topic1_habr_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src=\"../../img/ods_stickers.jpg\">\n",
    "## Открытый курс по машинному обучению\n",
    "Авторы материала: программист-исследователь Mail.ru Group, старший преподаватель Факультета Компьютерных Наук ВШЭ Юрий Кашницкий и Data Scientist в Segmento Екатерина Демидова. Материал распространяется на условиях лицензии [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Можно использовать в любых целях (редактировать, поправлять и брать за основу), кроме коммерческих, но с обязательным упоминанием автора материала."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Тема 1. Первичный анализ данных с Pandas</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Pandas](http://pandas.pydata.org)** — это библиотека Python, предоставляющая широкие возможности для анализа данных. С ее помощью очень удобно загружать, обрабатывать и анализировать табличные данные с помощью SQL-подобных запросов. В связке с библиотеками `Matplotlib` и `Seaborn` появляется возможность удобного визуального анализа табличных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные, с которыми работают дата саентисты и аналитики, обычно хранятся в виде табличек — например, в форматах `.csv`, `.tsv` или `.xlsx`. Для того, чтобы считать нужные данные из такого файла, отлично подходит библиотека Pandas.\n",
    "\n",
    "Основными структурами данных в Pandas являются классы `Series` и `DataFrame`. Первый из них представляет собой одномерный индексированный массив данных некоторого фиксированного типа. Второй - это двухмерная структура данных, представляющая собой таблицу, каждый столбец которой содержит данные одного типа. Можно представлять её как словарь объектов типа `Series`. Структура `DataFrame` отлично подходит для представления реальных данных: строки соответствуют признаковым описаниям отдельных объектов, а столбцы соответствуют признакам."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "\n",
    "## Демонстрация основных методов Pandas \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Чтение из файла и первичный анализ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Прочитаем данные и посмотрим на первые 5 строк с помощью метода `head`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "File b'../../data/telecom_churn.csv' does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-5c0b24d45018>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../../data/telecom_churn.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mE:\\Programs\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    653\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 655\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    656\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    657\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Programs\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    403\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    404\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 405\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    406\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    407\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Programs\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    760\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    761\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 762\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    763\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Programs\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m    964\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    965\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 966\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    967\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Programs\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1580\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1581\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1582\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1583\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1584\u001b[0m         \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__ (pandas\\_libs\\parsers.c:4209)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source (pandas\\_libs\\parsers.c:8873)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: File b'../../data/telecom_churn.csv' does not exist"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/telecom_churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В Jupyter-ноутбуках датафреймы `Pandas` выводятся в виде вот таких красивых табличек, и `print(df.head())` выглядит хуже.\n",
    "\n",
    "Кстати, по умолчанию `Pandas` выводит всего 20 столбцов и 60 строк, поэтому если ваш датафрейм больше, воспользуйтесь функцией `set_option`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-f10f123ea034>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'display.max_columns'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'display.max_rows'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 100)\n",
    "pyyd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А также укажем значение параметра `presicion` равным 2, чтобы отображать два знака после запятой (а не 6, как установлено по умолчанию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-9fafe8aa68e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'precision'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Посмотрим на размер данных, названия признаков и их типы**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3333, 20)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что в таблице 3333 строки и 20 столбцов. Выведем названия столбцов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['State', 'Account length', 'Area code', 'International plan',\n",
      "       'Voice mail plan', 'Number vmail messages', 'Total day minutes',\n",
      "       'Total day calls', 'Total day charge', 'Total eve minutes',\n",
      "       'Total eve calls', 'Total eve charge', 'Total night minutes',\n",
      "       'Total night calls', 'Total night charge', 'Total intl minutes',\n",
      "       'Total intl calls', 'Total intl charge', 'Customer service calls',\n",
      "       'Churn'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы посмотреть общую информацию по датафрейму и всем признакам, воспользуемся методом **`info`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 20 columns):\n",
      "State                     3333 non-null object\n",
      "Account length            3333 non-null int64\n",
      "Area code                 3333 non-null int64\n",
      "International plan        3333 non-null object\n",
      "Voice mail plan           3333 non-null object\n",
      "Number vmail messages     3333 non-null int64\n",
      "Total day minutes         3333 non-null float64\n",
      "Total day calls           3333 non-null int64\n",
      "Total day charge          3333 non-null float64\n",
      "Total eve minutes         3333 non-null float64\n",
      "Total eve calls           3333 non-null int64\n",
      "Total eve charge          3333 non-null float64\n",
      "Total night minutes       3333 non-null float64\n",
      "Total night calls         3333 non-null int64\n",
      "Total night charge        3333 non-null float64\n",
      "Total intl minutes        3333 non-null float64\n",
      "Total intl calls          3333 non-null int64\n",
      "Total intl charge         3333 non-null float64\n",
      "Customer service calls    3333 non-null int64\n",
      "Churn                     3333 non-null bool\n",
      "dtypes: bool(1), float64(8), int64(8), object(3)\n",
      "memory usage: 498.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bool`, `int64`, `float64` и `object` — это типы признаков. Видим, что 1 признак — логический (`bool`), 3 признака имеют тип `object` и 16 признаков — числовые.\n",
    "\n",
    "**Изменить тип колонки** можно с помощью метода `astype`. Применим этот метод к признаку `Churn` и переведём его в `int64`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Churn'] = df['Churn'].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод **`describe`** показывает основные статистические характеристики данных по каждому числовому признаку (типы `int64` и `float64`): число непропущенных значений, среднее, стандартное отклонение, диапазон, медиану, 0.25 и 0.75 квартили."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "      <td>3333.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>101.06</td>\n",
       "      <td>437.18</td>\n",
       "      <td>8.10</td>\n",
       "      <td>179.78</td>\n",
       "      <td>100.44</td>\n",
