{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas: Groupby\n",
    "\n",
    "`groupby` is an amazingly powerful function in pandas. But it is also complicated to use and understand.\n",
    "The point of this lesson is to make you feel confident in using `groupby` and its cousins, `resample` and `rolling`. \n",
    "\n",
    "These notes are loosely based on the [Pandas GroupBy Documentation](http://pandas.pydata.org/pandas-docs/stable/groupby.html).\n",
    "\n",
    "The \"split/apply/combine\" concept was first introduced in a paper by Hadley Wickham: <https://www.jstatsoft.org/article/view/v040i01>.\n",
    "\n",
    "\n",
    "Imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (12,7)\n",
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we read the Earthquake data from our previous assignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usc000mqlp</th>\n",
       "      <td>2014-01-31 23:08:03.660</td>\n",
       "      <td>-4.9758</td>\n",
       "      <td>153.9466</td>\n",
       "      <td>110.18</td>\n",
       "      <td>4.2</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.940</td>\n",
       "      <td>0.61</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>115km ESE of Taron, Papua New Guinea</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqln</th>\n",
       "      <td>2014-01-31 22:54:32.970</td>\n",
       "      <td>-28.1775</td>\n",
       "      <td>-177.9058</td>\n",
       "      <td>95.84</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.063</td>\n",
       "      <td>1.14</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>120km N of Raoul Island, New Zealand</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>New Zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqls</th>\n",
       "      <td>2014-01-31 22:49:49.740</td>\n",
       "      <td>-23.1192</td>\n",
       "      <td>179.1174</td>\n",
       "      <td>528.34</td>\n",
       "      <td>4.4</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.439</td>\n",
       "      <td>0.95</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>South of the Fiji Islands</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>South of the Fiji Islands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mf1x</th>\n",
       "      <td>2014-01-31 22:19:44.330</td>\n",
       "      <td>51.1569</td>\n",
       "      <td>-178.0910</td>\n",
       "      <td>37.50</td>\n",
       "      <td>4.2</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>72km E of Amatignak Island, Alaska</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqlm</th>\n",
       "      <td>2014-01-31 21:56:44.320</td>\n",
       "      <td>-4.8800</td>\n",
       "      <td>153.8434</td>\n",
       "      <td>112.66</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1.808</td>\n",
       "      <td>0.79</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>100km ESE of Taron, Papua New Guinea</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time  latitude  longitude   depth  mag magType  \\\n",
       "id                                                                             \n",
       "usc000mqlp 2014-01-31 23:08:03.660   -4.9758   153.9466  110.18  4.2      mb   \n",
       "usc000mqln 2014-01-31 22:54:32.970  -28.1775  -177.9058   95.84  4.3      mb   \n",
       "usc000mqls 2014-01-31 22:49:49.740  -23.1192   179.1174  528.34  4.4      mb   \n",
       "usc000mf1x 2014-01-31 22:19:44.330   51.1569  -178.0910   37.50  4.2      mb   \n",
       "usc000mqlm 2014-01-31 21:56:44.320   -4.8800   153.8434  112.66  4.3      mb   \n",
       "\n",
       "            nst    gap   dmin   rms net                   updated  \\\n",
       "id                                                                  \n",
       "usc000mqlp  NaN   98.0  1.940  0.61  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mqln  NaN  104.0  1.063  1.14  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mqls  NaN   80.0  5.439  0.95  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mf1x  NaN    NaN    NaN  0.83  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mqlm  NaN  199.0  1.808  0.79  us  2014-04-08T01:43:19.000Z   \n",
       "\n",
       "                                           place        type  \\\n",
       "id                                                             \n",
       "usc000mqlp  115km ESE of Taron, Papua New Guinea  earthquake   \n",
       "usc000mqln  120km N of Raoul Island, New Zealand  earthquake   \n",
       "usc000mqls             South of the Fiji Islands  earthquake   \n",
       "usc000mf1x    72km E of Amatignak Island, Alaska  earthquake   \n",
       "usc000mqlm  100km ESE of Taron, Papua New Guinea  earthquake   \n",
       "\n",
       "                              country  \n",
       "id                                     \n",
       "usc000mqlp           Papua New Guinea  \n",
       "usc000mqln                New Zealand  \n",
       "usc000mqls  South of the Fiji Islands  \n",
       "usc000mf1x                     Alaska  \n",
       "usc000mqlm           Papua New Guinea  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('http://www.ldeo.columbia.edu/~rpa/usgs_earthquakes_2014.csv', parse_dates=['time'], index_col='id')\n",
    "df['country'] = df.place.str.split(', ').str[-1]\n",
    "df_small = df[df.mag<4]\n",
    "df = df[df.mag>4]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Example\n",
    "\n",
    "This is an example of a \"one-liner\" that you can accomplish with groupby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112e20780>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9YlV9C9gFIMnGwA+Bo4FnAP9aVa+ffnySnYG9gLsBtwc+n2Snqrp2fWOQJEmShrRQZRuP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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d17cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('country').mag.count().nlargest(20).plot(kind='bar', figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112b42d68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C/S7M+0lEPD4zL2087kTbDM0S7xcRn2J+H9pWjouItSgdJi6kJHJfaRzD3Zn5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\ntXmr4h0l6T4R8T0WLv+4hTKD/aXMvL1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111793320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_small.groupby('country').mag.count().nlargest(20).plot(kind='bar', figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What Happened?\n",
    "\n",
    "Let's break apart this operation a bit. The workflow with `groubpy` can be divided into three general steps:\n",
    "\n",
    "1. **Split**: Partition the data into different groups based on some criterion.\n",
    "1. **Apply**: Do some caclulation within each group. Different types of \"apply\" steps might be\n",
    "  1. *Aggregation*: Get the mean or max within the group.\n",
    "  1. *Transformation*: Normalize all the values within a group\n",
    "  1. *Filtration*: Eliminate some groups based on a criterion.\n",
    "1. **Combine**: Put the results back together into a single object.\n",
    "\n",
    "![split-apply-combine](https://miro.medium.com/max/1840/1*JbF6nhrQsn4f-TaSF6IR9g.png)\n",
    "\n",
    "### The `groupby` method\n",
    "\n",
    "Both `Series` and `DataFrame` objects have a groupby method. It accepts a variety of arguments, but the simplest way to think about it is that you pass another series, whose unique values are used to split the original object into different groups.\n",
    "\n",
    "via <https://medium.com/analytics-vidhya/split-apply-combine-strategy-for-data-mining-4fd6e2a0cc99>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10626f358>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df.country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a shortcut for doing this with dataframes: you just pass the column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10626f208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `GroubBy` object\n",
    "\n",
    "When we call, `groupby` we get back a `GroupBy` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1131bcf60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = df.groupby('country')\n",
    "gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length tells us how many groups were found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the groups are available as a dictionary via the `.groups` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = gb.groups\n",
    "len(groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " 'Afghanistan',\n",
       " 'Alaska',\n",
       " 'Albania',\n",
       " 'Algeria',\n",
       " 'American Samoa',\n",
       " 'Angola',\n",
       " 'Anguilla',\n",
       " 'Antarctica',\n",
       " 'Argentina',\n",
       " 'Arizona',\n",
       " 'Aruba',\n",
       " 'Ascension Island region',\n",
       " 'Australia',\n",
       " 'Azerbaijan',\n",
       " 'Azores Islands region',\n",
       " 'Azores-Cape St. Vincent Ridge',\n",
       " 'Balleny Islands region',\n",
       " 'Banda Sea',\n",
       " 'Bangladesh',\n",
       " 'Barbados',\n",
       " 'Barbuda',\n",
       " 'Bay of Bengal',\n",
       " 'Bermuda',\n",
       " 'Bhutan',\n",
       " 'Bolivia',\n",
       " 'Bosnia and Herzegovina',\n",
       " 'Bouvet Island',\n",
       " 'Bouvet Island region',\n",
       " 'Brazil',\n",
       " 'British Indian Ocean Territory',\n",
       " 'British Virgin Islands',\n",
       " 'Burma',\n",
       " 'Burundi',\n",
       " 'California',\n",
       " 'Canada',\n",
       " 'Cape Verde',\n",
       " 'Carlsberg Ridge',\n",
       " 'Cayman Islands',\n",
       " 'Celebes Sea',\n",
       " 'Central East Pacific Rise',\n",
       " 'Central Mid-Atlantic Ridge',\n",
       " 'Chagos Archipelago region',\n",
