n",
       "      <td>111.87</td>\n",
       "      <td>114.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1173.84</td>\n",
       "      <td>147.68</td>\n",
       "      <td>550.09</td>\n",
       "      <td>111.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>330.96</td>\n",
       "      <td>550.09</td>\n",
       "      <td>669.33</td>\n",
       "      <td>111.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>330.96</td>\n",
       "      <td>1173.84</td>\n",
       "      <td>119.99</td>\n",
       "      <td>2.6458</td>\n",
       "      <td>0.3646</td>\n",
       "      <td>0.3282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687025</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>М</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.2382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687026</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.9847</td>\n",
       "      <td>0.9847</td>\n",
       "      <td>М</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9847</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.9847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>155.97</td>\n",
       "      <td>23.99</td>\n",
       "      <td>41.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>615.77</td>\n",
       "      <td>87.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>449.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687027</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3545</td>\n",
       "      <td>0.7263</td>\n",
       "      <td>М</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8318</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8318</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81.90</td>\n",
       "      <td>29.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>46.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687028</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Ж</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.8408</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9895</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.9895</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3536</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5264</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.1059</td>\n",
       "      <td>0.5745</td>\n",
       "      <td>0.2423</td>\n",
       "      <td>0.6481</td>\n",
       "      <td>0.3924</td>\n",
       "      <td>0.5264</td>\n",
       "      <td>0.2564</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.1059</td>\n",
       "      <td>0.5745</td>\n",
       "      <td>0.6481</td>\n",
       "      <td>0.3924</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5179</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.5834</td>\n",
       "      <td>0.1777</td>\n",
       "      <td>0.2541</td>\n",
       "      <td>1.2352</td>\n",
       "      <td>0.3381</td>\n",
       "      <td>0.5179</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2672</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5834</td>\n",
       "      <td>0.2541</td>\n",
       "      <td>1.2352</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.452</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.660</td>\n",
       "      <td>1.344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.660</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.344</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>531.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>916.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2407.56</td>\n",
       "      <td>1304.03</td>\n",
       "      <td>290.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>228.47</td>\n",
       "      <td>290.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>228.47</td>\n",
       "      <td>752.32</td>\n",
       "      <td>596.86</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  cheque_count_12m_g20  cheque_count_12m_g21  \\\n",
       "0       47.0                   3.0                  22.0   \n",
       "1       57.0                   1.0                   0.0   \n",
       "2       38.0                   7.0                   0.0   \n",
       "3       65.0                   6.0                   3.0   \n",
       "4       61.0                   0.0                   1.0   \n",
       "687024  35.0                   0.0                   0.0   \n",
       "687025  33.0                   0.0                   0.0   \n",
       "687026  36.0                   0.0                   0.0   \n",
       "687027  37.0                   0.0                   1.0   \n",
       "687028  40.0                   0.0                   1.0   \n",
       "\n",
       "        cheque_count_12m_g25  cheque_count_12m_g32  cheque_count_12m_g33  \\\n",
       "0                       19.0                   3.0                  28.0   \n",
       "1                        2.0                   1.0                   1.0   \n",
       "2                       15.0                   4.0                   9.0   \n",
       "3                       25.0                   2.0                  10.0   \n",
       "4                        2.0                   0.0                   2.0   \n",
       "687024                   4.0                   0.0                   2.0   \n",
       "687025                   0.0                   0.0                   0.0   \n",
       "687026                   3.0                   0.0                   0.0   \n",
       "687027                   2.0                   0.0                   0.0   \n",
       "687028                   0.0                   0.0                   2.0   \n",
       "\n",
       "        cheque_count_12m_g38  cheque_count_12m_g39  cheque_count_12m_g41  \\\n",
       "0                        8.0                   7.0                   6.0   \n",
       "1                        1.0                   0.0                   1.0   \n",
       "2                        5.0                   9.0                  14.0   \n",
       "3                       14.0                  11.0                   8.0   \n",
       "4                        1.0                   0.0                   3.0   \n",
       "687024                   0.0                   1.0                   0.0   \n",
       "687025                   0.0                   0.0                   0.0   \n",
       "687026                   0.0                   0.0                   1.0   \n",
       "687027                   0.0                   0.0                   0.0   \n",
       "687028                   0.0                   0.0                   2.0   \n",
       "\n",
       "        cheque_count_12m_g42  cheque_count_12m_g45  cheque_count_12m_g46  \\\n",
       "0                        1.0                  13.0                  12.0   \n",
       "1                        0.0                   1.0                   0.0   \n",
       "2                        7.0                   6.0                  10.0   \n",
       "3                        1.0                   0.0                   2.0   \n",
       "4                        2.0                   1.0                   1.0   \n",
       "687024                   3.0                   2.0                   2.0   \n",
       "687025                   0.0                   0.0                   0.0   \n",
       "687026                   0.0                   0.0                   1.0   \n",
       "687027                   1.0                   0.0                   1.0   \n",
       "687028                   2.0                   2.0                   2.0   \n",
       "\n",
       "        cheque_count_12m_g48  cheque_count_12m_g52  cheque_count_12m_g56  \\\n",
       "0                       16.0                   3.0                  15.0   \n",
       "1                        1.0                   0.0                   0.0   \n",
       "2                       14.0                   5.0                  11.0   \n",
       "3                        6.0                   7.0                   2.0   \n",
       "4                        5.0                   5.0                   0.0   \n",
       "687024                   3.0                   2.0                   1.0   \n",
       "687025                   2.0                   0.0                   0.0   \n",
       "687026                   0.0                   0.0                   0.0   \n",
       "687027                   0.0                   1.0                   0.0   \n",
       "687028                   3.0                   1.0                   1.0   \n",
       "\n",
       "        cheque_count_12m_g57  cheque_count_12m_g58  cheque_count_12m_g79  \\\n",
       "0                       11.0                   0.0                   4.0   \n",
       "1                        0.0                   0.0                   1.0   \n",
       "2                        0.0                   3.0                   2.0   \n",
       "3                        0.0                   0.0                   0.0   \n",
       "4                        0.0                   0.0                   1.0   \n",
       "687024                   0.0                   1.0                   0.0   \n",
       "687025                   0.0                   0.0                   0.0   \n",
       "687026                   0.0                   0.0                   0.0   \n",
       "687027                   0.0                   0.0                   0.0   \n",
       "687028                   2.0                   1.0                   4.0   \n",
       "\n",
       "        cheque_count_3m_g20  cheque_count_3m_g21  cheque_count_3m_g25  \\\n",
       "0                       0.0                  7.0                  8.0   \n",
       "1                       0.0                  0.0                  2.0   \n",
       "2                       2.0                  0.0                  3.0   \n",
       "3                       1.0                  0.0                  5.0   \n",
       "4                       0.0                  1.0                  1.0   \n",