       "      <td>30.56</td>\n",
       "      <td>200.98</td>\n",
       "      <td>100.11</td>\n",
       "      <td>17.08</td>\n",
       "      <td>200.87</td>\n",
       "      <td>100.11</td>\n",
       "      <td>9.04</td>\n",
       "      <td>10.24</td>\n",
       "      <td>4.48</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.82</td>\n",
       "      <td>42.37</td>\n",
       "      <td>13.69</td>\n",
       "      <td>54.47</td>\n",
       "      <td>20.07</td>\n",
       "      <td>9.26</td>\n",
       "      <td>50.71</td>\n",
       "      <td>19.92</td>\n",
       "      <td>4.31</td>\n",
       "      <td>50.57</td>\n",
       "      <td>19.57</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>408.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.20</td>\n",
       "      <td>33.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.00</td>\n",
       "      <td>408.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>143.70</td>\n",
       "      <td>87.00</td>\n",
       "      <td>24.43</td>\n",
       "      <td>166.60</td>\n",
       "      <td>87.00</td>\n",
       "      <td>14.16</td>\n",
       "      <td>167.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>7.52</td>\n",
       "      <td>8.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>101.00</td>\n",
       "      <td>415.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>179.40</td>\n",
       "      <td>101.00</td>\n",
       "      <td>30.50</td>\n",
       "      <td>201.40</td>\n",
       "      <td>100.00</td>\n",
       "      <td>17.12</td>\n",
       "      <td>201.20</td>\n",
       "      <td>100.00</td>\n",
       "      <td>9.05</td>\n",
       "      <td>10.30</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.00</td>\n",
       "      <td>510.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>216.40</td>\n",
       "      <td>114.00</td>\n",
       "      <td>36.79</td>\n",
       "      <td>235.30</td>\n",
       "      <td>114.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>235.30</td>\n",
       "      <td>113.00</td>\n",
       "      <td>10.59</td>\n",
       "      <td>12.10</td>\n",
       "      <td>6.00</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>243.00</td>\n",
       "      <td>510.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>350.80</td>\n",
       "      <td>165.00</td>\n",
       "      <td>59.64</td>\n",
       "      <td>363.70</td>\n",
       "      <td>170.00</td>\n",
       "      <td>30.91</td>\n",
       "      <td>395.00</td>\n",
       "      <td>175.00</td>\n",
       "      <td>17.77</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>5.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Account length  Area code  Number vmail messages  Total day minutes  \\\n",
       "count         3333.00    3333.00                3333.00            3333.00   \n",
       "mean           101.06     437.18                   8.10             179.78   \n",
       "std             39.82      42.37                  13.69              54.47   \n",
       "min              1.00     408.00                   0.00               0.00   \n",
       "25%             74.00     408.00                   0.00             143.70   \n",
       "50%            101.00     415.00                   0.00             179.40   \n",
       "75%            127.00     510.00                  20.00             216.40   \n",
       "max            243.00     510.00                  51.00             350.80   \n",
       "\n",
       "       Total day calls  Total day charge  Total eve minutes  Total eve calls  \\\n",
       "count          3333.00           3333.00            3333.00          3333.00   \n",
       "mean            100.44             30.56             200.98           100.11   \n",
       "std              20.07              9.26              50.71            19.92   \n",
       "min               0.00              0.00               0.00             0.00   \n",
       "25%              87.00             24.43             166.60            87.00   \n",
       "50%             101.00             30.50             201.40           100.00   \n",
       "75%             114.00             36.79             235.30           114.00   \n",
       "max             165.00             59.64             363.70           170.00   \n",
       "\n",
       "       Total eve charge  Total night minutes  Total night calls  \\\n",
       "count           3333.00              3333.00            3333.00   \n",
       "mean              17.08               200.87             100.11   \n",
       "std                4.31                50.57              19.57   \n",
       "min                0.00                23.20              33.00   \n",
       "25%               14.16               167.00              87.00   \n",
       "50%               17.12               201.20             100.00   \n",
       "75%               20.00               235.30             113.00   \n",
       "max               30.91               395.00             175.00   \n",
       "\n",
       "       Total night charge  Total intl minutes  Total intl calls  \\\n",
       "count             3333.00             3333.00           3333.00   \n",
       "mean                 9.04               10.24              4.48   \n",
       "std                  2.28                2.79              2.46   \n",
       "min                  1.04                0.00              0.00   \n",
       "25%                  7.52                8.50              3.00   \n",
       "50%                  9.05               10.30              4.00   \n",
       "75%                 10.59               12.10              6.00   \n",
       "max                 17.77               20.00             20.00   \n",
       "\n",
       "       Total intl charge  Customer service calls    Churn  \n",
       "count            3333.00                 3333.00  3333.00  \n",
       "mean                2.76                    1.56     0.14  \n",
       "std                 0.75                    1.32     0.35  \n",
       "min                 0.00                    0.00     0.00  \n",
       "25%                 2.30                    1.00     0.00  \n",
       "50%                 2.78                    1.00     0.00  \n",
       "75%                 3.27                    2.00     0.00  \n",
       "max                 5.40                    9.00     1.00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы посмотреть статистику по нечисловым признакам, нужно явно указать интересующие нас типы в параметре `include`. Можно также задать `include`='all', чтоб вывести статистику по всем имеющимся признакам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3333</td>\n",
       "      <td>3333</td>\n",
       "      <td>3333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>WV</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>106</td>\n",
       "      <td>3010</td>\n",
       "      <td>2411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State International plan Voice mail plan\n",
       "count   3333               3333            3333\n",
       "unique    51                  2               2\n",
       "top       WV                 No              No\n",
       "freq     106               3010            2411"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object', 'bool'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для категориальных (тип `object`) и булевых (тип `bool`) признаков  можно воспользоваться методом **`value_counts`**. Посмотрим на распределение нашей целевой переменной — `Churn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2850\n",
       "1     483\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2850 пользователей из 3333 — лояльные, значение переменной `Churn` у них — `0`.\n",
    "\n",
    "Посмотрим на распределение пользователей по переменной `Area code`. Укажем значение параметра `normalize=True`, чтобы посмотреть не абсолютные частоты, а относительные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415    0.50\n",
       "510    0.25\n",
       "408    0.25\n",
       "Name: Area code, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Area code'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сортировка\n",
    "\n",
    "`DataFrame` можно отсортировать по значению какого-нибудь из признаков. В нашем случае, например, по `Total day charge` (`ascending=False` для сортировки по убыванию):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>CO</td>\n",
       "      <td>154</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>350.8</td>\n",
       "      <td>75</td>\n",
       "      <td>59.64</td>\n",
