ischurov/Lesson16.ipynb

## Lesson16.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fitting-migration",
   "metadata": {},
   "source": [
    "## Наука о данных\n",
    "### Совместный бакалавриат ВШЭ-РЭШ, 2021-2022 учебный год\n",
    "_Илья Щуров_\n",
    "\n",
    "[Страница курса](http://math-info.hse.ru/s21/j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "stock-three",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "placed-oxford",
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.read_html(\"http://math-info.hse.ru/f/2014-15/nes-stat/climate.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "million-external",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "italic-meeting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "mineral-edition",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://math-info.hse.ru/f/2014-15/nes-stat/climate.html\"\n",
    "df = pd.read_html(url)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "indian-spelling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION_ID      int64\n",
       "STATION_NM     object\n",
       "DATE_OBS       object\n",
       "TMPMAX        float64\n",
       "Q               int64\n",
       "TMPMIN        float64\n",
       "Q.1             int64\n",
       "TMPMN         float64\n",
       "Q.2             int64\n",
       "PRECIP        float64\n",
       "Q.3             int64\n",
       "D               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "upper-pierce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['МОСКВА ВДНХ', 'МОСКВА ВДН??', 'МОСКВ?? ВДНХ', 'МОС??ВА ВДНХ',\n",
       "       'М??СКВА ВДНХ', 'МО??КВА ВДНХ', 'МОСКВА В??НХ', 'МОСК??А ВДНХ',\n",
       "       '??ОСКВА ВДНХ', 'МОСКВА ??ДНХ', 'МОСКВА ВД??Х'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['STATION_NM'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "connected-batman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27612])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['STATION_ID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "plastic-communist",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://math-info.hse.ru/f/2014-15/nes-stat/climate.html\"\n",
    "df = (pd.read_html(url)[0]\n",
    "      [['DATE_OBS', 'TMPMAX', 'TMPMIN', 'TMPMN', 'PRECIP']]\n",
    "      .dropna()\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "greenhouse-relevance",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dependent-warehouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='TMPMN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "tribal-amount",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "differential-enterprise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "df.iloc[:3650].plot(y='TMPMN', ax=ax)\n",
    "df.iloc[:3650].rolling(120).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "hungry-rachel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "df.iloc[:3650].plot(y='TMPMN', ax=ax)\n",
    "df.iloc[:3650].rolling(120, center=True).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dietary-canada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>-2.46</td>\n",
       "      <td>-9.58</td>\n",
       "      <td>-6.68</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>-4.40</td>\n",
       "      <td>-13.46</td>\n",
       "      <td>-8.54</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>-4.36</td>\n",
       "      <td>-12.78</td>\n",
       "      <td>-8.32</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>-3.98</td>\n",
       "      <td>-11.96</td>\n",
       "      <td>-7.94</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>-2.98</td>\n",
       "      <td>-10.30</td>\n",
       "      <td>-6.12</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>-1.22</td>\n",
       "      <td>-9.92</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "345      NaN     NaN    NaN     NaN\n",
       "346      NaN     NaN    NaN     NaN\n",
       "347      NaN     NaN    NaN     NaN\n",
       "348      NaN     NaN    NaN     NaN\n",
       "349    -2.46   -9.58  -6.68    0.10\n",
       "...      ...     ...    ...     ...\n",
       "3990   -4.40  -13.46  -8.54    1.88\n",
       "3991   -4.36  -12.78  -8.32    1.40\n",
       "3992   -3.98  -11.96  -7.94    1.40\n",
       "3993   -2.98  -10.30  -6.12    1.52\n",
       "3994   -1.22   -9.92  -5.34    1.62\n",
       "\n",
       "[3650 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3650].rolling(5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "behavioral-savannah",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>-2.46</td>\n",
       "      <td>-9.58</td>\n",
       "      <td>-6.68</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>-3.46</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>-7.32</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>-4.58</td>\n",
       "      <td>-11.16</td>\n",
       "      <td>-7.88</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>-3.98</td>\n",
       "      <td>-11.96</td>\n",
       "      <td>-7.94</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>-2.98</td>\n",
       "      <td>-10.30</td>\n",
       "      <td>-6.12</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>-1.22</td>\n",
       "      <td>-9.92</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "345      NaN     NaN    NaN     NaN\n",
       "346      NaN     NaN    NaN     NaN\n",
       "347    -2.46   -9.58  -6.68    0.10\n",
       "348    -3.46  -10.02  -7.32    0.10\n",
       "349    -4.58  -11.16  -7.88    0.04\n",
       "...      ...     ...    ...     ...\n",
       "3990   -3.98  -11.96  -7.94    1.40\n",
       "3991   -2.98  -10.30  -6.12    1.52\n",
       "3992   -1.22   -9.92  -5.34    1.62\n",
       "3993     NaN     NaN    NaN     NaN\n",
       "3994     NaN     NaN    NaN     NaN\n",
       "\n",
       "[3650 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3650].rolling(5, center=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "angry-academy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dykiH5ZDgA4hX8lO+H544ecGFZreOc7Ocyer/FsXEmMZ++hnZ0gw/OPk2ADdxzkcDmAzgBsbYaACzALzLOR8B4F3t70DhNjl+b5O0m3KYUO+LfWdklEHGSeqXbbuMEvNeg3hA1uZYZH1R9+gcjp95dHVl1x0G5xeKAHSYKJr7RHxP0a8oQ7RkFP6i0P07gk5YYkdQRcdJXxZvScRXN4qNKOJMdclLzgbKDNYuzEl2ZmcCIeoETMUdCtJvRZnIc853cs6Xar/rAKwFUApgJoAntNueAHC+alt+wjg5vDhhUDASgs02ziiAlePZ7hAO1njn5YIhXt0gMqH0d/ncYMZIEauIhxlk9PqkEARTQ5moUcRHX8xmov6SjdOLCGyjKereq4IvLHICpN6pKDcLAPDiUm8p/pxEcsa+u/Euou+5bpf4CY7i5I1QjSmjfzpKZm4XlsJotrnSJlxDIuGrTJ4xVg5gAoDPAPTlnOtnyl0A+to8cw1jbDFjbHFNjaKtqARx0OPR+AH9JPAOIfs0QleqmW13ReOEqHJ5MskUKI9JSrbsZRF9sXW/43W/jq5uCjCqnU02G76Il6gd7N5GH7l4Za79u3/2lbuBAMVNjx5QpNXtjZN3+sbrqzsJsq2Jr1bsFl21U8dB+VnQzxjfiWrdix5r1oudfiT6KegjIhaQ7esmVs3kCt+IPGOsAMALAH7EOY/zTOEx6kJ+Js75o5zziZzziSUlJUp9oCcHPTv81GZ//8lY9Me/f+CcpPnsh2PRFM2Lwckhyc8JI/PKfrQrUgVNQPz5Nm5E8buPLxKvjBgQOwcsM9w2V5GQy4D1hON0AjRClZsVnQt2Yki9fbsNVMf7WtA6mXnaFsfJ6zJ9cTpAwegI9vB7MZ8Qo6NaR3s242rcaJ2aTZSG0BcizxjLQozAP8U5f1Er3s0Y669d7w8g8GAtMpmVKGbRq238l9rxUjRtnlXe66lZaQTRjBOn5HWj8EtcYwwaJdsVcxeo50VPVnbvoxMD0Xkz3xTvSDSD18JN3sLoysIvhkTm84ty6qprjDzFOh9cOp60rTNBC98P6xoG4DEAaznnDxguvQrgCu33FQBeUW3LDU8SyT/McFK6BOl15oREEfkgbXFFYfYGpBaKano2HXZmhiKwGyrjwhQdT78c60RiCy0iEtv8eb7zCdMvyHhkOymBRYZVnyMlhZ3e5pR1DTf96xVeT8FuoslEwA9O/gQAlwE4lTG2TPtvBoB7AZzOGNsAYJr2d6AQ+RAdsa2Jm6XyYSrATOiCSGRBwfHoaLpIHUWp53/6wgproQOowG+WdpyuSay2IDglY40CuVV8RWFuZnxfiNczehmbEfQsk8mr4HQKomIWmdeIfvox8gP1WjAy450dtvOKc4F62m5LMxL2sWXdldr1A5nutziDc/4R7N/3NNX6pfoicS8lYwsC1OT62JSFhpp/e+qb0bvA35g4TpuJ1zUQBKfixCGLWgYxxuLe9sMNe3DZlHL1zhmgejKSlW5MO7KPKQWl/56sQYBi8HUxC+UYSOUzsOP8jd+gRWOeqMxQyuMi+K3NZsbZGfZ8dJeSyacKRL6DPuG8RBj0AqpPZgsKilg8Y4j1EmRfnPqgCtHTgPUmhzoFKKNuNWPcYOXDIphONlq7Roe6/wrkuO2oLYhThQ9Vqnp7e4Wuj1i1XUyP9sh78ZFEddpp5OQpKZ/u5U7Nb/OJWlTHoYOai+YAcamQNzetiLwIZ6MPOuX9eqjZuwxXvEed8bOd7tH7Sc2RNQIKZtkogNZxknrcVziJ5GsFTmB+iPTNNEHfsIzJy6l0i3ZY6sOJx9wnWacealN4Z6118/N7Q6KmkmzeV2uwuFitRgunLC1cgNEP40/a5kC9ku7lrMPJD4FymKQIuLnEaSwTpSJLKyJvHrS29qhl0J1oF+W4oCzLI543yyOd2jBGfNRBEXBzIhBK2ecsk7e/1nGP+y2+wMkjeJFJXksH3/LpiO4RVJ+82qjH1Wv6+1aJROqAuDWQypQXTYgtEwUTsHLi1BTJzcqwFmqglPCHiFwIdriLyHucfB5dDGlF5I2OGTrMu60sh6puekXVGV/qpLD6kvD+oxIhmL12qX47yuQFSKLshicy1lSV/zXEvrfWaXIkc9jMVNz4VU42nFvFgaoEgXNuGSsqBLHTN7IL62Gpw1Jn7F/jRqXfY3bk+8Pb1jaoHskmizcTeVkxyA9mWwO/mU98fnDWMt3a5yG8ghekFZHftjdeKUcfp+QmRxCmV+YiUbmkjhwiAJJIP7c7KC2FzNYkB4Ma6RdMrvWvrdhhuccuKTNg5eCoPukE4ROBENJ2AcFUFnyUc5z9cHzyEHIuyjIcpq8c1HHfvFHo40nJrK835a9tj3J8uct9PuvMC7VRUTB7J8tummbv5/W76yzvqWrltqe+2UJfnE5Pf/8wQaatCWklhSCrYAxCYXZkv6K4vx+zSStoB8oe2dpNa7/tkjuLQ30szIo+2YTm5jenQhPIbEZ2HKHKgucQk5fLRro030876KgLEcy16mKNuHKtM+b3jHLgg3XiuorvPPaZ0H0i39Tpzc1jd+/cL63jKfnJzZvey19st3Ti5w4iNbN+KSjnqLQm8pxzy4d/neAcnaCqxNtAiJCK8twtV504HC8pzQBgp4NFURCcvAirKjuxzSIHVRd2BuC15e5zQmbEo5xbxor6ZDJvnkj9gnn47tQidxrDL9j1h4N7Sv3nBrODnOxeZmbuopxbfDZkx/iJhfFiRWraNTmkJDWHeQgqFHpaE3kqO01ji5ycVvUIt3O/lbCK0CAqaYUep9prrtdfvLzK9prICScIM0vZGg+ZNj/KuUrWkuVBU7q2PfXNSqIQ6lnqk81bIx5SN9YnM6Gi2lb7Rv/++CvLnP9Ms82/1ZCC0a4ZUfGLLCwyecGwwjrM84YRdao7THElq7SgFLlpTeQ37LZq8B9+zxrL2Qnm7/7el3L21qTiVaqGTuhcAT2RvMtr29qjQn1KtIenCNrauUX5t1PCc5nD+j0qq+sJRZ94n0TH/plF9t6pZkQJxWsQuO9NqxgDsIb/tYsU6hRFUwXmaqnvsYCIFGkHxpjlPaNcjdAnMCeRFNKayFM7q0wYW4ormbdGTob80QarfFJkIjndUZCj7Kgchy17G8g+mY/Isqcaii6qRkSksGlP/GYuEjrBCL/yCeigxsmYCF2HXTxysk5ub/US37Yc9FAAOuzEC2YrJrtTHVXOuXrCEpF4RjI6JwZqHaptpOsDyGTmB9KbyCvurKt2HFASUXDOO466ceUCzzqRQq8yeSdQJoPW46zvzfrC/ZhlmTJ12m24atY11jKZbEd0ncFw8ne/YTVl9FtUBXSGHPAKv0WFjDFa3KVQZ0Nze8IcnGSQ1kT+w/U1yp6bKh/NLiuMSJ0vL7NXBpIcnOLkElO8ijcimqtUFVSX2hUJSlZGxOrxKjGRyFORD99nNhHqYpvJx+IbxwxUawh2VjvxfweZk5SC9Xuo1RcEE6O6EQU1omlN5J8QCD3sBAY1p5i2KCc/fCIUmB9KyCfJCogimSidW/c2OOZv7SxQV3aZszb92xQ2wvl5okyxTzSHqFZnblYGdpmso1ra2i3GBQOKc5XaAWwUx6a/7Wg8Ka7xgXyJiKpkEGHWd1CVyb+9Znfy3KwdkNZEvj3KlWTAsSNdAAQ5gIlg1h+IxNY3QiTl2s3PiYcVFn1HmaH4Yus+svyW5+P7peoP0B7laoSJeFQ1Rj4VzfBgU5uFKN34zDKldgCxbyIjk/9w/R5lTcyuA/6GAWdglknKeaKCftOgcgH4gbQm8qofjDH5QEpxzxNlO/Y3BhI/3uxZt6W2Acu37Rd+fiOhBDT3s77ZSlTscO1/ltBmbua8ORJDsXgzTeRVQLUf4+i810mf3tS4RLs5E4TURKSfdk5s1KMNLW3K4pV9JgsqVUaJEZz8tn2NyvWq6B7M7+gX0pvIq04EAFPuec9Up1ylZuL50hfbPdu563iDcPunuiUagIkhZh8dV0aYmAFAjaCjy/b9jXhl+XbX+1Q3vCBORYp8PPlsUIrTIByPRLq5xCZlIYf1m3yuyKFS4RRUk9pTMnlK5yGLf5nWUSogrYk8ACUPA0rZVi8RjpgxZo25YT0lSuOeuV9aylTFSq+bkkk32CyiX7+6RrhOERNBZYWxxL0kQSRFK9bNnDFxkQvthat2srQbpwWEia4q3iASi4vyJVR8espfRQZPEDqWB+aJBVuzA2Vds2TLPmWmgzI4EGUM/dhkKKQ/kVcANa9FXOCdngf8UUQZsbGmHv9xiNzoBdc/tZQkLH6EzPUTMicrUYsQKiwBIH4U30zoBGKcvIq4hkZxXrbnOu1AZWZS4ZbWKdqPUzb2TuECREDbyQNbleM7WSHqh/GRYJhmWfjrVZOCUBGMBJU4gzaB9E4Arnp8ERqJI62o0pk6sVBen4Cajb6q2R2pHJZ4XtTyg6pTRoH/MyLvbVRRqWc3P44qLSLLRVBtE8tI9FRotmoKCkGsQzujimUSeiwKdjoe632JU/GmPSevEpVP1TvTrmk7r0CvkM0QJAqqSxEFIu+UZNorZMZN9F7q+6gSGhll7oqq/ZYyu0dV5k2VjUksTaishbLRU72CIpKHJIwAKDCbeoOwpqMgG31VBWlP5CmIuljvbVAjnnbWJX572gEBKSCJSlWUxmYXeoBK6yYL8RcX3Vw55/hAIrWfUNsSZpmqoghVkNnMiG9nB0qkJzo/KYXqA6YAckAsgiPlTS4KO91YohhsVcWxDA5LIv/cEjGOciXBUanivje/pJV9KegtR9VJZaUSRUaEWThimUTSqotSNMBaNAr87f34hA5cghOnRkjm+1JDbBfdMQhlLsWEyITBNYfglYFMFEvRe5vbKFGmnfhODcm0s7eDL0SeMfYvxlg1Y2yVoawnY2weY2yD9m8PP9qS7htRtrHaf1kilYYuUeIaDppYUn2Sqpfokwonv2aneAas6jqrvJhqWkbML7qoY4pXMfm9KCjTQjtQIsZ/f7zZc9uyoN7THI7Z7r4g2rbDXz8Qy6z0qk2IEL/XoT0o8V/iMsT6xck/DuAsU9ksAO9yzkcAeFf7OyUQRFwVmSh7iQp14JQrVQhEl1Tm5vNLqoQXEWXloBouQDQ6JYf/30NG6Uxx8om0v6a6qhqpMwjauaVWrE9UEm87PxDR+bR4My0qoupct0vNhFQVvhB5zvmHAMxvPRPAE9rvJwCc70dbTuhblCN0nyihsk0Np8jl+T7hAzojJtfJWwxyilfrzQfJROD+cvGxOsX7quosJw66QyrELxVBOVPZiWtE8fB7lZYyuxDg64ict4nj44OVyfflnOteFbsA9KVuYoxdwxhbzBhbXFOjpuwSzRYTRExzGSQzucB2mYQagv1Ute55c5V94m4jqP6s3iEuAqLM3FftsEYKLSnMtXyj3725Tsn01S4ejqpyW/QbUXF/7J4NwrwvmDoVHmbWRCiSj1uQkxmB78dfH5AQxSuPfWHyk3DOH+WcT+ScTywpKUlEdyQ4ebpcXLZqc0FhdlJel3bhC6j2P/+q1nqfTVtUL0XN62Tw1w82Cd1HEcmbn1su3I5oP7My7JJ7W3HJo58Kty2qOJbKQiXIYW/daxV/2T0pOk4JNPUmITpOVDcZGBYToRnET1vWMplkNYmk+0ES+d2Msf4AoP2bOMNQA5SiUNo8SyYCoRarzfMqa+MpwvXZ7mSgqjiluK8kMyVKEJWLyxAvUTO+z77aa6vkNSNRY7zfJiBWIEHPfL9RDeZsYrLNU06B7VGb+EREYSLNZIMk8q8CuEL7fQWAVwJsC0Di5Ia1h9Rsu1U4X5mQqxSxkCLygvdRMk870OIzMcgM28KN1hMLZQa46wCRaN22ff/n1ztrrTmDg3Ctp+bcs4voWCnU2FEMi8xoUIYJ1Hh+KmH7rvI5vti6HxMGFXt+ntLXRaPckjDcDg2CwQP9gF8mlLMBLARwBGOsijF2FYB7AZzOGNsAYJr2d8KhlD3d1gTSWiZz7FaZnKr6BIrI2xH+F5dWCdW5RkIuLgpVbvaLbdajeDthKH/XHCr9Hf2BVPOUriBc5vcSuV+3EKIVO4jOJeo+KhQGoG56S+ERQlFJQVQ/A9BWMxTsdQ/WMtEAY5S4pi3KhWNb7dhPh5QIAr7EruGcX2pz6TQ/6leBisxThs6IpEzTIbpJBAEi94RtPxcQ2aWoW4Pout/xfQCgtV3teSoaIgW78fzWPz+zlFEctoy/megbUXPOTmQQxIllP+E9TjXzxkrxXLii4reqfeKbZhOxcVDNUIyRzAk9jF3jEfsIrojCO2vF1AN2Xn7UB1pPhFNtbaOfXyvhFGSGDIdLHSkpzlGGyNMiR5nJLXyr0rPUiUdYJm9Tbsf5qoAaO9UgWaLt2I0HqahMQV2MqKLzqc9osRT19NOEzkvUAkpuPLueM1RKgAoFS8XREHWlv/9tOmY1Nbc+22SVY977plUUYAdVUQCFD4n4K2S0SsUJJ0N8H5m/wVJGETXlBOzE0hJ1zZc53hvRu0A+7C9VZRDBq6i+t9nEeVARAdneK1gmA+U1Q7wANUdENz0ZhXW6WNekBB56x+qOLQqZZMXZmdah3LZXXEkqGk9nn2LQtFeWWbM1fYcQI9iBdpQRh6jIhDqVqRIFSiZPwS4Er1v7x5b3lOwRbRIrQywoLvP9ddZNgpqzbTbfgtogafl3Yk5wMlDNpUuBCqJHWddQeXgBYCeh3E8k0p7IL6+yOrtQkFE2URP2I0K0IYPueVlC9/33U7XsMQ0t7RjUMx8XTCjtKJNxWbfLeuQ38rMzlNohOVfBDeb6p5aS5TI5c0VBdclN/JWb5bxsqTlC1WiXBOWoAd0d6++o02U4Tx/d6f+oKuYTxV8/2IjR/Ysw7cg+np6nurSAOBFTjPjVJw0h69xNMA3GQH+/Of8o4f55QdoT+SBATU3KsUIGRYJEXhVZGRFHU9NrTx7q+LzfgbvsEAT3o5q0xO0bi9KsUf0KO35T4kS3frpZWNGiBI5hJd3iyuw2vZXbxRgjN5GD8XKiFI2fd/gjuMtDRDeeWS+utJSZ02UCwMtf0DmNC3Ot9i19CjtDsBQR1/1ESOQ1yITQddOij+4vn62H5pD9XxjZmRFwbr8ERg9w7ntxvlXuHITzDJX8WWYzoUQuMuFyg4Txs1JiEJluUnb+dKhja5ndZtIsLOtW87kI4mvE4sRz2w0om7WhFw5gAtuAY5o/RwY6xz8PTSjDLuQgJhLNRBt64mDH30ZQc2mHDWMyvqzYpc/BCujTPv2fKGTS2q124XRERS9GUOttzkpxm2EKk4b0tHhkZmdo0fc8zqsJg4rx+CfxZYHEJSFIgEziisoaq7XTs4v8z0ylin0e4v7kZkU6FOj3vmlN6m5HM9oa61CCA+jB6pGHZjS3DUAGctCOCKgJUTGw2KIUL0QD+rB9yEAUJU0cfbEPDByThpVg8cbdGBGpwni2CSMj29B/dy6uyq5GIRpRvJnhJ9nNaEQO6ng+GpCDjFdfx9UZDLW8CBksinw0IQNRZKMNzchCHlrQkx1EARpRwBrBwdDAc9CH7ccu3gMHUYBCNKAXO4hiVo8IomjaU46l0RHoVt8dgzK2YBTbiomRdShgjShEI7LRhgjT5lYdcEtODmp4dxSyBhSiEVloB3KBBp6DfBaTxX+75VZ8HB0r/Z10rHARGQetgw2JvAYZT1C3pAj9uudKt0/RSdXAX8cRRD5LUw55daqi+qkqBhFt558S6eYo81W5RMkcBWjEIeSC2xx4M9GG/qwWB3g39GJ1GNDcgEGsDtnc+fvnGGTqbmaAM8b2M9mOczz89RGY9d8P0JPVYXxkI86KfI4McLQhgt7sIEq2tOCW7AjyWDNaeSYakIvB7zfhsrbdgLFrbQBygf28G/byQhxAAZZFh6GGd0cLspDVPBjFkTYAHMdG1uH8jI9RygxWZOuBn+j1bUdH3VHOsJX3QWE0D5XIxS7eAy1ZBdjd2IRMtKOQNWAA9oBteBu3Zzl/kwaeg3rkoZlnoQ0RFEYasZv3wMTIehSgEXtRiH28EHtRCA6G4xo+wMn8LaARQBZQywuxOlqOndFe2I9uaIvkorqtADW8Owb1zEPf/cvQh+3D3mgR9qMA23lvlGA/ilgDDvJ8HEA3bI72c+yjG4IwiZVBSOQ1JNsOmI5QKF9PBFH0wgEUsQaU5g2Ou5aNVizcWAOOCF4Q9GY1Q8ZKgwZHDlrRjOyOPo1mW3BiZCVOzFiFEuxHPmtGzvpsTM/qh+XRYVjBh6KWF6GINaAbGsEAcDDUIxd1PB9jIpvRwHOwD4VoQwY4GHK2tuDijP0YwarQHYcQYVEc5N0QRQT1yMUBXoB65CIHreiJOnAw9GIHMJDVYBCrRhmrQS5rxX7eDUujI8DAMYhVo1VbMlFEMJTtQC4zKOyrgF/mAAfqCjEn81i8Hx2PhdExyELsBFLK9iAHLTi1eT9OzYz1a9DO/tgR6YMvowPRjCzUIx9tiCAL7eiGRpQ11+PUyHpURCpxYcYC9MYB5Dzfho8NxHp9tBQH0Q3Z4NjJe6KKFaOZN6Ihmos81oxctCC35wA8v6UbDqAb9vMCNCELM8vbsGnrNvTGAXRnh1DCDuDijPc7OFgcBGCQzr3XXoGnotNQxUvQhghOGpSNVdv2og0ZuKiiH55ftgtbeF+sjA7BIeThl2eMxp2vrwEATO/fD3Nr4x2d1v34TBz/i/+hB6tDE7LRwHPRjghakYlstKIZWWhEDihelyGKDETRZiJhp44oRvP+nRhRHMGrG5qxF/Hix8KcTNS1x77HmOwirG4bLzBn1RBJslA8JPIaqLjiRhTnZ2F/QwtKsB/tyEA31ohWnokWZFkmko5s3oIzI4tQxA6hCIfQmx1EBFHs5j0xmO1CC7Kwm/dAX7YPQ79cjPMjTWhEDuqRi144iDGbPsPPMjehjuejDnkAgEI0YgfvhUZkowVZaEUmhrCdOCuyCEdGtqAYhzqOo63v5+Oo7D7ow/YjF80oYo1o4xFs4v3Rk9WhBZnIQwtakYln26finfajkdvYD8PYdpwT+RSD+vbEht11aNSO9N3QhBGVSzAtAnwVGYSdbYVoQE6clcYA7MFAVoPhke04N2MhKlgl6pCHOp6Pg8hHb3YQZWwPankhWpGJ3jiATBZ7fmW0HGv4YLTwLAzJy0Fp85eYmrm883jtAY08G3sRU3QWoBEZiCIfzWSdB3k+tvI+2MBL8V50Amp5EYaz7Tgq8lXHt2pEDlqRiSy04ePoGGzm/dADdTiIfJSVlWNj1Q6cnbcG50U/wbcy36M7VQdEMxjqkYei6gacZlJzRDnr7N9WdBDa+e3j8Ro/Hl8/YSzu/6gGLTwTG3gpVvEhiCOExFT+8dCR+MumeHPifiWD8NSmmCVOUW4mDjbFuPZstCEHrZgx4BDW79wPDoY9KEIVj7dYeWNz5+8zjpqIZ5cspt8XnaazxtyqUc5Qi+6o5VZrngbkYvpR/TB3Fe0ByxFBG3HCamNZqGYlKMopwF5Yn1VJRO8nHjd4TwedPyAtiXxhbibqJOS3gHuc9dNG9cWbSzdgUe4NlmtVvDe6oQn1PA+70AOj1ldjVg5Q0NiMbtmd9Tbz2HDnsDbU8TzkZUSRGW1GM89Ezpo38JBZp1kJVGREOoigEw7wfLzZfhz2oQBVvAT1PA8/GlSJmu27sTI6BE3IRi0vwuTIGjQgF8ujw5DLWrCPF6KU7cEPMl/GDzJfBt4CztQV/3sBmNULa4B/6v3MBFp5Btre645jsguQgxYMjXQurC3RPniu/RRkINoh89zPC/G/6FT0Y3uRgSiqUYy10UH4NDo6brP8emkpXqzejgI0YCSrQjGrRxQR7OIxW/QIoujODqGM1WBJdGSsDTQgC+3IZq0oQCNW8qHYyXuiHfHmmEy7twBNaEVmx1F/QI9uqNon7ttgxpkFffFW+258lDkD1fV1mBhZh6PZBtQjDxmIopYXYh8Kkd9vJObtyEY7MvCt8UX4auVCDGG7kI1WdEMTclgLmnk2DiEXeb0G4pOaHOzgvVCDWAbNUypOwrMfLLDtxwnDe+HjynjnvAcN/iKFOZmoa27DkN6d1jaXTynHI/MrATC0IAstyMLGrFJ8wb1bjRm3UT0Qm/Eg6GbA0MuDc5nuADiibwF53ah7c/Jg/u9Vk/Cdx8T8RyYO7uFoeUX5y2ys8T8FqR3Sksh/94QhePhdq2elE9xk1Bwc7YjgF61XIhttOIBuyEMz8tCM8ZGNyEErmpCDErYfawqmYFNtE/Jys/FCwwRs5n1xgBfgIPKRixZ0QxNqUYSnr56E6/45H3XIwyPnD8Y/XpkPAMhlLajh3XHVtAr8fF41ctGCAjSBA2hCNvqxvchGG7LR2iEKWMmHohnZ2okjxspNGHctfrV5ddx7PNJ+AQDgjNF98faazgiIpa01GB3Zgv87tgD/XbwLi6NH4KiRw/HuulqM65uDjbsP4BBycf95Q/DYa/Pxl+nF+Odbi9CdHcLpvRh2VG1FE7LwbOvXsIqXYwfvja94P9iplS46pgzPL7EXGdVq+oh65GMpHylknjGkd7eYzb8L488RwUEU4CDiCcEJw3rj2cXqytkYw5CJhdExWIgx1hsMMawOsQL7+wAyQLcb41eY46z4P6JfoYUoUQxuEMlLjHDTR1Br8tzxA4SCgNmtZyOR3+RAaO1yClB45FtHY/I97wrfn2ikJZHP8HD8cXvkssmD8eLS7fhP+xnWi+3ANScPxaMfxpJfXDCgFC/t3o6B2XnYVh+/izchB03I0dqM4CBi3FRzdk8s48NjN2lzvyG7N4CauGcAYCOPOTJ945gyPGcilCK2yT3ys9C3KF45uB0l2B4twblDJ+C5z74AAAyLFKABh3AwUoQareZDOX2wmI9Cw+ipePSNmEIq68hR+O1XVisPJxhtxSl8QDigGJGVwSzes+PKuivlIp1HhP0NGkGYdb652jnIly6yiJsexAKQWUY5Lg5aFLiHqAQDe+QJ3WdnmSZKGxIVW6a0OC9wfWBoJ6/BONCUt9yEQT2EnxeF0XORslB5mQhBYMTXRln7aeS+KIcNILaHiPXX2qf7NJM9Yzsypo3F+TEuM4cIAyGDwlwrt3r5lMHEneKQiYvvF+xCKKigVzdnMcfUI2IZ2Nz8DmTm9InDe4vfrEEmk5IXDO6VDyDe8UjUVDoRND7CEOd5Hlg7gbeQBFAJvW8+Y6Sl7JFvTej4/dLSToJ60ggPaQgN81WfH25zuKxHfsdvSj7ploOScuAyTk79SH7WmHgTMM7FjuKHmtstderB3YwWA1RSYztce/IwAMB5FWqTm1qrY0uLleqkxtMuObNfWLTZWeZ9DeGB7CZarHUxvb1iSjmA+PmpStO8cL5uMvneBZ3rOC8rplcZWkLL2inojnvG2DPJtnQxgkMurLRXpNAr+4dvTBxoKRvYM99SZiSyxhyY48rEYnfYQfdilFHiUWFE3IJ50QvLWnbzmUfE/S0a93ohEVlThx1HRLlwP3RxRcdvnZDKOJ9RCOI43ZPggJ+86jjf25HBzWcc4X6TJPQN3nh4pDZ9ajOhGCg7uDnJuZneGmPfXHzsQORlZWBAsbgPCjVDhMU1wq145/o5T4xYKG2I/CqDFypFP8hwtobfRscjLyZNRsJpDHY2un8RRtpo+o3QvWQvJjYoO5AknlSgmQq4+nHUbox0cYwRA3uKyVFlEMTSMFqb6OhBhHFIJKjophRk9kz90/33006nvkxBRWP/7uLf0o2AzTbEbj+/YgDxfPzfWRksTvTi3r61TNSEUnV9XD91mHA7QZP5tCHy5/zpo47f1OQaW2rlzo0mVEZrAy9cZrmBQBiDTq3ZeZBMKGKGvsguOU6CyBPdZLDqFMzjETsm0u8ounHYjdDTV0+2uRLD3z7Y6HhdFJRITXVhUvNGpkpKh3pUqXwcIy+QEWPor2k0G6ZOMapJxt1uNVp3UToWY1uNLe1gjGFIb4n3JMrs5n2hRSwn/qLUnT89U+wEFrSNPJBGRN4N1GDaBWLyMu7G5MuXEQrAC48uc3z+h7NjFi2qH50xoFe3eG7HXGOUczDQnCsFknuz6SYlFjPerMuLqcdlRAElEhzdD04dLnRfxcBiS5lMHCJKPHG+ou6BApWcRMaijBLDUE9fcXy5RK+Idly6tKe+U1buFgpk+/5G6XVJbdp241TX7D2xtor6OBE2PIcNkRcSY3SUyw/93zXzSQAoI8y8ph/lHP9C33BkThEU90XKVk1FXBPX3Dp9lFA7d860seF2wFBDWNtBJOG34u7zvQeBcgIVOZPCxHKrBRWlY7ADxclfdeIQ4edFMKpfIXoVWDc4GU9O0ek9sEc+vm6y/mCAkPhRv9cMoxWOMUMbtUEaN6OPKvegtS0qJysnyr53YrmlrLxXvmXdJSrMSSI8cA8bIk/BzkpBPba79w8nM7kos04GYHPtIVOZWVzDEWEMFYOKhdrJz7ISOicLj/7dc+McTUS57vwca6IQGdj1iBI3nzC8l1idEh+EcqCxe/6ccf3j/hY1QczNosdIhlaIMjEcnOTmz1cw+7NrWsQW4BCZnYo2ebaD2T8EAF64/nhLmepmIoOgI2AfNkSelLfafJ1+xESQQW29NV2Y6ElCXVzDLJEnzVU2tUYBRh9dSa6bkskTZTrXK5LwQ11+Ln4vdTqiNikqO6BMN+84T/zEM2lo/CYjKjqzi2gocwKk7qTEbO1RbqmXg5afUzjlCG/ZmXRQpyj6u9PvTik/xxGx3XsV5FhOEqpWL4yxDrNPJ6zafkDY2s0rAifyjLGzGGPrGGOVjLFZQbdn2w+Je1VPUKWCXnnksTtB58Tm1ig5kccTcmlRxWt/DyGWzfjtBf6LbDII42jKSkPVOUfGEsc8ftMM5oJeIEOUqFup7x7l1s3j+ycNFc6jSjEMdmGpqVJKHEnB7tVPO9I6pnZ1Kn15m/YfvfwYS1lpcTxtqKyu79pEnjGWAeDPAKYDGA3gUsbY6CDbtMPJlDUGgO+dYJWZqu7imQRREa1SdYOhnD2C2DeoMdJd9I8Z3ClGOpaQczvBLX+pjssmi3u3UvmV+xCnNSrRd1B7rnkzj3JO6nJEISVeIF6KEjW1tUctDmKj+hUqEaUgcg/4AfMrSX12m1fKIiZeH5NxwYZqd8s7VQTNyR8HoJJzvolz3gLgGQAzA26TRGFupvVIy4CsTP9XMUVUThCUuaoqYkjLCRtRlWhLovfpFkbG+6nQC7F7aBGKKFEdUGwliHbkgzodUd6tNnmtLRhaQotWpMwLzfdymih4rk+DmXO0uycn0ypaWF9dT85HUUJNdckusiS1b5AnyASddP1oxlzFD08dTiZJ79KcPIBSAMawflVaWQcYY9cwxhYzxhbX1DgHpVIBs5FBqyaafOn/WZU2FFGhFhEFVXEN5dRC1SiTGUrUflzn5HcZ4rHIZqBSme92Y0fJq689xRougNpf7d6dVLIq2FZHHfKSquCnZ7nba+dl03NzbGl3cjNU+UZjCCIH0HF0vAQa9BOUbk6P+xMHm25a5g5jtD+Ch0BtMki64pVz/ijnfCLnfGJJiYeYMYJgjMUltwCA1dsPKtdrtkkH1Fz2Vaf1NEIOaaf0FV1D5Cbh8KzXmOyqa9pu3M3lPbtlkzFppgoqCiOM4bYZR8p30FSHW9lQQWUsYD9vTrU5SYlgfFl3sp92sdpFcP0pYp6gAJCpcLJRBefAUYQDZRDbjn7KP36YmMWXLIIexe0AjC6cZVqZr9hmiDsjAz+iDprXwNCSbkpEXjWmi2gMEmXrloS4ccRwtYKt+UkjelvG5AwbJadMhMJs4mQmNaZWJs/CuX6fCE5mh3KbDUFEBFRpIxdmjBafiW6GKmEFnDCij/dNRgZU/ylJld1aEJ0P/brnIjPCMEHQpFkWQRP5RQBGMMaGMMayAVwC4FW/G1mwQSZBcydUJWGUy3rfwlw1Tp54VGZS03JMqsxaeKWNh6PoZKWsKWztoiVGX8Vv4dHLJlrkwD8+3RqRVAY/OX0kqXcR/eoXHl1m2XgofYTMPLITg6jCPE9kTmkyYh3zvU5K6Nd+cGLc3374E5lPZhz095SRn8t2KyjRfKBEnnPeBuD/ALwFYC2A/3HOVzs/JY+qfd44edUocJSoBlCTJUYYs2wePfKzhb1OqeO5nbjFzIHYJX4gORWiiOI8g1YquSEvOwNFJrvubEUxQHnvbsIKbgonj+wtFFdFRj9DOXd9Z/Ig3+X8Mn2S4drNs8SpHbND2NnjrMHNZDHcJIKyi6BpjFyrw84rWkkR7yMCF3pxzt/gnI/knA/jnN8dRBterbJUCdDvLxpn+TiMqR1JI4zhm6ZIlJEIkJ8t5l5/bHlPayEpk1e04hGj+/jTu+Kx5u16pLpPkM5Q6vIqzzhv/ACrqSuhI5FpYlQ/66nyxtNGIsvU0A8F4/jY9cFsAugXzDkPZPK7HkfNeUXYTTkqyYedF7K10uQwPElXvPoBr8T62PKeGGZjDieCPkW5FmLBmBoBjRCmjRkRNQm4W9xuHTKyRdGjrF2y5ETOd4toxAd9BG2xJPg8o2swy8/94O7MDIdMgheqD0ExnKMHxG9SuYLWaEAwXDDnNglbpNqStCyTulscaUHk6yTSzxnRqyBbWZZJfUYVcQ1jzLIwrz15mPIEOHlkvOXSE59stsjFDzQ6ZxQyguKEg3J0oWLUy0AmYqUoZMJk0M9by8y6h0R5PzvBvBklqksJf3XL1OWYSJwQpHTrMvMhQEOGtCDyOw94M9nTozH6CQYmlWLMLB+OMKDEFO5gZF/npNeufWLWgFgNLe0WMdfsz7eBgugYqRJ5ypojLysDM8b2px8QhKh7vAyCWJLmOlXnpoxy22+o6j3kCKQ6zGNFnTQzIwxjJbLGNbfGm2wn62ukBZFPpm6PlMlLzFCz7X6EMUwx2ctSIhwZMDAyubOKToJW5npXtAFAz25Wjr2xtd33jVh1vozoU2ATPkLV/FWtPrN1lJlZ8AKvr2SnxBduN4EmuhTspoioU2OsjvhaWtuTs+2mBZH3SqwYoeySroOYjGoyeUZ4yikmJrDpjui4MTAUmSwIqDq/PWmQbNfiMLwPfWKR+bw/PG2E6z2qCy0SYRjU07suByAIOJFcXXYWmc0O/QgBIFLDTwiTVFVOXgrJl2oJwcmfZ+kvT8ePprnPXS84rIi8qiu6KJTs5IkvEpRsVpR4MmKTocZN2MoA9Lewq9dv+GHWaQzC5hfMn1n2uwcyTwSqpCxhnHLG3iKQGi/Z6ghqiqjEFgKcxWcFOZlSpwQZpAeRF4z98O1J8ZELfQlCRByxRZMvU4gwK5nzY/FSVVAiHPJZ+C8vDmpCdxGmDqeYFOFFeZkw916WVxBNxi0D62wUO7lSkVh1mH05Zox1zprmBgaGHIU1B1iJOsUIqDpdBR2jxg5pQeRF44B/yyROkIl6aAe/lxUVU0a1jSjntCJJgjNJVPS/VMSfLp3ge53m3LHHDO6pLJNXDYnhFVSzThuO6omFgqoXcyDg5j+5L965skgLIm/nndYVESFsqFUXgUiGGicwxgIZ41H9xKyGqMTVdghiLzrLJT8vQCcBl4U5aJrsuwQRtVGkSkrE5jTn/BbJMUaHjlYBNd3ziaB2snVefZJ4PCK/kBZEnrLcozzTKChPDZ+5bjqok1qdxfnZ/puKJogjKcrNlMt6lCSBjV2MecA5DosRPRRNPYPg5IVqJG5ySoAutHFIfXOx+52Gx0zUDzS2Wu559prJwn2iEOXWjTwRSAsiT5mKjSXChFLw27rmULPVMUvGGSdC2Io7yTdFoRan3VoWFDE1v/tVJ8pxPkEYqam+qaiOxl367QwRIj+zQi7OC+XRbbmHeM5JCW/V7yRmY5Zph8qlO7RELfoldRoWPc2qIC2IvCgHY0nxJTm3RgrE0V68ZZ+l7FSJhMZknHHBrzRlaDDxqJMpj5dlToNwuhV5f6dNz+wUY4cTRsRnDwtCJq/qWEaBmrPDHSKnmm+fdqRawm/GmNA8kRlN2XAnXz/aXXJgDt0AABcdUybVjhekBZH3yr0xw/9FQE1csaOncBOk4lWUk3dqp6uKa2TbEU0yLdUHkXscbhJlQr5mYga21B4Sek62HRmYa6ROpaRjmOOoxV+bWWElkLJvImJE4PSNzLPGLj6/HWZNH+Va5wjCcz0RDFR6EHnBdW1xXYb9h6fEPZQJlCoBsN5LmaiJPivejhNExAsM4km37UDJJz/dtFepzrYgiLzPXKIo1u6sk7rfT8VrvpYS0FylOWwzQBN0/Tkqu1WyDoZOG49ZlHLMIDk/CIoRM9MlSlyTCGubtCDylE0rtdRl5NLURBRxohlDHMlkSYA1KFTqmS8yxoSV23b487ePtpQdbLIqvGTQLmCMLKufEBLXOHKJ3j2y5e73b57owblEdC+DexHJYrR/f0ZwuH4zRsKWXzInaknqKxIfKVlGgGlC5L09J36gtG/HvLAoLthpwlKp6LyuVd+UoQLjKWrR4IS+RKJkc9uybbQnyeEkCEW0LFHwU1xzHZHo3A7UqddZdCii4xAHFYqbOiVSdfYNKD4+BYpJDChwaxzSgshTOzldZn3WbjLRE9Fagfmu8l7E8dSmDQA4gtCue12qiWb4g2jOzPWKvtMD3xwPAGgT4ORT8GDkC3w9+uufwaO5o15GPe738FMbLBVqgfru3QI0aTTPZSpKayJ8fNKEyHt7zmmxUwumt0BUvzvOs6bpS0ei4kdwNwoWCyhBkjBMM2+jZMBubQDA6TbJvUXhKK5J0DHd19g1WlXeT5UxUK/u97zJy85AqTk4G3Gf0/gE8YksMvkA2hBBWhB56hgkdswnIj5qoFPGETWYyihPPydCRXI6AVDPsaXFvtZnl93IjNJiMUcgHUeZjv5U7tIgMJhIQu4XEkbkfWTlJw+JjbvYKorhxOG9rYXk/SI6DvF3yc6MWCyTfnCqNaJjsnmtaUeqMRJekSZE3lpGimsExC06RLkiawgC4h5ZBZrc7UIIIjtSD4GMTRWDiqXqHGQitj3y7RVa7988teO3PsZe6anubTr7+2pejX4iiLAGolV62TDKexu+HbdvT+S9VH0+LiTsz8mNQ3EDdnJmMldNfZ9EMABpQuT9HymvR19SPun8gEhRSuIGgaTQ7YL5ZXWcbXLWqSc8iHVQtsx2U+Ekk6ORGXp8HD9i0PgF2WkdhDlesiy7rj5piO91OimmgyC2IvL2gT3lTrpekBZEXhSk4tXmuwtbKjDHP7U25MQ1yQZ14qGmaxDhgs0cpGjqPv1E5UdGn1SBrOmlauwbCnbz0xgTvuMUJZGfwP0e/1fGEEkHJxHIyPmp76kao14ESi0wxr7BGFvNGIsyxiaart3KGKtkjK1jjJ2p1s1gwJi9fJA82QlsEv7EqPfvFOEFMlZIQcMp0JUR+qsPtA0G1vlSVJ2qJpBOBI72nXCHbJ/8jsQI2M9nNwW3E71P1umgH2Wyqwg5e361571C1X5oFYCvA/i7sZAxNhrAJQDGABgA4B3G2EjOebtieyQ8W9cA6F1Icz9ebY5pczJPVcVB1btUFV4XpmrAMFlCd8YY97DAZPCsABdbv+7+ERenRC9iTltyLyqjJBWtOlkMAxXjnhO/ZOD4zqYqVfPeeoVSq5zztZzzdcSlmQCe4Zw3c86/AlAJ4DiVtkRxzcn2ThzUZmB3jKeOYaIR+GQgujCmH+V/YCkZeLXnTZVQ/15DXySybRE4JeNIZEKKyQqK0WTpnK47ZZjvdeZn2/PJ5rnkdG+QCGprKQWwzfB3lVZmAWPsGsbYYsbY4pqaGk+NGQdzfFmx8HNlPfIdrGuIdgQ8Xin44Q0pc7IY3d+beKCrwJxVSQRudDYVdSMUnILVBUFE7KY3Jf83rg8npiBZMf/N5rmAujPS+DKxkObJhCuRZ4y9wxhbRfw3048OcM4f5ZxP5JxPLCkpcX+ArKPztxMtNDtM2CWTjtXj30TUq3r66kmWa0Fwut870d0ywenEk0qgPoMX0VUys4f5ybnqc5ZyzPMqFlr968SqzFLReszr9MhQzPWQiGnpuvVzzqd5qHc7gIGGv8u0ssDhpOkXtdQw1uN6n3CNwDHlPSTulodMX3IlEx97nYuJiM0BuH8vt25MGuK/09W/rpzofpMDTjnCyvToIXVvPG04fvHKauk6qWFycu2Xi6DqvQ9dFU4xfkQIOBVj3m8EJa55FcAljLEcxtgQACMAfB5QWybxhD9TSFgmL2IO1vFvsDbx18pw5wly8S4WcJiSRc9uYo5dxuiI1IKbaiCig7R7VTkro4WLSBgMJ1BJaoKwoNFxloDS2gnx4hqHGyVewem07Se8fnYnEZkIg9O/e4rbyTPGLmCMVQGYAmAOY+wtAOCcrwbwPwBrALwJ4IagLGuAeEIiG7vd7n5RE0qxduzrdMOREvL144c7O/yIwk/Rhm4HPMBHCxPRdHrHD+scD+qNRFNEikDXCxm/QRCx7VXtqp16RG0qQcjPZUShQZhbFhpOLqpfyAvj52QhFQRUrWte4pyXcc5zOOd9OednGq7dzTkfxjk/gnM+V72r9qAmgqobv+jkljIxI6650dOjbI5zelIHz0iQjFrfMH5yxhEud/oPI9MrunGpjkqxQSlMRR2UATW3BhT7b+vdWbeVqwxCfi5TZRB8vPE9g1wGdnPuWC1Wf6KQdh6v+qSQTVhsW5GxyKO4pvNe+SlrNwdVJ7+0d6THxaDPc3/D4FIxiKwN+BqVURDGJoNIRXjJsYMAeN+MnEaEEi9JEWTjpup4nwwn3/n7xtOsQcf8goqvjRlO8ZaSgfQj8h2OGWoL3MYP1ltdurjG4ZosVAlYtwTZ7AZhdy4KN7NT6qqsQtoM43dxSmYtAuoTO9nJq0J1TwwgQVPceFKJQLyAx7lAxX6LigDNoOjMkf0LtbpTA+lH5GXvt5nZfsoCdS7TTzm/Kivf6pBcw8/Jqb9fEDH/3e4zKilF2xdJCO3YD8NvZXkvUaYspnNAHlG3Z09nh5eXqdIYubFAMMSFG6i+nTaqj7UwgHaAxDM+aUfk/YLoPFTdC9xsm+0miuoWNPvzrdJteoGXqqicoV5g5OSDXlgdOiCjuMangTRaAck4+8mCCu8bxLlBSplruJVKIq6KDnGi1zAmgvc5hSQOGmlH5P1iwP2IC69j0lAtKTJRgZtIwY44qZ406pqsIXydtP5eyZW+iJLhAGMcI5Le+tipG08bGasS8qcHN/zj8k57e32+BHEyCirukrVSb7f6tVEnjo9ODYFN2hF5v5RtokRUhCuZEECMclVF5pbaBkuZW8x1L1BNlKxiwmeUvJzgk3mpHXSZLiM4eadQBE7Q6zKaTSYrgqMsnAiyzCv4me2KwnjFten0LkYFrLAPQQBIOyLv1/mSVJJSZULOUN47delxg8hyp82sXEDckUMomvQ6RTI+ieKccTErJ+PE/r5LQgi30RJdI8YxGkEoQYNmUnXjmm9Nor+hDG4/+8g45WCiwjQEsal4VbwGgT9cNF7peafxGWajeE/0Pp1+RN4n6CKWZMPOptZpokwUsMOdfhTh3ajV+ey1UyyXuuV4U/glwm3bDnHimoS12flbN6GkNlTB2jp+XX3SUKy/a7pCz7QaFQjMBRPIGIMe+iDeiaAja1LKZgqieQ2MSJUIrGlB5I1zxq858c2JA91vEm0vCIcSwYVy7njaX4DaCPQTRxmReMOcKNlLmzpUubOBPawnFarKEwMW0VCQkcnrKQeN8L4hBA+Zr0a9+3+vmiRUzw/i0komju11OgGrKn2TaUqcujPKI/w6XooqoZIlIxVtVbfZNYPizP3imlzrcbnuNqb3XTROqB9Gu+pTRpbgrDH9sOCnXxN61isombwdKLvvaw0xz7uI+J2EvpEbR+BETefjZmxgTOpiDFniF2dsJ+pychD08i2SSdiNSD8iH0CdekyPXCLBiEic9yAWqyo37OQhqhpITfV13Z4XdYoxLrLcrAz87bJjMLBnJ7cWiMu+oU43U1AVBiEZ5KNIIo7/jdO8e6caiXCegeAHvekd0dfezNHPXKyh4tUDjEQpiImwfnc9AOBHp48kr09WkN97FSl4lZHrSCaXGETQK6pOt8XkpR8PXzrBuU7GyN90+4mHyti7+XSMMyTQ8JLYRUeyZNlXnlBue+2UkfK5LowbVDLl82lB5I2TK8isM24c5EMXV5DlTj2igkKJIIh0gF6USxTciFtTq3hA0mQeec8jdAtUmRFSs89NbCVTlyCCHE9RPZYbjD0cYkgYTq1tv0IdAM6nYy8n58LcLDylJQoybpCXTymXrksFaUHkjTbQSXG60SafauRLGYjaD8tsetRx3Mt4uorkPdR5sgsnRdXpJhMPguD5KdYSFeeoxsjxC37pp4yfzZgFjKp+7o0n+dImEL+h6JgwqBiAXF4E45o7YXhv/PGSCvzJcAIc1sfaTpBICyJvhOo0G9RT3qXeKRtV7HpybY3J54k+kTlsPVVuLeJxl8VZ2GxNFuqlH0GIa9zgdsK678KxzhWIhkQ23OaU2SnZoF4nJzOCK48vt33GuDnfcuYox/oHelivdtDl7sb4QHqoB5kcy2bMrChFcRIjU6YdkS9UNHWiFswxg3s4PtNB5BMoWtDb9DNXK9V//ZialyVOSNyOtm57nvFyZ1RR4eY74Jq8O4BTn5tn7cXHdjpGJUNmn6wk2nF9YAx3nDdG6F7j6divnv+/qcNtr735o5PwwS1WCyw/nc8S/Q3Sjsirh0u1fkzKnjmuTe2jqc4DmfR9uizy5zOOVGvUACr8uT6eN51BK50pKFpQdohm/nhJhXCbFFQXZmoYwLnDC5PpxEknG3bfza9N+cJjymyvjepXFL+xSLT57k2nAAAmDXE2xEi0nil1z3mSyMmMoLnNPnyuE/767aPRqyAH3/z7QpxB5Ll0M5/q5OTdMWVoLyzcVEtek0k0foXiInWau3HOZdoffooE3BbObTOOxFUnDkGZwenpptOPwPvrauzrJMoSlUTcDbquw7yJu9GPnCwxHswL7fNTYek3UuSzSWNYSQEW/PRrKPVoTBEUUvdLS2JoSQHW7jzo6dnpY2Ny1OW/PIO0MDnKJRfoueMGYMGGPRhW4q5QmX3NZJTPmuOpn0ao2u2ShFbjoIyMVLEHUzhXcYzLDZkZkTgCDwBjy5y/AYXueakxvbvnZWH5L8+Qioc+ql+hcJLnrhK0TBR2B7Aj+iU+RAbVl8wIs83fK6IjSLQ5ZWqsAh+gH/FU5nt3Gw36sBJn64VvHjsQFxxdakt4ExVMSgZ6XPLCnEzUNcfCDlO9fOmGE6TrdpM5JookDe+jFsNbZNMWhd3csoObAtfzjHIZ/GPLe5ARShMJO6soyvolUTBupDGdU+qtaTukDZHXkSzFkhNnbea07vn6WHLjSOS00TmOnKwI6ppjZVFio/Ry9HSNrBuwp6nwMzblOqfmp1JbFF7mgMyr9ytydmh67rrjfWlHx9GaCWJaQVXv508vhJF+RD4IAuJzfXbhg4NAcpyJ3Dj51BAv2I2MXp6MROCiMJ4OZbp5ZH/vIg8vM6mPy6Yi2o5I+OwQNJQEu4yx3zPGvmSMrWCMvcQYKzZcu5UxVskYW8cYO1O5py5IZgYiv5BsqY5f7dO5bLvO8VZH0HOJDHgXbJNdAmal8NJfnI65N57saxtunstO6GrfSNWEch6Aozjn4wCsB3ArADDGRgO4BMAYAGcB+AtjLLgMxOjkWIPgElXJU0ZK7zydfdNNFzMlbPKOI8zFUvltjTh+mDWnaVfBpCGdffcy51NxSo7Rcg+YTRB7dssWjvsuige+OR6rf+3Oe1JrX/WEl2iGR4nIc87f5pzryUI/BaAboM4E8AznvJlz/hWASgDHqbTl3pcga1eD6AT1EgTJT0zWvPsyJSx3RhPH/+QQkPhG3XwbAGDCIBcnN0Odj3/3WNw5U8yBRxZxGZ8EnzFaG3kZ79NH9wUAvP6DE+UfDgh6iGE/l/Klx9HxdDIzIp7NgtV9cdSel4WfMvnvAXhW+12KGNHXUaWVWcAYuwbANQAwaJC6rJr6AD87y9k1OlUgmkXp1FHiCTyChmj42USa+b170yno6YMbubHLUyWSpsjiaS2IlV3bXjDtSOf+jisrxuZ7zxaq6+yx/TFn5c7AT2j6AdJPInjP18dh9ufbPD9PvXMKHoIc4cqyMcbeYYytIv6babjnNgBtAJ6S7QDn/FHO+UTO+cSSEu+crD4vqA+QK+hUYgcqU1Iy8Y/LJ8b9rRLWVZWYDCXM2qjjrJuisL9LGFs3GOscVlLgmAAi1SBjPy+Kf15xrG91lSZo/nd6jqfwsRzAEB9NaxMB19nFOZ/mdJ0xdiWAcwCcxju/znYAxnNSmVYWGPywk7dDMu1zKZhfceGtp9o6Z8jWJYvzxg/Aj55dJlUntYZV06t5CSxnp3romEsqHRIAJUufPLQX/vRepW1uX7KeBLGWiSK9qU3igZF9CrFquzfHy2RAiYVgjJ0F4KcATuGcGz0oXgXwNGPsAQADAIwA8LlKW25wmhhd7Xgli/xs+88YNFNEhTz2IppRNfX04gGcip6iJwzvjTV3nun4Tc0I2iQ1UaPkFs01GSC7cpjJ5B8BkANgnrZgPuWcX8c5X80Y+x+ANYiJcW7gnItnivCCjoHzb0oylloTTkcK0iZlpOI4Jwrmd5ch8ED8fHj8u8di98EmH3rVCZlPM3FwD4vH6sezTkVjS5vNE51IRjTXwwFKRJ5zbhuzk3N+N4C7Ver3Aj8JYDoQnulHWQOuGdG7IAfVustrgIiLJ5/im1RJYQ52H2wOnNP3q3qjDiRIBbFId5+/3uoxK+o13XEiSfF1lyrOfKJIm1DDTvNCdc4YF6NKPtdkYKghfAKl3Pz3d/1T0Kkgldb1C9cfj/u/MV4pUYQIgnQ+64qQiebqhCumDFbuixOUTSjDUMNq8HO+p664xttb9i7Iwc4D8Uf5vh7czt3g5slJjWkqWVSU9chH2TGhG32i4ZdM/tczj1LvjAO62p6aNpy8vntTeVa9fhQ6HV5X+8QhUhXpwoH7BX1tueXmTSSuO2UYvn50aVz+hnJFa7uupnhNGVx5whBcecIQ1/vyszPQ0NKOrAzxFWYk7F1lYf7j8olobgtW122HUf2sIX7d5nXqLGt1PH31JFTtaxS+X3XRi7rZew2dfO64AXj0w02BO+Gl4trqnpeFB75ZEVcm4k3thDAKZcD4zcyjcNNzy3GiSy5OIHXFNSLQ3daNkJEF/vvKYz0nJrlgQhl+9sJKT8+mA44XmFt+opcL0bl8ymA8uXCL52xQY8u6k96x540fIBXnyIy/fvvouJP3/31tOD7/ai/GeUgQk0ikoumtEw4LIm/MlK57r/ZPoRRdb/zwJFTX+Wv2RkFmw/qaAtfmnhmKKPS4mb530ylYv7ve28NdHJ/eehqu/c9i3H3+WMf7ZlaU4smFW3wnTg9fOkHpeT0jm45JQ3th3V3TlepMBEb2VUtGk2j902FB5GdWdIYVnTS0F/54SQXOJHK5ikAPouQnRg8owmgkPrWZjlvOPEI4Bo0I3EgJFebV67QfWlIQZ0HUlXDRMWW4a85a9PMY0qFf91y88n/uAcb8sloJEcPIvrH5phouJVE4LIi8mYOZWUHGSnN4vvO3nvP0jnNHK/cr0bBjIG74mq27gydQUSyNbvpUHt3DEVedOATfPWFI4KaaHbV3VdljiqKrGGF0ja0ohaDLC8e4JPdORagucZWsQsb4P5RsWEW2m2yI6HcoMMYCJ/B6O4cznrp6Eh66uMK3+jK1/JYnj/T23UPFa4rjiuPLccLw3hihKJfrihjcMx9rd6oHZiomwgAfM9g5tnsq44IJcifDZCGd+Pglt08Tfp8TfFaEZ2dG8MEtU5V9TKgIrkEg5OQFYOSDGGNdlsDrvgSUiWOycZgzm4EiHYe2V0EOehdYfWIShcG9unnWzyVaahYSeQek2+LQ0/tdc/LQhLRHmXGaMWWonsYu3UY79RCK5FMFekLqxLQWimsckG5rYkBxnnA2IApOdvbmmPtUO09fPQnrd9fFlZ0zvj8WbqoVaj87M4KLjilzvzHBSPVTyOES3fHJ7x2HLXsb3G9MESRq2oREXgCHu+LKDe/edIrQ0fn44b0tjkL5Wv5bEYub9V3AhjoV0VWsQACgtbUVVVVVaGqS9xspAVDSHVi7dq3/HfMRre1R/OO8/sjKYNJ9zc3NRVlZGbKyxE2eQyLvAIb04+aDwDAFO/Xzxpeipq4Zl00u969DErhz5hjMWbEzKW0nCqmYjMMOVVVVKCwsRHl5edoyV02t7cDuOuRkZuAICf0Y5xy1tbWoqqrCkCHuIVx0hDJ5B3SBNZFQ9PAhObYZGRGGa04ehrxs/53MRHD5lHI8e+0UT8+ePS7msdlVaJHHDJEJRVNTE3r16pW2BF4FjDH06tVL+pQTEvkUxTPXTMb1U4cluxtxmKwpSd0SkRwuyOoitv1djV6GBN4eXsYmFNc4IJnimslDe3UQ1VSBPr+8Bi5LN3QBxjgOqRSzP0TiFK/hanVAEAk1ujLyNLvg7j7GuUkHpLpiM9X7l0qora1FRUUFKioq0K9fP5SWlnb8zRjDd77znY5729raUFJSgnPOOQcA8Pjjj6OkpAQVFRUYPXo0/vGPf3SUM8bwzjvvdDz73ptzcET/Ijz//PMAgKlTp2LixIkd1xcvXoypU6f68k4hkXfA89cfjwcv9pYG7qQRiQ03mwicProvfnXuaNw6Y1Syu5JSSHfTxMMJvXr1wrJly7Bs2TJcd911+PGPf9zxd7du3bBq1So0NsZyBcybNw+lpfHezhdffDGWLVuG999/Hz//+c+xe/duAMDYsWPxzDPPAIidAOe+8gJGjYmPHlpdXY25c+f6/k6huMYBpcV5uGCCN7vsf115LEbc5v8HSyYYY/iuQGKWEKmFrmRdY8SvX1uNNTvUw2gYMXpAEX517hjPz8+YMQNz5szBRRddhNmzZ+PSSy/FggULLPf16dMHw4YNw5YtWwAAJ510EhYsWIDW1lbU1zdg25ZNONJE5G+55RbcfffdmD7dX1PhkJMPCKHc+vBBqotDDhdnqETgkksuwTPPPIOmpiasWLECkyZNIu/btGkTNm3ahOHDYxFeGWOYNm0a3nrrLbzx2quYevp0i4RgypQpyM7Oxvz5833tsxInzxj7DYCZAKIAqgFcyTnfwWIq4D8CmAGgQStfqtrZwx3XTx2GZxdtS3Y3QnQxlPWIJSX/wakjktwTOahw3EFh3Lhx2Lx5M2bPno0ZM2ZYrj/77LP46KOPkJOTg7///e/o2bMzxPYll1yChx9+GAcOHMBd99yHB37/O8vzt99+O+666y7cd999vvVZld38Ped8HOe8AsDrAH6plU8HMEL77xoAf1VsJwSAn501Ckt/cXqyuxFCQ1cRfxTkZGLzvWfjXCJZSwh5nHfeebj55ptx6aWXWq7pMvnPPvsMF1xwQdy14447DitXrsSePXsw/qjRpGnrqaeeisbGRnz66ae+9VeJk+ecGwVm3dBpVTYTwJM8ZrP1KWOsmDHWn3Oe3q6FIQ5LhGbdhxe+973vobi4GGPHjsX7778v9ey9996L3Fxnq73bb78d1113HYYO9SeQoLLilTF2N4DLARwA8DWtuBSAUa5QpZVZiDxj7BrEuH0MGjRItTshQoQIESjKysrwwx/+0NOzIkrVGTNmoKSkxFP9FJibgwRj7B0AlIvjbZzzVwz33Qogl3P+K8bY6wDu5Zx/pF17F8DPOOeLndqaOHEiX7zY8RYplM+aA4COiJgIJLv9EMHih7O/wKvLd+CPl1RIp5QMQWPt2rU48sgjk92NlAY1RoyxJZzzidT9rpw853yaYNtPAXgDwK8AbAcw0HCtTCsLESJEiBAJhJLilTFmVNfPBPCl9vtVAJezGCYDOBDK40OkG7qI3jXEYQ5Vmfy9jLEjEDOh3ALgOq38DcTMJysRM6H8rmI7IUKECBHCA1Stay60KecAblCpO0SIECFCqCN0ywwRwiPCqI4hugJCIh8ihCLC+OchUhkhkQ8RQhEhR58+SFSo4ZdffhmMsYSEGk7rKJT3XTgWldX1ye5GiBAhugj0UMMAcMcdd6CgoAA333wzAKCgoKAj1HBeXp5tqOFHHnkE1dXVGDNmDM477zwAnaGGp02LWaTPnj0b48ePj3tWDzXsdxTKtCbyFx8betCGCB6huCYgzJ0F7Frpb539xgLT7/X8uB+hhpubm1FZWYmKioq4Z8JQwyFChAiRZPgRaviVV17p4PCNSMlQwyFCHM4IJfEBQ4HjDgp+hRq+//778dvf/tbyfCqGGg4R4rBHKKw5vOBHqOGRI0eSdadcqOEQIUKEONxw2IUaDhHicMVFR5dhzoqdqBhYnOyuhEgg0i7UcCLhd6jhZOP1FTtQlJuFk0f698FChEhnhKGG3eF7qOEQ3nHOuDDdWogQIZKLUPEaIkSIEGmMkMiHCBEipZBKIuRUg5exCYl8iBAhUga5ubmora0NCT0Bzjlqa2tdrXPMCGXyIUKESBmUlZWhqqoKNTU1ye5KSiI3NxdlZWVSz4REPkSIECmDrKwsDBkyJNndSCuE4poQIUKESGOERD5EiBAh0hghkQ8RIkSINEZKebwyxmoAbBG4tTeAPQF3pysjHB9nhOPjjHB8nJGK4zOYc0661qcUkRcFY2yxnQtviHB83BCOjzPC8XFGVxufUFwTIkSIEGmMkMiHCBEiRBqjqxL5R5PdgRRHOD7OCMfHGeH4OKNLjU+XlMmHCBEiRAgxdFVOPkSIECFCCCAk8iFChAiRxuhyRJ4xdhZjbB1jrJIxNivZ/UkkGGObGWMrGWPLGGOLtbKejLF5jLEN2r89tHLGGHtYG6cVjLGjDfVcod2/gTF2RbLeRxWMsX8xxqoZY6sMZb6NB2PsGG28K7Vnu1TObpvxuYMxtl2bQ8sYYzMM127V3nUdY+xMQzm55hhjQxhjn2nlzzLGshP3dmpgjA1kjM1njK1hjK1mjN2olaff/OGcd5n/AGQA2AhgKIBsAMsBjE52vxL4/psB9DaV/Q7ALO33LAD3ab9nAJgLgAGYDOAzrbwngE3avz203z2S/W4ex+NkAEcDWBXEeAD4XLuXac9OT/Y7+zA+dwC4mbh3tLaecgAM0dZZhtOaA/A/AJdov/8G4Ppkv7PE2PQHcLT2uxDAem0M0m7+dDVO/jgAlZzzTZzzFgDPAJiZ5D4lGzMBPKH9fgLA+YbyJ3kMnwIoZoz1B3AmgHmc872c830A5gE4K8F99gWc8w8B7DUV+zIe2rUizvmnPLZinzTU1SVgMz52mAngGc55M+f8KwCViK03cs1pXOmpAJ7XnjeOdcqDc76Tc75U+10HYC2AUqTh/OlqRL4UwDbD31Va2eECDuBtxtgSxtg1WllfzvlO7fcuAH2133Zjle5j6Nd4lGq/zeXpgP/TRA7/0sURkB+fXgD2c87bTOVdDoyxcgATAHyGNJw/XY3IH+44kXN+NIDpAG5gjJ1svKhxDKFNrIZwPEj8FcAwABUAdgK4P6m9STIYYwUAXgDwI875QeO1dJk/XY3Ibwcw0PB3mVZ2WIBzvl37txrAS4gdpXdrR0No/1Zrt9uNVbqPoV/jsV37bS7v0uCc7+act3POowD+gdgcAuTHpxYxkUWmqbzLgDGWhRiBf4pz/qJWnHbzp6sR+UUARmha/WwAlwB4Ncl9SggYY90YY4X6bwBnAFiF2PvrGv0rALyi/X4VwOWaVcBkAAe0Y+hbAM5gjPXQjupnaGXpAl/GQ7t2kDE2WZM/X26oq8tCJ2AaLkBsDgGx8bmEMZbDGBsCYARiikNyzWlc7nwAF2nPG8c65aF908cArOWcP2C4lH7zJ1naba//IablXo+Yxv+2ZPcnge89FDHLhuUAVuvvjphs9F0AGwC8A6CnVs4A/Fkbp5UAJhrq+h5iirVKAN9N9rspjMlsxEQOrYjJPK/yczwATESMCG4E8Ag0D/Gu8p/N+PxHe/8ViBGu/ob7b9PedR0MliB2a06bk59r4/YcgJxkv7PE2JyImChmBYBl2n8z0nH+hGENQoQIESKN0dXENSFChAgRQgIhkQ8RIkSINEZI5EOECBEijRES+RAhQoRIY4REPkSIECHSGCGRDxEiRIg0RkjkQ4QIESKN8f8BulzuopcfqcwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "df.plot(y='TMPMN', ax=ax)\n",
    "df.rolling(365 * 10, center=True).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "located-gardening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# df.plot(y='TMPMN', ax=ax)\n",
    "\n",
    "df.rolling(365 * 10, center=True).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "described-newfoundland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE_OBS     object\n",
       "TMPMAX      float64\n",
       "TMPMIN      float64\n",
       "TMPMN       float64\n",
       "PRECIP      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "professional-weekly",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE_OBS'] = pd.to_datetime(df['DATE_OBS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "turned-diabetes",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE_OBS    datetime64[ns]\n",
       "TMPMAX             float64\n",
       "TMPMIN             float64\n",
       "TMPMN              float64\n",
       "PRECIP             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "handed-mentor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948-12-11</th>\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>1948-12-12</th>\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>1948-12-13</th>\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>1948-12-14</th>\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>1948-12-15</th>\n",