       "687024                  0.0                  0.0                  3.0   \n",
       "687025                  NaN                  NaN                  NaN   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  1.0   \n",
       "687028                  0.0                  1.0                  0.0   \n",
       "\n",
       "        cheque_count_3m_g42  cheque_count_3m_g45  cheque_count_3m_g52  \\\n",
       "0                       0.0                  5.0                  1.0   \n",
       "1                       0.0                  1.0                  0.0   \n",
       "2                       2.0                  1.0                  1.0   \n",
       "3                       0.0                  0.0                  1.0   \n",
       "4                       0.0                  0.0                  2.0   \n",
       "687024                  2.0                  1.0                  2.0   \n",
       "687025                  NaN                  NaN                  NaN   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  1.0   \n",
       "687028                  1.0                  0.0                  0.0   \n",
       "\n",
       "        cheque_count_3m_g56  cheque_count_3m_g57  cheque_count_3m_g79  \\\n",
       "0                       6.0                  6.0                  1.0   \n",
       "1                       0.0                  0.0                  1.0   \n",
       "2                       0.0                  0.0                  2.0   \n",
       "3                       0.0                  0.0                  0.0   \n",
       "4                       0.0                  0.0                  1.0   \n",
       "687024                  1.0                  0.0                  0.0   \n",
       "687025                  NaN                  NaN                  NaN   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  0.0   \n",
       "687028                  1.0                  1.0                  3.0   \n",
       "\n",
       "        cheque_count_6m_g20  cheque_count_6m_g21  cheque_count_6m_g25  \\\n",
       "0                       0.0                 12.0                  9.0   \n",
       "1                       1.0                  0.0                  2.0   \n",
       "2                       6.0                  0.0                  9.0   \n",
       "3                       2.0                  1.0                 11.0   \n",
       "4                       0.0                  1.0                  2.0   \n",
       "687024                  0.0                  0.0                  3.0   \n",
       "687025                  0.0                  0.0                  0.0   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  1.0   \n",
       "687028                  0.0                  1.0                  0.0   \n",
       "\n",
       "        cheque_count_6m_g32  cheque_count_6m_g33  cheque_count_6m_g38  \\\n",
       "0                       1.0                  6.0                  4.0   \n",
       "1                       1.0                  1.0                  1.0   \n",
       "2                       2.0                  5.0                  1.0   \n",
       "3                       2.0                  3.0                  5.0   \n",
       "4                       0.0                  2.0                  1.0   \n",
       "687024                  0.0                  2.0                  0.0   \n",
       "687025                  0.0                  0.0                  0.0   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  0.0   \n",
       "687028                  0.0                  1.0                  0.0   \n",
       "\n",
       "        cheque_count_6m_g39  cheque_count_6m_g40  cheque_count_6m_g41  \\\n",
       "0                       2.0                  5.0                  1.0   \n",
       "1                       0.0                  3.0                  1.0   \n",
       "2                       7.0                  7.0                  8.0   \n",
       "3                       5.0                  4.0                  2.0   \n",
       "4                       0.0                  8.0                  2.0   \n",
       "687024                  0.0                  5.0                  0.0   \n",
       "687025                  0.0                  1.0                  0.0   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  1.0                  0.0   \n",
       "687028                  0.0                  0.0                  0.0   \n",
       "\n",
       "        cheque_count_6m_g42  cheque_count_6m_g45  cheque_count_6m_g46  \\\n",
       "0                       0.0                  5.0                  5.0   \n",
       "1                       0.0                  1.0                  0.0   \n",
       "2                       3.0                  2.0                  6.0   \n",
       "3                       1.0                  0.0                  1.0   \n",
       "4                       2.0                  1.0                  1.0   \n",
       "687024                  2.0                  2.0                  2.0   \n",
       "687025                  0.0                  0.0                  0.0   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  0.0   \n",
       "687028                  1.0                  0.0                  2.0   \n",
       "\n",
       "        cheque_count_6m_g48  cheque_count_6m_g52  cheque_count_6m_g56  \\\n",
       "0                       6.0                  1.0                  6.0   \n",
       "1                       1.0                  0.0                  0.0   \n",
       "2                       6.0                  3.0                  4.0   \n",
       "3                       3.0                  1.0                  0.0   \n",
       "4                       4.0                  3.0                  0.0   \n",
       "687024                  2.0                  2.0                  1.0   \n",
       "687025                  2.0                  0.0                  0.0   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  1.0                  0.0   \n",
       "687028                  2.0                  0.0                  1.0   \n",
       "\n",
       "        cheque_count_6m_g57  cheque_count_6m_g58  cheque_count_6m_g79  \\\n",
       "0                       9.0                  0.0                  1.0   \n",
       "1                       0.0                  0.0                  1.0   \n",
       "2                       0.0                  0.0                  2.0   \n",
       "3                       0.0                  0.0                  0.0   \n",
       "4                       0.0                  0.0                  1.0   \n",
       "687024                  0.0                  1.0                  0.0   \n",
       "687025                  0.0                  0.0                  0.0   \n",
       "687026                  0.0                  0.0                  0.0   \n",
       "687027                  0.0                  0.0                  0.0   \n",
       "687028                  1.0                  0.0                  3.0   \n",
       "\n",
       "        children  crazy_purchases_cheque_count_12m  \\\n",
       "0            0.0                              13.0   \n",
       "1            0.0                               0.0   \n",
       "2            3.0                               0.0   \n",
       "3            0.0                               2.0   \n",
       "4            2.0                               4.0   \n",
       "687024       3.0                               0.0   \n",
       "687025       0.0                               0.0   \n",
       "687026       0.0                               0.0   \n",
       "687027       0.0                               0.0   \n",
       "687028       3.0                               1.0   \n",
       "\n",
       "        crazy_purchases_cheque_count_1m  crazy_purchases_cheque_count_3m  \\\n",
       "0                                   3.0                              5.0   \n",
       "1                                   0.0                              0.0   \n",
       "2                                   0.0                              0.0   \n",
       "3                                   0.0                              0.0   \n",
       "4                                   1.0                              1.0   \n",
       "687024                              0.0                              0.0   \n",
       "687025                              0.0                              0.0   \n",
       "687026                              0.0                              0.0   \n",
       "687027                              0.0                              0.0   \n",
       "687028                              0.0                              0.0   \n",
       "\n",
       "        crazy_purchases_cheque_count_6m  crazy_purchases_goods_count_12m  \\\n",
       "0                                   8.0                             16.0   \n",
       "1                                   0.0                              0.0   \n",
       "2                                   0.0                              0.0   \n",
       "3                                   0.0                              3.0   \n",
       "4                                   2.0                              4.0   \n",
       "687024                              0.0                              0.0   \n",
       "687025                              0.0                              0.0   \n",
       "687026                              0.0                              0.0   \n",
       "687027                              0.0                              0.0   \n",
       "687028                              0.0                              1.0   \n",
       "\n",
       "        crazy_purchases_goods_count_6m  disc_sum_6m_g34  food_share_15d  \\\n",
       "0                                 11.0           153.09          0.6488   \n",
