667 lines
185 KiB
Plaintext
667 lines
185 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "34a4c8ac-8cb7-41f1-a816-9038d1b597ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from tqdm import tqdm\n",
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"\n",
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"# Загрузка данных\n",
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"df = pd.read_csv('yandex_top100.csv')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d52b258a-1d4b-44c4-8e18-d43772ffbcba",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 2. Очистка и подготовка данных\n",
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"# Преобразуем track_len в секунды\n",
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"def time_to_seconds(time_str):\n",
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" if pd.isna(time_str):\n",
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" return np.nan\n",
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" parts = list(map(int, time_str.split(':')))\n",
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" return parts[0] * 60 + parts[1]\n",
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"\n",
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"df['duration_sec'] = df['track_len'].apply(time_to_seconds)\n",
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"\n",
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"# Обработка числовых столбцов\n",
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"numeric_cols = ['monthly_listens_total', 'artists_likes_total']\n",
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"for col in numeric_cols:\n",
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" df[col] = pd.to_numeric(df[col], errors='coerce')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c16e9521-c8a9-4cb1-a8f2-5cca97aeddf1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Топ-10 самых популярных треков:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\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>name</th>\n",
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" <th>artist(s)</th>\n",
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" <th>monthly_listens_total</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>1</th>\n",
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" <td>Плохая Девочка</td>\n",
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" <td>['Винтаж', 'ТРАВМА', 'SKIDRI', 'DVRKLXGHT']</td>\n",
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" <td>16677658</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>89</th>\n",
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" <td>С тобой</td>\n",
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" <td>['Jakone', 'A.V.G', 'BAGARDI', 'Goro', 'Asatro']</td>\n",
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" <td>13509502</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>44</th>\n",
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" <td>Зари</td>\n",
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" <td>['Andro', 'ELMAN', 'TONI', 'MONA']</td>\n",
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" <td>10805529</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>Не вспоминай</td>\n",
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" <td>['NILETTO', 'Олег Майами', 'Лёша Свик']</td>\n",
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" <td>9595600</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>Поспешили</td>\n",
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" <td>['MACAN', 'Jakone']</td>\n",
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" <td>9094105</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Где прошла ты</td>\n",
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" <td>['Кравц', 'Гио Пика']</td>\n",
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" <td>9058111</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>30</th>\n",
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" <td>IVL</td>\n",
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" <td>['MACAN', 'SCIRENA']</td>\n",
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" <td>8454285</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>money</td>\n",
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" <td>['By Индия', 'The Limba']</td>\n",
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" <td>8014390</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>По весне</td>\n",
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" <td>['Jakone', 'SCIRENA']</td>\n",
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" <td>7249592</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>Она близко</td>\n",
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" <td>['A.V.G', 'Goro']</td>\n",
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" <td>6888389</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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"text/plain": [
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" name artist(s) \\\n",
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"1 Плохая Девочка ['Винтаж', 'ТРАВМА', 'SKIDRI', 'DVRKLXGHT'] \n",
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"89 С тобой ['Jakone', 'A.V.G', 'BAGARDI', 'Goro', 'Asatro'] \n",
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"44 Зари ['Andro', 'ELMAN', 'TONI', 'MONA'] \n",