       "      <td>216.5</td>\n",
       "      <td>94</td>\n",
       "      <td>18.40</td>\n",
       "      <td>253.9</td>\n",
       "      <td>100</td>\n",
       "      <td>11.43</td>\n",
       "      <td>10.1</td>\n",
       "      <td>9</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>NY</td>\n",
       "      <td>64</td>\n",
       "      <td>415</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>346.8</td>\n",
       "      <td>55</td>\n",
       "      <td>58.96</td>\n",
       "      <td>249.5</td>\n",
       "      <td>79</td>\n",
       "      <td>21.21</td>\n",
       "      <td>275.4</td>\n",
       "      <td>102</td>\n",
       "      <td>12.39</td>\n",
       "      <td>13.3</td>\n",
       "      <td>9</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2594</th>\n",
       "      <td>OH</td>\n",
       "      <td>115</td>\n",
       "      <td>510</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>345.3</td>\n",
       "      <td>81</td>\n",
       "      <td>58.70</td>\n",
       "      <td>203.4</td>\n",
       "      <td>106</td>\n",
       "      <td>17.29</td>\n",
       "      <td>217.5</td>\n",
       "      <td>107</td>\n",
       "      <td>9.79</td>\n",
       "      <td>11.8</td>\n",
       "      <td>8</td>\n",
       "      <td>3.19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>OH</td>\n",
       "      <td>83</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>337.4</td>\n",
       "      <td>120</td>\n",
       "      <td>57.36</td>\n",
       "      <td>227.4</td>\n",
       "      <td>116</td>\n",
       "      <td>19.33</td>\n",
       "      <td>153.9</td>\n",
       "      <td>114</td>\n",
       "      <td>6.93</td>\n",
       "      <td>15.8</td>\n",
       "      <td>7</td>\n",
       "      <td>4.27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>MO</td>\n",
       "      <td>112</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>335.5</td>\n",
       "      <td>77</td>\n",
       "      <td>57.04</td>\n",
       "      <td>212.5</td>\n",
       "      <td>109</td>\n",
       "      <td>18.06</td>\n",
       "      <td>265.0</td>\n",
       "      <td>132</td>\n",
       "      <td>11.93</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Account length  Area code International plan Voice mail plan  \\\n",
       "365     CO             154        415                 No              No   \n",
       "985     NY              64        415                Yes              No   \n",
       "2594    OH             115        510                Yes              No   \n",
       "156     OH              83        415                 No              No   \n",
       "605     MO             112        415                 No              No   \n",
       "\n",
       "      Number vmail messages  Total day minutes  Total day calls  \\\n",
       "365                       0              350.8               75   \n",
       "985                       0              346.8               55   \n",
       "2594                      0              345.3               81   \n",
       "156                       0              337.4              120   \n",
       "605                       0              335.5               77   \n",
       "\n",
       "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "365              59.64              216.5               94             18.40   \n",
       "985              58.96              249.5               79             21.21   \n",
       "2594             58.70              203.4              106             17.29   \n",
       "156              57.36              227.4              116             19.33   \n",
       "605              57.04              212.5              109             18.06   \n",
       "\n",
       "      Total night minutes  Total night calls  Total night charge  \\\n",
       "365                 253.9                100               11.43   \n",
       "985                 275.4                102               12.39   \n",
       "2594                217.5                107                9.79   \n",
       "156                 153.9                114                6.93   \n",
       "605                 265.0                132               11.93   \n",
       "\n",
       "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "365                 10.1                 9               2.73   \n",
       "985                 13.3                 9               3.59   \n",
       "2594                11.8                 8               3.19   \n",
       "156                 15.8                 7               4.27   \n",
       "605                 12.7                 8               3.43   \n",
       "\n",
       "      Customer service calls  Churn  \n",
       "365                        1      1  \n",
       "985                        1      1  \n",
       "2594                       1      1  \n",
       "156                        0      1  \n",
       "605                        2      1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Total day charge', \n",
    "        ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сортировать можно и по группе столбцов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>MN</td>\n",
       "      <td>13</td>\n",
       "      <td>510</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21</td>\n",
       "      <td>315.6</td>\n",
       "      <td>105</td>\n",
       "      <td>53.65</td>\n",
       "      <td>208.9</td>\n",
       "      <td>71</td>\n",
       "      <td>17.76</td>\n",
       "      <td>260.1</td>\n",
       "      <td>123</td>\n",
       "      <td>11.70</td>\n",
       "      <td>12.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2259</th>\n",
       "      <td>NC</td>\n",
       "      <td>210</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31</td>\n",
       "      <td>313.8</td>\n",
       "      <td>87</td>\n",
       "      <td>53.35</td>\n",
       "      <td>147.7</td>\n",
       "      <td>103</td>\n",
       "      <td>12.55</td>\n",
       "      <td>192.7</td>\n",
       "      <td>97</td>\n",
       "      <td>8.67</td>\n",
       "      <td>10.1</td>\n",
       "      <td>7</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>LA</td>\n",
       "      <td>67</td>\n",
       "      <td>510</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>310.4</td>\n",
       "      <td>97</td>\n",
       "      <td>52.77</td>\n",
       "      <td>66.5</td>\n",
       "      <td>123</td>\n",
       "      <td>5.65</td>\n",
       "      <td>246.5</td>\n",
       "      <td>99</td>\n",
       "      <td>11.09</td>\n",
       "      <td>9.2</td>\n",
       "      <td>10</td>\n",
       "      <td>2.48</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>SD</td>\n",
       "      <td>114</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>36</td>\n",
       "      <td>309.9</td>\n",
       "      <td>90</td>\n",
       "      <td>52.68</td>\n",
       "      <td>200.3</td>\n",
       "      <td>89</td>\n",
       "      <td>17.03</td>\n",
       "      <td>183.5</td>\n",
       "      <td>105</td>\n",
       "      <td>8.26</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.83</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>AL</td>\n",
       "      <td>141</td>\n",
       "      <td>510</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>308.0</td>\n",
       "      <td>123</td>\n",
       "      <td>52.36</td>\n",
       "      <td>247.8</td>\n",
       "      <td>128</td>\n",
       "      <td>21.06</td>\n",
       "      <td>152.9</td>\n",
       "      <td>103</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Account length  Area code International plan Voice mail plan  \\\n",
       "688     MN              13        510                 No             Yes   \n",
       "2259    NC             210        415                 No             Yes   \n",
       "534     LA              67        510                 No              No   \n",
       "575     SD             114        415                 No             Yes   \n",
       "2858    AL             141        510                 No             Yes   \n",
       "\n",
       "      Number vmail messages  Total day minutes  Total day calls  \\\n",
       "688                      21              315.6              105   \n",
       "2259                     31              313.8               87   \n",
       "534                       0              310.4               97   \n",
       "575                      36              309.9               90   \n",
       "2858                     28              308.0              123   \n",