       "      <td>-3.6</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-27</th>\n",
       "      <td>8.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-28</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-29</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-30</th>\n",
       "      <td>1.8</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-31</th>\n",
       "      <td>1.3</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                 \n",
       "1948-12-11     2.4    -4.5   -1.1     0.0\n",
       "1948-12-12     1.9    -8.7   -6.2     0.5\n",
       "1948-12-13    -7.4   -12.3  -10.1     0.0\n",
       "1948-12-14    -5.6   -13.2   -9.4     0.0\n",
       "1948-12-15    -3.6    -9.2   -6.6     0.0\n",
       "...            ...     ...    ...     ...\n",
       "2006-10-27     8.7     2.7    5.6     0.3\n",
       "2006-10-28    11.0     4.1    8.2     0.3\n",
       "2006-10-29     4.6     1.4    2.4     0.5\n",
       "2006-10-30     1.8    -1.9   -0.7     3.5\n",
       "2006-10-31     1.3    -7.7   -3.0     0.0\n",
       "\n",
       "[21142 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('DATE_OBS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "capable-annual",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://math-info.hse.ru/f/2014-15/nes-stat/climate.html\"\n",
    "df = (pd.read_html(url)[0]\n",
    "      [['DATE_OBS', 'TMPMAX', 'TMPMIN', 'TMPMN', 'PRECIP']]\n",
    "      .dropna()\n",
    "      .assign(DATE_OBS=lambda x: pd.to_datetime(x['DATE_OBS']))\n",
    "      .set_index('DATE_OBS')\n",
    "     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "beginning-rebecca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948-12-11</th>\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>1948-12-12</th>\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>1948-12-13</th>\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>1948-12-14</th>\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>1948-12-15</th>\n",
       "      <td>-3.6</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-27</th>\n",
       "      <td>8.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-28</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-29</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-30</th>\n",
       "      <td>1.8</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-31</th>\n",
       "      <td>1.3</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                 \n",
       "1948-12-11     2.4    -4.5   -1.1     0.0\n",
       "1948-12-12     1.9    -8.7   -6.2     0.5\n",
       "1948-12-13    -7.4   -12.3  -10.1     0.0\n",
       "1948-12-14    -5.6   -13.2   -9.4     0.0\n",
       "1948-12-15    -3.6    -9.2   -6.6     0.0\n",
       "...            ...     ...    ...     ...\n",
       "2006-10-27     8.7     2.7    5.6     0.3\n",
       "2006-10-28    11.0     4.1    8.2     0.3\n",
       "2006-10-29     4.6     1.4    2.4     0.5\n",
       "2006-10-30     1.8    -1.9   -0.7     3.5\n",
       "2006-10-31     1.3    -7.7   -3.0     0.0\n",
       "\n",
       "[21142 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "domestic-mitchell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE_OBS'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# df.plot(y='TMPMN', ax=ax)\n",
    "\n",
    "df.rolling(365 * 10, center=True).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "understanding-round",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE_OBS'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# df.plot(y='TMPMN', ax=ax)\n",
    "\n",
    "df.iloc[:365*10].rolling(365, center=True).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "tribal-consciousness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE_OBS'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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polftxqmySjYeOM7Eftps0x5M7ue4xuHzbUdtjkRpolftxpp9+VTVGC7Q+UrbhZhQfy5MjuWNNfs5VVZpdzgdmiZ61W6s3J1LsJ+3jqPSjvxqah9OlFTyxpoDdofSoWmiV+2CMYaVu3M5r3e0TkrdjgzpGsGFybG8vDpDa/U20k+Mahcy8orJOl6q3Srbodpa/b3vpFFYqsneDproVbuwYqejP/ZEbZ9vd4Z0jeB/rhjEsh05jH/iS3YePWl3SB2OJnrVLnyx7RjJcaE6TWA7dd3Y7nzyi/H4+njx4IJ0yiqr7Q6pQ9FEr9xezqkyNhwo4NJBcXaHolphUEI4f5gxgE0HT3DhUyt54rOd1NToxVRtwSnDFCvlSp9vPYoxaKL3AFcMSyAqxI8XV2bwwsp9hAb4cNekXjrctItpjV65vQ82ZtE/PozkuDC7Q1FOMKFPDG/eMpppg+L465Jd3PDqBvKLyu0Oy6NpoldubW9OEZuzCvnJ8AS7Q1FOJCI8N2cYj/5oAGsz8pn10lqdWNyFNNErt/blzmMAXDYkvpEtVXvj4+3Fjef34PWbRnMwv4S5H+q00q6iiV65tdV78ujbOYT48EC7Q1EuMq5XFDedn8RnW49y+ESp3eF4JE30ym2VVVazLrOACdp33uNdf14SAI8v3kG19sRxOk30ym19d/AEFVU1nN87yu5QlIslRATywCX9WJyezV8+32l3OB5HE71yW2mHTgAwrJsOYtYR3DmxF9eOTeSlVRmsy9ApCJ1JE71yW5sPnaB7VBCdgv3sDkW1kYemDyAm1J9/rdxndygeRRO9clubs06Q0i3C7jBUGwr08+ZnYxL5alcumXnFdofjMTTRK7d05EQp2YVlDO0aYXcoqo1dMyYRX2/hjTX77Q7FY2iiV25pxS7HaJUTdBLwDic2NIDLBsfzXmoWReVVdofjEZyS6EXkFRHJEZGtdZZFishSEdlj/dczaqrJVuzMoWunQHrHhtgdirLBjef3oKi8innrDtodikdwVo3+NeDSs5b9DlhujOkDLLfuK9Wo8qpqvtmbz5TkWB3sqoNK6RbB+b2jeHGVzkzlDE5J9MaYVUDBWYtnAq9bt18HrnBGWcrzbckqpLSymvN7a7NNR/b/LurL8ZIKbnk9larqGrvDaddc2Ubf2RiTbd0+CnR2YVnKg9T2oR6dFGlzJMpOI7pH8viVg1ifWcCyHcfsDqdda5OTscYYA9R7XbOI3C4iqSKSmpub2xbhKDe3LrOA5LhQ7T+vuGpENxIiAnnt2/12h9KuuTLRHxOReADrf059GxljXjLGjDTGjIyJ0TFNOrrK6ho2HjjO6B5am1fg7SX8bGwiazMK2K/96lvMlYl+EXCDdfsG4CMXlqU8xNbDhZRUVDOmh45voxyuHJaACHyUdsTuUNotZ3WvnAesAfqJSJaI3AI8AVwkInuAqdZ9pc5pfabjnP6oHtobVznEhwcyOimSjzYfxtEKrJrLKXPGGmPmNLDqQmccX3Uc6zIL6BkTTGxogN2hKDdyxbAEfr8gna2HTzK4a7jd4bQ7emWschvVNYYN+wsYo+3z6izTBsXh6y18mHbY7lDaJU30ym3syD7JqbIqbZ9XPxAR5MfEvjF8vvWo3aG0S5roldtYZ7XPa48bVZ/zekVz+EQpR3S6wWbTRK/cxrqMfLpFBtIlQueHVT80yrqALvXAcZsjaX800Su3UFJRxeo9eTo/rGpQ//hQgvy82bj/7NFWVGM00Su3sHxHDqWV1fxoSBe7Q1FuysfbixHdO7E2QxN9c2miV25hwaYsOof5a/u8Oqfze0ez69gpjhaW2R1Ku6KJXtlub84pVuzKZc7oRLy9dFhi1bALrKa9VXt0XKzm0ESvbPfKN/vx9/Hi+nFJdoei3Fz/+FBiQv1ZuUsTfXNoole2Kqus5uPNR7hsSDyROlqlaoSIcPGAzizfeYxinWawyTTRK1st23GMU2VV/GR4V7tDUe3E5UO7UFZZo2PUN4MmemWrt9ceJCEikLE99WpY1TSjkiKJDw9gkY5m2WSa6JVtdmSfZE1GPteN664nYVWTeXkJM4bEs3J3LseLK+wOp13QRK9s8+LKfQT6ejN7VDe7Q1HtzOVDE6iqMXySnt34xkoTvbJHRm4RizYf4bpx3YkI0pOwqnkGJYQxtGs4zy3fQ5GelG2UJnpli7fXHcTbS7htQk+7Q1HtkIjw6OUDyTlVzm/e30x1jU5Ici6a6FWbq6yu4aO0w1yY3JmYUH+7w1Ht1LDETsy9rD+fph/lzTX77Q7HrWmiV21u1e5c8ooq+MkI7VKpWufWCT0ZnRTJS6syqKiqsTsct6WJXrW5DzZlERXsx6R+OlKlar2fT+rFkcIyPtuqJ2YbooletakTJRUs257DzJQEfL317adab2LfGLp2CuTd1EN2h+K29JOm2tTHm49QUV3DT0Yk2B2K8hBeXsLVI7rxzd589uYU2R2OW9JEr9rU+5sOkxwXysAu4XaHojzINWMSCQ3w4b73NvPv1RlsyTrBqbJKPk3P5oH3NvPJlo59Fa2P3QGojmP3sVNsPnSCuZf1tzsU5WFiQv15aHp/frcgnc2HTgAQ7OdNcUU1wX7evLcxi4MFJdw1qbe9gdpEE71qM2+tPYCfjxc/1gHMlAvMHp3IjKFdOFVWSdrBE3ywKQsQnpmdwoML03ny811EB/vz0w54JbYmetUmDhWUsGDTYWbocMTKhUL8fQjx9yF+cCDTBsefXv5/Vw+loLiC3y9Mx8tLuKqDde3VNnrlcsXlVdz02ga8BO6Z3DF/Oit7+Xp78a9rRzAqqRP3v7eZpds71hDHmuiVyz2yaBsZuUW8cO0IesaE2B2O6qBC/H1465YxdAkP4I0OdiWtyxO9iOwXkXQRSRORVFeXp9zL1sOFvL8xizsm9uK83tF2h6M6OB9vL2aNSmT1njwO5BfbHU6baasa/WRjTIoxZmQblafcxDPL9xAW4MPPJ/WyOxSlAJg1qhveXsL8DR3nAittulEucyC/mGU7jnHDeUmEBfjaHY5SAMSFBzAlOZb3Ug91mPFx2iLRG+ALEdkoIrefvVJEbheRVBFJzc3Vmd09yVtrD+AtwrVju9sdilJnmD2qG3lFFXy7L8/uUNpEWyT68caY4cA04G4RuaDuSmPMS8aYkcaYkTExOsiVJ/lyZw7j+0TTOSzA7lCUOsP5vaMJ9vPmiw7S+8blid4Yc9j6nwMsBEa7ukxlv5NllezLLWZEYie7Q1HqBwJ8vZmUHMvS7ceo6QCTlrg00YtIsIiE1t4GLga2urJM5R7SswoBSEmMsDcQpRpw8YDO5J4q5ztryARP5uoafWfgaxHZDKwHFhtjPndxmcoNpFkfniEJEbbGoVRDJifH4ustfLH9qN2huJxLE70xJsMYM9T6G2iMecyV5Sn3sWF/AT2jgwkP0t42yj2FBfgytmcUS7Ye9fjmG+1eqZzuVFkl3+7NZ3JyrN2hKHVOPxnelf35Jby/McvuUFxKE71yupW7c6moruGSgXF2h6LUOc1M6cLopEj+97MdFJdX2R2Oy2iiV073WfpRokP8GNFde9wo9yYi/HZaMsdLKpm3/qDd4biMJnrlVKfKKlm24xiXDY7H20vsDkepRo3o3olxPaN44rOd3P3fTSzbfgxjPKvNXsejV061ZNsxyqtqmDlM54RV7cfz1wzj2eV7WJx+lMVbskmICGT2qG4MTAjD28uL8b2j23XFRRO9cqqP0g6TGBnEsG4RdoeiVJNFhfjzx5mDeHjGAD5MO8LHm4/w1NLdp9ffNqEHD102wMYIW0cTvXKanFNlfLM3j7sn90ak/dZ+VMfl4+3FVSO6ctWIruw6eoqSiireTT3Ey6szmdwvtt0Ota1t9MppPtmcTY1x9GRQqr3rFxfKsMROPPKjgXTtFMifPtlOVXX7HO1SE71ymo/SDjOwSxi9Y0PtDkUppwnw9eah6f3ZefQU8+qMYX+ooISck2U2RtZ0muiVU2TkFrE5q5ArUvQkrPI8lw6KY2zPSJ7+YhcnSipYuv0YF/1tJT954dt20f9eE71qtZoawx8+2kagrzeXa7ON8kAiwh9mDKSwtJJfzk/j7v9uolunILKOl/LHj7e5fXdMTfSqVYrKq/jF/O/4em8ef/jRAB17XnmsAV3CuHZsd1btzqV7ZBDv3DGOuyf15t3ULN5e594XW4k7fRONHDnSpKbq/OHtxeETpcx+aQ1Zx0v57aXJ3HFBT+1tozxadY0hv7icqGB/vL2EmhrDDa+u57uDJ/jyvonE2lTREZGN55qTW2v0qkWqawy3vp7KieJK3rtjHHdO7KVJXnk8by8hNjTg9MVTXl7Cn2YOoryqmhdXZdgcXcM00asWWb7jGDuyT/LnKwcxMinS7nCUsk2P6GAuHhDHgk1ZlFdV2x1OvTTRqxZ5c+0B4sICuGxwvN2hKGW7WaO6cbykki935NgdSr000atmy8gtYvWePK4Zk4iPt76FlDq/dzQRQb4s00SvPMXb6w7i6y3MHt3N7lCUcgveXsKEPjGs3J3rlrNVaaJXzVJYWsm7Gw5x6aB4YkO1K6VStSb1jSGvqJzt2SftDuUHNNGrZnntm/2cKq/i5xN72R2KUm5lUr8YvL2ET9Oz7Q7lBzTRqyY7VVbJK99kMrV/ZwZ0CbM7HKXcSlSIP+f3jubjLUfc7kpZTfSqyd5ae9BxCfiFve0ORSm3dPnQLhwqKOW7QyfsDuUMmuhVk1TXGN5cs5/xvaMZ0jXC7nCUcksXD+yMn48Xi9KO2B3KGTTRqyb5Zm8eRwrLtKeNUucQFuDL5H4xfLIlm2o36n2jM0ypBmXmFbNgUxa+3l68s+EQkcF+XDSgs91hKeXWZqYksGTbMb7em8fEvjF2hwNoolcN2JF9kqtfWEORNdZ2z5hg/vGz4fj7eNscmVLubUpyLOGBvnywMavjJHoRuRR4BvAG/m2MecLVZarWe/zTHfh4C9/8bgpRwX4E+GqCV6opAny9mZnShXc2HKKovIoQf/vr0y5toxcRb+AfwDRgADBHRNrvVOodxIqdOazek8ddk3qREBGoSV6pZrp0YBzlVTWsy8i3OxTA9SdjRwN7jTEZxpgKYD4w08VlqlYoqajiwYXp9IkN4YbzkuwOR6l2aURSJwJ8vVi9J8/uUADXJ/oE4FCd+1nWMuWmPko7QnZhGX+aOUjb45VqIX8fb0b3iGL1nly7QwHcoHuliNwuIqkikpqb6x5PSkdljOGttQdIjgtlbE8dY16p1pjQO5p9ucVkF5baHYrLE/1hoG7H667WstOMMS8ZY0YaY0bGxLjHGeqOKu3QCbYdOcm1Y7vrbFFKtdL4PtEArN6Tx6rdubz+7X4KSypticXVp4M3AH1EpAeOBD8buMbFZaoWenPtAYL9vLlimLauKdVayXGhRIf48+LKfRw5UUZpZTVf7szh9ZtHt3ksLq3RG2OqgHuAJcAO4F1jzDZXlqlapqi8isVbsrliWIJbdAdTqr0TES7o62i+8fEW5ozuxqo9uRw+0fZNOS7/RBtjPgU+dXU5qnVW7MyhvKqGmSlam1fKWR6a3p8pybEMTgjHS4R56w/xwcYsfnlhnzaNw/aTsco9fLY1m+gQf0Z072R3KEp5jKgQf2YM6UL3qGC6RQZxXq8o3tt4qM1nodJEryitqGbFzlwuGdgZby89CauUq1w9siuHCkp5YdU+sgtLMcaw+dAJ5q0/yPsbs/j7st0s/C7L6eVqY6xi5e5cSiurmTYo3u5QlPJolw6M57GQnTz5+S7+umQX3SOD2J9fcsY2SVFBXDmsq1PL1USv+HxrNhFBvozRvvNKuVSgnzerfzOZAwXFfJZ+lLUZ+dw8vgcT+sRQVllNz5hgl3TB1ETfwZVWVLN0+zFmDOmCr7e25CnlaoF+3iTHhZEcV/90nLFhzr8iXT/ZHdwX249SXFGtfeeV8mCa6DswYwzz1x+iS3gAY3pos41SnkoTfQe2cncuazLyuWVCT7y0t41SHkvb6DugNfvyeWfDQZbvzKFHdDDXje1ud0hKKRfSRN/BpGcVcvNrG/DxEvp0DuHZOcPw89Efdkp5Mk30HUhxeRX3zNtEpyBfPrznfGJDA+wOSSnVBjTRdyD/XXeQA/klzL99rCZ5pToQ/c3eQRhjmL/hIMMTIxjbM8rucJRSbUgTfQfxzd589uUWM2tUt8Y3Vkp5FE307VRldQ0b9hdQWlHd6LaFpZX86ZNtdIsM1GGIleqAtI2+HSoormDWi2vYk1NE5zB/3rvjPBKjgurd9tjJMi5//mtyT5XznxtGEeCrE34r1dFojb4denTRNvbnF/OnmQMpKqvi0Y+3YcwPx7cuKq/i9wvSOVFSyYK7zmdycqwN0Sql7KY1+nYmM6+YRZuP8Mspvbl+XBIVVTX8efEObnsjlUsHxRMfHkBJRTWbDh5n/vqDHC+p5OEZA0jpFmF36Eopm2iir6O4vIpgN58v9dP0bABmj04E4Obze1BVY/jHl3tZtiPn9HZeAhf0jeHeqX0ZqkleqQ7NvbOaC9Q2cYgI6VmFlFVVM6xbBC+vzuSpL3Zx16ReRIX4c/247oi43/gvn289Skq3CLpEBALg5SXcObEXt47vwcGCEo6dLMfPR+gfH0aQX4d7eZVS9ehQmWBfbhG/+O93dO0UyP+7uC8z//E1NQYSI4PIOl5CgK83z365F4CYUH+mD3avGZfyispJP1zIA5f0+8E6H28vesaE0DMmxIbIlFLuzOMT/cYDBTy7fC/RIf6s3pNLcXkV27NP8sX2Y4T4+zD3sv68vzGLKcmx3D25N3tyTvH4pzt4/NMdTBsU51a1+vWZBQCM66UXPCmlms6jE/3Jskru+e93VFbXUFxejQgsuOs8lmw9xlvrDnDr+B7MHp14ur0bHDX5q0d045FF2zhSWEaC1UTiDtZl5BPk583ghHC7Q1FKtSMenejnrTtIdmEZC+86j4SIQEoqqkmKDiY5LoxfTe3T4H7DEzsBsOnAcfdK9JkFjOjeSaf8U0o1i0dnjC+2H2NglzCGJXYiNiyApOjgJu2XHB9KgK8Xmw4ed3GETXe8uIKdR0/pTFBKqWbz2ESfe6qcTQePc9GAzs3e19fbiyFdI9h08ITzA2uh9fsd7fNjdEAypVQzuSzRi8ijInJYRNKsv+muKqs+b6zZjzFw6aC4Fu0/PLET248UUlbZ+FgybWFdRgH+Pl4M6art80qp5nF1jf5vxpgU6+9TF5cFQHlVNU8v3c2LKzO4IqULyXFhLTrO8MQIKqsNWw8XOjnCllmXmc+I7p3w99GxapRSzeNRTTdF5VXc89/veHb5Hs7vHcXcGQNafKzh3R0nZDcesL+dvrCkku3ZJxnTQ5ttlFLN5+pEf4+IbBGRV0SkkysKqL3S9WhhGZc9u5plO47x6I8G8OpNo4kO8W/xcaND/EmMDGJxejaFpZXOCrdFNuwvwBgY01NPxCqlmq9ViV5ElonI1nr+ZgL/AnoBKUA28FQDx7hdRFJFJDU3N7fZMTzw/hZufT2Vq174lvyiCubfNpYbz+/Rikf1vV9P7cP2Iye55uW1nCyzL9mvy8zHz8dLByZTSrVIq/rRG2OmNmU7EXkZ+KSBY7wEvAQwcuTIH46124j48AC+2ZtHVIgff5+Vwsgk59V6fzy8K52C/LjtjVQeXJDO89cMd9qxmyozr5h3NhxibM8oHUteKdUiLrtgSkTijTHZ1t0rga2uKOe+i/tx38U/HPvFWSZbQyM8s3wP148rYHQb92P/08fbEBEeu2JQm5arlPIcrmyjf1JE0kVkCzAZuNeFZbnUnRN70SnIl7fWHmjTcrMLS1m5O5frx3WnW2T9M0gppVRjXFajN8Zc56pjt7VAP28uHhDH4vRsyquq26yL44JNh6kxcNWIrm1SnlLKM3lU90pXunRwHEXlVXy9J69NyjPG8G7qIcb2jKR7VNOGblBKqfpoom+i83pFEejrzVe7mt8zqCXWZxZwIL+En47s1iblKaU8lyb6JvL38WZsz0i+3uv6Gr0xhqeW7iYiyJdpg9xr8hOlVPujib4ZJvSJITOvmEMFJS45/sYDxzl8opS/LdvD+swCfndpMoF+2qVSKdU6Hj0evbNN6BMNwNd785hTZ7ISZ0jPKuQn//r29P0fD0vQZhullFNoom+G3rEhxIUFsHpPrtMT/ao9jrb/X13Yh8EJ4VzYP9atpjFUSrVfmuibQUSY0CeaL7Yfo7rG4O3lvET89Z48+seHce9FfZ12TKWUAm2jb7bxfaIpLK3kOyfOPlVSUcXGg8c5Xyf9Vkq5gNbom+nC/p0J9ffhjTUHmj2uzvrMAv677gBhgb786sI+fLz5CP3iwjh6spSKqhqmtmA2LKWUaowm+mYK8fdh1qhuvPrtfuZe1p/YsIBG96msruHJz3fy768z6RTkx4mSCt5Yc+D08XrGBJMYGaTzwSqlXEKbblrgyuEJVNcYvt2X36TtP9iYxcurM5kzOpHVv5nMvNvGcv/FfXngkn4UlVexJauQa8cm6slXpZRLaI2+BZLjwggL8GFdZj5XDEtodPuvduWSEBHIY1cMQkQY0zPq9CTfYQE+RAT5cdlgvTBKKeUamuhbwNtLGN0jknUZBY1u66j55zFtUHy9NfbrxiW5IEKllPqeNt200OgekWTkFZNzsuyc223JOsHJsirOty62UkqptqaJvoVqJ+pel3nuWv031tg42nVSKWUXTfQtNLBLGCH+jnb6c1m9J48B8WFEtWKicqWUag1to28hH28vRnTvVG87vTGGD9MOExXsz6aDx7nZSZOVK6VUS2iib4WxPaP4y+c7yTlZdkZ/+hW7crj3nc2A48TtNO1Ro5SykTbdtMLEvjEAfLX7+8lIjDH876c7iQn152+zhvLlfRNJ6RZhU4RKKaWJvlX6x4fSOcyflXVmnUo7dII9OUU8cEk/rhzWVacBVErZThN9K4gIk/vFsnJ3LqfKKgH4eHM2ft5eXDoozubolFLKQRN9K80ZnUhReRXz1h/kVFklH6UdZlK/GMICfO0OTSmlAD0Z22pDu0VwXq8onvtyL+szj5NfXME9U3rbHZZSSp2mNXon+OvVQ4kO8Wf5zmPcNqEHQ7pG2B2SUkqdpjV6J0iICGTpvRdQXFFNeKA22Sil3Ismeifx8fYiPFB/ICml3E+rMpOIXC0i20SkRkRGnrXu9yKyV0R2icglrQtTKaVUS7W2Rr8V+DHwYt2FIjIAmA0MBLoAy0SkrzGmupXlKaWUaqZW1eiNMTuMMbvqWTUTmG+MKTfGZAJ7gdGtKUsppVTLuKpROQE4VOd+lrVMKaVUG2u06UZElgH1Xeb5kDHmo9YGICK3A7cDJCYmtvZwSimlztJoojfGTG3BcQ8D3erc72otq+/4LwEvAYwcOdK0oCyllFLn4Kqmm0XAbBHxF5EeQB9gvYvKUkopdQ5iTMsr0SJyJfAcEAOcANKMMZdY6x4CbgaqgF8bYz5rwvFOAbuAcKCwnk0aWt7Qumigspn7tKSc2uW+QJ4Tymms/PrKaWnMTdknuk55zn7O6lt+rtfNFY+zOc+nM8pv6+fz7DJbe6ym7HN2ea5+nO31s97UfRp7z/QzxoQ2cHzH+Onu8gekWv9famB9vcsbWgekNneflpRTu7w2/taW01j59ZXjysdZtzxnP2cNvQ/aopyWPJ/OKL+tn8+zy2yLx3l2ea5+nO31s96S57Ohx9/Q8Y0xbjvWzcfNXO5p+9hdfkv2sbv8ttrH7vJbuo/d5etr47x9mv06t6rpxtlEJNUYM7LxLe05nruU56mPS8vznDK1vLYtr7H17lajf8nNj+cu5Xnq49LyPKdMLa9tyzvnereq0SullHI+d6vRK6WUcrJ2n+hFpKgNy6oWkbQ6f0nn2Pars0f0bGIZRkTeqnPfR0RyReSTFobdnLKvsMpPdmEZdj6+NnuvNLXMlr5P6jmOy1+7s8p7yBq5dov1WRjTBmV2FZGPRGSPiOwTkWdExO8c2/9aRIJaUI4Rkafq3L9fRB5tYdhNKa82r2wTkc0icp+IODU3t/tE38ZKjTEpdf72u6CMYmCQiARa9y+igauKGyIiLR2VdA7wtfW/OeV5N2PzVj8+Va8WvXYtISLjgBnAcGPMEGAqZ45t5YoyBVgAfGiM6QP0BUKAx86x26+BZid6oBz4sYhEt2DflqjNKwNxfB6mAY84swCPSPQiEiIiy0Vkk4iki8hMa3mSiOwQkZetb8sv6iQYZ5U9QkRWishGEVkiIvF1Vl9nfVNvFZHmjN75KXCZdXsOMK9OeaNFZI2IfCci34pIP2v5jSKySES+BJa34HGEAOOBW3AMMY2ITBKRVSKy2JpX4IXamoaIFInIUyKyGRjXzOJa8vhWiUhKne2+FpGhLXick+r+ehCR50XkRuv2fhH5Y533kVNqx+cq00nHb+i1a+hxTheRndZ79tkW/JqKB/KMMeUAxpg8Y8yRhj4L1q+WZ1r4Wag1BSgzxrxqlVkN3AvcLCLBIvJ/1rG3iMgvROSXOIZIXyEiK5pZVhWOk5v3nr3CyilfWuUsF5FEEQkXkQN1PhvBInJIRJo93ZwxJgfH2F/3iIO3iPxVRDZYZd5RJ5bfWu/TzSLyxLmO6xGJHigDrjTGDAcmA09ZNQBwDL/wD+vb8gTwk1aUEyjfN9sstF7I54CrjDEjgFc4s4YRZIxJAe6y1jXVfBxDSAQAQ4B1ddbtBCYYY4YBfwAer7NuuBXLxOY+MBxDS39ujNkN5IvICGv5aOAXwACgF475BwCCgXXGmKHGmK+bWVZLHt9/gBsBRKQvEGCM2dzMcpsiz3of/Qu43wXHd4WGXrsfsJ7zF4Fp1ns2pgXlfQF0E5HdIvJPEZnows9CrYHAxroLjDEngYPArUASkGL9wnjbGPMscASYbIyZ3ILy/gH8TETCz1r+HPB6bTnAs8aYQiANqP3czQCWGGMqW1AuxpgMwBuIxfHlXWiMGQWMAm4TkR4iMg3H6z7GGDMUePJcx/SUqQQFeFxELgBqcAyJ3Nlal2mMSbNub8TxhmipUuvN6ihUZBAwCFhqfa94A9l1tp8HYIxZJSJhIhJhjDnRWCHGmC3iaP+fg6P2W1c48LqI9AEMjkuxay01xhQ090FZ5gDPWLfnW/c/AdZbbzxEZB6OmuP7QDXwQUsKauHjew94WEQewDG0xmstKbsJFlj/N/L9l5q7a+i1q08ykGEc80SA4z16e3MKM8YUWV8mE3BUrN4B/owLPgtNNAn4pzGmyiqjpZ+B04wxJ0XkDeCXQGmdVeP4/n3xJt8n2HeAWcAKHL+q/tnaGCwXA0NE5CrrfjiOyutU4FVjTIkV7zkfs6ck+p/hqJmMMMZUish+IMBaV15nu2rAmU03AmwzxjTUdHF239Xm9GVdBPwfjjdxVJ3l/wOsMMZcaSXLr+qsK27G8U8TkUgcP40Hi4jB8SE1wOJ6Yq69X2ZaN2NYsx6fMaZERJbiqMX8FGiw1tqIKs78JRtw1vra90s1zvt8NFZmi53jtfvIVWXC6aaTr4CvRCQduBvXfRYAtgNX1V0gImFAIrC/mcdqqr8Dm4BXm7DtIhyVzUgc780vW1qoiPTE8f7LwZFjfmGMWXLWNs2antVTmm7CgRwryU8GurdRubuAGHGcnEJEfEVkYJ31s6zl43H8/GpoUKP6vAL80RiTftbycL4/eXlji6L+oauAN40x3Y0xScaYbkAmjhrbaOunoheOx9PcZpqGtOTx/Rt4FthgjDnewnIPAAPEMbJqBHBhC4/jLmU29Np5NVDmLqCnfN9jbFZzCxSRftYvrlopwA5c91kAx3mnIBG53jqON/AUjl92S4A7xOqEYCVbgFNAwwN9NcKqJb+Lo/mk1rdY50FwVDBXW9sWARtw/LL6pKWVIBGJAV4AnjeOi5yWAD+vbe8Xkb4iEgwsBW4Sq1dRncdcr3Zdo7de2HIcbWUfWzWLVBztvC5njKmwflI9a7Xl+eCoBWyzNikTke9wND/c3MxjZ+FIamd7EkfTxlwcNW5nmAP85axlHwA/x/HmfR7ojeNn6UJnFNiSx2eM2SgiJ2laDesMte8VY8whEXkXx3zHmcB3zQ7evcps6LWbjSNJnVGmMaZURO4CPheRYhyvb3OFAM9ZXyBVOKYKvR3HCUynfxasuI04Rsv9p4g8jOOL7FPgQRy1377AFhGpBF7G8Z59yXqcR1rYTg+OL5N76tz/BfCq1YSYC9xUZ907OJoYJzWzjEARScPx3FThaBJ62lr3bxzNzZus8465wBXGmM/F0TkhVUQq+P65qFe7vjJWHL0uXjbG6Hy0LiAik4D7jTEzbA4FABHpgqO5INkYU9PMfdv8veKu708RCbHa2QXHScc9xpi/ubC8r3C8j1JdVYY6t3bbdCMid+I4wTPX7liU61k/2dfhmMKyuUm+zd8rbv7+vM2qQW7D0VT2or3hKFdr1zV6pZRSjWu3NXqllFJN064SvYi8IiI5IrK1zrKh4riSMl1EPra6XNXdJ1EcV3HeX2fZr8RxFd02Efl1Gz4EpZRqc+0q0ePoSnXpWcv+DfzOGDMYR4+QB85a/zRwer5a6yKn23Bc8TkUmCEivV0VsFJK2a1dJXpjzCrg7CvA+gKrrNtLqTPEgYhcgaNr2bY62/fHcel+iXUl3UrazxWQSinVbO0q0TdgG46rJQGuBrrB6YGefgv88azttwITRCTKuthgeu0+SinliTwh0d8M3CUiG3FcBVdhLX8U+Jt1xdppxpgdOC4w+QL4HMdgRK25lF8ppdxau+teaV26/YkxZlA96/oCbxljRovIar6vqUfgGOzsD8aY58/a53EgyxjjrEGIlFLKrbTrIRAARCTWGJNjjcUyF8c4ERhjJtTZ5lGgqDbJ19knEUf7/Ni2j1wppdpGu0r04hgmdxIQLSJZOGZhCRGRu61NFtC0cVA+EJEooBK424nDpSqllNtpd003SimlmscTTsYqpZQ6B030Sinl4TTRK6WUh9NEr5RSHk4TvVJKeThN9Eop5eE00SuPICLVIpJmDT29WUTusy6iq7vNhyKy1rp9ibV9mjWM9S7r9hsiMklECuusTxORqecou6uIfCQie0Rkn4g8IyJ+1rq6x9oiIstEJNZa109EvrLW7RCRl1z5HKmOSxO98hSlxpgUY8xA4CJgGo4L6gCwJrIeAYSLSE9jzBJr+xQcE8r/zLp/vbXL6tr11t+y+gq15l1dAHxojOmDYzTVEOCxOpvVHmsIjsm4ay/wexbHeEwpxpj+wHNOeSaUOosmeuVxjDE5wO3APVYiBsdQFx8D84HZTixuClBmjHnVKrsauBe42Rod9TQrllDguLUoHsiqE3e6E+NS6jRN9MojGWMyAG8g1lo0B8dk3fOs242ZcFbTTa8GthsIbDyr7JPAQaB2QpsJ1mTcB4GpwCvW8r8BX4rIZyJyr/WrQymn00SvPJ6IdAb6AF8bY3YDldZMY+dydtPNvlaEUHusbjjGYnoSwPoV0B94D8cYTmtFxL8V5ShVL030yiOJSE8c8wzkAD8FOgGZIrIfSKJptfqm2I6j7b9u2WFAIrC3nu0XARfU3jHGHDHGvGKMmQlUAY19ASnVbJrolccRkRgcw1U/bxyj9s0BLjXGJBljknAkZme10y8HgkTkeqtsb+Ap4DVjTEk9248H9lnbXioivtbtOCAKOOykuJQ6rV0NU6zUOQRa7eC+OGrGbwJPWxPVdAfW1m5ojMm0ujyOMcasa+B4te3qtf5sjHn/7I2MMUZErgT+KSIP46g8fQo8WM+xBCgEbrWWXww8IyJl1v0HjDFHm/GYlWoSHaZYKaU8nDbdKKWUh9OmG6WawJqRbHk9qy40xuS3dTxKNYc23SillIfTphullPJwmuiVUsrDaaJXSikPp4leKaU8nCZ6pZTycP8fdz+NTGdJvxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# df.plot(y='TMPMN', ax=ax)\n",
