1988 lines
321 KiB
Plaintext
1988 lines
321 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "33fe93ef",
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"metadata": {},
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"source": [
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"# 04. Клиентские агрегаты и сегментация\n",
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"\n",
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"Цели: агрегировать метрики на уровне клиента, сравнить эффективность по полу/возрасту/устройству, построить простые сегменты и оценить влияние 'заспамленности'."
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]
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},
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{
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"cell_type": "code",
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"id": "e8c8418b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-05T18:35:33.091038Z",
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"start_time": "2025-12-05T18:35:31.190803Z"
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}
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},
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"source": [
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from pathlib import Path\n",
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"from eda_utils import (\n",
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" load_data, DATA_PATH, CATEGORIES, ACTIVE_IMP_COLS, PASSIVE_IMP_COLS,\n",
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" ACTIVE_CLICK_COLS, PASSIVE_CLICK_COLS, ORDER_COLS, NUMERIC_COLS, CAT_COLS,\n",
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" describe_zero_share, safe_divide, build_daily, build_client, add_contact_density\n",
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")\n",
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"pd.set_option(\"display.max_columns\", None)\n",
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"pd.options.display.float_format = '{:,.3f}'.format\n",
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"sns.set_theme(style=\"ticks\", palette=\"deep\")\n",
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"\n",
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"df = load_data()\n",
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"client = build_client(df)\n",
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"client.head()"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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" id active_imp_ent active_imp_super active_imp_transport \\\n",
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"0 1 4.000 14.000 6.000 \n",
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"1 2 5.000 13.000 7.000 \n",
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"2 3 3.000 13.000 25.000 \n",
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"3 4 6.000 2.000 2.000 \n",
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"4 5 2.000 8.000 4.000 \n",
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"\n",
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" active_imp_shopping active_imp_hotel active_imp_avia passive_imp_ent \\\n",
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"0 8.000 0 1 9.000 \n",
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"1 2.000 0 0 9.000 \n",
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"2 11.000 3 2 43.000 \n",
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"3 5.000 1 4 3.000 \n",
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"4 4.000 0 5 1.000 \n",
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"\n",
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" passive_imp_super passive_imp_transport passive_imp_shopping \\\n",
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"0 3.000 4.000 6.000 \n",
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"1 1.000 18.000 13.000 \n",
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"2 18.000 59.000 60.000 \n",
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"3 9.000 4.000 6.000 \n",
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"4 0.000 2.000 4.000 \n",
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"\n",
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" passive_imp_hotel passive_imp_avia active_click_ent active_click_super \\\n",
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"0 12 1 2.000 8.000 \n",
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"1 34 14 2.000 8.000 \n",
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"2 22 34 2.000 7.000 \n",
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"3 5 10 4.000 1.000 \n",
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"4 7 6 1.000 6.000 \n",
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"\n",
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" active_click_transport active_click_shopping active_click_hotel \\\n",
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"0 1.000 3.000 0 \n",
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"1 8.000 1.000 0 \n",
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"2 18.000 8.000 1 \n",
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"3 3.000 5.000 0 \n",
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"4 5.000 0.000 0 \n",
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"\n",
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" active_click_avia passive_click_ent passive_click_super \\\n",
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"0 0 0.000 0.000 \n",
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"1 0 0.000 0.000 \n",
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"2 1 0.000 0.000 \n",
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"3 1 1.000 0.000 \n",
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"4 1 1.000 0.000 \n",
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"\n",
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" passive_click_transport passive_click_shopping passive_click_hotel \\\n",
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"0 0.000 2.000 1 \n",
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"1 0.000 0.000 2 \n",
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"2 0.000 0.000 0 \n",
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"3 0.000 0.000 0 \n",
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"4 0.000 0.000 0 \n",
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"\n",
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" passive_click_avia orders_amt_ent orders_amt_super orders_amt_transport \\\n",
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"0 0 0 0 0 \n",
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"1 2 0 0 3 \n",
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"2 0 0 2 0 \n",
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"3 0 0 0 0 \n",
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"4 2 0 0 1 \n",
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"\n",
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" orders_amt_shopping orders_amt_hotel orders_amt_avia age gender_cd \\\n",
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"0 0 0 0 58.000 M \n",
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"1 0 0 0 54.000 M \n",
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"2 0 0 0 70.000 F \n",
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"3 0 0 0 43.000 F \n",
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"4 0 0 0 46.000 M \n",
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"\n",
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" age_group device_platform_cd active_imp_total passive_imp_total \\\n",