       "1                                  0.0            55.99          0.0000   \n",
       "2                                  0.0           290.00          0.3739   \n",
       "3                                  0.0            51.81          0.0000   \n",
       "4                                  2.0           161.12          0.2882   \n",
       "687024                             0.0           130.90          0.6214   \n",
       "687025                             0.0             0.00          0.0000   \n",
       "687026                             0.0             0.00          0.9847   \n",
       "687027                             0.0             0.00          0.3545   \n",
       "687028                             0.0             0.00          0.0000   \n",
       "\n",
       "        food_share_1m gender  k_var_cheque_15d  k_var_cheque_3m  \\\n",
       "0              0.3254      Ж            0.7288           1.8741   \n",
       "1              1.0000      Ж            0.0000           0.9630   \n",
       "2              0.4768      М               NaN           0.3295   \n",
       "3              1.0000      Ж            0.0000           1.4933   \n",
       "4              0.2882      Ж            0.9301           0.9014   \n",
       "687024         0.6924      Ж            1.0931           1.1344   \n",
       "687025         0.0000      М            0.0000           0.0000   \n",
       "687026         0.9847      М               NaN              NaN   \n",
       "687027         0.7263      М               NaN           0.2269   \n",
       "687028         0.0000      Ж            0.0000           0.8408   \n",
       "\n",
       "        k_var_cheque_category_width_15d  k_var_cheque_group_width_15d  \\\n",
       "0                                0.5263                        0.7692   \n",
       "1                                0.0000                        0.0000   \n",
       "2                                   NaN                           NaN   \n",
       "3                                0.0000                        0.0000   \n",
       "4                                0.8165                        0.7542   \n",
       "687024                           0.8240                        0.8281   \n",
       "687025                           0.0000                        0.0000   \n",
       "687026                              NaN                           NaN   \n",
       "687027                              NaN                           NaN   \n",
       "687028                           0.0000                        0.0000   \n",
       "\n",
       "        k_var_count_per_cheque_15d_g24  k_var_count_per_cheque_15d_g34  \\\n",
       "0                                  NaN                             NaN   \n",
       "1                                  NaN                             NaN   \n",
       "2                                  0.0                             NaN   \n",
       "3                                  NaN                             NaN   \n",
       "4                                  0.0                             NaN   \n",
       "687024                             NaN                          0.4714   \n",
       "687025                             NaN                             NaN   \n",
       "687026                             0.0                          0.0000   \n",
       "687027                             0.0                          0.0000   \n",
       "687028                             NaN                             NaN   \n",
       "\n",
       "        k_var_count_per_cheque_1m_g24  k_var_count_per_cheque_1m_g27  \\\n",
       "0                              0.2917                            NaN   \n",
       "1                              0.0000                            0.0   \n",
       "2                              0.0000                            0.0   \n",
       "3                              0.0000                            0.0   \n",
       "4                              0.0000                            0.0   \n",
       "687024                         0.4359                            NaN   \n",
       "687025                            NaN                            NaN   \n",
       "687026                         0.0000                            0.0   \n",
       "687027                         0.0000                            0.0   \n",
       "687028                            NaN                            NaN   \n",
       "\n",
       "        k_var_count_per_cheque_1m_g34  k_var_count_per_cheque_1m_g44  \\\n",
       "0                              0.6682                         0.5592   \n",
       "1                              0.0000                         0.0000   \n",
       "2                              0.4159                         0.8485   \n",
       "3                              0.0000                         0.0000   \n",
       "4                                 NaN                         0.0000   \n",
       "687024                         0.4714                            NaN   \n",
       "687025                            NaN                            NaN   \n",
       "687026                         0.0000                            NaN   \n",
       "687027                         0.0000                         0.0000   \n",
       "687028                            NaN                            NaN   \n",
       "\n",
       "        k_var_count_per_cheque_1m_g49  k_var_count_per_cheque_3m_g24  \\\n",
       "0                               0.400                         0.5871   \n",
       "1                               0.000                         0.0000   \n",
       "2                               0.000                         0.0000   \n",
       "3                               0.000                         0.0000   \n",
       "4                               0.000                         0.0000   \n",
       "687024                          0.441                         0.4359   \n",
       "687025                            NaN                            NaN   \n",
       "687026                            NaN                         0.0000   \n",
       "687027                            NaN                         0.0000   \n",
       "687028                            NaN                         0.9895   \n",
       "\n",
       "        k_var_count_per_cheque_3m_g27  k_var_count_per_cheque_3m_g32  \\\n",
       "0                              0.4654                            NaN   \n",
       "1                              0.0000                            NaN   \n",
       "2                              0.0302                            0.0   \n",
       "3                                 NaN                            NaN   \n",
       "4                              0.6682                            0.0   \n",
       "687024                            NaN                            0.0   \n",
       "687025                            NaN                            NaN   \n",
       "687026                         0.0000                            0.0   \n",
       "687027                         0.0000                            0.0   \n",
       "687028                         0.9970                            0.0   \n",
       "\n",
       "        k_var_count_per_cheque_3m_g34  k_var_count_per_cheque_3m_g41  \\\n",
       "0                              0.6055                         0.0000   \n",
       "1                              1.0102                         0.0000   \n",
       "2                              0.6009                         0.6205   \n",
       "3                              0.0000                         0.0000   \n",
       "4                              0.7781                         0.0000   \n",
       "687024                         0.4714                         0.0000   \n",
       "687025                            NaN                            NaN   \n",
       "687026                         0.0000                         0.0000   \n",
       "687027                         0.0000                         0.0000   \n",
       "687028                         0.0000                         0.0000   \n",
       "\n",
       "        k_var_count_per_cheque_3m_g44  k_var_count_per_cheque_6m_g24  \\\n",
       "0                              0.5590                         0.6183   \n",
       "1                              0.0000                            NaN   \n",
       "2                              1.0035                         0.5712   \n",
       "3                                 NaN                            NaN   \n",
       "4                              0.0000                         0.4826   \n",
       "687024                            NaN                         0.4359   \n",
       "687025                            NaN                         0.0000   \n",
       "687026                            NaN                         0.0000   \n",
       "687027                         0.0000                         0.0000   \n",
       "687028                         0.6667                         0.9895   \n",
       "\n",
       "        k_var_count_per_cheque_6m_g27  k_var_count_per_cheque_6m_g32  \\\n",
       "0                              0.4845                            NaN   \n",
       "1                                 NaN                            NaN   \n",
       "2                              0.5762                         0.4714   \n",
       "3                              0.3295                         0.0000   \n",
       "4                              0.7526                         0.0000   \n",
       "687024                            NaN                         0.0000   \n",
       "687025                         0.0000                         0.0000   \n",
       "687026                         0.0000                         0.0000   \n",
       "687027                         0.0000                         0.0000   \n",
       "687028                         0.9970                         0.0000   \n",