    "\n",
    "df.iloc[:365].rolling(30, center=True).mean().plot(y='TMPMN', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "united-manhattan",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-fb7baf641372>:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df[\"1948\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948-12-11</th>\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>1948-12-12</th>\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>1948-12-13</th>\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>1948-12-14</th>\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>1948-12-15</th>\n",
       "      <td>-3.6</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-6.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-16</th>\n",
       "      <td>-2.6</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-4.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-17</th>\n",
       "      <td>-3.7</td>\n",
       "      <td>-14.4</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-18</th>\n",
       "      <td>-6.7</td>\n",
       "      <td>-17.5</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-19</th>\n",
       "      <td>-10.4</td>\n",
       "      <td>-17.3</td>\n",
       "      <td>-13.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-20</th>\n",
       "      <td>-6.7</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-21</th>\n",
       "      <td>-4.2</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-22</th>\n",
       "      <td>-3.1</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-23</th>\n",
       "      <td>-6.0</td>\n",
       "      <td>-9.6</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-24</th>\n",
       "      <td>-6.1</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-25</th>\n",
       "      <td>-8.6</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-26</th>\n",
       "      <td>-6.0</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-27</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-28</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-29</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-30</th>\n",
       "      <td>-3.3</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-31</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                 \n",
       "1948-12-11     2.4    -4.5   -1.1     0.0\n",
       "1948-12-12     1.9    -8.7   -6.2     0.5\n",
       "1948-12-13    -7.4   -12.3  -10.1     0.0\n",
       "1948-12-14    -5.6   -13.2   -9.4     0.0\n",
       "1948-12-15    -3.6    -9.2   -6.6     0.0\n",
       "1948-12-16    -2.6    -6.7   -4.3     0.0\n",
       "1948-12-17    -3.7   -14.4   -9.0     0.2\n",
       "1948-12-18    -6.7   -17.5  -15.9     0.0\n",
       "1948-12-19   -10.4   -17.3  -13.5     0.0\n",
       "1948-12-20    -6.7   -10.9   -8.8     0.0\n",
       "1948-12-21    -4.2    -7.8   -6.3     1.1\n",
       "1948-12-22    -3.1    -7.2   -4.8     0.0\n",
       "1948-12-23    -6.0    -9.6   -7.2     0.0\n",
       "1948-12-24    -6.1    -9.7   -8.3     0.0\n",
       "1948-12-25    -8.6   -12.5  -11.0     0.0\n",
       "1948-12-26    -6.0   -13.2   -9.5     0.0\n",
       "1948-12-27    -0.5    -6.3   -2.8     0.0\n",
       "1948-12-28    -0.2    -3.5   -1.2     0.6\n",
       "1948-12-29    -0.3    -4.9   -2.5     0.0\n",
       "1948-12-30    -3.3    -6.5   -5.2     0.0\n",
       "1948-12-31    -1.2    -5.3   -2.3     0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"1948\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "embedded-passenger",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-8bad1fa4609c>:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df[\"1950-01\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>-16.4</td>\n",
       "      <td>-22.7</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-02</th>\n",
       "      <td>-15.7</td>\n",
       "      <td>-23.1</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-03</th>\n",
       "      <td>-19.1</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>-24.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-04</th>\n",
       "      <td>-24.4</td>\n",
       "      <td>-31.8</td>\n",
       "      <td>-29.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-05</th>\n",
       "      <td>-27.5</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>-30.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-06</th>\n",
       "      <td>-23.7</td>\n",
       "      <td>-29.6</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-07</th>\n",
       "      <td>-23.7</td>\n",
       "      <td>-25.7</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-08</th>\n",
       "      <td>-24.7</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>-28.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-09</th>\n",
       "      <td>-27.6</td>\n",
       "      <td>-32.7</td>\n",
       "      <td>-29.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-10</th>\n",
       "      <td>-28.4</td>\n",
       "      <td>-36.8</td>\n",
       "      <td>-32.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-11</th>\n",
       "      <td>-17.2</td>\n",
       "      <td>-31.6</td>\n",
       "      <td>-23.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-12</th>\n",
       "      <td>-15.7</td>\n",
       "      <td>-22.5</td>\n",
       "      <td>-17.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-13</th>\n",
       "      <td>-12.5</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-14</th>\n",
       "      <td>-9.1</td>\n",
       "      <td>-13.6</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-15</th>\n",
       "      <td>-4.1</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-16</th>\n",
       "      <td>-6.6</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-17</th>\n",
       "      <td>-15.1</td>\n",
       "      <td>-26.4</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-18</th>\n",
       "      <td>-15.5</td>\n",
       "      <td>-28.9</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-19</th>\n",
       "      <td>-18.4</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-20</th>\n",
       "      <td>-10.6</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-21</th>\n",
       "      <td>-13.2</td>\n",
       "      <td>-19.9</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-22</th>\n",
       "      <td>-14.7</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>-18.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-23</th>\n",
       "      <td>-5.8</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-24</th>\n",
       "      <td>-3.7</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-25</th>\n",
       "      <td>-2.3</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-26</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-27</th>\n",
       "      <td>-4.8</td>\n",
       "      <td>-8.4</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-28</th>\n",
       "      <td>-7.0</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-29</th>\n",
       "      <td>-12.7</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-30</th>\n",
       "      <td>-12.7</td>\n",
       "      <td>-18.5</td>\n",
       "      <td>-15.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-31</th>\n",
       "      <td>-9.9</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>-14.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                 \n",
       "1950-01-01   -16.4   -22.7  -19.0     0.1\n",
       "1950-01-02   -15.7   -23.1  -19.1     0.0\n",
       "1950-01-03   -19.1   -29.0  -24.8     0.2\n",
       "1950-01-04   -24.4   -31.8  -29.1     0.1\n",
       "1950-01-05   -27.5   -33.0  -30.3     0.0\n",
       "1950-01-06   -23.7   -29.6  -26.8     0.0\n",
       "1950-01-07   -23.7   -25.7  -24.5     0.0\n",
       "1950-01-08   -24.7   -31.4  -28.9     0.0\n",
       "1950-01-09   -27.6   -32.7  -29.8     0.5\n",
       "1950-01-10   -28.4   -36.8  -32.8     0.2\n",
       "1950-01-11   -17.2   -31.6  -23.4     0.3\n",
       "1950-01-12   -15.7   -22.5  -17.7     0.0\n",
       "1950-01-13   -12.5   -24.4  -18.4     0.0\n",
       "1950-01-14    -9.1   -13.6  -11.6     1.8\n",
       "1950-01-15    -4.1    -9.5   -7.1     1.7\n",
       "1950-01-16    -6.6   -15.6  -13.1     0.2\n",
       "1950-01-17   -15.1   -26.4  -22.6     0.4\n",
       "1950-01-18   -15.5   -28.9  -20.8     0.0\n",
       "1950-01-19   -18.4   -21.4  -19.6     0.0\n",
       "1950-01-20   -10.6   -21.9  -15.9     0.6\n",
       "1950-01-21   -13.2   -19.9  -16.2     0.8\n",
       "1950-01-22   -14.7   -21.4  -18.5     0.1\n",
       "1950-01-23    -5.8   -19.8  -11.9     0.0\n",
       "1950-01-24    -3.7    -6.3   -4.6     0.1\n",
       "1950-01-25    -2.3    -5.7   -3.6     0.1\n",
       "1950-01-26    -2.0    -5.3   -3.5     0.0\n",
       "1950-01-27    -4.8    -8.4   -6.7     0.1\n",
       "1950-01-28    -7.0   -13.1  -10.9     0.0\n",
       "1950-01-29   -12.7   -20.3  -17.4     0.2\n",
       "1950-01-30   -12.7   -18.5  -15.5     0.2\n",
       "1950-01-31    -9.9   -19.8  -14.6     0.4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"1950-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "hollywood-tablet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE_OBS\n",
       "1948-12-11    -1.1\n",
       "1948-12-12    -6.2\n",
       "1948-12-13   -10.1\n",
       "1948-12-14    -9.4\n",
       "1948-12-15    -6.6\n",
       "              ... \n",
       "2006-10-27     5.6\n",
       "2006-10-28     8.2\n",
       "2006-10-29     2.4\n",
       "2006-10-30    -0.7\n",
       "2006-10-31    -3.0\n",
       "Name: TMPMN, Length: 21142, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TMPMN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "hispanic-transformation",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'1950-01-03'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '1950-01-03'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-34b9ee8f3910>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"1950-01-03\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '1950-01-03'"
     ]
    }
   ],
   "source": [
    "df[\"1950-01-03\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "affecting-occasions",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>-16.4</td>\n",
       "      <td>-22.7</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-02</th>\n",
       "      <td>-15.7</td>\n",
       "      <td>-23.1</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-03</th>\n",
       "      <td>-19.1</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>-24.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-04</th>\n",
       "      <td>-24.4</td>\n",
       "      <td>-31.8</td>\n",
       "      <td>-29.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-05</th>\n",
       "      <td>-27.5</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>-30.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-06</th>\n",
       "      <td>-23.7</td>\n",
       "      <td>-29.6</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-07</th>\n",
       "      <td>-23.7</td>\n",
       "      <td>-25.7</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-08</th>\n",
       "      <td>-24.7</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>-28.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-09</th>\n",
       "      <td>-27.6</td>\n",
       "      <td>-32.7</td>\n",
       "      <td>-29.8</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-10</th>\n",
       "      <td>-28.4</td>\n",
       "      <td>-36.8</td>\n",
       "      <td>-32.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-11</th>\n",
       "      <td>-17.2</td>\n",
       "      <td>-31.6</td>\n",
       "      <td>-23.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-12</th>\n",
       "      <td>-15.7</td>\n",
       "      <td>-22.5</td>\n",
       "      <td>-17.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-13</th>\n",
       "      <td>-12.5</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>-18.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-14</th>\n",
       "      <td>-9.1</td>\n",
       "      <td>-13.6</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-15</th>\n",
       "      <td>-4.1</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-16</th>\n",
       "      <td>-6.6</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-17</th>\n",
       "      <td>-15.1</td>\n",
       "      <td>-26.4</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-18</th>\n",
       "      <td>-15.5</td>\n",
       "      <td>-28.9</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-19</th>\n",
       "      <td>-18.4</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-20</th>\n",
       "      <td>-10.6</td>\n",
       "      <td>-21.9</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-21</th>\n",
       "      <td>-13.2</td>\n",
       "      <td>-19.9</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-22</th>\n",
       "      <td>-14.7</td>\n",
       "      <td>-21.4</td>\n",
       "      <td>-18.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-23</th>\n",
       "      <td>-5.8</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-24</th>\n",
       "      <td>-3.7</td>\n",
       "      <td>-6.3</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-25</th>\n",
       "      <td>-2.3</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-26</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-27</th>\n",
       "      <td>-4.8</td>\n",
       "      <td>-8.4</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-28</th>\n",
       "      <td>-7.0</td>\n",
       "      <td>-13.1</td>\n",
       "      <td>-10.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-29</th>\n",
       "      <td>-12.7</td>\n",
       "      <td>-20.3</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-30</th>\n",
       "      <td>-12.7</td>\n",
       "      <td>-18.5</td>\n",
       "      <td>-15.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-31</th>\n",
       "      <td>-9.9</td>\n",
       "      <td>-19.8</td>\n",
       "      <td>-14.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                 \n",
       "1950-01-01   -16.4   -22.7  -19.0     0.1\n",
       "1950-01-02   -15.7   -23.1  -19.1     0.0\n",
       "1950-01-03   -19.1   -29.0  -24.8     0.2\n",
       "1950-01-04   -24.4   -31.8  -29.1     0.1\n",
       "1950-01-05   -27.5   -33.0  -30.3     0.0\n",
       "1950-01-06   -23.7   -29.6  -26.8     0.0\n",
       "1950-01-07   -23.7   -25.7  -24.5     0.0\n",
       "1950-01-08   -24.7   -31.4  -28.9     0.0\n",
       "1950-01-09   -27.6   -32.7  -29.8     0.5\n",
       "1950-01-10   -28.4   -36.8  -32.8     0.2\n",
       "1950-01-11   -17.2   -31.6  -23.4     0.3\n",
       "1950-01-12   -15.7   -22.5  -17.7     0.0\n",
       "1950-01-13   -12.5   -24.4  -18.4     0.0\n",
       "1950-01-14    -9.1   -13.6  -11.6     1.8\n",
       "1950-01-15    -4.1    -9.5   -7.1     1.7\n",
       "1950-01-16    -6.6   -15.6  -13.1     0.2\n",
       "1950-01-17   -15.1   -26.4  -22.6     0.4\n",
       "1950-01-18   -15.5   -28.9  -20.8     0.0\n",
       "1950-01-19   -18.4   -21.4  -19.6     0.0\n",
       "1950-01-20   -10.6   -21.9  -15.9     0.6\n",
       "1950-01-21   -13.2   -19.9  -16.2     0.8\n",
       "1950-01-22   -14.7   -21.4  -18.5     0.1\n",
       "1950-01-23    -5.8   -19.8  -11.9     0.0\n",
       "1950-01-24    -3.7    -6.3   -4.6     0.1\n",
       "1950-01-25    -2.3    -5.7   -3.6     0.1\n",
       "1950-01-26    -2.0    -5.3   -3.5     0.0\n",
       "1950-01-27    -4.8    -8.4   -6.7     0.1\n",
       "1950-01-28    -7.0   -13.1  -10.9     0.0\n",
       "1950-01-29   -12.7   -20.3  -17.4     0.2\n",
       "1950-01-30   -12.7   -18.5  -15.5     0.2\n",
       "1950-01-31    -9.9   -19.8  -14.6     0.4"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"1950-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "behind-aspect",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE_OBS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-02-03</th>\n",
       "      <td>-5.6</td>\n",
       "      <td>-20.6</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-04</th>\n",
       "      <td>-16.9</td>\n",
       "      <td>-30.5</td>\n",
       "      <td>-23.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-05</th>\n",
       "      <td>-11.2</td>\n",
       "      <td>-19.3</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP\n",
       "DATE_OBS                                 \n",
       "1950-02-03    -5.6   -20.6  -16.4     0.1\n",
       "1950-02-04   -16.9   -30.5  -23.7     0.0\n",
       "1950-02-05   -11.2   -19.3  -15.6     0.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"1950-02-03\":\"1950-02-05\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "patent-peter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
       "            ...\n",
       "            10, 10, 10, 10, 10, 10, 10, 10, 10, 10],\n",
       "           dtype='int64', name='DATE_OBS', length=21142)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "specialized-domain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948,\n",
       "            ...\n",
       "            2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006],\n",
       "           dtype='int64', name='DATE_OBS', length=21142)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "small-theorem",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mon'] = df.index.month\n",