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"0 55+ Android 33.000 35.000 \n",
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"1 45-54 Android 27.000 89.000 \n",
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"2 55+ Android 57.000 236.000 \n",
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"3 35-44 Android 20.000 37.000 \n",
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"4 45-54 Android 23.000 20.000 \n",
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"\n",
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" active_click_total passive_click_total orders_amt_total click_total \\\n",
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"0 14.000 3.000 0 17.000 \n",
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"1 19.000 4.000 3 23.000 \n",
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"2 37.000 0.000 2 37.000 \n",
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"3 14.000 1.000 0 15.000 \n",
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"4 13.000 3.000 1 16.000 \n",
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"\n",
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" imp_total active_ctr passive_ctr ctr_all cr_click2order cr_imp2order \\\n",
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"0 68.000 0.424 0.086 0.250 0.000 0.000 \n",
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"1 116.000 0.704 0.045 0.198 0.130 0.026 \n",
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"2 293.000 0.649 0.000 0.126 0.054 0.007 \n",
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"3 57.000 0.700 0.027 0.263 0.000 0.000 \n",
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"4 43.000 0.565 0.150 0.372 0.062 0.023 \n",
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"\n",
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" has_active_comm has_passive_comm has_any_order order_categories_count \\\n",
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"0 1 1 0 0 \n",
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"1 1 1 1 1 \n",
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"2 1 1 1 1 \n",
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"3 1 1 0 0 \n",
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"4 1 1 1 1 \n",
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"\n",
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" contact_days max_impressions_per_day avg_impressions_per_contact_day \n",
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"0 13 9.000 5.231 \n",
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"1 15 14.000 7.733 \n",
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"2 31 21.000 9.452 \n",
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"3 12 14.000 4.750 \n",
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"4 10 9.000 4.300 "
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],
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"<style scoped>\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>active_imp_ent</th>\n",
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" <th>active_imp_super</th>\n",
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" <th>active_imp_transport</th>\n",
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" <th>active_imp_shopping</th>\n",
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" <th>active_imp_hotel</th>\n",
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" <th>active_imp_avia</th>\n",
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" <th>passive_imp_ent</th>\n",
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" <th>passive_imp_super</th>\n",
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" <th>passive_imp_transport</th>\n",
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" <th>passive_imp_shopping</th>\n",
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" <th>passive_imp_hotel</th>\n",
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" <th>passive_imp_avia</th>\n",
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" <th>active_click_ent</th>\n",
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" <th>active_click_super</th>\n",
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" <th>active_click_transport</th>\n",
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" <th>active_click_shopping</th>\n",
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" <th>active_click_hotel</th>\n",
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" <th>active_click_avia</th>\n",
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" <th>passive_click_ent</th>\n",
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" <th>passive_click_super</th>\n",
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" <th>passive_click_transport</th>\n",
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" <th>passive_click_shopping</th>\n",
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" <th>passive_click_hotel</th>\n",
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" <th>passive_click_avia</th>\n",
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" <th>orders_amt_ent</th>\n",
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" <th>orders_amt_super</th>\n",
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" <th>orders_amt_transport</th>\n",
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" <th>orders_amt_shopping</th>\n",
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" <th>orders_amt_hotel</th>\n",
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" <th>orders_amt_avia</th>\n",
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" <th>age</th>\n",
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" <th>gender_cd</th>\n",
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" <th>age_group</th>\n",
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" <th>device_platform_cd</th>\n",
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" <th>active_imp_total</th>\n",
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" <th>passive_imp_total</th>\n",
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" <th>active_click_total</th>\n",
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" <th>passive_click_total</th>\n",
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" <th>orders_amt_total</th>\n",
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" <th>click_total</th>\n",
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" <th>imp_total</th>\n",
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" <th>active_ctr</th>\n",
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" <th>passive_ctr</th>\n",
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" <th>ctr_all</th>\n",
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" <th>cr_click2order</th>\n",
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" <th>cr_imp2order</th>\n",
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" <th>has_active_comm</th>\n",
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" <th>has_passive_comm</th>\n",
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" <th>has_any_order</th>\n",
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" <th>order_categories_count</th>\n",
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" <th>contact_days</th>\n",
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" <th>max_impressions_per_day</th>\n",
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" <th>avg_impressions_per_contact_day</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>4.000</td>\n",
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" <td>14.000</td>\n",
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" <td>6.000</td>\n",
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" <td>8.000</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>9.000</td>\n",