       " 'Chile',\n",
       " 'China',\n",
       " 'Christmas Island',\n",
       " 'Colombia',\n",
       " 'Comoros',\n",
       " 'Cook Islands',\n",
       " 'Costa Rica',\n",
       " 'Crozet Islands region',\n",
       " 'Cuba',\n",
       " 'Cyprus',\n",
       " 'Davis Strait',\n",
       " 'Democratic Republic of the Congo',\n",
       " 'Djibouti',\n",
       " 'Dominica',\n",
       " 'Dominican Republic',\n",
       " 'Drake Passage',\n",
       " 'East Timor',\n",
       " 'East of Severnaya Zemlya',\n",
       " 'East of the Kuril Islands',\n",
       " 'East of the North Island of New Zealand',\n",
       " 'East of the Philippine Islands',\n",
       " 'East of the South Sandwich Islands',\n",
       " 'Easter Island region',\n",
       " 'Eastern Greenland',\n",
       " 'Ecuador',\n",
       " 'Ecuador region',\n",
       " 'Egypt',\n",
       " 'El Salvador',\n",
       " 'Eritrea',\n",
       " 'Ethiopia',\n",
       " 'Falkland Islands region',\n",
       " 'Federated States of Micronesia region',\n",
       " 'Fiji',\n",
       " 'Fiji region',\n",
       " 'France',\n",
       " 'French Polynesia',\n",
       " 'French Southern Territories',\n",
       " 'Galapagos Triple Junction region',\n",
       " 'Georgia',\n",
       " 'Greece',\n",
       " 'Greenland',\n",
       " 'Greenland Sea',\n",
       " 'Guadeloupe',\n",
       " 'Guam',\n",
       " 'Guatemala',\n",
       " 'Gulf of Alaska',\n",
       " 'Haiti',\n",
       " 'Hawaii',\n",
       " 'Honduras',\n",
       " 'Iceland',\n",
       " 'Idaho',\n",
       " 'India',\n",
       " 'India region',\n",
       " 'Indian Ocean Triple Junction',\n",
       " 'Indonesia',\n",
       " 'Iran',\n",
       " 'Iraq',\n",
       " 'Italy',\n",
       " 'Japan',\n",
       " 'Japan region',\n",
       " 'Jordan',\n",
       " 'Kansas',\n",
       " 'Kazakhstan',\n",
       " 'Kermadec Islands region',\n",
       " 'Kosovo',\n",
       " 'Kuril Islands',\n",
       " 'Kyrgyzstan',\n",
       " 'Labrador Sea',\n",
       " 'Laptev Sea',\n",
       " 'Macedonia',\n",
       " 'Macquarie Island region',\n",
       " 'Malawi',\n",
       " 'Malaysia',\n",
       " 'Mariana Islands region',\n",
       " 'Martinique',\n",
       " 'Mauritania',\n",
       " 'Mauritius',\n",
       " 'Mauritius - Reunion region',\n",
       " 'Mexico',\n",
       " 'Micronesia',\n",
       " 'Mid-Indian Ridge',\n",
       " 'Molucca Sea',\n",
       " 'Mongolia',\n",
       " 'Montana',\n",
       " 'Montenegro',\n",
       " 'Morocco',\n",
       " 'Mozambique',\n",
       " 'Mozambique Channel',\n",
       " 'Nepal',\n",
       " 'New Caledonia',\n",
       " 'New Mexico',\n",
       " 'New Zealand',\n",
       " 'Nicaragua',\n",
       " 'Niue',\n",
       " 'North Atlantic Ocean',\n",
       " 'North Indian Ocean',\n",
       " 'North Korea',\n",
       " 'North of Ascension Island',\n",
       " 'North of Franz Josef Land',\n",
       " 'North of New Zealand',\n",
       " 'North of Severnaya Zemlya',\n",
       " 'North of Svalbard',\n",
       " 'Northern East Pacific Rise',\n",
       " 'Northern Mariana Islands',\n",
       " 'Northern Mid-Atlantic Ridge',\n",
       " 'Northwest of Australia',\n",
       " 'Norway',\n",
       " 'Norwegian Sea',\n",
       " 'Off the coast of Central America',\n",
       " 'Off the coast of Ecuador',\n",
       " 'Off the coast of Oregon',\n",
       " 'Off the east coast of the North Island of New Zealand',\n",
       " 'Off the south coast of Australia',\n",
       " 'Off the west coast of northern Sumatra',\n",
       " 'Oklahoma',\n",
       " 'Oman',\n",
       " 'Oregon',\n",
       " 'Owen Fracture Zone region',\n",
       " 'Pacific-Antarctic Ridge',\n",
       " 'Pakistan',\n",
       " 'Palau',\n",
       " 'Palau region',\n",
       " 'Panama',\n",
       " 'Papua New Guinea',\n",
       " 'Peru',\n",
       " 'Peru-Ecuador border region',\n",
       " 'Philippine Islands region',\n",
       " 'Philippines',\n",
       " 'Poland',\n",
       " 'Portugal',\n",
       " 'Portugal region',\n",
       " 'Prince Edward Islands',\n",
       " 'Prince Edward Islands region',\n",
       " 'Puerto Rico',\n",
       " 'Republic of the Congo',\n",
       " 'Reykjanes Ridge',\n",
       " 'Romania',\n",
       " 'Russia',\n",
       " 'Russia region',\n",
       " 'Saint Helena',\n",
       " 'Saint Lucia',\n",
       " 'Saint Vincent and the Grenadines',\n",
       " 'Samoa',\n",
       " 'Santa Cruz Islands region',\n",
       " 'Saudi Arabia',\n",
       " 'Scotia Sea',\n",
       " 'Sea of Okhotsk',\n",
       " 'Serbia',\n",
       " 'Slovenia',\n",
       " 'Socotra region',\n",
       " 'Solomon Islands',\n",
       " 'Somalia',\n",
       " 'South Africa',\n",
       " 'South Atlantic Ocean',\n",
       " 'South Carolina',\n",
       " 'South Georgia Island region',\n",
       " 'South Georgia and the South Sandwich Islands',\n",
       " 'South Indian Ocean',\n",
       " 'South Napa Earthquake',\n",
       " 'South Sandwich Islands',\n",
       " 'South Sandwich Islands region',\n",
       " 'South Shetland Islands',\n",
       " 'South Sudan',\n",
       " 'South of Africa',\n",
       " 'South of Australia',\n",
       " 'South of Panama',\n",
       " 'South of Tasmania',\n",
       " 'South of Tonga',\n",
       " 'South of the Fiji Islands',\n",
       " 'South of the Kermadec Islands',\n",
       " 'South of the Mariana Islands',\n",
       " 'Southeast Indian Ridge',\n",
       " 'Southeast central Pacific Ocean',\n",
       " 'Southeast of Easter Island',\n",
       " 'Southern East Pacific Rise',\n",
       " 'Southern Mid-Atlantic Ridge',\n",
       " 'Southern Pacific Ocean',\n",
       " 'Southwest Indian Ridge',\n",
       " 'Southwest of Africa',\n",
       " 'Southwest of Australia',\n",
       " 'Southwestern Atlantic Ocean',\n",
       " 'Spain',\n",
       " 'Sudan',\n",
       " 'Svalbard and Jan Mayen',\n",
       " 'Sweden',\n",
       " 'Syria',\n",
       " 'Taiwan',\n",
       " 'Tajikistan',\n",
       " 'Tanzania',\n",
       " 'Thailand',\n",
       " 'Tonga',\n",
       " 'Tonga region',\n",
       " 'Trinidad and Tobago',\n",
       " 'Tristan da Cunha region',\n",
       " 'Turkey',\n",
       " 'Turkmenistan',\n",
       " 'Uganda',\n",
       " 'Ukraine',\n",
       " 'United Kingdom',\n",
       " 'Utah',\n",
       " 'Uzbekistan',\n",
       " 'Vanuatu',\n",
       " 'Vanuatu region',\n",
       " 'Venezuela',\n",
       " 'Vietnam',\n",
       " 'Wallis and Futuna',\n",
       " 'West Chile Rise',\n",
       " 'West of Australia',\n",
       " 'West of Macquarie Island',\n",
       " 'West of Vancouver Island',\n",
       " 'West of the Galapagos Islands',\n",
       " 'Western Australia',\n",
       " 'Western Indian-Antarctic Ridge',\n",
       " 'Yemen',\n",
       " 'Zambia',\n",
       " 'north of Ascension Island',\n",
       " 'northern Mid-Atlantic Ridge',\n",
       " 'south of Panama',\n",
       " 'western Xizang']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(groups.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating and selecting groups\n",
    "\n",
    "You can loop through the groups if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usc000mkc8</th>\n",
       "      <td>2014-01-25 16:10:38.760</td>\n",
       "      <td>-55.2925</td>\n",
       "      <td>-27.0527</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.1</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.553</td>\n",
       "      <td>0.35</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-27T18:15:40.000Z</td>\n",
       "      <td>156km N of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mkc7</th>\n",
       "      <td>2014-01-25 09:43:23.230</td>\n",
       "      <td>-55.9434</td>\n",
       "      <td>-27.6772</td>\n",
       "      <td>103.48</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.324</td>\n",
       "      <td>0.62</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-27T18:15:40.000Z</td>\n",
       "      <td>89km NNW of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mh0c</th>\n",
       "      <td>2014-01-19 15:42:45.510</td>\n",
       "      <td>-56.9656</td>\n",
       "      <td>-26.7803</td>\n",
       "      <td>120.25</td>\n",
       "      <td>4.6</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151.0</td>\n",
       "      <td>6.120</td>\n",
       "      <td>0.39</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-22T00:05:23.000Z</td>\n",
       "      <td>39km SE of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usb000m7f2</th>\n",
       "      <td>2014-01-10 13:50:38.730</td>\n",
       "      <td>-56.0110</td>\n",
       "      <td>-26.1186</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.2</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>6.192</td>\n",
       "      <td>0.71</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-15T03:38:58.000Z</td>\n",
       "      <td>101km NE of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usb000m78v</th>\n",
       "      <td>2014-01-07 06:51:09.160</td>\n",
       "      <td>-56.9651</td>\n",
       "      <td>-26.6696</td>\n",
       "      <td>124.37</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.0</td>\n",
       "      <td>6.176</td>\n",
       "      <td>0.59</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-03-07T00:26:01.000Z</td>\n",
       "      <td>43km SE of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time  latitude  longitude   depth  mag magType  \\\n",
       "id                                                                             \n",
       "usc000mkc8 2014-01-25 16:10:38.760  -55.2925   -27.0527   10.00  4.1      mb   \n",
       "usc000mkc7 2014-01-25 09:43:23.230  -55.9434   -27.6772  103.48  4.3      mb   \n",
