Groupby_NCexploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring North Carolina NO<sub>2</sub> hourly emmisions\n",
    "\n",
    "From the [EPA site on Nitrogen Dioxide pollution](https://www.epa.gov/no2-pollution), \"NO<sub>2</sub> primarily gets in the air from the burning of fuel. NO<sub>2</sub> forms from emissions from cars, trucks and buses, power plants, and off-road equipment.\"\n",
    "\n",
    "**Data Source:** [EPA pregenerated hourly data files](https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw)\n",
    "\n",
    "**I'm using hourly data so we can explore the data at many different time scales, and in doing so, learn how to do aggregations at many scales on dates and times.**\n",
    "\n",
    "The EPA has hourly data for Ozone, SO<sub>2</sub>, CO, and NO<sub>2</sub> for all sites in the US. **Because that data is quite large, I have filtered down to only North Carolina sites**, and I excluded Lee County because that data was incomplete for 2018. I have also filtered down to a subset of the columns, leaving only the State, County, Site, Date, Time and Measured value.\n",
    "\n",
    "Here is an example of the output if you load in one row from the original data. (Note that not all columns are visible in this output...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/EPA_data_head.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "*To preserve the mystery, select from the notebook menus*\n",
    "\n",
    "`Edit -> Clear All Outputs`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Load NC subset for exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Site Num</th>\n",
       "      <th>Date Local</th>\n",
       "      <th>Time Local</th>\n",
       "      <th>Sample Measurement</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>4.4</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>01:00</td>\n",
       "      <td>5.1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>02:00</td>\n",
       "      <td>3.6</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>03:00</td>\n",
       "      <td>4.1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>04:00</td>\n",
       "      <td>4.6</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Site Num  Date Local Time Local  Sample Measurement      State Name  \\\n",
       "0        22  2018-01-01      00:00                 4.4  North Carolina   \n",
       "1        22  2018-01-01      01:00                 5.1  North Carolina   \n",
       "2        22  2018-01-01      02:00                 3.6  North Carolina   \n",
       "3        22  2018-01-01      03:00                 4.1  North Carolina   \n",
       "4        22  2018-01-01      04:00                 4.6  North Carolina   \n",
       "\n",
       "  County Name  \n",
       "0     Forsyth  \n",
       "1     Forsyth  \n",
       "2     Forsyth  \n",
       "3     Forsyth  \n",
       "4     Forsyth  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/NC_NO2_hourly_2018.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Site Num                int64\n",
       "Date Local             object\n",
       "Time Local             object\n",
       "Sample Measurement    float64\n",
       "State Name             object\n",
       "County Name            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Be careful of data types on read\n",
    "\n",
    "If you don't specify things like the data types, character encoding, and value separator character explicitly, Pandas will try to guess. *Sometimes it guesses wrong!*\n",
    "\n",
    "- **Since the \"Site Num\" values look like integers but have leading zeros, you need to force Pandas to interpret that column as strings!**\n",
    "- The date and time columns are interpreted as strings by defult. \n",
    "    - If we accept this default, we typically have to convert timestamps into special datetime objects explicitly as a separate step after the read if we want to use the Pandas special date and time functionality. \n",
    "    - *We have the option, though, of combining the date and time columns, and parsing them as a datetime object during the CSV read!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Note on category data type*\n",
    "\n",
    "Because the State Name, County Name, and Site Num values are repeated many many times throughout the data set, this would be a great opportunity to tell Pandas to interpret them as **\"category\" data type**. This would save space in memory and make some computations faster, but introduces a complication in the GroupBy functionality since not all categorical combinations appear in the data set. (e.g. not all states have all counties, and not all counties have all site numbers, so when you do the groupby() you need to include `observed=True` as an argument) See the Groupby_NCcategorical.ipynb notebook in my more complete\n",
    "[pandas-jupyterlab repository](https://github.com/emonson/pandas-jupyterlab)\n",
    "for coverage of those issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In a single, chained command, we're\n",
    "\n",
    "- reading the CSV file\n",
    "    - combining the date + time columns, parsing them as a datetime object, and naming that column \"tstamp\"\n",
    "    - forcing the site number to be parsed as a string\n",
    "    - explicitly specifying the data value separator as a comma\n",
    "    - explicitly specifying the text character encoding as Unicode 'utf-8'\n",
    "- renaming four of the columns\n",
    "- setting the time stamp datetime column \"tstamp\" as the DataFrame Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    \"data/NC_NO2_hourly_2018.csv\",\n",
    "    parse_dates = {\"tstamp\":[\"Date Local\", \"Time Local\"]},\n",
    "    dtype = {'Site Num':'str'},\n",
    "    sep=',',\n",
    "    encoding='utf-8'\n",
    ").rename(\n",
    "    columns = {\n",
    "        \"State Name\": \"state\",\n",
    "        \"County Name\": \"county\",\n",
    "        \"Site Num\": \"site\",\n",
    "        \"Sample Measurement\": \"measure\"\n",
    "    }\n",
    ").set_index('tstamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>measure</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tstamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00</th>\n",
       "      <td>0022</td>\n",
       "      <td>4.4</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 01:00:00</th>\n",
       "      <td>0022</td>\n",
       "      <td>5.1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 02:00:00</th>\n",
       "      <td>0022</td>\n",
       "      <td>3.6</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 03:00:00</th>\n",
       "      <td>0022</td>\n",
       "      <td>4.1</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 04:00:00</th>\n",
       "      <td>0022</td>\n",
       "      <td>4.6</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Forsyth</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     site  measure           state   county\n",
       "tstamp                                                     \n",
       "2018-01-01 00:00:00  0022      4.4  North Carolina  Forsyth\n",
       "2018-01-01 01:00:00  0022      5.1  North Carolina  Forsyth\n",
       "2018-01-01 02:00:00  0022      3.6  North Carolina  Forsyth\n",
       "2018-01-01 03:00:00  0022      4.1  North Carolina  Forsyth\n",
       "2018-01-01 04:00:00  0022      4.6  North Carolina  Forsyth"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Too much data to see patterns without aggregation\n",
    "\n",
    "Even in this reduced data set, for a single year there are almost 40k rows!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39205, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# GroupBy\n",
    "\n",
    "## Mean emissions by county\n",
    "\n",
    "**To start exploring the data and seeing trends or patterns, we need to make comparisons between different counties, sites, dates and times.**\n",
    "\n",
    "We can see the same sort of behavior as in our basic example when grouping by a single categorical variable. Mecklenburg County has the highest mean emissions over the whole year for all sites in that county."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Forsyth</th>\n",
       "      <td>6.652983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mecklenburg</th>\n",
       "      <td>8.361926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wake</th>\n",
       "      <td>6.834003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              measure\n",
       "county               \n",
       "Forsyth      6.652983\n",
       "Mecklenburg  8.361926\n",
       "Wake         6.834003"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('county').agg({'measure':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy County and Site creates a multi-index\n",
    "\n",
    "When we group by multiple categorical variables and see that Pandas creates a multi-index. Now we can see that two of the counties have two sites, and their mean emissions vary quite a bit, with the one of the Wake County sites having higher mean emissions than the lower of the two Mecklenburg sites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <th>site</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Forsyth</th>\n",
       "      <th>0022</th>\n",
       "      <td>6.652983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mecklenburg</th>\n",
       "      <th>0041</th>\n",
       "      <td>6.282450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0045</th>\n",
       "      <td>10.198556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wake</th>\n",
       "      <th>0014</th>\n",
       "      <td>4.955026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0021</th>\n",
       "      <td>8.700441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    measure\n",
       "county      site           \n",
       "Forsyth     0022   6.652983\n",
       "Mecklenburg 0041   6.282450\n",
       "            0045  10.198556\n",
       "Wake        0014   4.955026\n",
       "            0021   8.700441"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_site_df = df.groupby(['county','site']).agg({'measure':'mean'})\n",
    "county_site_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use tuples for multi-index access\n",
    "\n",
    "To select values from a multi-index, combine the values in a tuple. **Order matters!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "measure    8.700441\n",
       "Name: (Wake, 0021), dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_site_df.loc[('Wake','0021'),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partial hierarchy return\n",
    "\n",
    "Including only the highest level value in a multi-index returns all sub-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>site</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0014</th>\n",
       "      <td>4.955026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0021</th>\n",
       "      <td>8.700441</td>\n",
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      "text/plain": [
       "       measure\n",
       "site          \n",
       "0014  4.955026\n",
       "0021  8.700441"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_site_df.loc[('Wake'),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Can get multi-index in both rows and columns\n",
    "\n",
    "MultiIndex is a bit of a pain, but that's what you get both in rows and columns when you either do multiple groupby levels or multiple aggregation functions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <th>site</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Forsyth</th>\n",
       "      <th>0022</th>\n",
       "      <td>0.4</td>\n",
       "      <td>6.652983</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mecklenburg</th>\n",
       "      <th>0041</th>\n",
       "      <td>0.3</td>\n",
       "      <td>6.282450</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0045</th>\n",
       "      <td>0.3</td>\n",
       "      <td>10.198556</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wake</th>\n",
       "      <th>0014</th>\n",
       "      <td>0.3</td>\n",
       "      <td>4.955026</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0021</th>\n",
       "      <td>0.1</td>\n",
       "      <td>8.700441</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 measure                 \n",
       "                     min       mean   max\n",
       "county      site                         \n",
       "Forsyth     0022     0.4   6.652983  41.0\n",
       "Mecklenburg 0041     0.3   6.282450  39.6\n",
       "            0045     0.3  10.198556  39.2\n",
       "Wake        0014     0.3   4.955026  38.2\n",
       "            0021     0.1   8.700441  35.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['county','site']).agg({'measure':['min','mean','max']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort results to see rank order\n",
    "\n",
    "Sorting values using `.sort_values()` can be a big help in exploration – it gives you a ranking in that measure – but remember:\n",
    "\n",
    "- select the sorting column(s) **using the tuple notation in a multi-index situation**\n",
    "- sort_values() **doesn't actually change the underlying DataFrame** – you need to reassign for that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <th>site</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mecklenburg</th>\n",
       "      <th>0045</th>\n",
       "      <td>0.3</td>\n",
       "      <td>10.198556</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wake</th>\n",
       "      <th>0021</th>\n",
       "      <td>0.1</td>\n",
       "      <td>8.700441</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forsyth</th>\n",
       "      <th>0022</th>\n",
       "      <td>0.4</td>\n",
       "      <td>6.652983</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mecklenburg</th>\n",
       "      <th>0041</th>\n",
       "      <td>0.3</td>\n",
       "      <td>6.282450</td>\n",
       "      <td>39.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wake</th>\n",
       "      <th>0014</th>\n",
       "      <td>0.3</td>\n",
       "      <td>4.955026</td>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 measure                 \n",
       "                     min       mean   max\n",
       "county      site                         \n",
       "Mecklenburg 0045     0.3  10.198556  39.2\n",
       "Wake        0021     0.1   8.700441  35.0\n",
       "Forsyth     0022     0.4   6.652983  41.0\n",
       "Mecklenburg 0041     0.3   6.282450  39.6\n",