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" <td>3.000</td>\n",
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" <td>4.000</td>\n",
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" <td>6.000</td>\n",
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" <td>12</td>\n",
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" <td>1</td>\n",
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" <td>2.000</td>\n",
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" <td>8.000</td>\n",
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" <td>1.000</td>\n",
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" <td>3.000</td>\n",
|
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" <td>0</td>\n",
|
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" <td>0</td>\n",
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" <td>0.000</td>\n",
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" <td>0.000</td>\n",
|
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" <td>0.000</td>\n",
|
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" <td>2.000</td>\n",
|
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" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
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" <td>0</td>\n",
|
|
" <td>0</td>\n",
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" <td>58.000</td>\n",
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" <td>M</td>\n",
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" <td>55+</td>\n",
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" <td>Android</td>\n",
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" <td>33.000</td>\n",
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" <td>35.000</td>\n",
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" <td>14.000</td>\n",
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" <td>3.000</td>\n",
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" <td>0</td>\n",
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" <td>17.000</td>\n",
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" <td>68.000</td>\n",
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" <td>0.424</td>\n",
|
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" <td>0.086</td>\n",
|
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" <td>0.250</td>\n",
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" <td>0.000</td>\n",
|
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" <td>0.000</td>\n",
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" <td>1</td>\n",
|
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" <td>1</td>\n",
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" <td>0</td>\n",
|
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" <td>0</td>\n",
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" <td>13</td>\n",
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" <td>9.000</td>\n",
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" <td>5.231</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>5.000</td>\n",
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" <td>13.000</td>\n",
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" <td>7.000</td>\n",
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" <td>2.000</td>\n",
|
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" <td>0</td>\n",
|
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" <td>0</td>\n",
|
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" <td>9.000</td>\n",
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" <td>1.000</td>\n",
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" <td>18.000</td>\n",
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" <td>13.000</td>\n",
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" <td>34</td>\n",
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" <td>14</td>\n",
|
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" <td>2.000</td>\n",
|
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" <td>8.000</td>\n",
|
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" <td>8.000</td>\n",
|
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" <td>1.000</td>\n",
|
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" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.000</td>\n",
|
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" <td>0.000</td>\n",
|
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" <td>0.000</td>\n",
|
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" <td>0.000</td>\n",
|
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" <td>2</td>\n",
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" <td>2</td>\n",
|
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" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
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" <td>54.000</td>\n",
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" <td>M</td>\n",
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" <td>45-54</td>\n",
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" <td>Android</td>\n",
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" <td>27.000</td>\n",
|
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" <td>89.000</td>\n",
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" <td>19.000</td>\n",
|
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" <td>4.000</td>\n",
|
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" <td>3</td>\n",
|
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" <td>23.000</td>\n",
|
|
" <td>116.000</td>\n",
|
|
" <td>0.704</td>\n",
|
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" <td>0.045</td>\n",
|
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" <td>0.198</td>\n",
|
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" <td>0.130</td>\n",
|
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" <td>0.026</td>\n",
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" <td>1</td>\n",
|
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" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>15</td>\n",
|
|
" <td>14.000</td>\n",
|
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" <td>7.733</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2</th>\n",
|
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" <td>3</td>\n",
|
|
" <td>3.000</td>\n",
|
|
" <td>13.000</td>\n",
|
|
" <td>25.000</td>\n",
|
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" <td>11.000</td>\n",
|
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" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>43.000</td>\n",
|
|
" <td>18.000</td>\n",
|
|
" <td>59.000</td>\n",
|
|
" <td>60.000</td>\n",
|
|
" <td>22</td>\n",
|
|
" <td>34</td>\n",
|
|
" <td>2.000</td>\n",
|
|
" <td>7.000</td>\n",
|
|
" <td>18.000</td>\n",
|
|
" <td>8.000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>70.000</td>\n",
|
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" <td>F</td>\n",
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" <td>55+</td>\n",
|
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" <td>Android</td>\n",
|
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" <td>57.000</td>\n",
|
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" <td>236.000</td>\n",
|
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" <td>37.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>37.000</td>\n",
|
|
" <td>293.000</td>\n",
|
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" <td>0.649</td>\n",
|
|
" <td>0.000</td>\n",
|
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" <td>0.126</td>\n",
|
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" <td>0.054</td>\n",
|
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" <td>0.007</td>\n",
|
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" <td>1</td>\n",
|