       "usc000mh0c 2014-01-19 15:42:45.510  -56.9656   -26.7803  120.25  4.6      mb   \n",
       "usb000m7f2 2014-01-10 13:50:38.730  -56.0110   -26.1186   10.00  4.2      mb   \n",
       "usb000m78v 2014-01-07 06:51:09.160  -56.9651   -26.6696  124.37  4.3      mb   \n",
       "\n",
       "            nst    gap   dmin   rms net                   updated  \\\n",
       "id                                                                  \n",
       "usc000mkc8  NaN   97.0  5.553  0.35  us  2014-03-27T18:15:40.000Z   \n",
       "usc000mkc7  NaN   87.0  5.324  0.62  us  2014-03-27T18:15:40.000Z   \n",
       "usc000mh0c  NaN  151.0  6.120  0.39  us  2014-03-22T00:05:23.000Z   \n",
       "usb000m7f2  NaN  106.0  6.192  0.71  us  2014-03-15T03:38:58.000Z   \n",
       "usb000m78v  NaN  109.0  6.176  0.59  us  2014-03-07T00:26:01.000Z   \n",
       "\n",
       "                                   place        type country  \n",
       "id                                                            \n",
       "usc000mkc8   156km N of Visokoi Island,   earthquake          \n",
       "usc000mkc7  89km NNW of Visokoi Island,   earthquake          \n",
       "usc000mh0c   39km SE of Visokoi Island,   earthquake          \n",
       "usb000m7f2  101km NE of Visokoi Island,   earthquake          \n",
       "usb000m78v   43km SE of Visokoi Island,   earthquake          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The key is \"\"\n"
     ]
    }
   ],
   "source": [
    "for key, group in gb:\n",
    "    display(group.head())\n",
    "    print(f'The key is \"{key}\"')\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you can get a specific group by key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>usc000mqlq</th>\n",
       "      <td>2014-01-31 20:00:16.000</td>\n",
       "      <td>-33.6550</td>\n",
       "      <td>-71.9810</td>\n",
       "      <td>25.10</td>\n",
       "      <td>4.5</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.63</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:19.000Z</td>\n",
       "      <td>34km WSW of San Antonio, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mql6</th>\n",
       "      <td>2014-01-31 13:48:23.000</td>\n",
       "      <td>-18.0690</td>\n",
       "      <td>-69.6630</td>\n",
       "      <td>149.10</td>\n",
       "      <td>4.3</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.77</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:18.000Z</td>\n",
       "      <td>17km NW of Putre, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqk8</th>\n",
       "      <td>2014-01-30 14:20:56.560</td>\n",
       "      <td>-19.6118</td>\n",
       "      <td>-70.9487</td>\n",
       "      <td>15.16</td>\n",
       "      <td>4.1</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1.227</td>\n",
       "      <td>1.34</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:17.000Z</td>\n",
       "      <td>107km NW of Iquique, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mdi2</th>\n",
       "      <td>2014-01-30 10:02:14.000</td>\n",
       "      <td>-32.1180</td>\n",
       "      <td>-71.7860</td>\n",
       "      <td>25.70</td>\n",
       "      <td>4.5</td>\n",
       "      <td>mwr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.10</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-01-30T21:28:21.955Z</td>\n",
       "      <td>64km NW of La Ligua, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usc000mqeh</th>\n",
       "      <td>2014-01-29 18:58:23.000</td>\n",
       "      <td>-18.6610</td>\n",
       "      <td>-69.6440</td>\n",
       "      <td>123.10</td>\n",
       "      <td>4.8</td>\n",
       "      <td>mb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.52</td>\n",
       "      <td>us</td>\n",
       "      <td>2014-04-08T01:43:16.000Z</td>\n",
       "      <td>51km S of Putre, Chile</td>\n",
       "      <td>earthquake</td>\n",
       "      <td>Chile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time  latitude  longitude   depth  mag magType  \\\n",
       "id                                                                             \n",
       "usc000mqlq 2014-01-31 20:00:16.000  -33.6550   -71.9810   25.10  4.5      mb   \n",
       "usc000mql6 2014-01-31 13:48:23.000  -18.0690   -69.6630  149.10  4.3      mb   \n",
       "usc000mqk8 2014-01-30 14:20:56.560  -19.6118   -70.9487   15.16  4.1      mb   \n",
       "usc000mdi2 2014-01-30 10:02:14.000  -32.1180   -71.7860   25.70  4.5     mwr   \n",
       "usc000mqeh 2014-01-29 18:58:23.000  -18.6610   -69.6440  123.10  4.8      mb   \n",
       "\n",
       "            nst    gap   dmin   rms net                   updated  \\\n",
       "id                                                                  \n",
       "usc000mqlq  NaN    NaN    NaN  1.63  us  2014-04-08T01:43:19.000Z   \n",
       "usc000mql6  NaN    NaN    NaN  1.77  us  2014-04-08T01:43:18.000Z   \n",
       "usc000mqk8  NaN  159.0  1.227  1.34  us  2014-04-08T01:43:17.000Z   \n",
       "usc000mdi2  NaN    NaN    NaN  1.10  us  2015-01-30T21:28:21.955Z   \n",
       "usc000mqeh  NaN    NaN    NaN  1.52  us  2014-04-08T01:43:16.000Z   \n",
       "\n",
       "                                     place        type country  \n",
       "id                                                              \n",
       "usc000mqlq  34km WSW of San Antonio, Chile  earthquake   Chile  \n",
       "usc000mql6         17km NW of Putre, Chile  earthquake   Chile  \n",
       "usc000mqk8      107km NW of Iquique, Chile  earthquake   Chile  \n",
       "usc000mdi2      64km NW of La Ligua, Chile  earthquake   Chile  \n",
       "usc000mqeh          51km S of Putre, Chile  earthquake   Chile  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.get_group('Chile').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregation\n",
    "\n",
    "Now that we know how to create a `GroupBy` object, let's learn how to do aggregation on it.\n",
    "\n",
    "One way us to use the `.aggregate` method, which accepts another function as its argument. The result is automatically combined into a new dataframe with the group key as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>depth</th>\n",
       "      <th>mag</th>\n",
       "      <th>magType</th>\n",
       "      <th>nst</th>\n",
       "      <th>gap</th>\n",
       "      <th>dmin</th>\n",
       "      <th>rms</th>\n",
       "      <th>net</th>\n",
       "      <th>updated</th>\n",
       "      <th>place</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>2014-12-31 14:49:19.200</td>\n",
       "      <td>-37.5219</td>\n",
       "      <td>78.9418</td>\n",
       "      <td>248.18</td>\n",
       "      <td>6.9</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195.0</td>\n",
       "      <td>28.762</td>\n",
       "      <td>1.47</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-03-17T02:38:27.040Z</td>\n",
       "      <td>99km NW of Visokoi Island,</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2014-12-27 06:37:50.010</td>\n",
       "      <td>37.0112</td>\n",
       "      <td>71.6062</td>\n",
       "      <td>248.39</td>\n",
       "      <td>5.6</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172.0</td>\n",
       "      <td>3.505</td>\n",
       "      <td>1.55</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-06-22T20:12:10.712Z</td>\n",
       "      <td>8km SE of Ashkasham, Afghanistan</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>2014-12-30 21:22:21.580</td>\n",
       "      <td>67.9858</td>\n",
       "      <td>179.9288</td>\n",
       "      <td>266.61</td>\n",
       "      <td>7.9</td>\n",
       "      <td>mww</td>\n",
       "      <td>152.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7.712</td>\n",
       "      <td>2.15</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-05-30T05:34:08.822Z</td>\n",
       "      <td>9km WSW of Little Sitkin Island, Alaska</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2014-05-20 04:43:25.500</td>\n",
       "      <td>41.5297</td>\n",
       "      <td>20.2804</td>\n",
       "      <td>28.26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>mwr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.299</td>\n",
       "      <td>1.34</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-01-30T15:28:03.533Z</td>\n",
       "      <td>6km NE of Durres, Albania</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>2014-12-26 17:55:18.140</td>\n",
       "      <td>36.9391</td>\n",
       "      <td>5.6063</td>\n",
       "      <td>21.40</td>\n",
       "      <td>5.5</td>\n",
       "      <td>mww</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>1.45</td>\n",
       "      <td>us</td>\n",
       "      <td>2015-03-17T02:37:18.040Z</td>\n",
       "      <td>5km SSW of Bougara, Algeria</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               time  latitude  longitude   depth  mag magType  \\\n",
       "country                                                                         \n",
       "            2014-12-31 14:49:19.200  -37.5219    78.9418  248.18  6.9     mww   \n",
       "Afghanistan 2014-12-27 06:37:50.010   37.0112    71.6062  248.39  5.6     mww   \n",
       "Alaska      2014-12-30 21:22:21.580   67.9858   179.9288  266.61  7.9     mww   \n",
       "Albania     2014-05-20 04:43:25.500   41.5297    20.2804   28.26  5.0     mwr   \n",
       "Algeria     2014-12-26 17:55:18.140   36.9391     5.6063   21.40  5.5     mww   \n",
       "\n",
       "               nst    gap    dmin   rms net                   updated  \\\n",
       "country                                                                 \n",