       "Wake        0014     0.3   4.955026  38.2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['county','site']).agg(\n",
    "    {'measure':['min','mean','max']}\n",
    ").sort_values(\n",
    "    ('measure','mean'),\n",
    "    ascending=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot table to explore the data\n",
    "\n",
    "GroupBy is important for creating tidy data that is ready for further analysis, but **sometimes you want to compare across groups visually in a table. In those cases it's easiest to see the patterns in a pivot table, which is a \"wide\" format, vs the \"tall\" format of tidy data**. We will also use pivot tables to prepare data for visualization using the Pandas built-in plotting functions.\n",
    "\n",
    "Note: This is similar to what you would do in Excel – take event data and aggregate by creating a pivot table, and then either view those numbers directly, or create charts from the pivot table.\n",
    "\n",
    "#### *Pivot table example*\n",
    "\n",
    "*How the number of data points varies across sites (vertical) and months (horizontal)*\n",
    "\n",
    "<img src='images/PivotTable_example.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot table syntax\n",
    "\n",
    "Pandas pivot_table() documentation: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html\n",
    "\n",
    "```\n",
    "df.pivot_table(index=None, columns=None, values=None, aggfunc='mean', ...)\n",
    "```\n",
    "\n",
    "#### I think of this as specifying how I want to build the eventual table\n",
    "\n",
    "- **index**: the variable(s) that will become the index *(row labels – vertical)*\n",
    "- **columns**: the variable(s) that will become the column(s) *(column labels – horizontal)*\n",
    "- **values**: the column that will be aggregated and become the **body of the table**\n",
    "- **aggfunc**: the aggregation function that will be used – same as in groupby(). *(A list will generate multiple tables \"side-by-side\" in the \"columns\" direction.)*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>site</th>\n",
       "      <th>0014</th>\n",
       "      <th>0021</th>\n",
       "      <th>0022</th>\n",
       "      <th>0041</th>\n",
       "      <th>0045</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</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>Forsyth</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54873.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mecklenburg</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44567.7</td>\n",
       "      <td>81914.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wake</th>\n",
       "      <td>39090.2</td>\n",
       "      <td>69098.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "site            0014     0021     0022     0041     0045\n",
       "county                                                  \n",
       "Forsyth          NaN      NaN  54873.8      NaN      NaN\n",
       "Mecklenburg      NaN      NaN      NaN  44567.7  81914.8\n",
       "Wake         39090.2  69098.9      NaN      NaN      NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    index='county',\n",
    "    columns='site',\n",
    "    values='measure',\n",
    "    aggfunc='sum'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring time trends with pivot_table()\n",
    "\n",
    "Since our DataFrame index is a datetime, we can use special built-in Pandas date and time functionailty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DatetimeIndex Attributes – *categorical discreet date/time parts*\n",
    "\n",
    "The DatetimeIndex class has a lot of optimized features, which are detailed in [the Index section of the Time series / date functionality documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#indexing)\n",
    "\n",
    "In Tableau there is the concept of **categorical discreet date parts**, which we use when we want to extract part of a date or time and ignore the rest of the information. For example:\n",
    "\n",
    "- **Is there a trend in our data through the days of the week?** *Maybe more emissions happen on weekdays, and less on weekends?* In that case we want to know what happens on Sundays, Mondays, etc, but ignore what month or year or hour the data comes from and aggregate over all of those attributes while preserving the weekday categorical distinction.\n",
    "- **Is there a seasonal trend over the months of the year?** *Maybe there are more emissions during the winter or summer?* In this case all we want to retain is whether the data is from a January, or a February, etc, and ignore (i.e. aggregate over) all other attributes like what hour or day or year they're from.\n",
    "- **Is there a trend across hours in the day?** *Maybe there are more emissions when more people are awake, or sleeping?* In this case we want to know which of the 24 hours during a day something occurred in, and aggregate across everything else.\n",
    "\n",
    "[Datetime index attributes](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html) – gives you categorical date/time parts like month, day, minute, etc\n",
    "\n",
    "*Note: One thing I find quite inconvenient is that the date and time attributes turn into integers (starting at 0 or 1, depending on the specifics) instead of staying nice, human-readable values. (e.g. February == 2, Monday == 0, etc.) DatetimeIndex has some methods like `.month_name()`, but by default the pivot tables will be sorted alphabetically rather than in calendar month order...*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling – *preserves continuous time, but aggregated to a coarser level*\n",
    "\n",
    "If you want to preserve a sense of continuous time, but aggregate up to a coarser time level than the original data date/time sampling level of detail, there is a concept in Pandas of **resampling** date and time data. Resampling is \"a time-based groupby, followed by a reduction method on each of its groups.\" **For example, if you have hourly data and want to convert that data (through aggregation like a mean or sum) into daily or monthly or yearly data, you need to resample.**\n",
    "\n",
    "This can be done in conjunction with a groupby() operation. I give one example of this at the very end of this lesson.\n",
    "\n",
    "- Resample in timeseries documentation: [Date/Time resampling](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling)\n",
    "- Series.resample() documentation: [Resample method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.resample.html)\n",
    "- Valid strings for resampling frequency: [date-offset object docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of records from each site over months of the year\n",
    "\n",
    "To see how complete our data is, we want to display the count of records over the 12 months of the year for each site. (The grouping over state that I do below is not necessary at all here.) To do this we'll create a pivot table where \n",
    "\n",
    "- the rows look like a groupby() combination of state, county and site\n",
    "- the columns are the extracted discreet datetime Index \"month\" part\n",
    "- the values populating the main part of the table are a \"count\" of the \"measure\" values\n",
    "\n",
    "**Some of the counties and sites are more complete than others.** For reference:\n",
    "\n",
    "- 28 days * 24 hours == 672 samples\n",
    "- 31 days * 24 hours == 744 samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tstamp</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>site</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 rowspan=\"5\" valign=\"top\">North Carolina</th>\n",
       "      <th>Forsyth</th>\n",
       "      <th>0022</th>\n",
       "      <td>704.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mecklenburg</th>\n",
       "      <th>0041</th>\n",
       "      <td>714.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0045</th>\n",
       "      <td>706.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>689.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wake</th>\n",
       "      <th>0014</th>\n",
       "      <td>705.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0021</th>\n",
       "      <td>696.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>597.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tstamp                              1      2      3      4      5      6   \\\n",
       "state          county      site                                             \n",
       "North Carolina Forsyth     0022  704.0  631.0  701.0  686.0  697.0  676.0   \n",
       "               Mecklenburg 0041  714.0  644.0  697.0  687.0  705.0  677.0   \n",
       "                           0045  706.0  635.0  712.0  683.0  703.0  690.0   \n",
       "               Wake        0014  705.0  509.0  699.0  678.0  692.0  681.0   \n",
       "                           0021  696.0  634.0  696.0  677.0  698.0  681.0   \n",
       "\n",
       "tstamp                              7      8      9      10     11     12  \n",
       "state          county      site                                            \n",
       "North Carolina Forsyth     0022  706.0  691.0  682.0  702.0  669.0  703.0  \n",
       "               Mecklenburg 0041  714.0  704.0  525.0  560.0  467.0    NaN  \n",
       "                           0045  667.0  713.0  530.0  690.0  614.0  689.0  \n",
       "               Wake        0014  692.0  673.0  537.0  636.0  683.0  704.0  \n",
       "                           0021  701.0  690.0  537.0  652.0  683.0  597.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    index=['state','county','site'],\n",
    "    columns=df.index.month,\n",
    "    values='measure',\n",
    "    aggfunc='count'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Output styling\n",
    "\n",
    "It's hard for people to see patterns in large groups of numbers, and sometimes extra visual elements like the \".0\" at the end of all of those counts, make it even harder.\n",
    "\n",
    "**Pandas has a concept of output styling in Jupyter for DataFrames (including pivot tables), similar to Excel's \"Conditional Formatting\"**\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html\n",
    "\n",
    "### *Two things to watch out for:*\n",
    "- Styling requires a unique index, so if you run into an error you may need to `.reset_index()`\n",
    "- **A styled DataFrame doesn't elide in the way Jupyter typically displays, so it will print out the whole DataFrame below the cell and clog up your window!!** So, don't try it with the original Emissions DataFrame (df) – do something like `.head()` first so the number of rows displayed will be limited.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set number precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8c\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"index_name level0\" >tstamp</th>        <th class=\"col_heading level0 col0\" >1</th>        <th class=\"col_heading level0 col1\" >2</th>        <th class=\"col_heading level0 col2\" >3</th>        <th class=\"col_heading level0 col3\" >4</th>        <th class=\"col_heading level0 col4\" >5</th>        <th class=\"col_heading level0 col5\" >6</th>        <th class=\"col_heading level0 col6\" >7</th>        <th class=\"col_heading level0 col7\" >8</th>        <th class=\"col_heading level0 col8\" >9</th>        <th class=\"col_heading level0 col9\" >10</th>        <th class=\"col_heading level0 col10\" >11</th>        <th class=\"col_heading level0 col11\" >12</th>    </tr>    <tr>        <th class=\"index_name level0\" >state</th>        <th class=\"index_name level1\" >county</th>        <th class=\"index_name level2\" >site</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row0\" class=\"row_heading level0 row0\" rowspan=5>North Carolina</th>\n",
       "                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row0\" class=\"row_heading level1 row0\" >Forsyth</th>\n",
       "                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel2_row0\" class=\"row_heading level2 row0\" >0022</th>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0\" class=\"data row0 col0\" >704</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col1\" class=\"data row0 col1\" >631</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col2\" class=\"data row0 col2\" >701</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col3\" class=\"data row0 col3\" >686</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col4\" class=\"data row0 col4\" >697</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col5\" class=\"data row0 col5\" >676</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col6\" class=\"data row0 col6\" >706</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col7\" class=\"data row0 col7\" >691</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col8\" class=\"data row0 col8\" >682</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col9\" class=\"data row0 col9\" >702</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col10\" class=\"data row0 col10\" >669</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col11\" class=\"data row0 col11\" >703</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row1\" class=\"row_heading level1 row1\" rowspan=2>Mecklenburg</th>\n",
       "                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel2_row1\" class=\"row_heading level2 row1\" >0041</th>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0\" class=\"data row1 col0\" >714</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col1\" class=\"data row1 col1\" >644</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col2\" class=\"data row1 col2\" >697</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col3\" class=\"data row1 col3\" >687</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col4\" class=\"data row1 col4\" >705</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col5\" class=\"data row1 col5\" >677</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col6\" class=\"data row1 col6\" >714</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col7\" class=\"data row1 col7\" >704</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col8\" class=\"data row1 col8\" >525</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col9\" class=\"data row1 col9\" >560</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col10\" class=\"data row1 col10\" >467</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel2_row2\" class=\"row_heading level2 row2\" >0045</th>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0\" class=\"data row2 col0\" >706</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col1\" class=\"data row2 col1\" >635</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col2\" class=\"data row2 col2\" >712</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col3\" class=\"data row2 col3\" >683</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col4\" class=\"data row2 col4\" >703</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col5\" class=\"data row2 col5\" >690</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col6\" class=\"data row2 col6\" >667</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col7\" class=\"data row2 col7\" >713</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col8\" class=\"data row2 col8\" >530</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col9\" class=\"data row2 col9\" >690</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col10\" class=\"data row2 col10\" >614</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col11\" class=\"data row2 col11\" >689</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row3\" class=\"row_heading level1 row3\" rowspan=2>Wake</th>\n",