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" <td>1</td>\n",
|
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" <td>1</td>\n",
|
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" <td>1</td>\n",
|
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" <td>31</td>\n",
|
|
" <td>21.000</td>\n",
|
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" <td>9.452</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
|
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" <td>6.000</td>\n",
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" <td>2.000</td>\n",
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" <td>2.000</td>\n",
|
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" <td>5.000</td>\n",
|
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" <td>1</td>\n",
|
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" <td>4</td>\n",
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" <td>3.000</td>\n",
|
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" <td>9.000</td>\n",
|
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" <td>4.000</td>\n",
|
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" <td>6.000</td>\n",
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" <td>5</td>\n",
|
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" <td>10</td>\n",
|
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" <td>4.000</td>\n",
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" <td>1.000</td>\n",
|
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" <td>3.000</td>\n",
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" <td>5.000</td>\n",
|
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" <td>0</td>\n",
|
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" <td>1</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>43.000</td>\n",
|
|
" <td>F</td>\n",
|
|
" <td>35-44</td>\n",
|
|
" <td>Android</td>\n",
|
|
" <td>20.000</td>\n",
|
|
" <td>37.000</td>\n",
|
|
" <td>14.000</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>15.000</td>\n",
|
|
" <td>57.000</td>\n",
|
|
" <td>0.700</td>\n",
|
|
" <td>0.027</td>\n",
|
|
" <td>0.263</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>12</td>\n",
|
|
" <td>14.000</td>\n",
|
|
" <td>4.750</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>5</td>\n",
|
|
" <td>2.000</td>\n",
|
|
" <td>8.000</td>\n",
|
|
" <td>4.000</td>\n",
|
|
" <td>4.000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>2.000</td>\n",
|
|
" <td>4.000</td>\n",
|
|
" <td>7</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" <td>6.000</td>\n",
|
|
" <td>5.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>46.000</td>\n",
|
|
" <td>M</td>\n",
|
|
" <td>45-54</td>\n",
|
|
" <td>Android</td>\n",
|
|
" <td>23.000</td>\n",
|
|
" <td>20.000</td>\n",
|
|
" <td>13.000</td>\n",
|
|
" <td>3.000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>16.000</td>\n",
|
|
" <td>43.000</td>\n",
|
|
" <td>0.565</td>\n",
|
|
" <td>0.150</td>\n",
|
|
" <td>0.372</td>\n",
|
|
" <td>0.062</td>\n",
|
|
" <td>0.023</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>9.000</td>\n",
|
|
" <td>4.300</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"execution_count": 1
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "faffcfc1",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.136101Z",
|
|
"start_time": "2025-12-05T18:35:33.122490Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"metrics = ['imp_total', 'click_total', 'orders_amt_total', 'ctr_all', 'cr_click2order', 'cr_imp2order']\n",
|
|
"by_gender = client.groupby('gender_cd')[metrics].mean().reset_index()\n",
|
|
"by_age = client.groupby('age_group')[metrics].mean().reset_index()\n",
|
|
"by_device = client.groupby('device_platform_cd')[metrics].mean().reset_index()\n",
|
|
"display(by_gender, by_age, by_device)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_30652/1000924793.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
|
" by_age = client.groupby('age_group')[metrics].mean().reset_index()\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" gender_cd imp_total click_total orders_amt_total ctr_all \\\n",
|
|
"0 F 82.524 19.760 1.555 0.261 \n",
|
|
"1 M 83.306 19.875 1.462 0.260 \n",
|
|
"\n",
|
|
" cr_click2order cr_imp2order \n",
|
|
"0 0.079 0.019 \n",
|
|
"1 0.076 0.018 "
|
|
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|
|
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|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>gender_cd</th>\n",
|
|
" <th>imp_total</th>\n",
|
|
" <th>click_total</th>\n",
|
|
" <th>orders_amt_total</th>\n",
|
|
" <th>ctr_all</th>\n",
|
|
" <th>cr_click2order</th>\n",
|
|
" <th>cr_imp2order</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>F</td>\n",
|
|
" <td>82.524</td>\n",
|
|
" <td>19.760</td>\n",
|
|
" <td>1.555</td>\n",
|
|
" <td>0.261</td>\n",
|
|
" <td>0.079</td>\n",
|
|
" <td>0.019</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>M</td>\n",
|
|
" <td>83.306</td>\n",
|
|
" <td>19.875</td>\n",
|
|
" <td>1.462</td>\n",
|
|
" <td>0.260</td>\n",
|
|
" <td>0.076</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
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|
|
]
|
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|
|
"data": {
|
|
"text/plain": [
|
|
" age_group imp_total click_total orders_amt_total ctr_all \\\n",
|
|
"0 <25 82.566 18.225 0.965 0.250 \n",
|
|
"1 25-34 85.387 19.188 1.501 0.246 \n",
|
|
"2 35-44 84.273 19.824 1.600 0.256 \n",
|
|
"3 45-54 81.277 20.217 1.391 0.269 \n",
|
|
"4 55+ 78.814 20.419 1.392 0.280 \n",
|
|
"\n",
|
|
" cr_click2order cr_imp2order \n",
|
|
"0 0.051 0.013 \n",
|
|
"1 0.078 0.018 \n",
|
|
"2 0.082 0.020 \n",
|
|
"3 0.072 0.018 \n",
|
|
"4 0.070 0.018 "
|
|
],
|
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|
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|
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|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>age_group</th>\n",
|
|
" <th>imp_total</th>\n",
|
|
" <th>click_total</th>\n",
|
|
" <th>orders_amt_total</th>\n",
|
|
" <th>ctr_all</th>\n",
|
|
" <th>cr_click2order</th>\n",
|
|
" <th>cr_imp2order</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td><25</td>\n",
|
|
" <td>82.566</td>\n",
|
|
" <td>18.225</td>\n",
|
|
" <td>0.965</td>\n",
|
|
" <td>0.250</td>\n",
|
|
" <td>0.051</td>\n",
|
|
" <td>0.013</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>25-34</td>\n",
|
|
" <td>85.387</td>\n",
|
|
" <td>19.188</td>\n",
|
|
" <td>1.501</td>\n",
|
|
" <td>0.246</td>\n",
|
|
" <td>0.078</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>35-44</td>\n",
|
|
" <td>84.273</td>\n",
|
|
" <td>19.824</td>\n",
|
|
" <td>1.600</td>\n",
|
|
" <td>0.256</td>\n",
|
|
" <td>0.082</td>\n",
|
|
" <td>0.020</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>45-54</td>\n",
|
|
" <td>81.277</td>\n",
|
|
" <td>20.217</td>\n",
|
|
" <td>1.391</td>\n",
|
|
" <td>0.269</td>\n",
|
|
" <td>0.072</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>55+</td>\n",
|
|
" <td>78.814</td>\n",
|
|
" <td>20.419</td>\n",
|
|
" <td>1.392</td>\n",
|
|
" <td>0.280</td>\n",
|
|
" <td>0.070</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
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|
|
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|
|
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|
|
}
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" device_platform_cd imp_total click_total orders_amt_total ctr_all \\\n",
|
|
"0 Android 80.464 19.793 1.595 0.266 \n",
|
|
"1 iOS 85.348 19.859 1.395 0.254 \n",
|
|
"2 iPadOS 91.081 21.000 1.442 0.248 \n",
|
|
"\n",
|
|
" cr_click2order cr_imp2order \n",
|
|
"0 0.083 0.020 \n",
|
|
"1 0.070 0.017 \n",
|
|
"2 0.068 0.015 "
|
|
],
|
|
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|
|
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|