       "               NaN  195.0  28.762  1.47  us  2015-03-17T02:38:27.040Z   \n",
       "Afghanistan    NaN  172.0   3.505  1.55  us  2015-06-22T20:12:10.712Z   \n",
       "Alaska       152.0  338.0   7.712  2.15  us  2015-05-30T05:34:08.822Z   \n",
       "Albania        NaN   69.0   1.299  1.34  us  2015-01-30T15:28:03.533Z   \n",
       "Algeria        NaN  174.0   3.250  1.45  us  2015-03-17T02:37:18.040Z   \n",
       "\n",
       "                                               place        type  \n",
       "country                                                           \n",
       "                         99km NW of Visokoi Island,   earthquake  \n",
       "Afghanistan         8km SE of Ashkasham, Afghanistan  earthquake  \n",
       "Alaska       9km WSW of Little Sitkin Island, Alaska  earthquake  \n",
       "Albania                    6km NE of Durres, Albania  earthquake  \n",
       "Algeria                  5km SSW of Bougara, Algeria  earthquake  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.aggregate(np.max).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the operation is applied to every column. That's usually not what we want. We can use both `.` or `[]` syntax to select a specific column to operate on. Then we get back a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "               6.9\n",
       "Afghanistan    5.6\n",
       "Alaska         7.9\n",
       "Albania        5.0\n",
       "Algeria        5.5\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.aggregate(np.max).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Chile                         8.2\n",
       "Alaska                        7.9\n",
       "Solomon Islands               7.6\n",
       "Papua New Guinea              7.5\n",
       "El Salvador                   7.3\n",
       "Mexico                        7.2\n",
       "Fiji                          7.1\n",
       "Indonesia                     7.1\n",
       "Southern East Pacific Rise    7.0\n",
       "                              6.9\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.aggregate(np.max).nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are shortcuts for common aggregation functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Chile                         8.2\n",
       "Alaska                        7.9\n",
       "Solomon Islands               7.6\n",
       "Papua New Guinea              7.5\n",
       "El Salvador                   7.3\n",
       "Mexico                        7.2\n",
       "Fiji                          7.1\n",
       "Indonesia                     7.1\n",
       "Southern East Pacific Rise    7.0\n",
       "                              6.9\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.max().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Mexico         4.01\n",
       "Oregon         4.02\n",
       "California     4.04\n",
       "               4.10\n",
       "Afghanistan    4.10\n",
       "Alaska         4.10\n",
       "Albania        4.10\n",
       "Algeria        4.10\n",
       "Angola         4.10\n",
       "Antarctica     4.10\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.min().nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "South Napa Earthquake          6.020000\n",
       "Bouvet Island region           5.750000\n",
       "South Georgia Island region    5.450000\n",
       "Barbados                       5.400000\n",
       "New Mexico                     5.300000\n",
       "Easter Island region           5.162500\n",
       "Malawi                         5.100000\n",
       "Drake Passage                  5.033333\n",
       "North Korea                    5.000000\n",
       "Saint Lucia                    5.000000\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.mean().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Barbados                              1.555635\n",
       "Bouvet Island region                  1.484924\n",
       "Puerto Rico                           0.957601\n",
       "Off the coast of Ecuador              0.848528\n",
       "Palau region                          0.777817\n",
       "East of the South Sandwich Islands    0.606495\n",
       "Southern East Pacific Rise            0.604508\n",
       "South Indian Ocean                    0.602194\n",
       "Prince Edward Islands region          0.595259\n",
       "Panama                                0.591322\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.std().nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also apply multiple functions at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>4.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4.582544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.410656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>4.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.515025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.391667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>4.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.583333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             amin  amax      mean\n",
       "country                          \n",
       "              4.1   6.9  4.582544\n",
       "Afghanistan   4.1   5.6  4.410656\n",
       "Alaska        4.1   7.9  4.515025\n",
       "Albania       4.1   5.0  4.391667\n",
       "Algeria       4.1   5.5  4.583333"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.mag.aggregate([np.min, np.max, np.mean]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112a09e80>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YlHn1RAn9n1WRXsNFLLMiYtcB982MiAkdPm6rLeZ9Iv+ADSRtTbpAKeCXEVHG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\n/wEz2s0FAPu8XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a097f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb.mag.aggregate([np.min, np.max, np.mean]).nlargest(10, 'mean').plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformation\n",
    "\n",
    "The key difference between aggregation and transformation is that aggregation returns a *smaller* object than the original, indexed by the group keys, while *transformation* returns an object with the same index (and same size) as the original object. Groupby + transformation is used when applying an operation that requires information about the whole group.\n",
    "\n",
    "In this example, we standardize the earthquakes in each country so that the distribution has zero mean and unit variance. We do this by first defining a function called `standardize` and then passing it to the `transform` method.\n",
    "\n",
    "I admit that I don't know why you would want to do this. `transform` makes more sense to me in the context of time grouping operation. See below for another example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "usc000mqlp   -0.915774\n",
       "usc000mqln   -0.675696\n",
       "usc000mqls   -0.282385\n",
       "usc000mf1x   -0.684915\n",
       "usc000mqlm   -0.666807\n",
       "Name: mag, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def standardize(x):\n",
    "    return (x - x.mean())/x.std()\n",
    "\n",
    "mag_standardized_by_country = gb.mag.transform(standardize)\n",
    "mag_standardized_by_country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Grouping\n",
    "\n",
    "We already saw how pandas has a strong built-in understanding of time. This capability is even more powerful in the context of `groupby`. With datasets indexed by a pandas `DateTimeIndex`, we can easily group and resample the data using common time units.\n",
    "\n",
    "To get started, let's load the timeseries data we already explored in past lessons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "import pandas as pd\n",
    "\n",
    "header_url = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/HEADERS.txt'\n",
    "with urllib.request.urlopen(header_url) as response:\n",
    "    data = response.read().decode('utf-8')\n",
    "lines = data.split('\\n')\n",
    "headers = lines[1].split(' ')\n",
    "\n",
    "ftp_base = 'ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/'\n",
    "dframes = []\n",
    "for year in range(2016, 2019):\n",
    "    data_url = f'{year}/CRND0103-{year}-NY_Millbrook_3_W.txt'               \n",
    "    df = pd.read_csv(ftp_base + data_url, parse_dates=[1],\n",
    "                     names=headers, header=None, sep='\\s+',\n",
    "                     na_values=[-9999.0, -99.0])\n",
    "    dframes.append(df)\n",
    "\n",
    "df = pd.concat(dframes)\n",
    "df = df.set_index('LST_DATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>3.4</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.147</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.144</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>5.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.141</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-14.4</td>\n",
       "      <td>-6.9</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.139</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>-15.5</td>\n",
       "      <td>-10.3</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            WBANNO  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "2016-01-01   64756   2.422     -73.74     41.79          3.4         -0.5   \n",
       "2016-01-02   64756   2.422     -73.74     41.79          2.9         -3.6   \n",
       "2016-01-03   64756   2.422     -73.74     41.79          5.1         -1.8   \n",
       "2016-01-04   64756   2.422     -73.74     41.79          0.5        -14.4   \n",
       "2016-01-05   64756   2.422     -73.74     41.79         -5.2        -15.5   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY ...  \\\n",
