{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing time with `pandas`\n",
    "\n",
    "Let's open up some data from [the Census bureau](https://www.census.gov/econ/currentdata/datasets/) - we're going to use **New Home Sales**. The data is formatted... oddly, so I've cleaned it up for you as **home-sales.csv** inside of the **data** folder.\n",
    "\n",
    "Open it **without moving it**. Tab autocomplete will help you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>val</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1963-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1963-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1963-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1963-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1963-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    val    per_name\n",
       "0  42.0  1963-01-01\n",
       "1  35.0  1963-02-01\n",
       "2  44.0  1963-03-01\n",
       "3  52.0  1963-04-01\n",
       "4  58.0  1963-05-01"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/home-sales.csv\", usecols=['val','per_name'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     is_adj   val cat_code                       cat_desc  cat_indent dt_code  \\\n",
       "636       0  39.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "637       0  45.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "638       0  49.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "639       0  57.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "640       0  51.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "\n",
       "        dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "636  All Houses       K       US  United States  2016-01-01  \n",
       "637  All Houses       K       US  United States  2016-02-01  \n",
       "638  All Houses       K       US  United States  2016-03-01  \n",
       "639  All Houses       K       US  United States  2016-04-01  \n",
       "640  All Houses       K       US  United States  2016-05-01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105de97b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEPCAYAAABP1MOPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHBUCRVGUEc7/B/9ki2QNOvbcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105da4438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='per_name', y='val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105d4a2b0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n35wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fb9630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='val')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a datetime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1963-01-01\n",
       "1    1963-02-01\n",
       "2    1963-03-01\n",
       "3    1963-04-01\n",
       "4    1963-05-01\n",
       "Name: per_name, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.per_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 963 µs, sys: 0 ns, total: 963 µs\n",
      "Wall time: 978 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0   1963-01-01\n",
       "1   1963-02-01\n",
       "2   1963-03-01\n",
       "3   1963-04-01\n",
       "4   1963-05-01\n",
       "Name: per_name, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "pd.to_datetime(df.per_name).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.46 ms, sys: 2 µs, total: 1.46 ms\n",
      "Wall time: 1.48 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0   1963-01-01\n",
       "1   1963-02-01\n",
       "2   1963-03-01\n",
       "3   1963-04-01\n",
       "4   1963-05-01\n",
       "Name: per_name, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "pd.to_datetime(df.per_name, format=\"%Y-%m-%d\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-01-01</td>\n",
       "      <td>1963-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-02-01</td>\n",
       "      <td>1963-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-03-01</td>\n",
       "      <td>1963-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-04-01</td>\n",
       "      <td>1963-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-05-01</td>\n",
       "      <td>1963-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_adj   val cat_code                       cat_desc  cat_indent dt_code  \\\n",
       "0       0  42.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "1       0  35.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "2       0  44.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "3       0  52.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "4       0  58.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "\n",
       "      dt_desc dt_unit geo_code       geo_desc    per_name       date  \n",
       "0  All Houses       K       US  United States  1963-01-01 1963-01-01  \n",
       "1  All Houses       K       US  United States  1963-02-01 1963-02-01  \n",
       "2  All Houses       K       US  United States  1963-03-01 1963-03-01  \n",
       "3  All Houses       K       US  United States  1963-04-01 1963-04-01  \n",
       "4  All Houses       K       US  United States  1963-05-01 1963-05-01  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df.per_name, format=\"%Y-%m-%d\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_adj                 int64\n",
       "val                  float64\n",
       "cat_code              object\n",
       "cat_desc              object\n",
       "cat_indent             int64\n",
       "dt_code               object\n",
       "dt_desc               object\n",
       "dt_unit               object\n",
       "geo_code              object\n",
       "geo_desc              object\n",
       "per_name              object\n",
       "date          datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the index to the datetime\n",
    "\n",
    "Normally the index of the column is just a number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "1963-01-01       0  42.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-02-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-03-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-04-01       0  52.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-05-01       0  58.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "1963-01-01   TOTAL  All Houses       K       US  United States  1963-01-01  \n",
       "1963-02-01   TOTAL  All Houses       K       US  United States  1963-02-01  \n",
       "1963-03-01   TOTAL  All Houses       K       US  United States  1963-03-01  \n",
       "1963-04-01   TOTAL  All Houses       K       US  United States  1963-04-01  \n",
       "1963-05-01   TOTAL  All Houses       K       US  United States  1963-05-01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('date', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-01-01</td>\n",
       "      <td>1963-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-02-01</td>\n",
       "      <td>1963-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-03-01</td>\n",