|
"<style scoped>\n",
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|
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|
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|
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|
|
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|
|
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|
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|
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>device_platform_cd</th>\n",
|
|
" <th>imp_total</th>\n",
|
|
" <th>click_total</th>\n",
|
|
" <th>orders_amt_total</th>\n",
|
|
" <th>ctr_all</th>\n",
|
|
" <th>cr_click2order</th>\n",
|
|
" <th>cr_imp2order</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Android</td>\n",
|
|
" <td>80.464</td>\n",
|
|
" <td>19.793</td>\n",
|
|
" <td>1.595</td>\n",
|
|
" <td>0.266</td>\n",
|
|
" <td>0.083</td>\n",
|
|
" <td>0.020</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>iOS</td>\n",
|
|
" <td>85.348</td>\n",
|
|
" <td>19.859</td>\n",
|
|
" <td>1.395</td>\n",
|
|
" <td>0.254</td>\n",
|
|
" <td>0.070</td>\n",
|
|
" <td>0.017</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>iPadOS</td>\n",
|
|
" <td>91.081</td>\n",
|
|
" <td>21.000</td>\n",
|
|
" <td>1.442</td>\n",
|
|
" <td>0.248</td>\n",
|
|
" <td>0.068</td>\n",
|
|
" <td>0.015</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 2
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "ed0c732f",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.354936Z",
|
|
"start_time": "2025-12-05T18:35:33.187946Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
|
|
"sns.barplot(data=by_gender, x='gender_cd', y='ctr_all', ax=axes[0])\n",
|
|
"axes[0].set_title('CTR по полу')\n",
|
|
"sns.barplot(data=by_age, x='age_group', y='cr_click2order', ax=axes[1])\n",
|
|
"axes[1].set_title('CR click->order по возрасту')\n",
|
|
"axes[1].tick_params(axis='x', rotation=20)\n",
|
|
"sns.barplot(data=by_device, x='device_platform_cd', y='ctr_all', ax=axes[2])\n",
|
|
"axes[2].set_title('CTR по платформам')\n",
|
|
"axes[2].tick_params(axis='x', rotation=20)\n",
|
|
"plt.tight_layout()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1500x400 with 3 Axes>"
|
|
],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 3
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "488849b9",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.457343Z",
|
|
"start_time": "2025-12-05T18:35:33.358912Z"
|
|
}
|
|
},
|
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"source": [
|
|
"funnel_rows = []\n",
|
|
"for channel, imp_tpl, click_tpl in [('active', 'active_imp_{}', 'active_click_{}'), ('passive', 'passive_imp_{}', 'passive_click_{}')]:\n",
|
|
" for cat in CATEGORIES:\n",
|
|
" imp_col, click_col, order_col = imp_tpl.format(cat), click_tpl.format(cat), f'orders_amt_{cat}'\n",
|
|
" impressions = df[imp_col].sum()\n",
|
|
" clicks = df[click_col].sum()\n",
|
|
" orders = df[order_col].sum()\n",
|
|
" funnel_rows.append({\n",
|
|
" 'channel': channel,\n",
|
|
" 'category': cat,\n",
|
|
" 'impressions': impressions,\n",
|
|
" 'clicks': clicks,\n",
|
|
" 'orders': orders,\n",
|
|
" 'CTR': safe_divide(clicks, impressions),\n",
|
|
" 'CR_click2order': safe_divide(orders, clicks),\n",
|
|
" 'CR_imp2order': safe_divide(orders, impressions),\n",
|
|
" })\n",
|
|
"funnel = pd.DataFrame(funnel_rows)\n",
|
|
"display(funnel)\n",
|
|
"plt.figure(figsize=(12, 5))\n",
|
|
"sns.barplot(data=funnel, x='category', y='CTR', hue='channel')\n",
|
|
"plt.title('CTR по категориям (active vs passive)')\n",
|
|
"plt.tight_layout()\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" channel category impressions clicks orders CTR CR_click2order \\\n",
|
|
"0 active ent 37,103.000 28,399.000 1182 0.765 0.042 \n",
|
|
"1 active super 44,939.000 32,569.000 2574 0.725 0.079 \n",
|
|
"2 active transport 67,877.000 52,353.000 6280 0.771 0.120 \n",
|
|
"3 active shopping 30,088.000 23,481.000 904 0.780 0.038 \n",
|
|
"4 active hotel 16,688.000 4,167.000 464 0.250 0.111 \n",
|
|
"5 active avia 22,800.000 6,370.000 1035 0.279 0.162 \n",
|
|
"6 passive ent 65,268.000 3,223.000 1182 0.049 0.367 \n",
|
|
"7 passive super 33,042.000 1,049.000 2574 0.032 2.454 \n",
|
|
"8 passive transport 93,796.000 2,409.000 6280 0.026 2.607 \n",
|
|
"9 passive shopping 81,396.000 1,282.000 904 0.016 0.705 \n",
|
|
"10 passive hotel 116,615.000 6,796.000 464 0.058 0.068 \n",
|
|
"11 passive avia 83,023.000 3,338.000 1035 0.040 0.310 \n",
|
|
"\n",
|
|
" CR_imp2order \n",
|
|
"0 0.032 \n",
|
|
"1 0.057 \n",
|
|
"2 0.093 \n",
|
|
"3 0.030 \n",
|
|
"4 0.028 \n",
|
|
"5 0.045 \n",
|
|
"6 0.018 \n",
|
|
"7 0.078 \n",
|
|
"8 0.067 \n",
|
|
"9 0.011 \n",
|
|
"10 0.004 \n",
|
|
"11 0.012 "
|
|
],
|
|
"text/html": [
|
|
"<div>\n",
|
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"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>channel</th>\n",
|
|
" <th>category</th>\n",
|
|
" <th>impressions</th>\n",
|
|
" <th>clicks</th>\n",
|
|
" <th>orders</th>\n",
|
|
" <th>CTR</th>\n",
|
|
" <th>CR_click2order</th>\n",
|
|
" <th>CR_imp2order</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>active</td>\n",
|
|
" <td>ent</td>\n",
|
|
" <td>37,103.000</td>\n",
|
|
" <td>28,399.000</td>\n",
|
|
" <td>1182</td>\n",
|
|
" <td>0.765</td>\n",
|
|
" <td>0.042</td>\n",
|
|
" <td>0.032</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>active</td>\n",
|
|
" <td>super</td>\n",
|
|
" <td>44,939.000</td>\n",
|
|
" <td>32,569.000</td>\n",
|
|
" <td>2574</td>\n",
|
|
" <td>0.725</td>\n",
|
|
" <td>0.079</td>\n",
|
|
" <td>0.057</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>active</td>\n",
|
|
" <td>transport</td>\n",
|
|
" <td>67,877.000</td>\n",
|
|
" <td>52,353.000</td>\n",
|
|
" <td>6280</td>\n",
|
|
" <td>0.771</td>\n",
|
|
" <td>0.120</td>\n",
|
|
" <td>0.093</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>active</td>\n",
|
|
" <td>shopping</td>\n",
|
|
" <td>30,088.000</td>\n",
|
|
" <td>23,481.000</td>\n",
|
|
" <td>904</td>\n",
|
|
" <td>0.780</td>\n",
|
|
" <td>0.038</td>\n",
|
|
" <td>0.030</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>active</td>\n",
|
|
" <td>hotel</td>\n",
|
|
" <td>16,688.000</td>\n",
|
|
" <td>4,167.000</td>\n",
|
|
" <td>464</td>\n",
|
|
" <td>0.250</td>\n",
|
|
" <td>0.111</td>\n",
|
|
" <td>0.028</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>active</td>\n",
|
|
" <td>avia</td>\n",
|
|
" <td>22,800.000</td>\n",
|
|
" <td>6,370.000</td>\n",
|
|
" <td>1035</td>\n",
|
|
" <td>0.279</td>\n",
|
|
" <td>0.162</td>\n",
|
|
" <td>0.045</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>passive</td>\n",
|
|
" <td>ent</td>\n",
|
|
" <td>65,268.000</td>\n",
|
|
" <td>3,223.000</td>\n",
|
|
" <td>1182</td>\n",
|
|
" <td>0.049</td>\n",
|
|
" <td>0.367</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>passive</td>\n",
|
|
" <td>super</td>\n",
|
|
" <td>33,042.000</td>\n",
|
|
" <td>1,049.000</td>\n",
|
|
" <td>2574</td>\n",
|
|
" <td>0.032</td>\n",
|
|
" <td>2.454</td>\n",
|
|
" <td>0.078</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>passive</td>\n",
|
|
" <td>transport</td>\n",
|
|
" <td>93,796.000</td>\n",
|
|
" <td>2,409.000</td>\n",
|
|
" <td>6280</td>\n",
|
|
" <td>0.026</td>\n",
|
|
" <td>2.607</td>\n",
|
|
" <td>0.067</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>passive</td>\n",
|
|
" <td>shopping</td>\n",
|
|
" <td>81,396.000</td>\n",
|
|
" <td>1,282.000</td>\n",
|
|
" <td>904</td>\n",
|
|
" <td>0.016</td>\n",
|
|
" <td>0.705</td>\n",
|
|
" <td>0.011</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>passive</td>\n",
|
|
" <td>hotel</td>\n",
|
|
" <td>116,615.000</td>\n",
|
|
" <td>6,796.000</td>\n",
|
|
" <td>464</td>\n",
|
|
" <td>0.058</td>\n",
|
|
" <td>0.068</td>\n",
|
|
" <td>0.004</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>passive</td>\n",