       "                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel2_row3\" class=\"row_heading level2 row3\" >0014</th>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0\" class=\"data row3 col0\" >705</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col1\" class=\"data row3 col1\" >509</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col2\" class=\"data row3 col2\" >699</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col3\" class=\"data row3 col3\" >678</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col4\" class=\"data row3 col4\" >692</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col5\" class=\"data row3 col5\" >681</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col6\" class=\"data row3 col6\" >692</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col7\" class=\"data row3 col7\" >673</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col8\" class=\"data row3 col8\" >537</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col9\" class=\"data row3 col9\" >636</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col10\" class=\"data row3 col10\" >683</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col11\" class=\"data row3 col11\" >704</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                        <th id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8clevel2_row4\" class=\"row_heading level2 row4\" >0021</th>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0\" class=\"data row4 col0\" >696</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col1\" class=\"data row4 col1\" >634</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col2\" class=\"data row4 col2\" >696</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col3\" class=\"data row4 col3\" >677</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col4\" class=\"data row4 col4\" >698</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col5\" class=\"data row4 col5\" >681</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col6\" class=\"data row4 col6\" >701</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col7\" class=\"data row4 col7\" >690</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col8\" class=\"data row4 col8\" >537</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col9\" class=\"data row4 col9\" >652</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col10\" class=\"data row4 col10\" >683</td>\n",
       "                        <td id=\"T_0ba0c0ba_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col11\" class=\"data row4 col11\" >597</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7feed0aafd30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    index=['state','county','site'],\n",
    "    columns=df.index.month,\n",
    "    values='measure',\n",
    "    aggfunc='count'\n",
    ").style.set_precision(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background gradient (heatmap) – Higher mean emissions in the winter months\n",
    "\n",
    "- **Color scale range is determined column-wise by default**. You can change the `axis=1` for row-wise auto-scaling of max and min color values\n",
    "- The only way to get a common scale across the whole pivot table is to explicitly set `vmin=` and `vmax=`\n",
    "- You can set a fractional threshold for dark to light text transition with `text_color_threshold`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling color independently across rows:\n",
    "\n",
    "*Allows us to compare across months, but distorts the comparison between sites!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col5 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col6 {\n",
       "            background-color:  #f0eaf4;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col7 {\n",
       "            background-color:  #fbf4f9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col8 {\n",
       "            background-color:  #d6d6e9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col9 {\n",
       "            background-color:  #b3c3de;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col10 {\n",
       "            background-color:  #2c89bd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col11 {\n",
       "            background-color:  #1278b4;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col1 {\n",
       "            background-color:  #86b0d3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col2 {\n",
       "            background-color:  #c2cbe2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col3 {\n",
       "            background-color:  #dedcec;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col4 {\n",
       "            background-color:  #f6eff7;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col5 {\n",
       "            background-color:  #f9f2f8;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col6 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col7 {\n",
       "            background-color:  #e8e4f0;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col8 {\n",
       "            background-color:  #efe9f3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col9 {\n",
       "            background-color:  #5ea0ca;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col10 {\n",
       "            background-color:  #88b1d4;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col11 {\n",
       "            background-color:  #056caa;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col1 {\n",
       "            background-color:  #71a8ce;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col2 {\n",
       "            background-color:  #348ebf;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col3 {\n",
       "            background-color:  #4c99c5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col4 {\n",
       "            background-color:  #a8bedc;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col5 {\n",
       "            background-color:  #d9d8ea;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col6 {\n",
       "            background-color:  #f5eff6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col7 {\n",
       "            background-color:  #bfc9e1;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col8 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col9 {\n",
       "            background-color:  #81aed2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col10 {\n",
       "            background-color:  #60a1ca;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col11 {\n",
       "            background-color:  #0a73b2;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }</style><table id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8c\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"index_name level0\" >tstamp</th>        <th class=\"col_heading level0 col0\" >1</th>        <th class=\"col_heading level0 col1\" >2</th>        <th class=\"col_heading level0 col2\" >3</th>        <th class=\"col_heading level0 col3\" >4</th>        <th class=\"col_heading level0 col4\" >5</th>        <th class=\"col_heading level0 col5\" >6</th>        <th class=\"col_heading level0 col6\" >7</th>        <th class=\"col_heading level0 col7\" >8</th>        <th class=\"col_heading level0 col8\" >9</th>        <th class=\"col_heading level0 col9\" >10</th>        <th class=\"col_heading level0 col10\" >11</th>        <th class=\"col_heading level0 col11\" >12</th>    </tr>    <tr>        <th class=\"index_name level0\" >county</th>        <th class=\"index_name level1\" >site</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row0\" class=\"row_heading level0 row0\" >Forsyth</th>\n",
       "                        <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row0\" class=\"row_heading level1 row0\" >0022</th>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0\" class=\"data row0 col0\" >10.9</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col1\" class=\"data row0 col1\" >7.9</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col2\" class=\"data row0 col2\" >6.5</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col3\" class=\"data row0 col3\" >4.9</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col4\" class=\"data row0 col4\" >5.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col5\" class=\"data row0 col5\" >5.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col6\" class=\"data row0 col6\" >4.6</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col7\" class=\"data row0 col7\" >5.4</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col8\" class=\"data row0 col8\" >4.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col9\" class=\"data row0 col9\" >6.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col10\" class=\"data row0 col10\" >7.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col11\" class=\"data row0 col11\" >10.8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row1\" class=\"row_heading level0 row1\" rowspan=2>Mecklenburg</th>\n",
       "                        <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row1\" class=\"row_heading level1 row1\" >0041</th>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0\" class=\"data row1 col0\" >13.1</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col1\" class=\"data row1 col1\" >6.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col2\" class=\"data row1 col2\" >6.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col3\" class=\"data row1 col3\" >5.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col4\" class=\"data row1 col4\" >4.1</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col5\" class=\"data row1 col5\" >4.8</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col6\" class=\"data row1 col6\" >4.5</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col7\" class=\"data row1 col7\" >4.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col8\" class=\"data row1 col8\" >4.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col9\" class=\"data row1 col9\" >7.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col10\" class=\"data row1 col10\" >7.9</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row2\" class=\"row_heading level1 row2\" >0045</th>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0\" class=\"data row2 col0\" >15.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col1\" class=\"data row2 col1\" >11.0</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col2\" class=\"data row2 col2\" >10.1</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col3\" class=\"data row2 col3\" >10.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col4\" class=\"data row2 col4\" >8.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col5\" class=\"data row2 col5\" >7.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col6\" class=\"data row2 col6\" >8.1</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col7\" class=\"data row2 col7\" >7.5</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col8\" class=\"data row2 col8\" >9.1</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col9\" class=\"data row2 col9\" >10.0</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col10\" class=\"data row2 col10\" >12.5</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col11\" class=\"data row2 col11\" >13.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row3\" class=\"row_heading level0 row3\" rowspan=2>Wake</th>\n",
       "                        <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row3\" class=\"row_heading level1 row3\" >0014</th>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0\" class=\"data row3 col0\" >9.0</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col1\" class=\"data row3 col1\" >5.6</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col2\" class=\"data row3 col2\" >4.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col3\" class=\"data row3 col3\" >4.0</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col4\" class=\"data row3 col4\" >3.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col5\" class=\"data row3 col5\" >3.1</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col6\" class=\"data row3 col6\" >2.9</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col7\" class=\"data row3 col7\" >3.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col8\" class=\"data row3 col8\" >3.5</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col9\" class=\"data row3 col9\" >6.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col10\" class=\"data row3 col10\" >5.6</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col11\" class=\"data row3 col11\" >7.6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row4\" class=\"row_heading level1 row4\" >0021</th>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0\" class=\"data row4 col0\" >11.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col1\" class=\"data row4 col1\" >9.0</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col2\" class=\"data row4 col2\" >9.7</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col3\" class=\"data row4 col3\" >9.4</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col4\" class=\"data row4 col4\" >8.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col5\" class=\"data row4 col5\" >7.3</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col6\" class=\"data row4 col6\" >6.5</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col7\" class=\"data row4 col7\" >7.9</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col8\" class=\"data row4 col8\" >6.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col9\" class=\"data row4 col9\" >8.8</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col10\" class=\"data row4 col10\" >9.2</td>\n",
