Lecture 13.ipynb

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   "source": [
    "# Программирование на языке Python для сбора и анализа данных\n",
    "\n",
    "*Текст лекции: Щуров И.В., НИУ ВШЭ*\n",
    "\n",
    "Данный notebook является конспектом лекции по курсу «Программирование на языке Python для сбора и анализа данных» (НИУ ВШЭ, 2015-16). Он распространяется на условиях лицензии [Creative Commons Attribution-Share Alike 4.0](http://creativecommons.org/licenses/by-sa/4.0/). При использовании обязательно упоминание автора курса и аффилиации. При наличии технической возможности необходимо также указать активную гиперссылку на [страницу курса](http://math-info.hse.ru/s15/m). Фрагменты кода, включенные в этот notebook, публикуются как [общественное достояние](http://creativecommons.org/publicdomain/zero/1.0/).\n",
    "\n",
    "Другие материалы курса, включая конспекты и видеозаписи лекций, а также наборы задач, можно найти на [странице курса](http://math-info.hse.ru/s15/m)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Лекция 13: ещё о pandas\n",
    "\n",
    "Эта история началась с невинного разговора о погоде. Примерно такого:\n",
    "\n",
    "— Смотри-ка, сегодня 10-е марта, а за окном плюс семь. Прямо весна наступила неожиданно.\n",
    "\n",
    "— Да… Ну, если март тёплый, значит, апрель будет холодный. Так всегда бывает.\n",
    "\n",
    "— Правда? Сейчас проверим…\n",
    "\n",
    "И я пошёл искать статистические данные по погоде в Москве. Они нашлись быстро — [вот здесь](http://cliware.meteo.ru/inter/data.html) можно взять информацию за последние 50 лет. Судя по заглавной странице, это система Всероссийского научно-исследовательского института гидрометеорологической информации — звучит внушительно. С помощью найденных данных и математической статистики мы не только проверим сформулированную выше «народную примету», но и получим довольно неожиданную информацию о связи погоды в разные месяцы, а также своими глазами посмотрим на глобальное потепление.\n",
    "\n",
    "Поехали!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Планируем\n",
    "Мы нашли информацию о температуре за каждый день за последние 50 лет, но что мы теперь с ней будем делать? Чтобы подтвердить или опровергнуть нашу гипотезу, её надо переформулировать математически. Утверждение, «тепло в марте, значит, будет холодно в апреле», означает, что между двумя величинами — средней температурой за март и средней температурой за апрель — наблюдается *отрицательная корреляция*. То есть мы предполагаем, что в те года, в которые средняя температура за март была относительно высокой, средняя температура за апрель была относительно низкой. Конечно, всегда может встретиться аномальный год с жарким мартом и жарким апрелем — само по себе это не будет противоречить нашей гипотезе — но мы предполагаем, что такие случаи редки.\n",
    "\n",
    "Чтобы проверить эту гипотезу, нам потребуется вычислить среднюю температуру за каждый из интересующих нас месяцев каждого из 50 лет и вычислить величину, которая называется [коэффициентом корреляции Пирсона](https://ru.wikipedia.org/wiki/%D0%9A%D0%BE%D1%80%D1%80%D0%B5%D0%BB%D1%8F%D1%86%D0%B8%D1%8F#.D0.9A.D0.BE.D1.8D.D1.84.D1.84.D0.B8.D1.86.D0.B8.D0.B5.D0.BD.D1.82_.D0.BA.D0.BE.D1.80.D1.80.D0.B5.D0.BB.D1.8F.D1.86.D0.B8.D0.B8_.D0.9F.D0.B8.D1.80.D1.81.D0.BE.D0.BD.D0.B0). К счастью, нам даже не нужно знать, по какой формуле её считать (хотя это и полезно), поскольку Python посчитаёт её за нас. Самое главное — подготовить для этого данные."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Грузим\n",
    "Для анализа данных в Python используется модуль под милым названием `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чтобы загрузить эту таблицу в `pandas`, можно было скопировать её в какой-нибудь табличный процессор (например, Excel, Numbers или OpenOffice.org Calc), сохранить как CSV и загрузить с помощью `pd.read_csv`, но на самом деле всё проще: можно загружать сразу html-таблички.\n",
    "\n",
    "Есть одна небольшая проблема: ссылка, упомянутая выше, указывает не на страницу с таблицей, а на форму — собственно табличку вы получаете после выбора параметров в форме и нажатия на кнопку, при этом сами параметры не передаются в адресной строке (это так называемый POST-запрос). Поэтому загрузить эти данные в `pandas`, просто подставив адрес нужной страницы, не удастся. Так что я сохранил html-файл на свой локальный компьютер. Потом мы обсуждали этот пример со студентами и, похоже, своими запросами временно «положили» источник данных, так что пришлось пользоваться [сохранённой копией](http://math-info.hse.ru/f/2014-15/nes-stat/climate.html) на страничке [нашего курса](http://math-info.hse.ru/s14/s).\n",
    "\n",
    "Итак, данные у нас есть — поехали."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "dat = pd.read_html(\"http://math-info.hse.ru/f/2014-15/nes-stat/climate.html\", header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Параметр `header=0` показывает, что нулевая (то есть первая) строчка должна использоваться как строка заголовков (имён переменных). Без него возникает множество технических проблем, которые, впрочем, тоже преодолимы. \n",
    "\n",
    "Одако, если вывести сейчас переменную `dat`, получится что-то не очень понятное — вместо красивой таблички нечто текстовое. А если попытаться работать с `dat` как с dataframe (так в `pandas`, вслед за `R`, называются таблицы с данными), то и вообще ничего не получится. Дело в том, что `dat` сейчас — не dataframe. А что же?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Логично. Это список. На html-страничке ведь может быть несколько таблиц. `pd.read_html` каждую из них поместит в свой dataframe и создаст список dataframe'ов. В нашем случае этот список очень простой. Если бы мы читали документацию, то сразу бы об этом узнали."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В нём всего один элемент. Извлечём его из списка и назовём тем же именем, что раньше называли список. (Сам список нам больше не понадобится.)"