       "\n",
       "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "688              53.65              208.9               71             17.76   \n",
       "2259             53.35              147.7              103             12.55   \n",
       "534              52.77               66.5              123              5.65   \n",
       "575              52.68              200.3               89             17.03   \n",
       "2858             52.36              247.8              128             21.06   \n",
       "\n",
       "      Total night minutes  Total night calls  Total night charge  \\\n",
       "688                 260.1                123               11.70   \n",
       "2259                192.7                 97                8.67   \n",
       "534                 246.5                 99               11.09   \n",
       "575                 183.5                105                8.26   \n",
       "2858                152.9                103                6.88   \n",
       "\n",
       "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "688                 12.1                 3               3.27   \n",
       "2259                10.1                 7               2.73   \n",
       "534                  9.2                10               2.48   \n",
       "575                 14.2                 2               3.83   \n",
       "2858                 7.4                 3               2.00   \n",
       "\n",
       "      Customer service calls  Churn  \n",
       "688                        3      0  \n",
       "2259                       3      0  \n",
       "534                        4      0  \n",
       "575                        1      0  \n",
       "2858                       1      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['Churn', 'Total day charge'],\n",
    "        ascending=[True, False]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Индексация и извлечение данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame` можно индексировать по-разному. В связи с этим рассмотрим различные способы индексации и извлечения нужных нам данных из датафрейма на примере простых вопросов.\n",
    "\n",
    "Для извлечения отдельного столбца можно использовать конструкцию вида `DataFrame['Name']`. Воспользуемся этим для ответа на вопрос: **какова доля нелояльных пользователей в нашем датафрейме?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14491449144914492"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14,5% — довольно плохой показатель для компании, с таким процентом оттока можно и разориться."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Очень удобной является логическая индексация `DataFrame` по одному столбцу. Выглядит она следующим образом: `df[P(df['Name'])]`, где `P` - это некоторое логическое условие, проверяемое для каждого элемента столбца `Name`. Итогом такой индексации является `DataFrame`, состоящий только из строк, удовлетворяющих условию `P` по столбцу `Name`. \n",
    "\n",
    "Воспользуемся этим для ответа на вопрос: **каковы средние значения числовых признаков среди нелояльных пользователей?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Account length            102.66\n",
       "Area code                 437.82\n",
       "Number vmail messages       5.12\n",
       "Total day minutes         206.91\n",
       "Total day calls           101.34\n",
       "Total day charge           35.18\n",
       "Total eve minutes         212.41\n",
       "Total eve calls           100.56\n",
       "Total eve charge           18.05\n",
       "Total night minutes       205.23\n",
       "Total night calls         100.40\n",
       "Total night charge          9.24\n",
       "Total intl minutes         10.70\n",
       "Total intl calls            4.16\n",
       "Total intl charge           2.89\n",
       "Customer service calls      2.23\n",
       "Churn                       1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Churn'] == 1].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скомбинировав предыдущие два вида индексации, ответим на вопрос: **сколько в среднем в течение дня разговаривают по телефону нелояльные пользователи**?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206.91407867494814"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Churn'] == 1]['Total day minutes'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Какова максимальная длина международных звонков среди лояльных пользователей (`Churn == 0`), не пользующихся услугой международного роуминга (`'International plan' == 'No'`)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.899999999999999"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Churn'] == 0) & (df['International plan'] == 'No')]['Total intl minutes'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Датафреймы можно индексировать как по названию столбца или строки, так и по порядковому номеру. Для индексации **по названию** используется метод **`loc`**, **по номеру** — **`iloc`**.\n",
    "\n",
    "В первом случае мы говорим _«передай нам значения для id строк от 0 до 5 и для столбцов от State до Area code»_, а во втором — _«передай нам значения первых пяти строк в первых трёх столбцах»_. \n",
    "\n",
    "В случае `iloc` срез работает как обычно, однако в случае `loc` учитываются и начало, и конец среза. Да, неудобно, да, вызывает путаницу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>415</td>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code\n",
       "0    KS             128        415\n",
       "1    OH             107        415\n",
       "2    NJ             137        415\n",
       "3    OH              84        408\n",
       "4    OK              75        415\n",
       "5    AL             118        510"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:5, 'State':'Area code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code\n",
       "0    KS             128        415\n",
       "1    OH             107        415\n",
       "2    NJ             137        415\n",
       "3    OH              84        408\n",
       "4    OK              75        415"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод `ix` индексирует и по названию, и по номеру, но он вызывает путаницу, и поэтому был объявлен устаревшим (deprecated)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если нам нужна первая или последняя строчка датафрейма, пользуемся конструкцией `df[:1]` или `df[-1:]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
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       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>TN</td>\n",
       "      <td>74</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>234.4</td>\n",
       "      <td>113</td>\n",
       "      <td>39.85</td>\n",
       "      <td>265.9</td>\n",
       "      <td>82</td>\n",
       "      <td>22.6</td>\n",
       "      <td>241.4</td>\n",
       "      <td>77</td>\n",
       "      <td>10.86</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     State  Account length  Area code International plan Voice mail plan  \\\n",
       "3332    TN              74        415                 No             Yes   \n",
       "\n",
       "      Number vmail messages  Total day minutes  Total day calls  \\\n",
       "3332                     25              234.4              113   \n",
       "\n",
       "      Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "3332             39.85              265.9               82              22.6   \n",
       "\n",
       "      Total night minutes  Total night calls  Total night charge  \\\n",
       "3332                241.4                 77               10.86   \n",
       "\n",
       "      Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "3332                13.7                 4                3.7   \n",
       "\n",
       "      Customer service calls  Churn  \n",
       "3332                       0      0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Применение функций: `apply`, `map` и др."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Применение функции к каждому столбцу:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                          WY\n",
       "Account length                243\n",
       "Area code                     510\n",
       "International plan            Yes\n",
       "Voice mail plan               Yes\n",
       "Number vmail messages          51\n",
       "Total day minutes         3.5e+02\n",
       "Total day calls               165\n",
       "Total day charge               60\n",