       "\n",
       "        k_var_count_per_cheque_6m_g44  k_var_days_between_visits_15d  \\\n",
       "0                              0.5471                         0.4554   \n",
       "1                              0.0000                         0.0000   \n",
       "2                              0.9830                         0.0000   \n",
       "3                              0.0000                         0.0000   \n",
       "4                                 NaN                         0.4714   \n",
       "687024                            NaN                         0.6614   \n",
       "687025                         0.0000                         0.0000   \n",
       "687026                            NaN                         0.0000   \n",
       "687027                         0.0000                         0.0000   \n",
       "687028                         0.6667                         0.0000   \n",
       "\n",
       "        k_var_days_between_visits_1m  k_var_days_between_visits_3m  \\\n",
       "0                             0.6479                        0.8240   \n",
       "1                             0.0000                        1.0027   \n",
       "2                                NaN                        0.5559   \n",
       "3                             0.0000                        0.7432   \n",
       "4                             0.4714                        0.9980   \n",
       "687024                        0.5162                        0.5162   \n",
       "687025                        0.0000                        0.0000   \n",
       "687026                        0.0000                        0.0000   \n",
       "687027                           NaN                           NaN   \n",
       "687028                        0.0000                        0.3536   \n",
       "\n",
       "        k_var_disc_per_cheque_15d  k_var_disc_share_12m_g32  \\\n",
       "0                          1.4055                    1.4080   \n",
       "1                          0.0000                       NaN   \n",
       "2                             NaN                    0.9780   \n",
       "3                          0.0000                    0.1315   \n",
       "4                          1.3497                    0.0000   \n",
       "687024                     1.0710                    0.0000   \n",
       "687025                     0.0000                    0.0000   \n",
       "687026                        NaN                    0.0000   \n",
       "687027                        NaN                    0.0000   \n",
       "687028                     0.0000                    0.0000   \n",
       "\n",
       "        k_var_disc_share_15d_g24  k_var_disc_share_15d_g34  \\\n",
       "0                            NaN                       NaN   \n",
       "1                            NaN                       NaN   \n",
       "2                            0.0                       NaN   \n",
       "3                            NaN                       NaN   \n",
       "4                            0.0                       NaN   \n",
       "687024                       NaN                    1.1557   \n",
       "687025                       NaN                       NaN   \n",
       "687026                       0.0                    0.0000   \n",
       "687027                       0.0                    0.0000   \n",
       "687028                       NaN                       NaN   \n",
       "\n",
       "        k_var_disc_share_15d_g49  k_var_disc_share_1m_g24  \\\n",
       "0                            NaN                   0.5208   \n",
       "1                            NaN                   0.0000   \n",
       "2                            NaN                   0.0000   \n",
       "3                            NaN                   0.0000   \n",
       "4                         0.0000                   0.0000   \n",
       "687024                    1.1622                   0.0058   \n",
       "687025                       NaN                      NaN   \n",
       "687026                       NaN                   0.0000   \n",
       "687027                    0.0000                   0.0000   \n",
       "687028                       NaN                      NaN   \n",
       "\n",
       "        k_var_disc_share_1m_g27  k_var_disc_share_1m_g34  \\\n",
       "0                           NaN                   0.5462   \n",
       "1                           0.0                   0.0000   \n",
       "2                           0.0                   0.0078   \n",
       "3                           0.0                   0.0000   \n",
       "4                           0.0                      NaN   \n",
       "687024                      NaN                   1.1557   \n",
       "687025                      NaN                      NaN   \n",
       "687026                      0.0                   0.0000   \n",
       "687027                      0.0                   0.0000   \n",
       "687028                      NaN                      NaN   \n",
       "\n",
       "        k_var_disc_share_1m_g40  k_var_disc_share_1m_g44  \\\n",
       "0                           NaN                   0.1559   \n",
       "1                           NaN                   0.0000   \n",
       "2                           NaN                   0.8362   \n",
       "3                        0.0000                   0.0000   \n",
       "4                           NaN                   0.0000   \n",
       "687024                   1.0531                      NaN   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                      NaN   \n",
       "687027                      NaN                   0.0000   \n",
       "687028                      NaN                      NaN   \n",
       "\n",
       "        k_var_disc_share_1m_g49  k_var_disc_share_1m_g54  \\\n",
       "0                        0.0449                   0.0000   \n",
       "1                        0.0000                   0.0000   \n",
       "2                        1.3183                   0.8560   \n",
       "3                        0.0000                      NaN   \n",
       "4                        0.0000                   1.2273   \n",
       "687024                   1.3709                   0.0000   \n",
       "687025                      NaN                      NaN   \n",
       "687026                      NaN                   0.0000   \n",
       "687027                      NaN                   0.0000   \n",
       "687028                      NaN                      NaN   \n",
       "\n",
       "        k_var_disc_share_3m_g24  k_var_disc_share_3m_g26  \\\n",
       "0                        0.8300                   0.0115   \n",
       "1                        0.0000                   0.1094   \n",
       "2                        0.0000                   0.0000   \n",
       "3                        0.0000                      NaN   \n",
       "4                        0.0000                   0.0249   \n",
       "687024                   0.0058                      NaN   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                   0.0000   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                   0.5264                   0.0000   \n",
       "\n",
       "        k_var_disc_share_3m_g27  k_var_disc_share_3m_g32  \\\n",
       "0                        0.3846                      NaN   \n",
       "1                        0.0000                      NaN   \n",
       "2                        0.6077                      0.0   \n",
       "3                           NaN                      NaN   \n",
       "4                        0.9172                      0.0   \n",
       "687024                      NaN                      0.0   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                      0.0   \n",
       "687027                   0.0000                      0.0   \n",
       "687028                   0.2564                      0.0   \n",
       "\n",
       "        k_var_disc_share_3m_g33  k_var_disc_share_3m_g34  \\\n",
       "0                        0.7418                   0.5004   \n",
       "1                           NaN                   1.1289   \n",
       "2                        0.7665                   1.2056   \n",
       "3                        0.7904                   0.0050   \n",
       "4                           NaN                   0.6549   \n",
       "687024                   0.2821                   1.1557   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                   0.0000   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                      NaN                   0.0000   \n",
       "\n",
       "        k_var_disc_share_3m_g38  k_var_disc_share_3m_g40  \\\n",
       "0                        1.2014                   1.3485   \n",
       "1                        0.0000                   0.6188   \n",
       "2                           NaN                   1.0002   \n",
       "3                           NaN                      NaN   \n",
       "4                        0.0000                   0.6684   \n",
       "687024                   0.0000                   1.0531   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                   0.0000   \n",
       "687027                   0.0000                      NaN   \n",
       "687028                   0.0000                   0.0000   \n",
       "\n",
       "        k_var_disc_share_3m_g41  k_var_disc_share_3m_g44  \\\n",
       "0                        0.0000                   1.2304   \n",
       "1                        0.0000                   0.0000   \n",
       "2                        0.0541                   0.8461   \n",
       "3                        0.0000                      NaN   \n",
       "4                        0.4566                   0.0000   \n",