|
|
" <td>avia</td>\n",
|
|
" <td>83,023.000</td>\n",
|
|
" <td>3,338.000</td>\n",
|
|
" <td>1035</td>\n",
|
|
" <td>0.040</td>\n",
|
|
" <td>0.310</td>\n",
|
|
" <td>0.012</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x500 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 4
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "7cc4ed80",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.594597Z",
|
|
"start_time": "2025-12-05T18:35:33.461730Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"seg_rows = []\n",
|
|
"for seg_col in ['gender_cd', 'age_group', 'device_platform_cd']:\n",
|
|
" grouped = df.groupby(seg_col).agg({\n",
|
|
" 'imp_total': 'sum',\n",
|
|
" 'click_total': 'sum',\n",
|
|
" 'orders_amt_total': 'sum',\n",
|
|
" })\n",
|
|
" grouped['CTR'] = safe_divide(grouped['click_total'], grouped['imp_total'])\n",
|
|
" grouped['CR_click2order'] = safe_divide(grouped['orders_amt_total'], grouped['click_total'])\n",
|
|
" grouped['CR_imp2order'] = safe_divide(grouped['orders_amt_total'], grouped['imp_total'])\n",
|
|
" grouped['segment'] = grouped.index\n",
|
|
" grouped['seg_col'] = seg_col\n",
|
|
" seg_rows.append(grouped.reset_index(drop=True))\n",
|
|
"seg_funnel = pd.concat(seg_rows, ignore_index=True)\n",
|
|
"display(seg_funnel)\n",
|
|
"plt.figure(figsize=(12, 5))\n",
|
|
"sns.barplot(data=seg_funnel, x='segment', y='CR_click2order', hue='seg_col')\n",
|
|
"plt.title('CR click->order по сегментам')\n",
|
|
"plt.tight_layout()\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_30652/2411478308.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
|
" grouped = df.groupby(seg_col).agg({\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" imp_total click_total orders_amt_total CTR CR_click2order \\\n",
|
|
"0 216,709.000 51,889.000 4084 0.239 0.079 \n",
|
|
"1 475,926.000 113,547.000 8355 0.239 0.074 \n",
|
|
"2 14,284.000 3,153.000 167 0.221 0.053 \n",
|
|
"3 133,887.000 30,087.000 2354 0.225 0.078 \n",
|
|
"4 298,241.000 70,156.000 5662 0.235 0.081 \n",
|
|
"5 169,301.000 42,111.000 2897 0.249 0.069 \n",
|
|
"6 76,922.000 19,929.000 1359 0.259 0.068 \n",
|
|
"7 322,501.000 79,329.000 6394 0.246 0.081 \n",
|
|
"8 362,301.000 84,301.000 5921 0.233 0.070 \n",
|
|
"9 7,833.000 1,806.000 124 0.231 0.069 \n",
|
|
"\n",
|
|
" CR_imp2order segment seg_col \n",
|
|
"0 0.019 F gender_cd \n",
|
|
"1 0.018 M gender_cd \n",
|
|
"2 0.012 <25 age_group \n",
|
|
"3 0.018 25-34 age_group \n",
|
|
"4 0.019 35-44 age_group \n",
|
|
"5 0.017 45-54 age_group \n",
|
|
"6 0.018 55+ age_group \n",
|
|
"7 0.020 Android device_platform_cd \n",
|
|
"8 0.016 iOS device_platform_cd \n",
|
|
"9 0.016 iPadOS device_platform_cd "
|
|
],
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
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|
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"\n",
|
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" .dataframe thead th {\n",
|
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" 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>imp_total</th>\n",
|
|
" <th>click_total</th>\n",
|
|
" <th>orders_amt_total</th>\n",
|
|
" <th>CTR</th>\n",
|
|
" <th>CR_click2order</th>\n",
|
|
" <th>CR_imp2order</th>\n",
|
|
" <th>segment</th>\n",
|
|
" <th>seg_col</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>216,709.000</td>\n",
|
|
" <td>51,889.000</td>\n",
|
|
" <td>4084</td>\n",
|
|
" <td>0.239</td>\n",
|
|
" <td>0.079</td>\n",
|
|
" <td>0.019</td>\n",
|
|
" <td>F</td>\n",
|
|
" <td>gender_cd</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>475,926.000</td>\n",
|
|
" <td>113,547.000</td>\n",
|
|
" <td>8355</td>\n",
|
|
" <td>0.239</td>\n",
|
|
" <td>0.074</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" <td>M</td>\n",
|
|
" <td>gender_cd</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>14,284.000</td>\n",
|
|
" <td>3,153.000</td>\n",
|
|
" <td>167</td>\n",
|
|
" <td>0.221</td>\n",
|
|
" <td>0.053</td>\n",
|
|
" <td>0.012</td>\n",
|
|
" <td><25</td>\n",
|
|
" <td>age_group</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>133,887.000</td>\n",
|
|
" <td>30,087.000</td>\n",
|
|
" <td>2354</td>\n",
|
|
" <td>0.225</td>\n",
|
|
" <td>0.078</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" <td>25-34</td>\n",
|
|
" <td>age_group</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>298,241.000</td>\n",
|
|
" <td>70,156.000</td>\n",
|
|
" <td>5662</td>\n",
|
|
" <td>0.235</td>\n",
|
|
" <td>0.081</td>\n",
|
|
" <td>0.019</td>\n",
|
|
" <td>35-44</td>\n",
|
|
" <td>age_group</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>169,301.000</td>\n",
|
|
" <td>42,111.000</td>\n",
|
|
" <td>2897</td>\n",
|
|
" <td>0.249</td>\n",
|
|
" <td>0.069</td>\n",
|
|
" <td>0.017</td>\n",
|
|
" <td>45-54</td>\n",
|
|
" <td>age_group</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>76,922.000</td>\n",
|
|
" <td>19,929.000</td>\n",
|
|
" <td>1359</td>\n",
|
|
" <td>0.259</td>\n",
|
|
" <td>0.068</td>\n",
|
|
" <td>0.018</td>\n",
|
|
" <td>55+</td>\n",
|
|
" <td>age_group</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>322,501.000</td>\n",
|
|
" <td>79,329.000</td>\n",
|
|
" <td>6394</td>\n",
|
|
" <td>0.246</td>\n",
|
|
" <td>0.081</td>\n",
|
|
" <td>0.020</td>\n",
|
|
" <td>Android</td>\n",
|
|
" <td>device_platform_cd</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>362,301.000</td>\n",
|
|
" <td>84,301.000</td>\n",
|
|
" <td>5921</td>\n",
|
|
" <td>0.233</td>\n",
|
|
" <td>0.070</td>\n",
|
|
" <td>0.016</td>\n",
|
|
" <td>iOS</td>\n",
|
|
" <td>device_platform_cd</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>7,833.000</td>\n",
|
|
" <td>1,806.000</td>\n",
|
|
" <td>124</td>\n",
|
|
" <td>0.231</td>\n",
|
|
" <td>0.069</td>\n",
|
|
" <td>0.016</td>\n",
|
|
" <td>iPadOS</td>\n",
|
|
" <td>device_platform_cd</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
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|
]
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|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 5
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "5586e7b7",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.605066Z",
|
|
"start_time": "2025-12-05T18:35:33.599434Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"from scipy import stats\n",
|
|
"groups = [g.dropna() for _, g in client.groupby('gender_cd')['ctr_all']]\n",
|
|
"stat, p = stats.mannwhitneyu(groups[0], groups[1], alternative='two-sided')\n",
|
|
"print({'stat': stat, 'pvalue': p})"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'stat': np.float64(7612187.5), 'pvalue': np.float64(0.2769307059762368)}\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 6
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "607472ee",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.617651Z",
|
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"start_time": "2025-12-05T18:35:33.608114Z"
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}
|
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},
|
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"source": [
|
|
"client['channel_segment'] = np.select([\n",
|
|
" (client['has_active_comm'] == 1) & (client['has_passive_comm'] == 0),\n",
|
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" (client['has_active_comm'] == 0) & (client['has_passive_comm'] == 1),\n",
|
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" (client['has_active_comm'] == 1) & (client['has_passive_comm'] == 1),\n",
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|
"], ['only_active', 'only_passive', 'both'], default='none')\n",
|
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"segment_summary = client.groupby('channel_segment')[metrics + ['has_any_order']].agg(['mean', 'count'])\n",
|