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"6 Не вспоминай ['NILETTO', 'Олег Майами', 'Лёша Свик'] \n",
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"7 Поспешили ['MACAN', 'Jakone'] \n",
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"4 Где прошла ты ['Кравц', 'Гио Пика'] \n",
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"30 IVL ['MACAN', 'SCIRENA'] \n",
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"10 money ['By Индия', 'The Limba'] \n",
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"5 По весне ['Jakone', 'SCIRENA'] \n",
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"14 Она близко ['A.V.G', 'Goro'] \n",
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"\n",
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" monthly_listens_total \n",
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"1 16677658 \n",
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"89 13509502 \n",
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"44 10805529 \n",
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"6 9595600 \n",
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"7 9094105 \n",
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"4 9058111 \n",
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"30 8454285 \n",
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"10 8014390 \n",
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"5 7249592 \n",
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"14 6888389 "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Статистика по жанрам:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th {\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>\n",
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" <th></th>\n",
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" <th colspan=\"3\" halign=\"left\">monthly_listens_total</th>\n",
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" <th>duration_sec</th>\n",
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" <th colspan=\"2\" halign=\"left\">chart</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>count</th>\n",
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" <th>sum</th>\n",
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" <th>mean</th>\n",
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" <th>mean</th>\n",
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" <th>min</th>\n",
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" <th>max</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>genre</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>русский рэп</th>\n",
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" <td>44</td>\n",
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" <td>181475383</td>\n",
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" <td>4.124441e+06</td>\n",
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" <td>152.295455</td>\n",
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" <td>2</td>\n",
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" <td>98</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>русская поп-музыка</th>\n",
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" <td>39</td>\n",
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" <td>172165551</td>\n",
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" <td>4.414501e+06</td>\n",
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" <td>172.743590</td>\n",
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" <td>1</td>\n",
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" <td>100</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>панк</th>\n",
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" <td>7</td>\n",
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" <td>30689647</td>\n",
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" <td>4.384235e+06</td>\n",
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" <td>223.857143</td>\n",
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" <td>16</td>\n",
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" <td>88</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>русский рок</th>\n",
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" <td>1</td>\n",
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" <td>4805309</td>\n",
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" <td>4.805309e+06</td>\n",
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" <td>191.000000</td>\n",
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" <td>22</td>\n",
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" <td>22</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>электроника</th>\n",
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" <td>3</td>\n",
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" <td>4686638</td>\n",
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" <td>1.562213e+06</td>\n",
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" <td>154.333333</td>\n",
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" <td>50</td>\n",
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" <td>99</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>поп</th>\n",
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" <td>2</td>\n",
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" <td>3492475</td>\n",