   ]
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   "cell_type": "code",
   "execution_count": 5,
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION_ID</th>\n",
       "      <th>STATION_NM</th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>Q</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>Q.1</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>Q.2</th>\n",
       "      <th>PRECIP</th>\n",
       "      <th>Q.3</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27612</td>\n",
       "      <td>МОСКВА ВДНХ</td>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27612</td>\n",
       "      <td>МОСКВА ВДНХ</td>\n",
       "      <td>1948-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27612</td>\n",
       "      <td>МОСКВА ВДНХ</td>\n",
       "      <td>1948-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATION_ID   STATION_NM    DATE_OBS  TMPMAX  Q  TMPMIN  Q.1  TMPMN  Q.2  \\\n",
       "0       27612  МОСКВА ВДНХ  1948-01-01     NaN  9     NaN    9    NaN    9   \n",
       "1       27612  МОСКВА ВДНХ  1948-01-02     NaN  9     NaN    9    NaN    9   \n",
       "2       27612  МОСКВА ВДНХ  1948-01-03     NaN  9     NaN    9    NaN    9   \n",
       "\n",
       "   PRECIP  Q.3  D  \n",
       "0     NaN    9  9  \n",
       "1     NaN    9  9  \n",
       "2     NaN    9  9  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = dat[0]\n",
    "dat.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь в `dat` лежит честный dataframe. Часть столбцов имеют понятный без специальных пояснений смысл: `TMPMAX` — максимальная температура за день, `TMPMIN` — минимальная, `TMPMN` — средняя (от слова *mean*), `PRECIP` — уровень осадков (*precipitation*). Ещё есть непонятные столбцы `Q` и `D`, значения которых мне не ведомы. Название и идентификатор станции нам тоже не слишком нужны. От них можно избавиться, например, вот так."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dat.drop(['STATION_ID', 'STATION_NM', 'Q','Q.1','Q.2','Q.3','D'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Параметр `axis=1` означает, что нужно выкинуть столбцы, а не строки, `inplace=True` означает, что это нужно сделать «на месте», то есть модифицировать dataframe, а не вернуть его версию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "0  1948-01-01     NaN     NaN    NaN     NaN\n",
       "1  1948-01-02     NaN     NaN    NaN     NaN\n",
       "2  1948-01-03     NaN     NaN    NaN     NaN\n",
       "3  1948-01-04     NaN     NaN    NaN     NaN\n",
       "4  1948-01-05     NaN     NaN    NaN     NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В строчках за 1948 год нет никаких полезных данных и их надо бы выкинуть. Проще всего это сделать с помощью `.dropna` — это команда, выкидывающая строки (или столбцы, если вызывать с `axis=1`), в которых есть неопределенные ячейки (например, содержащие `NaN`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1948-12-11</td>\n",
       "      <td>2.4</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1948-12-12</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>1948-12-13</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>1948-12-14</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>1948-12-15</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "345  1948-12-11     2.4    -4.5   -1.1     0.0\n",
       "346  1948-12-12     1.9    -8.7   -6.2     0.5\n",
       "347  1948-12-13    -7.4   -12.3  -10.1     0.0\n",
       "348  1948-12-14    -5.6   -13.2   -9.4     0.0\n",
       "349  1948-12-15    -3.6    -9.2   -6.6     0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.dropna(inplace=True)\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видимо, 1948 год начинается не с начала и в нём есть только кусок декабря, так что мы его тоже, пожалуй, выкинем, от греха подальше. Я это сделал, просмотрев табличку вручную и заметив номер нужной мне строчки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1948-12-31</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1949-01-02</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>1949-01-03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1949-01-04</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1949-01-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "365  1948-12-31    -1.2    -5.3   -2.3     0.0\n",
       "366  1949-01-01    -2.1    -6.7   -4.2     0.0\n",
       "367  1949-01-02    -0.5    -6.7   -1.2     4.2\n",
       "368  1949-01-03     1.1    -2.1   -0.7     0.0\n",
       "369  1949-01-04     3.3     0.9    2.3     0.0\n",
       "370  1949-01-05     4.0    -0.9    1.1     0.8"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.loc[365:370]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1949-01-02</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>1949-01-03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1949-01-04</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1949-01-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "366  1949-01-01    -2.1    -6.7   -4.2     0.0\n",
       "367  1949-01-02    -0.5    -6.7   -1.2     4.2\n",
       "368  1949-01-03     1.1    -2.1   -0.7     0.0\n",
       "369  1949-01-04     3.3     0.9    2.3     0.0\n",
       "370  1949-01-05     4.0    -0.9    1.1     0.8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.drop(range(345,366),inplace=True)\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Рисуем\n",
    "Чтобы убедиться, что всё в порядке, нарисуем несколько картинок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZGAN2IiIiIiIbY8BORERERGRjDNjJclFRUQH9dOzY0Z1Ho0aN3K+3bNnS7zpHjRrllVdG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\nhgV2hmEYhmEYhkkw/w8v+XqECUw/ZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cbe85c0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 374
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "dat.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что-нибудь видно? Не очень. Это потому, что мы попытались вывести на одном графике четыре разных параметра, да ещё и за кучу лет. Давайте возьмём поменьше данных для