    "df['year'] = df.index.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "honey-framing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1948-12-11', '1948-12-12', '1948-12-13', '1948-12-14',\n",
       "               '1948-12-15', '1948-12-16', '1948-12-17', '1948-12-18',\n",
       "               '1948-12-19', '1948-12-20',\n",
       "               ...\n",
       "               '2006-10-22', '2006-10-23', '2006-10-24', '2006-10-25',\n",
       "               '2006-10-26', '2006-10-27', '2006-10-28', '2006-10-29',\n",
       "               '2006-10-30', '2006-10-31'],\n",
       "              dtype='datetime64[ns]', name='DATE_OBS', length=21142, freq=None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "vanilla-brazilian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.month.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "threatened-advantage",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = (df['1949':]\n",
    " .pivot_table(index='year', columns='mon', values='TMPMN')\n",
    " .corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eight-complaint",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\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>mon</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>1</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>2</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>3</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>4</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>5</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>6</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>7</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>8</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>9</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>10</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>11</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>12</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": [
       "mon        1         2         3         4         5         6         7   \\\n",
       "mon                                                                         \n",
       "1    1.000000  0.384755  0.257302  0.216484  0.047153  0.039691  0.206568   \n",
       "2    0.384755  1.000000  0.379095  0.216300  0.017000  0.028284  0.252928   \n",
       "3    0.257302  0.379095  1.000000  0.328143  0.071455  0.177538  0.091281   \n",
       "4    0.216484  0.216300  0.328143  1.000000  0.041331  0.017182  0.159124   \n",
       "5    0.047153  0.017000  0.071455  0.041331  1.000000 -0.121644  0.028159   \n",
       "6    0.039691  0.028284  0.177538  0.017182 -0.121644  1.000000  0.306204   \n",
       "7    0.206568  0.252928  0.091281  0.159124  0.028159  0.306204  1.000000   \n",
       "8   -0.120758 -0.097432  0.022202 -0.117359  0.118944  0.093280  0.411425   \n",
       "9    0.134955  0.033740 -0.081379  0.028134  0.038196 -0.060803  0.131770   \n",
       "10   0.117936  0.071301 -0.037189  0.037973  0.178622  0.111169  0.132230   \n",
       "11  -0.142560  0.059700 -0.031521  0.077483  0.175155 -0.370548  0.000195   \n",
       "12  -0.059667 -0.012276 -0.075481  0.000211 -0.156485  0.150160  0.053797   \n",
       "\n",
       "mon        8         9         10        11        12  \n",
       "mon                                                    \n",
       "1   -0.120758  0.134955  0.117936 -0.142560 -0.059667  \n",
       "2   -0.097432  0.033740  0.071301  0.059700 -0.012276  \n",
       "3    0.022202 -0.081379 -0.037189 -0.031521 -0.075481  \n",
       "4   -0.117359  0.028134  0.037973  0.077483  0.000211  \n",
       "5    0.118944  0.038196  0.178622  0.175155 -0.156485  \n",
       "6    0.093280 -0.060803  0.111169 -0.370548  0.150160  \n",
       "7    0.411425  0.131770  0.132230  0.000195  0.053797  \n",
       "8    1.000000  0.222137  0.179547  0.123741  0.002483  \n",
       "9    0.222137  1.000000  0.277824  0.077645  0.054884  \n",
       "10   0.179547  0.277824  1.000000  0.146520 -0.031469  \n",
       "11   0.123741  0.077645  0.146520  1.000000 -0.112312  \n",
       "12   0.002483  0.054884 -0.031469 -0.112312  1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "rolled-conservation",
   "metadata": {},
   "outputs": [],
   "source": [
    "months = ['jan', 'feb', 'mar', 'apr', 'may', \n",
    "          'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "embedded-result",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "certain-enzyme",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(corr, cmap='bwr', vmin=-1, vmax=1)\n",
    "plt.colorbar()\n",
    "plt.yticks(range(12), months)\n",
    "plt.xticks(range(12), months);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "hydraulic-fraud",
   "metadata": {},
   "outputs": [],
   "source": [
    "multiindexed_df = df['1949':].groupby(['year', 'mon']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sorted-ferry",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{pmatrix}\n",
    "a_{11} & a_{12} \\\\\n",
    "a_{21} & a_{22}\n",
    "\\end{pmatrix}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "undefined-bloom",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{pmatrix}\n",
    "a_{11} \\\\\n",
    "a_{12} \\\\\n",
    "a_{21} \\\\\n",
    "a_{22} \\\\\n",
    "\\end{pmatrix}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "tamil-fireplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(1949,  1),\n",
       "            (1949,  2),\n",
       "            (1949,  3),\n",
       "            (1949,  4),\n",
       "            (1949,  5),\n",
       "            (1949,  6),\n",
       "            (1949,  7),\n",
       "            (1949,  8),\n",
       "            (1949,  9),\n",
       "            (1949, 10),\n",
       "            ...\n",
       "            (2006,  1),\n",
       "            (2006,  2),\n",
       "            (2006,  3),\n",
       "            (2006,  4),\n",
       "            (2006,  5),\n",
       "            (2006,  6),\n",
       "            (2006,  7),\n",
       "            (2006,  8),\n",
       "            (2006,  9),\n",
       "            (2006, 10)],\n",
       "           names=['year', 'mon'], length=694)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "outdoor-webmaster",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mon</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.116129</td>\n",
       "      <td>-7.996774</td>\n",
       "      <td>-6.112903</td>\n",
       "      <td>1.361290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.144828</td>\n",
       "      <td>-5.231034</td>\n",
       "      <td>-2.703448</td>\n",
       "      <td>1.917241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.745161</td>\n",
       "      <td>-3.977419</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>1.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.996667</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>11.110000</td>\n",
       "      <td>0.893333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.480645</td>\n",
       "      <td>5.025806</td>\n",
       "      <td>10.825806</td>\n",
       "      <td>1.177419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.120000</td>\n",
       "      <td>11.136667</td>\n",
       "      <td>16.236667</td>\n",
       "      <td>3.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.583871</td>\n",
       "      <td>15.554839</td>\n",
       "      <td>19.332258</td>\n",
       "      <td>5.390323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.345161</td>\n",
       "      <td>12.490323</td>\n",
       "      <td>16.754839</td>\n",
       "      <td>2.687097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.306667</td>\n",
       "      <td>6.256667</td>\n",
       "      <td>10.043333</td>\n",
       "      <td>1.696667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.416129</td>\n",
       "      <td>4.206452</td>\n",
       "      <td>7.180645</td>\n",
       "      <td>1.016129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.976667</td>\n",
       "      <td>-1.980000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>1.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.061290</td>\n",
       "      <td>-4.148387</td>\n",
       "      <td>-2.625806</td>\n",
       "      <td>2.638710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TMPMAX     TMPMIN      TMPMN    PRECIP\n",
       "mon                                           \n",
       "1    -4.116129  -7.996774  -6.112903  1.361290\n",
       "2    -0.144828  -5.231034  -2.703448  1.917241\n",
       "3     2.745161  -3.977419  -0.700000  1.483871\n",
       "4    15.996667   6.120000  11.110000  0.893333\n",
       "5    16.480645   5.025806  10.825806  1.177419\n",
       "6    21.120000  11.136667  16.236667  3.850000\n",
       "7    23.583871  15.554839  19.332258  5.390323\n",
       "8    21.345161  12.490323  16.754839  2.687097\n",
       "9    14.306667   6.256667  10.043333  1.696667\n",
       "10   10.416129   4.206452   7.180645  1.016129\n",
       "11    1.976667  -1.980000  -0.050000  1.440000\n",
       "12   -1.061290  -4.148387  -2.625806  2.638710"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.loc[2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "general-maker",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.loc[(2000, 3), 'TMPMN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "atmospheric-works",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mon</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.300000</td>\n",
       "      <td>-6.493548</td>\n",
       "      <td>-3.670968</td>\n",
       "      <td>0.854839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>2</td>\n",
       "      <td>-3.571429</td>\n",
       "      <td>-11.075000</td>\n",
       "      <td>-7.339286</td>\n",
       "      <td>0.789286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>3</td>\n",
       "      <td>1.390323</td>\n",
       "      <td>-6.470968</td>\n",
       "      <td>-2.780645</td>\n",
       "      <td>2.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>4</td>\n",
       "      <td>9.056667</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>4.280000</td>\n",
       "      <td>0.603333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>5</td>\n",
       "      <td>21.377419</td>\n",
       "      <td>9.093548</td>\n",
       "      <td>15.225806</td>\n",
       "      <td>1.016129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>6</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>12.706667</td>\n",
       "      <td>18.203333</td>\n",
       "      <td>1.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>7</td>\n",
       "      <td>23.222581</td>\n",
       "      <td>12.909677</td>\n",
       "      <td>17.977419</td>\n",
       "      <td>1.112903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>8</td>\n",
       "      <td>21.822581</td>\n",
       "      <td>13.819355</td>\n",
       "      <td>17.535484</td>\n",
       "      <td>4.170968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>9</td>\n",
       "      <td>18.156667</td>\n",
       "      <td>9.280000</td>\n",
       "      <td>13.303333</td>\n",
       "      <td>2.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>10</td>\n",
       "      <td>9.470968</td>\n",
       "      <td>4.687097</td>\n",
       "      <td>7.003226</td>\n",
       "      <td>1.693548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>694 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mon     TMPMAX     TMPMIN      TMPMN    PRECIP\n",
       "year                                                \n",
       "1949    1  -1.300000  -6.493548  -3.670968  0.854839\n",
       "1949    2  -3.571429 -11.075000  -7.339286  0.789286\n",
       "1949    3   1.390323  -6.470968  -2.780645  2.419355\n",
       "1949    4   9.056667  -0.066667   4.280000  0.603333\n",
       "1949    5  21.377419   9.093548  15.225806  1.016129\n",
       "...   ...        ...        ...        ...       ...\n",
       "2006    6  23.666667  12.706667  18.203333  1.820000\n",
       "2006    7  23.222581  12.909677  17.977419  1.112903\n",
       "2006    8  21.822581  13.819355  17.535484  4.170968\n",
       "2006    9  18.156667   9.280000  13.303333  2.013333\n",
       "2006   10   9.470968   4.687097   7.003226  1.693548\n",
       "\n",
       "[694 rows x 5 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.reset_index(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "accredited-kansas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</th>\n",
       "      <th>PRECIP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mon</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>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>-1.300000</td>\n",
       "      <td>-6.493548</td>\n",
       "      <td>-3.670968</td>\n",
       "      <td>0.854839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>-14.348387</td>\n",
       "      <td>-21.616129</td>\n",
       "      <td>-18.022581</td>\n",
       "      <td>0.261290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1951</td>\n",
       "      <td>-8.709677</td>\n",
       "      <td>-15.745161</td>\n",
       "      <td>-12.138710</td>\n",
       "      <td>1.070968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1952</td>\n",
       "      <td>-1.648387</td>\n",
       "      <td>-6.722581</td>\n",
       "      <td>-4.135484</td>\n",
       "      <td>1.606452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1953</td>\n",
       "      <td>-7.300000</td>\n",
       "      <td>-14.067742</td>\n",
       "      <td>-10.403226</td>\n",
       "      <td>1.080645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954</td>\n",
       "      <td>-10.796774</td>\n",
       "      <td>-17.754839</td>\n",
       "      <td>-14.306452</td>\n",
       "      <td>0.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1955</td>\n",
       "      <td>-3.664516</td>\n",
       "      <td>-9.209677</td>\n",
       "      <td>-6.312903</td>\n",
       "      <td>2.025806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1956</td>\n",
       "      <td>-7.525806</td>\n",
       "      <td>-13.932258</td>\n",
       "      <td>-10.916129</td>\n",
       "      <td>1.661290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>-3.216129</td>\n",
       "      <td>-9.051613</td>\n",
       "      <td>-6.038710</td>\n",
       "      <td>1.748387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>-3.861290</td>\n",
       "      <td>-9.987097</td>\n",
       "      <td>-6.932258</td>\n",
       "      <td>1.741935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>-1.758065</td>\n",
       "      <td>-6.987097</td>\n",
       "      <td>-4.335484</td>\n",
       "      <td>2.132258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960</td>\n",
       "      <td>-6.941935</td>\n",
       "      <td>-12.125806</td>\n",
       "      <td>-9.351613</td>\n",
       "      <td>2.174194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>-3.741935</td>\n",
       "      <td>-8.735484</td>\n",
       "      <td>-6.174194</td>\n",
       "      <td>0.964516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962</td>\n",
       "      <td>-1.670968</td>\n",
       "      <td>-7.893548</td>\n",
       "      <td>-4.248387</td>\n",
       "      <td>1.135484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1963</td>\n",
       "      <td>-12.864516</td>\n",
       "      <td>-19.506452</td>\n",
       "      <td>-15.929032</td>\n",
       "      <td>0.654839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964</td>\n",
       "      <td>-3.874194</td>\n",
       "      <td>-12.522581</td>\n",
       "      <td>-8.074194</td>\n",
       "      <td>0.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965</td>\n",
       "      <td>-6.551613</td>\n",
       "      <td>-12.522581</td>\n",
       "      <td>-9.529032</td>\n",
       "      <td>1.790323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1966</td>\n",
       "      <td>-6.922581</td>\n",
       "      <td>-12.993548</td>\n",
       "      <td>-9.664516</td>\n",
       "      <td>1.951613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1967</td>\n",
       "      <td>-10.077419</td>\n",
       "      <td>-17.651613</td>\n",
       "      <td>-13.922581</td>\n",
       "      <td>1.374194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1968</td>\n",
       "      <td>-12.158065</td>\n",
       "      <td>-18.693548</td>\n",
       "      <td>-15.548387</td>\n",
       "      <td>1.841935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>-12.458065</td>\n",
       "      <td>-19.509677</td>\n",
       "      <td>-16.170968</td>\n",
       "      <td>0.803226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970</td>\n",
       "      <td>-6.567742</td>\n",
       "      <td>-14.032258</td>\n",
       "      <td>-10.374194</td>\n",
       "      <td>2.696774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1971</td>\n",
       "      <td>-1.477419</td>\n",
       "      <td>-5.829032</td>\n",
       "      <td>-3.538710</td>\n",
       "      <td>1.335484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1972</td>\n",
       "      <td>-11.470968</td>\n",
       "      <td>-18.167742</td>\n",
       "      <td>-14.874194</td>\n",
       "      <td>0.148387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1973</td>\n",
       "      <td>-6.406452</td>\n",
       "      <td>-13.812903</td>\n",
       "      <td>-10.248387</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1974</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>-13.354839</td>\n",