       "Total eve minutes         3.6e+02\n",
       "Total eve calls               170\n",
       "Total eve charge               31\n",
       "Total night minutes         4e+02\n",
       "Total night calls             175\n",
       "Total night charge             18\n",
       "Total intl minutes             20\n",
       "Total intl calls               20\n",
       "Total intl charge             5.4\n",
       "Customer service calls          9\n",
       "Churn                           1\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.max) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод `apply` можно использовать и для того, чтобы применить функцию к каждой строке. Для этого нужно указать `axis=1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Применение функции к каждой ячейке столбца**\n",
    "\n",
    "Допустим, по какой-то причине нас интересуют все люди из штатов, названия которых начинаются на 'W'. В данному случае это можно сделать по-разному, но наибольшую свободу дает связка `apply`-`lambda` – применение функции ко всем знаениям в столбце."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
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       "      <th>Total night calls</th>\n",
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       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WV</td>\n",
       "      <td>141</td>\n",
       "      <td>415</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>37</td>\n",
       "      <td>258.6</td>\n",
       "      <td>84</td>\n",
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       "      <td>222.0</td>\n",
       "      <td>111</td>\n",
       "      <td>18.87</td>\n",
       "      <td>326.4</td>\n",
       "      <td>97</td>\n",
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       "      <th>26</th>\n",
       "      <td>WY</td>\n",
       "      <td>57</td>\n",
       "      <td>408</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>39</td>\n",
       "      <td>213.0</td>\n",
       "      <td>115</td>\n",
       "      <td>36.21</td>\n",
       "      <td>191.1</td>\n",
       "      <td>112</td>\n",
       "      <td>16.24</td>\n",
       "      <td>182.7</td>\n",
       "      <td>115</td>\n",
       "      <td>8.22</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>WI</td>\n",
       "      <td>64</td>\n",
       "      <td>510</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>67</td>\n",
       "      <td>26.18</td>\n",
       "      <td>225.8</td>\n",
       "      <td>118</td>\n",
       "      <td>19.19</td>\n",
       "      <td>265.3</td>\n",
       "      <td>86</td>\n",
       "      <td>11.94</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WY</td>\n",
       "      <td>97</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>133.2</td>\n",
       "      <td>135</td>\n",
       "      <td>22.64</td>\n",
       "      <td>217.2</td>\n",
       "      <td>58</td>\n",
       "      <td>18.46</td>\n",
       "      <td>70.6</td>\n",
       "      <td>79</td>\n",
       "      <td>3.18</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>WY</td>\n",
       "      <td>87</td>\n",
       "      <td>415</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>83</td>\n",
       "      <td>25.67</td>\n",
       "      <td>219.7</td>\n",
       "      <td>116</td>\n",
       "      <td>18.67</td>\n",
       "      <td>203.9</td>\n",
       "      <td>127</td>\n",
       "      <td>9.18</td>\n",
       "      <td>9.7</td>\n",
       "      <td>3</td>\n",
       "      <td>2.62</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  Account length  Area code International plan Voice mail plan  \\\n",
       "9     WV             141        415                Yes             Yes   \n",
       "26    WY              57        408                 No             Yes   \n",
       "44    WI              64        510                 No              No   \n",
       "49    WY              97        415                 No             Yes   \n",
       "54    WY              87        415                 No              No   \n",
       "\n",
       "    Number vmail messages  Total day minutes  Total day calls  \\\n",
       "9                      37              258.6               84   \n",
       "26                     39              213.0              115   \n",
       "44                      0              154.0               67   \n",
       "49                     24              133.2              135   \n",
       "54                      0              151.0               83   \n",
       "\n",
       "    Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "9              43.96              222.0              111             18.87   \n",
       "26             36.21              191.1              112             16.24   \n",
       "44             26.18              225.8              118             19.19   \n",
       "49             22.64              217.2               58             18.46   \n",
       "54             25.67              219.7              116             18.67   \n",
       "\n",
       "    Total night minutes  Total night calls  Total night charge  \\\n",
       "9                 326.4                 97               14.69   \n",
       "26                182.7                115                8.22   \n",
       "44                265.3                 86               11.94   \n",
       "49                 70.6                 79                3.18   \n",
       "54                203.9                127                9.18   \n",
       "\n",
       "    Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "9                 11.2                 5               3.02   \n",
       "26                 9.5                 3               2.57   \n",
       "44                 3.5                 3               0.95   \n",
       "49                11.0                 3               2.97   \n",
       "54                 9.7                 3               2.62   \n",
       "\n",
       "    Customer service calls  Churn  \n",
       "9                        0      0  \n",
       "26                       0      0  \n",
       "44                       1      0  \n",
       "49                       1      0  \n",
       "54                       5      1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['State'].apply(lambda state: state[0] == 'W')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод `map` можно использовать и для **замены значений в колонке**, передав ему в качестве аргумента словарь вида `{old_value: new_value}`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>KS</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
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       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>True</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code  International plan Voice mail plan  \\\n",
       "0    KS             128        415               False             Yes   \n",
       "1    OH             107        415               False             Yes   \n",
       "2    NJ             137        415               False              No   \n",
       "3    OH              84        408                True              No   \n",
       "4    OK              75        415                True              No   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  \n",
       "0                       1      0  \n",
       "1                       1      0  \n",
       "2                       0      0  \n",
       "3                       2      0  \n",
       "4                       3      0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'No' : False, 'Yes' : True}\n",
    "df['International plan'] = df['International plan'].map(d)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогичную операцию можно провернуть с помощью метода `replace`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code  International plan  Voice mail plan  \\\n",
       "0    KS             128        415               False             True   \n",
       "1    OH             107        415               False             True   \n",
       "2    NJ             137        415               False            False   \n",
       "3    OH              84        408                True            False   \n",
       "4    OK              75        415                True            False   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  \n",
       "0                       1      0  \n",
       "1                       1      0  \n",
       "2                       0      0  \n",
       "3                       2      0  \n",