       "      <td>1963-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-04-01</td>\n",
       "      <td>1963-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-05-01</td>\n",
       "      <td>1963-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_adj   val cat_code                       cat_desc  cat_indent dt_code  \\\n",
       "0       0  42.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "1       0  35.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "2       0  44.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "3       0  52.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "4       0  58.0     SOLD  New Single-family Houses Sold           0   TOTAL   \n",
       "\n",
       "      dt_desc dt_unit geo_code       geo_desc    per_name       date  \n",
       "0  All Houses       K       US  United States  1963-01-01 1963-01-01  \n",
       "1  All Houses       K       US  United States  1963-02-01 1963-02-01  \n",
       "2  All Houses       K       US  United States  1963-03-01 1963-03-01  \n",
       "3  All Houses       K       US  United States  1963-04-01 1963-04-01  \n",
       "4  All Houses       K       US  United States  1963-05-01 1963-05-01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's the column on the far left - `0`, `1`, `2`, `3`, `4`... boring and useless! If we use **.set_index** to replace the index with the datetime, though, we can start to have some fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be sure you use `inplace=True` or else it won't save the new index!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting specific(-ish) dates via the index\n",
    "\n",
    "Now that our index is a datetime, we can select date ranges super super easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1975-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "1975-01-01       0  29.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-02-01       0  34.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-03-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-04-01       0  54.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-05-01       0  57.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-06-01       0  51.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-07-01       0  51.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-08-01       0  53.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-09-01       0  46.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-10-01       0  46.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-11-01       0  46.0     SOLD  New Single-family Houses Sold           0   \n",
       "1975-12-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "1975-01-01   TOTAL  All Houses       K       US  United States  1975-01-01  \n",
       "1975-02-01   TOTAL  All Houses       K       US  United States  1975-02-01  \n",
       "1975-03-01   TOTAL  All Houses       K       US  United States  1975-03-01  \n",
       "1975-04-01   TOTAL  All Houses       K       US  United States  1975-04-01  \n",
       "1975-05-01   TOTAL  All Houses       K       US  United States  1975-05-01  \n",
       "1975-06-01   TOTAL  All Houses       K       US  United States  1975-06-01  \n",
       "1975-07-01   TOTAL  All Houses       K       US  United States  1975-07-01  \n",
       "1975-08-01   TOTAL  All Houses       K       US  United States  1975-08-01  \n",
       "1975-09-01   TOTAL  All Houses       K       US  United States  1975-09-01  \n",
       "1975-10-01   TOTAL  All Houses       K       US  United States  1975-10-01  \n",
       "1975-11-01   TOTAL  All Houses       K       US  United States  1975-11-01  \n",
       "1975-12-01   TOTAL  All Houses       K       US  United States  1975-12-01  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['1975']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List slices with datetimes\n",
    "\n",
    "We can also use **list slicing** with datetimes! Usually we would say things like `df10\n",
    "\n",
    "Just for review, you can use `:` to only select certain parts of a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('banana', 'orange', 'apple', 'blueberries', 'strawberries')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make our list of fruits\n",
    "ranked_fruits = ('banana', 'orange', 'apple', 'blueberries', 'strawberries')\n",
    "ranked_fruits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('banana', 'orange')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start from the beginning, get the first two\n",
    "ranked_fruits[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('orange', 'apple', 'blueberries')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start from two, get up until the fourth element\n",
    "ranked_fruits[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('blueberries', 'strawberries')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Starting from the third element, get all the rest\n",
    "ranked_fruits[3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of using boring ol' numbers, we can use **dates instead**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting rows after a certain date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>557 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "1970-01-01       0  34.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-02-01       0  29.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-03-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-04-01       0  42.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-05-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-06-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-07-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-08-01       0  48.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-09-01       0  45.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-10-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-11-01       0  40.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-12-01       0  37.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-01-01       0  45.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-02-01       0  49.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-03-01       0  62.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-04-01       0  62.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-05-01       0  58.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-06-01       0  59.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-07-01       0  64.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-08-01       0  62.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-09-01       0  50.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-10-01       0  52.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-11-01       0  50.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-12-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-01-01       0  51.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-02-01       0  56.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-03-01       0  60.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-04-01       0  65.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-05-01       0  64.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-06-01       0  63.0     SOLD  New Single-family Houses Sold           0   \n",