       "      <td>-10.219355</td>\n",
       "      <td>0.767742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>-1.232258</td>\n",
       "      <td>-6.074194</td>\n",
       "      <td>-3.670968</td>\n",
       "      <td>1.393548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1976</td>\n",
       "      <td>-9.348387</td>\n",
       "      <td>-15.287097</td>\n",
       "      <td>-12.241935</td>\n",
       "      <td>1.748387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1977</td>\n",
       "      <td>-8.458065</td>\n",
       "      <td>-13.874194</td>\n",
       "      <td>-11.254839</td>\n",
       "      <td>0.832258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1978</td>\n",
       "      <td>-4.593548</td>\n",
       "      <td>-10.167742</td>\n",
       "      <td>-7.270968</td>\n",
       "      <td>0.632258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979</td>\n",
       "      <td>-7.612903</td>\n",
       "      <td>-12.751613</td>\n",
       "      <td>-9.954839</td>\n",
       "      <td>2.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980</td>\n",
       "      <td>-7.541935</td>\n",
       "      <td>-14.503226</td>\n",
       "      <td>-11.312903</td>\n",
       "      <td>0.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>-3.293548</td>\n",
       "      <td>-7.532258</td>\n",
       "      <td>-5.393548</td>\n",
       "      <td>2.074194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1982</td>\n",
       "      <td>-7.022581</td>\n",
       "      <td>-13.648387</td>\n",
       "      <td>-10.158065</td>\n",
       "      <td>1.967742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1983</td>\n",
       "      <td>-1.838710</td>\n",
       "      <td>-6.348387</td>\n",
       "      <td>-3.967742</td>\n",
       "      <td>2.164516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1984</td>\n",
       "      <td>-2.309677</td>\n",
       "      <td>-6.651613</td>\n",
       "      <td>-4.409677</td>\n",
       "      <td>1.070968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>-6.396774</td>\n",
       "      <td>-13.977419</td>\n",
       "      <td>-10.012903</td>\n",
       "      <td>2.277419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>-3.970968</td>\n",
       "      <td>-9.732258</td>\n",
       "      <td>-6.725806</td>\n",
       "      <td>2.464516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1987</td>\n",
       "      <td>-14.448387</td>\n",
       "      <td>-20.945161</td>\n",
       "      <td>-17.506452</td>\n",
       "      <td>1.167742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988</td>\n",
       "      <td>-5.264516</td>\n",
       "      <td>-9.280645</td>\n",
       "      <td>-7.241935</td>\n",
       "      <td>0.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989</td>\n",
       "      <td>0.138710</td>\n",
       "      <td>-4.461290</td>\n",
       "      <td>-2.112903</td>\n",
       "      <td>1.474194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>-3.116129</td>\n",
       "      <td>-8.309677</td>\n",
       "      <td>-5.667742</td>\n",
       "      <td>1.696774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>-2.372414</td>\n",
       "      <td>-7.582759</td>\n",
       "      <td>-4.875862</td>\n",
       "      <td>1.631034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992</td>\n",
       "      <td>-2.300000</td>\n",
       "      <td>-8.203226</td>\n",
       "      <td>-5.270968</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1993</td>\n",
       "      <td>-2.248387</td>\n",
       "      <td>-6.990323</td>\n",
       "      <td>-4.416129</td>\n",
       "      <td>2.629032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994</td>\n",
       "      <td>-1.393548</td>\n",
       "      <td>-5.880645</td>\n",
       "      <td>-3.422581</td>\n",
       "      <td>2.135484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995</td>\n",
       "      <td>-3.845161</td>\n",
       "      <td>-8.054839</td>\n",
       "      <td>-5.874194</td>\n",
       "      <td>2.167742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996</td>\n",
       "      <td>-7.167742</td>\n",
       "      <td>-12.712903</td>\n",
       "      <td>-10.019355</td>\n",
       "      <td>0.558065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997</td>\n",
       "      <td>-4.680645</td>\n",
       "      <td>-10.851613</td>\n",
       "      <td>-7.732258</td>\n",
       "      <td>1.477419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>-2.703226</td>\n",
       "      <td>-6.719355</td>\n",
       "      <td>-4.725806</td>\n",
       "      <td>1.409677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>-2.212903</td>\n",
       "      <td>-6.825806</td>\n",
       "      <td>-4.577419</td>\n",
       "      <td>2.341935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>-4.116129</td>\n",
       "      <td>-7.996774</td>\n",
       "      <td>-6.112903</td>\n",
       "      <td>1.361290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>-2.335484</td>\n",
       "      <td>-6.438710</td>\n",
       "      <td>-4.312903</td>\n",
       "      <td>1.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>-1.948387</td>\n",
       "      <td>-7.290323</td>\n",
       "      <td>-4.754839</td>\n",
       "      <td>1.532258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>-4.338710</td>\n",
       "      <td>-10.606452</td>\n",
       "      <td>-7.351613</td>\n",
       "      <td>1.438710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>-4.187097</td>\n",
       "      <td>-8.767742</td>\n",
       "      <td>-6.458065</td>\n",
       "      <td>2.845161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005</td>\n",
       "      <td>-1.080645</td>\n",
       "      <td>-4.835484</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>3.145161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>-8.241935</td>\n",
       "      <td>-13.625806</td>\n",
       "      <td>-10.787097</td>\n",
       "      <td>0.861290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year     TMPMAX     TMPMIN      TMPMN    PRECIP\n",
       "mon                                                 \n",
       "1    1949  -1.300000  -6.493548  -3.670968  0.854839\n",
       "1    1950 -14.348387 -21.616129 -18.022581  0.261290\n",
       "1    1951  -8.709677 -15.745161 -12.138710  1.070968\n",
       "1    1952  -1.648387  -6.722581  -4.135484  1.606452\n",
       "1    1953  -7.300000 -14.067742 -10.403226  1.080645\n",
       "1    1954 -10.796774 -17.754839 -14.306452  0.774194\n",
       "1    1955  -3.664516  -9.209677  -6.312903  2.025806\n",
       "1    1956  -7.525806 -13.932258 -10.916129  1.661290\n",
       "1    1957  -3.216129  -9.051613  -6.038710  1.748387\n",
       "1    1958  -3.861290  -9.987097  -6.932258  1.741935\n",
       "1    1959  -1.758065  -6.987097  -4.335484  2.132258\n",
       "1    1960  -6.941935 -12.125806  -9.351613  2.174194\n",
       "1    1961  -3.741935  -8.735484  -6.174194  0.964516\n",
       "1    1962  -1.670968  -7.893548  -4.248387  1.135484\n",
       "1    1963 -12.864516 -19.506452 -15.929032  0.654839\n",
       "1    1964  -3.874194 -12.522581  -8.074194  0.387097\n",
       "1    1965  -6.551613 -12.522581  -9.529032  1.790323\n",
       "1    1966  -6.922581 -12.993548  -9.664516  1.951613\n",
       "1    1967 -10.077419 -17.651613 -13.922581  1.374194\n",
       "1    1968 -12.158065 -18.693548 -15.548387  1.841935\n",
       "1    1969 -12.458065 -19.509677 -16.170968  0.803226\n",
       "1    1970  -6.567742 -14.032258 -10.374194  2.696774\n",
       "1    1971  -1.477419  -5.829032  -3.538710  1.335484\n",
       "1    1972 -11.470968 -18.167742 -14.874194  0.148387\n",
       "1    1973  -6.406452 -13.812903 -10.248387  0.600000\n",
       "1    1974  -7.000000 -13.354839 -10.219355  0.767742\n",
       "1    1975  -1.232258  -6.074194  -3.670968  1.393548\n",
       "1    1976  -9.348387 -15.287097 -12.241935  1.748387\n",
       "1    1977  -8.458065 -13.874194 -11.254839  0.832258\n",
       "1    1978  -4.593548 -10.167742  -7.270968  0.632258\n",
       "1    1979  -7.612903 -12.751613  -9.954839  2.354839\n",
       "1    1980  -7.541935 -14.503226 -11.312903  0.935484\n",
       "1    1981  -3.293548  -7.532258  -5.393548  2.074194\n",
       "1    1982  -7.022581 -13.648387 -10.158065  1.967742\n",
       "1    1983  -1.838710  -6.348387  -3.967742  2.164516\n",
       "1    1984  -2.309677  -6.651613  -4.409677  1.070968\n",
       "1    1985  -6.396774 -13.977419 -10.012903  2.277419\n",
       "1    1986  -3.970968  -9.732258  -6.725806  2.464516\n",
       "1    1987 -14.448387 -20.945161 -17.506452  1.167742\n",
       "1    1988  -5.264516  -9.280645  -7.241935  0.564516\n",
       "1    1989   0.138710  -4.461290  -2.112903  1.474194\n",
       "1    1990  -3.116129  -8.309677  -5.667742  1.696774\n",
       "1    1991  -2.372414  -7.582759  -4.875862  1.631034\n",
       "1    1992  -2.300000  -8.203226  -5.270968  1.700000\n",
       "1    1993  -2.248387  -6.990323  -4.416129  2.629032\n",
       "1    1994  -1.393548  -5.880645  -3.422581  2.135484\n",
       "1    1995  -3.845161  -8.054839  -5.874194  2.167742\n",
       "1    1996  -7.167742 -12.712903 -10.019355  0.558065\n",
       "1    1997  -4.680645 -10.851613  -7.732258  1.477419\n",
       "1    1998  -2.703226  -6.719355  -4.725806  1.409677\n",
       "1    1999  -2.212903  -6.825806  -4.577419  2.341935\n",
       "1    2000  -4.116129  -7.996774  -6.112903  1.361290\n",
       "1    2001  -2.335484  -6.438710  -4.312903  1.225806\n",
       "1    2002  -1.948387  -7.290323  -4.754839  1.532258\n",
       "1    2003  -4.338710 -10.606452  -7.351613  1.438710\n",
       "1    2004  -4.187097  -8.767742  -6.458065  2.845161\n",
       "1    2005  -1.080645  -4.835484  -3.000000  3.145161\n",
       "1    2006  -8.241935 -13.625806 -10.787097  0.861290"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.reset_index(level=0).loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "warming-vault",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>mon</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2006</th>\n",
       "      <th>6</th>\n",
       "      <td>23.666667</td>\n",
       "      <td>12.706667</td>\n",
       "      <td>18.203333</td>\n",
       "      <td>1.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.222581</td>\n",
       "      <td>12.909677</td>\n",
       "      <td>17.977419</td>\n",
       "      <td>1.112903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.822581</td>\n",
       "      <td>13.819355</td>\n",
       "      <td>17.535484</td>\n",
       "      <td>4.170968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.156667</td>\n",
       "      <td>9.280000</td>\n",
       "      <td>13.303333</td>\n",
       "      <td>2.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.470968</td>\n",
       "      <td>4.687097</td>\n",
       "      <td>7.003226</td>\n",
       "      <td>1.693548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>694 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TMPMAX     TMPMIN      TMPMN    PRECIP\n",
       "year mon                                           \n",
       "1949 1    -1.300000  -6.493548  -3.670968  0.854839\n",
       "     2    -3.571429 -11.075000  -7.339286  0.789286\n",
       "     3     1.390323  -6.470968  -2.780645  2.419355\n",
       "     4     9.056667  -0.066667   4.280000  0.603333\n",
       "     5    21.377419   9.093548  15.225806  1.016129\n",
       "...             ...        ...        ...       ...\n",
       "2006 6    23.666667  12.706667  18.203333  1.820000\n",
       "     7    23.222581  12.909677  17.977419  1.112903\n",
       "     8    21.822581  13.819355  17.535484  4.170968\n",
       "     9    18.156667   9.280000  13.303333  2.013333\n",
       "     10    9.470968   4.687097   7.003226  1.693548\n",
       "\n",
       "[694 rows x 4 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "latter-start",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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>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",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>-14.306452</td>\n",
       "      <td>-14.000000</td>\n",
       "      <td>-3.425806</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>12.883871</td>\n",
       "      <td>18.963333</td>\n",
       "      <td>21.025806</td>\n",
       "      <td>18.354839</td>\n",
       "      <td>12.286667</td>\n",
       "      <td>5.738710</td>\n",
       "      <td>-1.573333</td>\n",
       "      <td>-4.974194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>-6.312903</td>\n",
       "      <td>-6.792857</td>\n",
       "      <td>-4.664516</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>10.506452</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>17.906452</td>\n",
       "      <td>17.916129</td>\n",
       "      <td>13.856667</td>\n",
       "      <td>7.712903</td>\n",
       "      <td>-3.023333</td>\n",
       "      <td>-14.306452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>-10.916129</td>\n",
       "      <td>-18.503448</td>\n",
       "      <td>-3.600000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>10.664516</td>\n",
       "      <td>20.570000</td>\n",
       "      <td>15.190323</td>\n",
       "      <td>14.625806</td>\n",
       "      <td>8.423333</td>\n",
       "      <td>4.670968</td>\n",
       "      <td>-5.180000</td>\n",
       "      <td>-4.254839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>-6.038710</td>\n",
       "      <td>-1.760714</td>\n",
       "      <td>-6.319355</td>\n",
       "      <td>6.640000</td>\n",
       "      <td>14.435484</td>\n",
       "      <td>15.240000</td>\n",
       "      <td>18.554839</td>\n",
       "      <td>17.087097</td>\n",
       "      <td>12.313333</td>\n",
       "      <td>5.241935</td>\n",
       "      <td>-0.893333</td>\n",
       "      <td>-4.622581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>-6.932258</td>\n",
       "      <td>-7.710714</td>\n",
       "      <td>-6.080645</td>\n",
       "      <td>4.063333</td>\n",
       "      <td>13.290323</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>18.367742</td>\n",
       "      <td>15.696774</td>\n",
       "      <td>9.080000</td>\n",
       "      <td>6.038710</td>\n",
       "      <td>-0.786667</td>\n",
       "      <td>-7.641935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>-4.335484</td>\n",
       "      <td>-5.492857</td>\n",
       "      <td>-1.425806</td>\n",
       "      <td>6.630000</td>\n",
       "      <td>11.490323</td>\n",
       "      <td>16.886667</td>\n",
       "      <td>20.538710</td>\n",
       "      <td>16.932258</td>\n",
       "      <td>8.253333</td>\n",
       "      <td>2.229032</td>\n",
       "      <td>-5.110000</td>\n",
       "      <td>-10.990323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>-9.351613</td>\n",
       "      <td>-7.651724</td>\n",
       "      <td>-5.480645</td>\n",
       "      <td>5.046667</td>\n",
       "      <td>11.625806</td>\n",
       "      <td>18.486667</td>\n",
       "      <td>21.009677</td>\n",
       "      <td>16.190323</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>2.338710</td>\n",
       "      <td>-3.620000</td>\n",
       "      <td>0.167742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>-6.174194</td>\n",
       "      <td>-2.335714</td>\n",
       "      <td>0.245161</td>\n",
       "      <td>4.310000</td>\n",
       "      <td>12.058065</td>\n",
       "      <td>19.173333</td>\n",
       "      <td>19.374194</td>\n",
       "      <td>16.825806</td>\n",
       "      <td>9.686667</td>\n",
       "      <td>6.590323</td>\n",
       "      <td>-1.550000</td>\n",
       "      <td>-8.070968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>-4.248387</td>\n",
       "      <td>-6.057143</td>\n",
       "      <td>-5.012903</td>\n",
       "      <td>7.590000</td>\n",
       "      <td>13.225806</td>\n",
       "      <td>13.506667</td>\n",
       "      <td>16.361290</td>\n",
       "      <td>14.832258</td>\n",
       "      <td>10.790000</td>\n",
       "      <td>6.451613</td>\n",
       "      <td>1.343333</td>\n",
       "      <td>-7.351613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>-15.929032</td>\n",
       "      <td>-10.057143</td>\n",
       "      <td>-9.429032</td>\n",
       "      <td>3.886667</td>\n",
       "      <td>17.022581</td>\n",
       "      <td>13.540000</td>\n",
       "      <td>19.132258</td>\n",
       "      <td>17.796774</td>\n",
       "      <td>13.216667</td>\n",
       "      <td>5.651613</td>\n",
       "      <td>-0.216667</td>\n",
       "      <td>-8.751613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>-8.074194</td>\n",
       "      <td>-9.975862</td>\n",
       "      <td>-6.190323</td>\n",
       "      <td>4.206667</td>\n",
       "      <td>11.529032</td>\n",
       "      <td>18.983333</td>\n",
       "      <td>19.990323</td>\n",
       "      <td>16.006452</td>\n",
       "      <td>11.806667</td>\n",
       "      <td>6.893548</td>\n",
       "      <td>-2.306667</td>\n",
       "      <td>-2.858065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>-9.529032</td>\n",
       "      <td>-10.071429</td>\n",
       "      <td>-3.303226</td>\n",
       "      <td>2.523333</td>\n",
       "      <td>9.738710</td>\n",
       "      <td>16.016667</td>\n",
       "      <td>16.661290</td>\n",
       "      <td>15.735484</td>\n",
       "      <td>12.923333</td>\n",
       "      <td>3.806452</td>\n",