       "LST_DATE                                                           ...   \n",
       "2016-01-01           1.5          1.3           0.0           1.69 ...   \n",
       "2016-01-02          -0.4         -0.3           0.0           6.25 ...   \n",
       "2016-01-03           1.6          1.1           0.0           5.69 ...   \n",
       "2016-01-04          -6.9         -7.5           0.0           9.17 ...   \n",
       "2016-01-05         -10.3        -11.7           0.0           9.34 ...   \n",
       "\n",
       "           SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  \\\n",
       "LST_DATE                                                    \n",
       "2016-01-01                  0.233                   0.204   \n",
       "2016-01-02                  0.227                   0.199   \n",
       "2016-01-03                  0.223                   0.196   \n",
       "2016-01-04                  0.220                   0.194   \n",
       "2016-01-05                  0.213                   0.191   \n",
       "\n",
       "            SOIL_MOISTURE_50_DAILY  SOIL_MOISTURE_100_DAILY  \\\n",
       "LST_DATE                                                      \n",
       "2016-01-01                   0.155                    0.147   \n",
       "2016-01-02                   0.152                    0.144   \n",
       "2016-01-03                   0.151                    0.141   \n",
       "2016-01-04                   0.148                    0.139   \n",
       "2016-01-05                   0.148                    0.138   \n",
       "\n",
       "            SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  SOIL_TEMP_20_DAILY  \\\n",
       "LST_DATE                                                                \n",
       "2016-01-01                4.2                 4.4                 5.1   \n",
       "2016-01-02                2.8                 3.1                 4.2   \n",
       "2016-01-03                2.6                 2.8                 3.8   \n",
       "2016-01-04                1.7                 2.1                 3.4   \n",
       "2016-01-05                0.4                 0.9                 2.4   \n",
       "\n",
       "            SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY      \n",
       "LST_DATE                                                 \n",
       "2016-01-01                 6.0                  7.6 NaN  \n",
       "2016-01-02                 5.7                  7.4 NaN  \n",
       "2016-01-03                 5.2                  7.2 NaN  \n",
       "2016-01-04                 4.9                  6.9 NaN  \n",
       "2016-01-05                 4.3                  6.6 NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This timeseries has daily resolution, and the daily plots are somewhat noisy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112f05b00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jBmKUM+m8vHy7K+2rqOG/8H7tZiwNQx7kmSxz5ZX/+aNLJFuv9b9KNmRMU1m5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\nEwIfoIkHP+HnHw53EhMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a09518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.T_DAILY_MEAN.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common way to analyze such data in climate science is to create a \"climatology,\" which contains the average values in each month or day of the year. We can do this easily with groupby. Recall that `df.index` is a pandas `DateTimeIndex` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>2.924731</td>\n",
       "      <td>-7.120430</td>\n",
       "      <td>-2.100000</td>\n",
       "      <td>-1.905376</td>\n",
       "      <td>2.480645</td>\n",
       "      <td>5.811613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240292</td>\n",
       "      <td>0.199547</td>\n",
       "      <td>0.150376</td>\n",
       "      <td>0.162533</td>\n",
       "      <td>0.168817</td>\n",
       "      <td>0.232258</td>\n",
       "      <td>0.788172</td>\n",
       "      <td>1.749462</td>\n",
       "      <td>3.394624</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.487882</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>6.431765</td>\n",
       "      <td>-5.015294</td>\n",
       "      <td>0.712941</td>\n",
       "      <td>1.022353</td>\n",
       "      <td>4.080000</td>\n",
       "      <td>8.495176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244286</td>\n",
       "      <td>0.203089</td>\n",
       "      <td>0.154306</td>\n",
       "      <td>0.165506</td>\n",
       "      <td>1.217647</td>\n",
       "      <td>1.183529</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>1.602353</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>7.953763</td>\n",
       "      <td>-3.035484</td>\n",
       "      <td>2.455914</td>\n",
       "      <td>2.643011</td>\n",
       "      <td>2.786022</td>\n",
       "      <td>13.210645</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224202</td>\n",
       "      <td>0.190742</td>\n",
       "      <td>0.153140</td>\n",
       "      <td>0.161763</td>\n",
       "      <td>3.469892</td>\n",
       "      <td>3.407527</td>\n",
       "      <td>3.379570</td>\n",
       "      <td>3.472043</td>\n",
       "      <td>3.792473</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>14.793333</td>\n",
       "      <td>1.816667</td>\n",
       "      <td>8.302222</td>\n",
       "      <td>8.574444</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>15.295222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208600</td>\n",
       "      <td>0.183911</td>\n",
       "      <td>0.150022</td>\n",
       "      <td>0.159989</td>\n",
       "      <td>9.402222</td>\n",
       "      <td>9.138889</td>\n",
       "      <td>8.438889</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>6.633333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>21.235484</td>\n",
       "      <td>8.460215</td>\n",
       "      <td>14.850538</td>\n",
       "      <td>15.121505</td>\n",
       "      <td>3.016129</td>\n",
       "      <td>17.287849</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198925</td>\n",
       "      <td>0.175452</td>\n",
       "      <td>0.146032</td>\n",
       "      <td>0.157710</td>\n",
       "      <td>16.889247</td>\n",
       "      <td>16.691398</td>\n",
       "      <td>15.569892</td>\n",
       "      <td>14.193548</td>\n",
       "      <td>12.344086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.627778</td>\n",
       "      <td>11.837778</td>\n",
       "      <td>18.733333</td>\n",
       "      <td>19.026667</td>\n",
       "      <td>3.053333</td>\n",
       "      <td>21.912111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132856</td>\n",
       "      <td>0.127044</td>\n",
       "      <td>0.128311</td>\n",
       "      <td>0.154978</td>\n",
       "      <td>22.372222</td>\n",
       "      <td>22.198889</td>\n",
       "      <td>20.916667</td>\n",
       "      <td>19.417778</td>\n",
       "      <td>17.452222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.568817</td>\n",
       "      <td>15.536559</td>\n",
       "      <td>22.054839</td>\n",
       "      <td>22.012903</td>\n",
       "      <td>3.867742</td>\n",
       "      <td>21.569140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100871</td>\n",
       "      <td>0.084957</td>\n",
       "      <td>0.112538</td>\n",
       "      <td>0.153278</td>\n",
       "      <td>25.453763</td>\n",
       "      <td>25.408602</td>\n",
       "      <td>24.141935</td>\n",
       "      <td>22.725806</td>\n",
       "      <td>20.994624</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.473118</td>\n",
       "      <td>15.351613</td>\n",
       "      <td>21.410753</td>\n",
       "      <td>21.378495</td>\n",
       "      <td>4.476344</td>\n",
       "      <td>18.492688</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150946</td>\n",
       "      <td>0.121290</td>\n",
       "      <td>0.125409</td>\n",
       "      <td>0.162650</td>\n",
       "      <td>24.784946</td>\n",
       "      <td>24.897849</td>\n",
       "      <td>24.117204</td>\n",
       "      <td>23.311828</td>\n",
       "      <td>22.278495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.488667</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.084444</td>\n",
       "      <td>12.032222</td>\n",
       "      <td>18.057778</td>\n",
       "      <td>17.866667</td>\n",
       "      <td>3.728889</td>\n",
       "      <td>13.625000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131144</td>\n",
       "      <td>0.113678</td>\n",
       "      <td>0.117722</td>\n",
       "      <td>0.156965</td>\n",
       "      <td>21.060000</td>\n",
       "      <td>21.218889</td>\n",
       "      <td>20.947778</td>\n",
       "      <td>20.823333</td>\n",
       "      <td>20.717778</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.533688</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>19.548000</td>\n",
       "      <td>6.704000</td>\n",
       "      <td>13.124000</td>\n",
       "      <td>13.149333</td>\n",
       "      <td>3.408000</td>\n",
       "      <td>9.659733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127653</td>\n",
       "      <td>0.087333</td>\n",
       "      <td>0.103053</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>15.598667</td>\n",
       "      <td>15.749333</td>\n",
       "      <td>15.986667</td>\n",
       "      <td>16.608000</td>\n",
       "      <td>17.434667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.522000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>10.986667</td>\n",
       "      <td>-1.536667</td>\n",