       "...            ...   ...      ...                            ...         ...   \n",
       "2013-12-01       0  31.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-01-01       0  33.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-02-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-03-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-04-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-05-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-06-01       0  38.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-07-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-08-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-09-01       0  37.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-10-01       0  38.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-11-01       0  31.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-12-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-01-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-02-01       0  45.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-03-01       0  46.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-04-01       0  48.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-05-01       0  47.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-06-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-07-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-08-01       0  41.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-09-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-10-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-11-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-12-01       0  38.0     SOLD  New Single-family Houses Sold           0   \n",
       "2016-01-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "2016-02-01       0  45.0     SOLD  New Single-family Houses Sold           0   \n",
       "2016-03-01       0  49.0     SOLD  New Single-family Houses Sold           0   \n",
       "2016-04-01       0  57.0     SOLD  New Single-family Houses Sold           0   \n",
       "2016-05-01       0  51.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "1970-01-01   TOTAL  All Houses       K       US  United States  1970-01-01  \n",
       "1970-02-01   TOTAL  All Houses       K       US  United States  1970-02-01  \n",
       "1970-03-01   TOTAL  All Houses       K       US  United States  1970-03-01  \n",
       "1970-04-01   TOTAL  All Houses       K       US  United States  1970-04-01  \n",
       "1970-05-01   TOTAL  All Houses       K       US  United States  1970-05-01  \n",
       "1970-06-01   TOTAL  All Houses       K       US  United States  1970-06-01  \n",
       "1970-07-01   TOTAL  All Houses       K       US  United States  1970-07-01  \n",
       "1970-08-01   TOTAL  All Houses       K       US  United States  1970-08-01  \n",
       "1970-09-01   TOTAL  All Houses       K       US  United States  1970-09-01  \n",
       "1970-10-01   TOTAL  All Houses       K       US  United States  1970-10-01  \n",
       "1970-11-01   TOTAL  All Houses       K       US  United States  1970-11-01  \n",
       "1970-12-01   TOTAL  All Houses       K       US  United States  1970-12-01  \n",
       "1971-01-01   TOTAL  All Houses       K       US  United States  1971-01-01  \n",
       "1971-02-01   TOTAL  All Houses       K       US  United States  1971-02-01  \n",
       "1971-03-01   TOTAL  All Houses       K       US  United States  1971-03-01  \n",
       "1971-04-01   TOTAL  All Houses       K       US  United States  1971-04-01  \n",
       "1971-05-01   TOTAL  All Houses       K       US  United States  1971-05-01  \n",
       "1971-06-01   TOTAL  All Houses       K       US  United States  1971-06-01  \n",
       "1971-07-01   TOTAL  All Houses       K       US  United States  1971-07-01  \n",
       "1971-08-01   TOTAL  All Houses       K       US  United States  1971-08-01  \n",
       "1971-09-01   TOTAL  All Houses       K       US  United States  1971-09-01  \n",
       "1971-10-01   TOTAL  All Houses       K       US  United States  1971-10-01  \n",
       "1971-11-01   TOTAL  All Houses       K       US  United States  1971-11-01  \n",
       "1971-12-01   TOTAL  All Houses       K       US  United States  1971-12-01  \n",
       "1972-01-01   TOTAL  All Houses       K       US  United States  1972-01-01  \n",
       "1972-02-01   TOTAL  All Houses       K       US  United States  1972-02-01  \n",
       "1972-03-01   TOTAL  All Houses       K       US  United States  1972-03-01  \n",
       "1972-04-01   TOTAL  All Houses       K       US  United States  1972-04-01  \n",
       "1972-05-01   TOTAL  All Houses       K       US  United States  1972-05-01  \n",
       "1972-06-01   TOTAL  All Houses       K       US  United States  1972-06-01  \n",
       "...            ...         ...     ...      ...            ...         ...  \n",
       "2013-12-01   TOTAL  All Houses       K       US  United States  2013-12-01  \n",
       "2014-01-01   TOTAL  All Houses       K       US  United States  2014-01-01  \n",
       "2014-02-01   TOTAL  All Houses       K       US  United States  2014-02-01  \n",
       "2014-03-01   TOTAL  All Houses       K       US  United States  2014-03-01  \n",
       "2014-04-01   TOTAL  All Houses       K       US  United States  2014-04-01  \n",
       "2014-05-01   TOTAL  All Houses       K       US  United States  2014-05-01  \n",
       "2014-06-01   TOTAL  All Houses       K       US  United States  2014-06-01  \n",
       "2014-07-01   TOTAL  All Houses       K       US  United States  2014-07-01  \n",
       "2014-08-01   TOTAL  All Houses       K       US  United States  2014-08-01  \n",
       "2014-09-01   TOTAL  All Houses       K       US  United States  2014-09-01  \n",
       "2014-10-01   TOTAL  All Houses       K       US  United States  2014-10-01  \n",
       "2014-11-01   TOTAL  All Houses       K       US  United States  2014-11-01  \n",
       "2014-12-01   TOTAL  All Houses       K       US  United States  2014-12-01  \n",
       "2015-01-01   TOTAL  All Houses       K       US  United States  2015-01-01  \n",
       "2015-02-01   TOTAL  All Houses       K       US  United States  2015-02-01  \n",
       "2015-03-01   TOTAL  All Houses       K       US  United States  2015-03-01  \n",
       "2015-04-01   TOTAL  All Houses       K       US  United States  2015-04-01  \n",
       "2015-05-01   TOTAL  All Houses       K       US  United States  2015-05-01  \n",
       "2015-06-01   TOTAL  All Houses       K       US  United States  2015-06-01  \n",
       "2015-07-01   TOTAL  All Houses       K       US  United States  2015-07-01  \n",
       "2015-08-01   TOTAL  All Houses       K       US  United States  2015-08-01  \n",
       "2015-09-01   TOTAL  All Houses       K       US  United States  2015-09-01  \n",