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" <td>1.746238e+06</td>\n",
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" <td>212.500000</td>\n",
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" <td>26</td>\n",
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" <td>53</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>хаус</th>\n",
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" <td>1</td>\n",
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" <td>3389073</td>\n",
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" <td>3.389073e+06</td>\n",
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" <td>252.000000</td>\n",
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" <td>95</td>\n",
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" <td>95</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>кавказская музыка</th>\n",
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" <td>1</td>\n",
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" <td>2035200</td>\n",
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" <td>2.035200e+06</td>\n",
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" <td>175.000000</td>\n",
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" <td>96</td>\n",
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" <td>96</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>индастриал</th>\n",
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" <td>1</td>\n",
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" <td>1840688</td>\n",
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" <td>1.840688e+06</td>\n",
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" <td>272.000000</td>\n",
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" <td>87</td>\n",
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" <td>87</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>гиперпоп</th>\n",
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" <td>1</td>\n",
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" <td>903121</td>\n",
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" <td>9.031210e+05</td>\n",
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" <td>145.000000</td>\n",
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" <td>34</td>\n",
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" <td>34</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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"text/plain": [
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" monthly_listens_total \\\n",
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" count sum mean \n",
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"genre \n",
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"русский рэп 44 181475383 4.124441e+06 \n",
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"русская поп-музыка 39 172165551 4.414501e+06 \n",
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"панк 7 30689647 4.384235e+06 \n",
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"русский рок 1 4805309 4.805309e+06 \n",
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"электроника 3 4686638 1.562213e+06 \n",
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"поп 2 3492475 1.746238e+06 \n",
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"хаус 1 3389073 3.389073e+06 \n",
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"кавказская музыка 1 2035200 2.035200e+06 \n",
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"индастриал 1 1840688 1.840688e+06 \n",
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"гиперпоп 1 903121 9.031210e+05 \n",
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"\n",
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" duration_sec chart \n",
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" mean min max \n",
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"genre \n",
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"русский рэп 152.295455 2 98 \n",
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"русская поп-музыка 172.743590 1 100 \n",
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"панк 223.857143 16 88 \n",
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"русский рок 191.000000 22 22 \n",
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"электроника 154.333333 50 99 \n",
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"поп 212.500000 26 53 \n",
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"хаус 252.000000 95 95 \n",
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"кавказская музыка 175.000000 96 96 \n",
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"индастриал 272.000000 87 87 \n",
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"гиперпоп 145.000000 34 34 "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"## 3. Анализ данных\n",
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"# Топ-10 самых популярных треков\n",
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"top_songs = df.sort_values('monthly_listens_total', ascending=False).head(10)\n",
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"print(\"\\nТоп-10 самых популярных треков:\")\n",
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"display(top_songs[['name', 'artist(s)', 'monthly_listens_total']])\n",
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"\n",