       "4                       3      0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.replace({'Voice mail plan': d})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Группировка данных\n",
    "\n",
    "В общем случае группировка данных в Pandas выглядит следующим образом:\n",
    "\n",
    "```\n",
    "df.groupby(by=grouping_columns)[columns_to_show].function()\n",
    "```\n",
    "\n",
    "1. К датафрейму применяется метод **`groupby`**, который разделяет данные по `grouping_columns` – признаку или набору признаков.\n",
    "3. Индексируем по нужным нам столбцам (`columns_to_show`). \n",
    "2. К полученным группам применяется функция или несколько функций."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Группирование данных в зависимости от значения признака `Churn` и вывод статистик по трём столбцам в каждой группе.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Total day minutes</th>\n",
       "      <th colspan=\"6\" halign=\"left\">Total eve minutes</th>\n",
       "      <th colspan=\"6\" halign=\"left\">Total night minutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>50%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>175.18</td>\n",
       "      <td>50.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177.2</td>\n",
       "      <td>315.6</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>199.04</td>\n",
       "      <td>50.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>199.6</td>\n",
       "      <td>361.8</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>200.13</td>\n",
       "      <td>51.11</td>\n",
       "      <td>23.2</td>\n",
       "      <td>200.25</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>483.0</td>\n",
       "      <td>206.91</td>\n",
       "      <td>69.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>217.6</td>\n",
       "      <td>350.8</td>\n",
       "      <td>483.0</td>\n",
       "      <td>212.41</td>\n",
       "      <td>51.73</td>\n",
       "      <td>70.9</td>\n",
       "      <td>211.3</td>\n",
       "      <td>363.7</td>\n",
       "      <td>483.0</td>\n",
       "      <td>205.23</td>\n",
       "      <td>47.13</td>\n",
       "      <td>47.4</td>\n",
       "      <td>204.80</td>\n",
       "      <td>354.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total day minutes                                   Total eve minutes  \\\n",
       "                  count    mean    std  min    50%    max             count   \n",
       "Churn                                                                         \n",
       "0                2850.0  175.18  50.18  0.0  177.2  315.6            2850.0   \n",
       "1                 483.0  206.91  69.00  0.0  217.6  350.8             483.0   \n",
       "\n",
       "                                         Total night minutes                 \\\n",
       "         mean    std   min    50%    max               count    mean    std   \n",
       "Churn                                                                         \n",
       "0      199.04  50.29   0.0  199.6  361.8              2850.0  200.13  51.11   \n",
       "1      212.41  51.73  70.9  211.3  363.7               483.0  205.23  47.13   \n",
       "\n",
       "                            \n",
       "        min     50%    max  \n",
       "Churn                       \n",
       "0      23.2  200.25  395.0  \n",
       "1      47.4  204.80  354.9  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_show = ['Total day minutes', 'Total eve minutes', 'Total night minutes']\n",
    "\n",
    "df.groupby(['Churn'])[columns_to_show].describe(percentiles=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сделаем то же самое, но немного по-другому, передав в `agg` список функций:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Total day minutes</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Total eve minutes</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Total night minutes</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
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       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>175.18</td>\n",
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       "      <td>199.04</td>\n",
       "      <td>50.29</td>\n",
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       "      <td>395.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206.91</td>\n",
       "      <td>69.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.8</td>\n",
       "      <td>212.41</td>\n",
       "      <td>51.73</td>\n",
       "      <td>70.9</td>\n",
       "      <td>363.7</td>\n",
       "      <td>205.23</td>\n",
       "      <td>47.13</td>\n",
       "      <td>47.4</td>\n",
       "      <td>354.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total day minutes                    Total eve minutes               \\\n",
       "                   mean    std amin   amax              mean    std  amin   \n",
       "Churn                                                                       \n",
       "0                175.18  50.18  0.0  315.6            199.04  50.29   0.0   \n",
       "1                206.91  69.00  0.0  350.8            212.41  51.73  70.9   \n",
       "\n",
       "             Total night minutes                      \n",
       "        amax                mean    std  amin   amax  \n",
       "Churn                                                 \n",
       "0      361.8              200.13  51.11  23.2  395.0  \n",
       "1      363.7              205.23  47.13  47.4  354.9  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_show = ['Total day minutes', 'Total eve minutes', 'Total night minutes']\n",
    "\n",
    "df.groupby(['Churn'])[columns_to_show].agg([np.mean, np.std, np.min, np.max])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сводные таблицы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Допустим, мы хотим посмотреть, как наблюдения в нашей выборке распределены в контексте двух признаков — `Churn` и `Customer service calls`. Для этого мы можем построить **таблицу сопряженности**, воспользовавшись методом **`crosstab`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>International plan</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2664</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>346</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "International plan  False  True \n",
       "Churn                           \n",
       "0                    2664    186\n",
       "1                     346    137"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Churn'], df['International plan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Voice mail plan  False  True \n",
       "Churn                        \n",
       "0                 0.60   0.25\n",
       "1                 0.12   0.02"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Churn'], df['Voice mail plan'], normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим, что большинство пользователей — лояльные и пользуются дополнительными услугами (международного роуминга / голосовой почты)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Продвинутые пользователи `Excel` наверняка вспомнят о такой фиче, как **сводные таблицы** (`pivot tables`). В `Pandas` за сводные таблицы отвечает метод **`pivot_table`**, который принимает в качестве параметров:\n",
    "\n",
    "* `values` – список переменных, по которым требуется рассчитать нужные статистики,\n",
    "* `index` – список переменных, по которым нужно сгруппировать данные,\n",
    "* `aggfunc` — то, что нам, собственно, нужно посчитать по группам — сумму, среднее, максимум, минимум или что-то ещё.\n",
    "\n",
    "Давайте посмотрим среднее число дневных, вечерних и ночных звонков для разных `Area code`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total night calls</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>100.50</td>\n",
       "      <td>99.79</td>\n",
       "      <td>99.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>100.58</td>\n",
       "      <td>100.50</td>\n",
       "      <td>100.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>100.10</td>\n",
       "      <td>99.67</td>\n",
       "      <td>100.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total day calls  Total eve calls  Total night calls\n",
       "Area code                                                     \n",
       "408                 100.50            99.79              99.04\n",
       "415                 100.58           100.50             100.40\n",