       "      <td>-5.786667</td>\n",
       "      <td>-1.493548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>-9.664516</td>\n",
       "      <td>-8.857143</td>\n",
       "      <td>0.009677</td>\n",
       "      <td>8.716667</td>\n",
       "      <td>15.422581</td>\n",
       "      <td>16.540000</td>\n",
       "      <td>19.212903</td>\n",
       "      <td>16.645161</td>\n",
       "      <td>9.543333</td>\n",
       "      <td>6.087097</td>\n",
       "      <td>-0.900000</td>\n",
       "      <td>-10.525806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>-13.922581</td>\n",
       "      <td>-10.435714</td>\n",
       "      <td>0.338710</td>\n",
       "      <td>6.566667</td>\n",
       "      <td>16.945161</td>\n",
       "      <td>16.453333</td>\n",
       "      <td>17.706452</td>\n",
       "      <td>18.361290</td>\n",
       "      <td>11.353333</td>\n",
       "      <td>9.003226</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>-9.583871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>-15.548387</td>\n",
       "      <td>-8.265517</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>12.383871</td>\n",
       "      <td>18.330000</td>\n",
       "      <td>15.725806</td>\n",
       "      <td>17.712903</td>\n",
       "      <td>10.790000</td>\n",
       "      <td>2.954839</td>\n",
       "      <td>-2.533333</td>\n",
       "      <td>-5.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>-16.170968</td>\n",
       "      <td>-13.435714</td>\n",
       "      <td>-6.912903</td>\n",
       "      <td>5.973333</td>\n",
       "      <td>11.125806</td>\n",
       "      <td>14.736667</td>\n",
       "      <td>17.803226</td>\n",
       "      <td>16.445161</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>4.690323</td>\n",
       "      <td>1.786667</td>\n",
       "      <td>-9.158065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>-10.374194</td>\n",
       "      <td>-8.325000</td>\n",
       "      <td>-2.851613</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>15.820000</td>\n",
       "      <td>19.316129</td>\n",
       "      <td>16.258065</td>\n",
       "      <td>11.090000</td>\n",
       "      <td>5.393548</td>\n",
       "      <td>-1.823333</td>\n",
       "      <td>-5.887097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>-3.538710</td>\n",
       "      <td>-9.417857</td>\n",
       "      <td>-4.161290</td>\n",
       "      <td>3.826667</td>\n",
       "      <td>12.774194</td>\n",
       "      <td>16.380000</td>\n",
       "      <td>17.467742</td>\n",
       "      <td>16.729032</td>\n",
       "      <td>10.780000</td>\n",
       "      <td>3.222581</td>\n",
       "      <td>-0.630000</td>\n",
       "      <td>-5.651613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>-14.874194</td>\n",
       "      <td>-7.403448</td>\n",
       "      <td>-2.541935</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>12.574194</td>\n",
       "      <td>18.983333</td>\n",
       "      <td>22.412903</td>\n",
       "      <td>20.632258</td>\n",
       "      <td>10.983333</td>\n",
       "      <td>5.167742</td>\n",
       "      <td>-0.186667</td>\n",
       "      <td>-0.906452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>-10.248387</td>\n",
       "      <td>-3.385714</td>\n",
       "      <td>-1.016129</td>\n",
       "      <td>7.880000</td>\n",
       "      <td>13.254839</td>\n",
       "      <td>18.240000</td>\n",
       "      <td>17.977419</td>\n",
       "      <td>15.909677</td>\n",
       "      <td>7.633333</td>\n",
       "      <td>3.845161</td>\n",
       "      <td>-2.060000</td>\n",
       "      <td>-5.861290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>-10.219355</td>\n",
       "      <td>-1.460714</td>\n",
       "      <td>-0.619355</td>\n",
       "      <td>3.403333</td>\n",
       "      <td>9.641935</td>\n",
       "      <td>16.446667</td>\n",
       "      <td>18.180645</td>\n",
       "      <td>15.874194</td>\n",
       "      <td>13.103333</td>\n",
       "      <td>8.680645</td>\n",
       "      <td>1.776667</td>\n",
       "      <td>-2.335484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>-3.670968</td>\n",
       "      <td>-6.385714</td>\n",
       "      <td>1.238710</td>\n",
       "      <td>10.066667</td>\n",
       "      <td>15.638710</td>\n",
       "      <td>17.776667</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>15.019355</td>\n",
       "      <td>13.663333</td>\n",
       "      <td>4.093548</td>\n",
       "      <td>-3.296667</td>\n",
       "      <td>-4.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>-12.241935</td>\n",
       "      <td>-11.455172</td>\n",
       "      <td>-2.558065</td>\n",
       "      <td>5.723333</td>\n",
       "      <td>10.925806</td>\n",
       "      <td>13.713333</td>\n",
       "      <td>16.103226</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>9.653333</td>\n",
       "      <td>-0.948387</td>\n",
       "      <td>-0.803333</td>\n",
       "      <td>-3.690323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>-11.254839</td>\n",
       "      <td>-6.332143</td>\n",
       "      <td>-0.851613</td>\n",
       "      <td>7.030000</td>\n",
       "      <td>14.229032</td>\n",
       "      <td>16.836667</td>\n",
       "      <td>18.764516</td>\n",
       "      <td>15.825806</td>\n",
       "      <td>9.506667</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1.726667</td>\n",
       "      <td>-8.174194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>-7.270968</td>\n",
       "      <td>-9.500000</td>\n",
       "      <td>0.390323</td>\n",
       "      <td>4.613333</td>\n",
       "      <td>10.493548</td>\n",
       "      <td>14.313333</td>\n",
       "      <td>16.345161</td>\n",
       "      <td>15.774194</td>\n",
       "      <td>9.723333</td>\n",
       "      <td>3.341935</td>\n",
       "      <td>2.003333</td>\n",
       "      <td>-14.509677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>-9.954839</td>\n",
       "      <td>-8.828571</td>\n",
       "      <td>-0.883871</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>17.106452</td>\n",
       "      <td>17.153333</td>\n",
       "      <td>16.674194</td>\n",
       "      <td>16.938710</td>\n",
       "      <td>11.730000</td>\n",
       "      <td>3.838710</td>\n",
       "      <td>-0.913333</td>\n",
       "      <td>-5.658065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>-11.312903</td>\n",
       "      <td>-7.151724</td>\n",
       "      <td>-6.370968</td>\n",
       "      <td>5.846667</td>\n",
       "      <td>8.370968</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>14.709677</td>\n",
       "      <td>10.536667</td>\n",
       "      <td>5.190323</td>\n",
       "      <td>-2.040000</td>\n",
       "      <td>-4.248387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>-5.393548</td>\n",
       "      <td>-4.907143</td>\n",
       "      <td>-3.135484</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>14.019355</td>\n",
       "      <td>19.813333</td>\n",
       "      <td>21.512903</td>\n",
       "      <td>17.396774</td>\n",
       "      <td>10.816667</td>\n",
       "      <td>7.806452</td>\n",
       "      <td>-0.583333</td>\n",
       "      <td>-3.509677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>-10.158065</td>\n",
       "      <td>-8.792857</td>\n",
       "      <td>-0.648387</td>\n",
       "      <td>5.336667</td>\n",
       "      <td>11.977419</td>\n",
       "      <td>13.856667</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>16.645161</td>\n",
       "      <td>11.753333</td>\n",
       "      <td>4.064516</td>\n",
       "      <td>2.033333</td>\n",
       "      <td>-1.135484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>-3.967742</td>\n",
       "      <td>-6.878571</td>\n",
       "      <td>-1.403226</td>\n",
       "      <td>9.290000</td>\n",
       "      <td>15.612903</td>\n",
       "      <td>14.550000</td>\n",
       "      <td>17.938710</td>\n",
       "      <td>16.032258</td>\n",
       "      <td>12.413333</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>-1.466667</td>\n",
       "      <td>-3.180645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>-4.409677</td>\n",
       "      <td>-10.268966</td>\n",
       "      <td>-2.354839</td>\n",
       "      <td>7.466667</td>\n",
       "      <td>15.970968</td>\n",
       "      <td>15.590000</td>\n",
       "      <td>17.567742</td>\n",
       "      <td>15.125806</td>\n",
       "      <td>12.410000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>-3.543333</td>\n",
       "      <td>-9.590323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>-10.012903</td>\n",
       "      <td>-14.014286</td>\n",
       "      <td>-3.067742</td>\n",
       "      <td>5.346667</td>\n",
       "      <td>12.974194</td>\n",
       "      <td>14.663333</td>\n",
       "      <td>16.438710</td>\n",
       "      <td>19.419355</td>\n",
       "      <td>10.123333</td>\n",
       "      <td>6.070968</td>\n",
       "      <td>-3.333333</td>\n",
       "      <td>-6.535484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>-6.725806</td>\n",
       "      <td>-13.539286</td>\n",
       "      <td>0.158065</td>\n",
       "      <td>6.680000</td>\n",
       "      <td>13.648387</td>\n",
       "      <td>18.633333</td>\n",
       "      <td>17.796774</td>\n",
       "      <td>16.516129</td>\n",
       "      <td>8.580000</td>\n",
       "      <td>4.174194</td>\n",
       "      <td>-0.093333</td>\n",
       "      <td>-7.454839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>-17.506452</td>\n",
       "      <td>-6.057143</td>\n",
       "      <td>-5.312903</td>\n",
       "      <td>2.810000</td>\n",
       "      <td>12.832258</td>\n",
       "      <td>17.736667</td>\n",
       "      <td>16.764516</td>\n",
       "      <td>15.087097</td>\n",
       "      <td>9.046667</td>\n",
       "      <td>3.590323</td>\n",
       "      <td>-3.646667</td>\n",
       "      <td>-6.983871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>-7.241935</td>\n",
       "      <td>-6.113793</td>\n",
       "      <td>-0.993548</td>\n",
       "      <td>5.346667</td>\n",
       "      <td>13.848387</td>\n",
       "      <td>19.530000</td>\n",
       "      <td>21.587097</td>\n",
       "      <td>16.474194</td>\n",
       "      <td>11.280000</td>\n",
       "      <td>4.870968</td>\n",
       "      <td>-4.440000</td>\n",
       "      <td>-6.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>-2.112903</td>\n",
       "      <td>-0.503571</td>\n",
       "      <td>1.990323</td>\n",
       "      <td>7.656667</td>\n",
       "      <td>13.396774</td>\n",
       "      <td>20.053333</td>\n",
       "      <td>19.161290</td>\n",
       "      <td>16.248387</td>\n",
       "      <td>12.176667</td>\n",
       "      <td>5.329032</td>\n",
       "      <td>-2.633333</td>\n",
       "      <td>-5.293548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>-5.667742</td>\n",
       "      <td>0.382143</td>\n",
       "      <td>1.980645</td>\n",
       "      <td>8.120000</td>\n",
       "      <td>10.803226</td>\n",
       "      <td>14.506667</td>\n",
       "      <td>17.535484</td>\n",
       "      <td>15.954839</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>5.358065</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>-3.393548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>-4.875862</td>\n",
       "      <td>-6.678571</td>\n",
       "      <td>-1.219355</td>\n",
       "      <td>7.030000</td>\n",
       "      <td>13.387097</td>\n",
       "      <td>18.783333</td>\n",
       "      <td>18.080645</td>\n",
       "      <td>16.090323</td>\n",
       "      <td>10.973333</td>\n",
       "      <td>6.538710</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>-3.974194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>-5.270968</td>\n",
       "      <td>-4.313793</td>\n",
       "      <td>1.654839</td>\n",
       "      <td>5.140000</td>\n",
       "      <td>11.932258</td>\n",
       "      <td>16.693333</td>\n",
       "      <td>18.622581</td>\n",
       "      <td>18.041935</td>\n",
       "      <td>13.146667</td>\n",
       "      <td>2.154839</td>\n",
       "      <td>-2.533333</td>\n",
       "      <td>-4.396774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>-4.416129</td>\n",
       "      <td>-4.882143</td>\n",
       "      <td>-1.932258</td>\n",
       "      <td>5.730000</td>\n",
       "      <td>14.490323</td>\n",
       "      <td>14.023333</td>\n",
       "      <td>17.506452</td>\n",
       "      <td>15.425806</td>\n",
       "      <td>6.870000</td>\n",
       "      <td>4.645161</td>\n",
       "      <td>-8.003333</td>\n",
       "      <td>-3.612903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>-3.422581</td>\n",
       "      <td>-11.300000</td>\n",
       "      <td>-2.929032</td>\n",
       "      <td>7.226667</td>\n",
       "      <td>9.848387</td>\n",
       "      <td>14.546667</td>\n",
       "      <td>17.561290</td>\n",
       "      <td>15.870968</td>\n",
       "      <td>13.676667</td>\n",
       "      <td>4.958065</td>\n",
       "      <td>-2.526667</td>\n",
       "      <td>-7.948387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-5.874194</td>\n",
       "      <td>-0.846429</td>\n",
       "      <td>0.587097</td>\n",
       "      <td>9.116667</td>\n",
       "      <td>14.470968</td>\n",
       "      <td>19.683333</td>\n",
       "      <td>17.548387</td>\n",
       "      <td>16.793548</td>\n",
       "      <td>12.823333</td>\n",
       "      <td>6.683871</td>\n",
       "      <td>-2.816667</td>\n",
       "      <td>-9.506452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-10.019355</td>\n",
       "      <td>-9.689655</td>\n",
       "      <td>-3.045161</td>\n",
       "      <td>6.403333</td>\n",
       "      <td>15.703226</td>\n",
       "      <td>16.526667</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>17.274194</td>\n",
       "      <td>9.880000</td>\n",
       "      <td>6.051613</td>\n",
       "      <td>3.863333</td>\n",
       "      <td>-7.019355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-7.732258</td>\n",
       "      <td>-4.682143</td>\n",
       "      <td>-0.932258</td>\n",
       "      <td>4.650000</td>\n",
       "      <td>11.093548</td>\n",
       "      <td>17.830000</td>\n",
       "      <td>18.729032</td>\n",
       "      <td>17.122581</td>\n",
       "      <td>8.516667</td>\n",
       "      <td>3.677419</td>\n",
       "      <td>-0.843333</td>\n",
       "      <td>-7.538710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-4.725806</td>\n",
       "      <td>-7.642857</td>\n",
       "      <td>-1.348387</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>13.712903</td>\n",
       "      <td>19.976667</td>\n",
       "      <td>18.864516</td>\n",
       "      <td>15.487097</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>5.658065</td>\n",
       "      <td>-8.030000</td>\n",
       "      <td>-5.938710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-4.577419</td>\n",
       "      <td>-6.253571</td>\n",
       "      <td>-0.835484</td>\n",
       "      <td>9.683333</td>\n",
       "      <td>8.674194</td>\n",
       "      <td>21.423333</td>\n",
       "      <td>21.719355</td>\n",
       "      <td>16.406452</td>\n",
       "      <td>11.770000</td>\n",
       "      <td>7.374194</td>\n",
       "      <td>-4.933333</td>\n",
       "      <td>-1.706452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>-6.112903</td>\n",
       "      <td>-2.703448</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>11.110000</td>\n",
       "      <td>10.825806</td>\n",
       "      <td>16.236667</td>\n",
       "      <td>19.332258</td>\n",
       "      <td>16.754839</td>\n",
       "      <td>10.043333</td>\n",
       "      <td>7.180645</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>-2.625806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-4.312903</td>\n",
       "      <td>-7.192857</td>\n",
       "      <td>-2.125806</td>\n",
       "      <td>10.966667</td>\n",
       "      <td>11.251613</td>\n",
       "      <td>16.266667</td>\n",
       "      <td>22.967742</td>\n",
       "      <td>16.964516</td>\n",
       "      <td>12.193333</td>\n",
       "      <td>4.806452</td>\n",
       "      <td>-0.526667</td>\n",
       "      <td>-10.580645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>-4.754839</td>\n",
       "      <td>-0.425000</td>\n",
       "      <td>2.229032</td>\n",
       "      <td>7.183333</td>\n",
       "      <td>12.732258</td>\n",
       "      <td>17.310000</td>\n",
       "      <td>22.638710</td>\n",
       "      <td>17.041935</td>\n",
       "      <td>12.030000</td>\n",
       "      <td>2.545161</td>\n",
       "      <td>-1.476667</td>\n",
       "      <td>-12.561290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>-7.351613</td>\n",
       "      <td>-8.653571</td>\n",
       "      <td>-2.729032</td>\n",
       "      <td>4.673333</td>\n",
       "      <td>15.535484</td>\n",
       "      <td>12.820000</td>\n",
       "      <td>20.641935</td>\n",
       "      <td>16.948387</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>5.567742</td>\n",
       "      <td>1.146667</td>\n",
       "      <td>-2.061290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>-6.458065</td>\n",
       "      <td>-7.006897</td>\n",
       "      <td>1.345161</td>\n",
       "      <td>4.556667</td>\n",
       "      <td>11.412903</td>\n",
       "      <td>15.283333</td>\n",
       "      <td>19.022581</td>\n",
       "      <td>18.383871</td>\n",
       "      <td>12.133333</td>\n",