визуализации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3Nz1pNuecY3rXNtggbHZFhcTYr2uSYtglZAaIj2F3teEDBpgH75kzjTs/aVLp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\nku4YPcOp/oz33jPr668fHZdFwV5sNtggvO0S7FEOe2enqai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1K73Hqy3Yo5zJJIddo0N9\ntCDQ5VjQgp0OOwHCgl03+pUIdv1eGTuxaFHrxbDb7aLd9pHW5oorgnWp8+175M9/NumH11rLjEnU\nZo4mrcOuy1xU25JU9pOgYF+FK/73oouAb3wDOPdcE3KQJNg1LpGjf8RaOOxaUEycmO66SXOhBbuL\nffYxy4MOCvaJG97RYZyHtA57FPVw2O3yJw8reQh2PWmScOKJwI47AiefHB96RFoHXa+vt16wnpQA\nQOdijxp0CgRtwMKFzeewd3UB++5rZmQGkkNiKNhJFFLn270wvb2By37WWcntnE0Wh/2664DDDwcO\nPTTbtdswD/sqxowp3ffyy+HtLJWgS7BLpdq3L3DAASYMZ7/9sg06tSeimTUL+NvfzLpdaQ0fbpz1\nAQNK88yT1mDttY1od2U/Acw0zH/+sxlUKbS3G4G9fLn5Sxp02tZmBHFUt2IRHHYdtmMfk0aw62PE\n3dMPxNtvDzz5ZPnXS5oPXa+vtx7wzjtmPa3D3tFR2h2/8cbButTpCxY0n2B/+mmTmenBB41op2An\n5SJ1vn2P9PaaGYn/8AezHdUrFRUSIyZQGsF+8snmr1Io2Ffh6sZec82waH/vvfTnc3WvyI/Yr58R\n07Nnmx/UngkvTZYYcehffz14zTXA4YUXop8QSfNz883A978PTJ7sfn211Uw8n82QIebeW7o02Xnw\nPHPvRVV4/fvX3mGXaz7jDHNtu+5qtst12HUX/AcfmGWl00yT5sYW7EKWkBjtCk6YEBbskkhg/vzm\nE+y6vli8uLR32zWwnZD/b+/O4yUp63uPf3+HWWAWhmWYwQGBgIAQERFBwUQQE1EIccEoYBQxqEGG\nEFCuerlXJ68LroQsiiIxsrvgKBo1SBRQWQwmhjBEI5sgyCYuM6yznuf+8fSTfrpOVVdVd1VXndOf\n9+vVrz6nurpPzVRX1bd//aun0vSrsMeFrAcfTH9+Vn4KHyLL9LAPi8DekXYFqt/8pvf3O+4Y7m8k\nV2LYySR34EXGYU8boi8ZiuK/hfG0xx7SpZeWf96CBf7KpE88kd/DLvn3clZgb7Il5vjjfRXlnnv8\n74MG9viCZ//yL/6eE93QTxUtMeHEUjPpllt655vJFfZ4+e++mx52DC6rwu5c7/EgmbWCrAp7E4Gd\nHvY+6g7sQTKw9zvpNFTYFy/ufSNNTEgvetFwywcE4T35+OPFevv6HTCbbIkJ21pY1rSWmKzLRYd/\n09NP956Ue+WV/p4KO/qJg/kzn9n9Oa/CHt6zGzd236Mvf/nUbSiusCfHlp7uJ53Gy3/XXVMDOxV2\nFBVCeTJ3TU72Bvas8dnbVGEnsPfx61/3/72srJNDk6N3xDurrB3x5pv3jq3+0Y8OP2QQEITKXpGW\nGKn/jqmOCrtzvrKYJSuwD1Jhz9ruCezoJ94+4nOk8gJ7/F5NhoJYqLDX2RIzOSm95S3SJz5RzesV\nFR/37r13amBPtpwS2JEl7POTwbtMYL/gAum5z+2dnjx+ENhH7Nhj/X24CmmoqoUgnPWVSVFFV2Jc\nVY+DzeRkbx98HNiTl0cHhhFCRb+WmPgbnlFW2CcnpaOO8ide//CH6fMkt7WwrIME9qwPBoQE9BMH\nhPg6H3mBPXzjs2lT//do2P/X2RJzzTXSxRdLp5xSzesVFf971qzJ3gcFbIvIEj7cJb9JTQb2fied\nvu1t0n/+Z+90KuwNu+wyP/b64Yf3Tg9fbQ5bFRwksMcfEuLnm/WO/EJgR5WqbompssJ+443SN7/p\nq+zJkZyCsDMNO+m0lph+1Uup+2+KLyqV9jiQZp99/AndH/pQb2DP62EP79W4JaZohT1861NVYA8n\nWI9aXO18/HECOwYXQnleYM9riUl2MCS3zbvv7l4ck8A+AhMT/mTOZPhtS2APb6hwsltcYY+vgAcM\nq2xLTL9LLs+fX22FPQyPJ2VXRapsiSGwYxBm/oTv9763O2a6VK4lpt95FmknnYZjVVU97P3azupE\nYMewwrdCy5f7+7TAHr+vsgJ71rEtefz45je7V5QnsI9QMvyGHWxaS8x11xV/3awe9qSswJ58PhV2\n1CVuiclqBYsDe7+K3tKlU7eprJ1jEfEwqFmvU2VLTBjzObnjZgQmFBVX1fP2/2VbYuIKe9huq6qw\nJwdeGJX4AweBHYP4+7/3x67nPMf/ngzsyVFisj7kZp10GrcnBw8/LP3VX3VHdGJYxxFIhot+Ffa8\nrzdjg1TYN270t1mzuuEkvEGWLevOR2BHlcKB/6GH0ocLlbJ7dJOWLp36eNZFlop46KHuz1mV+jKj\nxGRtj2F6qLAvWND9ylMiJKC4JUv8KF7bbZc/OEDcElPkpNO0CntVgZ0KO6azeGCAYVtiksIY7slv\nj8PVeSUC+0iUaYnp1wqQVDSwJ6/itnatDwthpxxaYp797O48tMSgSmFH98EPdqftsENvT2v83t9v\nP3+RrjRLlvS2BEjpFxYrKq6wZwX2rB72YSrs8+f3BnYq7ChqYkK66aZi84b36t13Szfc4H8uWmGv\nOrD/6lfdnzdtGt1F+Kiwo2qDBva0jHfQQdL//t/+52Rgj9ESMwLJamC/nsMyO7CwE0pbiXEVMxlC\nQktCssK+117deaiwo0ppF+baYYfe3+P3/lFHZb/WkiVT359VVdhH2RKT3A8QElCGWbGhd0OwOP/8\n7knVae/R+fP99Kef9mOVS9336G9/mx9yi/jlL7s/D9PGVhYVdlQtb1jHrOJP/Lx3vcuPInj99d1t\njcDesGQ1sF/byyAV9rSdy0c/2v052TMcfk9W2OPAToUdVTr2WOmss3qn9Qvsr3mN7xlMs3RpenVj\nUEUq7FW0xCRPOk3uB6iwow5pRaC095pZt8oeruQbQsS55/pvvYYVf9s7ysBOhR1VyzvptN+wjsE5\n50jXXtu7jRLYG1Z3YE9bicuXS1//uv85K7AnK+zbbOPPhF6+nB0WqrVokXTmmdLznted1i+wm/n3\n4hlnTH2tJUumThumwp51nYL4tetqiUl7HKhSWmDPuhpvPPCAJB1wQPfnrCFP0zzxhPTgg1Onx9ta\nmcDunPTGN6bvD4qgwo6q5Z10WraHPcg6x0sisI9EXS0xeQEhnMSQ7D/MqrBLvqr58Y8XXwagjPhb\nnDiwZ329H39TFDzjGVOnDRPY44N3WmAPO+GJie4H6nA/Odn922Ur7Mn9ABV21CEtnGeFiXho3/e9\nb/BvWvfc02/fcQuMlD6scBEPPih97nO+IjmIrMCe9cGFwI48VZ90GsQV9re/vbfIRWAfgc026+25\nrarC3q+HXcreGX34w/4E01/8wv/Ozgmjcuqp0steJr3//b3fPPV73//TP/X+nnZ+xTAtMXk72bQg\nHn/AKBvYQ4U9+dUngR11SAsIa9akzxtX2BcsmPqeLHryaaiu/9u/9U4ftMKe96E6T9yesH5994ND\nXKyKcUxEnmGudNpPfFyYN89fFTUgsI9I0QtdVNXDLmWv3C9+Ubr99m71MmunBVTthS+UvvMdP7Zs\nHCT6VR2OOqq3ypCmzgp7VhBPtsWUDexz5vRuu4QE1CFt21q9On3euMK+YMHU92RyxLE8jz/e+3tc\nYS8TvOO/m7Xs/SQ/HITXyNrm2BaRZxQV9nnzegu8BPYRiXeE/SrsVbbEZFXYg1AFiZcNGJWigV2S\n7r+//+NVVdjTQkTW1+fDBvbZs3uDARV21CHtOJAVeuMK+8KFU9+TWVfpzZKcf9CWmLgyP0hgT1Y7\nw/CSVNgxqKqvdBrEgX2LLQjsjaizwp53oZYsYae13XbF/yZQlTKBPZz89pKXdKddcUX356oq7EVb\nYqTePvZ+8wXJHnYq7BiFYSrsyfdysmKeJm6biS+UtGlT7/ZVJrDHFfajjpK+9KXiz037W2mBPf5/\nYltEnrxhHQetsMfvyblzey/WVGQY1zII7BmKVtj7BfZkFTFUDbJ2LnkV9hBUFi/uPx9Qh/i9nrcT\n++xnfRvNypXdaX/yJ769SxousOdV2KtuiQnLSoUdo5C2bZ12Wvq8yR725IgVRSrscbh+5JHuz8lt\na9DAftdd0utfX/y50tQK+6OP+vt4+4uP0QR25Bl0lJi8omwylNfZssyVTjNU0RKzYUPvyhu2wh5Q\nYUcT4vd63k7sGc/wJ6omhZ3bMC0xo+5hj3+nwo66xdvZtttK//7v0i67pM+bDOxxdU8qFtjj9pV4\nlJh4ujR4YB9E+FtmPliFCnsysIfpfHhGnrzRlwatsCfVGdipsGdYurT786AtMckqwbA97AEVdjSh\nTEtMlrC91NkSk9XDXrYlJjmdCjtGIX7fzpuXHdal3sLSwoVTx2Uv0hKTFdiT1wMZtId9EOHYGa64\nnNYSEx+Xy7SmYjyl5av4fZo1SkzZY12dhRze5hniS7MP2hJTNrAXDQAEdjShisA+bIU9HkddosKO\nmSfetvpdlEWaWmFPDkhQtsKe9bPUTIU9HIfT2knjwN7vGA1I+RX2rItzlf0wuPPO5eYvg5aYDPHF\nXqoK7MP2sAe0xKAJVQb2QSvscc+hNNrAToUdoxBvW8kWl6TkSafJb4PL9rDHJ6AOU2GvOrAHyQr7\nrbf6fUDe/xOQlq/ijBYC+0knSTfd5N9bUrljnXM+O15/vW9nqxqBPUO8oxj0SqdlK+xF3xjJrz2B\nUWhDS0wysI9ilJj4dyrsqFscLPKCaLLCniwulW2JiY9Zw1TYq26JCZIV9uc+d7i/g/GRdsyK39Ph\neLB4sfTa13YDe5EK+/bbSw8/LB16qP/9935vqEXNREtMhjIV9ngkjFjZwF5U2pUjgbrFO65Be0aH\nbYlJfm05inHYAyrsGIVBK+zz5k0dsSIepjHLb37T/TmusCdD96AXTupncjL9dUOQis8lk3q3P9pg\nUEbRlpjNNitfnPrJT/zJ4QceONwy5iGwZygT2I8+WvrIR6Y+VkVg33vvqfOzo0IT2lRhDwduetgx\n05QJ7LNnS5dfLn3+8+kfom+7rf/z163rHXIxDuwvf/nUeYsqGtgPPthf8yQ5fzh2Jr9Njlti4gvW\nAHnqDOxbby3tv/9wy1cELTEZFiyQ3vY2vxPsN0xPWJlpB/2yPexJv/u70g03+DdS6FufmOCMeDSj\niZNOH3hAWras+7ywU91iC789bdjgw39cWSzbEpN17ggVdjShTEuMJB13XPZjt9zS/7nxuOtS9xiV\nbD2T6gnsN9/s7//7v6UXvGDq34ovYCj1HovpW0cZafv5+ANq+HliotwQxqPUokVpnwsukM4/v3/A\nDiuzSGAvW2H/l3/xOyxGhUEbjPqk0yuvlHbcUXr727vT4gp7WIZkmwwVdkxnZUaJSfPGN3Z/vv/+\n3paXpGQLTdgm4naYUMmuuoc9DksLF/Y+Fo6dixb1To+3OQI7yih6YcrNNuudd9BjXR0I7AVstln2\np6y6Avvznucri0BbVNkSU6TCfu65/v4zn+lOi3eq4eCd3M7yetgHPemUCjtGoUxLTJpLL5VWr5b2\n2sv//uCD2fMmK+lhmwgV8i226F4ArUxgf+yx/Hl+/evs5ShSYa/zAjWYefICe3jvJ1tiqLBPQ3kj\nu5RpiSlyoE9WPoCmVbETK1NhT5snHNhnzcoO7OFgnwzcYZnDa4RgXyawxz21VNhRh7ItMUlmvjId\nvpkNFx1Kk/zgHLalENiXLu0G46oDe7xc8bdkznX/FhV2VCXvmBXeg8mWGCrs01DWziG8CdJWalaF\nvciBnsCOtol3eKM46TRtnrQKe/zVutQdPzrZTlBFS8yzn939nQo76jBshT0I5z09+mj2PGFbCKPN\nJCvs8+fXF9jj5YoD+xFHdEe3ocKOquRlqrjCHs+bHKmoSQT2grLGPg8rtkxgp8KO6WjUJ532C+z9\nKuxh5JiswD5MS8wee3R/b9NXpZg5qgrsRSrsIbCHa40kA/uCBYMF9jVrpk5LbvNpFfaNG6Vvfas7\nPRnY422SwI4qxcWg3/99f8XSj3xE2nXXZpcrxigxBW27rXTPPb3TJia6ASTt4E1gx0wy6mEd00J9\nCBj9etizKuzJlpi87TE5ffZsaffd85cbGEbcEjPISadBkQp72MbCB4NkS0yVFfYNG3pDdlqFPXll\n1uQ1R2iJQV3iwL7PPtK99za6OKkI7AWlXWY2r0VgmGEdCexom7ZV2IOigT1uiXGufIV9iy18YD/2\nWK42jPpUXWEv0hIT3uuTk/4WgnMc2IteOGlyMv0Kqxs39gb2+KTTsF0nn7f55n6fEfYFtMSgLvGw\njm1FYC8oL7CnreRkb20VFXaCPJoy6pNO8yrsYRmyAnsy7ITtbsOG7uv0u65B2gXLzKTPfS5/2YFB\nVd3DXuSk08028+/3DRv8bZgK+xNPpG/fyeNhPFZ7eCwZ2M38/0HYphmHHXWJK+xt1eLPEu2SFtjz\nKo5xkHAuf1SKWDKYH3usv4/H2AVGadQtMUV72JNBIKuHPZ6/yIfn5PBeXGEYozDsKDFBqLB/4QvS\nTjtJp5wi7buvdPvt3XniD65xi1kc2MMyFA3soR0m+Q1V8noJ8VjtWRV2qXc7pocddSGwzyBpX4Hn\nVdjjwB6/GYpUyZPzfPrT/vLT552X/1ygDqNuiam6hz2E83CF1HhaljggcCl0jELVFXbJX0DpE5+Q\nVq1KvxBZqLBLvRX2QU46DSecLlnSO71fhb1oYKclBnWZDi0xLV60dkme/CKVC+xl+telqYF94ULp\nmGO6Z/MDo9amYR37jRKTF9iLVtil3sBEhR2jUHUPe1IclJMtMdLUCnvZwB4q7MnAXnWFnZYYVIkK\newFmNsvMTjWzz5rZLWa2zswmzeytBZ57vJndbGaPm9lqM7vOzI6sYzmHbYkp078u0auO9mnDSadF\nLpwUWmKyetjLVNjjD+oEdoxCVaPEZAX2eLtKa4kZtoc9BPbkMTNZYR8ksFNhR12mQ2Bvw0mn8yX9\njSQn6RFJD0l6Zt6TzOwcSadLul/SBZLmSDpG0tfNbLlz7pNVLuQxx0h33ukv9/ynf+qnlamwE9gx\n3bXhpNN4pxoH8FhWhb1sD7vUe6VFWmIwClVV2Iu8X7NaYtJGiSka2MN8yWUftMIe/zuosKNutMT0\n95SkV0pa5pxbJunCvCeY2UHyYf1OSfs4597lnDtF0v6SfiPpHDPbqcqFnDtXOussf9JOUGZYRwI7\nprs2nHRapMJepCUmbXjINHEVr82VF8wcVQX2IsKH4uRJp/FVucsG9qz2z0FGiZE46RSj1eb9fOOB\n3Tm3wTl3tXPukRJPO0m+In+2c+5/LtHgnLtP0nmS5ko6odol9bJCet097EDT4vd70Q+eScOedBpX\n2LNGiSly0mkc/PsZ9N8JDKqqUWKypLXEJCvsYZ6JifKBPas4VUUPOy0xqBuBvXov7dxfnfLYVZJM\n0mF1/OGskE6FHTNd/B4ftLd