       "2015-10-01   TOTAL  All Houses       K       US  United States  2015-10-01  \n",
       "2015-11-01   TOTAL  All Houses       K       US  United States  2015-11-01  \n",
       "2015-12-01   TOTAL  All Houses       K       US  United States  2015-12-01  \n",
       "2016-01-01   TOTAL  All Houses       K       US  United States  2016-01-01  \n",
       "2016-02-01   TOTAL  All Houses       K       US  United States  2016-02-01  \n",
       "2016-03-01   TOTAL  All Houses       K       US  United States  2016-03-01  \n",
       "2016-04-01   TOTAL  All Houses       K       US  United States  2016-04-01  \n",
       "2016-05-01   TOTAL  All Houses       K       US  United States  2016-05-01  \n",
       "\n",
       "[557 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"1970\":]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting rows between a certain date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "1970-02-01       0  29.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-03-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-04-01       0  42.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-05-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-06-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "1970-02-01   TOTAL  All Houses       K       US  United States  1970-02-01  \n",
       "1970-03-01   TOTAL  All Houses       K       US  United States  1970-03-01  \n",
       "1970-04-01   TOTAL  All Houses       K       US  United States  1970-04-01  \n",
       "1970-05-01   TOTAL  All Houses       K       US  United States  1970-05-01  \n",
       "1970-06-01   TOTAL  All Houses       K       US  United States  1970-06-01  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Between Feb of 1973\n",
    "# and July of 1975\n",
    "df[\"1970-02\":\"1975-07\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count the number of houses sold in the 70's and count the number sold in the 80's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"val\" is thousands of houses sold\n",
    "# so say, give me everything in the 70's\n",
    "# and then grab the 'val' column\n",
    "# and sum it up\n",
    "df[\"1970\":\"1979\"].val.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6088.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll do the same thing for the 80's!\n",
    "df[\"1980\":\"1989\"].val.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do we select every single February?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1963-01-01    False\n",
       "1963-02-01     True\n",
       "1963-03-01    False\n",
       "1963-04-01    False\n",
       "1963-05-01    False\n",
       "Name: per_name, dtype: bool"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.per_name.str.contains(\"-02-\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1964-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1964-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1964-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1965-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1965-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1965-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1966-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1966-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1966-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1967-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1967-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1967-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1968-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1968-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1968-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1969-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1969-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1969-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1970-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1971-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1972-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2006-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2006-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2006-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2007-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2007-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2007-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2008-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2008-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2008-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2009-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2009-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2009-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2011-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2011-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2011-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2012-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2012-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2012-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2013-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2014-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "1963-06-01       0  48.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-07-01       0  62.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-08-01       0  56.0     SOLD  New Single-family Houses Sold           0   \n",
       "1964-06-01       0  53.0     SOLD  New Single-family Houses Sold           0   \n",
       "1964-07-01       0  54.0     SOLD  New Single-family Houses Sold           0   \n",
       "1964-08-01       0  56.0     SOLD  New Single-family Houses Sold           0   \n",
       "1965-06-01       0  57.0     SOLD  New Single-family Houses Sold           0   \n",
       "1965-07-01       0  51.0     SOLD  New Single-family Houses Sold           0   \n",
       "1965-08-01       0  58.0     SOLD  New Single-family Houses Sold           0   \n",
       "1966-06-01       0  40.0     SOLD  New Single-family Houses Sold           0   \n",
       "1966-07-01       0  40.0     SOLD  New Single-family Houses Sold           0   \n",
       "1966-08-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "1967-06-01       0  47.0     SOLD  New Single-family Houses Sold           0   \n",
       "1967-07-01       0  46.0     SOLD  New Single-family Houses Sold           0   \n",
       "1967-08-01       0  47.0     SOLD  New Single-family Houses Sold           0   \n",
       "1968-06-01       0  41.0     SOLD  New Single-family Houses Sold           0   \n",
       "1968-07-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1968-08-01       0  47.0     SOLD  New Single-family Houses Sold           0   \n",
       "1969-06-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1969-07-01       0  39.0     SOLD  New Single-family Houses Sold           0   \n",