       "                        <td id=\"T_0bd2ac24_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col11\" class=\"data row4 col11\" >10.3</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7feeb92a3250>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    index=['county','site'],\n",
    "    columns=df.index.month,\n",
    "    values='measure',\n",
    "    aggfunc='mean'\n",
    ").style.set_precision(1).background_gradient(axis=1).highlight_null('lightgray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Common color scale across whole table\n",
    "\n",
    "*Lets us in an undistorted way compare not only across months, but across sites.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0 {\n",
       "            background-color:  #2987bc;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col1 {\n",
       "            background-color:  #9ab8d8;\n",
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       "            background-color:  #c1cae2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
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       "            background-color:  #e5e1ef;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col4 {\n",
       "            background-color:  #dedcec;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col5 {\n",
       "            background-color:  #dedcec;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col6 {\n",
       "            background-color:  #ebe6f2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col7 {\n",
       "            background-color:  #dad9ea;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col8 {\n",
       "            background-color:  #e8e4f0;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col9 {\n",
       "            background-color:  #c9cee4;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col10 {\n",
       "            background-color:  #afc1dd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col11 {\n",
       "            background-color:  #2c89bd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0 {\n",
       "            background-color:  #046096;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col1 {\n",
       "            background-color:  #c8cde4;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col2 {\n",
       "            background-color:  #bcc7e1;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col3 {\n",
       "            background-color:  #dedcec;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col4 {\n",
       "            background-color:  #f1ebf5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col5 {\n",
       "            background-color:  #e7e3f0;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col6 {\n",
       "            background-color:  #ece7f2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col7 {\n",
       "            background-color:  #e9e5f1;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col8 {\n",
       "            background-color:  #e8e4f0;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col9 {\n",
       "            background-color:  #b0c2de;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col10 {\n",
       "            background-color:  #97b7d7;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col11 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "            background-color:  lightgray;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col1 {\n",
       "            background-color:  #2685bb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col2 {\n",
       "            background-color:  #4697c4;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col3 {\n",
       "            background-color:  #4094c3;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col4 {\n",
       "            background-color:  #8cb3d5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col5 {\n",
       "            background-color:  #abbfdc;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col6 {\n",
       "            background-color:  #91b5d6;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col7 {\n",
       "            background-color:  #a5bddb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col8 {\n",
       "            background-color:  #6fa7ce;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col9 {\n",
       "            background-color:  #4a98c5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col10 {\n",
       "            background-color:  #0569a5;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col11 {\n",
       "            background-color:  #046198;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0 {\n",
       "            background-color:  #75a9cf;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col1 {\n",
       "            background-color:  #d7d6e9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col2 {\n",
       "            background-color:  #e9e5f1;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col3 {\n",
       "            background-color:  #f2ecf5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col4 {\n",
       "            background-color:  #fcf4fa;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col5 {\n",
       "            background-color:  #fef6fa;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col6 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col7 {\n",
       "            background-color:  #f6eff7;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col8 {\n",
       "            background-color:  #f8f1f8;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col9 {\n",
       "            background-color:  #cacee5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col10 {\n",
       "            background-color:  #d7d6e9;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col11 {\n",
       "            background-color:  #a4bcda;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0 {\n",
       "            background-color:  #0d75b3;\n",
       "            color:  #f1f1f1;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col1 {\n",
       "            background-color:  #75a9cf;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col2 {\n",
       "            background-color:  #589ec8;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col3 {\n",
       "            background-color:  #63a2cb;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col4 {\n",
       "            background-color:  #8eb3d5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col5 {\n",
       "            background-color:  #abbfdc;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col6 {\n",
       "            background-color:  #c2cbe2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col7 {\n",
       "            background-color:  #9ab8d8;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col8 {\n",
       "            background-color:  #cccfe5;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col9 {\n",
       "            background-color:  #7dacd1;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col10 {\n",
       "            background-color:  #6da6cd;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }    #T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col11 {\n",
       "            background-color:  #3f93c2;\n",
       "            color:  #000000;\n",
       "            : ;\n",
       "        }</style><table id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8c\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"index_name level0\" >tstamp</th>        <th class=\"col_heading level0 col0\" >1</th>        <th class=\"col_heading level0 col1\" >2</th>        <th class=\"col_heading level0 col2\" >3</th>        <th class=\"col_heading level0 col3\" >4</th>        <th class=\"col_heading level0 col4\" >5</th>        <th class=\"col_heading level0 col5\" >6</th>        <th class=\"col_heading level0 col6\" >7</th>        <th class=\"col_heading level0 col7\" >8</th>        <th class=\"col_heading level0 col8\" >9</th>        <th class=\"col_heading level0 col9\" >10</th>        <th class=\"col_heading level0 col10\" >11</th>        <th class=\"col_heading level0 col11\" >12</th>    </tr>    <tr>        <th class=\"index_name level0\" >county</th>        <th class=\"index_name level1\" >site</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row0\" class=\"row_heading level0 row0\" >Forsyth</th>\n",
       "                        <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row0\" class=\"row_heading level1 row0\" >0022</th>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0\" class=\"data row0 col0\" >10.9</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col1\" class=\"data row0 col1\" >7.9</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col2\" class=\"data row0 col2\" >6.5</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col3\" class=\"data row0 col3\" >4.9</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col4\" class=\"data row0 col4\" >5.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col5\" class=\"data row0 col5\" >5.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col6\" class=\"data row0 col6\" >4.6</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col7\" class=\"data row0 col7\" >5.4</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col8\" class=\"data row0 col8\" >4.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col9\" class=\"data row0 col9\" >6.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col10\" class=\"data row0 col10\" >7.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col11\" class=\"data row0 col11\" >10.8</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row1\" class=\"row_heading level0 row1\" rowspan=2>Mecklenburg</th>\n",
       "                        <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row1\" class=\"row_heading level1 row1\" >0041</th>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0\" class=\"data row1 col0\" >13.1</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col1\" class=\"data row1 col1\" >6.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col2\" class=\"data row1 col2\" >6.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col3\" class=\"data row1 col3\" >5.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col4\" class=\"data row1 col4\" >4.1</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col5\" class=\"data row1 col5\" >4.8</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col6\" class=\"data row1 col6\" >4.5</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col7\" class=\"data row1 col7\" >4.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col8\" class=\"data row1 col8\" >4.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col9\" class=\"data row1 col9\" >7.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col10\" class=\"data row1 col10\" >7.9</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row2\" class=\"row_heading level1 row2\" >0045</th>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0\" class=\"data row2 col0\" >15.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col1\" class=\"data row2 col1\" >11.0</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col2\" class=\"data row2 col2\" >10.1</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col3\" class=\"data row2 col3\" >10.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col4\" class=\"data row2 col4\" >8.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col5\" class=\"data row2 col5\" >7.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col6\" class=\"data row2 col6\" >8.1</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col7\" class=\"data row2 col7\" >7.5</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col8\" class=\"data row2 col8\" >9.1</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col9\" class=\"data row2 col9\" >10.0</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col10\" class=\"data row2 col10\" >12.5</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col11\" class=\"data row2 col11\" >13.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row3\" class=\"row_heading level0 row3\" rowspan=2>Wake</th>\n",
       "                        <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row3\" class=\"row_heading level1 row3\" >0014</th>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0\" class=\"data row3 col0\" >9.0</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col1\" class=\"data row3 col1\" >5.6</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col2\" class=\"data row3 col2\" >4.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col3\" class=\"data row3 col3\" >4.0</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col4\" class=\"data row3 col4\" >3.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col5\" class=\"data row3 col5\" >3.1</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col6\" class=\"data row3 col6\" >2.9</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col7\" class=\"data row3 col7\" >3.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col8\" class=\"data row3 col8\" >3.5</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col9\" class=\"data row3 col9\" >6.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col10\" class=\"data row3 col10\" >5.6</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col11\" class=\"data row3 col11\" >7.6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row4\" class=\"row_heading level1 row4\" >0021</th>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0\" class=\"data row4 col0\" >11.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col1\" class=\"data row4 col1\" >9.0</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col2\" class=\"data row4 col2\" >9.7</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col3\" class=\"data row4 col3\" >9.4</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col4\" class=\"data row4 col4\" >8.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col5\" class=\"data row4 col5\" >7.3</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col6\" class=\"data row4 col6\" >6.5</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col7\" class=\"data row4 col7\" >7.9</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col8\" class=\"data row4 col8\" >6.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col9\" class=\"data row4 col9\" >8.8</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col10\" class=\"data row4 col10\" >9.2</td>\n",
       "                        <td id=\"T_0c04d1ea_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col11\" class=\"data row4 col11\" >10.3</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7feeb918d4f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    index=['county','site'],\n",