       "      <td>4.721667</td>\n",
       "      <td>4.858333</td>\n",
       "      <td>2.481667</td>\n",
       "      <td>6.991500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197867</td>\n",
       "      <td>0.159883</td>\n",
       "      <td>0.140783</td>\n",
       "      <td>0.150367</td>\n",
       "      <td>7.018333</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.860000</td>\n",
       "      <td>9.181667</td>\n",
       "      <td>11.268333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.522000</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>2.845161</td>\n",
       "      <td>-6.472581</td>\n",
       "      <td>-1.819355</td>\n",
       "      <td>-1.677419</td>\n",
       "      <td>2.454839</td>\n",
       "      <td>4.752903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225093</td>\n",
       "      <td>0.186726</td>\n",
       "      <td>0.154403</td>\n",
       "      <td>0.171903</td>\n",
       "      <td>1.732258</td>\n",
       "      <td>1.845161</td>\n",
       "      <td>2.622581</td>\n",
       "      <td>3.914516</td>\n",
       "      <td>6.038710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          WBANNO    CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "1          64756  2.488667     -73.74     41.79     2.924731    -7.120430   \n",
       "2          64756  2.487882     -73.74     41.79     6.431765    -5.015294   \n",
       "3          64756  2.488667     -73.74     41.79     7.953763    -3.035484   \n",
       "4          64756  2.488667     -73.74     41.79    14.793333     1.816667   \n",
       "5          64756  2.488667     -73.74     41.79    21.235484     8.460215   \n",
       "6          64756  2.488667     -73.74     41.79    25.627778    11.837778   \n",
       "7          64756  2.488667     -73.74     41.79    28.568817    15.536559   \n",
       "8          64756  2.488667     -73.74     41.79    27.473118    15.351613   \n",
       "9          64756  2.488667     -73.74     41.79    24.084444    12.032222   \n",
       "10         64756  2.533688     -73.74     41.79    19.548000     6.704000   \n",
       "11         64756  2.522000     -73.74     41.79    10.986667    -1.536667   \n",
       "12         64756  2.522000     -73.74     41.79     2.845161    -6.472581   \n",
       "\n",
       "          T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY ...  \\\n",
       "LST_DATE                                                         ...   \n",
       "1            -2.100000    -1.905376      2.480645       5.811613 ...   \n",
       "2             0.712941     1.022353      4.080000       8.495176 ...   \n",
       "3             2.455914     2.643011      2.786022      13.210645 ...   \n",
       "4             8.302222     8.574444      2.400000      15.295222 ...   \n",
       "5            14.850538    15.121505      3.016129      17.287849 ...   \n",
       "6            18.733333    19.026667      3.053333      21.912111 ...   \n",
       "7            22.054839    22.012903      3.867742      21.569140 ...   \n",
       "8            21.410753    21.378495      4.476344      18.492688 ...   \n",
       "9            18.057778    17.866667      3.728889      13.625000 ...   \n",
       "10           13.124000    13.149333      3.408000       9.659733 ...   \n",
       "11            4.721667     4.858333      2.481667       6.991500 ...   \n",
       "12           -1.819355    -1.677419      2.454839       4.752903 ...   \n",
       "\n",
       "          SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  \\\n",
       "LST_DATE                                                   \n",
       "1                       0.240292                0.199547   \n",
       "2                       0.244286                0.203089   \n",
       "3                       0.224202                0.190742   \n",
       "4                       0.208600                0.183911   \n",
       "5                       0.198925                0.175452   \n",
       "6                       0.132856                0.127044   \n",
       "7                       0.100871                0.084957   \n",
       "8                       0.150946                0.121290   \n",
       "9                       0.131144                0.113678   \n",
       "10                      0.127653                0.087333   \n",
       "11                      0.197867                0.159883   \n",
       "12                      0.225093                0.186726   \n",
       "\n",
       "          SOIL_MOISTURE_50_DAILY  SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  \\\n",
       "LST_DATE                                                                       \n",
       "1                       0.150376                 0.162533           0.168817   \n",
       "2                       0.154306                 0.165506           1.217647   \n",
       "3                       0.153140                 0.161763           3.469892   \n",
       "4                       0.150022                 0.159989           9.402222   \n",
       "5                       0.146032                 0.157710          16.889247   \n",
       "6                       0.128311                 0.154978          22.372222   \n",
       "7                       0.112538                 0.153278          25.453763   \n",
       "8                       0.125409                 0.162650          24.784946   \n",
       "9                       0.117722                 0.156965          21.060000   \n",
       "10                      0.103053                 0.141000          15.598667   \n",
       "11                      0.140783                 0.150367           7.018333   \n",
       "12                      0.154403                 0.171903           1.732258   \n",
       "\n",
       "          SOIL_TEMP_10_DAILY  SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  \\\n",
       "LST_DATE                                                               \n",
       "1                   0.232258            0.788172            1.749462   \n",
       "2                   1.183529            1.280000            1.602353   \n",
       "3                   3.407527            3.379570            3.472043   \n",
       "4                   9.138889            8.438889            7.600000   \n",
       "5                  16.691398           15.569892           14.193548   \n",
       "6                  22.198889           20.916667           19.417778   \n",
       "7                  25.408602           24.141935           22.725806   \n",
       "8                  24.897849           24.117204           23.311828   \n",
       "9                  21.218889           20.947778           20.823333   \n",
       "10                 15.749333           15.986667           16.608000   \n",
       "11                  7.100000            7.860000            9.181667   \n",
       "12                  1.845161            2.622581            3.914516   \n",
       "\n",
       "          SOIL_TEMP_100_DAILY      \n",
       "LST_DATE                           \n",
       "1                    3.394624 NaN  \n",
       "2                    2.460000 NaN  \n",
       "3                    3.792473 NaN  \n",
       "4                    6.633333 NaN  \n",
       "5                   12.344086 NaN  \n",
       "6                   17.452222 NaN  \n",
       "7                   20.994624 NaN  \n",
       "8                   22.278495 NaN  \n",
       "9                   20.717778 NaN  \n",
       "10                  17.434667 NaN  \n",
       "11                  11.268333 NaN  \n",
       "12                   6.038710 NaN  \n",
       "\n",
       "[12 rows x 27 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_climatology = df.groupby(df.index.month).mean()\n",
    "monthly_climatology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in this new dataframe respresents the average values for the months (1=January, 2=February, etc.)\n",
    "\n",
    "We can apply more customized aggregations, as with any groupby operation. Below we keep the mean of the mean, max of the max, and min of the min for the temperature measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.100000</td>\n",
       "      <td>16.9</td>\n",
       "      <td>-26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.712941</td>\n",
       "      <td>24.9</td>\n",
       "      <td>-24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.455914</td>\n",
       "      <td>26.8</td>\n",
       "      <td>-16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.302222</td>\n",
       "      <td>30.6</td>\n",
       "      <td>-11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.850538</td>\n",
       "      <td>33.4</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          T_DAILY_MEAN  T_DAILY_MAX  T_DAILY_MIN\n",
       "LST_DATE                                        \n",
       "1            -2.100000         16.9        -26.0\n",
       "2             0.712941         24.9        -24.7\n",
       "3             2.455914         26.8        -16.5\n",
       "4             8.302222         30.6        -11.3\n",
       "5            14.850538         33.4         -1.6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_T_climatology = df.groupby(df.index.month).aggregate({'T_DAILY_MEAN': 'mean',\n",
    "                                                              'T_DAILY_MAX': 'max',\n",
    "                                                              'T_DAILY_MIN': 'min'})\n",