2FBX2rB72OOAX7VcksGPU6q6wx9tV2kmnJ57YW3kve+GkrOLUoIE9\n/j+gJQZV2GWX7McI7BUys3mSdpD0REZV/s7O/R51/P2swE4PO2a6+D0+aD93VVc6HWZYxzItMQR2\njFoVH4yDs87y7/GlS9Mfj086DVce/eEP0wN71S0xaYE9nhZkVdWpsKOs22+XrrxSOvTQ7HnoYa9W\nOA1sTcbjYfpWdfzxrKp6VRX2gw/296997WDLB9SliiBR5qTTvGEdy550OkhLTN7jQNWqbIk580x/\nAukLX5j+eNwSE1e3Q2APVxqVyrfEzJnjrx0SJCvsaeOwp/2NrJBOhR1l7bGH9OpX99+vz/gKu5nd\n2xmKsejtkir+bhMGrbAX7WG/6irp29+WTj558GUE6hC/9wetsJdpiRn2wkm0xGA6qrolZu7c3g+v\nWS0xq1d3p4ftY2LChxszv+2F7a+fsD3Onu1HVzvkEP97vwp7eCyt7SYO6XHQKno+GJDUb78/4wO7\nfBvKT0vcHhjib4UK+qKMx8P01RmPpzKzzNuKFSv+Z766e9i33FL6gz9o95sG46nKCvuwLTFFetj7\ntcRw0inaqo4e9qztNW6JiYVK98SE32bLtMUki1NhG4or7Bs3pl8JPO314/+DuFBA2ygG1W+/X0VL\nzIoVKzLz5DAq+cLXOfeHVbxOwb/1lJk9IGmZmS1N6WPfvXN/R8nXLTRfmVFi4p1P2R52oG3aWmHf\nsMFva5s2+eUKQSDZ4xq3xBStsB9wgPSVr1DNw+jUMUpMVmCPK+yxcOwK+WLuXP9B+Le/Td/2f/lL\n6YQTpFNPnRrYw78nrrCHb8GCsD3GFfaVK7t/O5g7V7r/flrVMJy6W2JWrFjRU+iNDRPap2MPuyRd\n27l/RcpjR3Tur6njD5epsMc7pbLDOgJt0+YK+4EHSjvsIP3kJ91gkNwpxwG/aIX99NOlj31MWrUq\nf3mBKsTHlaqCadYH7KwKezhehel7dIZwyMgg+rM/k/75n6XDD59anEqrsMf96/Fj4YPCdddJRx/t\nf05eC2HHHaXtt09fDqCIse5hb8D58kM3nmlm/3NyqZntIulkSWslXVTHHy7Tw57Wo0eFHdNVFRX2\nYa90mjWs46pVvgf3r/+6e/BPbmtpo8Tk7ZznzJHe/W5pzz3zlxeowhZbSMuXS+95T3VtH1k97PE3\nVrG4JUaSzj/f319xRW8ryx13+G3pm9/sTitSYc8K7GlDsnLxMlSNwD4EM3uPmV1oZhdKerV8GH9r\nmGZmfxbP75z7gaRzJe0maZWZnWtmn5D0b/Kjw7yrcxGlymWF9LwKO4Ed0138fm9qHPY4aIdtKd7O\nnnwyu8Ke1hLDV+too49/XPrwh6t7vfib3VWr/PB2Um9LzFlndedJBvbnP1/aZx/pscekG2/00664\nwn+QPfHE7rb6O79TrIc9Od56ssKeNTJMm8MUpo9+7yOGdcz3Cklv7tyeK38V04OiaS9OPsE59275\nq5k+JOltkt4k6TZJf+Sc+1RdC5o1lCMVdsx08Xt80N7aQa90GgJBWoX9iSe6823cmF1hH6QlBpgJ\nksenwzqXFYxbYs48s9tqkuxhl6S99/b3j3TOGrvoIn9/8cXdebbZpneUGCm9wh5vs/Fj4e/G+5f4\nZ7ZXVGG6Vthb8fZ3zr00f67U510iaaRDRJbpYY8DOz3smEkGvWhJmQp7fIDftMnvZNMq7PHX6+vW\n+dc2mxpSBmmJAWaC5Lbw4IP+PtkSs3Ch9PDDU3vYJWnBAn8fwvbuu/thiGPr1/eOwy6lV9iTgT3Z\nEkOFHXWaroG9LRX2aSOrJSatwk5LDGaqQT94lqmwpw37FlfGw0433s7CAT9tOxvkwknATJD1NX9y\nlJiwjSRbYiRp/nx/Hz4gP/OZU19v/fpiPexZgT2twk5gR9XqHtaxLi1etHbKaonJq7AT2DGTDFph\nL3PSaXyADwf0uDIedrrxdhbCe9oOeZALJwEzQfLk1UWdq5UkR4npF9hDhf2BPldRiQN7eK3wGvGH\n9KwedirsGIXpeuEk6ksllRklhgo7ZqpBK+xpB+8saYE9royHbTEtsFNhB7qSx6dly/x9VoU9hO44\n6IfA/rGPSTvvnH7V03XrplbYw2vH84cK++zZvR+gOekUo0BLzJgo0xKzcWM3dNDDjplkFBX2tJaY\ntAp7/MG4X4U9hJGVK6Xbbuu+DjDTJY9PS5f6+7vv7n28SEuM5IedTAvsa9dO7WEP21j8IT0E9q06\ngzL3a4mJj5ltblfA9EFLzJiIV2ZcfUge+MN8ofpHhR0zycKFgz2v6Emnk5O984TtJx6nBUt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fjCmwo70Cuvwh73sMfbT16FncAODK/qHnYq7EA1Go+QnRNMr5QP359xzr2jwNPOl2+XOdPMvuac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\n5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc8db70>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 374
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# только средняя температура и данные за три года\n",
    "dat.iloc[0:365*3]['TMPMN'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Это график температуры за первые три года наблюдений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ty6kIdv/7N2pkO+wDBhjLffow8ebp7MoKWrGeA/mCh7mBTbQMjJ537+4J/9JS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\nYa4d9pYtbSEPiGBPEhHsQiTxBLvpIIM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IAbtNiZcmy3/vkp3tfaYlJcqk+/hjFW/u51Ye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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d028f28>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 374
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat.iloc[0:365*3]['TMPMN'].plot()\n",
    "pd.rolling_mean(dat.iloc[0:365*3]['TMPMN'],30).plot(lw=2,color='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А здесь на него наложено *скользящее среднее* (*rolling mean*) за месяц, сглаживающее резкие колебания."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, что происходит в масштабе десятилетий."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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++eb08ltv8bfjXf95543iXD/wgPj4JhMGea5qui4s117LL4/bxJlnZr+sxIwe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/Ol8rz77sB11xW/Lb6IqzybRhikeBdeGD0/j+OTlWf/sfJM5CdPIhO5oL7B6iqBzz8f/PQ1POuC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d024cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 359
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(dat['TMPMN'],365).plot(lw=2,color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь было сглаживание по годам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gxI1nrfqdKfEcNiqQK4LEc5MmjhU8Tl2ME+EkKbwdCZqkDLhFL8/+CABHHRX8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\nxg8AnAAUvTAWh57b2p0alcPdIlatij8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lw65wHpB164CDMRF9H9yPOVDz3r6I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\nE0IIIYQQEpH/Bx4t+Pp618pHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2056a0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 359
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(dat['TMPMN'],365).plot(color='blue')\n",
    "pd.rolling_mean(dat['TMPMN'],3650).plot(lw=2,color='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сглаживание по годам и десятилетиям на одной картинке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tWyb95jfSlCnBuQcfdK0PxP2W8ZXSuXPq8emnu+opffumnj/iCOmhh6R99nHH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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2f7ef0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 369
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(dat['TMPMN'],3650).plot(color='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И, наконец, красный график отдельно. Если вас когда-нибудь интресовал вопрос о том, есть или нет глобальное потепление, то теперь, наверное, он отпал. (Вообще-то такого вопроса нет: климат меняется и действительно теплеет; вопрос, который широко обсуждается — является ли это результатом действий челвоека или это естественный процесс. Но на него мы так сходу не ответим.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упорядочиваем\n",
    "Картинки красивые, но по вертикальной оси написана какая-то ерунда. Это потому, что строчки сейчас индексируются числами (да ещё и отсчёт начинается с 367). Для более разумного поведения было бы хорошо индексировать строчки датами из колонки `DATE_OBS`. В нашем случае проблема осложняется тем, что система не воспринимает эту колонку как колонку с датами. Давайте же решим эту проблему."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "366     1949-01-01\n",
       "367     1949-01-02\n",
       "368     1949-01-03\n",
       "369     1949-01-04\n",
       "370     1949-01-05\n",
       "371     1949-01-06\n",
       "372     1949-01-07\n",
       "373     1949-01-08\n",
       "374     1949-01-09\n",
       "375     1949-01-10\n",
       "376     1949-01-11\n",
       "377     1949-01-12\n",
       "378     1949-01-13\n",
       "379     1949-01-14\n",
       "380     1949-01-15\n",
       "381     1949-01-16\n",
       "382     1949-01-17\n",
       "383     1949-01-18\n",
       "384     1949-01-19\n",