    "    columns=df.index.month,\n",
    "    values='measure',\n",
    "    aggfunc='mean'\n",
    ").style.set_precision(1).background_gradient(vmin=3, vmax=15).highlight_null('lightgray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**To try the exercise below, select this cell and from the Jupyter menus choose**\n",
    "\n",
    "`Run -> Run All Above Selected Cell`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE\n",
    "\n",
    "**Explore with a heatmap-styled pivot table, how mean emissions at each site vary by hour of the day**\n",
    "\n",
    "Reminder: [Datetime index attributes](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.html) – gives you categorical date/time parts like month, day, minute, etc\n",
    "\n",
    "*Note: Type instead of using copy/paste for better retention*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Expected output*\n",
    "\n",
    "<img src='images/Emissions_heatmap_hourofday.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal Bar styling\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.formats.style.Styler.bar.html\n",
    "\n",
    "- **Make sure bars go to zero with `vmin=0` so you don't distort perception of the data! That's not the default!**\n",
    "- **Also explicily set `vmax` to get a common maximum bar length across all columns!**\n",
    "\n",
    "#### Higher median in the winter\n",
    "\n",
    "*Notice how we just swapped what is the index and what is the columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#988675 20.8%, transparent 20.8%);\n",
       "        }    #T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow10_col4 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#988675 46.7%, transparent 46.7%);\n",
       "        }    #T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#988675 43.3%, transparent 43.3%);\n",
       "        }    #T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col2 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#988675 69.2%, transparent 69.2%);\n",
       "        }    #T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col3 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#988675 27.0%, transparent 27.0%);\n",
       "        }    #T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col4 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#988675 50.6%, transparent 50.6%);\n",
       "        }</style><table id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8c\" ><thead>    <tr>        <th class=\"index_name level0\" >county</th>        <th class=\"col_heading level0 col0\" >Forsyth</th>        <th class=\"col_heading level0 col1\" colspan=2>Mecklenburg</th>        <th class=\"col_heading level0 col3\" colspan=2>Wake</th>    </tr>    <tr>        <th class=\"index_name level1\" >site</th>        <th class=\"col_heading level1 col0\" >0022</th>        <th class=\"col_heading level1 col1\" >0041</th>        <th class=\"col_heading level1 col2\" >0045</th>        <th class=\"col_heading level1 col3\" >0014</th>        <th class=\"col_heading level1 col4\" >0021</th>    </tr>    <tr>        <th class=\"index_name level0\" >tstamp</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0\" class=\"data row0 col0\" >7.45</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col1\" class=\"data row0 col1\" >9.95</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col2\" class=\"data row0 col2\" >15.00</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col3\" class=\"data row0 col3\" >4.60</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col4\" class=\"data row0 col4\" >10.00</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0\" class=\"data row1 col0\" >5.80</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col1\" class=\"data row1 col1\" >4.90</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col2\" class=\"data row1 col2\" >9.90</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col3\" class=\"data row1 col3\" >3.50</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col4\" class=\"data row1 col4\" >7.70</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0\" class=\"data row2 col0\" >5.10</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col1\" class=\"data row2 col1\" >4.30</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col2\" class=\"data row2 col2\" >8.10</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col3\" class=\"data row2 col3\" >2.70</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col4\" class=\"data row2 col4\" >8.70</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0\" class=\"data row3 col0\" >3.80</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col1\" class=\"data row3 col1\" >3.80</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col2\" class=\"data row3 col2\" >8.30</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col3\" class=\"data row3 col3\" >2.20</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col4\" class=\"data row3 col4\" >8.30</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0\" class=\"data row4 col0\" >4.20</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col1\" class=\"data row4 col1\" >3.00</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col2\" class=\"data row4 col2\" >7.70</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col3\" class=\"data row4 col3\" >2.40</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col4\" class=\"data row4 col4\" >7.50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow5_col0\" class=\"data row5 col0\" >4.40</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow5_col1\" class=\"data row5 col1\" >3.90</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow5_col2\" class=\"data row5 col2\" >6.20</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow5_col3\" class=\"data row5 col3\" >2.30</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow5_col4\" class=\"data row5 col4\" >6.50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow6_col0\" class=\"data row6 col0\" >3.50</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow6_col1\" class=\"data row6 col1\" >3.25</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow6_col2\" class=\"data row6 col2\" >7.30</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow6_col3\" class=\"data row6 col3\" >2.15</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow6_col4\" class=\"data row6 col4\" >5.50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow7_col0\" class=\"data row7 col0\" >4.10</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow7_col1\" class=\"data row7 col1\" >3.50</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow7_col2\" class=\"data row7 col2\" >6.70</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow7_col3\" class=\"data row7 col3\" >2.80</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow7_col4\" class=\"data row7 col4\" >7.40</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow8_col0\" class=\"data row8 col0\" >3.40</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow8_col1\" class=\"data row8 col1\" >3.60</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow8_col2\" class=\"data row8 col2\" >8.40</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow8_col3\" class=\"data row8 col3\" >2.80</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow8_col4\" class=\"data row8 col4\" >5.50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow9_col0\" class=\"data row9 col0\" >4.60</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow9_col1\" class=\"data row9 col1\" >5.10</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow9_col2\" class=\"data row9 col2\" >9.10</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow9_col3\" class=\"data row9 col3\" >4.00</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow9_col4\" class=\"data row9 col4\" >7.90</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow10_col0\" class=\"data row10 col0\" >5.00</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow10_col1\" class=\"data row10 col1\" >6.20</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow10_col2\" class=\"data row10 col2\" >11.60</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow10_col3\" class=\"data row10 col3\" >3.70</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow10_col4\" class=\"data row10 col4\" >8.30</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col0\" class=\"data row11 col0\" >7.70</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col1\" class=\"data row11 col1\" >nan</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col2\" class=\"data row11 col2\" >12.30</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col3\" class=\"data row11 col3\" >4.80</td>\n",
       "                        <td id=\"T_0c1fc3f6_4f6f_11eb_b1ae_c8bcc8f49a8crow11_col4\" class=\"data row11 col4\" >9.00</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fee60a7bb50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    columns=['county','site'],\n",
    "    index=df.index.month,\n",
    "    values='measure',\n",
    "    aggfunc='median'\n",
    ").style.set_precision(2).bar(vmin=0, vmax=16, color='#988675', width=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby results can be styled, too\n",
    "\n",
    "*Just make sure the DataFrame isn't too long, or you choose with head() or tail() to only display part of it, or it will overwhelm your browser window.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 65.2%, transparent 65.2%);\n",
       "        }    #T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 61.6%, transparent 61.6%);\n",
       "        }    #T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 100.0%, transparent 100.0%);\n",
       "        }    #T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 48.6%, transparent 48.6%);\n",
       "        }    #T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0 {\n",
       "            width:  10em;\n",
       "             height:  80%;\n",
       "            background:  linear-gradient(90deg,#d65f5f 85.3%, transparent 85.3%);\n",
       "        }</style><table id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8c\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >measure</th>    </tr>    <tr>        <th class=\"index_name level0\" >county</th>        <th class=\"index_name level1\" >site</th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row0\" class=\"row_heading level0 row0\" >Forsyth</th>\n",
       "                        <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row0\" class=\"row_heading level1 row0\" >0022</th>\n",
       "                        <td id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow0_col0\" class=\"data row0 col0\" >6.7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row1\" class=\"row_heading level0 row1\" rowspan=2>Mecklenburg</th>\n",
       "                        <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row1\" class=\"row_heading level1 row1\" >0041</th>\n",
       "                        <td id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow1_col0\" class=\"data row1 col0\" >6.3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row2\" class=\"row_heading level1 row2\" >0045</th>\n",
       "                        <td id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow2_col0\" class=\"data row2 col0\" >10.2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel0_row3\" class=\"row_heading level0 row3\" rowspan=2>Wake</th>\n",
       "                        <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row3\" class=\"row_heading level1 row3\" >0014</th>\n",
       "                        <td id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow3_col0\" class=\"data row3 col0\" >5.0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8clevel1_row4\" class=\"row_heading level1 row4\" >0021</th>\n",
       "                        <td id=\"T_0c25c8d2_4f6f_11eb_b1ae_c8bcc8f49a8crow4_col0\" class=\"data row4 col0\" >8.7</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7feeb92bd310>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\n",
    "    ['county','site']\n",
    ").agg(\n",
    "    {'measure':'mean'}\n",
    ").style.set_precision(1).bar(vmin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Time series plots like Excel\n",
    "\n",
    "The pivot tables themselves provide a visualization of the data, but **a line plot is a very natural visualization style for a time series.**\n",
    "\n",
    "To use the built-in plotting functions of Pandas for data like this, just like in Excel we need to be careful which data goes in which direction in the pivot table.\n",
    "\n",
    "- **Intended X-axis needs to be on the row index** – so, we need time to be going down the row index if we want time to be horizontal, from left to right, in our line plot\n",
    "- **Separate lines will be made from each column** – so we want our counties and sites to be our columns\n",
    "\n",
    "*This happens to be the same arrangement we made for our horizontal bars styling above.*\n",
    "\n",
    "#### Pandas line plot documentation\n",
    "\n",
    "- General plot: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html\n",