    "monthly_T_climatology.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f0577b8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nnxnsjtrNqfhTALg6uRJUJYg2/m2yDwiVPSpf9xoyxLTkZFg0r684wPwtp+nT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\niB46AYpR6MA7jBNdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129e5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_T_climatology.plot(marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to do it on a finer scale, we can group by day of year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1129e5c88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OGpISadA8+H+7eCa+cx6pTBit88LnRyArKhysYjOAHY3deGVLPa5/ahNWbKHs\n57cqm+hzNbpnXt3aCwesH+CNB4/j+qc2YWstnx18KZr/WHC4qNeueD+y+8lMxeQWk1efOZ2eI+a9\nd2nxJdHgs8Hig5+euDT6NIJt8EeKgHbTykFenzs5O34vQ1TnmNv8nQiOfMrfiwWsGMyUDkBqHtHL\nypmrbdtPXlnqBFpmD2jYT0aZeVyilNObTrV0WKPrrBl0Hb3NNEhEm/YDfBCK1+APh9IBGXFmbIeS\nRS6anA6fx4npY5Nw49dowKtv70dlXScq6zoNdIu4zCib3DGJSPZyLzysqPjle1S587wZkQlgigo8\nvGo3Kus60dDBs0/Z7EBEZV0n3t/dDAWAyymhOD/DIK5ix3tifbW+fFKNfUM53XeN5UaD3LCVXrsa\nIvdJnUCGfWIRFxI0aXV0WIwKMPH/MaTRR0qBjb8GKl7kEs/TEDalM1KIlA4z+GKThqEgTnNrNwEF\nk0d2PWIvUavgp1mlA9DD1VXPU9szZ1Ad9UAv7wMLcE+LSeScHjL+oofvS6ft+ttomWmpuxqofHIs\nJA7XwzcZ+GhBWw2FeRk4Y2IadjR2Y/KY2IPD2j3NGAjKuHBODr45bxxe39qIzTUduPHpTVBUitW7\nnRLOn5WNjQeOIyQrcDsl5GfRNXT0BXHWtCw4HdWQNbKdce6fH7aeWSiqite31uNwG2/DGFZU/Pe7\n1Phl99FuSABkRdHjCrKi4uuzsnHVwkn4f+/sgshCKVbJbqOEYZWIiJbctfF/6HXNj8mjF50AbxpR\nkwfe5+UWWI3+d39EbTZziyNnB9F6/b50ubAiSgb6aQDbwx8pREqn7wQMvti1590fjdzzYMXYAEpE\nMt/QVpSOv5skb+zBcnvJe/d3EcevG3zm4Q+SkWcds0SD702nIHZXPVXBZPVrQv1Dd9NilI5IF8WC\n2cDHMVB0DRCHfvh4P25+ZjP+fWVVRGC0sq4TP3x1OwDgxU21eGtbo65dCitkuFUAQVnFur0tCMqK\nvnywhWZQ975MpZ0fXTY/ghqSFRVeNz16eWMSde/c43SgpTcyHyMQVvDQ27uwYks9XtlSj5UVFJtx\nSJybv2XxZLz+3bNx8dwcfRaj/0xO4wrzDMWMeD6/+Zky/H7dgZiBZx1iVUxmkKtW8PiRHIr0yr1p\ndL8dsijOJm7fslf4QLJuIh/h8Z9gkuQ/AWyDP1IERA9fKw9rrtkdC32t/L1icePHgpiByGAoUGU2\nNeCUjjgV9ncZjfHOlZTx2K99jxQti/LA+9S1SgmT1I7VSxcrYbKBpL2aEmd6hL6jhzfEHtASh0np\nsO0YtdNeHX1bDS29/HsGZRU8CJfzAAAgAElEQVRvVDZFGC0xYCtrxtxsNIcC495vWTwZd5+TD4D/\nNTwuByakkVTxBxdOx79fTDOfR6+aj2DIomcAjJXimRd/zaKJBm6+MC8Dy79dhL/cVmjYJySrqKgl\nJ6SyrhO3PluG3609gBuf3qzHDRiYlDOWMS+raUdQVmLGGAzILQamX0SB/jtWk5GufJF/blV/x5uu\nxX0sCt6JXnzdZzDIga2ayFuJJ76MUuRfQdgGf6RglI4S4nLIUH98deUBIy0xnIp/LAPxo18aeVF2\nXk+K9TUEB4i6EeukM/qFPTiq1gC7v9X4+ZFPgZevoveuBM7duwSOmU2x2w+RwWeBNoCyIWMNaHEG\nbcuPtONPH1ejsV+7XvY937o/5oASCMvwh5QIDzgUVvDmtkbdqy2Zmql73W6XA9cumoSHL58b85rM\nEFUxM8fRjIUZ4Icvn4exKTRIKqqKi+ZSsL+paxCba+KfHa7Zad3E++I5kV7+Z9X0NyyraYc/RINY\nWFHx0Du7DEb/o/0tCCsqFBUIhOh3MaMgN1L9M9SsADnz6e+kKEThiEPYjIsjvXJvmjGLmvVM8KYb\nqRhWKwigezZa+0qXSOFFmQmcBhixwZckKVeSpI8lSdonSdIeSZL+VVs/RpKkDyVJqtZeT2LU6AuE\nqKxpO8jfx0vrON2cMjk3SjDTCqxbkLlTFuPoEzOsy9EG+0ndInrlA5pBZ4XLnB564Njsg3WPEhOq\nXD5u8J0mSgegWY6/m3P47PixBjQ2OzjyaYThZgbl+c9qcMPTZfjd2oN4YWsLvy7AmhoQ0NpDdMnd\n504xzH2cDgmvldfjd2vJq4WqIsXrwvwJqboH3eMPRxhRK0wfm4RbF0/Gq/dxz7vVRNPsPtqNCs0w\n/nzVHnT00+fPlR7RzaADwIJJaRHBWBHRlEbbG7oiNPrzJ5LBNKuPVCFYDACpXt7lSgXwRmVjhBFn\n23icJBkNhGRc/9Sm2BRPcg45RTtfQ0Rnq+SxkdszEUHLbno+xuTTspnrZwlfGVOBrFnRnx9DXkiU\nmcBpgNHw8MMAHlBVdQ6AEgDflyRpLoCfAtigquoMABu05X8+iMqanib+XlTLxELIz1UwqePjP69o\nOJ1uvsy8Xd8Yaw+/s5YGCXN/UgAo/g4vspWUxT37cWeA66I1g3DkE57Jaw7aMhzfD6x9iCdclXwv\n9oDGZhR7VhlmLVSuYDN+t+6ArnRRAWQommFxuOMq/lVaTcqhsKwYDGlhXobOy4fCCl4uq0P3YBhn\nTeMByZKpmfC4HHBKgCuG5a/vGDRo6gHg7GlZ+mAhSUBbbwCqFlANyQre1Tz13gDPjHa5HGSkBdto\nPq0jSl5AWU17BJk3dSzNmhZNTkeix5jrISaGVbcapcGyHDmoHDpO2wRlBTNykvHm9kYoKmLOCvRY\nTrDfuN7htq50yQz+sZ0Ui2ISXHNje4DuqXFC7MqK6mQVOdn705DOAUbB4KuqekxV1W3a+14A+wBM\nBLAMgFZcBi8BuGqk5/pKwipDFQC2PBVfADbs555tvDQQYDScN60wFjYD6MGQg8YqlQ3l1KEo2Au8\n/u3IY3bV8cQVTzKXVo5fAIybTw/euf9G6/a/xyksc9BWhBzkwVWxnaMV9NiHcdZSVtOOkKxCVbnS\nZZF0EPc73+P7Ft5hqbpgM4MVW+rx89Wkdnm5rN5QrXlQqGvjdEg6VfLS5jrdWy3My8Ar95bgJ0tn\n4ZFl8+F1OywfHlmJNJCFeRn4+kySYqoqsPFAK1xOGjzcLoc5CwIAaf6vWTQJCdp5XA4J9y+Zqp/X\n5ZDwyLL5lgqZkqmZSHBrx9diDwNBug/+sesY+oPGOIHbyWmZt7Y1GT+zSNhi9BAAHOvyG2cMAF7f\n2hDp5bMclda9NDAvupNaJ971D2sngBn8nkYgfXJsgw9QfCDQy6nODSaqEyow5wrqLJc547Skc4BR\nlmVKkpQPYCGALQByVFU9BtCgIEnSyW83NNpgBtQKx3ZYdxgyw2DwB6JvFws58/l73eCP4cuMrxcb\niVuVY97/HjWduGM17cMqaLp9QNpkQK3jJY9F19MZxcNnTbOTsylfYSj1zfSLgc+f1Atw7fcuwIaP\nDyHJ5JECQIljH1wSa4WoRHZZAp8ZULKTpMsjVUWF0yFBUYmrVrT1iR4nrl44Uee0mXcrBkXZ+1nj\nUvD4+oP4rLpN/yUkRM9onZSRBIAMpayouLE4FxPTffq2KysaENSisR4tbsAGGVH+ePG8cUPKIcX9\nkjxO/OLdvejTZg+rqriGXavxip9dNhuFeRn4vw0HDVRQoseJl+9ZbDhPZV0n3t7OB4XS6uM40Gyc\nFYQVFW9tazReHyuf3LIbSMwCFt4a+7kQ80J6j1ln2YpISCFHRm/Qo9KztUNzhsJBID2X7v/2w9HP\n+0+OUTP4kiQlA3gTwI9VVe2RYpGPxv3uB3A/AEyePEIN+peN2lJjcTEASJ1EXkm8pXjDfu3mliI9\n/Hg7aYkKG5HSYZ8xg693DFI0Db15RiFcs3hMt4/L5PQAmVP77qoxaCuWXFh4O5VP+OBBWj6+nypX\nRoNQgGu/dwGWrQohGD4AtyvSly5T5kB2uOFQw1GpnI/2tyDEJC2qCkkiD9vjduDhy+ehsXMAf954\nGDubSG46EJTxrTPG49VymgHEKkdQmJeBH180E1trOxAKK3A6JFxflBtB5zBctXAiXq9oQFhW9ECw\nuN2r95+FN7c1QgIiSiqL25mXo4Ftt6OBcif6NYPPDL9TAlxOBwJhBWk+ouna+6mAnwSa6QwGZXx+\n6Lh+PCCyONwLm2rR1Bk5M43IABCTqwa0rmp3rol+X4sGv/YzbvB9Ub57Qgp5+Hnn8mJrUIHtK4gy\nkoPkmLiTImml0wijYvAlSXKDjP0rqqq+pa1ukSRpvObdjwfQarWvqqrPAHgGAIqKir64TJEvAvlL\noFsRhmkXAtv/irjr6bAkJrGSJKB10rqUKBlzkggbCMRjMOhB2zHGZYA3MPckApc/ATy/1HgtjAf3\nZVJmIkP7YW7w/V1AygSg+F5g15vU+ET08Bu38vc7X6cOSMeqaPnj/wHyzomrb+qGjw8hEKaSvyGt\nHILbKekGfJs6E4cvXYHZ/h1RB0SmhAHIyM8el4Ka4/144a5iFOZlQFZU/GXjYYNxaukJIMHlQJrP\njR99Y2ZM42rlgcfadsV90beN15APF0kJNDvqC4ShKCp2NXVjSlYizp6WhcvOGI9bn92C5u4AKus6\n8bfNNLNxOiScP2ss1u9rxePrq/HnjYfx8OXz0DkQRKrXaDKsjL1TAq5dNMm48milcXkoZ8hQjVXl\njlUsD19ViHrMmgkc36edJ0Caf1Wme9vtO7GZdEM5zYBnf+uUpoNGbPAlcuWfA7BPVdU/CB+tBnAH\ngP/VXleN9FxfOeQWA6m5QLegZR6n0SvTLgTO/2nsm0NVySN3+7QbUcy6LTU2AGcPR4OWni62RLTy\n8NmDYe4jKgeBiefxBt8iCu+gYmbmmUvzLqJqAj2UDu9NJSN7+GP6XOTwzT1U963ix1LCcTcfMXvW\nEoCHL5+Lfc292NvUg6rGLuSeeT6QcFHUYzi0WWaSx4m/3rMYf/jwAKZnJ+uG1emQIjzR367bj8GQ\nAn8ogEfW7NHLGkfDcAx1YV4GnL46VLRshLO1CAXZBXHtNxKwImsDQRmPvbcXvf4w+vxhHOtuxDWL\nJiElwYWWHj/Katoha46LqqroHCBvX1GpyNt/vbMLikp0EwDMyknGgRYum5RAA7LL6cDX8vlvwjJy\nv5G8ALOdHl4GfChnSKcO2Qk0rz2WwQfIy08eyw0+AFS9op3TrTXNGabBbyjn/R3K/kx1oE5Roz8a\nKp1zANwO4EJJkqq0/5eBDP3FkiRVA7hYW/7ngxIyTjPZVDSephKy1hrRlaB5+ILBj6bCEeWY+nGE\nWvqMpokWF/B3R1YnZKh6lZ9bLGWcfy7fp/cocPwADTrsekWDb+6hOmcZvQ6zhV5hXgZ8bvJOVe3/\nY//Yh2sXTcLtZ+UBiJQ7ihADkP1BGRv2taC2fQA5qV7DNmYc7fLr5xztksJVrVW4d929eHLbk7hv\n3X2oaq0atWNHAzP4B5p78NzntQBg6Kmbk+ZFc7cf8yeQR83iEFecOUFfdjgkncZhxefuOXcKnIL1\ncDgk/OLK+Zg3IRUBbRvWtP336w7gqtUh7L/kVaDoLgrWRqFzWJC9aU8pp3EgUaAVMCYqivAIBl9R\neMwA4M+H/pwNWPdYjobaUqH38zCTI79iGLGHr6qqmOpmxjdGevyvPAK9pBMe1IyHO5GkZkOVEQC4\nca4vNy4Dxofhqr8YE010jpIdx4rDz4j8TA7Rze5Nt1YQsZnEkgeAb/wc+PC/aH3eOabsYc17Z4Xj\nRErHqhFGztwTaqGXkejGYDf/nswAL5hEgeGnPzmM64ty9WYfjC4BgBuf3oywQDb/5ZPDUFXStjOU\n1bTDIcG6rjxGv6RwRUsFgnIQKlQE5SAqWiq+cC8/yUOPuDlJi0k61+5uxo7GLpRMJQrwusJJuKl4\nMgrzMvDbtQcwc1wKri/MxYNv7zLsPxCUcdPXJuMVlrSlzQompPuwrZ6ehbKadt34h8IKNvTlY/bl\nj0e91sq6TmrqoqgodadghccDh6KV0u7Q1HDrHgTGnxl5H+kefg/Je7Nm0jMpOkM9R0luzAK68fRO\nBozP3HCSI7+CsDNtRwJFpmzAJKH6oTuRPImwdqNZaYIZ6jbT66EPqTDU8QPW24pVIXOLIwOfBoM/\nQElRLFt128v8eCwr2JsqUC8MppjDjIuF8/uMckumvmFp72LQll2jWBEzVoXMGOgTdOmiAW7rI8/+\n71sbcOuzZVixpR63LC/TE6fe2tZoMPYAd+j6/PyYorbe45QMOveL5+aMeknhopwiMDGD0+FEUU7R\nEHvEj6rWKjy769mIWUOVFrRt6+MBWSbpBIA9R3twrNuPR9dQTZpLzxinf+dJGYnISk7AzcW5EbkH\nv3xvH+ZNSIPXzeWlVI1UQlPnILbWdhiSvOIZPD+rPg5Za9iyJTQdT095nPJCFt4mdEgLW3vYIqXT\n1wqMnU37ieis5RLh4dA6ucU0ywWAc350ytI5gG3wRwaWdCXyim6vZvD9MTTBGg5/pL3RSIvWvXzb\nujK+3dbnjPuOmWI8jiFoq8UEWH5A1Qp+buaRe9MiqZeiu4yB4fRcfkyH00gDLXmAtmXaahenSYaL\naCn5gbCMHsE4Ox0SHr58HgrzMlDXTioLRru8v/sYAmEqFxAIKTjWHf1hzk7lsxFRW//q/Wfh6oW8\n8cqnWpLWaKIguwBnTzgbAHDHvDtGzbvf3rodd6+9G09uexL3rL0Hj2x+RDf8ZkrqzElp+Pt3zsIt\niydrihutZpA2IH7vlW3632JcmhctPX58uJfKLYjCO0Xz6Nnv98q9JQCANTuPQlGB257dgpAs6zGS\np28rjDl4VtR2GH5zFcAf96ejcvLdFFdyJsSmBZnB3/ZXEhYkZ9N+YkOh7LlCu0VNqRPLIRPB8gCs\navucQrDLI48ErKyCaPBdPjKA4QCw41XufVupEjKIi6bpImOqtaSjmo/5dofW0b7MIIsUChBJ6bgT\nhSqCQhLTtAtpVULq0H1ezfVsRIN/wUOkTqp4npadJg8/TjCONyQrEZ2d2vuMPX7FQOK5M8bi8fXV\nUEEDAeP6AfoFS6vJyOmNRgRnf3XVMdxcnGcpe/xwL69cKvaGHU1MSCJuPDtx9AzH3/b+DSGt8mRQ\nCWLlwZV4q/otPLT4IZRMvQhOByBrDvI+QTNfMjUTLkH5BHBuvzAvA+PTvNjR2IUfrKDKoQ4AcEhQ\nVRUezWMXf78/fXxIz3UIhBU8+DbP5k5PjH6P6L13TdSangdxgfFeLQ9Pw+b1B3HujLH878OCvLs1\nkWCwj+7pedcCu16ndWNnQR+1aj4BmiopoCuHyUmLlTPD6NbgKDQpOok49Tz8us1UMOyr0MCAUSQG\nSkfz8HuOAtv/xtdbpXMzmqTke1zdAxBPOP5M47ZivRxzfCDCw/cCU9i5BKrG3FIuXqqlodxo8NlD\nwwK7roTIfeLApsNtCIStqy4y2sbtlAyUAcCNdIrXBRXAur0thuMyo/Posvl4YOksjBO8+rBFJizD\nxXPHIcHliDjfaCJB+636Q6OnBR+bGFmLRlZl/GrLr1DjX49FZ2yHw0dF7MRSCYV5GbihKNewn1iu\nYVyaF10DIYRkLhC48Wu5eEDz6M2DYcnUTAP1U3Ocf8e6juizLlIIRa43/A2EnsQ3L9+CP66vNtbt\nadmj7aUdqFkbbFg/XEAL2mqOzOofAJUvaBy/qR6VFdizM5q9p08CTi0PnzUyUMLApiepXd7J5NMs\nKR0feeC9R43ZrPnnRl4rq59/wUPUB7ZZC4xd+P+4KoFBnMqaE0ciPHwfMOXrtCzKQ/eupnXRVDoi\nxAH1pSuBm18zfpZbLBRPG56Hv+lQm6HdIGB8uCvrOvHkBipEd8+5U5DidUdo14unjMG2+k5LsYWk\n5UacPzsbE9N92F7fieYeUnd4hkimiqWVjxdVrVWoaKlAUU6k9JLV0OkaxabbszKol3JeSh7qe+uh\ngtE0Mh7b8hgUVUHiZBcC9ffCGZ5i+P7XLJqEN7c1IhhS4DCVaxifZqTqrBLGRBTmZeD6olweyBWw\ncmsDJqb7DPtW1HZgw75W5Jqa0UgAEqL0HC6radcHdMMsbPpFwKe/4RuyAnxiYJbp8K1gDsaakx5Z\nXalgDIMfb6LkScSpZfCjadNHgpH8kXRKR/DwXT5eOtjh4k0esmZE7t/bTGVfPYnGG9Gbapw6pk0C\nrntBqJdj8pbMiVdu7XgOF9XBYfvpHn4qhkRtKc/KlYPA3nf4Z6xkhF4eOX4Pv7KuE7c/Xw5ZUcFK\nzLsckv5wU3ONzXqZgec+O4LX7j8r4sGfmZMCRTWGnbNSPGjrDWJSug8NnQPI0RKvzpyUjvX7WjEp\nw4cnblo4arp6K1S1VuHOD+6ErMrwODxYNn0Zrpx2pW74BzVqoDMwRNOQYYDROflp+cj0ZWJbK7UC\ndEgOyJqay+mQcd6CXnxvodGIxkoeY0FzFSwPYt6Qv801iyYZykQwlB5qw9a6DsPf+aZnyhBWiB5K\ncDqQl5WIr+WPQSAs461tTYYyzPx6+TqXUxi8Jy8mR4vlp7D2nh6hnIfTbWqaI3H1zWW/MyY2Pn8J\nHYMlPTKDH83DrysDXriEjjkUPXQScepQOg3lpGRh2tzRqHinB1Ufpdfh0kQ6pWMO2nrp9cwbaJ3L\nB0vlam8zby4iKnF6m/lg4k0HvBnGmyceD1+StIJSQjVP9j4eDz9/iTFQJkn8t2eDLaucOYygreih\nMZsQVlTsO9qNP318CE9/cthgLMKyaknBhBWaHYhmZalWV76uYwBJHhd2NNIANyGdBtPslBOjnoaD\nvx/4u25kGZ8uau79Mv2tulkAfQRgypxDnRSg7w32Ynwyr7j63QXf1d97nB788OxvWhrswrwMfP+C\n6RGfdfQb4ygshhILhXkZePX+swzyVwaRtiuradeVVMGwgoCs4KqFE/HY1WegIJeqlzJaT0RDB5cu\n/+jC6QDAg/6i9p7lrhg8/ATjcv65wJk30vtMoYz3ofW8NIMcJHEFo+BYm0Uzaj6GIQb3FdXqnxoe\nvphdyp7w5HEjP66h0FJg+DMGK0rH5SOZYjhIgU9vOpU/7rYoGdvXwm9S8Ubsa+aeRHIOv9