       "385     1949-01-20\n",
       "386     1949-01-21\n",
       "387     1949-01-22\n",
       "388     1949-01-23\n",
       "389     1949-01-24\n",
       "390     1949-01-25\n",
       "391     1949-01-26\n",
       "392     1949-01-27\n",
       "393     1949-01-28\n",
       "394     1949-01-29\n",
       "395     1949-01-30\n",
       "           ...    \n",
       "21459   2006-10-02\n",
       "21460   2006-10-03\n",
       "21461   2006-10-04\n",
       "21462   2006-10-05\n",
       "21463   2006-10-06\n",
       "21464   2006-10-07\n",
       "21465   2006-10-08\n",
       "21466   2006-10-09\n",
       "21467   2006-10-10\n",
       "21468   2006-10-11\n",
       "21469   2006-10-12\n",
       "21470   2006-10-13\n",
       "21471   2006-10-14\n",
       "21472   2006-10-15\n",
       "21473   2006-10-16\n",
       "21474   2006-10-17\n",
       "21475   2006-10-18\n",
       "21476   2006-10-19\n",
       "21477   2006-10-20\n",
       "21478   2006-10-21\n",
       "21479   2006-10-22\n",
       "21480   2006-10-23\n",
       "21481   2006-10-24\n",
       "21482   2006-10-25\n",
       "21483   2006-10-26\n",
       "21484   2006-10-27\n",
       "21485   2006-10-28\n",
       "21486   2006-10-29\n",
       "21487   2006-10-30\n",
       "21488   2006-10-31\n",
       "Name: DATE_OBS, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['DATE_OBS'] = pd.to_datetime(dat['DATE_OBS'])\n",
    "dat['DATE_OBS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим `dtype: datetime64`, что означает, что теперь типом данных являются даты. Сделаем теперь этот столбец индексом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE_OBS</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>1949-01-01</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-02</th>\n",
       "      <td>1949-01-02</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-03</th>\n",
       "      <td>1949-01-03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-04</th>\n",
       "      <td>1949-01-04</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-05</th>\n",
       "      <td>1949-01-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                            \n",
       "1949-01-01 1949-01-01    -2.1    -6.7   -4.2     0.0\n",
       "1949-01-02 1949-01-02    -0.5    -6.7   -1.2     4.2\n",
       "1949-01-03 1949-01-03     1.1    -2.1   -0.7     0.0\n",
       "1949-01-04 1949-01-04     3.3     0.9    2.3     0.0\n",
       "1949-01-05 1949-01-05     4.0    -0.9    1.1     0.8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.index=dat['DATE_OBS']\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j8PdZtCj+2jJpaJB+/Wt3X+fO7narVtLDD0tr1kh9+oTPdc01ya4NFYtAHAAA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\nvvXWqv2WHvPmhVrx444rXd/VBWuumU+ZRESkYygRl2KotkZco6NI0qB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c20c438>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 260,
       "width": 369
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(dat['TMPMN'],3650).plot(lw=2,color='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь горизонтальная ось подписана верно. Кстати, срезы с датами тоже работают."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE_OBS</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>2001-01-25</th>\n",
       "      <td>2001-01-25</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-26</th>\n",
       "      <td>2001-01-26</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-27</th>\n",
       "      <td>2001-01-27</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>2.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-28</th>\n",
       "      <td>2001-01-28</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-29</th>\n",
       "      <td>2001-01-29</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-30</th>\n",
       "      <td>2001-01-30</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-31</th>\n",
       "      <td>2001-01-31</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-01</th>\n",
       "      <td>2001-02-01</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-7.4</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-02</th>\n",
       "      <td>2001-02-02</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-8.1</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-03</th>\n",
       "      <td>2001-02-03</td>\n",
       "      <td>-9.3</td>\n",
       "      <td>-16.7</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-04</th>\n",
       "      <td>2001-02-04</td>\n",
       "      <td>-8.4</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-10.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02-05</th>\n",
       "      <td>2001-02-05</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                            \n",
       "2001-01-25 2001-01-25    -6.6   -14.8  -11.0     0.0\n",
       "2001-01-26 2001-01-26    -3.5    -8.7   -5.3     0.7\n",
       "2001-01-27 2001-01-27    -0.4    -4.5   -2.5     2.7\n",
       "2001-01-28 2001-01-28     1.4    -0.4    0.8     3.7\n",
       "2001-01-29 2001-01-29     2.9     0.8    1.4     1.1\n",
       "2001-01-30 2001-01-30     1.5    -0.1    0.6     1.4\n",
       "2001-01-31 2001-01-31     0.1    -1.8   -0.7     0.6\n",
       "2001-02-01 2001-02-01    -1.7    -7.4   -4.5     0.0\n",
       "2001-02-02 2001-02-02    -3.0   -12.3   -8.1    13.2\n",
       "2001-02-03 2001-02-03    -9.3   -16.7  -13.0     2.6\n",
       "2001-02-04 2001-02-04    -8.4   -16.2  -10.7    13.4\n",
       "2001-02-05 2001-02-05   -11.0   -22.1  -16.0     0.9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "dat[datetime(2001,1,25):datetime(2001,2,5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Усредняем\n",
    "Впрочем, мы отвлеклись. Вернёмся к нашей задаче: верно ли, что более тёплый март — это признак того, что апрель будет более холодным? Иными словами, правда ли, что средняя температура за март и апрель отрицательно скоррелированы? Чтобы это понять, нам потребуется сформировать новую таблицу, в которой наблюдением (то есть строчкой) будет один год, а переменными (то есть столбцами) будет средняя температура за каждый месяц.\n",
    "\n",
    "Для начала добавим в нашу таблицу столбцы, соответствующие году, месяцу и дню."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-02</th>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-03</th>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-04</th>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-05</th>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year  Mon  Day\n",
       "DATE_OBS                  \n",
       "1949-01-01  1949    1    1\n",
       "1949-01-02  1949    1    2\n",
       "1949-01-03  1949    1    3\n",
       "1949-01-04  1949    1    4\n",
       "1949-01-05  1949    1    5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['Year']=dat.index.year\n",
    "dat['Mon']=dat.index.month\n",
    "dat['Day']=dat.index.day\n",