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"segment_summary"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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" imp_total click_total orders_amt_total \\\n",
|
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" mean count mean count mean count \n",
|
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"channel_segment \n",
|
|
"both 83.060 8339 19.839 8339 1.492 8339 \n",
|
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"\n",
|
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" ctr_all cr_click2order cr_imp2order \\\n",
|
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" mean count mean count mean count \n",
|
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"channel_segment \n",
|
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"both 0.260 8339 0.077 8339 0.018 8339 \n",
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"\n",
|
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" has_any_order \n",
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" mean count \n",
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"channel_segment \n",
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"both 0.565 8339 "
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],
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" <th></th>\n",
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" <th colspan=\"2\" halign=\"left\">imp_total</th>\n",
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" <th colspan=\"2\" halign=\"left\">click_total</th>\n",
|
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" <th colspan=\"2\" halign=\"left\">orders_amt_total</th>\n",
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" <th colspan=\"2\" halign=\"left\">ctr_all</th>\n",
|
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" <th colspan=\"2\" halign=\"left\">cr_click2order</th>\n",
|
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" <th colspan=\"2\" halign=\"left\">cr_imp2order</th>\n",
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" <th colspan=\"2\" halign=\"left\">has_any_order</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>mean</th>\n",
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" <th>count</th>\n",
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" <th>mean</th>\n",
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" <th>count</th>\n",
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" <th>mean</th>\n",
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" <th>count</th>\n",
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" <th>mean</th>\n",
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" <th>count</th>\n",
|
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" <th>mean</th>\n",
|
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" <th>count</th>\n",
|
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" <th>mean</th>\n",
|
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" <th>count</th>\n",
|
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" <th>mean</th>\n",
|
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" <th>count</th>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>channel_segment</th>\n",
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" <th></th>\n",
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" <th></th>\n",
|
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>both</th>\n",
|
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" <td>83.060</td>\n",
|
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" <td>8339</td>\n",
|
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" <td>19.839</td>\n",
|
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" <td>8339</td>\n",
|
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" <td>1.492</td>\n",
|
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" <td>8339</td>\n",
|
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" <td>0.260</td>\n",
|
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" <td>8339</td>\n",
|
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" <td>0.077</td>\n",
|
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" <td>8339</td>\n",
|
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" <td>0.018</td>\n",
|
|
" <td>8339</td>\n",
|
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" <td>0.565</td>\n",
|
|
" <td>8339</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
|
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]
|
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},
|
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"execution_count": 7,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
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],
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"execution_count": 7
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},
|
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{
|
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"cell_type": "code",
|
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"id": "524d15e5",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.651467Z",
|
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"start_time": "2025-12-05T18:35:33.643689Z"
|
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}
|
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},
|
|
"source": [
|
|
"seg_order_bins = pd.cut(client['order_categories_count'], bins=[-0.1, 0.9, 1.9, 2.9, 10], labels=['0', '1', '2', '3+'])\n",
|
|
"order_seg = client.copy()\n",
|
|
"order_seg['order_bin'] = seg_order_bins\n",
|
|
"orders_summary = order_seg.groupby('order_bin')[metrics + ['has_any_order']].mean()\n",
|
|
"orders_summary"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_30652/172415356.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
|
" orders_summary = order_seg.groupby('order_bin')[metrics + ['has_any_order']].mean()\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" imp_total click_total orders_amt_total ctr_all cr_click2order \\\n",
|
|
"order_bin \n",
|
|
"0 75.508 18.831 0.000 0.268 0.000 \n",
|
|
"1 83.024 19.882 1.964 0.259 0.107 \n",
|
|
"2 96.538 21.661 3.603 0.246 0.181 \n",
|
|
"3+ 118.997 24.103 5.873 0.225 0.260 \n",
|
|
"\n",
|
|
" cr_imp2order has_any_order \n",
|
|
"order_bin \n",
|
|
"0 0.000 0.000 \n",
|
|
"1 0.027 1.000 \n",
|
|
"2 0.043 1.000 \n",
|
|
"3+ 0.057 1.000 "
|
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],
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>imp_total</th>\n",
|
|
" <th>click_total</th>\n",
|
|
" <th>orders_amt_total</th>\n",
|
|
" <th>ctr_all</th>\n",
|
|
" <th>cr_click2order</th>\n",
|
|
" <th>cr_imp2order</th>\n",
|
|
" <th>has_any_order</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>order_bin</th>\n",
|
|
" <th></th>\n",
|
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" <th></th>\n",
|
|
" <th></th>\n",
|
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>75.508</td>\n",
|
|
" <td>18.831</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.268</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" <td>0.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>83.024</td>\n",
|
|
" <td>19.882</td>\n",
|
|
" <td>1.964</td>\n",
|
|
" <td>0.259</td>\n",
|
|
" <td>0.107</td>\n",
|
|
" <td>0.027</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>96.538</td>\n",
|
|
" <td>21.661</td>\n",
|
|
" <td>3.603</td>\n",
|
|
" <td>0.246</td>\n",
|
|
" <td>0.181</td>\n",
|
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" <td>0.043</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3+</th>\n",
|
|
" <td>118.997</td>\n",
|
|
" <td>24.103</td>\n",
|
|
" <td>5.873</td>\n",
|
|
" <td>0.225</td>\n",
|
|
" <td>0.260</td>\n",
|
|
" <td>0.057</td>\n",
|
|
" <td>1.000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"execution_count": 8