    "- Line plot: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.line.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Higher emissions variance in winter months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feeb017e850>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lpd04Y8WKFdx7773a6xkZGUyePJmQkBBCQkLYv38/gHYFqp+fH/7+/qxevbpaP5mZmQwZMoS//vqr2vny8nJefPFFQkJC8Pf314p57dq1ixEjRjBlyhQ8PT158MEHq+ncfPzxx4SGhhIaGqqVRnj44YdZtWqVtk3laHnXrl2MHDmSBx54AD8/P9RqNf/85z/x8fFh/PjxjB07ttp9NVFVRnjKlCls374dIQSbN29m1KhRWFpaYmFhwahRo9i0aVO11/bRRx9Vs2Vra0tISAh6eno39XP33Xc3eU2ApH3pWouYKrHzAaGGjDjoFdje3nRONr4MV6Ja1qa9H4yZV+vlmiR/QSMNsHz5cuzt7dHR0cHR0VGrivif//yH5557juHDh5OUlER4eDixsbHMnTsXc3NzoqI0r6Hq6DUtLY0JEybw7rvvMmrUqGopjUWLFmFubs6RI0coLi5m2LBhjB49GoATJ04QHR2No6Mjw4YNY//+/QwfPhwAMzMzIiIiWLp0Kc8++2y1Xxs1ERERwenTp+nXrx+rVq0iMTGRqKgo0tPT8fLyYtasWXXeX1ViWFdXF3Nzc7KysmqUHr58+TKgkTuYMGGCVn64Ifj6+nLkyJEGt5d0HLpmcK/cuCM9Rgb3TkRNkr8Ad955J3PmzMHOzq6apC/Atm3bqu2gdPXqVa5du8a2bdtYvny59ryFhQUApaWlhIWF8dVXX9UoOLZlyxZOnTqlHTnn5eURHx+Pvr4+oaGhODk5ARAQEEBiYqI2uN9///3a43PPPVfvaw0NDaVfv34A7Nu3j6lTp6JSqbC3t9du1FEXNS0+VBSl1vMpKSmsXLmSXbt21Wu7Kjo6Oujr63Pt2jV69OjRqHsl7UvXDO6W/UDXSO7K1BzqGGG3FrVJ/urr6xMUFMQnn3xCdHQ069ev115Tq9UcPHjwJglcIUSNeuq6uroEBQWxefPmGoO7EIIvv/yS8PDwaud37dpVbSONG2WAq/ZV+VhXVxd1xWI6IUS1/V+rShM3ZZV4pcSwk5MTZWVl5OXlYWlpiZOTU7UAnpyczIgRIzhx4gQJCQm4uroCmn1aXV1dtSmkuiguLsbQ0LDRPkral66Zc1fpaEoiZXDvVNQl+fvf//6XDz/8ECsrq2rnR48ezcKFC7XPIyMjazxfmZZRFIXFixcTFxdXbRPrSsLDw/n6668pLS0FNPK6169fr9f3yknHFStWMGTIEEBTRXPs2DFAkyOvtHkjw4cPZ/Xq1ajVatLS0qoF51deeYU1a9bcdE9VGeFVq1Zx++23oygK4eHhbNmyhZycHHJyctiyZQvh4eGMGzeOK1eukJiYSGJiIsbGxg0K7FlZWdjY2NSYj5d0bLrmyB00qZn4ze3thaSRVEr+3ihp6+PjU2OVzIIFC3j66afx9/enrKyMW2+9lW+++YbXX3+dp59+Gl9fX3R0dHjzzTe1Oyvp6OiwfPly7rrrLszMzBg7dqzW3qOPPkpiYiKBgYEIIbCxsdHueVoXxcXFDBo0CLVaza+//grAY489xsSJEwkNDSUsLKzaaL0qkydPZvv27fj6+uLu7s6gQYMwNzcHICoqSqsLX5VHHnmEGTNm4OrqiqWlpTYFZWlpyZw5cwgJCQHgjTfewNLSsk7fr1y5QnBwMFevXkWlUvH5558TExODmZkZO3furPb+SDoRtclFtuVfi0n+VuXAVxr532tpLW+7i9IRJFw7kqRtW3Lt2jUhhBCZmZnCxcVFpKamCiGEGD16dHu6JSZNmiTi4uJqvNYRPi/dHbqN5G9VKjfuSIsGU9u620o6DK0t+dtRGT9+PLm5uZSUlDBnzhzs7e0B2Ly5/X59lpSUcPfdd+Ph4dFuPkiaTtcN7pUVM2nR0L/+6gNJx6G+MsCuSGOrWNoCfX19HnroofZ2Q9JEuuaEKoCpDZjYypWqEomkW9Lpg7u6sBBRSxUCdt6yYkYikXRLOnVwj9v1BzHBQZzb81fNDex8NatU1U3T75BIJJLOSqcO7j29/FHUgvgdN9cBA5qNO8qKNDozEolE0o3o1MHd3s6F9F7GlJ+oRQNFWzEj5X87C1Lyt2G0pOTvrl27MDc3JyAggICAAN555x1ASv52dpoV3BVFeU5RlGhFUU4rivKroiiGiqJYKoqyVVGU+IqjRUs5WxOqQH96Xywk/koNE6c2nqCo5MYdnQgp+dswWlLyF+CWW24hMjKSyMhI3njjDUBK/nZ2mhzcFUXpBfwbCBZC+AI6wDTgZWC7EMIN2F7xvNVwHTkR/XI4vOPnmy/qGYFlf1kx04mQkr9tL/lbF1Lyt/PS3Dp3XcBIUZRSwBhIAV4BRlRcXwLsAl5qZj+1Yj90JHkKZB7Yjbi/BrEoOx9IjWyt7rssH0Z8SFx2XIva9LT05KXQ2j8KUvK3/SR/Dx48yIABA3B0dGT+/PnaX0dS8rfz0uTgLoS4rCjKfCAJKAS2CCG2KIpiJ4RIrWiTqihKjctDFUV5HHgcoE+fPk11Ax1zc4qc7XGMT+Nszlk8LG9YTWfnAzF/QHE+GNycb5V0HKTkb/tI/gYGBnLx4kVMTU3ZsGEDd999N/Hx8YCU/O3MNDm4V+TSJwL9gFxgpaIo0xt6vxDiO+A7gODg4MZrnlbBasgtqH5byZaEDXiE3hDcbSsmVdNjoXdIc7rpVtQ1wm4tpORvw2lJyV8zMzNt+7Fjx/LPf/6TzMxMrK2tASn521lpzoTqHcAFIUSGEKIU+B0YCqQpiuIAUHFMb76bdWM59FYMyiBu/583/0exq9y4Qy5m6uhIyd/2kfy9cuWK9v9NREQEarVa+z5Lyd/OS3OCexIwWFEUY0UzVAkDYoF1wMyKNjOBtc1zsW4uZRegG6DZbckq9gqx2bHVG/TsC3omsmKmk1Ap+XsjPj4+2gnEqixYsICjR4/i7++Pt7c333zzDQCvv/46OTk5+Pr6MmDAAHbu3Km9p1Lyd+fOnfzf//1fNXuPPvoo3t7eBAYG4uvryxNPPNGgUsBKyd8vvviCzz77DNBI/u7evZvQ0FAOHz5cp+Svk5OTtr8bJX8rRcSq8sgjj5CVlYWrqyuffvqp9ouqquRvSEhIgyR/V61apX2f/v3vf7N8+XLtrw8p+duJqU0usiF/wNtAHHAa+AkwAKzQVMnEVxwt67PTVMnf/QkZou9Lf4qdcWni7PhxYvmdXuLTo5/e3PB/YUIsHtukProTHUHCVUr+SslfScOhDsnfZtW5CyHeFEJ4CiF8hRAzhBDFQogsIUSYEMKt4pjdnD7qIrCPBSb6OmyOTqPHoCF4XVbYem7TzakZW29NWqYJuU1J21JV8rc7MX78eAICArjllluk5K+kRejUkr+GejqM8LBla0waLwcHo/fzzxjGXyY6Kxpf6yorDu184PgSuJYKZo7t57CkQUjJ346BlPzt3HRq+QGA0T52ZOYXk+CgqQLwTVbYnHjDaKdyUlXm3SUSSTeh0wf3kZ626OkobLlcgoGbK0OvmLE5cXP11Iy2HFJWzEgkku5Bpw/uZoZ6DHaxYnP0FYyCQ+idmE/atRROZZ76u5GxJfRwlNruEomk29DpgztAuI89iVkFXPXwQ1VYgluabg2pGW+ZlpFIJN2GLhHcR3nbAbDLoBcAY3J7syVxC2qh/ruRrbdm447yWnZtkkgkki5ElwjudmaGDOzTkz+TS9B3cWHAZV3SCtI4mXGySiNfUJdCVkL7OSqpl6p67pMmTeKPP/7QXvPw8ODdd9/VPp88eTK///57rbZaSmd906ZNeHh44OrqWm1Va3Z2NqNGjcLNzY1Ro0ZpV8FmZWUxcuRITE1Na9WdnzBhQjUN+YULF/LDDz8021eJpJIuEdwBRnvbE3U5DzEgENPYSxiiVz01o924Q+bdOzJV9dyHDh3KgQMHAE3ANDU15eDBg9q2Bw8eZOjQoa3qT3l5OU8//TQbN24kJiaGX3/9VStUNm/ePMLCwoiPjycsLEwb+A0NDZk7dy7z58+v0ebvv/9+06Yhs2bNYsGCBa36WiTdi05d516VcB87PtwUR4xNf9zyrzNRDGZL4hZmh8xGpajA2h1Uuprg7jelvd3t8Fx5/32KY1tW8tfAyxP7V1+ts82yZcv45ZdfABg2bBizZ88G4MCBA4wfP56NGzcihCAxMREjIyPs7e156qmnOHLkCIWFhUyZMoW33367ms3CwkImTZrE5MmTeeCBB3jmmWeIioqirKyMt956q5p+/I1ERETg6uqKi4sLoJEfXrt2Ld7e3qxdu1Zbnz5z5kxGjBjBhx9+iImJCcOHD9dqt1QlPz+fTz/9lO+++66aLr2xsTHOzs5EREQQGhpa/5spkdRDlxm5u9iY4mpryno0K/tGZtqSUZjB8bTjmga6BmDlJjfu6MDcqOceFBTE6dOnKSkp4cCBAwwZMgQPDw9iY2M5cOAAw4YNA+C9997j6NGjnDp1it27d3Pq1N+VUvn5+dx111088MADPPbYY7z33nvcfvvtHDlyhJ07d/Liiy/WKQxWlz56WlqaVhvdwcGB9PT6NfLmzJnDf//7X4yNjW+6FhwczN69e+t/oySSBtBlRu6gGb1/s/s8L/btS6/4HAztDdmcuJlg+2BNAztvuBTRvk52EuobYbcGN+q5GxgY4OPjw/Hjxzl06BCzZ8/m/PnzHDhwgBMnTmhTMr/99hvfffcdZWVlpKamEhMTg7+/PwATJ05k9uzZPPjgg4BGr33dunXalElRURFJSUl4eXnV6NNNUhZQo5RwQ4iMjCQhIYHPPvus2gYhldja2hIX17K/liTdly4zcgdN3r1cLchw8aH4eCS3Og5n68WtlKsrdErsfCDvEhTlta+jkhqpSc996NCh7Nmzh2vXrmFhYcHgwYM5cOCAduR+4cIF5s+fz/bt2zl16hTjxo2rZmPYsGHaVA5ogvXq1au1+4XWFdjhb930SpKTk3F01EhY2NnZkZqaCkBqaiq2tjXuS6Pl4MGDHDt2DGdnZ4YPH87Zs2cZMWKE9npRUdFNuvSSrs1b66JZtO9Cq9juUsHd38kcezNDDvfog/raNcapfckqyuJYmkZTG9tKbffY2o1I2o2a9NyHDRvGt99+y4ABAwDw9/fn0KFDJCUl4ePjw9WrVzExMcHc3Jy0tDQ2btxYzeY777yDlZUV//znPwGNXvuXX36pDfYnTpwANOmXsLCwm3wKCQkhPj6eCxcuUFJSwvLly5kwYQJQXVN9yZIldebuAZ566ilSUlJITExk3759uLu7V9OUOXv2bLUKGknXJiu/mJ8PXeRKXmGr2O9SwV1RFEb72LG6XDOC8r6oxkjX6O+qGa3GzOl28lBSHzfquQ8dOpTz589rN8DQ1dXF1taW4OBgVCoVAwYMYODAgfj4+DBr1ixtHr4qn3/+OUVFRcyePZs5c+ZQWlqKv78/vr6+zJkzB9CMvHV1b85S6urqsnDhQsLDw/Hy8uLee+/V7i/68ssvs3XrVtzc3Ni6dSsvv/z3XvDOzs48//zz/Pjjjzg5OVXbCrA29u/fzx133NG4N0zSaVl3MoUytWBykFOr2Fdqyim2NcHBwaJyd/vmsj8hkwe/P8y6Q59i4ePFgnuNiLgSwfap29FVdGBeX021zPhPW6S/rkRsbGydKYq24MSJE3z66af89NNPbdrvwoUL6dOnj3ZU3ta01+tuDh3h89KZGf+lZvL8z2duabINRVGOCSGCa7rWpUbuAKH9LDE30uOcowcFR48S3mcU2UXZHLlyBBQFbL1kxUwHpr303P/1r3+1W2AHzWTy3Llz261/SdsSd+Uqpy9fZXJg64zaoQsGdz0dFWGetmzX74U6L4+Q63YY6xpXT82kxciNOzows2bNQkdHp73daFNGjRqlLQGVdH1WH0tGT0dhYkCvVuujywV30Gi8HzbrC0D58VOM6D2CbUnbKFWXasohi/MgL7mdvZRIJN2RsnI1a06kMNLDFksT/Vbrp0sG91vdbbhqZkW+pS0FRyIIdw4nrziPiNQIjcYMSBkCiUTSLuyJzyAzv5gprTSRWkmXDO7G+rrc4mZDpEU/Co4cZajDEEz1TDWpGduKCSC5cYdEImkHVh1LxtJEnxEeda+LaC5dMriDZjf89U0AACAASURBVLXqYTNnynNz4cIlRvYeqUnN6BmDeW+p7S6RSNqc3IIStsWkMzHAEX3d1g2/XTa43+Flx2mb/gAURBwh3DmcayXXOJh6sGJSVY7cOyJdQfJ369atBAUF4efnR1BQEDt27NDe89prr9G7d++bVCGl5G/3YP3JFErK1a1aJVNJlw3uFib6OPu6km1qScGRIwxxHEIPvR4VqRlvyIqHsuL2dlNyA11B8tfa2pr169cTFRXFkiVLmDFjhtbeXXfdRUTEzfpGUvK3e7Dq+GU87Xvg42jW6n11KeGwGwn3see4RT8sD0fQS6XH7X1uZ0fSDkp8/o2+ugwyz4K9X3u72SHZ+9tZMi/lt6hN696m3HKve51tuoLk78CBA7X3+/j4UFRURHFxMQYGBgwePLjGfqTkb9cnIf0aJy/l8vo4ryaLzzWGLjtyB832e1HW/SE3h5Jz5zSpmdJrHFBVbLUn8+4diq4o+bt69WoGDhyIgYFBva9fSv52bVYdu4yOqnVr26vSpUfuThbGFPv4w4nfKDhyhMH3TcFM34zNOacZodKTFTN1UN8IuzXoapK/0dHRvPTSS2zZsqVB7aXkb9elXC1YcyKZEe422PSo/4u+JejSI3eAoMF+ZBiZk33gEHoqPe7oewc7k3dTbOMhJ1U7GF1J8jc5OZlJkyaxdOlS+vfv36DXLyV/uy77EjJJu1rcaiJhNdHlg3u4rwNRVv25HnEEIQThfcO5XnqdfZYOMi3Twegqkr+5ubmMGzeODz74oEaVytqQkr9dl1XHkjE30iPMq3Vr26vSqYN7bloBm76NIj+n9qoXdztTUvp6opeXQ8mFREIdQulp0JPNumVwLQUKstvQY0l9dAXJ34ULF5KQkMDcuXMJCAggICBAm4+fPXs2Tk5OFBQU4OTkxFtvvaXtS0r+dk2uFpWyJfoKEwY4YqDbhppJQoh2/wsKChJNIS+jQHz11A6xb+XZOtt9/uM2EePhKVJ+WiaEEOKtA2+JkKWBouAtcyEu7G1S312RmJiY9nZBHD9+XEyfPr3N+/3yyy/F2rVr27zfStrrdTeHjvB56Qz8cvii6PvSnyIyKafFbQNHRS1xtVOP3M2sjXANsiV6bwpF10trbTfs1gCyDM1I2qEZEYY7h1OoLmGfkWGHTs0silrEsthllKnL2tuVNkNK/kq6GquOJeNqa4q/k3mb9tupgztAYHhfSovLOb37cq1tBva15KydK6qoSIQQBNsFY2loyWaznh12V6ZNiZv4/PjnzIuYx7Q/p3Ey42Sb9Cs6gBSylPzt+HSEz0ln4ELmdY5dzGFKkFOb1LZXpVnBXVGUnoqirFIUJU5RlFhFUYYoimKpKMpWRVHiK44WLeVsTVg7mdLX14pTOy9RVlLzaE9HpUBAIKbXcsg/dwFdlS6j+o5ij6E+BR2wHDK9IJ25B+fiZ+3H/Nvmk1Ocw4wNM3jn4DvkFbfe5t6GhoZkZWXJ/7iSOhFCkJWVhaGhYXu70uFZfSwZlQKTBrZNbXtVmlvn/gWwSQgxRVEUfcAYeBXYLoSYpyjKy8DLwEvN7KdOAsP7sOaTE8QeSMVvRM2lRq6jb4W13xO9cReDn3Eh3DmcFWdWsOfqOe5Uq0HVMX7ECCF4Y/8blJSX8P7w93E2d2Z4r+F8FfkVv8T+wvak7bwQ/ALjXca3+EjAycmJ5ORkMjIyWtSupOthaGiIk1PblfV1RtRqwe/Hk7nFzQY7s7b/ImxycFcUxQy4FXgYQAhRApQoijIRGFHRbAmwi1YO7g6uPbF3MePE1iR8bnFEpXNzoA69dSAnDHuQtf8QPDOLQNtArHVN2GxYwJ25F8GyX2u62GB+O/Mb+1P289qg13A2dwbARM+E2SGzmdB/AnMPzuXVfa+yJmENrw96HZeeLi3Wt56eHv36dYz3QSLp7Bw8n0VKXhEvj22ffWabM1x1ATKAHxRFOaEoyveKopgAdkKIVICKY42FnYqiPK4oylFFUY42d6SoKAqB4X25llVEwrGbl4ADGOrpkuHiTc+zUZSVq9FR6TDKfjB7jQy5nnqiWf23FIl5iXxy7BOGOQ7jPo/7brruaenJT2N/4o0hbxCXHcfk9ZNZcHwBhWWF7eCtRCKpi9XHkulhqMtob7t26b85wV0XCAS+FkIMBK6jScE0CCHEd0KIYCFEsI2NTTPc0ODsZ42FgwnHNyfVmjM2HzwIq4JcIiM0efZw9ykUq1TsTtza7P6bS5m6jFf3vYqeSo93hr1Ta8pFpaiY6j6V9XevZ2y/sfwv6n9MWjuJPcl72thjiURSG/nFZWw8fYXx/o4Y6rVPcUBzgnsykCyEOFzxfBWaYJ+mKIoDQMWx5qF0C6OoFAJH9yHrcj5J0TUvTPIbNxKAuI27ABjoNBRbNWzKjmoLF+vk+6jvicqMYs7gOdga17+KzcrIiveGv8fi8MXo6+jz9PaneX7X81y5fqUNvJVIJHWxISqVwtLyVt9Kry6aHNyFEFeAS4qieFScCgNigHXAzIpzM4G1zfKwEbiF2GFqYcDxzRdrvG7h7cF14x6UHD+mKfJXVIzWtWJfeR75JS0rb9sYorOi+fbkt4zpN4Y7+93ZqHtD7ENYfddq/j3w3+xJ3sPEPyayNHppt6qNl0g6GquOJdPP2oTAPj3rb9xKNLdE5BlgmaIop4AA4H1gHjBKUZR4YFTF8zZBR1dFwB19SInP5cr5m0sGFUWhxHsALpfPcibtGgDh1gMpVWBnYsOU+1qaorIiXtn7CpZGlrw26LUm2dDT0eMx/8dYM3ENQXZBfHz04zatjZdIJH+TlFVAxIXsdqltr0qzgrsQIrIib+4vhLhbCJEjhMgSQoQJIdwqjm0q3uI1zAEDE91aR++9Rg7DrjCH3bs1mt/+vYdjX1bG5vg1bemmli+Of8GFvAu8O+xdzA2at4Ktd4/efBX2FZ+N+Izc4lxmbJjB2wffbtXaeIlEUp3Vx5NR2qm2vSodo7i7BdE31MVvhBMXTmaSnXLzJgx2wzUa4Cm79wOgsvdn9PUC9med4mrJ1Tb19VDqIX6O/ZkHPB9giOOQFrGpKAp39L2DtXevZYb3DNbEr2HCHxNYd26dXJwkkbQyarXg9xPJDOtvjWPP9pVv7nLBHcB/pBO6eipObL159G7g5kqpSQ8sE6K5lF0Ali6EF5VRJtTsTNrZZj5eLbnK6/tex9nMmWeDnm1x+yZ6JrwY8iIrxq/AqYcTr+17jVmbZ3E+93yL9yWRSDREJGZzKbuQyUHtO2qHLhrcjUz18R7uyNnDaVzLrr75g6JSYRAUjF/mObbGpIFKBz9zFxzRZVPipjbz8f3D75NZmMkHt3yAkW7rfcN7WHrw05ifeHPIm5zNOcvk9ZP54vgXsjZeImkFVh9LxtRAl3Af+/Z2pWsGd4ABd/RGACe3Xbrpms3wwTgUZHPooKbeXbHzI/x6EYdSDrVJfnpz4mb+Ov8XT/g/ga9162/OoFJUTHGfwrq71zG231i+j/pe1sZLJC1MQUkZG6JSGetnj7F+++9g2mWDu5mVEe4hdkTvT6Eov7ocsHHF7vLlkcfJvl4Ctt6E52ZSJsrYkbSjVf3KKMhg7qG5+Fr58qj/o63a141UrY030DHg6e1P89zO52RtvETSAmw6fYXrJeVMCepdf+M2oMsGd4CBo/tQVlxO1O7kaucN3N0RPczwzTzHttg0sPPBu6QEJ0PrVk3NCCF448AbFJcV8/4t76On0mu1vuoixD6EVXet4j+B/2Hf5X1M/GMiS6KXyNp4iaQZrDqWTB9LY0KcW1UIt8F06eBu1csUZz8rTu1IprT4bzlgRaWiR0gQA7MvsCVaE9wVINyoN4dTD5NTlNMq/qw8u5J9l/fxfPDz9DNvX4EuPR09HvV7lDUT1xBsH8z8o/OZ9uc0Mgsz29UviaQzkpxTwMHzWUwObN/a9qp06eAOms08iq6XEnsgpdp549BQ7K5lEH0qngJ9SzC25s5ShXJRzvak7S3ux8WrF5l/dD5DHYcyzWNai9tvKk49nFh4+0I+G/EZCbkJLI1e2t4uSSSdjjXHLyME3BPY8CoZIQQ/nv6x1VLBXT64O7j2xMHVnBNbkygvV2vPG4eEAOBxJYE9ZzPAzgePrET6mvVt8dRMmbqMV/dWiIINrV0UrL2orI2/vc/t/J7wO0VlRfXfJJFIAE2QXn08mcEulvS2NG7QPeXqcj488iGfHPukVQaT0A2CO0Dg6L7kZxeTcCRNe87Q0xNVjx4E5SWyuTI1k36G0X1GceTKEbIKs1qs/0VRiziVeYrXB7+OnUn7yH82hPs97yevOI+NFza2tysSSafh2MUcErMKmBzYMJGw4vJiXtzzIstilzHDewZzh7XO3rndIrj39bXC0tGE41uSEGrNKk1FRwfjoCCCcy6wPTaNMhtvKCvkTgtf1ELdYt+m0VnRfHPyG8Y4j2FMvzEtYrO1CLYLxrWnK7/G/SpXs0okDWT18WSM9HQY4+dQb9u84jwe3/I4Wy9u5YXgF5gdMhuV0jphuFsEd0Wl2cwjO+U6F0//PSI3DgmhZ1YqOrnZnC7V5MrcCq/Rz7xfi6RmisqKeHXvq1gaWvLa4KaJgrUliqIwzWMasdmxnMo81d7uSCQdnqLScv48mcoYP3tMDequbU/NT2XmxplEZUbx0a0fMdNnZp3tm0u3CO4ArsG2mFpWlwM2DtXk3QNzL7AuxQxQUNJjCXcO5+iVo82uHPni+BeczzvP3OFzGy0KVlZSzvovI9m/OqFNR9Hj+4/HRM+E5XHL26xPiaSzsjn6CteKy5hST0rmTPYZpm+YTnpBOt+O+rZNfsV3m+Cuo6Ni4Kg+pJ7LIyUhFwBDLy9UJiaMLrnMhrg8hFV/SDtNeN9wBIItzZABPpx6mJ9jf+Z+z/sZ6ji0UfcKIdj1yxmSorOJ3JpE5NabV9m2FiZ6JkzsP5HNiZtbdN5BIumKrDqWTK+eRgx2saq1zeHUwzy86WFQ4McxPxJiH9ImvnWb4A7gNcwRQ1M9TlSM3hVdXYyCAvG4ksCVq0Xk9XCD9BhcLVxx7enK5sTNTernaslVXt+vEQV7Lui5Rt9/akcyZw5dIWScM65Bthz4PYFzx9tkQysA7vO8j1J1Kb/H/95mfUoknY0reUXsT8jknsBeqFQ1V8BtOL+BJ7c9ib2JPcvGLsPdwr3N/OtWwV1PXwf/kU4kRmWRdVmz85JJaCgGKUlYleRzuswJsi9AyXVGO4/mRPoJ0q6n1WP1Zj44/AEZBRlNEgVLPpPD/tUJ9BtgTci4foTN9MLexYytP8Rw5ULb6LK7mLsw2GEwv539Ta5alUhq4fcTyagFNVbJVNawv7T3JQJsAlgyZgn2Jm0rJtatgjuA321O6BrocGJLEvB3vfvdqnS2ZFoBAtLjCHfWpGa2Xmzc5tlbErfw5/k/edz/8UaLgl3NKmTz/07T09aIOx72RlEp6OrrMPYpf0zM9dnwf6e4mtk2ao7TPKdx5foVdl/a3Sb9SSSdCSEEq48lE9zXAmdrk2rXqtawhzuH8+2obzHTN2tzH7tdcDc01cNnmCNnj6RxNasQQ29vFGNjbim4xO68io2p06NxMXfB3cK9UamZjIIM3jn0Dj5WPjzm/1ij/CotKWfjN1Goy9SMedIPfaO/Z96Neugz/l8DUJcL/vzqFMUFpXVYahluc7oNBxMHfj3za6v3JZF0NiIv5XIu4/pNG2DfWMP+0a0foa+j3y4+drvgDho5YAWI3HYJRU8P48BAHBJjSBK2lKoMIU0jBRzuHE5kRmSDVBMrRcGKyooaLQomhGDXz3FkJucz6hEfLOxNbmpjYW/CmCf8yEsvYNN3p6uttm0NdFW63OtxL4dTD8sNPiSSG1h9PBkDXRVj/f+ubW/LGvaG0C2Dew9LQ9wH2RG7L4XC/BKMQ0JQnz/HICtdLqj6VAvuQING71pRsKDncTF3aZQ/J7df4mxEGoPu6oezn3Wt7Xp5WDByuifJcTns/uVMq5dI3uN2D3oqPZafkWWREkklRaXlrItM4U5fe8wMNYO4tq5hbwjdMrgDDBzdl7JSNad2Jmvz7pNU6Rwr6oX6SjQIQV+zvnhZetVbEpl0NYn5R+czxGEI0zwbJwp2KS6bA7+fwyXAhqA7nett7znEgeCxzsTuT9XOG7QWloaW3Ol8J+vOreN66c370Uok3ZHtselcLSrTTqQ2p4Y989vvuLq1cfN6DaXbBndLBxP6DbAmamcyum6eKEZGDMg6zxnRG1VRNuRrqmRGO4/mVOYpLudfrtFOmbqMV/a9gq5Kl7nD5jbqZ9jVzEK2/C+annbGhD3shVJLOdWNhN7VD7cQOw6uOUfCsdYtkZzmOY3rpddZf259q/YjkXQWVh27hL2ZIcNcrZtVw3794EEyPv+c63v2toqf3Ta4g0YOuLigjNiITIwHBqAffVJT6w43pWZqG70vPr2YUxmneH1Q40TBSkvK2fhtFGq1YOyTfugbNnxbLkVRuP0hTxz6m7PthxiunG+9Ekk/az98rHyk3oxEAqRfLWJPfCaTAnuxOXFjk2vYy7KzSZn9EvouLti9+kqr+Nqtg7u9izmObj2J3JaEQVAIxWfO4NrXC4DCZI22Su8evfGx8qkx7x6TFcPXkV9zp/OdjHUZ2+B+hRDs/EkzgTr6ER962jVMJrQquno6jHnKDxMLAzZ8fYq8jNYpkVQUhWme0zifd54jV460Sh8SSWfhj8jLlKvV6PTc3eQadiEEqa+8SnleHr0+mY/KqHFrYRpKtw7uoBm95+cUc8UyAIBhXCdN9CTj3Altm3DncKKzorl07W8ZgOLyYq0o2OuDX29Un5FbLxF/JI3BE13o61v7suX6MDLV566KEsm/vjpJ0fXWKZG80/lOehr0lBOrkm6NEIJVxy7h1H8LS84sbHINe85PP5O/eze2s2dj6OnZSt7K4E4fH0usepkSHQcYGGJ/IZbzKmeU9Ghtm5qqZr44/gXn8s7xzrB3GiUKdikmm4NrEugfaENgeN9m+9/TzpixT/mRl1HIpu+iKC9r+RJJQ11DJrlNYkfSDrmZtqTbcjwpgyTd78jT39nkGvai2FjSP/4Y05EjsXjwgVbyVEO3D+6KohAY3oectEKuBo2n4MgRSq29sC26SFFxMQCOpo74W/tr8+4RqRH8FPMT0zymMazXsAb3dTWzkM2LTmPhYMLtD3m12I5Mjm4W3P6QF5fP5LKrlUok7/O4D7VQs/Lsyha3LZF0dPKK83hh39PomUXxrwHPN6mGXV1QwOXn/4uOhQUO77/X6juydfvgDuAaZEsPK0MumA+iKC4OGwdvDJRSTkQe1bYZ7Tya2OxYojOjeW3/azibOfN88PMN7qO0uJwNX0eBgLFPNW4CtSF4DLInZJwzcQdSObbpYv03NJJepr24zek2Vp1dRUl5SYvbl0g6Kqn5qTy08SEyS+Px1HmKJwL+0SQ7aR98QEliIo4ffYSuhUULe3kzMrgDqgo54KxCY3LN+uNQbgrA+dOHtW0qUzNPbnuSjIIM3h/+foNFwYQQ7FgaS3aKZgLV3KbxE6gNIWR8P9wH2XF47XnijzRe8Kw+7ve8n+yi7Ebr7UgknZXKGvaU/DQKkmbxz+ApTbJzdeNGcleuwuqJxzEZPKiFvawZGdwr8BrqgKGpHknO4RQnXKEcFYXJpymv2JbP3sSeAJsAcotzecz/Mfxs/Bps+8SWJBKOpTP47v708Wn6BGp9KIrC7dO9cHA1Z/uSWFIrdOtbisGOg+lr1pdf46TejKTrU7WG3a38JSx1vLnFrfYV5LVRknyZ1DfexCggAJunn255R2tBBvcKdPV1GHB7b7IsvLly4iIFPfrRt+wCRxOztW0e9XuUCf0n8Lj/4w22mxSdxaE/zuEaZMvA0X1aw/Vq6OipGPukP6aWBmz4Oorc9IIWs61SVEzzmMbJjJPEZMW0mF2JpKNRVYf9y9sWE3HGgEkDe6Gr07iQKcrKSHnhBRACx/nzUfQarjnVXJod3BVF0VEU5YSiKH9WPLdUFGWroijxFcfWTy61EL639UJXVU58mQt61t54qi6xJebv9MZtvW/jveHvNVgULC+jgC2LorF0bNkJ1PowNNVj/L8GAPDXV6datERygusEjHSN5DZ8ki5JTTrsB8+qKVOLGnXb6yPjq68ojIzE4Z230Xfq1Qoe105LjNz/A8RWef4ysF0I4QZsr3jeKTA00cPDy4A0myCys5zorWSw9/T5JlWflBSVaSZQgTFP+qNnoNPS7tZJT1tjxjzlx9WsQjZ+03Ilkmb6ZoxzGceGCxvIK26bzUMkkragcge1G3XYVx9Lxq+XOR72PRpl7/rhCLK++RbzyfdgNrbhixxbimYFd0VRnIBxwPdVTk8EllQ8XgLc3Zw+2prAqQNQhJpTFzXKjqZ58cSmXmuUjcoJ1JzU64Q/6ou5TeusQKsPR9eehM30IiU+l50/x7VYieQ0j2kUlxezJn5Ni9iTSNqbvcl7mfTHJP46/xdPDnhSW8Mek3KVmNSrN+m210dZTg4pL76IvrMz9q+91kpe101zR+6fA7OBqsNCOyFEKkDF0bamGxVFeVxRlKOKohzNyMhophsth5m9OU5l57hQ5ExBuTmeqktsjm7cwp3jmy9y7ngGQya50tvbsunOZJ+Hwpym3w+4h9gzaEI/zhy6wtENic2yVYmHpQeBtoGsOLMCtWhdXXmJpDW5VnKNNw+8yT+3/5Me+j1YNnYZTwc8ra1hX308GT0dhQkDHBtsUwhB6muvU56To5EXMG6d6rj6aHJwVxRlPJAuhDjWlPuFEN8JIYKFEME2NjZNdaNV8PEENSpOFk7iVvO0ann3+rh4OotDa8/jFmJHwKjeTXcieg18NQh+vAvKiptuBwga44znYHsi1l/gzOGWWWF6v9f9JOcns+/yvhaxJ5G0NQdSDnDPunv4I+EPHvF9hBV3rcDH2kd7vbRczR8nLhPmaYeFScNXoub88gv5O3Zg++ILGHp7t4brDaI5I/dhwARFURKB5cDtiqL8DKQpiuIAUHFsXU3aVsB++ABsMk9y+vpovHUyiE29yqXs+qtOctML2Lo4Gqtepoyc4dn0CdTD38LKf4BFP0iLgh1zm2anAkVRGDHdk17uPdnxUywp8c0vkQzrE4aNkY0si5R0Oq6XXuedg+/wxNYnMNQx5KcxP/Fs0LMY6BhUa7f7TAZZ10uY3IiUTNGZM6R/+BGmt92GxYwZLe16o2hycBdCvCKEcBJCOAPTgB1CiOnAOqByG5KZwNpme9nGGAUE0DdlJyXCiMxMZ0DUm5opKSpj4zdRKIrC2Cf90NNvwgSqELDtLdg4GzzGwhO7IfgROPAlnN/VhFfyNzq6Ku58wg8zKyM2fHOK3LTmlUjqqfSY6j6V/Zf3k3S1dTcNkUhaisOph7ln7T2sOruKh30eZuVdK/G38a+x7erjyViZ6DPCo2GZBXVhIZf/+19U5mY4fPB+m1XH1UZr1LnPA0YpihIPjKp43qlQGRtj52yGVVEip/JGcYt1MVuia0/NCCHYvkQzgTr6MR/MrJswgVpeCn88Bfs+g6B/wL1LQc8IRr8L1u6w5ikoyK7fTh0Ymugx/l/+KIrCnwtPUpTfvBLJKe5T0FF0WHFmRbPsSCS1IYTg+uEI1IXNk7QuKC3gvUPv8eiWR9HT0WPpmKX8N/i/GOoa1tg+53oJ22LTmBjQC70G1ranzfuQknPn6fXRR+haNmOurYVokeAuhNglhBhf8ThLCBEmhHCrODYvIrUTxiEhOMVv4LrainAdNUcvZpOZX3Pu+9jGi5w/kcHQya709mzCP2pxPvw6DU7+CiNfg/GfgU6F9oy+MdzzP7ieAev/oxndNwNzG2PGPuVPfk4xG745RXlp0ydEbYxtuKPvHaxJWENhWevoyUu6N/nbt5M0cyYXpkylKC6uSTaOXjnK5HWTWXFmBdO9prPyrpUE2AbUec/6UymUlgsmBzWsNv3q5i3krliB1aOPYjJkSJP8bGnkCtVaMA4NxTIrGktxAfVlY4QatsfePHpPjMrk8PrzuIfaMSCsCROo+RmwZDyc2wF3LYDbZsONP+ccA+D21yF2HUQua+Ir+huH/uaEPexFakIe25fGNqtEcprnNK6VXGPD+Q3N9ksiuZGs7xeha2uL+upVEqfeS/bSpQ3+vBaWFfJhxIf8Y7NG6Gtx+GJeCn2pQZpQq44l4+Vgho9j/XLepSkppM6Zg6G/Pzb/fqZBvrUFMrjXgvHAABQdHdwLdnAt34hQfaObUjO5aQVsXRSNtZMpI6Y3YQI1+wIsHg3pcTDtFwiqY8f0of8G51tg40uQda4Jr6g6bsF2DL7bhfgjaRz580KT7QTaBuJu4c7yM8vlNnySFqXg+HEKIyOxevxx+q39A5Phw0l7/wMuPfEEZZmZdd57Iv0EU9ZN4efYn7nf835WT1hNsH1wg/o9m3aNU8l5TA6sf9Quysq4/OJsKC+n1/yP21ReoD5kcK8FlYkJhr4+WKSexUw/i6EleuyNzyS/uAyAksIyNnx9CpWuijFNmUBNOQGLRmnq2GeuA496dkxXqWDSN6DSgd8f1+Tom0lgeF+8hjpw5K9E4g6lNslG5TZ8cdlxRGZENtsniaSSrEWL0TE3p+c9k9C1tMTp/77C7o05FByO4PzEu8nfe/PG0kVlRcw/Mp+ZG2dSLspZNHoRrw56FWO9htearz6WjK5K4e6B9Qf3zK+/ofDYMezfegv9Pq2vHdUYZHCvA5PQUIpTCwgwWI1uXil2RbDnbAZCLdj2Ywy56YWEP+aLmVUjJ1DP7YAfx4OuIczaAr1DG3afuRPc9QVcPgp7Pm78C7oBRVG47UEPnDwt2PlTHJfPNm3B1Lh+4+ih10OWRUpajOLz58nfvh2LBx/ULgJSFAXLBx7AT+iGfwAAIABJREFUeeVv6Fpacumxx0n74APUJZr9BU5mnGTq+qksiVnCVPeprJ6wmlCHBv7fqqCsXM2aE5cZ4WGDtalBnW0Ljhwh8+uvMb/7bszvGt+0F9qKyOBeB8YhIVCups+1PRiZKAwv02dz9BWObkzkwslMhk12xcmjkbpoJ1fAsqlg4QyPbAWbhu+YDoDPJBjwgCa4Jx1q3L01oKOj4s7HNRIJG7+JIjO5cVILAMZ6xkx0ncjWi1vJLKz757JE0hCyf/gBxcAAi+kP3nTN0N0d55W/YTF9OtlLlnJh6lS+W/cGD218iKLyIr4d9S1zhszBRM+k0f3uPptB+rXieuUGynNzufzibPR798bu9cbtodxWyOBeB0aBgaBSUZyuYoBfPk7FClkRGUSsv4DHYHv8b2+E3oQQsH8BrHkc+gyBf2wAM4emOTbmQ+jZB35/DIquNs1GFQyMNSqSuvo6rJl/nOQzjR/BT/OcRpm6jFVnVzXbH0n3piwjg7w/1mI+6e5aSwpVhobYv/4a5R++THbyOUJfXcl/k/35/a7fGeo4tEn97k/I5NkVkfTqacRIzxpVU4AKeYE5cyjLysLxk0/QMW38l0hbIIN7HeiYmmLo7U1BhgG+NifQNdBhSIEuV3TU7DAto7ihSotqNWx+DbbO0Yy8p68Gw4Zvqn0Thmaa8si8y7DhxabbqYKZtRGTZwdhYmHI+i8jiT/auJ2c+pr1ZZjjMFaeXUmpuuUkhiXdj+yffkaUlWH1j9q3syspL2HB8QVMz/uM95+2RhngTcjSo+S98CplOY0fnPx29BIzF0fgYG7Ib08OwUC39jm03BUruLZ1G7bPP4+Rr0+t7dobGdzrwTg0lKIsffQyThJ0Z19MLQwwvd2eX45eYtL/HeBC5vW6DZQVw++PwqGvYNCTMHkx6Nady2sQvUM1ZZOnlsPp1c23B/SwNOSeFwKxczZjy6JoTu281Kj7p3lOI70gnZ1JO1vEH0n3ozz/OjnLl9Nj1Cj0+/atsU1MVgz3/Xkf/4v6H+NdxrNk+jr8f1qJ7f+3d97hURXrH//MbrLJbnojgYSa0AkdVKQo2BULNtRrvxdFQLzqRVCxXAt4LVeu/ixgAVFBRRFBQAQLHemEXkJJ722zfXd+f5wlBQIksCEQ5vM85zmz58yZM5Psfs973pl5Z9w4yv74k4M330L52nU1XnssUkre+mUP4+Zs45LEKOaM7Ed8+In70Gx795IzaTJBAwYQef99p9XGs4U4F4av9e7dW27YsOHUGRuAsj/+IP3RkbS4AYLe2oX0SIRO8NvuHJ78ditOl4fJt3ZlaE1R42yl8M09cHA5XPEyXDr2+DHsZ4LbBZ9fC/l74NFVEH4Ggcqq4HK4WfLpDg5uzafnNS25+KY2tRrm6fa4uX7u9TQLbsZnV3/mk7ooLiwKZ8wgZ9JkWn0zG2O3btXOOd1OpqZMZdq2aUQGRvLiJS8yqPmganmsO3aQ+dTTOA4fJuof/yBmzOgTDk+0Od2Mm7ONn7ZmMrxPc165uctJZ6N6bDYO3X4HrqIi2vw4F7/o01hyz+aiMLOcggwzBelmCjLLiW0dSr9hSXUuC0AIsVFKWeMYT7/TKvECwtSrFwiB5WApQdYihFHrQB3cIZafHx/AmK83MWbWZtYdLOD56zsR6O99nSvLhi9vg7xdcMvH0G247yun94NhU+Gj/jD3UW1Ipe7MFwXxM+i55pFkls/aw6bFh7EU27ns3g7oTzENW6/Tc0f7O/jvxv+yr2gfbSPannFdFBcO0umkYPoMTL17Hyfsewr38NzK59hTtIcb2tzA+L7jCQs43rVp7NyZ1t/PIXvSJAqmTqV87Vri33rzuGGKReUORszcwPpDRYy7pj0jByWe0oDJ/c9/sO/bR/NPPjmlsHvcHopzrBRkmjUhz9AEvazAVpHHP1BPVLMgjMG1jzhZF5S4nwJ9SAiBbeKx5Nogdxe0rOysiQ838s0jl/DmL3uYujyVzUeK+eCenrSUmTBzGFgK4O5vIOmK+qtgZGu47k0tLs3q/0H/f/qkWJ1OMOju9gSFB/DX/INYypxcM6LLKVeUGpY0jA+2fMA3e77h+YvPzVEEinOT0sWLcWVlEffCxIpjLo+LT1I+4eOtHxMaEMq7l7/LkBZDTlqOLiiIZq++SnD//mS98CIHb76FuBdfIOymmwA4lF/Og9PXk1Fs5b27etT81n0MZUuXUvT1LCIfeojg/pdWHJdSYilxVBPwgkwzRVmWitXPhE4QHmsitnUonS5tRlR8EFHxwYREBdZrcDHllqkFOf+eSNHs72g3dSy6/iNrzLN0Zw5PfbeVzp69TA94E4OfH9zzHcT3rP8KSglzHoRd8+HvS6FZD58Wv3NlJn98tZuYFiHcMLobxpCTWxrPr3yeJYeXsOz2ZYQY6rY0meLCRErJwZtvQbpctJn/E0KnI60sjfF/PsO2ghSuDUliQtu7iWjSSRspFlC775UzM5OMceOwbthI6NChZD4wmhHfazFqpt3Xm96tTh0LypmVRerNt6BLaEXw6+9TmGunILOcwgwz+Rlm7OWuirxBYQai4oOJjA8mOj6IyPhgIuJM+PnXzzKbJ3PLKHGvBWXLlpE+ajQtR/bBNPaLE+bL3ziPkPn/INsTxo9d3uPRYVeetNfdp1iL4MNLwd+khQo2+HZ41sGtefzyyQ6CIwIYOqb7SZcO3FGwg+ELhjO+73ju6Xj8OGWF4ljMK1eR9ve/0/S11wgbdgvzU+fz2trX0LsdTMzJ5tryY0JUGyM1kY9oCeEtvelW2j68hRZR1Yt0uymYOpXc994nJzCcmUMe5oVn7qRVdM2/EY/bQ0melYKMcvLTSklfsIJSVzDWwKiKPH4BmkslKj5Ys8SbBRMVH0xg8NkNP6DE/Qxxl5Sw96KLie4XSsxnJ+iF3zQT5o/FE5fMlCavMWVdCV0Twnj/rp60iDpLy2wdXA4zboReD8DQd31efNaBEn7+YCs6vY6ho7sR0+LE1tM9P99DqaOUn27+qcHjWivOfY489BD2ffuJWfg9r236D4sPLaZXaCKTti+naa+HYdAzUHwYig5r++Ij3vQRbXMfE7E1ONYr9C2R4S34I9fIoj+zuXXTGkItZcSMHkXUiBEIfaXxVZxjYf3CgxzYlFcRLVUgMVpyiG4TRVyfdl4xDyY0KhCha/jvtRJ3H5A6+CL0nkJa/r63+ogXKWH5W/D7q5A4WIvDHhDCkh3ZPP3dViTw5m3duKZL3Nmp6K8vwKopMHwWdPD9iuuFWeXMf28LdouLax9JpnnHml9r5x+Yz7Mrn2XqlVO5pNm5EQJVcW5i3bGDQ7fehn3EHfyrxWryLfmM6vwgD/7+PnpjBIz4o5olfhweD5hzvEJ/uNpDQBYfwVOUhh43AG6HIHtjOKWHjRgTAoi/pzvlUV3ZsK8de/eb0PsJ2l8UR1xiOKayNEqfHEHEdVcR/5//nJW/RV1R4u4Dssf+jeKl62n32wJ0sd5RIB63Nolow6fQ9U648X3wq/RHpxVaGP31Jraml/Dgpa2YcG1HDH71PLXA5YBPhkBpBoxcAyGxPr+FucjO/Pe2UJxjYcgDHWnX5/gHl91t56o5V9Etphv/G/w/n9dB0XhIe/JJin9byj9GSqKbtGRy/0l0WTYZ9i2Bvy+DpjWvlHQqSm1OHvtyE2v25/BMv1D+kaxHeC39kt/+4sDiIg42v5qcmD7ohZMupkX0CPoRk8GK+7FdpN5+B8LPn9Y/fI8+ONjHrfYNaiikDzBd3I+iXzZiW/ULpmFtwWmF7/8Ouxdo49eHvKRFbqxC80gT3z3aj0mLdvH5qkNsOlLM+3f1oHlkPbpp/Axw6yfw8SCY9xjcM8e3Y+uB4IgAhj3dk4UfpvDrpzuxlDjofkX1oWYB+gCGtR3GZ9s/I8ucRdPg0wy1oGjUHNi1FtviRSzqLbgm+Q7G9RmHadt32u/qqldPW9jTiyw8NH09qXnlTLqtB3f0rpwDUpxjYcPhQ+ztnY3wOElI/40uHVy0eug6dLaeSHMeWS+/gis3j1azvj5nhf1UqBmqtcQ0eCgAlnVrtM7LmbdoX8CrJ8GV/z5O2I9i8NPx4tDOfPS3nqTmmbn+fytYcor1WM+YmPZw9auwfyn8NbVebhFg8mfo491I7BHDqjn7WfX9fqSn+lvgHe3uAODbvd/WSx0U5y9SSubsncOCSSPwAL0ff5mX+r2EqSQTFo+H1oPg4lGnVfa29GJu+WA1WSU2ZjzUt0LYi3MtLJu+k69fXseBjbl0G9Kc+17vz8VXNMG+cAEHn5qCNbAPxTktKVuyhCb/fAJjcrIPW312UW6ZOpDaryM6YwCx/fygLAMunwBJQxA6nWYdezdxNK3TAULreBGCzGIbLyzYye5sM8P7NOfRwW3x99NXv65KWUIIdGFhp9chKaW2dN+B3zWfZWwnH/81NDweycpv9pLyZwbt+sYy+L6O6Ku4nsb+NpbNuZv59fZfj1tdXnFhUmQr4qXVL7Fu7zKmfiAJuvJKEt9+V1uj4NOroDAVRq6GsNotcVeVJTuyGTt7C5FBBqY/2Ie2sSEU51rYuPAQe/7KQacXdBkUT48rWxAUVvl9LF/3F5njxuEqLETodJh69aL5J9O03/Y5jPK5+4icBwZSuDbvrN7TkJhIzOhRhFx9dd2/aOY8+PASbeTA35eBf82LAZ8pUko2Lj7MunmpNO8YwTWPJGMI1Dx+a7PW8o8l/+D1/q8zNHFovdxfcf6wOnM1z698nmJ7MZNT+9B81nJaz5tHYPt2sOwVWPEW3D4DOt9c57I/W3mQV37eSdf4MKbd35sAm4cNCw+xZ51X1AfG0+Oq6qJeFVdREdkvvYwtJYWWs2fh3+TEkSHPFZS4+wj3yqlYf/wA2e8JbXytBKQHpER6PN7PsvKYlOCRgASPR/ssAY+HbWnFzNmYhh7JHb0T6BAbXP0aKZEOO8U/zMVx4AAB7doRPWY0IVdcUTdLft+v8NVt2ivuNa/Xzx/Gy67Vmfz+5R6iE4K5YXQ3TKEGpJTcNO8mgv2D+fr6r+v1/opzF4fbwZRNU/hi5xe0CWvD5IteQX/bYwR27kSLqVPh8BqYfh10uwtu/qBOZbs9klcW7GT66kNc1SmWV4Z0YPuyNPasza6VqB+LlPK8Gb6rxP0c5VB+OY99tYmdWaU8MrANT1/d/rjARdLtpnThIvL/7/9wHDpEQKeOxIwZQ/Bll9X+C7jwX5rv/d652nDNeuRQSj6/TN2OKczA0Me7E97ExNe7vmbSX5OYdf0sukR3qdf7K8499hft55kVz7C3aC/D2w/nyd5PYvt+PtkvvkiL6dMJ6t4BPuyvuSQfXVnr2acA5XYXY2dvZumuXEb0aE5fmx97j1rqA+LpcXXtRf18RIn7OYzN6ebVn3fy5doj9GoZwXt39aBZDSFHpctFyfwF5H/wAc60NG2l9TFjCOp/6alF3mmFqZeBtVjzZQZFnTz/GZJ9sISf39+G0KFZ8E11DPluCFe0vILX+r9Wr/dWnDtIKZm1exbvbHyHIP8g/t3v3wxqPgjpdpN6/Q3ogoJoNec7xNxHIGUOPPQLNO9T6/JzSm08PGM9GWlljGwSgyvVjE4n6DywGT2vbtmoRf0oStzPA37amsmE77dh8NPxzp3dubx9zf4+6XRSMm8e+R98iDMzE2PPnsQ8PgbTRRedXOSzU2DaYGh7Fdz5pc+HRx5LUXY589/bitXs5NoRXfii7EPm7pvL0tuXEhFYx6UJFecd+dZ8Xlj1AisyVtA/vj+vXPoK0UYtkmLpr7+SMeZx4v/7DqEJ5fD9w3DZBLhsfK3L351dypip62lX6KGTQ49ep9NE/aqWBIU3flE/ihL384SDXjfNrqxSRl6WyFNXtsPvBGF2pcNB8Q8/kP/hR7hycjD16UPM2Mcx9a7x/6yx+n1Y8hwM/R/0ur+eWlFJeYmd+e9tpSiznM63RTIq7W880fMJHk5+uN7vrWg4lqcvZ+KqiZgdZp7q/RR3dbirwvCQUnJ4+F24CgpInD0VMW2QNnT3wUVaCOtasHR9Bj9+tYv2NoFeryN5YLxmqV9Aon4UJe7nETanm5fn72TWX0fo0yqC9+7qSVzYiUe5eOx2ir/9jvypH+POyyeo3yVEjxmDqUcNkSE9HvjyFkj7Cx5ZAdGnt0BAXbBbXSz6KIWMPUWkd9nIptilLLx1IXofxJ1XnFvYXDbe3vA2s/fMpl1EO94Y8AZJEdW/Y5aNGzl8z9+Ife5ZIl2zIGur5mePbH3K8kvzrXz7xQ4se0tACNpeEseAoYkER1x4on4UJe7nIfO2ZDDhhxQMfjomD+t6ytg0HpuNolmzKZg2DXdhIUEDBxAzZszxkzBKM+HDfhDRGh5eAvr6j2LndnpYOmMn+zfksrXp7wx/4HIGt6zfjl3F2WV34W7GLx/PgZID3NvpXsb2HFvjvIa0x0Zh3bSJpFdvRrfydbj5Q+h+90nLLs23smHRIXauzsItJQVNDIx6rAexcefnzFFfosT9PCU1z8wT32xhW3oJd/VtzsQbOmEynPzV1WOxUPjVVxR+8inukhKCBw8mZsxoAjt2rMy08yf49l4Y8DQMmXjiwnyI9EiWf7eX7b9nUJxwmPHP3I/e/9yeIKI4NR7pYebOmUzZNIXwgHBe7f8q/Zr1qzGvPTWV1OuuJ/q+YcS4PoION8Dt00/Y/1NeYmfDwkPsXJmJyyPZ4u+i6SVNeOmOrid0V15oKHE/j3G4PPx36V4++vMAraOCmDK8B8kJxy8vdixus5mimTMp+Hw6ntJSQq66iujRowhs107LMG80bPkKHvi52upS9YmUko9mfIdnbTRRiYEMG90Xg1GFNzpfybXk8tzK51ibtZbLm1/Oy/1ePmlneebzz1M6fwFJ9+jx01tg5CowHR9V1FbuZPOSI2z7LQ23W5IWLpjvKmfM9R0YMbB26/leKChxbwSsOVDAk99uId9s56mr2jNiQBt0tYgn7S4tpXD6DApnzMBjsRB67TVEjx5NQHwT+HiAtsj2yJUQeOoHhi/It+Yz8oPxDNo/nIgmwXQb0px2fWMrZrT6CiklZqeZfGt+xRbsH0xSeBJxQXFKIM6QZYeX8eKaF3G4HYzrM45b29560r+pMzeXA0OuIKx3HE1b/gX3zYM21Re3dtrdbPs9jc1LjmC3ujC0DubzkiKK9ZK3bu/Gdckq+NyxKHFvJBRbHDw7N4WFKdlc0iaKd+7sRtOwk8S5roK7uJiCz6dTOHMm0mYjbOgNRA8biGHxvdDlVrh1Wj3XvpJnlj/DvpRMbisYRVGmBb8APe36xNJ5QDOatAw96bVOj5MCawEF1gLyrfnkWfOqCfjRrcBagM1tq7GMIP8gEsMSSQzXtqTwJBLDE4k1xZ5Xou+RHvKt+WSYMyiwFuCSLtweN27pPn7vTbukC4/0nDDt8rhwS3f1tMdTUbZHeihzlrExZyOdojoxecBkWoedujM0953/UjBtGonXZWO4+jEt4qMXt8vDzpWZbFh4CEupg9gO4SzR2VmSWcglbaL4z21d6zeS6nlMvYi7EKI58AUQB3iAqVLKKUKISOAboBVwCLhDSll0srKUuNceKSXfbUznpZ924K/XMXlYMtfWwaJxFRZS8OmnFH31NdLpJOyiNkRHrsRw38fQ9fZ6rHklW3K3cO+ie3n+ookMCriaHSsy2bchB7fDg6mpDmOyHXvrXArclcKdZ82jwFpAkb3mr1JYQBjRgdFEm6KJNkYTHRhNjCmGKGMU0cZoogKjKHWUcqD4APuL97O/eD8Hig9QaCusKCPEP+Q4wU8KTyLaGN1gol/mKCO9LJ0McwYZ5gzSy9JJN2ufM82Z2I9dgaiO+On88BN+6IQOvU5fY9pP54de6NHpdBXH+sf355Guj+Bfiw55t7mc/ZcNIii6lISbm2hxjvwC8Hgk+9bn8Nf8VErzbTRNCqOsXRD/2XAIgAnXdeTuvi1q9YZ6oVJf4t4UaCql3CSECAE2AjcDDwCFUsrJQojxQISU8pmTlaXEve4cyi9n7OzNbE0v4Y7eCbw4tDNBAbV3bbjy8ij45BOKZs1GuhyEJzmJfmMW/h1rP0PwdJFScueCO0k3pxNqCCXfmo+0C9rm96ZjTj+iLfE4dDZSYzaT3XI3hlhPhUjHGGOqpaON0UQZozDoT75o94kotBVWCH7VfbG9uCJPqCG0QuyrCn9UYNQZi77D7SDTnKkJdplXwM3pFYJe6iitlj/EEEJCcALxwfEkhGj7+OB4Ykwx+Ov80Qu9tulOsK+S1omz0ylZ8Pl0ct94g1bXlGB89jdkTHsObctn7bxUCjPLiW4eTOLgeN5JOcLq1EIuTYpi8jBlrdeGs+KWEULMA973bpdJKbO8D4A/pJTtT3atEvfTw+n2MGXpPv7vj/20jDQxZXgPujUPr1sZOTkU/O9tiuZqa502eWYCEffeW++W6uqM1Xyx8wsiAiM0S9u7RQVG4Z8fSt5GJ4c3FeFyeohpEULnAc1o28f3vvmakFJSYCuoJvZH01XFNjwgvJqFf3RftVPRIz3kWnKrWd9VLfA8Sx6Syt+gQWegWXAz4kPiSQhO0IQ8JL5CxMMCzk7fiK+QTif7B12KQZ9Py7cnkBF2G2vnHSA7tZSwJkb63tCatW4bkxftRgDPXd+Ju/o2P6/cYw1JvYu7EKIVsBzoAhyRUoZXOVckpTzpfHMl7mfGutQC/vnNFnLL7PzzynY8OigRfR1fZZ2/TSX71UmYMwMJG9KXuLc/RhdYPyGCa4vd4mTvXznsWJFBQUY5/gF62vaNpcuA+JMuzl1fSCnJt+ZXs/KPps1Oc0W+yMBIWoS0oMheRKY5E6fHWXFOIIgNiq0Q64SQhApL/KgFfrYs6rNByZcfkfnqFIy39mVbkydI21VEUHgAfa5vRXCHMMbP3c6a1AIGtI1m0rBkEiKUtV4X6lXchRDBwJ/Aa1LKH4QQxbURdyHECGAEQIsWLXodPnz4jOpxoVNicfLsjyn8vC2Li1pH8t87u9cYgOxkyJ3zyX/9WfI3uAhsGkDCu2/j321IPdW4DvWSkpyDpexYmcn+9TkNYs2fsn6WnGqWflpZGpGBkRWuk6MWeNOgpqftQjrfkE4bKVdfx76Ya8mO6EVAkB+9rm5F54HNmL05ncmLdqMTguev78idfZS1fjrUm7gLIfyBBcAvUsp3vMf2oNwyDYKUkh82ZfDCvO3odYLXhyVzQ9dmdSvE7aRs6vNk/t88hN5Dwv09MD30FoQ3P/W1ZwG7xcmedZo1X5ipWfPt+sbSuYGseUXNlBXaWPPGF+wvbo1eD92vSaT7lS3IttgZN2cb6w4WMrBdDJOGJRNfRyNEUUl9dagKYAZa5+kTVY6/CRRU6VCNlFKOO1lZStx9y+GCcp74ZgubjxRzW68EXrqxM8F16GwFsO/YQvqjI3DklxLXu5zwex9EDHzqrI2HPxUV1vyKDPZvyMXl9NCkZQid+jeMNS+lxGF1YSl1YCl1YC93ERQRQESs6YKaqGUtc7Bx8WG2/5GGdDpJyF/D5dOexhQRxBdrDvHG4j346QQTb+jE7b0TlLV+htSXuPcHVgApaEMhAZ4F1gHfAi2AI8DtUsrCGgvxosTd9zjdHt5bto/3f99P80gT797ZnR4t6hZq111aSsYTYyhf/RdhbSzEDdCjG/wM9H7orMSkqS31ac077W4spXYspU5tX+LAUqYJuKVE21u9gu52eWoswxRqIDzWREScifBYU0U6JMrYaIb5Oawutiw9wpalabgcbpL81xD950JajvwbZbfdx7jvt/HXwUIGtYth8q3JtZ6foTg5ahLTBcz6Q4U8MXsL2aU2/nlFW0ZellSnzlbpdpP33nsUfPQxxmYBxPc5jH98a7jiJeg4tN7jwteFCmt+eQb7Nubi9lrznQfEk9S7SYU173Z6KgW61IGlxF5NpKtuTrv7+BsJMAb7YwoNwBRmwBRqwBRiqEyHGggw+VNWaKM4x0JRjoXibAtFOeXYy10Vxej8BOFNqgh+rInwOG0fYDp3Hp4nw+V0s/3PDDYuPozN7CSxRwx9DVOxzl6AOS+CtZO/YNKKNPz1Os1a76WsdV+ixP0Cp8TqZOKP2/lpayZ9W0Xyzp3d6jwqofSXJWROGI8uwJ+EIS5Mfvuh+cXaTMM6rJ5ztrCVO9n7VzY7VmRq1nygnuDwAM1lYnHVeE2AyU8T5zCvWFcRb2NopXAbg/3RnWbgKqvZ4RV6S+U+x0JpnhWPp/K3aAzx91r4QZXCH2siNDrwtO/tSzxuD7vXZLP+54OYi+w07xjBxTcn0qRwPo6vxnBgYRwre1zFa82v5PL2Mbw+TFnr9YESdwUAczenM/HHHQgBr92SzI3d6tbZatu7l/TRY3BmZhJ33xAidAvBnAOdb4EhL9YqJvfZRkpJdmopu1Zn4rC4KsTbGGLAFBZQIdimEIPvolRmbYPNX0J0W23N2sg2p3zDcbs9lOXbKMourxD84hwLRdkWbObKoZQ6vSAsxniMmyeI4IgApJTa2uwebaF1j6fys5aWVc7VcNyD9llWHvNUPe4953J42LU6i+IcC01ahXLJzW1I6BAJhanIjwaQuikOyw4bo4e+wJg7L+XWnvHKWq8nlLgrKkgrtDB29mY2HSlmWM94Xr6xMyGBtXcBuEtKyHj6X5SvWEH4rbcQO8iEbv3/gdsJfUfAwKdrjPR3QVB0GH57FVK+BZ0feLxvCOEtNZFPHAytB4KxbhPNbOXOCqEvzin37i2U5FnxuBvm9xvRNIiLb2pD627e0AxuF7apV+JI20vqvCj2d+xL/0/fP+lCM4ozR4m7ohout4f3f9/P/5btIyHCxLvDu9OzDp2t0u0m790pFEybhrF7d+Jffw7/7R9r1mpACAz8lyb0fhfICjnlBbD4xP0MAAARFUlEQVTiLVj/CQgdXDwSLn0CLAVw4DdtO7gcHGYQekjoXSn2zXrWenm5Y/G4PZQW2CjOtlBeYkfoBDqdQOgEQgc6nQ6hAyGqH6+aT6cTCHGC495razpuMPpVWONuj2TrzGfoefBjvtxxFb1SttNq3o8Y2590BLTCByhxV9TIxsOFjJ29hawSG48PbsuoyxPrtAhC6eLFZE54Fn1wMAnv/Q9jUwP8+gLs/xXCW2iums7DQNfwPuJ6wVEOaz+AlVPAWQ7d79EWeg6LPz6v2wnp6zWh378MMjcDUhta2npQpdhHtMTl9mB1urE63Fi8W+VnV7VzR9MSSYTJQFSwgaigACKDtHRkkIEAv/pb0vBAnplpX8/mtcKnWWe8nMifcjF16UyLqVPr7Z6KSpS4K05Iqc3Ji/N2MHdzBj1bhHNFp1iahRlpFm6kaVggcWGB+J9E8G179pA+ajSunBxiX5hIxO23w4HfYclEyEnRLNOrXoVWl57FVtUzbidsngl/TNb6HNpfD0NegCYdsDhc7M0xcyDXTJnNiaUGIbY4XOhtxSSZN9DZtpEejo00kQUAHJRxLHcns9zTlbWeTpRz6k5IndAsbLen5t9ySIAfkcEGooIMRAYFEFVF+E/3YeD2SD5dmcqHS7Yw3288ESZ/7M3GkfPqG7SYMYOgi/rW7W+qOC2UuCtOybwtGbz28y5yy6qHkBUCmoQE0DTMSLxX8JuFG2kWHkhT70Mg3Gkh61//onzVKsLvGk7chAkIPz1s+0bzQZdmaAJ45ctaJ+P5ipSw6ydY9m8o2I81rjeb2z/JWmcSu7PL2JNTxpFCC8f+pIQAk78eo0HbTP5+Wtpfj8mgx+ivo4XMINm6gbbl62lZugl/jw2P8KMgsjtFcf0pix+AO7YbpkBD9WsNegzeh2+pzUWB2U5huYOCcgcFZgeF5XbyzQ4Ky7Ut33u+sNyB6yQPg6NCHxkUQHRF2kB0cAAmg54P/zzA5iPFfB09nUvKl8J9P5P66L/RhYTQ6rtvVQfqWUKJu6LWlNtdZJVYySy2kVViJaPYRlaxlawSG5nFVjJLrNic1SfrGPQ6moYYuHf7z1y6YRGFrTuS/sREmrSMJz4YWuybQeCaKeC0QO8HYdB4CI5poBbWDSkluWV2srYupen6ScSWbueIrjmvO+9ksbMHINAJaBUdRMe4UNrHhdA+LoS2TYIJNxkwGfQE+OnqJnYuO6Stq3ThZG/Tjhsjoc1llS6cmtw/dWhXqdVFQbndK/pHHwCVD4OCcrv3AXH8wyDc5M+0Xmn0Wf8kDPwXpe6LyBjzOPH/fYfQa6897Xop6oYSd4XPkFJSZHGSeYzgZxVr6YTNK7l/1ZeY/U28ctH97I1oAUCLgHKeCpjLDY7FOHWBbG7+AJkdHyQ4JJRwoz9hJn/CjP6EGw0E+tdRDH2E2e5iT3aZdytld3YZ7uztjHR9xRD9ZrJkJJ/4DWd/06G0axpO+7hQOsSFkNQkmED/+vNrY86D1D8qO2fN2drx6PaQNEQT+uZ9ISC03iaVHfswSAosJXz6IIhKRD64mMP33IersJDERQsRfhdOuIWGRom74qxi2bmLtFGjcefnkfvwE+ztOYhMr/jrCvczvOQTLpPrKZLBrPJ0ZrWnC6s8nTksYwGBQa+rEHtN8LV9qNGf8KMPgSrnw4yGirTB79Sdty63h4P55ZorJbvM61IpJa3QWpEnyVDEs6a5XGZbhssviKzkxwgdNIqI8LoNY/Q5UkLuzkqhP7waXN7lBIVeG60UGAoBYd59aOW+4lyo1pFb9dzRvSH41B3gHg/MvAnSN8KjK7AcKubwPX8jduLzRN5zT/3/DRQVKHFXnHVcRUVkPPkkljVribj7bmInjEf4V46ntx9YgWv9dAxHVuBvyQHAbGxGWlgf9gX1IsXQlQxXKMUWJyVW72ZxUmaveXbpUUwGfRXRr/4gyDc72J1dxoFcMw635lrS6wSto4NoHxdCh9gQkiPd9DzyOSHbPkcAXDQC+j957o7dd1rhyBpt4pS9FOxlYCvV0rZSsJdU/yxrCKdQDaE9BGoS/qN7cw5snQU3vgc97yPtsVFYN20i6fff0BnVLNSziRJ3RYMgXS5y336Hws8/x9S7N/FT3sUvKuqYTBLy98HBPzXXw6EVYCvRzsV0hDaDtKGCrS6FwDBcbg+lNleF4BdbHNXEv8TqpPiYz9oxB+FGgybiXr94+7gQEmO8LhWnFdZ9BCv+qwlh97u1YY3nSKhjnyCl1u9Ro/iXVTlWdV9y/HGPE7oOh1s+wp6aSur1NxA9ahQxY0Y3dAsvOJS4KxqUkvkLyJo4EX14OAnvvYcxucuJM3vckLXVK/Z/alapy6a5HOJ7akLfZhAk9AV/H8x+dLtg69fw+yQoy4R212jDGmM7n3nZjREptQ5f798+8/nnKZ2/gKTff8Mv8hx9u2nEKHFXNDi2nTtJHz0GV34+cS+/TPgtN9fuQpcd0v6qtOwzNmmuBb9AaHFxpdg37Q66OnRqSgl7FsLSlyF/DyT0gSteblzj8esZZ24uB4ZcQdhtt9L0xRcbujoXJErcFecErqIiMp74J5Z164i4915ix/2rmh++VthK4fAqzao/+KfWuQhaB2GrAdpQwdaDtPH0Jxo5cmStNpM2bR1EJWkzac+x8MXnA7lvv0PBp5+SuGghhpYtG7o6FyRK3BXnDNLlIvfNNymc8QUBHTsS3L8/gcldMCYn4xcXV/chkOZcLW5L6h+a2Bcf0Y6HNK206lsP0saE5+6GZS9rFntwHFw2Hnrce9qxXS5k3OZy9l9+OUH9+pEw5d2Grs4Fy8nEXX2rFWcV4edH7IQJBHZJpvDzzyn4/HNwaSNg9NHRGJOTK8Q+sEsX/CJOEdAsuAkk36ZtAIUHK/31+3+FbbO14xGtNOE3BMPgiVpwL0NQ/TW0kVP83Xd4ysqIevihhq6K4gQoy13RoHjsduy7d2PdloJtewrWlO04Dh7k6Bx+/4QEjF2TCeySjDG5C4GdOqELqqUoezyQu0MT+sOrtLjq/Z+EoKhTX3se4C4rw5mejiM9HXdxMTqTCV1QEPqgIHRHN+8xYTT6bGKYdDrZf9XVGBISaDnzC5+UqTg9lOWuOGfRBQRg7NYNY7duFcfcZjO27TuwpmzDlrIdy5YtlC5c5L1AR0BiGwKTu2pi3yWZwPbtEAZDDYXrIC5Z2/qdf8P0PHY7zoyMCgF3pmtpZ3o6jowMPCUltS9Mp6sQel0N4q8LqjynP2EebTP/8SeurCziXnyh/hqvOGOU5a44L3Dl52Pdvh3bthSs21OwpWzHXVQEgPD3J6BjR4xduhCYrFn4htatEfp6DAngA6TLhTM7RxPsjOMF3JWXVy2/MBjwj4/HPyEB/4R4DAkJ+Mcn4J+QgF9EOB6rFU95OZ7yctzl5UiLBbf3s7ZZKtOWKukqm3Q4alX3gLZJtJ43D9FYwzmfJ6gOVUWjQ0qJMyNTc+VsS8GWkoJtxw48FgsAOpOJwC5dqvjvk/FvEqNZ8zodCFHv8WuklLjz8ytF+1gBz86u6G/QKq3DPy7OK95VBNwr4n4x0fUuptLpPE743VUfDhZtHzxwAIEdOtRrXRSnRom74oJAut04Dh7EmrIdW0oK1pQU7Lt3I53OE1/kFXshRIXoH/u5Iq3TgU4gENWvqzgvEMKbz+3GmZODtNmq3U4fHY2hwvo+RsDj4uo+NFRxQaN87ooLAqHXE5CUREBSEngnSXkcDux79mLbsR13cTFIifR4wCNBerS0BO+K0UiP1DpzPR6kPJqvSt6j10mppY9eV+WzlB6EEARffvkxLpR4FXtFcdZQ4q5o1OgMBozJXU4e8kChaISo3hCFQqFohChxVygUikaIEneFQqFohChxVygUikaIEneFQqFohChxVygUikaIEneFQqFohChxVygUikbIORF+QAiRBxxu6HrUkmggv6ErUY805vaptp2/NOb2nUnbWkopY2o6cU6I+/mEEGLDiWI5NAYac/tU285fGnP76qttyi2jUCgUjRAl7gqFQtEIUeJed6Y2dAXqmcbcPtW285fG3L56aZvyuSsUCkUjRFnuCoVC0QhR4q5QKBSNECXutUQI0VwI8bsQYpcQYocQYmxD18nXCCH0QojNQogFDV0XXyKECBdCzBFC7Pb+/y5p6Dr5EiHEP73fye1CiFlCiMCGrtPpIoT4TAiRK4TYXuVYpBDiVyHEPu8+oiHreCacoH1ver+b24QQc4UQ4b64lxL32uMCnpJSdgQuBkYJITo1cJ18zVhgV0NXoh6YAiyWUnYAutGI2iiEiAceB3pLKbsAemB4w9bqjJgOXHPMsfHAMillW2CZ9/P5ynSOb9+vQBcpZVdgLzDBFzdS4l5LpJRZUspN3nQZmkDEN2ytfIcQIgG4HvikoeviS4QQocBA4FMAKaVDSlncsLXyOX6AUQjhB5iAzAauz2kjpVwOFB5z+CZghjc9A7j5rFbKh9TUPinlEimly/txLZDgi3spcT8NhBCtgB7AuoatiU95FxgHeBq6Ij6mDZAHfO51OX0ihAhq6Er5CillBvAWcATIAkqklEsatlY+J1ZKmQWakQU0aeD61CcPAYt8UZAS9zoihAgGvgeekFKWNnR9fIEQ4gYgV0q5saHrUg/4AT2BD6WUPYByzu/X+mp4/c83Aa2BZkCQEOJvDVsrxekghHgOzf37lS/KU+JeB4QQ/mjC/pWU8oeGro8PuRS4UQhxCJgNDBZCfNmwVfIZ6UC6lPLoW9YcNLFvLFwBHJRS5kkpncAPQL8GrpOvyRFCNAXw7nMbuD4+RwhxP3ADcI/00eQjJe61RAgh0Py2u6SU7zR0fXyJlHKClDJBStkKrTPuNyllo7D+pJTZQJoQor330BBgZwNWydccAS4WQpi839EhNKIOYy8/Afd70/cD8xqwLj5HCHEN8Axwo5TS4qtylbjXnkuBe9Gs2i3e7bqGrpSiVowBvhJCbAO6A683cH18hveNZA6wCUhB+02ft1P1hRCzgDVAeyFEuhDiYWAycKUQYh9wpffzeckJ2vc+EAL86tWVj3xyLxV+QKFQKBofynJXKBSKRogSd4VCoWiEKHFXKBSKRogSd4VCoWiEKHFXKBSKRogSd0WjxRsN8rFT5Hn2bNVHoTibqKGQikaLNwbQAm+0xBPlMUspg89apRSKs4Sy3BWNmclAondiyDQhxHJversQYoAQYjJaNMUtQoivAIQQPwohNnrjo484WpAQwiyEeMN7bqkQoq8Q4g8hRKoQ4kZvngeEEPOEEIuFEHuEEC82TLMVCmW5KxoxVS13IcRTQKCU8jUhhB4wSSnLjrXchRCRUspCIYQRWA8MklIWCCEkcJ2UcpEQYi4QhBYiuRMwQ0rZXQjxADAJ6AJYvNc/IKXccPZarVBo+DV0BRSKs8R64DNv8LcfpZRbTpDvcSHELd50c6AtUAA4gMXe4ymAXUrpFEKkAK2qXP+rlLIAQAjxA9AfUOKuOOsot4zigsC7SMJAIAOYKYS479g8QojL0KIsXiKl7AZsBo4uWeesEq3PA9i95XqobiQd+yqsXo0VDYISd0VjpgwtIBNCiJZoMeunoUX3PBr21+m15gHCgCIppUUI0QFtOcW6cqV3zU8j2opBq86oBQrFaaLcMopGi9dXvsq7GHEQUC6EcAJm4KjlPhXYJoTYhLYKzqPe6JF70JY8qysrgZlAEvC18rcrGgrVoapQ+Ahvh2pvKeXohq6LQqHcMgqFQtEIUZa7QqFQNEKU5a5QKBSNECXuCoVC0QhR4q5QKBSNECXuCoVC0QhR4q5QKBSNkP8HrLfDgBBJSqsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    columns=['county','site'],\n",
    "    index=df.index.month,\n",
    "    values='measure',\n",
    "    aggfunc='var'\n",
    ").plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Emissions are lower on the weekends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feeb04512e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    columns=['county','site'],\n",
    "    index=[df.index.dayofweek,df.index.day_name()],\n",
    "    values='measure',\n",
    "    aggfunc='sum'\n",
    ").plot.line(rot=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**To try the exercise below, select this cell and from the Jupyter menus choose**\n",
    "\n",
    "`Run -> Run All Above Selected Cell`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE\n",
    "\n",
    "**Produce a line plot of mean emissions by hour of day, with a line for each site**\n",
    "\n",
    "*Note: Type instead of using copy/paste for better retention*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Expected output*\n",
    "\n",
    "<img src='images/LinePlot_hourofday.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select certain columns to plot & figure size\n",
    "\n",
    "- You can use the `y=` argument to select certain columns to plot\n",
    "- **Notice how we have to use a tuple here to select the column(s)!**\n",
    "- and the figure size with `figsize=`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feed0b52f40>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    columns=['county','site'],\n",
    "    index=df.index.week,\n",
    "    values='measure',\n",
    "    aggfunc='mean'\n",
    ").plot.line(y=('Mecklenburg'),figsize=(12,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots for one line per plot\n",
    "\n",
    "- Use the `subplots=True` argument to make small multiples if the plots overlap too much\n",
    "- The `figsize` is for the combination of all subplots (not the size of each individual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fee703da760>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7feeb0229940>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    columns=['county','site'],\n",
    "    index=[df.index.year, df.index.month, df.index.day],\n",
    "    values='measure',\n",
    "    aggfunc='mean'\n",
    ").plot(y=('Mecklenburg'),figsize=(12,4),subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "**To try the exercise below, select this cell and from the Jupyter menus choose**\n",
    "\n",
    "`Run -> Run All Above Selected Cell`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXERCISE\n",
    "\n",
    "**Produce subplots, one for each county and site, showing the fully-detailed hourly data**\n",
    "\n",
    "*Note: Type instead of using copy/paste for better retention*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expected output:\n",
    "\n",
    "<img src='images/LineSubPlots_hourly.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Resample for continuous time plots\n",
    "\n",
    "Here's an example of resampling to the \"month start\" `('MS')` level in time while aggregating with both the mean and minimum.\n",
    "\n",
    "- Valid strings for resampling frequency: [date-offset object docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects)\n",
    "- General Resample documentation: [Resample docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling) *I have some trouble understanding this...*\n",
    "\n",
    "Because the groupby object still contains the same rows as the original, with the same index, you can apply `.resample()` after the `.groupby()`, which resamples within each group before the aggregation operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <th>site</th>\n",
       "      <th>tstamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Forsyth</th>\n",
       "      <th rowspan=\"12\" valign=\"top\">0022</th>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>10.902699</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>7.871474</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>6.535093</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>4.920262</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>5.289096</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>5.273817</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4.588669</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>5.444139</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>4.699853</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>6.278775</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>7.186697</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>10.812233</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">Mecklenburg</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">0041</th>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>13.098319</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>6.308696</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>6.706456</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>5.268268</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>4.110922</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>4.808567</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>4.535994</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>4.657102</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               measure     \n",
       "                                  mean  min\n",
       "county      site tstamp                    \n",
       "Forsyth     0022 2018-01-01  10.902699  1.0\n",
       "                 2018-02-01   7.871474  1.2\n",
       "                 2018-03-01   6.535093  1.2\n",
       "                 2018-04-01   4.920262  0.5\n",
       "                 2018-05-01   5.289096  0.9\n",
       "                 2018-06-01   5.273817  1.1\n",
       "                 2018-07-01   4.588669  1.1\n",
       "                 2018-08-01   5.444139  1.2\n",
       "                 2018-09-01   4.699853  0.4\n",
       "                 2018-10-01   6.278775  1.0\n",
       "                 2018-11-01   7.186697  1.3\n",
       "                 2018-12-01  10.812233  1.4\n",
       "Mecklenburg 0041 2018-01-01  13.098319  0.5\n",
       "                 2018-02-01   6.308696  0.5\n",
       "                 2018-03-01   6.706456  0.9\n",
       "                 2018-04-01   5.268268  0.3\n",
       "                 2018-05-01   4.110922  0.7\n",
       "                 2018-06-01   4.808567  1.1\n",
       "                 2018-07-01   4.535994  0.4\n",
       "                 2018-08-01   4.657102  0.6"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['county','site']).resample('MS').agg({'measure':['mean','min']}).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas-native plotting still requires a pivot table\n",
    "\n",
    "The only way I know how to arrange the data for Pandas-style plotting is to \n",
    "\n",
    "- do the `.groupby()`\n",
    "- do the `.resample()` on the groups – here at the day `('D')` level\n",
    "- `.reset_index()` to get the data ready for a pivot_table\n",
    "- create the `.pivot_table()` like above\n",
    "- `.plot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fee80d97160>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fee703e5490>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fee502b2640>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fef005197c0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7feec08899a0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\n",
    "    ['county','site']\n",
    ").resample(\n",
    "    'D'\n",
    ").agg(\n",
    "    {'measure':'mean'}\n",
    ").reset_index(\n",
    ").pivot_table(\n",
    "    columns=['county','site'],\n",
    "    index='tstamp',\n",
    "    values='measure'\n",
    ").plot(\n",
    "    subplots=True, \n",
    "    figsize=(10,10)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Plotting tidy data right out of groupby()\n",
    "\n",
    "I like Altair, Seaborn, and Plotly for plotting in Python because they want tidy data that I can get right from a `groupby()` result without having to create a pivot table. That way I don't have to worry about which axis is which in my Pandas DataFrame – I can assign any variable to either plot axis.\n",
    "\n",
    "- No need to create a pivot table – work directly with groupby()\n",
    "- Capabilities for creating faceted plots\n",
    "- Seaborn is better for built-in complicated statistical plots, but has hard-to-remember names for the plotting functions\n",
    "- Altair has a more consistent interface for all types of plots, and it has built-in aggregation, including time-series, but for large data it works out better to filter and group first rather than letting Altair do the work\n",
    "- Plotly has nice interaction built in, but Jupyter extension requires setup\n",
    "\n",
    "**Note: You almost always have to run .reset_index() on the grouped, agregated DataFrame before you can plot with these modules.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faceted line plot with Seaborn\n",
    "\n",
    "`relplot()` is the Seaborn \"Figure-level interface for drawing relational plots onto a FacetGrid\"\n",
    "\n",
    "- https://seaborn.pydata.org/examples/faceted_lineplot.html\n",
    "- https://seaborn.pydata.org/generated/seaborn.relplot.html\n",
    "\n",
    "*If you don't specify a categorical color palette, Seaborn will look at the values in \"site\" and re-interpret them as integers, then use a sequential (numerical) palette!!*\n",
    "\n",
    "- https://seaborn.pydata.org/generated/seaborn.color_palette.html\n",
    "\n",
    "**You need to use `.reset_index()` to access the index column(s) to plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fef004430d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.5x540 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(\n",
    "    data=df.groupby(['county','site']).resample('W').agg({'measure':'mean'}).reset_index(), \n",
    "    x='tstamp', \n",
    "    y='measure', \n",
    "    hue='site',\n",
    "    palette='deep',\n",
    "    row='site',\n",
    "    kind='line',\n",
    "    aspect=3.5,\n",
    "    height=1.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Altair for data visualization\n",
    "\n",
    "Altair is a very nice alternative module for visualizing data that's in tidy form, right out of the aggregated groupby results. Another nice feature is that Altair has a lot of built-in aggregation itself, as well as filtering, calculations, and interaction (including linking multiple plots), so you have extra options for date/time funcitons and nice display.\n",
    "\n",
    "You can see examples of this in the Groupby_NCcategorical.ipynb\n",
    "notebook within my more complete \n",
    "[python-jupyterlab repository](https://github.com/emonson/pandas-jupyterlab). \n",
    "More in-depth instruction can be found in \n",
    "[the video of my Altair workshop](https://library.capture.duke.edu/Panopto/Pages/Viewer.aspx?id=df15cf8e-86e1-4c6f-ac16-aab600f06c48) \n",
    "and the \n",
    "[accompanying GitHub repository](https://github.com/emonson/altair-vis-python)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Example Altair output:*\n",
    "\n",
    "<img src='images/Altair_monthdaysite.svg' />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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