    "dates=dat[['Year','Mon','Day']]\n",
    "dates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь переопределим индекс более удобным для нас образом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dat.index=pd.MultiIndex.from_tuples(dates.values.tolist(), names=dates.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Предыдущей командой мы создали так называемый *мультииндекс*, то есть индекс с несколькими измерениями. Простейший пример мультииндекса из математики — индекс у матрицы $a_{i,j}$. Можно считать $i$ и $j$ двумя разными индексами, а можно считать пару $(i,j)$ одним мульииндексом. Мультииндекс — способ записать в обычную таблицу многомерную информацию. В данном случае у нас в мультииндексе три измерения — год, месяц, день. Вот так теперь выглядит наш dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "      <th>Year</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Day</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 rowspan=\"5\" valign=\"top\">1949</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-01-02</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-01-03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-01-04</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1949-01-05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DATE_OBS  TMPMAX  TMPMIN  TMPMN  PRECIP  Year  Mon  Day\n",
       "Year Mon Day                                                          \n",
       "1949 1   1   1949-01-01    -2.1    -6.7   -4.2     0.0  1949    1    1\n",
       "         2   1949-01-02    -0.5    -6.7   -1.2     4.2  1949    1    2\n",
       "         3   1949-01-03     1.1    -2.1   -0.7     0.0  1949    1    3\n",
       "         4   1949-01-04     3.3     0.9    2.3     0.0  1949    1    4\n",
       "         5   1949-01-05     4.0    -0.9    1.1     0.8  1949    1    5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нам нужно для каждого года и каждого месяца найти среднюю температуру за этот месяц. Для этого следует сгруппировать элемены таблички по году и месяцу и к тому, что получилось, применить метод `.mean()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "      <th>Year</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Mon</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 rowspan=\"5\" valign=\"top\">1949</th>\n",
       "      <th>1</th>\n",
       "      <td>-1.300000</td>\n",
       "      <td>-6.493548</td>\n",
       "      <td>-3.670968</td>\n",
       "      <td>0.854839</td>\n",
       "      <td>1949</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.571429</td>\n",
       "      <td>-11.075000</td>\n",
       "      <td>-7.339286</td>\n",
       "      <td>0.789286</td>\n",
       "      <td>1949</td>\n",
       "      <td>2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.390323</td>\n",
       "      <td>-6.470968</td>\n",
       "      <td>-2.780645</td>\n",
       "      <td>2.419355</td>\n",
       "      <td>1949</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.056667</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>4.280000</td>\n",
       "      <td>0.603333</td>\n",
       "      <td>1949</td>\n",
       "      <td>4</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.377419</td>\n",
       "      <td>9.093548</td>\n",
       "      <td>15.225806</td>\n",
       "      <td>1.016129</td>\n",
       "      <td>1949</td>\n",
       "      <td>5</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TMPMAX     TMPMIN      TMPMN    PRECIP  Year  Mon   Day\n",
       "Year Mon                                                            \n",
       "1949 1    -1.300000  -6.493548  -3.670968  0.854839  1949    1  16.0\n",
       "     2    -3.571429 -11.075000  -7.339286  0.789286  1949    2  14.5\n",
       "     3     1.390323  -6.470968  -2.780645  2.419355  1949    3  16.0\n",
       "     4     9.056667  -0.066667   4.280000  0.603333  1949    4  15.5\n",
       "     5    21.377419   9.093548  15.225806  1.016129  1949    5  16.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_day_mean=dat.groupby(level=[0,1]).mean()\n",
    "year_day_mean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Почти то, что нужно. Теперь оставим лишь интересующий нас параметр — среднюю температуру за день (усредненную потом ещё и за месяц), то есть `TMPMN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Mon\n",
       "1949  1      -3.670968\n",
       "      2      -7.339286\n",
       "      3      -2.780645\n",
       "      4       4.280000\n",
       "      5      15.225806\n",
       "Name: TMPMN, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmpmn=year_day_mean['TMPMN']\n",
    "tmpmn.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И сделаем из одномерной таблицы с мультииндексом двумерную таблицу с обычными одномерными индексами:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/site-packages/pandas/core/index.py:4281: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return np.sum(name == np.asarray(self.names)) > 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mon</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>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>-3.670968</td>\n",
       "      <td>-7.339286</td>\n",
       "      <td>-2.780645</td>\n",
       "      <td>4.280000</td>\n",
       "      <td>15.225806</td>\n",
       "      <td>16.973333</td>\n",
       "      <td>17.425806</td>\n",
       "      <td>16.074194</td>\n",
       "      <td>11.506667</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>-0.423333</td>\n",
       "      <td>-4.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>-18.022581</td>\n",
       "      <td>-6.764286</td>\n",
       "      <td>-2.225806</td>\n",
       "      <td>9.043333</td>\n",
       "      <td>11.738710</td>\n",
       "      <td>15.123333</td>\n",
       "      <td>16.177419</td>\n",
       "      <td>14.080645</td>\n",
       "      <td>11.990000</td>\n",
       "      <td>4.745161</td>\n",
       "      <td>-0.433333</td>\n",
       "      <td>-5.503226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>-12.138710</td>\n",
       "      <td>-12.264286</td>\n",
       "      <td>-3.996774</td>\n",
       "      <td>8.403333</td>\n",
       "      <td>9.738710</td>\n",
       "      <td>17.706667</td>\n",
       "      <td>18.590323</td>\n",
       "      <td>18.325806</td>\n",
       "      <td>11.990000</td>\n",
       "      <td>2.780645</td>\n",
       "      <td>-4.810000</td>\n",
       "      <td>-1.251613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>-4.135484</td>\n",
       "      <td>-7.096552</td>\n",
       "      <td>-9.106452</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>10.358065</td>\n",
       "      <td>17.340000</td>\n",
       "      <td>17.870968</td>\n",
       "      <td>16.848387</td>\n",
       "      <td>12.136667</td>\n",
       "      <td>3.925806</td>\n",
       "      <td>-1.133333</td>\n",