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"# Анализ по жанрам\n",
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"genre_stats = df.groupby('genre').agg({\n",
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" 'monthly_listens_total': ['count', 'sum', 'mean'],\n",
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" 'duration_sec': 'mean',\n",
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" 'chart': ['min', 'max']\n",
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"}).sort_values(('monthly_listens_total', 'sum'), ascending=False)\n",
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"\n",
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"print(\"\\nСтатистика по жанрам:\")\n",
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"display(genre_stats)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "30a0fb8c-a255-40ea-9d97-6c7c17bc5409",
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"metadata": {},
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"outputs": [
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{
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"data": {
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Ofv31V+nWrZsp5F933XVl3b51wj6gMnniI03Ou8ncH9whSpoElh86ALBfmPjP5T0lwMdLVuw6Kp+u3md1SAAAwMU4sz6yZMkSk7RYsWKF/Pzzz1JYWGgSJ9nZ/+s5OnHiRPn222/ls88+M9sfPHhQxowZUwefDAAAAEB1eUsNtWrVStavXy9z5syRDRs2mNZRN998s4wdO7bc5H2Anba63y4txNM3QMI886V3qzCrQ4ILi40IlHuGdZInvo+XJ7+Pl/M6R5thqgAAAJxdH5k3b165x++++67pubFmzRo555xzJD09Xd5++2356KOP5PzzzzfbzJ49W7p27WqSIQMGDHDqZwMAAABQR4kN8yJvbxk3blxtXopG6M+DGZIuQVJSkCedQ9NNq3zgRG4Y1Ea+WX9QNuxPl8e/2yyvXNfH6pAAAIALqav6iCYyVEREhLnVBIf24hg6dGjZNl26dJG4uDj57bffKk1s5Ofnm8UuIyPD6XECAAAAjV2NExvffPPNCZ8fPXr0qcSDBiY7v0iW7kgx99OWfSABlzAPC07O28tTpo3pKaNeXibfbTgkV/dPlrM7NrU6LAAA4ALqqj6ik5JPmDBBBg8eLD169DDrEhMTxdfXV8LDw8tt26xZM/NcVfN2TJ06tVYxAAAAAKijxIZOqOdIW9/bJ/jV+8XFxTXdJRqwxVuTzUTQwZIre1d/I0JiA9XUPSZMxg9qI7N/3SOPfv2nzJtwtvh5e1kdFgAAsFhd1Ud0ro1NmzbJsmXLTim+yZMny6RJk8r12IiNjT2lfQIAAAA4xcnDtSWT4xIYGCg7duww90lqwNGOw1myIzlLPD1EOsghEVuJ1SHBzUy8sJM0DfGT3SnZ8saSXVaHAwAAXEBd1EfuuOMO+e6772TRokVmDg+75s2bS0FBgaSlpZXbPikpyTxXGT8/PwkNDS23AAAAALA4sVER8yWgMvlFxbJ462Fzv2/rJhIs/xtnGKiuUH8feXhkV3P/lUU7JOFIjtUhAQAAF3Mq9RHt6aFJja+++koWLlwobdu2Lfd83759xcfHRxYsWFC2buvWrZKQkCADBw48pbgBAAAAWJTY2LNnj2RnZ0tISMip7AYN0IqdRyW7oFjCA3zkjDalky8CtTG6d4wMah8p+UUl8ti3f1odDgAAcCGnWh/R4ac++OAD+eijj8w+dN4MXXJzc83zYWFhcvPNN5uhpbQ3h04mfuONN5qkRmUThwMAAABw0Tk2xowZY261sL9ixQq54IILpGlTJvXF/xzOyJP1+0u765/XJdpMBA2cSivMxy/tIRfN+EUWbjlsegIN6RxtdVgAAMAizqyPzJo1y9wOGTKk3PrZs2fLDTfcYO6/8MIL4unpKVdccYXk5+fL8OHD5dVXXz3lzwEAAACgHhMb2mpJ6Ziyo0aNkptuuukU3h4NTYnNJgu3HhadvrFTs2CJiwi0OiQ0AB2ig81E4m8v2y3//m6zDO4QJT4kzAAAaJScWR+xTzp+Iv7+/jJz5kyzAAAAAHDTxIa2XgKqsulAuiRl5Iuvl6ec05GePHCeuy7oKF/9cUB2JmfLByv2yo2Dy4+BDQAAGgfqIwAAAABqnNjIyMg44fOhoaGnEg/cWHZ+kfy684i5P7B9pAT51fjnBVQpLMBHJl3YSR6eu0lmzN8ul53WUpoE+VodFgAAqGfURwAAAADU+MpzeHi4GfO+sm7cur64uNhZscHN/LbriBQUlUh0iJ/0alU6RADgTNf0jzW9NbYkZsoL87eZuTcAAEDjQn0EAAAAQI0TG+3atZPDhw/Lgw8+KIMHD66bqOB2kjLy5M+Dpa3nzu3UVDwrqWwCp0onon/0km5y3Vsr5cOVCTJuQGvp1CzE6rAAAEA9oj4CAAAAoMaJjfj4eHn55ZflySeflD/++EOefvppaduWse4bM20dt2RbsrnfuXmIxIQHWB0SGrBBHaJkePdm8uOfSWYi8fdvOsPqkAAAQD2iPgIAAADAs6Yv8PHxkUmTJsn27dulZcuW0qtXL7nnnnskLS2tbiKEy9ualCmH0vPE29NDzmofZXU4aAQeuribmaB+6fYUWRB/2OpwAABAPaI+AgAAAKDGiQ27iIgImTFjhmkltWfPHunQoYN5jMZF59RYtiPF3O/fJkKC/ZkwHHUvLjJQbjqrtGXmk/+Nl8Jim9UhAQCAekZ9BAAAAGi8anwV+vTTTz9usj4diig/P9+0lJowYYIz44OLW733qGTnF0uov7f0iQu3Ohw0Inec30E+X7Nfdqdky3931DpHCwAA3Az1EQAAAAA1TmxcdtlldRMJ3E56bqGsTSjt8n92x6ZmYmegvgT7ecv9wzvL/V9skM82Z4lnYJjVIQEAgHpAfQQAAABAjRMbU6ZMqZtI4HaWbk+W4hKbxDYJkPZNg6wOB43QlX1byfsr9simAxkSPvhaq8MBAAD1gPoIAAAAgBonNjIyMk74fGho6KnEAzex72iO7EzOFh0F4JxOTY8bDgCoD56eHvKvi7vKdW+ulODeF0lmYYnVIQEAgDpGfQQAAABAjRMb4eHhlV7E1nFtdX1xcbGzYoOLKimxyZJtyeZ+r5ZhEhXsZ3VIaMQGtY+SPi38ZO0hkT/TSqSr1QEBAIA6RX0EAAAAQI0TG+rzzz+XiIgI50cDt7DxQLocyS4Qfx9PGdAu0upwAPlrzxBZczBXDuR6SmJ6njQP87c6JAAAUIeojwAAAACNW60SG4MHD5bo6GjnRwOXl19YLCt2HzH3Nanh7+NldUiAtA73kexNCyS454WybEeKXNGnJcOjAQDQgFEfAQAAABo3z9q8aPPmzRIfHy8JCQlSUFDg/KjgslbtTZW8whKJCPSVnjFhVocDlElb+qF4etjkQFqu7D6SbXU4AACgDlEfAQAAABq3WiU2LrjgAunevbu0bdtWgoKCpGfPnvLCCy84Pzq4lIy8Qlm3L83cH9wh0kzcDLiK4swU6RBcOnn48h1HpMRmszokAABQR6iPAAAAAI1bjYei2r17t5mYr7CwUDIyMuTgwYPy+++/yyOPPCJFRUVy33331U2ksNyKXUekuMQmLcMDpG1UkNXhAMfpHFYse3N9zBww8YcypDu9igAAaHCojwAAAACocWKjdevW5R737dtXRo0aJZ06dZLHH3+cikQDlZyZL/GHMs39szpEMX8BXJKvp8gZbSJk6Y4UWbHrqHRqFiI+XrXqmAYAAFwU9REAAAAAtZo8vDLXXHON6Q6OhunXHSnmtmN0sDQP87c6HKBKvVqFybr9aZKZV2SGTuvfJsLqkAAAQD2gPgIAAAA0HrVObKxZs8ZM2Ke6desmffr0MQsanr1HsmXv0RzRKTUGtY+0OhzghLy9PGVgu0j5aXOSrN6bKj1ahkmAj5fVYQEAACejPgIAAAA0XjVObBw+fNi0hlq8eLGEh4ebdWlpaXLeeefJnDlzpGnTpk4N8MCBA/LAAw/IDz/8IDk5OdKhQweZPXu29OvXz6nvg8rp+MW/7jhi7vdqFS7hgb5WhwScVJfmIbI2IVVSsgpk1e6jck4n5/5dAgAA1qnv+ggAAAAA11PjwefvvPNOyczMlD///FOOHj1qlk2bNpmJ++666y6nBpeamiqDBw8WHx8fk9jYvHmzPPfcc9KkSROnvg+qtjUxU5Kz8sXXy9PMXQC4A50DRueCURv2p0tGbqHVIQEAACepz/oIAAAAgAbSY2PevHkyf/586dq1a9k67fo9c+ZMGTZsmFODmz59usTGxpoeGnZt27Z16nugakXFJbJ8V2lvjX5tmkiAL8P5wH20jgyS2CYBsi81V1bsPiLDujW3OiQAAOAE9VkfAQAAANBAEhslJSWmB0VFuk6fc6ZvvvlGhg8fLn/5y19kyZIl0rJlS/nnP/8pf//736t8TX5+vlnstOUWamf9/nQzAXOwn7ecHlvazR+uyz7GtKvuzwqDOkTJJ6v2SfyhTOkT10Sigv2sDgkAAJyi+qyPAAAAAGggiY3zzz9f7r77bvn4448lJiambB6MiRMnygUXXODU4Hbt2iWzZs2SSZMmyb/+9S9ZtWqV6V7u6+sr48ePr/Q106ZNk6lTpzo1jsaooFhk1Z6j5r5OxKwTMsM1ZRxNNrfjxo2rk/1nZWWJu2oe6i8dmgbLjuQs+W3nERnVu/RvFgAAcF/1WR8BAAAA0EASG6+88oqMHj1a2rRpY4aJUvv27ZMePXrIBx984NTgtMWVThL+n//8xzw+/fTTzfi5r732WpWJjcmTJ5tEiGOPDXucqL6tmV6SX1QikcG+0qVFiNXh4ARys0p7JY289SHp3Kuv0/Yb//sS+eG9FyUvL0/c2cD2kbIzOUt2pWTLofRcaREWYHVIAADgFNRnfQQAAABAA0lsaOVh7dq1ZlzbLVu2mHU6vu3QoUOdHlyLFi3MeLmO9L2++OKLKl/j5+dnFtSeZ1C47Mws7aExqH2keHp4WB0SqiEyprW06tjdaftLStgpDUFEkK90iwmVPw9myK87jsgVfVqaycUBAIB7qs/6CAAAAAA3T2xkZmZKSEhpy329KHjhhReaxZEOFdW/f3+nBTd48GDZunVruXXbtm2T1q1bO+09cLywAVdJsc3DDOPTNjLI6nCAU3Zm2wjZkpgpB9JyZe/RHGnD7xoAALdjRX0EAAAAgGuq9sQJw4YNq3Ks/aKiInn44YdNIsKZdJzcFStWmKGoduzYIR999JG88cYbcvvttzv1ffA/KTnFEnLaiLIhfGjZjoYgxN9HercKM/eX7zgiNpvN6pAAAEANWVEfAQAAAODmiQ1tIaXdu3XOCkc654W2inr33Xdl7ty5Tg1O9/vVV1+ZiQF1zNx///vfMmPGDBk7dqxT3wf/89nmLPHw9pEovxKJbcJcBGg4+rWJEF8vT0nOypfth913QnQAABorK+ojAAAAANw8sbFo0SLJzs423b21MqEtnqdPn24m99YxbTdu3CgXX3yx0wO85JJLzL51AuP4+Hj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tt956q7lfUlJia968ue2ZZ54pez4tLc3m5+dn+/jjj+vsczS241BRUVGRLSQkxPbee++VrRs/frzt0ksvrZN4G6qaHofZs2fbwsLCqtwf54M158MLL7xgzoesrKyydZwPp0b/ef3qq69OuM39999v6969e7l1V199tW348OFOO7aNXXWOQ2W6detmmzp1atnjKVOm2Hr37u3k6BqH6hyDRYsWme1SU1Or3IZzof7PBd3ew8PDtmfPnrJ1nAtwV/o3w+7LL7884d8bAAAAuAd6bNRAQUGBac2pQ+PYeXp6msfaC6Ayut5xe6UtDO3ba4tdHWrBcZuwsDAzxEJV+2zsanMcKsrJyZHCwkLTErFizw5tIdq5c2e57bbb5MiRI06Pv7Efh6ysLGndurXpNXPppZfKn3/+WfYc54M158Pbb78t11xzjekd44jzoW6d7N8HZxxb1K5Fb2Zm5nH/PuiQeDqkT7t27WTs2LGSkJBgWYwN1WmnnWaGIdQeNL/++mvZes4Fa+i/Dfod67/ZjjgX4I5/1/VvhnrwwQfliiuukOuuu07S0tKsDg0wPd+Sk5NNHQmuh1ED4MpK266gIcnNzbU6BLdDYqMGUlJSpLi4WJo1a1ZuvT6uOA60na4/0fb225rss7GrzXGo6IEHHjCVcseLJDrszvvvvy8LFiyQ6dOny5IlS2TEiBHmveCc46AXyN955x35+uuv5YMPPjAVzUGDBsn+/fvN85wP9X8+6Bj1OhSVDoHkiPOh7lX170NGRoYp0Djjbx1q7tlnnzUXF6666qqydZpc1flP5s2bJ7NmzTJJWJ23SRMgOHWazNDhB7/44guzaOJb5wLSIacU50L9O3jwoPzwww/H/dvAuQB3TmpMnDjR/K3597//bf6dHTdunKSmplodIhox/S3qcH+9evWSO++887i5OGEtrQdpeTA/P9/qUABj6dKl5ne5cOFC81jnOiO50XDMnz/fzDe7Y8cOq0NxK95WBwDUt6eeekrmzJljWqM7TlytLdbtdJJ3LWC2b9/ebHfBBRdYFG3DopPy6mKnSY2uXbvK66+/bgr2sKZFrv7ezzjjjHLrOR/QGH300UcydepUk3x1nN9Bk3p2ei7oxV1txf7pp5+aObRwajTprYvjvw07d+6UF154Qf7v//7P0tgaq/fee0/Cw8PNvHCOOBfgTvRij2NSQxtsaC8vLXvq71aTc3/961/N35kmTZpYHS4aGb149eqrr5p6kLe3t7mv861p0k17FcHavx16wXj16tXi5+dnFsBqDz30kClvaWMfHx8fOeuss0xdXn+rcH96bPUajP690bLLrbfeanpH4+TosVEDUVFRZoLdpKSkcuv1cfPmzSt9ja4/0fb225rss7GrzXFwbImriY2ffvrJVMhPRP+I6HuRLXX+cbDTf5BPP/30su+Y86F+j4NOLKtJvupcjOJ8cL6q/n0IDQ2VgIAAp5xjqD49F7R1uhYqKw4RVpFe8O3UqRPnQx3SZKv9++VcqP8LOtq7Ui/4+vr6nnBbzgW4MvvFHi37v/jii/Lzzz+bpIa69tpr5fbbbzc9Nq6//nqGpUK90t/iV199Jd99951JYgQHB8svv/xiGlVMnjzZNLCAdew91LWuqvUlVVRUZG5pHQ8rTJs2Td566y2ToI+PjzdDgc6ePdv8W4aGk9SYO3eu6Rmto5tosnvXrl1Wh+YWSGzUgFbu+vbta7p+OXZv1seOrdAd6XrH7e0FGfv2bdu2NZVyx210GJKVK1dWuc/GrjbHQT399NOmV4AOn9CvX7+Tvo8Oj6RzCugQGXDecahYaNy4cWPZd8z5UL/H4bPPPjNdq3UohpPhfHC+k/374IxzDNXz8ccfy4033mhuR44cedLtdagq7VHA+VB31q1bV/b9ci7ULx16UBMV1Ul6cy7A1emcetrqukePHqbMqXP2KE2WXn311Sa5kZ6ebnojMaQa6kvHjh1NeUN7vemwf3pB65VXXjEXsvTfN+1hRI9Fayxbtsz0WtTjYG/UpckNe7KD1vGob/ob1OGjtdGJlnt1uKLnn39e7rrrLvnxxx+rVZeH69JhePUYau+b0aNHm7LJ448/buqlJDeqyerZy93NnDlzbH5+frZ3333XtnnzZtstt9xiCw8PtyUmJprn//rXv9oefPDBsu1//fVXm7e3t+3ZZ5+1xcfH26ZMmWLz8fGxbdy4sWybp556yuzj66+/tm3YsMF26aWX2tq2bWvLzc215DM2xOOg37Gvr6/t888/tx06dKhsyczMNM/r7b333mv77bffbLt377bNnz/f1qdPH1vHjh1teXl5ln3OhnYcpk6davvxxx9tO3futK1Zs8Z2zTXX2Pz9/W1//vln2TacD3V/HOzOOuss29VXX33ces6H2tHv7Y8//jCL/vP6/PPPm/t79+41z+sx0GNht2vXLltgYKDtvvvuM/8+zJw50+bl5WWbN29etY8tTv04fPjhh+bfaf3+Hf99SEtLK9vmnnvusS1evNicD/rv+tChQ21RUVG2w4cPW/IZG9oxeOGFF2xz5861bd++3ZSP7r77bpunp6f522PHuVD3x8Fu3LhxtjPPPLPSfXIuwNWVlJQcty41NdX8rgcOHGj+1hcWFpY9V1RUZHvzzTdtt956q624uLieo0Vjk5KSUnY/KyvL/P5GjRple+SRR8p+uyNHjrR179690r/PqDv271/rRs2bN7d9/PHHtlmzZtk8PDxMPej888839aPXXnvNtnz5csofqDf6b9Zbb71l/n5o2atVq1bmt6kmTJhgfqPDhw+3OkzUwrZt28zxu/POO81jx3LI22+/bWvZsuX/s3cfUE5V39vHN7333nvvRQGRoigIFlCsoDRFQVBRREVQpKgUEVApIgioKFhAFAUVEFBApYoFAenSRXov867n/N6bfyaTGQaYTKZ8P2uFIbkpN8lNcu/ZZ+/txgp0PUSPwMZlePPNNyOKFi3qBsqvvvrqiJ9++sm3rFGjRhHt27ePdP2PP/44omzZsu762kn56quvovyIamcmX7587qC9SZMmEevXr4+355Mc3odixYq5g/rAkwJNcuLEiYimTZtG5MmTxwWedP3OnTuzwxLH74N+eL3rantv0aJFxKpVqyLdH5+H+Ple+uuvv9xn4Ntvv41yX3weLs/3338f9HvGe+31V+9F4G2qV6/u3reSJUtGTJo06ZLeW1z5+6D/x3R97yC3QIEC7j3QDqbO//3332F5fknxPRgyZEhEqVKlXKA7Z86cEY0bN45YsGBBlPvlsxD67yQF9DJkyBAxfvz4oPfJZwEJmf+AgAJ7GjhWUEMOHDgQ0aZNG19wQwPKwW5HcAOhMnz48Ijbb789UvBN26f2/4YNG+Y7r+9VjR8EC9IhdLzvClEgtEqVKu79UqBJk2A0kNyqVSu3367Tzp07w7q+SPoWLVrkfrvE+z7QOMl9993nft9E3x133XWX21b5/UqcQasOHTpE5MqVK+Lrr792l/l/92tsQMdHffr0iThz5kwY1zRhS6F/YpvdAQAAAABAQqKyMV6j8CFDhrhyMhs2bLCiRYu6clOtWrVy/TT0/23btrkeMuqrpKbNQHw0ou7bt68rsfvGG2/4lqnE0aOPPupK+qk0lUos6rIlS5a47dl/u0bo/PTTT64PT5cuXaxRo0buMn1HTJ061SpWrOjej2zZsvneD5XnzZUrV7hXG0mYStR16tTJNm7c6HrweN8ld955p+3fv9/15Dl58qS1adPGmjVr5rZd4TsjcerQoYMrSaVS4TfddJPvd0P0PaTegyphiODY4gEAAAAAiZY3kPPss8+6vnpqyDxs2DB32R133OH6FeTIkcP1MShZsqSrT/7ll1+Gea2RHHh9XRRE8/q4eHNLM2XK5Gqra5ucPn26r4k4QY34tXv3bvv1119djXsFMUTfGQ8//LB7z9Tk/dChQ773I2fOnGFeYyRV3ndD1qxZLV++fJYhQwbf5Rro1ja5atUqu+qqq1y/DfXfUJDew3dG4ugjOGPGDNcwXEFtUcNw7avcfffdriew3mtvW1CjeIIaMWOrBwAAAAAkat999519/fXXLmCh2Y9p06a1lStX2g033OAGfpTFoQFJzZjXYLKadAKhNGnSJBs0aJCdOnXKBdaWLVtm+/btcyfPjTfeaO+9954tWrTIZs6caWnSpLFz584xQBmPbr/9dhs/frz99ttvLvi5dOlSd/m4ceOsYcOG9tRTT9lXX33lG2ikgThCZefOnb6gp7Y3ZXD5b3PXXHONff7551a7dm33G7Z69WoXNPWa2yNhU1bGddddZ4MHD3ZZYcq46d+/v1s2ZcoUF9xQIOOLL77ge+YSUIoKAAAAAJCorV+/3ubNm+fKTX3wwQf22GOP2auvvupmQN56661uEKhXr16RBow1GJQqVaqwrjeSHmVbaFCqffv2LhNAJWU08//FF1+0WrVq2d69e92AucoZVapUyVq2bOlmZ3u3JagRev/8848LNmXMmNG9VyoTVqhQITdTvnHjxq5EWP369d11O3fubM8995yVKlUq3KuNJEyz+J988kn3+1WiRAlXSvHdd991ZdAU0ND2qoB9IAVCKauY8Clw2qRJExfs1uSK//77z958801bsGCB+w1QuULRPovK461bt84FuHBx/GICAAAAABINDf4GKleunMvUkAkTJrhBAtUd98rGbN++PcqAMUENhMKWLVvcYLkyNho0aODKTCmTSDNxx4wZ4wazypcv7wa6lKmRJ08e320JaoSW5vWqb4EGjZWlofdpx44drpeGAhjK2FB5n7ffftu+//57d5t33nmHoAZCToPYNWrU8GUIqQyV/nbt2tVlduk7o2nTpq7/hj+CGgmbl0ugQIWC2QpcKKBauHBhF8jSb4Te0z179rjrqUTVzz//TFDjEvAJAAAAAAAkmkECb/BXA8R//fWXa6aqIIYGJzUYoLJUKukjmomtzAz1MQBCTeWkevfuba+99prdcsstrin1E0884fo3aABSGRqqj+/N0PVq55OpET/0WqtevQaPX3jhBTtx4oQLcCija/jw4W653qeePXtaunTprG7dupY+fXrKwiBkfvjhBze4rSbgysgYO3asK1WUO3due/nlly1//vyuD8z8+fNdaSoFOZB4aP8kb968LhtM3zcqN5Y9e3b3na/3VtlhI0eOdGXFmjdv7m5ToECBcK92osIvJwAAAAAg0ZT4EQ0eq7TPwYMHXWmfe+65xw1MbtiwwQU1Tp48aStWrLB7773XDSZocBkINQ1YKXihgUnNtlZWkPq6aPBK26EyAtSI2uM1iSWoEb8UeNJ3goIbRYoUsSFDhvi+W1S6TgGp559/3s2aJ6iBUNFvVqNGjXxliNR/QQ3Ca9asaQcOHHD9eDQgroxEfYeonCI9NRIPZeqp/KCy9lROTL8BCnKr75L3na9SY9WrV/c1iselI2MDAAAAAJDgeQMB//77ryvboGbh9erV82VvqGSMSjz06dPHzXBVfXLNulYTcW8wiPJTCCUNTGrWtQbGBw4c6IIWytwYNmyYG0hXKSrNxlXjWA8D56GnMjCqaa9+BOpvotdcs+H1/aD36cMPP3R1773yL97MaSCU9Hsl+l5QkH706NF2ww03uOC8vjv0naHvjuuvvz7S7fgdS/hUUkoTK5Shp+8Yfe8ro0+BLO2LqDShgqoKfGt/pnTp0uFe5USLaQEAAABAIrF48WI3m7RgwYJuYObzzz+/pNu/9NJL7naBJ2r5IrFQ3wI1+f3ll18sS5YsvsvVLLxdu3ZukECzXFV6SrOx1ZBVg0Qa0GQwCKHMJvJ6v6jptEoZqYa6Bs9nz57tgnIKdmg7bdOmTbhXN1nRYKIGi9WrQH87duzomvOKsr6efvppV+tegVHNqAbiS/Hixd2pf//+NmvWLPf9IBr81v81m/+9994L92riMoMaU6dOtU8++cRNwHjooYfc94tKj3322WfWqlUra9y4sQuq6jdCvxe4PAQ2AABIpjS7VTNIRDOEdGAHIGFTfeVq1aq5z+zl0OdcM9n9T+pLcNddd8X5ugKhcPXVV7vZqyo55ZX0OXPmjPv7+OOPuxnYXtNfzZD0Sv3QYBWhsGvXLvdXgQud/vzzT5s3b54bkOzRo4cbtHz11Vft008/dcsV8FCAjVIy8UP9djSg2K9fP1u7dq0rDaOBYpUAWrJkibuO3h9dR0Eo77sECJUPPvjADXh7mUH58uVzv1sKsulyr2yisr9UMk2Zh0g8FKRQUOOjjz6y++67z4oWLer2TRTE6N69u509e9Y2btzoghuaiLFs2TJXegyXj8AGgCSvQ4cOLiIe2MSpcuXKVqdOHTt8+HDY1g0Ip7vvvtumTJniZrIOGjTIHnzwwXCvEoCL0EGwPq+333570OWnT592wQvNaFcWhn7nFi5c6FueOXNm16zQO6k3gQbi+PwjsVD/ghEjRrhtW/t4O3bscKV/vBJVGiBSTXJ/lPpBKEyePNk6d+7sGyD/4osv7Oabb3YzrRUw/u2333xNqL/99lt3HQXZhOyh+JkIoIwtZWqob8HmzZvde6NG4UuXLnUZjN57pwFG/RaqRwoQCvrsq3H0+++/70pPaVa/lChRwu2LaRtVkE39GNq3b++WacBbAVEvGwwJ27Fjx1xgSuWn1FPDU6VKFV9wQ71SvCwONYxXBjauDIENAMmOghqa6acGTTrI0IwqIDnSDCHNeN22bZsbGKpQoUK4VwnAFdJsMM3+mjZtmpudqkyMm266yc0OC2bChAlWtmxZa9CgQbyvK3C5ypcv7xpw5s6d2227KvGjma4aGNLApAaXgfioj6/a6JokombhKoX27LPPumDy66+/7gbRixUr5hqGjxs3zt2GIFv8He8puK9JAOpnov4F+j1Ufw2VcHz77bddSRhlaXjBDX2fAKGkSSfKzFBvBWXOK7tQ2UJfffWVHT161E06e+aZZ1yQ3j+Y4fWXQsKmyUMKWuh7fsCAAfbHH3/4lmlSrYIeXtb1kSNHfIFuXBk+HQCSFe0kNGnSxM2cUmqyf1Bj+/bt1rJlS/eDpJl+2rHQ7Al/W7duDVqb3CuFoJk/1atX911f6cxqBOV/nWAZJIF10jXIrMfXwXnOnDndeumx/SktVbMW9VxUakGDWaKU92DrqJNmlnmP5530XG+88UbbtGmT776186+Dsxw5criDNs0Qjm5QzP85BDspDd+jdVPpI6Vl6mBDO3eB5VRi8z4ojVwzIdKnT+8OQgJnLgerIe+95trJ0I5FIL1vqsUd7D1as2aNuw//90AzLgKf28iRI33n58+fH+lxvdt466PAmh5z7ty5vuXLly9374Wek7ZN1VddtWpVlNc5sKb+5a6LbqMdZc0U0fuvzA3/7RdA4qLvT/Uf0EGVBntLlSrlsjeuvfZad3mgU6dOucFgsjWQkChgoR4Z/oLNVi1XrpwbUFYvDdXHV/8Zle74+eeffY3CgVDSPurzzz/v9tW076nAWpcuXdzEEQXXqlat6hpWK3uDWdfxR8cJNWrUcL+FTZs2dd8Vc+bMcVkyep+8YzQNMOrYUMEnIeiEUNFxuzKF9LukHjwa4FbWrIIcOk7TALcCGzr21veI15eH74zER1k2Y8eOdb8LOh73D25o7EbjDeqnonEOvnPiBoENAMnGgQMHXMM4HewqqOGfaqydBg2m6+Bj0aJFbrnSle+5555I9+FF1VU7V3XJVRsxJpqhFTgofzGqu6i0RDXE1EwizSLSIL9m3Hp1X/Vj2a1bN7fjozR3pb4rgOINjnt109WESj+o3nn/56NBLl2mgQANIOjAzKOB/RUrVrj71cxfPe8WLVq4dYuJd5/eSSmWgYYNG+YOJFavXm3PPfec27HT6x3b90EzWhTI0ProPrQzqHrb/rS+2nHw1kMHnp5OnTrZunXr3Ovk0f1oZrOaCcYFPQ+VHtD7FkglC7ROv//+uwuweKnGoh1anf/xxx9dU8MyZcq456nLQ7Eu/nr16uUCRQASL/0e6KBZGRj6zHsnfZ/6B6/9G6p63ztAQinjoOC9NztVv80S3QCPBiw1QUKZuNrOtS3rt0wl2Sj1g1BQ5oVmVGuWtbRu3dqVB9Q+ncrMeBNSevfu7b5/tS/nYdZ16Km8j44lNHlKk5nUmNebtKXSVPpuEL1PyuBQdg1NexFK586dc6WvlSGv3yjtp+kYWQF5baf6ndNxqfbJdAyr7w2vNxTfGYmTAqvKiPaCGypz51GFBAW1EHfooAYgWdDOrIIa+lGpVatWlNrLGhzXgNCWLVtcaqiosZwGx7WjoewA8Qb2vdrkyqaIjgbndaCjlHQvE0A0U18D29FRXU0dvOvH0IviK2CgQIx2vjXzSPerwWqvuZh465gnTx7fZTqo18z/YD+euj9drvVREMXLXlFmhgIaCqh4tSE1o1eviwYbYmow692nx6t57U+zVBTQEA2+6XFUK1uZCrF5H5QyroZc/fv3992nAiX+9D7peXnrov97BzI6eFHgSK+p95rp/8qO0KzPuKAZpHo8HVhpkMafMmC0XtrJzZs3b6SsIQ3M+Bs/frx7TTVYc8stt8T5uniUBq1awzpI9xquAkh89BnX9/7KlSujDOoGC27qd0bfLZpdDISbJh1oQsc333zjBnReeeUV69u3rxtI1kQOL7gRONCjQQLVK1e5Gf2Wa1+CQQOEggIayn7WPpXK/SkrTgEMTT46efKkDRkyxJWX0basCVU67ohuu0Vogho6RtBxi443dMygTHHte996663Wp08fu//++93xya+//uq+b7QMCCVNqtTENjWR1jHwI4884kqh6Thb3w2aCCmaDOk/g5/Z/EkjuKEsPn0P9evXz5XRRNzj1xVAsqCsBO04qKTQ33//bUOHDo20XAcgGkj3BtNFaeMaVNYyj2ohisooXYxmCakkgkqA+NOMLs3G1+B9MNrR1joq2ODNtlUARSVDNONW2RW7du1yJbWuhMpB6b6V8qoZu2pWJnq+2gFTU05Prly53KxI/9ficgVmcei8d7+xeR/0Hl7suet9iuk90s7lRx995F5TZcFoNpcyOeLCiRMn3ECMtjG9joE0+KLXXcEWNY9T4MGjHVqtm2b3KeChAJwGKlVeJth75510YHY56yIaPFKQTDtb9JsBEv9BlGYC6ndCWXz+p8CBXv0GKZBJGSokBMrQ1H6Iskc1wUOTNFQixmvq+9prr8WYuaGJEh988IGbNKABTMp3IK4NHz7clZPRtvrOO++4LF/tQ2kfUvuTyiZW4ENZ3dr+FNRQiU9tkwQ1Qk+lexTU0P699pM1iKwyvSrLqPdFs+VVpq5u3bqu7Koy0lUqDAgVZed7dLymiXcqN63guwLx2l/TOIEmKmobVVkqJL39cgWuNKmV4+zQIWMDQLKgmfjaiVDvAg0sa7aOV/v2UiigoIOTi80EVNaDIvQahP/nn38iLdMAulJNtU7BBt81kK2sEs02CqQD/bg6OFKWhLJY1PtDM5hUfko1aRM6BQRi8z6pb0R0NOih3iR6HzRrSzuad955Z5ysn0ptKQikxwhWqqxt27bu9dZBsIIayoDRwa+CGEpP1gw/NUFVvV+towI/XgmywPfO/z4vZ128jBil5ms2iWa2AUjY9Buh4Ld/gEK/NQqAa3BX3wfqkaRBOB1QqYGqfv/0e+ffUFkDdOrPpB5KQLhp8FGZm5qY4GXVamajsonuuOMON0teAXqvN1SwGfCaFPD111+7bCUGkhHXNMlG+2fKElZmgDKJVEtdmRrq27B+/Xq3rSozWPvx2g4V1IhuYgni9ndRg8PKxPayzaVKlSr2+OOPu/9rlrwy3v170AGhov4uOq7Sb5Z3jK3MQx2b6VhW3x+6XFUDdKznTfzjOyPp0feSempS8jl02OMDkCxox1ZBDdFAsg48NPDjDRirjIFmUOjk0WCzBv2VMeBROSQdaF/sh0nlp1TWx+t74U87M5rNtWfPHjcYpZM/HSQpMKIyRYEzbhXpVyaHmkNroOpKKDij+6xdu7Y99thjrneFBvj1WminSrOaPBps1wGb/2txuZStEnhejxnb90GDczE9dw12qJ6lBvSiox1GBRG0M6mTZnjFJmByMZqNocFEnaKj91CvuzJ3lCWhesy//PKLW6ayXDoAU18NrzG8N2M12HvnnYKte2zWRRkdCrKodIJmFQJI+NT/SN9v3nfcU0895f7vzfTTd5p+35SJpcBmq1at3G+XZq/6f09qoFgH1fQhQELg7Vfp990riaj+WJoEot9/BeA0MUUDy6LAhdf3zJ9KA2kfCYgrKi2lfXZlt2miiGhb1ACltlUNWGqfS5NlRH3f9L2q2dgMUMYPzYbXQLJK9yhj3r9Zr/a3FfRQlob2dxU8DfbdAcQlHV9rkp2CoNr+2rRp4yam6Zhb/1eQQyWmvUll3jbJd0bSRFAjtPjUAEiW1GhSO7rq06AdCs1+V/BDM101k0cD+48++qir1awdEwVAVBrh9ddfj9TbIRjNpFXpIP8ZtcFoFmKwuuZaBx0sqX6vds4180vp0zNmzHBpqjqvmWGaYa/ghw72VUpKg+LaWYotBQt0oKZmZhMnTnSDBxrc1oxHPbZKIqn+pwIpqgeq5ma6/EppPVUaSYNtSufXgYiCKnKx90EUDFApKg1eKCCh62iGpoJJCojotVEZlsDG74EUePICKlqnQBr4U1aFeAEw9arwLgtW5kLblZpIxhRUUTBBr7vuSxkb2oH1AmB67VWeSs9VB15q6H25AZfYrIvKJ2hWod4LAIlD48aNYxyU0fe4fqdi+q3SoLB/ABkIN23TmtSh33INQCqwr5nX+o3XpAv9tmvQUr2ntM+jMovUH0eoaR957Nixbr9e++ZeNpH2X70JIdqvU+DYv8edEDSOX5oYpvdK+/c6htBMeU0SEv1Vv0OV3w3sswiEQokSJVwJIjUI13G2AvVe6U/1ddGxrsYBvB6K/J4Bl4+MDQDJkkp2qD6uZu4oM0E7E7NmzXI7vA0bNnQD7BroVzBD1NBaB9XaKdbs2JiorI9mwcfUWDwm2tlRTxAdJCmzRIPv2hHSgLq3M65sA+20a8aYdta1U6QBgUuh2sAqQ6L0SDVX//TTT33LNONXA966X6XGasBBwYO4mNWvWcTejGOlhCtYpGbecrH3wRvUUzBEDc41+KGG217Gg2ZyKqD07bffRpqdHIyCCBo0UQaOfz8Rj2bgKaigk7dc1/UuC9bXQsGOi5Vz0nan110lYzSLRyXHvNml2vHVe6GDM9VeVfaGgleXIzbrooPxmDI6AAAIFf8JAvr91+/i4MGDXQlP/Z5v3rzZlY9R7XyV7tQMV9Uj12xsZlwjPmhyz2233WYrV650ATZRsM3bH1bgQ5N/lBXuXwIJ4W3Wq8xtHScp69uj46mLlRIGroSOYdUTStugKh3oGFYTzXQ8u3XrVtcHyqPfNE0Y9LK7AFy+FBHsFQIA4okG8DWDSqdw08+fghvKCLlYsAoAAISGGoMrC8PLMvr888/tlVdecdmMylJVbeqlS5e6cjMq+aPBSQVC9DvOLFeEmrY5lUPTBCeVlBENRGpilBreK/tN5f4U7NDlZGqE3+rVq11muwaWlemtiUlAKD399NOucb2yMBQE3bBhg+tjqAoCCmrou0O9YJS5oYA9gLhDxgYAINlRM12lB6sklDJXAABA/FNWhspsKmNRA8ei8oi9e/d2ZTg1qKwMDq9kpDIeCWoglD777DNbtmyZ77y2OQ2Sq/+LMjS07Sl4oRnXymxWFrKCGsrkIKiRcDI3tJ+v7w/1tgNC/Z2hvmXK2NB3h3ppKpCh7HuVW9bEPmVyqAyxqkAAiFv02AAAJDsq76SyAarVrbJXAAAg9AIDEhoc1qxqDR6rrKRKfg4cONBuv/12d12VplJPMJWZ9MpWCkENxDVtb+rR1rVrVytWrJjLEFK5TmUO3X///a40qwYlVQ5NqlWr5k5Co/CER6V2le1F016EmrK2VB5ZPRL1PaKsDZW7ViZi9+7dXXlnfaeoX6aOP4XgPBB3+PUFAMQbpeImBFRhBAAg/nkDOTt37rRChQq5AaBbb73VDQwVKVLEDUSqub3KUqnPmAaL1Y+K0h0INdXEV8ki9WVQH5ehQ4e6gIYGInv16uV6vOkyZQMoIOePTI2EiaAG4oN+s1T+TIEM9eXxStIpQK+ejcr0UglFr2+i+kvpNgDiBp8mAAAAAEC8ePvtt61t27a2aNEid75cuXJuoOfll1+266+/3r755htfvw01blbDVQU4VOoHCIW+ffu6PhrTpk1zgYxGjRq5EjJqYq/Gv+3atXN9NNTrRUE40TYLAE2aNHEZGQMGDHDljr1Ap4IZyvw6depUpOsT1ADiFp8oAAAAAEBIBA4Aq1yHMjZGjBhhv/76q2uuqkaryurUzHiV+lHTVfXd8M+ypNQPQsULVnTq1Mlte54bb7zRXnzxRVc3X43CVb5UAThhcBJInhQAHTlypAvSqxdUpUqVrE2bNvbDDz/Ys88+637XdHruuedcVmKVKlXCvcpAksbeIQAAAAAgJLwB4AULFljNmjVdvfGZM2faXXfdZc8//7yrQZ4rVy5bu3at3Xvvva6ZuJqtdujQwd2OOuQIFa8kzIMPPujO58yZ0zp27Ogu1/bnBdVUokonlaBS7fzt27db0aJFw7z2AOKbAhejR492v2UqP6UA/MMPP2xPP/20ZcyY0QU9VK6ucuXKrizVwoUL3XcM5aeA0EkRQaFxAAAAAECIKJChgR9lZnhlOtSI+e6777ZrrrnG9u7da2nTpnWDQvrr8WqVA3FN5WHSpUvnAme7du2yW265xQ1QquTZE088YZMmTXIlqAL7cKjXhsqlVa1aNWzrDiD+bdq0yQXf33rrLatTp479999/LhCvvjwK0Ctr4+zZs7Zq1SrLmjWrK7OoYIa+U8g4BEKHkCEAAAAAIGTlp/LkyWPZs2d3/9cgjwIWKs8xffp0V+Zn8eLFLtARGMQgqIFQUImYBg0a2MCBA23dunVWsGBBV2Lq+++/t9atW1ufPn2sc+fONm7cuEi3W7JkiR05csSVpAKQfAwePNi6detmBQoUcKWnvAyvQYMGucveffddd1maNGlc0KNChQq+TA2CGkBoEdgAAAAAAMQZr+TG0aNH3V/NYtXp5MmTkQZ5NOv9s88+cyV+NCBE2SmEkopVqLmvBiFXrlzpysS0aNHCDU6qmb2yOLTNqgnwo48+alOnTvWVo9L2q6DcL7/8YkWKFAn3UwEQT/QdUKJECfd9oc+/MgxFQQs1CO/du7crtbhixYoot6X8FBB6fMoAAAAAAHFKg8LFihVzzVU1EKwyVJ988ombFe8/MKSZrT///LPL2PBmuAKhoMCZsoe+/fZbVyZGfTO6dOliefPmtZ9++sm+/PJLmzdvnrvua6+95jKJdBttkwq8KYvDm60NIHlkd33wwQd2zz332KeffmqHDh2y119/3Y4dO+YLWiizsFSpUpY+ffpwry6QLJETBQAAAAC4IoHNUUuXLu0GgW+99VZ79dVXXWPV7777zr7++mtXukONVXWZSnzUrVvX3YaeGgj1NqrZ19WrV7fJkydb27ZtrV69evbUU09Zp06d7IcffnDbrXjboa7vbddkFAHJR8+ePW3EiBG2du1ad159eBTkUC+N48ePu7J1+i3r16+f66lRsWLFcK8ykCzRPBwAAAAAECdBDWVkaLBHs+FV7kdlOpYuXeoCGcrMUPBCZX3mzJnjZr+qnwE1yBEfNPSh4MTWrVutePHitnz5cjdIqQCcyk/REByAqOeOvhNUeqpatWqRfuNUPvGBBx5wpeuU8fXvv//ahx9+6H7HCM4D8Y9SVAAAAACAy+I/o/3ZZ591Az1qCK5eBbVq1bL+/fvbjTfe6IIYolrlZcuWtSeeeMLNdPUGg4BQUW8ML6ihcmhqHP7XX3/ZVVddZR999JGtX7/eevToYX/++We4VxVAmOm74IUXXnDl5xS80O+TfzaiMjX0PZIuXTrLli2by+IgqAGED4ENAAAAAMBl8crzqCeByvtMmDDB1SNXhoao1I/qlOtvs2bNbMuWLVHug8EghML8+fNdLXwNOmo7nTlzprVr185tj8oo0izs2rVr28SJE12fDV0GIPnq2LGj6w+l3lD6HWvRooVrGh5Y6Obmm2+29957z4YPH+6CIKdPn+Z3DAgTAhsAAAAAgMuiAZ8TJ07Y3LlzXcaGZsOrd4Z4mRh16tRxA0CaAatBZSDUVAJNg4/aJpU9JBqgHDlypHXr1s2d95rVX3PNNTZt2jSa1wPJ/Ldsz549Nnv2bCtSpIgLeN50000uwLFgwYIowY277rrLZXwNGzbMXnnllbCtN5Dc0WMDAAAAABBrXlkfz8GDB61mzZr20ksvWfv27SOV5FApj+3bt7vyUyr5o+bMzGxFKD355JP27rvvuubgv//+u2sWPmTIEMuQIUO4Vw1AAqQAxe23327p06d358+cOWNp06Z1/9f3yLfffmvTp0+36667LtJvn8yaNcv9vlWoUCEs6w4kd2RsAAAAAABiRTPavYGdXbt2ub85cuRwpXxmzJjhzitw4c1837Bhg73//vu2d+9eK1eunFtGTw2EMqgxZcoU1xh8zJgx1rRpU9e03j9zg6wMAB4F3B955BHbt2+f7zIFNbzfKZWmat68ud1www22atWqKLdv2bIlQQ0gjAhsAAAAAAAuSgPCXhPVV1991bp27WqLFy925zVw/Ntvv1n37t3deQU/lK2hyzUYpMCHh4wNhMKLL75oY8eOdWVjNINatP3ddtttLtDRp08f13ND2zCFKwCIAhhqBK5ePP78g/Dqp9GvXz+X/QUgYaEUFQAAAADgkvoXqP74uHHjrFq1alaqVCk7dOiQy8xQE3FlcBQqVMj+/fdfO3nypK1cudL11wgsYQXEFTXvVa8MlZAZPXq0+783UHn27FlXB//rr7+2kiVL2ttvv01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|
||
"text/plain": [
|
||
"<Figure size 1600x1200 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 4. Визуализация\n",
|
||
"plt.figure(figsize=(16, 12))\n",
|
||
"\n",
|
||
"# Распределение прослушиваний\n",
|
||
"plt.subplot(2, 2, 1)\n",
|
||
"sns.histplot(df['monthly_listens_total'], bins=30, kde=True)\n",