       "687024                   0.0000                      NaN   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                      NaN   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                   0.0000                   1.1059   \n",
       "\n",
       "        k_var_disc_share_3m_g46  k_var_disc_share_3m_g48  \\\n",
       "0                        0.7229                   0.5943   \n",
       "1                        0.0000                      NaN   \n",
       "2                        0.5812                   0.6945   \n",
       "3                        0.0000                      NaN   \n",
       "4                        0.0000                   0.0138   \n",
       "687024                   0.8648                   1.2152   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                   0.0000   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                   0.5745                   0.2423   \n",
       "\n",
       "        k_var_disc_share_3m_g49  k_var_disc_share_3m_g54  \\\n",
       "0                        1.5156                   0.0147   \n",
       "1                        0.4981                   0.6382   \n",
       "2                        1.7252                   0.7579   \n",
       "3                        0.0166                   0.5362   \n",
       "4                        1.2899                   1.6070   \n",
       "687024                   1.3709                   0.0000   \n",
       "687025                      NaN                      NaN   \n",
       "687026                      NaN                   0.0000   \n",
       "687027                      NaN                   0.0000   \n",
       "687028                   0.6481                   0.3924   \n",
       "\n",
       "        k_var_disc_share_6m_g24  k_var_disc_share_6m_g27  \\\n",
       "0                        0.8036                   0.6366   \n",
       "1                           NaN                      NaN   \n",
       "2                        0.6608                   0.8560   \n",
       "3                           NaN                   0.5780   \n",
       "4                        1.0710                   0.9058   \n",
       "687024                   0.0058                      NaN   \n",
       "687025                   0.0000                   0.0000   \n",
       "687026                   0.0000                   0.0000   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                   0.5264                   0.2564   \n",
       "\n",
       "        k_var_disc_share_6m_g32  k_var_disc_share_6m_g34  \\\n",
       "0                           NaN                   0.7793   \n",
       "1                           NaN                   1.1289   \n",
       "2                        0.9266                   1.1554   \n",
       "3                        0.1315                   0.3219   \n",
       "4                        0.0000                   0.5955   \n",
       "687024                   0.0000                   0.7422   \n",
       "687025                   0.0000                   0.0000   \n",
       "687026                   0.0000                   0.0000   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                   0.0000                   0.0000   \n",
       "\n",
       "        k_var_disc_share_6m_g44  k_var_disc_share_6m_g46  \\\n",
       "0                        1.2143                   1.0723   \n",
       "1                        0.0000                   0.0000   \n",
       "2                        0.7782                   0.7471   \n",
       "3                        1.1290                      NaN   \n",
       "4                           NaN                      NaN   \n",
       "687024                      NaN                   0.8648   \n",
       "687025                   0.0000                   0.0000   \n",
       "687026                      NaN                   0.0000   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                   1.1059                   0.5745   \n",
       "\n",
       "        k_var_disc_share_6m_g49  k_var_disc_share_6m_g54  \\\n",
       "0                        1.3947                   0.0123   \n",
       "1                        0.4981                   0.6382   \n",
       "2                        2.0674                   0.8871   \n",
       "3                        1.7975                   1.2530   \n",
       "4                        1.6232                   1.3194   \n",
       "687024                   1.4424                      NaN   \n",
       "687025                      NaN                      NaN   \n",
       "687026                      NaN                   0.0000   \n",
       "687027                      NaN                   0.0000   \n",
       "687028                   0.6481                   0.3924   \n",
       "\n",
       "        k_var_discount_depth_15d  k_var_discount_depth_1m  \\\n",
       "0                         0.4621                   0.4864   \n",
       "1                         0.0000                   0.0000   \n",
       "2                            NaN                   0.1201   \n",
       "3                         0.0000                   0.0000   \n",
       "4                         0.4903                   0.4903   \n",
       "687024                    0.6335                   0.6177   \n",
       "687025                    0.0000                   0.0000   \n",
       "687026                       NaN                      NaN   \n",
       "687027                       NaN                      NaN   \n",
       "687028                    0.0000                   0.0000   \n",
       "\n",
       "        k_var_sku_per_cheque_15d  k_var_sku_price_12m_g32  \\\n",
       "0                         0.7067                   0.0589   \n",
       "1                         0.0000                      NaN   \n",
       "2                            NaN                   0.6629   \n",
       "3                         0.0000                   0.2354   \n",
       "4                         0.9423                   0.0000   \n",
       "687024                    0.9331                   0.0000   \n",
       "687025                    0.0000                   0.0000   \n",
       "687026                       NaN                   0.0000   \n",
       "687027                       NaN                   0.0000   \n",
       "687028                    0.0000                   0.0000   \n",
       "\n",
       "        k_var_sku_price_15d_g34  k_var_sku_price_15d_g49  \\\n",
       "0                           NaN                      NaN   \n",
       "1                           NaN                      NaN   \n",
       "2                           NaN                      NaN   \n",
       "3                           NaN                      NaN   \n",
       "4                           NaN                   0.0000   \n",
       "687024                   0.0437                   1.3395   \n",
       "687025                      NaN                      NaN   \n",
       "687026                   0.0000                      NaN   \n",
       "687027                   0.0000                   0.0000   \n",
       "687028                      NaN                      NaN   \n",
       "\n",
       "        k_var_sku_price_1m_g24  k_var_sku_price_1m_g26  \\\n",
       "0                       0.5946                  0.0823   \n",
       "1                       0.0000                  0.0000   \n",
       "2                       0.0000                  0.0000   \n",
       "3                       0.0000                  0.0000   \n",
       "4                       0.0000                  0.0000   \n",
       "687024                  0.0223                     NaN   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                     NaN                     NaN   \n",
       "\n",
       "        k_var_sku_price_1m_g27  k_var_sku_price_1m_g34  \\\n",
       "0                          NaN                  0.1414   \n",
       "1                          0.0                  0.0000   \n",
       "2                          0.0                  0.0666   \n",
       "3                          0.0                  0.0000   \n",
       "4                          0.0                     NaN   \n",
       "687024                     NaN                  0.0437   \n",
       "687025                     NaN                     NaN   \n",
       "687026                     0.0                  0.0000   \n",
       "687027                     0.0                  0.0000   \n",
       "687028                     NaN                     NaN   \n",
       "\n",
       "        k_var_sku_price_1m_g40  k_var_sku_price_1m_g44  \\\n",
       "0                          NaN                  0.8669   \n",
       "1                          NaN                  0.0000   \n",
       "2                          NaN                  0.4668   \n",
       "3                       0.0000                  0.0000   \n",
       "4                          NaN                  0.0000   \n",
       "687024                  0.5187                     NaN   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                     NaN   \n",
       "687027                     NaN                  0.0000   \n",
       "687028                     NaN                     NaN   \n",
       "\n",
       "        k_var_sku_price_1m_g49  k_var_sku_price_1m_g54  \\\n",
       "0                       0.3707                  0.0000   \n",
       "1                       0.0000                  0.0000   \n",
       "2                       1.3422                  0.3536   \n",
       "3                       0.0000                     NaN   \n",
       "4                       0.0000                  0.2284   \n",
       "687024                  1.6498                  0.0000   \n",
       "687025                     NaN                     NaN   \n",
       "687026                     NaN                  0.0000   \n",
       "687027                     NaN                  0.0000   \n",