       "      <td>-5.883871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>-10.403226</td>\n",
       "      <td>-15.614286</td>\n",
       "      <td>-2.606452</td>\n",
       "      <td>7.183333</td>\n",
       "      <td>11.645161</td>\n",
       "      <td>19.193333</td>\n",
       "      <td>19.025806</td>\n",
       "      <td>17.290323</td>\n",
       "      <td>10.046667</td>\n",
       "      <td>5.790323</td>\n",
       "      <td>-3.156667</td>\n",
       "      <td>-5.632258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Mon          1          2         3         4          5          6   \\\n",
       "Year                                                                   \n",
       "1949  -3.670968  -7.339286 -2.780645  4.280000  15.225806  16.973333   \n",
       "1950 -18.022581  -6.764286 -2.225806  9.043333  11.738710  15.123333   \n",
       "1951 -12.138710 -12.264286 -3.996774  8.403333   9.738710  17.706667   \n",
       "1952  -4.135484  -7.096552 -9.106452  5.166667  10.358065  17.340000   \n",
       "1953 -10.403226 -15.614286 -2.606452  7.183333  11.645161  19.193333   \n",
       "\n",
       "Mon          7          8          9         10        11        12  \n",
       "Year                                                                 \n",
       "1949  17.425806  16.074194  11.506667  4.800000 -0.423333 -4.322581  \n",
       "1950  16.177419  14.080645  11.990000  4.745161 -0.433333 -5.503226  \n",
       "1951  18.590323  18.325806  11.990000  2.780645 -4.810000 -1.251613  \n",
       "1952  17.870968  16.848387  12.136667  3.925806 -1.133333 -5.883871  \n",
       "1953  19.025806  17.290323  10.046667  5.790323 -3.156667 -5.632258  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mon_mean=tmpmn.unstack()\n",
    "mon_mean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вот теперь совсем то, что нужно! Дадим месяцам осмысленные имена."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mon_mean.columns=['Jan','Feb','Mar','Apr','May','Jun','Jul',\n",
    "                  'Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Считаем!\n",
    "Барабанная дробь! Торжественный момент: мы наконец можем посчитать интересующие нас корреляции! Та-дам!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jan</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.384755</td>\n",
       "      <td>0.257302</td>\n",
       "      <td>0.216484</td>\n",
       "      <td>0.047153</td>\n",
       "      <td>0.039691</td>\n",
       "      <td>0.206568</td>\n",
       "      <td>-0.120758</td>\n",
       "      <td>0.134955</td>\n",
       "      <td>0.117936</td>\n",
       "      <td>-0.142560</td>\n",
       "      <td>-0.059667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>0.384755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.379095</td>\n",
       "      <td>0.216300</td>\n",
       "      <td>0.017000</td>\n",
       "      <td>0.028284</td>\n",
       "      <td>0.252928</td>\n",
       "      <td>-0.097432</td>\n",
       "      <td>0.033740</td>\n",
       "      <td>0.071301</td>\n",
       "      <td>0.059700</td>\n",
       "      <td>-0.012276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>0.257302</td>\n",
       "      <td>0.379095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.328143</td>\n",
       "      <td>0.071455</td>\n",
       "      <td>0.177538</td>\n",
       "      <td>0.091281</td>\n",
       "      <td>0.022202</td>\n",
       "      <td>-0.081379</td>\n",
       "      <td>-0.037189</td>\n",
       "      <td>-0.031521</td>\n",
       "      <td>-0.075481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>0.216484</td>\n",
       "      <td>0.216300</td>\n",
       "      <td>0.328143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041331</td>\n",
       "      <td>0.017182</td>\n",
       "      <td>0.159124</td>\n",
       "      <td>-0.117359</td>\n",
       "      <td>0.028134</td>\n",
       "      <td>0.037973</td>\n",
       "      <td>0.077483</td>\n",
       "      <td>0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>0.047153</td>\n",
       "      <td>0.017000</td>\n",
       "      <td>0.071455</td>\n",
       "      <td>0.041331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.121644</td>\n",
       "      <td>0.028159</td>\n",
       "      <td>0.118944</td>\n",
       "      <td>0.038196</td>\n",
       "      <td>0.178622</td>\n",
       "      <td>0.175155</td>\n",
       "      <td>-0.156485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>0.039691</td>\n",
       "      <td>0.028284</td>\n",
       "      <td>0.177538</td>\n",
       "      <td>0.017182</td>\n",
       "      <td>-0.121644</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306204</td>\n",
       "      <td>0.093280</td>\n",
       "      <td>-0.060803</td>\n",
       "      <td>0.111169</td>\n",
       "      <td>-0.370548</td>\n",
       "      <td>0.150160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>0.206568</td>\n",
       "      <td>0.252928</td>\n",
       "      <td>0.091281</td>\n",
       "      <td>0.159124</td>\n",
       "      <td>0.028159</td>\n",
       "      <td>0.306204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.411425</td>\n",
       "      <td>0.131770</td>\n",
       "      <td>0.132230</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.053797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>-0.120758</td>\n",
       "      <td>-0.097432</td>\n",
       "      <td>0.022202</td>\n",
       "      <td>-0.117359</td>\n",
       "      <td>0.118944</td>\n",
       "      <td>0.093280</td>\n",
       "      <td>0.411425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.222137</td>\n",
       "      <td>0.179547</td>\n",
       "      <td>0.123741</td>\n",
       "      <td>0.002483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>0.134955</td>\n",
       "      <td>0.033740</td>\n",
       "      <td>-0.081379</td>\n",
       "      <td>0.028134</td>\n",
       "      <td>0.038196</td>\n",
       "      <td>-0.060803</td>\n",
       "      <td>0.131770</td>\n",
       "      <td>0.222137</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277824</td>\n",
       "      <td>0.077645</td>\n",
       "      <td>0.054884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>0.117936</td>\n",
       "      <td>0.071301</td>\n",
       "      <td>-0.037189</td>\n",
       "      <td>0.037973</td>\n",
       "      <td>0.178622</td>\n",
       "      <td>0.111169</td>\n",
       "      <td>0.132230</td>\n",
       "      <td>0.179547</td>\n",
       "      <td>0.277824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146520</td>\n",
       "      <td>-0.031469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>-0.142560</td>\n",
       "      <td>0.059700</td>\n",
       "      <td>-0.031521</td>\n",
       "      <td>0.077483</td>\n",
       "      <td>0.175155</td>\n",
       "      <td>-0.370548</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.123741</td>\n",