       "      <td>5.935484</td>\n",
       "      <td>-1.560000</td>\n",
       "      <td>-2.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-8.903571</td>\n",
       "      <td>-6.029032</td>\n",
       "      <td>7.116667</td>\n",
       "      <td>14.832258</td>\n",
       "      <td>16.506667</td>\n",
       "      <td>19.309677</td>\n",
       "      <td>17.638710</td>\n",
       "      <td>13.123333</td>\n",
       "      <td>6.022581</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>-4.148387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>-10.787097</td>\n",
       "      <td>-13.307143</td>\n",
       "      <td>-3.719355</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>12.432258</td>\n",
       "      <td>18.203333</td>\n",
       "      <td>17.977419</td>\n",
       "      <td>17.535484</td>\n",
       "      <td>13.303333</td>\n",
       "      <td>7.003226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "1954 -14.306452 -14.000000 -3.425806   3.030000  12.883871  18.963333   \n",
       "1955  -6.312903  -6.792857 -4.664516   1.466667  10.506452  15.100000   \n",
       "1956 -10.916129 -18.503448 -3.600000   3.940000  10.664516  20.570000   \n",
       "1957  -6.038710  -1.760714 -6.319355   6.640000  14.435484  15.240000   \n",
       "1958  -6.932258  -7.710714 -6.080645   4.063333  13.290323  14.890000   \n",
       "1959  -4.335484  -5.492857 -1.425806   6.630000  11.490323  16.886667   \n",
       "1960  -9.351613  -7.651724 -5.480645   5.046667  11.625806  18.486667   \n",
       "1961  -6.174194  -2.335714  0.245161   4.310000  12.058065  19.173333   \n",
       "1962  -4.248387  -6.057143 -5.012903   7.590000  13.225806  13.506667   \n",
       "1963 -15.929032 -10.057143 -9.429032   3.886667  17.022581  13.540000   \n",
       "1964  -8.074194  -9.975862 -6.190323   4.206667  11.529032  18.983333   \n",
       "1965  -9.529032 -10.071429 -3.303226   2.523333   9.738710  16.016667   \n",
       "1966  -9.664516  -8.857143  0.009677   8.716667  15.422581  16.540000   \n",
       "1967 -13.922581 -10.435714  0.338710   6.566667  16.945161  16.453333   \n",
       "1968 -15.548387  -8.265517 -1.000000   6.020000  12.383871  18.330000   \n",
       "1969 -16.170968 -13.435714 -6.912903   5.973333  11.125806  14.736667   \n",
       "1970 -10.374194  -8.325000 -2.851613   5.833333  12.600000  15.820000   \n",
       "1971  -3.538710  -9.417857 -4.161290   3.826667  12.774194  16.380000   \n",
       "1972 -14.874194  -7.403448 -2.541935   5.880000  12.574194  18.983333   \n",
       "1973 -10.248387  -3.385714 -1.016129   7.880000  13.254839  18.240000   \n",
       "1974 -10.219355  -1.460714 -0.619355   3.403333   9.641935  16.446667   \n",
       "1975  -3.670968  -6.385714  1.238710  10.066667  15.638710  17.776667   \n",
       "1976 -12.241935 -11.455172 -2.558065   5.723333  10.925806  13.713333   \n",
       "1977 -11.254839  -6.332143 -0.851613   7.030000  14.229032  16.836667   \n",
       "1978  -7.270968  -9.500000  0.390323   4.613333  10.493548  14.313333   \n",
       "1979  -9.954839  -8.828571 -0.883871   3.310000  17.106452  17.153333   \n",
       "1980 -11.312903  -7.151724 -6.370968   5.846667   8.370968  17.870000   \n",
       "1981  -5.393548  -4.907143 -3.135484   3.340000  14.019355  19.813333   \n",
       "1982 -10.158065  -8.792857 -0.648387   5.336667  11.977419  13.856667   \n",
       "1983  -3.967742  -6.878571 -1.403226   9.290000  15.612903  14.550000   \n",
       "1984  -4.409677 -10.268966 -2.354839   7.466667  15.970968  15.590000   \n",
       "1985 -10.012903 -14.014286 -3.067742   5.346667  12.974194  14.663333   \n",
       "1986  -6.725806 -13.539286  0.158065   6.680000  13.648387  18.633333   \n",
       "1987 -17.506452  -6.057143 -5.312903   2.810000  12.832258  17.736667   \n",
       "1988  -7.241935  -6.113793 -0.993548   5.346667  13.848387  19.530000   \n",
       "1989  -2.112903  -0.503571  1.990323   7.656667  13.396774  20.053333   \n",
       "1990  -5.667742   0.382143  1.980645   8.120000  10.803226  14.506667   \n",
       "1991  -4.875862  -6.678571 -1.219355   7.030000  13.387097  18.783333   \n",
       "1992  -5.270968  -4.313793  1.654839   5.140000  11.932258  16.693333   \n",
       "1993  -4.416129  -4.882143 -1.932258   5.730000  14.490323  14.023333   \n",
       "1994  -3.422581 -11.300000 -2.929032   7.226667   9.848387  14.546667   \n",
       "1995  -5.874194  -0.846429  0.587097   9.116667  14.470968  19.683333   \n",
       "1996 -10.019355  -9.689655 -3.045161   6.403333  15.703226  16.526667   \n",
       "1997  -7.732258  -4.682143 -0.932258   4.650000  11.093548  17.830000   \n",
       "1998  -4.725806  -7.642857 -1.348387   3.900000  13.712903  19.976667   \n",
       "1999  -4.577419  -6.253571 -0.835484   9.683333   8.674194  21.423333   \n",
       "2000  -6.112903  -2.703448 -0.700000  11.110000  10.825806  16.236667   \n",
       "2001  -4.312903  -7.192857 -2.125806  10.966667  11.251613  16.266667   \n",
       "2002  -4.754839  -0.425000  2.229032   7.183333  12.732258  17.310000   \n",
       "2003  -7.351613  -8.653571 -2.729032   4.673333  15.535484  12.820000   \n",
       "2004  -6.458065  -7.006897  1.345161   4.556667  11.412903  15.283333   \n",
       "2005  -3.000000  -8.903571 -6.029032   7.116667  14.832258  16.506667   \n",
       "2006 -10.787097 -13.307143 -3.719355   6.020000  12.432258  18.203333   \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  \n",
       "1954  21.025806  18.354839  12.286667  5.738710 -1.573333  -4.974194  \n",
       "1955  17.906452  17.916129  13.856667  7.712903 -3.023333 -14.306452  \n",
       "1956  15.190323  14.625806   8.423333  4.670968 -5.180000  -4.254839  \n",
       "1957  18.554839  17.087097  12.313333  5.241935 -0.893333  -4.622581  \n",
       "1958  18.367742  15.696774   9.080000  6.038710 -0.786667  -7.641935  \n",
       "1959  20.538710  16.932258   8.253333  2.229032 -5.110000 -10.990323  \n",
       "1960  21.009677  16.190323   9.800000  2.338710 -3.620000   0.167742  \n",
       "1961  19.374194  16.825806   9.686667  6.590323 -1.550000  -8.070968  \n",
       "1962  16.361290  14.832258  10.790000  6.451613  1.343333  -7.351613  \n",
       "1963  19.132258  17.796774  13.216667  5.651613 -0.216667  -8.751613  \n",
       "1964  19.990323  16.006452  11.806667  6.893548 -2.306667  -2.858065  \n",
       "1965  16.661290  15.735484  12.923333  3.806452 -5.786667  -1.493548  \n",
       "1966  19.212903  16.645161   9.543333  6.087097 -0.900000 -10.525806  \n",
       "1967  17.706452  18.361290  11.353333  9.003226  0.420000  -9.583871  \n",
       "1968  15.725806  17.712903  10.790000  2.954839 -2.533333  -5.322581  \n",
       "1969  17.803226  16.445161  10.220000  4.690323  1.786667  -9.158065  \n",
       "1970  19.316129  16.258065  11.090000  5.393548 -1.823333  -5.887097  \n",
       "1971  17.467742  16.729032  10.780000  3.222581 -0.630000  -5.651613  \n",
       "1972  22.412903  20.632258  10.983333  5.167742 -0.186667  -0.906452  \n",
       "1973  17.977419  15.909677   7.633333  3.845161 -2.060000  -5.861290  \n",
       "1974  18.180645  15.874194  13.103333  8.680645  1.776667  -2.335484  \n",
       "1975  18.500000  15.019355  13.663333  4.093548 -3.296667  -4.032258  \n",
       "1976  16.103226  14.500000   9.653333 -0.948387 -0.803333  -3.690323  \n",
       "1977  18.764516  15.825806   9.506667  3.100000  1.726667  -8.174194  \n",
       "1978  16.345161  15.774194   9.723333  3.341935  2.003333 -14.509677  \n",
       "1979  16.674194  16.938710  11.730000  3.838710 -0.913333  -5.658065  \n",
       "1980  17.200000  14.709677  10.536667  5.190323 -2.040000  -4.248387  \n",
       "1981  21.512903  17.396774  10.816667  7.806452 -0.583333  -3.509677  \n",
       "1982  18.400000  16.645161  11.753333  4.064516  2.033333  -1.135484  \n",
       "1983  17.938710  16.032258  12.413333  6.200000 -1.466667  -3.180645  \n",
       "1984  17.567742  15.125806  12.410000  6.800000 -3.543333  -9.590323  \n",
       "1985  16.438710  19.419355  10.123333  6.070968 -3.333333  -6.535484  \n",
       "1986  17.796774  16.516129   8.580000  4.174194 -0.093333  -7.454839  \n",
       "1987  16.764516  15.087097   9.046667  3.590323 -3.646667  -6.983871  \n",
       "1988  21.587097  16.474194  11.280000  4.870968 -4.440000  -6.935484  \n",
       "1989  19.161290  16.248387  12.176667  5.329032 -2.633333  -5.293548  \n",
       "1990  17.535484  15.954839   9.320000  5.358065  0.250000  -3.393548  \n",
       "1991  18.080645  16.090323  10.973333  6.538710  0.970000  -3.974194  \n",
       "1992  18.622581  18.041935  13.146667  2.154839 -2.533333  -4.396774  \n",
       "1993  17.506452  15.425806   6.870000  4.645161 -8.003333  -3.612903  \n",
       "1994  17.561290  15.870968  13.676667  4.958065 -2.526667  -7.948387  \n",
       "1995  17.548387  16.793548  12.823333  6.683871 -2.816667  -9.506452  \n",
       "1996  18.900000  17.274194   9.880000  6.051613  3.863333  -7.019355  \n",
       "1997  18.729032  17.122581   8.516667  3.677419 -0.843333  -7.538710  \n",
       "1998  18.864516  15.487097  10.700000  5.658065 -8.030000  -5.938710  \n",
       "1999  21.719355  16.406452  11.770000  7.374194 -4.933333  -1.706452  \n",
       "2000  19.332258  16.754839  10.043333  7.180645 -0.050000  -2.625806  \n",
       "2001  22.967742  16.964516  12.193333  4.806452 -0.526667 -10.580645  \n",
       "2002  22.638710  17.041935  12.030000  2.545161 -1.476667 -12.561290  \n",
       "2003  20.641935  16.948387  11.333333  5.567742  1.146667  -2.061290  \n",
       "2004  19.022581  18.383871  12.133333  5.935484 -1.560000  -2.935484  \n",
       "2005  19.309677  17.638710  13.123333  6.022581  1.370000  -4.148387  \n",
       "2006  17.977419  17.535484  13.303333  7.003226       NaN        NaN  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df['TMPMN'].unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "necessary-delay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1949    -1.300000\n",
       "1950   -14.348387\n",
       "1951    -8.709677\n",
       "1952    -1.648387\n",
       "1953    -7.300000\n",
       "1954   -10.796774\n",
       "1955    -3.664516\n",
       "1956    -7.525806\n",
       "1957    -3.216129\n",
       "1958    -3.861290\n",
       "1959    -1.758065\n",
       "1960    -6.941935\n",
       "1961    -3.741935\n",
       "1962    -1.670968\n",
       "1963   -12.864516\n",
       "1964    -3.874194\n",
       "1965    -6.551613\n",
       "1966    -6.922581\n",
       "1967   -10.077419\n",
       "1968   -12.158065\n",
       "1969   -12.458065\n",
       "1970    -6.567742\n",
       "1971    -1.477419\n",
       "1972   -11.470968\n",
       "1973    -6.406452\n",
       "1974    -7.000000\n",
       "1975    -1.232258\n",
       "1976    -9.348387\n",
       "1977    -8.458065\n",
       "1978    -4.593548\n",
       "1979    -7.612903\n",
       "1980    -7.541935\n",
       "1981    -3.293548\n",
       "1982    -7.022581\n",
       "1983    -1.838710\n",
       "1984    -2.309677\n",
       "1985    -6.396774\n",
       "1986    -3.970968\n",
       "1987   -14.448387\n",
       "1988    -5.264516\n",
       "1989     0.138710\n",
       "1990    -3.116129\n",
       "1991    -2.372414\n",
       "1992    -2.300000\n",
       "1993    -2.248387\n",
       "1994    -1.393548\n",
       "1995    -3.845161\n",
       "1996    -7.167742\n",
       "1997    -4.680645\n",
       "1998    -2.703226\n",
       "1999    -2.212903\n",
       "2000    -4.116129\n",
       "2001    -2.335484\n",
       "2002    -1.948387\n",
       "2003    -4.338710\n",
       "2004    -4.187097\n",
       "2005    -1.080645\n",
       "2006    -8.241935\n",
       "Name: (TMPMAX, 1), dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.unstack()['TMPMAX', 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cardiac-rescue",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2771</th>\n",
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       "      <td>PRECIP</td>\n",
       "      <td>2.013333</td>\n",
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       "    <tr>\n",
       "      <th>2772</th>\n",
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       "      <td>9.470968</td>\n",
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       "    <tr>\n",
       "      <th>2773</th>\n",
       "      <td>2006</td>\n",
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       "      <td>TMPMIN</td>\n",
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       "      <th>2774</th>\n",
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       "<p>2776 rows × 4 columns</p>\n",
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      ],
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       "      year  mon level_2         0\n",
       "0     1949    1  TMPMAX -1.300000\n",
       "1     1949    1  TMPMIN -6.493548\n",
       "2     1949    1   TMPMN -3.670968\n",
       "3     1949    1  PRECIP  0.854839\n",
       "4     1949    2  TMPMAX -3.571429\n",
       "...    ...  ...     ...       ...\n",
       "2771  2006    9  PRECIP  2.013333\n",
       "2772  2006   10  TMPMAX  9.470968\n",
       "2773  2006   10  TMPMIN  4.687097\n",
       "2774  2006   10   TMPMN  7.003226\n",
       "2775  2006   10  PRECIP  1.693548\n",
       "\n",
       "[2776 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiindexed_df.stack().to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "passive-regression",
   "metadata": {},
   "outputs": [
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       "            TMPMAX  TMPMIN  TMPMN  PRECIP  mon  year\n",
       "DATE_OBS                                            \n",
       "1948-12-11     2.4    -4.5   -1.1     0.0   12  1948\n",
       "1948-12-12     1.9    -8.7   -6.2     0.5   12  1948\n",
       "1948-12-13    -7.4   -12.3  -10.1     0.0   12  1948"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "local-illness",
   "metadata": {},
   "outputs": [
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       "      <th>1948-12-14</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1948</td>\n",
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      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP  mon  year\n",
       "DATE_OBS                                            \n",
       "1948-12-11     2.4    -4.5   -1.1     0.0   12  1948\n",
       "1948-12-12     1.9    -8.7   -6.2     0.5   12  1948\n",
       "1948-12-13    -7.4   -12.3  -10.1     0.0   12  1948\n",
       "1948-12-14    -5.6   -13.2   -9.4     0.0   12  1948"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:][:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "apart-project",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948-12-11</th>\n",
       "      <td>2.4</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-12</th>\n",
       "      <td>1.9</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>12</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-13</th>\n",
       "      <td>-7.4</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-10.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-14</th>\n",
       "      <td>-5.6</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN  PRECIP  mon  year\n",
       "DATE_OBS                                            \n",
       "1948-12-11     2.4    -4.5   -1.1     0.0   12  1948\n",
       "1948-12-12     1.9    -8.7   -6.2     0.5   12  1948\n",
       "1948-12-13    -7.4   -12.3  -10.1     0.0   12  1948\n",
       "1948-12-14    -5.6   -13.2   -9.4     0.0   12  1948"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "substantial-hygiene",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TMPMAX</th>\n",
       "      <th>TMPMIN</th>\n",
       "      <th>TMPMN</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>1948-12-11</th>\n",
       "      <td>2.4</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-12</th>\n",
       "      <td>1.9</td>\n",
       "      <td>-8.7</td>\n",
       "      <td>-6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-13</th>\n",
       "      <td>-7.4</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-14</th>\n",
       "      <td>-5.6</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>-9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948-12-15</th>\n",
       "      <td>-3.6</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>-6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-27</th>\n",
       "      <td>8.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-28</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-29</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-30</th>\n",
       "      <td>1.8</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-31</th>\n",
       "      <td>1.3</td>\n",
       "      <td>-7.7</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21142 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TMPMAX  TMPMIN  TMPMN\n",
       "DATE_OBS                         \n",
       "1948-12-11     2.4    -4.5   -1.1\n",
       "1948-12-12     1.9    -8.7   -6.2\n",
       "1948-12-13    -7.4   -12.3  -10.1\n",
       "1948-12-14    -5.6   -13.2   -9.4\n",
       "1948-12-15    -3.6    -9.2   -6.6\n",
       "...            ...     ...    ...\n",
       "2006-10-27     8.7     2.7    5.6\n",
       "2006-10-28    11.0     4.1    8.2\n",
       "2006-10-29     4.6     1.4    2.4\n",
       "2006-10-30     1.8    -1.9   -0.7\n",
       "2006-10-31     1.3    -7.7   -3.0\n",
       "\n",
       "[21142 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, :3]"
   ]
  }
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