       "510                 100.10            99.67             100.60"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(['Total day calls', \n",
    "                'Total eve calls', \n",
    "                'Total night calls'], ['Area code'], \n",
    "               aggfunc='mean').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Преобразование датафреймов\n",
    "\n",
    "Как и многие другие вещи, добавлять столбцы в `DataFrame` можно несколькими способами."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Например, мы хотим посчитать общее количество звонков для всех пользователей. Создадим объект `total_calls` типа `Series` и вставим его в датафрейм:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Total calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
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       "      <td>415</td>\n",
       "      <td>False</td>\n",
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       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>303</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code  International plan  Voice mail plan  \\\n",
       "0    KS             128        415               False             True   \n",
       "1    OH             107        415               False             True   \n",
       "2    NJ             137        415               False            False   \n",
       "3    OH              84        408                True            False   \n",
       "4    OK              75        415                True            False   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  Total calls  \n",
       "0                       1      0          303  \n",
       "1                       1      0          332  \n",
       "2                       0      0          333  \n",
       "3                       2      0          255  \n",
       "4                       3      0          359  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_calls = df['Total day calls'] + df['Total eve calls'] + \\\n",
    "              df['Total night calls'] + df['Total intl calls']\n",
    "df.insert(loc=len(df.columns), column='Total calls', value=total_calls) \n",
    "# loc - номер столбца, после которого нужно вставить данный Series\n",
    "# мы указали len(df.columns), чтобы вставить его в самом конце\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавить столбец из имеющихся можно и проще, не создавая промежуточных `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Total calls</th>\n",
       "      <th>Total charge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
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       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>75.56</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>332</td>\n",
       "      <td>59.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
       "      <td>62.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "      <td>66.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "      <td>52.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code  International plan  Voice mail plan  \\\n",
       "0    KS             128        415               False             True   \n",
       "1    OH             107        415               False             True   \n",
       "2    NJ             137        415               False            False   \n",
       "3    OH              84        408                True            False   \n",
       "4    OK              75        415                True            False   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "1                     26              161.6              123   \n",
       "2                      0              243.4              114   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "1             27.47              195.5              103             16.62   \n",
       "2             41.38              121.2              110             10.30   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   Customer service calls  Churn  Total calls  Total charge  \n",
       "0                       1      0          303         75.56  \n",
       "1                       1      0          332         59.24  \n",
       "2                       0      0          333         62.29  \n",
       "3                       2      0          255         66.80  \n",
       "4                       3      0          359         52.09  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total charge'] = df['Total day charge'] + df['Total eve charge'] + \\\n",
    "                     df['Total night charge'] + df['Total intl charge']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы удалить столбцы или строки, воспользуйтесь методом `drop`, передавая в качестве аргумента нужные индексы и требуемое значение параметра `axis` (`1`, если удаляете столбцы, и ничего или `0`, если удаляете строки):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>State</th>\n",
       "      <th>Account length</th>\n",
       "      <th>Area code</th>\n",
       "      <th>International plan</th>\n",
       "      <th>Voice mail plan</th>\n",
       "      <th>Number vmail messages</th>\n",
       "      <th>Total day minutes</th>\n",
       "      <th>Total day calls</th>\n",
       "      <th>Total day charge</th>\n",
       "      <th>Total eve minutes</th>\n",
       "      <th>Total eve calls</th>\n",
       "      <th>Total eve charge</th>\n",
       "      <th>Total night minutes</th>\n",
       "      <th>Total night calls</th>\n",
       "      <th>Total night charge</th>\n",
       "      <th>Total intl minutes</th>\n",
       "      <th>Total intl calls</th>\n",
       "      <th>Total intl charge</th>\n",
       "      <th>Customer service calls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AL</td>\n",
       "      <td>118</td>\n",
       "      <td>510</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>223.4</td>\n",
       "      <td>98</td>\n",
       "      <td>37.98</td>\n",
       "      <td>220.6</td>\n",
       "      <td>101</td>\n",
       "      <td>18.75</td>\n",
       "      <td>203.9</td>\n",
       "      <td>118</td>\n",
       "      <td>9.18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MA</td>\n",
       "      <td>121</td>\n",
       "      <td>510</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>24</td>\n",
       "      <td>218.2</td>\n",
       "      <td>88</td>\n",
       "      <td>37.09</td>\n",
       "      <td>348.5</td>\n",
       "      <td>108</td>\n",
       "      <td>29.62</td>\n",
       "      <td>212.6</td>\n",
       "      <td>118</td>\n",
       "      <td>9.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account length  Area code  International plan  Voice mail plan  \\\n",
       "0    KS             128        415               False             True   \n",
       "3    OH              84        408                True            False   \n",
       "4    OK              75        415                True            False   \n",
       "5    AL             118        510                True            False   \n",
       "6    MA             121        510               False             True   \n",
       "\n",
       "   Number vmail messages  Total day minutes  Total day calls  \\\n",
       "0                     25              265.1              110   \n",
       "3                      0              299.4               71   \n",
       "4                      0              166.7              113   \n",
       "5                      0              223.4               98   \n",
       "6                     24              218.2               88   \n",
       "\n",
       "   Total day charge  Total eve minutes  Total eve calls  Total eve charge  \\\n",
       "0             45.07              197.4               99             16.78   \n",
       "3             50.90               61.9               88              5.26   \n",
       "4             28.34              148.3              122             12.61   \n",
       "5             37.98              220.6              101             18.75   \n",
       "6             37.09              348.5              108             29.62   \n",
       "\n",
       "   Total night minutes  Total night calls  Total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "5                203.9                118                9.18   \n",
       "6                212.6                118                9.57   \n",
       "\n",
       "   Total intl minutes  Total intl calls  Total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "5                 6.3                 6               1.70   \n",