       "      <td>0.077645</td>\n",
       "      <td>0.146520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.112312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>-0.059667</td>\n",
       "      <td>-0.012276</td>\n",
       "      <td>-0.075481</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>-0.156485</td>\n",
       "      <td>0.150160</td>\n",
       "      <td>0.053797</td>\n",
       "      <td>0.002483</td>\n",
       "      <td>0.054884</td>\n",
       "      <td>-0.031469</td>\n",
       "      <td>-0.112312</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Jan       Feb       Mar       Apr       May       Jun       Jul  \\\n",
       "Jan  1.000000  0.384755  0.257302  0.216484  0.047153  0.039691  0.206568   \n",
       "Feb  0.384755  1.000000  0.379095  0.216300  0.017000  0.028284  0.252928   \n",
       "Mar  0.257302  0.379095  1.000000  0.328143  0.071455  0.177538  0.091281   \n",
       "Apr  0.216484  0.216300  0.328143  1.000000  0.041331  0.017182  0.159124   \n",
       "May  0.047153  0.017000  0.071455  0.041331  1.000000 -0.121644  0.028159   \n",
       "Jun  0.039691  0.028284  0.177538  0.017182 -0.121644  1.000000  0.306204   \n",
       "Jul  0.206568  0.252928  0.091281  0.159124  0.028159  0.306204  1.000000   \n",
       "Aug -0.120758 -0.097432  0.022202 -0.117359  0.118944  0.093280  0.411425   \n",
       "Sep  0.134955  0.033740 -0.081379  0.028134  0.038196 -0.060803  0.131770   \n",
       "Oct  0.117936  0.071301 -0.037189  0.037973  0.178622  0.111169  0.132230   \n",
       "Nov -0.142560  0.059700 -0.031521  0.077483  0.175155 -0.370548  0.000195   \n",
       "Dec -0.059667 -0.012276 -0.075481  0.000211 -0.156485  0.150160  0.053797   \n",
       "\n",
       "          Aug       Sep       Oct       Nov       Dec  \n",
       "Jan -0.120758  0.134955  0.117936 -0.142560 -0.059667  \n",
       "Feb -0.097432  0.033740  0.071301  0.059700 -0.012276  \n",
       "Mar  0.022202 -0.081379 -0.037189 -0.031521 -0.075481  \n",
       "Apr -0.117359  0.028134  0.037973  0.077483  0.000211  \n",
       "May  0.118944  0.038196  0.178622  0.175155 -0.156485  \n",
       "Jun  0.093280 -0.060803  0.111169 -0.370548  0.150160  \n",
       "Jul  0.411425  0.131770  0.132230  0.000195  0.053797  \n",
       "Aug  1.000000  0.222137  0.179547  0.123741  0.002483  \n",
       "Sep  0.222137  1.000000  0.277824  0.077645  0.054884  \n",
       "Oct  0.179547  0.277824  1.000000  0.146520 -0.031469  \n",
       "Nov  0.123741  0.077645  0.146520  1.000000 -0.112312  \n",
       "Dec  0.002483  0.054884 -0.031469 -0.112312  1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=mon_mean.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На табличку с цифрами смотреть не очень приятно, так что мы лучше нарисуем картинку."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jXR7R7s+cmfC1ngTcvc99bgPe197nmTRJ343ARxYXuSRJGinf6RupYaX8rwP2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\n3jDjvgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2f7160>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 255,
       "width": 318
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(corr,cmap='seismic',interpolation='none',vmin=-1,vmax=1)\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(corr)),corr.columns)\n",
    "plt.yticks(range(len(corr)),corr.columns);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Красные квадратики означают положительную корреляцию, синие — отрицательную. Как видим, исходное утверждение «если март тёплый, то апрель будет холодным», подтвердилось с точностью до наоборот — корреляция положительная. А вот между июнем и ноябрём — отрицательная. (Интересно, почему так? Это действительно есть такой эффект или просто артефакт наших данных?)\n",
    "\n",
    "Проиллюстрируем утверждение о корреляциях на графиках, которые называются точечными диаграммами или диаграммами рассеяния (scatter plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B+IE+PmcvPlZ8fWdK6W82b0gp/WtEvAl4EjADvP1qD2aYkMrHicTjp1arFd+d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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2ecbe0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon_mean.plot(kind='scatter',x='Mar',y='Apr');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Каждая точка на графике — это один год, по горизонтальной оси отмечена средняя температура за март, а по вертикальной — за апрель. Видно, что график немного вытянут вдоль диагонали, которая идёт «из левого нижнего угла в правый верхний» — это и соответствует наличию положительной корреляции.\n",
    "\n",
    "А вот аналогичный график для пары июнь — ноябрь:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pG5n5L122359iqNRHMvO7B/bAQ9aeqH0t8H3ANZm5UPL+nLwtSdoyufoSupcl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\no9XjcdHZd6jnnXabVygivhp4E0X1jkO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0479e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "width": 395
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon_mean.plot(kind='scatter',x='Jun',y='Nov');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Постскриптум\n",
    "— Неправда твоя! Данные ясно показывают, что если март тёплый, то и апрель тёплый.\n",
    "\n",
    "— Не верю что-то.\n",
    "\n",
    "— У меня статистика за 50 лет.\n",
    "\n",
    "— А у меня личный опыт! Вот, помню, несколько лет назад ноябрь был тёплый, а декабрь холодный…\n",
    "\n",
    "— Ну, у ноября с декабрём как раз отрицательная корреляция.\n",
    "\n",
    "— Вот! Я же говорил! Я был прав!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вопросы для размышления\n",
    "1. На сколько градусов растёт в среднем температура за один год? (Посчитать регрессию с помощью `statsmodels`.)\n",
    "2. На сколько градусов вырастет в среднем температура в апреле, если она выросла на 1 градус в марте? \n",
    "3. А на сколько она упадёт в ноябре, если она выросла на один градус в декабре? \n",
    "4. Значимы ли коэффициенты регрессий в этих задачах?\n",
    "5. А если учесть [поправку на множественные сравнения](https://ru.wikipedia.org/wiki/%D0%9A%D0%BE%D1%80%D1%80%D0%B5%D0%BA%D1%86%D0%B8%D1%8F_%D0%BD%D0%B0_%D0%BC%D0%BD%D0%BE%D0%B6%D0%B5%D1%81%D1%82%D0%B2%D0%B5%D0%BD%D0%BD%D0%BE%D0%B5_%D1%82%D0%B5%D1%81%D1%82%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5)?\n",
    "6. Известна поговорка «после дождичка в четверг» (о маловероятном событии). Проверить с помощью теста Стьюдента, отличается ли статистически значимо уровень осадков (`PRECIP`), выпадающих по четвергам, от уровня осадков в остальные дни недели? (Подсказка: `dat.index.weekday`.)\n",
    "7. Положительную корреляцию можно объяснить глобальным потеплением: более тёплый апрель скорее всего попадает в более позднюю часть выборки и ему соответствует более тёплый май и остальные месяцы. Скорректировать результаты с учётом наличия глобального потепления. "
   ]
  }
 ],
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