       "687028                     NaN                     NaN   \n",
       "\n",
       "        k_var_sku_price_3m_g24  k_var_sku_price_3m_g26  \\\n",
       "0                       0.7177                  0.0866   \n",
       "1                       0.0000                  0.2072   \n",
       "2                       0.0000                  0.0000   \n",
       "3                       0.0000                     NaN   \n",
       "4                       0.0000                  0.1325   \n",
       "687024                  0.0223                     NaN   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                  0.5179                  0.0000   \n",
       "\n",
       "        k_var_sku_price_3m_g27  k_var_sku_price_3m_g32  \\\n",
       "0                       1.3485                     NaN   \n",
       "1                       0.0000                     NaN   \n",
       "2                       0.0100                     0.0   \n",
       "3                          NaN                     NaN   \n",
       "4                       0.2146                     0.0   \n",
       "687024                     NaN                     0.0   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                     0.0   \n",
       "687027                  0.0000                     0.0   \n",
       "687028                  0.2672                     0.0   \n",
       "\n",
       "        k_var_sku_price_3m_g33  k_var_sku_price_3m_g34  \\\n",
       "0                       0.4640                  0.3956   \n",
       "1                          NaN                  0.3993   \n",
       "2                       0.0457                  0.2615   \n",
       "3                       0.1655                  0.6560   \n",
       "4                          NaN                  0.1934   \n",
       "687024                  0.0444                  0.0437   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                     NaN                  0.0000   \n",
       "\n",
       "        k_var_sku_price_3m_g40  k_var_sku_price_3m_g41  \\\n",
       "0                       0.1930                  0.0000   \n",
       "1                       0.8333                  0.0000   \n",
       "2                       0.5856                  0.2870   \n",
       "3                          NaN                  0.0000   \n",
       "4                       0.5189                  0.0058   \n",
       "687024                  0.5187                  0.0000   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                     NaN                  0.0000   \n",
       "687028                  0.0000                  0.0000   \n",
       "\n",
       "        k_var_sku_price_3m_g44  k_var_sku_price_3m_g46  \\\n",
       "0                       0.8019                  0.1895   \n",
       "1                       0.0000                  0.0000   \n",
       "2                       0.5238                  0.2017   \n",
       "3                          NaN                  0.0000   \n",
       "4                       0.0000                  0.0000   \n",
       "687024                     NaN                  0.4438   \n",
       "687025                     NaN                     NaN   \n",
       "687026                     NaN                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                  0.5834                  0.1777   \n",
       "\n",
       "        k_var_sku_price_3m_g48  k_var_sku_price_3m_g49  \\\n",
       "0                       0.6128                  2.1596   \n",
       "1                          NaN                  0.6192   \n",
       "2                       0.2840                  1.8758   \n",
       "3                          NaN                  0.1326   \n",
       "4                       0.3868                  2.0293   \n",
       "687024                  1.0347                  1.6498   \n",
       "687025                     NaN                     NaN   \n",
       "687026                  0.0000                     NaN   \n",
       "687027                  0.0000                     NaN   \n",
       "687028                  0.2541                  1.2352   \n",
       "\n",
       "        k_var_sku_price_3m_g54  k_var_sku_price_6m_g24  \\\n",
       "0                       0.6810                  0.6546   \n",
       "1                       0.5405                     NaN   \n",
       "2                       0.6338                  0.2654   \n",
       "3                       0.1477                     NaN   \n",
       "4                       0.6860                  0.6113   \n",
       "687024                  0.0000                  0.0223   \n",
       "687025                     NaN                  0.0000   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                  0.3381                  0.5179   \n",
       "\n",
       "        k_var_sku_price_6m_g26  k_var_sku_price_6m_g27  \\\n",
       "0                       0.1300                  1.2374   \n",
       "1                       0.2072                     NaN   \n",
       "2                       0.0000                  0.4481   \n",
       "3                       0.7469                  0.2352   \n",
       "4                       1.0926                  0.2211   \n",
       "687024                     NaN                     NaN   \n",
       "687025                  0.0000                  0.0000   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                  0.0000                  0.2672   \n",
       "\n",
       "        k_var_sku_price_6m_g32  k_var_sku_price_6m_g41  \\\n",
       "0                          NaN                     NaN   \n",
       "1                          NaN                     NaN   \n",
       "2                       0.7673                  0.2393   \n",
       "3                       0.2354                  0.6846   \n",
       "4                       0.0000                  0.0058   \n",
       "687024                  0.0000                  0.0000   \n",
       "687025                  0.0000                  0.0000   \n",
       "687026                  0.0000                  0.0000   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                  0.0000                  0.0000   \n",
       "\n",
       "        k_var_sku_price_6m_g42  k_var_sku_price_6m_g44  \\\n",
       "0                       0.0000                  0.8756   \n",
       "1                       0.0000                  0.0000   \n",
       "2                       0.2851                  0.5170   \n",
       "3                          NaN                  0.2671   \n",
       "4                       0.2496                     NaN   \n",
       "687024                  0.1156                     NaN   \n",
       "687025                  0.0000                  0.0000   \n",
       "687026                  0.0000                     NaN   \n",
       "687027                  0.0000                  0.0000   \n",
       "687028                     NaN                  0.5834   \n",
       "\n",
       "        k_var_sku_price_6m_g48  k_var_sku_price_6m_g49  main_format  \\\n",
       "0                       0.6718                  2.0876            0   \n",
       "1                          NaN                  0.6192            1   \n",
       "2                       0.2407                  2.5227            0   \n",
       "3                       0.1028                  3.0736            1   \n",
       "4                       0.2195                  1.4917            0   \n",
       "687024                  1.0347                  1.7847            0   \n",
       "687025                  1.2382                     NaN            0   \n",
       "687026                  0.0000                     NaN            1   \n",
       "687027                  0.0000                     NaN            0   \n",
       "687028                  0.2541                  1.2352            0   \n",
       "\n",
       "        mean_discount_depth_15d  months_from_register  \\\n",
       "0                        0.6055                  18.0   \n",
       "1                        0.0000                   4.0   \n",
       "2                        0.7256                  34.0   \n",
       "3                        0.0000                  40.0   \n",
       "4                        0.7128                  20.0   \n",
       "687024                   0.5756                  59.0   \n",
       "687025                   0.0000                   4.0   \n",
       "687026                   0.9847                  66.0   \n",
       "687027                   0.8318                   9.0   \n",
       "687028                   0.0000                  13.0   \n",
       "\n",
       "        perdelta_days_between_visits_15_30d  promo_share_15d  response_sms  \\\n",
       "0                                    1.3393           0.5821      0.923077   \n",
       "1                                    0.0000           0.0000      1.000000   \n",
       "2                                    0.0000           0.7256      1.000000   \n",
       "3                                    0.0000           0.0000      0.909091   \n",
       "4                                    0.0000           0.7865      1.000000   \n",
       "687024                               1.3333           0.4002      0.000000   \n",
       "687025                               0.0000           0.0000      1.000000   \n",
       "687026                               0.0000           0.9847      1.000000   \n",
       "687027                               0.0000           0.8318      1.000000   \n",
       "687028                               0.0000           0.0000      1.000000   \n",
       "\n",
       "        response_viber  sale_count_12m_g32  sale_count_12m_g33  \\\n",
       "0             0.071429                10.0              84.314   \n",
       "1             0.000000                 1.0               1.000   \n",