       "6                 7.5                 7               2.03   \n",
       "\n",
       "   Customer service calls  Churn  \n",
       "0                       1      0  \n",
       "3                       2      0  \n",
       "4                       3      0  \n",
       "5                       0      0  \n",
       "6                       3      0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# избавляемся от созданных только что столбцов\n",
    "df = df.drop(['Total charge', 'Total calls'], axis=1) \n",
    "\n",
    "df.drop([1, 2]).head() # а вот так можно удалить строчки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "\n",
    "\n",
    "\n",
    "## Первые попытки прогнозирования оттока\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, как отток связан с признаком *\"Подключение международного роуминга\"* (`International plan`). Сделаем это с помощью сводной таблички `crosstab`, а также путем иллюстрации с `Seaborn` (как именно строить такие картинки и анализировать с их помощью графики – материал следующей статьи.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# надо дополнительно установить (команда в терминале)\n",
    "# чтоб картинки рисовались в тетрадке\n",
    "# !conda install seaborn \n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams['figure.figsize'] = (8, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>International plan</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2664</td>\n",
       "      <td>186</td>\n",
       "      <td>2850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>346</td>\n",
       "      <td>137</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>3010</td>\n",
       "      <td>323</td>\n",
       "      <td>3333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "International plan  False  True   All\n",
       "Churn                                \n",
       "0                    2664   186  2850\n",
       "1                     346   137   483\n",
       "All                  3010   323  3333"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Churn'], df['International plan'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Jkw4lI6OUQOZtlqkq2tsq6cgSUVp6nvwu9hZ93gssBHYXHu8t/G+sX5t2LBl4LNUS3R3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\nMvCSJBXIwEuSVKD/D+RVlfs3oH8HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11397e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='International plan', hue='Churn', data=df);\n",
    "plt.savefig('int_plan_and_churn.png', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что когда роуминг подключен, доля оттока намного выше – интересное наблюдение! Возможно, большие и плохо контролируемые траты в роуминге очень конфликтогенны и приводят к недовольству клиентов телеком-оператора и, соответственно, к их оттоку. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее посмотрим на еще один важный признак – *\"Число обращений в сервисный центр\"* (`Customer service calls`). Также построим сводную таблицу и картинку."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Customer service calls</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>605</td>\n",
       "      <td>1059</td>\n",
       "      <td>672</td>\n",
       "      <td>385</td>\n",
       "      <td>90</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>122</td>\n",
       "      <td>87</td>\n",
       "      <td>44</td>\n",
       "      <td>76</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>697</td>\n",
       "      <td>1181</td>\n",
       "      <td>759</td>\n",
       "      <td>429</td>\n",
       "      <td>166</td>\n",
       "      <td>66</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Customer service calls    0     1    2    3    4   5   6  7  8  9   All\n",
       "Churn                                                                  \n",
       "0                       605  1059  672  385   90  26   8  4  1  0  2850\n",
       "1                        92   122   87   44   76  40  14  5  1  2   483\n",
       "All                     697  1181  759  429  166  66  22  9  2  2  3333"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Churn'], df['Customer service calls'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mR8QE4B5gYWaui4gLgSVUAwFPy8wnOlmkJEm9pGMBn5mzRzzdZyP75wPz21aQxowT5i1q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\n/w+JKMC485UW8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d93e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Customer service calls', hue='Churn', data=df);\n",
    "plt.savefig('serv_calls__and_churn.png', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Может быть, по сводной табличке это не так хорошо видно (или скучно ползать взглядом по строчкам с цифрами), а вот картинка красноречиво свидетельствует о том, что доля оттока сильно возрастает начиная с 4 звонков в сервисный центр. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим теперь в наш DataFrame бинарный признак — результат сравнения `Customer service calls > 3`. И еще раз посмотрим, как он связан с оттоком. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Churn</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Many_service_calls</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2721</td>\n",
       "      <td>345</td>\n",
       "      <td>3066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129</td>\n",
       "      <td>138</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2850</td>\n",
       "      <td>483</td>\n",
       "      <td>3333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn                  0    1   All\n",
       "Many_service_calls                 \n",
       "0                   2721  345  3066\n",
       "1                    129  138   267\n",
       "All                 2850  483  3333"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Many_service_calls'] = (df['Customer service calls'] > 3).astype('int')\n",
    "\n",
    "pd.crosstab(df['Many_service_calls'], df['Churn'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFyCAYAAAAZA8jBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11802a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Many_service_calls', hue='Churn', data=df);\n",
    "plt.savefig('many_serv_calls__and_churn.png', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объединим рассмотренные выше условия и построим сводную табличку для этого объединения и оттока."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Churn</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>2841</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Churn     0    1\n",
       "row_0           \n",
       "False  2841  464\n",
       "True      9   19"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Many_service_calls'] & df['International plan'] , \n",
    "            df['Churn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значит, прогнозируя лояльность клиента в случае, когда число звонков в сервисный центр меньше 4 и не подключен роуминг (и прогнозируя отток – в противном случае), можно ожидать процент \"угадывания лояльности клиента\" около 85.8% (ошибаемся всего 464 + 9 раз). Эти 85.8%, которые мы получили с помощью очень простых рассуждений – это неплохая отправная точка (*baseline*) для дальнейших моделей машинного обучения, которые мы будем строить. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В целом до появления машинного обучения процесс анализа данных выглядел примерно так. Прорезюмируем:\n",
    "    \n",
    "- Доля лояльных клиентов в выборке – 85.5%. Самая наивная модель, ответ которой \"Клиент всегда лоялен\" на подобных данных будет угадывать примерно в  85.5% случаев. То есть доли правильных ответов (*accuracy*) последующих моделей должны быть как минимум не меньше, а лучше, значительно выше этой цифры;\n",
    "- С помощью простого прогноза , который условно можно выразить такой формулой: \"International plan = True & Customer Service calls > 3 => Churn = 1, else Churn = 0\", можно ожидать долю угадываний 85.8%, что еще чуть выше 85.5%\n",
    "- Эти два бейзлайна мы получили без всякого машинного обучения, и они служат отправной точной для наших последующих моделей. Если окажется, что мы громадными усилиями увеличиваем долю правильных ответов всего, скажем, на 0.5%, то возможно, мы что-то делаем не так, и достаточно ограничиться простой моделью из двух условий. \n",
    "- Перед обучением сложных моделей рекомендуется немного покрутить данные и проверить простые предположения. Более того, в бизнес-приложениях машинного обучения чаще всего начинают именно с простых решений, а потом экспериментируют с их усложнением. "
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