       "1969-08-01       0  40.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-06-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-07-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "1970-08-01       0  48.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-06-01       0  59.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-07-01       0  64.0     SOLD  New Single-family Houses Sold           0   \n",
       "1971-08-01       0  62.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-06-01       0  63.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-07-01       0  63.0     SOLD  New Single-family Houses Sold           0   \n",
       "1972-08-01       0  72.0     SOLD  New Single-family Houses Sold           0   \n",
       "...            ...   ...      ...                            ...         ...   \n",
       "2006-06-01       0  98.0     SOLD  New Single-family Houses Sold           0   \n",
       "2006-07-01       0  83.0     SOLD  New Single-family Houses Sold           0   \n",
       "2006-08-01       0  88.0     SOLD  New Single-family Houses Sold           0   \n",
       "2007-06-01       0  73.0     SOLD  New Single-family Houses Sold           0   \n",
       "2007-07-01       0  68.0     SOLD  New Single-family Houses Sold           0   \n",
       "2007-08-01       0  60.0     SOLD  New Single-family Houses Sold           0   \n",
       "2008-06-01       0  45.0     SOLD  New Single-family Houses Sold           0   \n",
       "2008-07-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "2008-08-01       0  38.0     SOLD  New Single-family Houses Sold           0   \n",
       "2009-06-01       0  37.0     SOLD  New Single-family Houses Sold           0   \n",
       "2009-07-01       0  38.0     SOLD  New Single-family Houses Sold           0   \n",
       "2009-08-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "2010-06-01       0  28.0     SOLD  New Single-family Houses Sold           0   \n",
       "2010-07-01       0  26.0     SOLD  New Single-family Houses Sold           0   \n",
       "2010-08-01       0  23.0     SOLD  New Single-family Houses Sold           0   \n",
       "2011-06-01       0  28.0     SOLD  New Single-family Houses Sold           0   \n",
       "2011-07-01       0  27.0     SOLD  New Single-family Houses Sold           0   \n",
       "2011-08-01       0  25.0     SOLD  New Single-family Houses Sold           0   \n",
       "2012-06-01       0  34.0     SOLD  New Single-family Houses Sold           0   \n",
       "2012-07-01       0  33.0     SOLD  New Single-family Houses Sold           0   \n",
       "2012-08-01       0  31.0     SOLD  New Single-family Houses Sold           0   \n",
       "2013-06-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "2013-07-01       0  33.0     SOLD  New Single-family Houses Sold           0   \n",
       "2013-08-01       0  31.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-06-01       0  38.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-07-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "2014-08-01       0  36.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-06-01       0  44.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-07-01       0  43.0     SOLD  New Single-family Houses Sold           0   \n",
       "2015-08-01       0  41.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "1963-06-01   TOTAL  All Houses       K       US  United States  1963-06-01  \n",
       "1963-07-01   TOTAL  All Houses       K       US  United States  1963-07-01  \n",
       "1963-08-01   TOTAL  All Houses       K       US  United States  1963-08-01  \n",
       "1964-06-01   TOTAL  All Houses       K       US  United States  1964-06-01  \n",
       "1964-07-01   TOTAL  All Houses       K       US  United States  1964-07-01  \n",
       "1964-08-01   TOTAL  All Houses       K       US  United States  1964-08-01  \n",
       "1965-06-01   TOTAL  All Houses       K       US  United States  1965-06-01  \n",
       "1965-07-01   TOTAL  All Houses       K       US  United States  1965-07-01  \n",
       "1965-08-01   TOTAL  All Houses       K       US  United States  1965-08-01  \n",
       "1966-06-01   TOTAL  All Houses       K       US  United States  1966-06-01  \n",
       "1966-07-01   TOTAL  All Houses       K       US  United States  1966-07-01  \n",
       "1966-08-01   TOTAL  All Houses       K       US  United States  1966-08-01  \n",
       "1967-06-01   TOTAL  All Houses       K       US  United States  1967-06-01  \n",
       "1967-07-01   TOTAL  All Houses       K       US  United States  1967-07-01  \n",
       "1967-08-01   TOTAL  All Houses       K       US  United States  1967-08-01  \n",
       "1968-06-01   TOTAL  All Houses       K       US  United States  1968-06-01  \n",
       "1968-07-01   TOTAL  All Houses       K       US  United States  1968-07-01  \n",
       "1968-08-01   TOTAL  All Houses       K       US  United States  1968-08-01  \n",
       "1969-06-01   TOTAL  All Houses       K       US  United States  1969-06-01  \n",
       "1969-07-01   TOTAL  All Houses       K       US  United States  1969-07-01  \n",
       "1969-08-01   TOTAL  All Houses       K       US  United States  1969-08-01  \n",
       "1970-06-01   TOTAL  All Houses       K       US  United States  1970-06-01  \n",
       "1970-07-01   TOTAL  All Houses       K       US  United States  1970-07-01  \n",
       "1970-08-01   TOTAL  All Houses       K       US  United States  1970-08-01  \n",
       "1971-06-01   TOTAL  All Houses       K       US  United States  1971-06-01  \n",
       "1971-07-01   TOTAL  All Houses       K       US  United States  1971-07-01  \n",
       "1971-08-01   TOTAL  All Houses       K       US  United States  1971-08-01  \n",
       "1972-06-01   TOTAL  All Houses       K       US  United States  1972-06-01  \n",
       "1972-07-01   TOTAL  All Houses       K       US  United States  1972-07-01  \n",
       "1972-08-01   TOTAL  All Houses       K       US  United States  1972-08-01  \n",
       "...            ...         ...     ...      ...            ...         ...  \n",
       "2006-06-01   TOTAL  All Houses       K       US  United States  2006-06-01  \n",
       "2006-07-01   TOTAL  All Houses       K       US  United States  2006-07-01  \n",
       "2006-08-01   TOTAL  All Houses       K       US  United States  2006-08-01  \n",
       "2007-06-01   TOTAL  All Houses       K       US  United States  2007-06-01  \n",
       "2007-07-01   TOTAL  All Houses       K       US  United States  2007-07-01  \n",
       "2007-08-01   TOTAL  All Houses       K       US  United States  2007-08-01  \n",
       "2008-06-01   TOTAL  All Houses       K       US  United States  2008-06-01  \n",
       "2008-07-01   TOTAL  All Houses       K       US  United States  2008-07-01  \n",
       "2008-08-01   TOTAL  All Houses       K       US  United States  2008-08-01  \n",
       "2009-06-01   TOTAL  All Houses       K       US  United States  2009-06-01  \n",
       "2009-07-01   TOTAL  All Houses       K       US  United States  2009-07-01  \n",
       "2009-08-01   TOTAL  All Houses       K       US  United States  2009-08-01  \n",