       "2             0.250000                 5.0              21.102   \n",
       "3             0.000000                 2.0              12.544   \n",
       "4             0.100000                 0.0               1.454   \n",
       "687024        0.166667                 0.0               3.000   \n",
       "687025        0.000000                 0.0               0.000   \n",
       "687026        0.000000                 0.0               0.000   \n",
       "687027        0.000000                 0.0               0.000   \n",
       "687028        0.100000                 0.0               6.452   \n",
       "\n",
       "        sale_count_12m_g49  sale_count_12m_g54  sale_count_12m_g57  \\\n",
       "0                     98.0                16.0                11.0   \n",
       "1                      2.0                 2.0                 0.0   \n",
       "2                     50.0               109.0                 0.0   \n",
       "3                     49.0                39.0                 0.0   \n",
       "4                     25.0                25.0                 0.0   \n",
       "687024                14.0                 2.0                 0.0   \n",
       "687025                 1.0                 1.0                 0.0   \n",
       "687026                 5.0                 3.0                 0.0   \n",
       "687027                 1.0                 0.0                 0.0   \n",
       "687028                25.0                17.0                 3.0   \n",
       "\n",
       "        sale_count_3m_g24  sale_count_3m_g33  sale_count_3m_g57  \\\n",
       "0                 137.282             28.776                6.0   \n",
       "1                   0.000              1.000                0.0   \n",
       "2                   0.000              7.594                0.0   \n",
       "3                   0.000              2.778                0.0   \n",
       "4                   0.000              0.454                0.0   \n",
       "687024             19.856              3.000                0.0   \n",
       "687025                NaN                NaN                NaN   \n",
       "687026              0.000              0.000                0.0   \n",
       "687027              0.000              0.000                0.0   \n",
       "687028              6.660              1.344                1.0   \n",
       "\n",
       "        sale_count_6m_g24  sale_count_6m_g25  sale_count_6m_g32  \\\n",
       "0                 169.658             10.680                7.0   \n",
       "1                   1.744              2.000                1.0   \n",
       "2                  25.294             11.084                3.0   \n",
       "3                   2.000             34.212                2.0   \n",
       "4                   3.036             12.000                0.0   \n",
       "687024             19.856             29.000                0.0   \n",
       "687025              0.000              0.000                0.0   \n",
       "687026              0.000              0.000                0.0   \n",
       "687027              0.000              0.476                0.0   \n",
       "687028              6.660              0.000                0.0   \n",
       "\n",
       "        sale_count_6m_g33  sale_count_6m_g44  sale_count_6m_g54  \\\n",
       "0                  28.776               21.0                8.0   \n",
       "1                   1.000                0.0                2.0   \n",
       "2                  11.158               31.0               59.0   \n",
       "3                   3.778                2.0               13.0   \n",
       "4                   1.454                8.0               23.0   \n",
       "687024              3.000               15.0                1.0   \n",
       "687025              0.000                0.0                1.0   \n",
       "687026              0.000               15.0                0.0   \n",
       "687027              0.000                0.0                0.0   \n",
       "687028              1.344               18.0                4.0   \n",
       "\n",
       "        sale_count_6m_g57  sale_sum_12m_g24  sale_sum_12m_g25  \\\n",
       "0                     9.0           4469.86            658.85   \n",
       "1                     0.0            113.39             62.69   \n",
       "2                     0.0           1564.91            971.09   \n",
       "3                     0.0            358.22           3798.18   \n",
       "4                     0.0            226.98            168.05   \n",
       "687024                0.0            550.09            695.32   \n",
       "687025                0.0              0.00              0.00   \n",
       "687026                0.0              0.00            155.97   \n",
       "687027                0.0              0.00             81.90   \n",
       "687028                1.0            531.25              0.00   \n",
       "\n",
       "        sale_sum_12m_g26  sale_sum_12m_g27  sale_sum_12m_g32  \\\n",
       "0                1286.32           7736.05            418.80   \n",
       "1                  58.71             93.35             87.01   \n",
       "2                 177.93           3257.49            975.21   \n",
       "3                 680.93           1425.07            175.73   \n",
       "4                 960.37           1560.21              0.00   \n",
       "687024            111.87            114.21              0.00   \n",
       "687025              0.00              0.00              0.00   \n",
       "687026             23.99             41.51              0.00   \n",
       "687027             29.82              0.00              0.00   \n",
       "687028              0.00            916.44              0.00   \n",
       "\n",
       "        sale_sum_12m_g44  sale_sum_12m_g54  sale_sum_3m_g24  sale_sum_3m_g26  \\\n",
       "0                3233.31            811.73          2321.61           182.82   \n",
       "1                   0.00            122.98             0.00            58.71   \n",
       "2                2555.27           6351.29             0.00             0.00   \n",
       "3                 602.81           3544.76             0.00           119.99   \n",
       "4                 342.45           1039.85             0.00            66.18   \n",
       "687024           1173.84            147.68           550.09           111.87   \n",
       "687025              0.00             28.01              NaN              NaN   \n",
       "687026            615.77             87.47             0.00             0.00   \n",
       "687027              0.00              0.00             0.00             0.00   \n",
       "687028           2407.56           1304.03           290.01             0.00   \n",
       "\n",
       "        sale_sum_3m_g32  sale_sum_3m_g33  sale_sum_6m_g24  sale_sum_6m_g25  \\\n",
       "0                283.84          3648.23          3141.25           356.67   \n",
       "1                 87.01           179.83           113.39            62.69   \n",
       "2                  0.00           783.87          1239.19           533.46   \n",
       "3                 73.24           346.74           139.68          1849.91   \n",
       "4                  0.00            87.94           226.98           168.05   \n",
       "687024             0.00           330.96           550.09           669.33   \n",
       "687025              NaN              NaN             0.00             0.00   \n",
       "687026             0.00             0.00             0.00             0.00   \n",
       "687027             0.00             0.00             0.00            46.72   \n",
       "687028             0.00           228.47           290.01             0.00   \n",
       "\n",
       "        sale_sum_6m_g26  sale_sum_6m_g32  sale_sum_6m_g33  sale_sum_6m_g44  \\\n",
       "0                237.25           283.84          3648.23          1195.37   \n",
       "1                 58.71            87.01           179.83             0.00   \n",
       "2                 83.37           593.13          1217.43          1336.83   \n",
       "3                360.40           175.73           496.73           172.58   \n",
       "4                461.37             0.00           237.93           225.51   \n",
       "687024           111.87             0.00           330.96          1173.84   \n",
       "687025             0.00             0.00             0.00             0.00   \n",
       "687026             0.00             0.00             0.00           449.01   \n",
       "687027             0.00             0.00             0.00             0.00   \n",
       "687028             0.00             0.00           228.47           752.32   \n",
       "\n",
       "        sale_sum_6m_g54  stdev_days_between_visits_15d  \\\n",
       "0                535.42                         1.7078   \n",
       "1                122.98                         0.0000   \n",
       "2               3709.82                         0.0000   \n",
       "3               1246.21                         0.0000   \n",
       "4                995.27                         1.4142   \n",
       "687024           119.99                         2.6458   \n",
       "687025            28.01                         0.0000   \n",
       "687026             0.00                         0.0000   \n",
       "687027             0.00                         0.0000   \n",
       "687028           596.86                         0.0000   \n",
       "\n",
       "        stdev_discount_depth_15d  stdev_discount_depth_1m  \n",
       "0                         0.2798                   0.3008  \n",
       "1                         0.0000                   0.0000  \n",
       "2                            NaN                   0.0803  \n",
       "3                         0.0000                   0.0000  \n",
       "4                         0.3495                   0.3495  \n",