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "6d26da08",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.800320Z",
|
|
"start_time": "2025-12-05T18:35:33.725190Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"spam_bins = pd.qcut(client['avg_impressions_per_contact_day'].fillna(0), q=5, duplicates='drop')\n",
|
|
"spam_table = client.groupby(spam_bins)[['ctr_all', 'cr_click2order', 'cr_imp2order', 'imp_total', 'click_total']].mean().reset_index().rename(columns={'avg_impressions_per_contact_day': 'spam_bin'})\n",
|
|
"plt.figure(figsize=(10, 4))\n",
|
|
"sns.lineplot(data=spam_table, x=spam_table.index, y='ctr_all', marker='o')\n",
|
|
"plt.title('CTR vs уровень контактов (спамность)')\n",
|
|
"plt.tight_layout()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_30652/3650128803.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
|
" spam_table = client.groupby(spam_bins)[['ctr_all', 'cr_click2order', 'cr_imp2order', 'imp_total', 'click_total']].mean().reset_index().rename(columns={'avg_impressions_per_contact_day': 'spam_bin'})\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1000x400 with 1 Axes>"
|
|
],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 9
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "2c4dd7e9",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.882163Z",
|
|
"start_time": "2025-12-05T18:35:33.804842Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"plt.figure(figsize=(8,4))\n",
|
|
"sns.histplot(client['avg_impressions_per_contact_day'].dropna(), bins=30)\n",
|
|
"plt.title('Распределение avg_impressions_per_contact_day')\n",
|
|
"plt.tight_layout()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 800x400 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
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},
|
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"metadata": {},
|
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"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 10
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "3c304f36",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:33.893854Z",
|
|
"start_time": "2025-12-05T18:35:33.885888Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"contact_summary = client[['contact_days', 'avg_impressions_per_contact_day', 'max_impressions_per_day']].describe().T\n",
|
|
"contact_summary"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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" count mean std min 25% 50% \\\n",
|
|
"contact_days 8,339.000 14.173 4.762 4.000 11.000 13.000 \n",
|
|
"avg_impressions_per_contact_day 8,339.000 5.861 1.892 2.412 4.561 5.467 \n",
|
|
"max_impressions_per_day 8,339.000 13.392 6.670 4.000 9.000 12.000 \n",
|
|
"\n",
|
|
" 75% max \n",
|
|
"contact_days 16.000 52.000 \n",
|
|
"avg_impressions_per_contact_day 6.737 28.533 \n",
|
|
"max_impressions_per_day 16.000 134.000 "
|
|
],
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
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"\n",
|
|
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
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" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>count</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std</th>\n",
|
|
" <th>min</th>\n",
|
|
" <th>25%</th>\n",
|
|
" <th>50%</th>\n",
|
|
" <th>75%</th>\n",
|
|
" <th>max</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>contact_days</th>\n",
|
|
" <td>8,339.000</td>\n",
|
|
" <td>14.173</td>\n",
|
|
" <td>4.762</td>\n",
|
|
" <td>4.000</td>\n",
|
|
" <td>11.000</td>\n",
|
|
" <td>13.000</td>\n",
|
|
" <td>16.000</td>\n",
|
|
" <td>52.000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>avg_impressions_per_contact_day</th>\n",
|
|
" <td>8,339.000</td>\n",
|
|
" <td>5.861</td>\n",
|
|
" <td>1.892</td>\n",
|
|
" <td>2.412</td>\n",
|
|
" <td>4.561</td>\n",
|
|
" <td>5.467</td>\n",
|
|
" <td>6.737</td>\n",
|
|
" <td>28.533</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max_impressions_per_day</th>\n",
|
|
" <td>8,339.000</td>\n",
|
|
" <td>13.392</td>\n",
|
|
" <td>6.670</td>\n",
|
|
" <td>4.000</td>\n",
|
|
" <td>9.000</td>\n",
|
|
" <td>12.000</td>\n",
|
|
" <td>16.000</td>\n",
|
|
" <td>134.000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"execution_count": 11
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "f3941445",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:34.019445Z",
|
|
"start_time": "2025-12-05T18:35:33.919670Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"orders_by_age = df.groupby('age_group')[ORDER_COLS].sum()\n",
|
|
"orders_share = orders_by_age.div(orders_by_age.sum(axis=1), axis=0)\n",
|
|
"orders_share.plot(kind='bar', stacked=True, figsize=(10, 5))\n",
|
|
"plt.title('Доли категорий заказов по возрастным группам')\n",
|
|
"plt.tight_layout()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_30652/1573599539.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
|
" orders_by_age = df.groupby('age_group')[ORDER_COLS].sum()\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1000x500 with 1 Axes>"
|
|
],
|
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 12
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "d47bb30f",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:42.485923Z",
|
|
"start_time": "2025-12-05T18:35:34.046060Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"from sklearn.cluster import KMeans\n",
|
|
"cluster_features = client[['imp_total', 'click_total', 'orders_amt_total', 'ctr_all', 'cr_click2order']].fillna(0)\n",
|
|
"scaled = StandardScaler().fit_transform(cluster_features)\n",
|
|
"kmeans = KMeans(n_clusters=5, random_state=42, n_init='auto')\n",
|
|
"client['cluster'] = kmeans.fit_predict(scaled)\n",
|
|
"cluster_profile = client.groupby('cluster')[['imp_total', 'click_total', 'orders_amt_total', 'ctr_all', 'cr_click2order']].mean()\n",
|
|
"display(cluster_profile)\n",
|
|
"cluster_profile.plot(kind='bar', figsize=(10,5))\n",
|
|
"plt.title('Профиль кластеров')\n",
|
|
"plt.tight_layout()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
" imp_total click_total orders_amt_total ctr_all cr_click2order\n",
|
|
"cluster \n",
|
|
"0 60.920 21.577 0.886 0.359 0.042\n",
|
|
"1 119.465 23.728 1.191 0.204 0.052\n",
|
|
"2 84.278 18.400 5.184 0.233 0.287\n",
|
|
"3 64.708 14.656 0.561 0.233 0.039\n",
|
|
"4 203.629 38.609 3.286 0.203 0.091"
|
|
],
|
|
"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>imp_total</th>\n",
|
|
" <th>click_total</th>\n",
|
|
" <th>orders_amt_total</th>\n",
|
|
" <th>ctr_all</th>\n",
|
|
" <th>cr_click2order</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>cluster</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>60.920</td>\n",
|
|
" <td>21.577</td>\n",
|
|
" <td>0.886</td>\n",
|
|
" <td>0.359</td>\n",
|
|
" <td>0.042</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>119.465</td>\n",
|
|
" <td>23.728</td>\n",
|
|
" <td>1.191</td>\n",
|
|
" <td>0.204</td>\n",
|
|
" <td>0.052</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>84.278</td>\n",
|
|
" <td>18.400</td>\n",
|
|
" <td>5.184</td>\n",
|
|
" <td>0.233</td>\n",
|
|
" <td>0.287</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>64.708</td>\n",
|
|
" <td>14.656</td>\n",
|
|
" <td>0.561</td>\n",
|
|
" <td>0.233</td>\n",
|
|
" <td>0.039</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>203.629</td>\n",
|
|
" <td>38.609</td>\n",
|
|
" <td>3.286</td>\n",
|
|
" <td>0.203</td>\n",
|
|
" <td>0.091</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1000x500 with 1 Axes>"
|
|
],
|
|
"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 13
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"name": "python3",
|
|
"language": "python",
|
|
"display_name": "Python 3 (ipykernel)"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|