|
||
"plt.title('Распределение месячных прослушиваний')\n",
|
||
"plt.xlabel('Количество прослушиваний')\n",
|
||
"plt.ylabel('Количество треков')\n",
|
||
"\n",
|
||
"# Топ жанров\n",
|
||
"plt.subplot(2, 2, 2)\n",
|
||
"top_genres = df['genre'].value_counts().nlargest(5)\n",
|
||
"top_genres.plot(kind='bar', color=sns.color_palette(\"husl\"))\n",
|
||
"plt.title('Топ-5 самых популярных жанров')\n",
|
||
"plt.xlabel('Жанр')\n",
|
||
"plt.ylabel('Количество треков')\n",
|
||
"plt.xticks(rotation=45)\n",
|
||
"\n",
|
||
"# Зависимость между прослушиваниями и лайками\n",
|
||
"plt.subplot(2, 2, 3)\n",
|
||
"sns.scatterplot(data=df, x='monthly_listens_total', y='artists_likes_total', \n",
|
||
" hue='genre', palette='viridis', alpha=0.7)\n",
|
||
"plt.title('Зависимость прослушиваний и лайков')\n",
|
||
"plt.xlabel('Месячные прослушивания')\n",
|
||
"plt.ylabel('Лайки артиста')\n",
|
||
"\n",
|
||
"# Продолжительность треков по жанрам\n",
|
||
"plt.subplot(2, 2, 4)\n",
|
||
"sns.boxplot(data=df, x='genre', y='duration_sec', \n",
|
||
" order=df['genre'].value_counts().nlargest(5).index)\n",
|
||
"plt.title('Продолжительность треков по жанрам (топ-5)')\n",
|
||
"plt.xlabel('Жанр')\n",
|
||
"plt.ylabel('Длительность (сек)')\n",
|
||
"plt.xticks(rotation=45)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('yandex_music_analysis.png', dpi=120)\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "8a800c10-b146-403a-a7c1-a138782a110c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Анализ артистов: 100%|███████████████████████| 74/74 [00:00<00:00, 3977.38it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Топ-10 артистов по средней популярности:\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"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>artist</th>\n",
|
||
" <th>mean_listens</th>\n",
|
||
" <th>tracks_count</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>['Винтаж', 'ТРАВМА', 'SKIDRI', 'DVRKLXGHT']</td>\n",
|
||
" <td>16677658.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>65</th>\n",
|
||
" <td>['Jakone', 'A.V.G', 'BAGARDI', 'Goro', 'Asatro']</td>\n",
|
||
" <td>13509502.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>34</th>\n",
|
||
" <td>['Andro', 'ELMAN', 'TONI', 'MONA']</td>\n",
|
||
" <td>10805529.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>['NILETTO', 'Олег Майами', 'Лёша Свик']</td>\n",
|
||
" <td>9595600.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>['MACAN', 'Jakone']</td>\n",
|
||
" <td>9094105.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>['Кравц', 'Гио Пика']</td>\n",
|
||
" <td>9058111.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>['MACAN', 'SCIRENA']</td>\n",
|
||
" <td>8454285.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>['By Индия', 'The Limba']</td>\n",
|
||
" <td>8014390.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>['Jakone', 'SCIRENA']</td>\n",
|
||
" <td>7249592.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>['A.V.G', 'Goro']</td>\n",
|
||
" <td>6888389.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" artist mean_listens \\\n",
|
||
"1 ['Винтаж', 'ТРАВМА', 'SKIDRI', 'DVRKLXGHT'] 16677658.0 \n",
|
||
"65 ['Jakone', 'A.V.G', 'BAGARDI', 'Goro', 'Asatro'] 13509502.0 \n",
|
||
"34 ['Andro', 'ELMAN', 'TONI', 'MONA'] 10805529.0 \n",
|
||
"6 ['NILETTO', 'Олег Майами', 'Лёша Свик'] 9595600.0 \n",
|
||
"7 ['MACAN', 'Jakone'] 9094105.0 \n",
|
||
"4 ['Кравц', 'Гио Пика'] 9058111.0 \n",
|
||
"25 ['MACAN', 'SCIRENA'] 8454285.0 \n",
|
||
"10 ['By Индия', 'The Limba'] 8014390.0 \n",
|
||
"5 ['Jakone', 'SCIRENA'] 7249592.0 \n",
|
||
"14 ['A.V.G', 'Goro'] 6888389.0 \n",
|
||
"\n",
|
||
" tracks_count \n",
|
||
"1 1 \n",
|
||
"65 1 \n",
|
||
"34 1 \n",
|
||
"6 1 \n",
|
||
"7 1 \n",
|
||
"4 1 \n",
|
||
"25 1 \n",
|
||
"10 1 \n",
|
||
"5 1 \n",
|
||
"14 1 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 5. Дополнительный анализ с tqdm\n",
|
||
"# Расчет средней популярности для каждого артиста\n",
|
||
"artists_popularity = []\n",
|
||
"unique_artists = df['artist(s)'].unique()\n",
|
||
"\n",
|
||
"for artist in tqdm(unique_artists, desc='Анализ артистов'):\n",
|
||
" artist_data = df[df['artist(s)'] == artist]\n",
|
||
" mean_popularity = artist_data['monthly_listens_total'].mean()\n",
|
||
" artists_popularity.append({\n",
|
||
" 'artist': artist,\n",
|
||
" 'mean_listens': mean_popularity,\n",
|
||
" 'tracks_count': len(artist_data)\n",
|
||
" })\n",
|
||
"\n",
|
||
"artists_df = pd.DataFrame(artists_popularity)\n",
|
||
"top_artists = artists_df.sort_values('mean_listens', ascending=False).head(10)\n",
|
||
"\n",
|
||
"print(\"\\nТоп-10 артистов по средней популярности:\")\n",
|
||
"display(top_artists)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "5785d7b7-429f-48ec-b416-ded10d3db9c7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Анализ завершен! Результаты сохранены в файлы:\n",
|
||
"- yandex_top100_processed.csv\n",
|
||
"- yandex_music_analysis.png\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# 6. Сохранение результатов\n",
|
||
"df.to_csv('yandex_top100_processed.csv', index=False)\n",
|
||
"\n",
|
||
"print(\"- yandex_top100_processed.csv\")\n",
|
||
"print(\"- yandex_music_analysis.png\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ad8105aa-183c-4f26-a411-ee990c47f283",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|