       "687024                    0.3646                   0.3282  \n",
       "687025                    0.0000                   0.0000  \n",
       "687026                       NaN                      NaN  \n",
       "687027                       NaN                      NaN  \n",
       "687028                    0.0000                   0.0000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.data.head().append(dataset.data.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29eca179",
   "metadata": {},
   "source": [
    "* There are 193 columns in the dataset\n",
    "* The dataset contains: \n",
    "    - basic information about clients (age, number of children)\n",
    "    - information about some groups of goods\n",
    "    - statistical information (variation of discounts, prices)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fdbce5",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "627f41a1",
   "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>Total</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_15d_g49</th>\n",
       "      <td>496259</td>\n",
       "      <td>0.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_15d_g49</th>\n",
       "      <td>496159</td>\n",
       "      <td>0.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_count_per_cheque_15d_g34</th>\n",
       "      <td>468551</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_15d_g34</th>\n",
       "      <td>468551</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_15d_g34</th>\n",
       "      <td>468467</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_count_per_cheque_15d_g24</th>\n",
       "      <td>442121</td>\n",
       "      <td>0.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_15d_g24</th>\n",
       "      <td>442054</td>\n",
       "      <td>0.643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_1m_g49</th>\n",
       "      <td>414473</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_count_per_cheque_1m_g49</th>\n",
       "      <td>414473</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_1m_g49</th>\n",
       "      <td>414369</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_1m_g54</th>\n",
       "      <td>388217</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_1m_g54</th>\n",
       "      <td>388139</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_1m_g34</th>\n",
       "      <td>385078</td>\n",
       "      <td>0.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_count_per_cheque_1m_g34</th>\n",
       "      <td>385078</td>\n",
       "      <td>0.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_1m_g34</th>\n",
       "      <td>384997</td>\n",
       "      <td>0.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_1m_g44</th>\n",
       "      <td>383315</td>\n",
       "      <td>0.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_count_per_cheque_1m_g44</th>\n",
       "      <td>383315</td>\n",
       "      <td>0.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_1m_g44</th>\n",
       "      <td>383219</td>\n",
       "      <td>0.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_sku_price_1m_g40</th>\n",
       "      <td>380641</td>\n",
       "      <td>0.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k_var_disc_share_1m_g40</th>\n",
       "      <td>380559</td>\n",
       "      <td>0.554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Total  Percentage\n",
       "k_var_sku_price_15d_g49         496259       0.722\n",
       "k_var_disc_share_15d_g49        496159       0.722\n",
       "k_var_count_per_cheque_15d_g34  468551       0.682\n",
       "k_var_sku_price_15d_g34         468551       0.682\n",
       "k_var_disc_share_15d_g34        468467       0.682\n",
       "k_var_count_per_cheque_15d_g24  442121       0.644\n",
       "k_var_disc_share_15d_g24        442054       0.643\n",
       "k_var_sku_price_1m_g49          414473       0.603\n",
       "k_var_count_per_cheque_1m_g49   414473       0.603\n",
       "k_var_disc_share_1m_g49         414369       0.603\n",
       "k_var_sku_price_1m_g54          388217       0.565\n",
       "k_var_disc_share_1m_g54         388139       0.565\n",
       "k_var_sku_price_1m_g34          385078       0.560\n",
       "k_var_count_per_cheque_1m_g34   385078       0.560\n",
       "k_var_disc_share_1m_g34         384997       0.560\n",
       "k_var_sku_price_1m_g44          383315       0.558\n",
       "k_var_count_per_cheque_1m_g44   383315       0.558\n",
       "k_var_disc_share_1m_g44         383219       0.558\n",
       "k_var_sku_price_1m_g40          380641       0.554\n",
       "k_var_disc_share_1m_g40         380559       0.554"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check NaN values ratio\n",
    "pd.DataFrame({\"Total\" : dataset.data.isna().sum().sort_values(ascending = False),\n",
    "              \"Percentage\" : round(dataset.data.isna().sum().sort_values(ascending = False) / len(dataset.data), 3)}).head(20)\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9fd9e418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total missed data percentage: 19.34 %\n"
     ]
    }
   ],
   "source": [
    "print('Total missed data percentage:', \n",
    "      round(100*dataset.data.isna().sum().sum()/(dataset.data.shape[0]*dataset.data.shape[1]), 2), '%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f32cd27",
   "metadata": {},
   "source": [
    "## Data transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2e2d3e",
   "metadata": {},
   "source": [
    "Transform categorical columns `gender` and `treatment` into binary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e16eec30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make treatment binary\n",
    "treat_dict = {\n",
    "    'test': 1,\n",
    "    'control': 0\n",
    "}\n",
    "dataset.treatment = dataset.treatment.map(treat_dict)\n",
    "\n",
    "# make gender binary\n",
    "gender_dict = {\n",
    "    'M': 1,\n",
    "    'Ж': 0\n",
    "}\n",
    "dataset.data.gender = dataset.data.gender.map(gender_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e00a3d",
   "metadata": {},
   "source": [
    "## Feature correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99dec536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(dataset.data.corr(), fignum=f.number)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "plt.title('Correlation Matrix', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c175f66",
   "metadata": {},
   "source": [
    "## Train/test split\n",
    "\n",
    "- stratify by two columns: treatment and target. \n",
    "\n",
    "`Intuition:` In a binary classification problem definition we stratify train set by splitting target `0/1` column. In uplift modeling we have two columns instead of one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ace8f106",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (480920, 193)\n",
      "Validation shape: (206109, 193)\n"
     ]
    }
   ],
   "source": [
    "stratify_cols = pd.concat([dataset.treatment, dataset.target], axis=1)\n",
    "\n",
    "X_train, X_val, trmnt_train, trmnt_val, y_train, y_val = train_test_split(\n",
    "    dataset.data,\n",
    "    dataset.treatment,\n",
    "    dataset.target,\n",
    "    stratify=stratify_cols,\n",
    "    test_size=0.3,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "print(f\"Train shape: {X_train.shape}\")\n",
    "print(f\"Validation shape: {X_val.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f480b37",
   "metadata": {},
   "source": [
    "## Pipeline with CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9e2cc44",
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_mode = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "estimator = CatBoostClassifier(verbose=100,\n",
    "                               random_state=42,\n",
    "                               thread_count=1)\n",
    "ct_model = ClassTransformation(estimator=estimator)\n",
    "\n",
    "my_pipeline = Pipeline([\n",
    "    ('imputer', imp_mode),\n",
    "    ('model', ct_model)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e3b792",
   "metadata": {},
   "source": [
    "Usual fit pipeline but with aditional treatment parameter\n",
    "`model__treatment = trmnt_train`.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c842c0ba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.143939\n",
      "0:\tlearn: 0.6695107\ttotal: 421ms\tremaining: 7m\n",
      "100:\tlearn: 0.5950043\ttotal: 34.2s\tremaining: 5m 4s\n",
      "200:\tlearn: 0.5908539\ttotal: 1m 5s\tremaining: 4m 21s\n",
      "300:\tlearn: 0.5870115\ttotal: 1m 39s\tremaining: 3m 51s\n",
      "400:\tlearn: 0.5835003\ttotal: 2m 13s\tremaining: 3m 19s\n",
      "500:\tlearn: 0.5800551\ttotal: 2m 47s\tremaining: 2m 46s\n",
      "600:\tlearn: 0.5768127\ttotal: 3m 21s\tremaining: 2m 13s\n",
      "700:\tlearn: 0.5736896\ttotal: 3m 54s\tremaining: 1m 39s\n",
      "800:\tlearn: 0.5706878\ttotal: 4m 27s\tremaining: 1m 6s\n",
      "900:\tlearn: 0.5676374\ttotal: 5m 2s\tremaining: 33.2s\n",
      "999:\tlearn: 0.5647908\ttotal: 5m 35s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "my_pipeline = my_pipeline.fit(\n",
    "    X=X_train, \n",
    "    y=y_train, \n",
    "    model__treatment=trmnt_train\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f5568e",
   "metadata": {},
   "source": [
    "Predict uplift and calculate basic uplift metric **uplift@30%** at first 30%. Read more about the metric [in docs](https://www.uplift-modeling.com/en/latest/api/metrics/uplift_at_k.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4813beb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uplift@30%: 0.0504\n"
     ]
    }
   ],
   "source": [
    "uplift_predictions = my_pipeline.predict(X_val)\n",
    "\n",
    "uplift_30 = uplift_at_k(y_val, uplift_predictions, trmnt_val, strategy='overall')\n",
    "print(f'uplift@30%: {uplift_30:.4f}')"
   ]
  }
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