kYIjx8\nU+IVux5vKh+UAB7IbdkH5JXE/m7moC5AiVlaYTPkLwEOfkDrh0HpMKmemYn5+eo9eoliEdG4dKuU\n/sljEuFxOhCUFfQGwrj12TK8cm8J+gLkAW+v79LXfRFlDKpaq/CPmn9ErA8pIV1zPxgaHQ+/orkC\nd6+9GwDg1Mr+dge6kZ7APeCF2Qv198uXLh+2KujC2Tn408dUaMwhRWY/R0NhXgYevmIebn22DMGQ\nAkbciX/LkqmZcAAQK1H5tSqeLDmuudsfkSgn5gNUNXbj8Q3UM9jjcmDrhBykdNfT/cjo3i6BXnJ6\njA2Hxp0J/Y4TM9JFh8jp4a08AaB+M6c0RUwsNO7zFdXqnxoeviG7VLtFehqtpY7xon6LccYwVHMO\nK+gGn1E62nTO5aXrHeykkgSeFKCpIvJaO+tom4Zyo/Kmt4VzhcnZkZrhXmOQ0pCGPtBBN3lDudHD\nbygHdmqZtC9fFd/vJgZ12QCg1cuvVGagvF67xmFQOoV5GXp9FxGyhbFfMCnN0EhExLkzxkbMmXr9\nxgAjU5z0DIb1QWa0s2dFVLRUQDGYMILb4dY194zSYRx+NP38UFh1aBVU7R+bUbQOtmIgPAC3ZqBq\nuvl9caISUF7HP3YxxKrWKjy6+VFdEsqooge+OQvLCkiZ9PiNBQZ1VGaK0VFo6SGjO44Z/B6jOEFs\nPQlQLCgkq3r5h4M9mv+aOpEbZNGRcnmMlE7nEf6cibNkFkvzJNM9L5ZOYVSRGdlz+PuvKJ0DnCoG\nP3+JUU/LcKJTp7rNVDis4kXjOYb7R2rdTwMG66XKqBRXAs0YBrtotG+qIG9eHKDqt5An37KH1ncI\ndfV7j3FKJ2VcpEc/0GZc7qyl14ZyUge1aseEBHQ3kc54x6u8Nv6J/m5MKSFPx03PbMYnh8lo7Tse\nvcSBGYGwjL4Az55lKf1meN0OPHxFdM64MC8DxVOMnx3rMhoIpjg5Z3oWrw//BalvAEqsckjGR2qM\nd4zBu2aUTvNAM1YeWIm7196N/9v2fxGlFipbKmMOBA29nFpg5+wN9qKprwkTkymf4HDXyMoAiwOj\nqlpTawDwScMnuP392/H6wdex8iB9J2b0v3/BdNxeQvLjD/e2GPItVNXY1GVCBhnnnDRyIFZWGOvq\nl0zNhEPYQazrrwA43Ef79bnH8IOag7atQo2dg2t5FU9m8BvKgZ1/p/fBPpoVN4vNgiRrxzAUJUv+\nK4ZTw+DnFgPzrolcf6JTpxoh4YmVKDCXOR4KDeXArpW036s30zrGZbu8RD8NdpLGV88SFAytmHQl\nB6mzD0P7IUrCAijRI9hvrP0huYhbZx18mCy0thS83V+Qzt+ym5K5tv+Nz2ZGOOV8cVMtQrKKgEoe\nVdWx+Bu3tJn09WbjO2NsElK9rrhoF7O642tTxhgahjDFiZhgNRw6Z7jed0F2AaanTzesc0pOg3fd\nMUitL4NyEI9teQwhJQQVqk77AMCHtR/izg/utBwI2HVVtvLqk4lCv9aG3gYkuylJSPTwTwRiJnKs\ngbK00eg8iN8F4M1X3trGG8aHZQWdAyGcN4MLHv7y8WFU1nWivp0cnA37Wg3Sy8K8DJw1NRNet7XZ\n6lApQNsqC4FaMTbm9ABNW6GbPVXmXeqYwTYXDnzvJ+SwAaScS8y0NuhW7US/gjg1OHwgMrsUAG5/\nx/rHH0p5M9Eipb0rUkoWE1bNRNjN5dQ8fH8XZax2N5BaRyyCNo7p7B00exE7ZwV6KGNQclJvWlUm\nA65TJwpv/Lz5L1zPr38vTXvv9EDP4lXCQPJ4QFKB618akRfCgp/jJTJeueF6PPj2roha7lZoNU3T\nO/qDBk6/PyRjzvjUIY1yZV2noZkHAH2gsFKcDFd9s/LASl3SmOBMMHjpsWSXACVXHe2na+sKdEFV\nVZ0S6Q7yYK0iGBanxEstrDmyBgAMA4F4nq3NQglqAL0hYzJQUAnC5/KN2MOPt/zz7MzZhmWX5DKU\njdil9RsQKbW2vgBkRdUHA4DaPRpmFYhMgEtwOTA+zYuGjsGI8hldKg10KVkThIvhMYCdzYM42D4F\n1zg9cMh+coBc2gyAZcabg7JizSqH21ibR4QYAwgH+LP6FZNqnhoePmAdGJy4MHKdrrx5JLryJntu\n5LruxqF5bTENe/LZ2krNuMJByhyAl1YY7KLEj/O1dr5XPMH/6GwAm3+NVivEdC4lTPSQrv8VArfB\nASAjn/h1bwp5KaW/5wXO5i4jHjFH6PPpcAG+tFFp7zY9JwWLpIP4tutDAEDR9gexv3w9XtlSj5uX\nR2lireG4qcIl88rZRP1olz+qBydCVPswPPD6DgCwVJwMB1WtVXhsy2OQVdlQ6Ix9Fsv77g/1Y3Iq\nb+QTUkI6bw8AkvYPoHo6CVrsZkYGl+0eH+AlBkT+n2Fe1jzEwoSkCUhPSEeHv0Nfpwx3BqshmoJH\nnP3kphiTt66afpVhgCqZmqnn6rldDmQkevBDLXt3W30XPE7jLCJWDZ7+YBhjk7346aXGQQYAEtJo\ntjB2nHA9gof/3RW78J9bEnBL8EH4U/Ko6Tlrbt52EHjhMiPNa0bbAaJa2Wy75hPg48fIHogePqvo\naVABjiDeOIo4dQy+w2IyooQj1+nKG3DljRlWo7QSii3NNEs4GSUz5Twyrq4E7i2w0gqDnVS1Mler\nPZ8qeB5ME7/wVqr54UpAxJ9DCVMdHcDI44f6+Y2sKiQJ2/BL4O3v0LrCO8moj53J97ngQQDS0G0G\n40AgJKPEsQ8OLUDpAi0DFCi1bGKtocI0GJw9LROv3FuCpfO46qq0uk1vMxgNjG4QQ4lmD/FEUdFS\noQdCAQpYMqO7tXmrPhCYqQuAePTclFzdqAPk5TOE1TAuyrsIPqcPOYlcRrinfQ/uW3cfVh5YiV1t\nXIly8+ybDTOLZ3c9i14tvlM8znrgnpQySR9IGPzxVG+NE+ZBb2/7XsPn7xx+xzAQFuZl4LwZWUhJ\noBlY50BQD64rqorriiYZ6LbCvAxMG5uEKVlJERTcQFBGYoIT35wXqdJTfdrAcGwnf47d3MPvlx1Q\nVGBreDqOevLJw2eOVFu1ljOjGXOPxXPCpJosqevlq4FPfkPGXKRkP3uce/ZhPwyd5E4yTh2Db2Wk\nrfqyigFeKUqAJdrNH6vWtblXZunvaH2DVuTM6TF6+ME+ukG86VwmKZYYZpmW3nSugPnGfwGFdxnP\nywKtTKkTDtJAwORlereDnIAAACAASURBVNKVQvECgMo1A8auQcnjKJnLXCPnBNDrD6NMmYOw5IYq\nORGCC2UKVym8Udlo6eVX1nXi+c+OGNaNSfKgMC8D8yfya1VU4OFVu2POFBjdcPPiyXHxzMNBUU4R\nXIKDceHkC1HRUoGq1irMzeSzQ7P3raoq+kP9SEtI03l0wGjw/WE/EpwJCCgBNPU1ISA0sgkpIbx/\n5H3Dtfx1719R1VqlG9kntj2BBz+jlpF3zrsTWT7yamdncI+3J9CD+h7jgDkQPsF+yRZgAyIb9PZ1\n7DN8LityxEC4IDcDfcEwzpiYZvD4Wf9e8yxifLoP6YnuiJlFXyCMpAQXJqT74HIalUPOAc2zPriW\ne9SChx/UGGy3y4G0tAxynJjBTzENIKm8kB7RQoKpXHEDiSBUQQRxbIfwAz1P5/dlQs+/OREVYDTE\nW/DNAqeOwReNNOOsrTz83GKiNABTVp14rCiqEmeMWtf5SwCH8HOZy7U63VwRIMoUfRlRDH6Uujbz\nTcFpNuVkWnz2yr6bmAPAri9VM/hiRq2/m7T9Hm6IThS9gTC2qTPx8/RfYd+cH+LW4INoTOa1gMJh\nBY+vPxhhsMVkGwBwShKStQYdZ0/LMig2lBiqEIbCvAz86uoz8Op9ww/IxkJBdgGum3Gdvry+bj2e\n2PYE7lt3H/o0GV9+ar7O6zPPu7y5HLIqoyfQg74Qz8jc1kKZr2EljJASQttgm15iQYTb4UZhTqFh\nnaIqqGipMMw6wtp9n56QritypmXwmjFt/rYIeWhls6nF4AggDnJuhxuTkifp7wFjPIIhN8MHVQWO\ndg0CKjWRz0h0R83eTfW50TMY6dANBGQkeZyoauiCbMrmdQc6QQZWSH4SVDohzeC/cm8JMjPSyYli\nBt83xnAsZM/i729fBUw7H7rxZo2L2LLTY4oxaucfbOf6/KJ7R4fDZ923YlHWMXAKGXzBSPs0g2/l\n4QPcsAZ7rX+UaAb/W3+I/kfJLQZyz4pcLypeeo7RuUSD33+cNyMZ4JwqN/im9HFxOXsucPEj2ncZ\nML4yDz9RuFHHziaPhnn2kiBlbdlDN3fC8A2+WMKYNRoBgPW9eVi2YzG2qTPRORCCU7PYCoDPqtuM\nxa2gyeoEo+5xObCtnrzfwrwM/PKqM+B0UF36aDVvrJQz0XjmoRBLhTPGy39XVpsmpIRQ3lyuf86M\n/b3r7sX/bfs/fH/D9wEYOXgA2HmcpvuMy5+aNtUwg0hxpcApObF86XIUj6f7j0ktWd38MzLP0Ld3\naAN7ujddr7qZ6knV1TrT0qbpyVgMrNzCaKAguwAepwfZidlYvnS5fg2Pn/84HJIDl0y5JCKYzRRV\nf95YjWuf2oyQrKJzIIRfvLvHciaX6nUbymMz9GsefllNO8ypAR8FZkMVS37nL9FpVhUSwqDfZGFu\nOm9mzgx+r7FBDFKFnrx5JcD5P+OZ5Q4XNUZPzKJz3bGaz6oBGAoWpmnHqf98dDj8EXbfOoUMfpwe\nPgB0CLSB1Y8iRzH4WTOt1zOMO0NYkHidDYAGmfZqmsodFaZ3G/+HmngnpBo9/EFG6Zh06D7B4Oef\nyz2E0ADdMJv/RMvMwx8UHpaW3TTYsEbiLYJ+eMcr9L2tuMkYYKVrf7f2AG5+ZjNuemYzdmolCzr6\ng3oijKKouGgO56StkpwK8zIwJtGD/MxESAAGQ7JhULhl8WS8/p2zolZjrGqtwj1r7xmVFoFVrVW4\nZ130Yw2GB5HgTECK8Hu5HC7kplJAkFW7rGipQEAOEL2hPYizM2cjwZmgG21mEBmPPi19Gh5a/JB+\n3NlZsyGrMmZmzESnn36LR89+FG6HG+dPOh8F2QWo66nTt182jWawGQkZOqWT6EpEqjbQT8+YjocW\nPwSX5NJjCdPShaqRI0RYCSMoB5HsTkZBdoH+vYrGFSHZnWz4zRiYwX+9osmwPloiXKrPFeHhq6qK\n/mAYSR6XHsMRHYgKeQZ6b3hTTw5EbjHNep0JUJ0eMI+8vV/Lgg/1c1vQdtB4AWkCpcMya5f+kpa/\n+RgthweJ1hm/gNunrJlULoWdv0dTkh3bMTqBW5GyjsVIRMGpafCZUVSiePjiQGDVkiyahz9UYEus\nmZM+GUgaS39UcUCRg5T4pF+LTJ8njon08D3J3GtgED38xCzOQR7bQVO5sj/Tcr3WLcucdTvYyW8s\nMUDLYgHD9PAZDaMCCMmqoXa6osKgvjh7mnFa7HQavfRAWEa71gaP7Wc1KFh56xXNFXhi2xMIKkEo\nUCwDpsPBO4feQVCOfqzB8CC8Li98Tk4J/Kz4Z7r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28Vr7zOBHa4AeDdESQYMnn9I5YUiS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o9tHzlx2c8lu3gmc+MZN44ZrJ10cT5M6d0JfHwhNnZ/f/7vbYNYmqGlHN7L8pge0US39TFVQ4Vb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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11260d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily_T_climatology = df.groupby(df.index.dayofyear).aggregate({'T_DAILY_MEAN': 'mean',\n",
    "                                                            'T_DAILY_MAX': 'max',\n",
    "                                                            'T_DAILY_MIN': 'min'})\n",
    "daily_T_climatology.plot(marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating anomalies\n",
    "\n",
    "A common mode of analysis in climate science is to remove the climatology from a signal to focus only on the \"anomaly\" values. This can be accomplished with transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rpa/miniconda3/envs/geo_scipy/lib/python3.6/site-packages/pandas/core/ops.py:692: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  lambda x: op(x, rvalues))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ef63828>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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09lBBTQtEkZ1QO1WgXBq7+jvVf5g3vU+L1DWvvRHLVAZxiqfa3xnfugWv/NIf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f72ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def standardize(x):\n",
    "    return (x - x.mean())/x.std()\n",
    "\n",
    "anomaly = df.groupby(df.index.month).transform(standardize)\n",
    "anomaly.plot(y='T_DAILY_MEAN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling\n",
    "\n",
    "Another common operation is to change the resolution of a dataset by resampling in time. Pandas exposes this through the [resample](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling) function. The resample periods are specified using pandas [offset index](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases) syntax.\n",
    "\n",
    "Below we resample the dataset by taking the mean over each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1126a5278>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KHbcCO4GFSqkmpdRngM3A1Uqpd4CrfV/HvHpbd9x1dJzM2spC9hzvZMjtifZQ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10549d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('M').mean().plot(y='T_DAILY_MEAN', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like with `groupby`, we can apply any aggregation function to our `resample` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10efc6160>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fWICBcTdnlKqcU0MT+NHzh3DRklp8Ksb8p0rPSGQJo9cvcNHiWtSU6VFdUoyaUj1+8NcD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef636d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('M').max().plot(y='T_DAILY_MAX', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling Operations\n",
    "\n",
    "The final category of operations applies to \"rolling windows\". (See [rolling](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html) documentation.) We specify a function to apply over a moving window along the index. We specify the size of the window and, optionally, the weights. We also use the keyword `centered` to tell pandas whether to center the operation around the midpoint of the window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112cd4b38>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9ODI26WjFF7NE1FDb1mm0lPAzoGe1bZdp5KdM84v3JFCGP0HiBgJ55TNmGytk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\n4Sn/+1jCYebEDUHnAciaZ6T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n3GzDgJ4hsV2mkp6gRvjhxJqYi1ea2bVvL5p25nqoD29o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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127c8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.rolling(30, center=True).T_DAILY_MEAN.mean().plot()\n",
    "df.rolling(30, center=True, win_type='triang').T_DAILY_MEAN.mean().plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}