       "2010-06-01   TOTAL  All Houses       K       US  United States  2010-06-01  \n",
       "2010-07-01   TOTAL  All Houses       K       US  United States  2010-07-01  \n",
       "2010-08-01   TOTAL  All Houses       K       US  United States  2010-08-01  \n",
       "2011-06-01   TOTAL  All Houses       K       US  United States  2011-06-01  \n",
       "2011-07-01   TOTAL  All Houses       K       US  United States  2011-07-01  \n",
       "2011-08-01   TOTAL  All Houses       K       US  United States  2011-08-01  \n",
       "2012-06-01   TOTAL  All Houses       K       US  United States  2012-06-01  \n",
       "2012-07-01   TOTAL  All Houses       K       US  United States  2012-07-01  \n",
       "2012-08-01   TOTAL  All Houses       K       US  United States  2012-08-01  \n",
       "2013-06-01   TOTAL  All Houses       K       US  United States  2013-06-01  \n",
       "2013-07-01   TOTAL  All Houses       K       US  United States  2013-07-01  \n",
       "2013-08-01   TOTAL  All Houses       K       US  United States  2013-08-01  \n",
       "2014-06-01   TOTAL  All Houses       K       US  United States  2014-06-01  \n",
       "2014-07-01   TOTAL  All Houses       K       US  United States  2014-07-01  \n",
       "2014-08-01   TOTAL  All Houses       K       US  United States  2014-08-01  \n",
       "2015-06-01   TOTAL  All Houses       K       US  United States  2015-06-01  \n",
       "2015-07-01   TOTAL  All Houses       K       US  United States  2015-07-01  \n",
       "2015-08-01   TOTAL  All Houses       K       US  United States  2015-08-01  \n",
       "\n",
       "[159 rows x 11 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index.month.isin([6,7,8])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Info on our time series\n",
    "\n",
    "If you try to `.plot`, pandas will automatically use the index (the date) as the x axis for you. This makes like **perfect.** because you don't have to think about anything, and calculations automatically have a good axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105d4ae80>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n35wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x102822710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y='val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10615b748>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEPCAYAAACJPZVzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXmYVOW1r99FA8qoINgMzSDK4AAKGgRNtI0GRYhDFOOU\nYMabWfBejeZ4AxhPIufEkMSTnGjiQO4xGowxjqhRbKdIEqM4C8TQTErTNJPI0GB/949V266urura\nu2oPVd3rfZ56qNq1q/aiuuq31/5961ufOOcwDMMwyoNOSQdgGIZh+MdE2zAMo4ww0TYMwygjTLQN\nwzDKCBNtwzCMMsJE2zAMo4zwJdoicrmIvJa6fSe1rY+IPC4iy0XkMRE5INpQDcMwjLyiLSJHAl8C\njgOOAaaLyGHA1cATzrnRwBLgmigDNQzDMPxl2ocDS51ze5xzHwLPAOcCZwELU/ssBM6JJkTDMAzD\nw49ovw6clLJDugNnAkOASudcHYBzbgPQP7owDcMwDIDO+XZwzr0tIvOBJ4D3gWXAvqgDMwzDMFqT\nV7QBnHO3A7cDiMi/A2uBOhGpdM7VicgAYGO214qINTcxDMMoAOecZG7zWz3SP/XvUNTPvgt4ALgs\ntctM4P42DpzzNmfOnDaf97OPn/c4+eSTIz9Gvn3yxdCe4ojjb9Ke4rDvRun9TZKOIxe+Mm3gXhHp\nC+wFvuGc25ayTBaJyBeBNcAMn+/Vgurq6qL38fMew4cPj/wY+fbJF0N7iiOOv0l7isO+G8Hi6Ejf\njVbkO0sUe9NDJM+cOXOSDqEkYnDO4sikFOIohRicszgySTKOlHa20tQOMyMy8NmsncYAFkcmpRBH\nKcQAFkcmpRJHOuLa8E5COYCIi/oYhmEY7Q0RwRU6EBkFw4cPR0Q61M2PX2gYhtEWiWXaqbNIpMcu\nNTri/9kwjMIouUzbMAzDCI6JtmEYRhlhom0YhlFGmGiHyNNPP82QIUOSDsMwjBjYtw927oz/uCba\nISPSatzAMIx2yI9+BF//evzH9TuN3TAMw0ixezf813/B0KHxH9sy7SzMnz+fGTNatlKZNWsWs2bN\n4o477uCII46gd+/eHHbYYdxyyy0JRWkYRlLcdRcMHw4rV0LcVbwm2lm46KKLWLx4MTt27ACgqamJ\nRYsWcfHFF1NZWcnDDz/M9u3buf3225k9ezbLli1LOGLDMOLCOViwAK67DioqoL4+3uOXrGiLhHMr\nhKFDhzJhwgT+9Kc/AfDkk0/So0cPJk6cyNSpUznkkEMA+MQnPsGUKVN49tlnw/pvG4ZR4ixZAh9+\nCFOmwMiRmm3HScmKtnPh3Arloosu4q677gLgrrvu4uKLLwZg8eLFTJ48mYMOOog+ffqwePFiNm3a\nFMZ/2TCMMmDBApg1S5PCUaNgxYp4j1+yop00M2bMoKamhvXr13PfffdxySWX0NjYyPnnn89VV11F\nfX09W7ZsYerUqTY13TA6CKtXw1//Cpdeqo9NtEuIfv36cfLJJ/OFL3yBESNGMGrUKBobG2lsbKRf\nv3506tSJxYsX8/jjjycdqmEYMfHmmzBhAnTrpo/NHikxLr74Yp588kkuueQSAHr27MnPf/5zZsyY\nQd++fbn77rs5++yzE47SMIy4WLcO0ufPJZFpW5e/GOmI/2fDiIslS+DAAzUTjoo5c9TLnjtXH+/Y\nAZWV8P770CnkFNi6/BmG0W55/nk480y4885oj7N2LVRVNT/u2RMOOADWr4/2uOmYaBuGUda8/Tac\ndx5cfLHaF1Gybl1L0Yb4LRKbxm4YRlmxZw8sWqT/Oqc9QObPhxEj4Oqroz12pqcNzYORp56qj5ct\ng8MPh/32iyYGE23DMMqGpiaYOVNtisMP123XXqvbVq1KPtPesQOqq+FXv4ILL4wmBhNtwzDKhu9+\nV/3jJ5+E/fdv+dzgwfDeezpbsaIi/GNv26aZfe/eLbePGgXPPKP377gDdu2CpUujE21fnraIzBaR\n10XkVRG5U0S6ishwEVkqIstF5C4RsROAYbRDGhpUCJPmppvgoYfg/vtbCzZA167Qty/U1UVzfC/L\nzmyPMXKkZtpNTfCzn2llyQsvRBMD+BBtERkEfBuY4Jwbh2bnFwHzgRudc6OBrcCXghx42LBhia+O\nHvdt2LBhwf9ChpEwl1wCSc8h27tXs+xHHlFhzkVVVXQWSTZrBODQQ3Wm5J/+pCWHl18Or7+u7Vuj\nwG/1SAXQI5VNdwPeBU4B7k09vxA4N8iBa2trcc51qFttbW2Qj8gwSoING/SWJLW1Wg+d6tWWkyhF\ne+3a1oOQoAOOgwbBVVfB7NnQvTuMGQMvvRRNHHlF2zn3LnAjsAZYD2wDXgK2OueaUrutAwZFE6Jh\nGEnS0BB/+9FMVq5U7zgfQ4aouEZBrkwb1CLZvRu8NvyTJqmvHQV5fWgRORA4GxiGCvY9wNQsu+ac\n6jfXmz4EVFdXU11dHTBMwzCSoqEBkm5kuWKFP9GO2h6ZODH7c5Mm6eSeLl308eTJ6r0Hoaamhpqa\nmrz7+Rk8PA34l3NuM4CI3AecABwoIp1S2XYVaplkJV20DcMoH3bt0lvSmfaKFWo55KOqSuuko2Dd\nOvjMZ7I/N29ey8eTJsE11wR7/8yEdl7mm6bw42mvASaJyP6iq9aeCrwBPAV4a3LNBAKeVwzDKHUa\nGvTfpDNtv/ZIEp52Ng49VO2SKGLx42n/DfgD8DLwCiDALcDVwBUisgLoC9wafniGYSTJ5s36bylk\n2qXsaWciEp2v7au22jk3D8jM1VcBx4cekWEYJUNDg1ZGJJlp79qltdd+Vj4fNAjefVdrpsPsurd9\nO+zbpyV9fpk8Weu1zz8/vDjAGkYZhtEGDQ0wenSymfY772ipX2cfKeb++6uwbtwYbgzr12efWNMW\nUWXaJtqGYeSkoUEbMe3cqQ2akmDFCi2p80sUvnYQP9tj4kR4+WX43Of09oMfhBOLibZhGDlpaICD\nDoJ+/ZoHJePGr5/tEYWvHcTP9ujZE+67T1dtnzxZp+GHgfULMQwjJ5s3w8EHq2jX16tnHDcrV8Lx\nAUbPosi0CxFtgNNP138bG3V6u3PBLJZsWKZtGEZOvEy7f//kBiNLwR4pVLQ9unbV244dxcdiom0Y\nRk7S7ZGkBiOD2iOl4mln0rdvcwllMZhoG4aRk6Qz7W3b4IMPgtkypeJpZ9KnD2zZUnwsJtqGYeQk\n6Ux75Uo47LBgPnAp2iOgmbaJtmEYkdLQoGKTVKYd1BoBXcFm/XqdYBMGH3ygE3za6uPthz59zB4x\nDCNCmpo0M/REO6lMO6hod+sGvXqFd5J57z0YOLD4qg+zRwzDiJTt27Whf9euao8klWkHqRzxGDIE\n3nhDM9v33y8uBk+0iyUse8TqtA3DyIrnZ0Nymfa//qUd84IycSKcd57ef/99Xflm8ODCYnjvvXDq\n080eMQwjUtJFO6lMe+NGXWYsKDffrAK5ebP2APnnPwuPodQybRNtwzCykk20Xc71qaJh0ybN8oth\n+HDNtAslLNG2TNtodzQ1wRlnhDfqbxTH5s3Not21q/rb27bFd/w9e7RR1QEHFPc+w4fDqlWFvz5M\n0bZM22hX7NgBjz2W/CophuKV+3nE7Ws3NGiGX2zVxiGHlEambfaI0e7wsrj33ks2DkNJt0cg/gk2\n9fV6zGIp1h55912zRwwjK9u367/v5lwi2vDD0qVw773Fv0+maMc9waa+vng/G0rHHrFM22h3eKJt\nmXZxPPII/PGPxb9P0pn2pk3hZNpDhuh3au/e4K/ds0dLBsOI44AD9L0+/LC49zHRNkoGE+1wqK0N\nJyNuL5l2ly6aKRfSj2TDBi05DGO9yYoKXRih2MFcE22jZDDRDodVq8LJiNOrRyB4pv3UU8U1bgor\n04bCfe2wrBGPMCwSE22jZNi+XTMRE+3iCDPTzqweCfK+3/42LFpU+PHDyrShcF87bNEOYzDSprEb\nJcP27bryt4l24TQ26iV9587FL21VjKe9erX2/njttcKPb5l2dvJm2iIySkReFpGXUv9uE5HviEgf\nEXlcRJaLyGMiUmQJvNHR2b4dxowx0S6GtWu177OIthQtlMZGbUeaPrElPdNuaoJHH9WFa++7Txs7\npfPww3oCLka0w8y0C63VjiLTjly0nXMrnHPjnXMTgGOBD4D7gKuBJ5xzo4ElwDXFhWJ0dLxMe8OG\n+KdLtxdWrdKssthBw82bVWDSM/X0TPvqq2H2bPjtb+G22+CTn2xZnfHww3DllfDWW4VXS4Qxhd2j\nPdkjQT3t04B3nHNrgbOBhantC4FzigvF6Ohs2wYDBuiU6a1bk46mPKmtVYEqtjwv0xqB5hPBTTfB\ngw/C889rlv3gg7q6zB/+oPvt3AnPPqtd9iorC2/WFNbkGuhg9kgGnwV+l7pf6ZyrA3DObQBCOica\nHZXt26F3b/2RmEWiGeqqVXqrrfV39VFbq1ZAGJl2pmj37g27d8MNN8DixS0HKWfPhgULNMYlS2DC\nBDjwQBg7tjCLxLnmaexhUFWlHQMbG1s/19SUfTuENxvSIwx7xPdApIh0Ac4Cvpva5PsCdu7cuR/d\nr66uprq62u9LjQ5Eumi/+y4ccUTSESXL7Nlw993Qo4f2ZTnrLPjNb9oeXKythSlT1GMOO9MWgc98\nBq66SjPXdKZPh//9v+Evf1FrZNo03e6J9vnnBzv+1q3NCzCEQefO2hN77dqW/bn37tXP9Zhj4Ec/\nav26sHppe/TpA8uXZ3+upqaGmpqavO8RpHpkKvAP55x3/q4TkUrnXJ2IDAA25nphumgbRi4s025m\n82b4f/8P3nxTP48dO+CUU+C662DOnNyvC8vTziz387j77uz7V1TA5Zdrtv23v2njL1DR/v3vgx8/\nTD/bw/O1PdF2Dr72NXjpJf3eZbJvn34OBx8cXgxt2SOZCe28efOy7hfEHrkIuCvt8QPAZan7M4H7\nA7yXYbTCRLuZW27RDNC7NO/ZEx56CBYu1IG/XETpaefjC1+AJ5/UrHbMGN1WqD0Spp/tkelrX3cd\nvPqqfp7Z+t1s3KifQecQC6Njq9MWkW7oIORX0zbPBxaJyBeBNcCM4kIxOjrpor1+fdLRJMfevfBf\n/6UinU5lpXrJH/+4VmtkWhR79miGOniwZqnFdLYrpEa6Z0+dUNPY2GzhjBqlf8sPPlCbJ8jxo8i0\nvc/kttv0BPjCCzoAni1JCHsQEsIZiPQl2s65XWQMNDrnNqNCbhihkC7aL76YdDTJcc89upjtMce0\nfm70aBXsZ55pLdpr1uiAW0VF8Zn2u+9qlhyUefNaDph27qwxv/GGrtvolygy7UMOUdvm0Ufhe9+D\np5/WE2H37irQmZORohDtWOq0DSMOnNMOaL16dWx7xDn1hWfPzr3P5MnafjUTz8+G4j1tb5JOUERa\nN1cqxCKJKtN+7jn4/Oe1C+Lo0bq9Vy+NO3PV9qhEO+46bcOIhA8+gG7dmkf5O6pov/iiZmLTp+fe\nZ9IkvazPxCv3g+Iz7XXrChPtbBQi2lFk2iNG6P/r5pvhhBNaPpctUYhCtHv1UhurkDaxHibaRkmw\nbVvzCH5HzrTfeQeOO67tVqDjx2tJX+Y0dW8QEorLtJ1TH3rw4MJen8nYsTrgF4QoMu2qKrWQzj23\n9XNxibaI1q8XY5GYaBslgedng/774YetL1c7Alu36o+6LfbbD8aNg7//veX2dNHu00dPhPv2BY9h\n0yYdVOzePfhrs+Fl2kFaE0SRaUPuE1Fcog06GFmMRWKibZQE6aIt0nGzbT+iDWqRZPra6Z52RYUK\nd0ND8BgK9bNzMWiQnoTr6vy/JopMuy28CV3phD0b0iN9MPKFF1qffPNhom2UBOmiDR1XtLds8Sfa\nkye39rXTPW3QTLUQiyRMPxv0JDxuXDBfO8wOf37INo6SfuUSJt5gpHPwrW/B7bcHe72JtlESmGgr\nQTNtz3LYtUsFPz0z7N+/sMHIsEUbgg9GhtlL2w+Z37etW/UzHTAg/GN5tdrPPqte/8qVwV5vom20\nIls5WdSYaCt+RXvIEK208dqNrl6t29IHMEsl04Zgg5F79mhjqmxTy6Mi8/u2cqXWyheziEQuPHtk\nwQKd+p/ZizwfJtpGC/bsgRNP1J7WcWKirfgVbZHmbHvfPrjiiuYmTR7FZNpDhgR/XVsEybS9LDsK\nwcxF5vdtxQqdzRkFfftqaeezz8L3v69e/65d/l9vom204F//0laVxSzIWggm2srWrZqJ+cHztb/+\ndX38n//Z8vlCM+2wByIBjjrK/4IIcfvZEK9o9+kDd94JX/6yfucPOURLPf1iom20wLtUS0K005e2\nGjhQm+cvW6a3Yqf+lgt+M23QTPvXv9YudYsWQZcuLZ8vJU+7Vy/1h/0siBC3nw0qpLt3N2e8nj0S\n1bFEdBAS9OQQxCIx0TZa4A2KxC3a6ZNrAI48UmeNXXYZnHMOzJoVbzxJEUS0jz0Wzj5b+1f37Nn6\n+UIm2DgXjWiDf4skiUxbRE8qXrYdZaY9bhx897vNn/HIkSbaRhGsWKF+5tq18R430x6pqlLfb9ky\nbVOarXVme8M5vaI4wOcS2d26aa/qXBUOhUxl37xZ3zdIRz6/+BXtJDJtaK7Vdi7aTHvCBLj++ubH\no0YFqyAx0TZasGKFdpFL2tNO5+CDtbdxe2fXLp0Us//+4bxfIZl2FH62h98KkiQybWj2tTduVKsp\n2yIQUWD2iFEUK1eWnmhXVgabTRc1772nPzQRvR1wQDhlkkGsET8UkmlHZY2A/wk2SWXa3gSbKK2R\nbJg9YhTMjh16eX788aUl2v3763TspqZ4Y8rG++9rad3nPqfxNDXB736nTYgKXXXcIwrR3rQpWM+P\nKEV75Ei1HzIbXWWyeXN8WW46XqYdpTWSjUGD9DPZts3f/ibaxkesXAmHHaae9vr1wX7sxdKWaHfu\nrNlsIX00wmTvXrjgAu3Cd+21zZn2tGna/H/q1OLaoQYp9/ND9+5qt+zY4f81UYp2+oIIbbFlS7if\ng1880Y470xbRk4RfX9tE2/gIL8Po3l2rEYoRoKC0JdpQGhbJD36gMw5/+cvWEz+++lX47Gdh5szC\n3z/sTBuC+9pr14Y/sSYdP4ORpZBpxynaEMwiMdE2PiI9w6iqis8icS6/aJfCYOSSJXDllbkXev3G\nN7RmulCiEO2gvnaUmTb4G4wshUw7TnsEglWQmGgbH5GUaO/cCV27tp4ckk7SmbZzmiG2tW7iwQer\nhVNID2uILtMuJdH2MxiZpGivX6+zEw87LN5jB6kgMdE2PiJ9ACZO0c6XZUPymfaaNWoZHXRQ7n06\nd9bnCz25+G3LGoQgmXaUE2s8xo6FV17J3WujqUkH5ML+HPzQv78eu0+f7JOVosTsEaMg0jPtIUNK\nS7STzrTzZdkexfRMiSLTHjZMOwD6PX6XLjrlPCoGDoQzz4RLLsneh2T7dp3Yk8uCipJOnfR7Fref\nDc32iJ/BfxNtA2i+rPcmNVRVxTcr0q9oJ5lpl6toB8ngopxY4yECt96q/9dZs1qLVFLWiMfAgcmI\n9kEHaaWPn6siX6ItIgeIyD0i8paIvCEix4tIHxF5XESWi8hjIuJz8q1Ringj5l5VRCnaI+WQaRez\nknwUoh1kgCtqa8Sja1f44x+hpgZ++tOWzyVVOeIxaFD8g5Aefk+wfjPtnwGPOOcOB44G3gauBp5w\nzo0GlgDXFBaqUQpkjpibPdKSuDLtsLNMTwj8XHZHtSZiNg48EG67TbPudJLOtL/1reyrtceB5/fn\nI69oi0gv4BPOudsBnHP7nHPbgLOBhandFgLnFBytkTiZEwoGD1bRjmOCTakPRDY26mzHww/Pv2+p\n2SMHHaRerZ/L7o0b9eQYF0OHtv6bbtmSbKY9ZQocemgyx540qfW6n9nwk2mPADaJyO0i8pKI3CIi\n3YFK51wdgHNuA5BAixcjLDInFPTood3e4piFGCTTjuMk8uGHWvrl8fbb2qjeTyOnUhNtEf8WSV1d\nvKJ90EEq0uklkps3J5tpJ4m3ElE+/IzRdgYmAN90zr0oIgtQa8T3z2fu3Lkf3a+urqa6utrvS42Y\nWLWq5Ure0OxrR928x49od++uFQU7dkRb3QC6DNSll+pn0qWLf2sESk+0odkiOfHEtvfbuFH7zsRF\n584q0A0NzSeLpO2RJKmrq2HduhquvLLt1rh+RHsdsNY592Lq8b2oaNeJSKVzrk5EBgA5L17TRdso\nTerqWvdl9nztY46J9tjbtrVd/+zhDUZGLdr19Zpp33MPXHxxPKLtnIq2317aQfA7caOuTj/jOPGu\noDzR3rzZ33ehPfLJT1Zz0knVnHQSfPrTMG/evKz75bVHUhbIWhHxLp5PBd4AHgAuS22bCdxfZMxG\nQjiX/QebWUESlTXhJ9OGtgcjw4xtyxY9Yf3kJ/5mQqYzYIDGGLQj4QcfaFVF167B481Hqdoj0Loq\nqCNn2qDrfuazSPxWj3wHuFNElqHVIz8E5gOfEpHlwGnADYWHaiTJ++/rpWrmJVm6aF99tWadUeBX\ntHMNRjqn1k4hi9hmY/Nm7ea3bRs891ww0d5vP70SCBpLVNYI+C8l27gxmUw7/W/a0UXbz2Ckr3lH\nzrlXgI9leeq04GEZpUauDGvIEHjqKbjpJl3WascOFcjMDnfFUmymvXmzzvp79VVdwKFYtmzRS/TL\nL4c5c/Tx8OH+X+9ZJEEEMIpyP4+RI7X6palJK0mysW+fxhD34gOZmXbSddpJc/zxusxeW6vW24xI\nI2eGVVUFjz4KN9yg4r3//tpMJ2yCiHa2TLu2Vv/1s5SVH7yys8sug5dfhqOOyi122SjE144y0+7V\nS987vSImk02b9P9cURFNDLnIPBF39Ey7b1+d4PP667n3iV20d+2Cs86Kt8G+0Ta5Mu1DD4Xdu+HB\nBzXT9FuSFJTMldhzkWtWpCfafpay8oNXdtazp7Zb/Vi2a8w2KDXRhvwWSRKDkJDdHunImTbk97Vj\nF+01a1QEwvIfjeLJJdqHHKI/qAkT9PHkyf6K/4Pi95I4V6a9ahVMnBieaKdne9dfDz/+cbDXFzKV\nPWrRzjcYmcQgJGS3Rzpypg35fe3YRdsb2AqykKURLW0NQO23X/P9qDLthoZgJX+Z1NbC9Onw5pvh\nrCOZfhIRCV7RUUimHUVb1nTylf0lMQgJLU/Ee/dqb/WoSzpLnZLLtD3R9tvExogev1nWhAk6OzB9\nYdb//m847zy9XXJJ8H4lu3bpIFhbkwk8cg1E1tbC0UfrINq//tW8ffly7W8RlGJ91XK1R5LOtL3P\nIMj4QXvkyCO1D0wuEhHtLl0s0y4l/GZZ+++vg3L/+Ic+rq+H730PZszQcsDBg3VxW7+rSkPzZAo/\nFSm5Sv5qa9Vzz1x/8Pbb4X/+x38sHu1RtPPZI0ln2s7ZIKRHRQX84Q+5n09EtCdNMtEuJYJkWem+\n9q9+pRn2hRfqv/PnQ3W1dklrbPT3fn6tEdAf9M6dsGdP8zbncov2Qw8F752yb5+WNhYzM3HgwLYz\npWxELdqHHqplkXv3Zn8+qUx7//31tm2biXY6U6bkfi520V67VmtpzR5RXnxRLYegLF8Oy5aFE0OQ\nH6zna+/Zo6uSz5rV/JyI9kc+4AD4ylf8vV8Q0RZpvebhpk3qu/fu3XLR2NWr9TsWVLS9qeTFXKJ7\nmXaQCqko67RBP6OBA3XQNhtJiTY0WyQdvUbbL4lk2qecoj+oMAaNyplXXtET2L//e/DX3nQT3Hhj\nOHEEuTT2Mu2771aRPOqols9XVMCdd2qFkJ9sM4hoQ+vBSC/LhpaZ9sMPa/+GoKIdRrbXo4cOXm7d\n6v81UWfaoL+7XJfdSdkj0DxWYZm2PxIR7cMPz1/s395Zs0YrHubOhcWL254BlY0XXginxG33brUc\n/P5Yhg7VjPf//l+YPTv7Pt276+XdI4/kf7+gop05GLlqVbNojx6tn+uuXSraM2Zotrtzp//3Dyvb\nC+prxyHal18Ov/hFdusq6Ux740ar0fZLrKK9a5f2uejXT0ezO6pFsm2bLm56xRV6GzwY/vpX/6/f\nuRPeekvHBXJ5lH6pr9cfjd+p6SKabffoAaefnnu/adNUOPMRVCQzByNra5tbynbpot+rF1/U9qqn\nn64nhM2b/b9/WNleKYr20UfDmDGwaFHL7c6VRqZtNdr+iFW0169XgerUyX+7yPbIvffqwJCXqfoV\nOI8XX1QroKqq+BNfITPhvvY1+NnP2vZ9p06FJUtaDhpmI2imfdhhLaf4ptsjAOPGwc9/ruWJBx6o\nJ4QgFklSmXbUddoes2fDggUt/fatW3XBCz+LPESBV0Fi9og/YhXttWu1CRF0bNFetQrGj29+PG2a\nVjr4ZelSzXbTB94KpZAlpqZMaXt0G/Rq6sgj4Zln2t4vqGifcUbLE1y6PQL6mdx7r36moO8dRLST\nyLSbmvTqK4pe2pmceaZWxzz3XPO2uJcZy8QbpzB7xB+xinb6as8d2R5Jv6QHrchYv15Pan544QV9\nTWaJWyFE2XPCz8koqGgfe6z+uL3GVZmf5dixmkVOn66PkxLtQYP0+712rd7Sl9TKZMeO5pV5oqZT\nJ/W2Fyxo3pZU3xEPs0eCkZhod+RMO/OSvqKidQaZC+daZtphiHZUWdb06fp/aqv0Lahod+qk2aL3\nvqtXw7Bhzc8fd5w+P2aMPg4q2mHZI8ccowPMJ5ygVxw//3nbx4xTrGbO1K6N3oBukoOQ0HIg0kQ7\nP4mJ9ogR+oNrKwNpr2SKNvj3tVev1n+HDg1HtKO8NB43Tj3ttk7OQUUbmj+rjRt1QLRnz+bnDj5Y\nn/MGVpMaiDzttOYs+/rrW06vz+S999ROiYsePTS+xYv1cZKDkNAy0zZ7JD+xi7bnae+/v35Rvbaa\nHYU9e/RHMnhwy+2nnw5PP60VNm3hZdkiOpi5caNW5BRKlJfGIpr1tmWRFLIm4Kc+BX/5i56wMhcj\nziQpeySdzGXbMolbtKFlkpB0pm112sGIfSDSy7ShY1oka9eqYGf6l337ql97660tt//lL9rPec0a\nfez52aCVoDKsAAAZ9klEQVS2yhFHtN0wPR9RD0J9+tPwxz9mf67QfhO9e+sKH7/5Tf4VZZKqHkmn\nFEV76lT485+1ZjvpTLt37+Y4TLTzk5g9Av7XrmtPZLNGPG65RS+lvcvWFSvgM59RT3TqVBU4L9P2\nKLaCJOpBqDPO0BOV12QqnW3btNSskMVsp03TKpF8om2ZdnYqKzVpeu655DNtET1+p046IGu0TWyi\nvXu3/kjTBcLvKtHtibZEe+RIuO8++PznVbinTtUp7nfcoSV2Z52lWfWxxza/plhfO+pMu3Nn+Pa3\ntSdJJoX42R7Tpul4SNiiHUWmXVmp75urZj0J0YbmgeKkRRtUF/r0CX/90fZIbKL97rtaBpU+IWPU\nqOIu7cuRzBK1TCZP1ox72jS49FL40pd0+4036g/riCNaZiPFiPaHH6qg9e9f2Ov98uUvq6+d2Yuk\nGNEeNUpPciNGtL1fUgOR6VRUtN35LynRTh/QTdIeAf1u2yCkP2IT7UxrBOCkk3S1kY6UbWdOBsnG\nuedqF7+5c5u3deoEv/sd3H9/y3090S5kzc2GBp3QEXV9cJ8+ukDCL37R+viFijbAE09oFURbBMm0\nd+/WtgB+FmQISlsWSVKiPX68Xv2uWpV8pl1ZaX62X2IT7cxBSNCM8atf1SnRHYW27JF0Ro5sfanY\ntateraRTWamiG7R/M8Q7E+7yy/UKIr15UyGVI+kMHZr/hNO3r2bPfjpKejPyorhEL0XR9mreO3f2\nt7BylHj2iJEfX6ItIrUi8oqIvCwif0tt6yMij4vIchF5TETanISbLdMG+OY3tZXnli2FhF84mza1\nvTpEVPgV7SCMG6dtXoMS50y4kSPV+klfSaahIfpL4s6dNXP2s5pOlCVnQ4Zkn/G6b59+DknZE9Om\n6Yk7aS/ZMm3/+M20m4Bq59x459zE1LargSecc6OBJcA1bb1Beo12OoMG6YDIr3/tP+gwWLJEW3f+\n9rfxHXPPHv2BZmbLxXLCCVBTE/x1cfecuOACePLJ5sfF2iN+8WuRRCnauTLtujrt0xLHFPZsTJsW\n/28vGxdcAP/n/yQdRXngV7Qly75nAwtT9xcC57T1BrkybdDOYzfdVHyb0SBs2qQLEFx5pdarxsHq\n1foZVFSE+75BuwR6xF01kFmeGKdo+xmMjHJGXi7RTsoa8dhvv7Zb7MbF4MHaOtbIj1/RdsBjIvJ3\nEflyalulc64OwDm3AWizBiGbp+0xYYJWAXz/+2qV3HmnimqU1NfDiSfCPffoIFmx3fL8EIU1Ajr5\npr4++OzSuBsFjRmjMe7erY8t005etI3yw+9F2QnOuQ0i0h94XESWo0Luizlz5vL66yqQO3dWU11d\n3WqfG2/UzmNr1sAbb+iP+9/+ze8RgrNpk/qsJ52kK4r/8Ie6hFaURCXa6U2UvvlN/69btw5OPTX8\neHKx33469f6tt7RyodREO8pMO5enbaJteNTU1FDjw+f0JdqpTBrnXL2I/AmYCNSJSKVzrk5EBgAb\nc73+y1+ey803w3/+Z+5jHHecZtig055vv91PZIVTX69eMKi3fd11OigUpbeYr0a7GKZNg9tuCyba\nK1bA178eTTy58EoUx48vvnrEL6WQaQ8YoDE0NracAWqibXhUV7dMaOfNm5d1v7z2iIh0F5Geqfs9\ngCnAa8ADwGWp3WYC92d9A/RHOnasv8ChecXvQmqP/bJpkw4Agfppw4drn48o8VOjXShTpuiU5A8+\n8Le/cyrao0ZFE08u0icDxVE9AqUh2hUVOn6QuTCCibYRFD+ediXwnIi8DCwFHnTOPQ7MBz6VskpO\nA27I9QZBRXvQIK3h9hrdR0F9fcuZgIUO5gUhKnsEdJLMxz6mVTF+8EQsjkw3nUzRjuP4ffsmPxAJ\n2X1tE20jKHlF2zm3yjl3TKrcb6xz7obU9s3OudOcc6Odc59yzm3N9R6vvaa1xEGYNEk72kVFeqYN\nwZf8KoQo7REIduLxsuy463PHjdPvQ2OjtqGNY4mtUsi0QX1tE22jWGKZERk004ZmiyQKnGst2oVW\nYPhl1y4VhQEDonl/aBZtP7bSypXxWyOgsxh37IB//jO+BkGlItpVVa0HI020jaDEItorV2qjoyBM\nnhxdpr19uw4Gpa8+XVGhXfWiski8kse2VjAvltGjdSD17bfz77tihVbPxI0IHHWUTgaKy5opheoR\naG2PNDVp2WWUJ3Kj/RGLaA8Zon2TgzB+vDZN8juwFoRNm7J3tovS1960KfqaaBG9ovEr2klk2qAx\nPv106Yl2HJl2umg3NECvXloKaRh+iUW0g1ojoF/kcePgxRfDj6e+vqU14jFlCjz7bDQnirgqJfyu\nBrRyZTKZNuj3Ie5MO99ApHPRL7Cb6Wl77YoNIwglK9oQna+dK9M+8EBdxmriRPW4TzihecXqYomr\nUsLPakBNTcmK9rhx2vckrv7JvXrpmEJjY+vnZs7Uv/XHPqZ2WbplFjaZnrb52UYhxNKmJmjliMfk\nydpDOmxyZdqgnf+8/t7XXKOX8RdcUPwx4xLtUaOaJynl4t13tRVnUu04jzpK/40r0xZpLvtL94+b\nmmDRIu3L3bWrnrSjZMAA/e7t3QtduphoG4VRFpl22JNscmXaoD9cL/M67bTwBkPjFO18mXaSWTao\nBVFVFW+NeDZfe8MGPXGdeKL+vaP+TLp00e/dhg362ETbKIRYRDvfklC5GDJEqzouu0wXS7jyynAE\nvK1MO53Jk1vbM/X1hU2xj2vK9qBB8P77WiGTiyQHIT3Gjk1etKOc7JSLIUN0gB1MtI3CiEW0Cy1z\nE9FL/RNP1N4kt9yiolksbWXa6Rx3nHb/S1+Q9fe/h//4j+DHjCvTFtGMsa0l3JIq90vnxz+G886L\n73jZBiOjnuyUjW9+E77yFc22TbSNQkio9bp/qqv1BvDLX+roe7Glc5lT2HPRo4fWPr/8slo1oCWB\nmf0j/BBX9Qg0WyTpq7ans3IlfPzj8cSSi6B1+8VSKpn25z6nx502TT11E20jKLGtERkGudpbBiVz\nNmRbpE+n/+ADeP55rURIX+vQD3Fl2uAv007aHombvn1bi3aUDbza4tprtYf8smUm2kZwykq021oc\nNQh+M21o6Ws/+aRaJoMGBc+24xTttgYj9+3TTO/QQ+OJpVSoqmrdoiAJewTUwvrlL3VB6yROGkZ5\n0yFFO0imnT6d/uGH9bJ24MDyFe3Vq7X0LMp65FIkvbugRxL2iEeXLvCd7yS3NqRRvnQ40W5sVJvD\nb03uoYeqHbJ+vYr29OnBRXvXLv23e/fg8RaCZ49kq7QphUHIJPBE2/tMPvxQrbZhw5KNyzCCUlai\nHYan7WW8frvLiaivffPNmp2OGhVctOMchITm/1+2dTaXLMk9QNme6d9f/37eSf+99/Rv0tGuOIzy\np6xEO4xMO4if7TFpkq5fOW2aimEhoh1nTbJIdotkxw6tMf/a1+KLpZTwenlDstaIYRRDWYn24MEq\n2sVMsAniZ3tMnqyCN326Pi510YbsFSR33KHlkx1VrNJ97aQqRwyjWMpKtHv0UF/YT5vNXBSSaX/s\nY3D00bpyOwSvHklCtDMz7aYmrVaYNSveOEqJsWN1shQkVzliGMVSVqINxfvafqewp9Orl9bUen2P\nyyHTzhTthx7SwdcTT4w3jlIiPdM2e8QoV8pOtIv1tf1OYW+LgQO1U55f4uo7kk6mPbJgAcyeHf+a\nkKXEEUfoZ7J3r9kjRvlSdlWixYp2fT2MGVNcDP36aUOmxkZt6ZmPhob4Z76NHKmNiUaP1jGAPXtg\nxox4Yyg1unXTNSqXL7dM2yhfOpxoh5Fpd+qk/U82bFARyEdDQ3MP6bjo1UsXz/VW4Tn4YJ3Q0dEZ\nO1atrvXr/f3tDKPUKDvRHjJEa40LpRBPOxuer+1XtOO2R0BPcEZLxo2DRx/VE7etzWiUI749bRHp\nJCIvicgDqcfDRWSpiCwXkbtEJJYTQClk2tB6MPLBB3OvZ5mUaButGTsWHnnErBGjfAkyEHk58Gba\n4/nAjc650cBW4EthBpaLMDztMDNtjx//WBdpyIaJdukwdqyuum7lfka54ku0RaQKOBP4TdrmTwL3\npu4vBM4NN7TseKJdyAQb51RAwxbtvXvhH/+At95SvzSTJKpHjOyMGKG1/pZpG+WK30x7AXAl4ABE\n5CBgi3OuKfX8OmBQ+OG1pmdP9SIzVyHxw7ZtWkEQhpeZLtqvvaaNh2bN0tK6dJqaYOtWXRfRSJ5O\nneDII020jfIlrw8tItOAOufcMhGp9janbunkzH3nzp370f3q6mqqvaVoCsTLtoNmr2FZI6CzIr1a\n7aVLdar7V7+qXQHTl5Hatk1ncloLztLh+uvjr+YxjHzU1NRQU1OTdz8/UnIicJaInAl0A3oBPwUO\nEJFOqWy7Csg53SRdtMPAE+2jjw72uro6qKwMJ4b0TPuFF+Dkk7Vr3IUXaoP7H/xAnzM/u/SYMiXp\nCAyjNZkJ7bx587Lul9cecc59zzk31Dk3ArgQWOKcuxR4CvCma8wE7i8yZt8UOhgZlWgvXdq8huSs\nWdrG1euhbaJtGEaYFDON/WrgChFZAfQFbg0npPwMGVKYaG/cGJ5oV1Zq+eCGDfq+3kK1o0drLfAj\nj+hjE23DMMIkkGg75552zp2Vur/KOXe8c26Uc+6zzrm90YTYmqqqwppG1dUVv5K7R+fOaoc8+CBM\nnKgDXB5nn62r3IAOmMa5AIJhGO2bsmsYBYXbI2Fm2qAWyX336SBkOtOmaabd1GSZtmEY4VKWoj16\ntNZDv/12sNeFmWmDivYTTzT72R4jRmiJ3z/+YaJtGEa4lKVoDxumMxDPPFM9Zb+EORAJKtp798Lx\nx7d+bvp0tUhMtA3DCJOyFG2Ayy6DL3xBrYgdO/y9Jgp7ZNSo7KI8bZouPGCibRhGmJStaANcey0c\ncwxccYW//cO2R0aM0PrsbJx4IrzzDrzxhom2YRjhIa6YVXL9HEDERXmMtWth/HgV5IqK3Pvt3g29\ne+tiAGGt3tLUpLdcsx0vuADuuQf+/nc47rhwjmkYRsdARHDOtVKrss60QWu2Bw+Gv/617f02btQs\nO8zltjp1ant6urd6u2XahmGERdmLNqh/7NVF5yLsQUg/TJ2q2X9Y/U4MwzA6jGh7mXac9O+v7Vp7\n9Yr3uIZhtF/ahWhPmqSTbdqaJZlEpg26wK5hGEZYtAvRrqiAM85o7veRjbDL/QzDMJKgXYg25LdI\nwi73MwzDSIJ2I9qnnw41Nc0tUTNJyh4xDMMIk3Yj2n376kSbXAs/JDEQaRiGETbtRrQBTj0Vnnkm\n+3OWaRuG0R5oV6I9bpwuspsNG4g0DKM90O5E+9VXW2//8ENdjMAmuRiGUe60K9E+5BAV561bW27f\ntAkOPNBWRDcMo/xpV6LdqRMceSS8/nrL7TYIaRhGe6FdiTbA2LGtfW0bhDQMo73QIUTbMm3DMNoL\n7U60s1WQWKZtGEZ7od2Jtpdpp6+7YKJtGEZ7Ia9oi8h+IvJXEXlZRF4TkTmp7cNFZKmILBeRu0Sk\nJGoz+vWDbt1advwze8QwjPZCXtF2zu0BTnHOjQeOAaaKyPHAfOBG59xoYCvwpUgjDUCmr22ZtmEY\n7QVf9ohzbmfq7n5AZ8ABpwD3prYvBM4NPboCySbalmkbhtEe8CXaItJJRF4GNgB/Bt4BtjrnmlK7\nrAMGRRNicNJFe98+WL/eMm3DMNoHvnzolDiPF5HewH3A4dl2y/X6uXPnfnS/urqa6urqQEEGZdw4\n+MlPdDDyG9+Ao4+GoUMjPaRhGEZR1NTUUJOrTWka4lxOrc3+ApHvAzuBq4ABzrkmEZkEzHHOTc2y\nvwt6jGLZtUtbtV51FTz4IDz9tK3TaBhGeSEiOOckc7uf6pF+InJA6n434DTgTeApYEZqt5nA/eGF\nWxzdusGwYbBwoa5mY4JtGEZ7IW+mLSJj0YHGTqnb751z/y4ihwB3A32Al4FLnXN7s7w+9kwbYNEi\ntUVGj4790IZhGEWTK9MObI8UcOBERNswDKOcKdgeMQzDMEoHE23DMIwywkTbMAyjjDDRNgzDKCNM\ntA3DMMoIE23DMIwywkTbMAyjjDDRNgzDKCNMtA3DMMoIE23DMIwywkTbMAyjjDDRNgzDKCNMtA3D\nMMoIE23DMIwywkTbMAyjjDDRNgzDKCNMtA3DMMoIE23DMIwywkTbMAyjjDDRNgzDKCNMtA3DMMoI\nE23DMIwyIq9oi0iViCwRkTdF5DUR+U5qex8ReVxElovIYyJyQPThGoZhdGz8ZNr7gCucc0cAk4Fv\nisgY4GrgCefcaGAJcE10YRZPTU1N0iGURAxgcWRSCnGUQgxgcWRSKnGkk1e0nXMbnHPLUvd3AG8B\nVcDZwMLUbguBc6IKMgxK4cMvhRjA4sikFOIohRjA4sikVOJIJ5CnLSLDgWOApUClc64OVNiB/oUE\n4OdDybePn/eora2N/Bj59skXQ3uKI46/SXuKw74bweLoSN+NTHyLtoj0BP4AXJ7KuF2gI+WgXD78\ncvlBlEoc5fSDKIU47LsRLI6O9N3IRJzLr70i0hl4CFjsnPtZattbQLVzrk5EBgBPOecOz/LaUMTd\nMAyjo+Gck8xtnX2+9jbgTU+wUzwAXAbMB2YC9/s9qGEYhlEYeTNtETkReAZ4DbVEHPA94G/AImAI\nsAaY4ZzbGmm0hmEYHRxf9ohhGIZRGgSeESkit4pInYi8mrZtnIj8RUReEZH7U4OWiEgXEblNRF4V\nkZdF5OS010xIbV8hIj9NMI7rRWSNiGwPGkNYcYhINxF5SETeSk1g+mGCn8fi1LbXROSXIuLb3gor\nhrTXPpD+Xgl8Fk+JyNup7S+JSL+E4ugiIjeLTmR7U0TOjTsOEemZ9jm8LCL1IvKTBD6Li1Lbl4nI\nIyLSN+7PIvXcZ1P7vyYiNwSJoWicc4FuwMfRsr9X07b9Dfh46v5lwHWp+98Abk3d7w+8mPaavwIT\nU/cfAU5PKI6JQCWwPehnEVYcQDfg5NT9zqgdldTn0TPt/h+AC+KOIbXtXOB/0t8rgc/iKWB8Id+L\nkOOY6+2Xetw3iTgy3vNF4MSYfycVQB3QJ/V4PvD9uD8LoC+w2vs7ALcDpxT6PQl6C5xpO+eeA7Zk\nbB6V2g7wBPCZ1P0jgCdTr6sHtorIcaLVJr2cc39L7fdbAk7OCSOO1OO/uVS9eSGEEYdzbpdz7unU\n9n3AS+gEpljjSD3eAZplAF0JUNoZVgwi0gOYDVzv99hRxJGi4P48IcbxReBHae+7OaE4ABCRkUB/\n59zzMcfgXfX1Sl0B9gbe9RtDiHGMAJan/R2eBM4LEkcxhNUw6nUR+XTq/gXo4CTAK8DZIlIhIocA\nx6aeGwysS3v9utS2uOOIioLjEJEDgU+T+rIkEYeIPApsALaj2XbcMfwA+DGwq8hjFxsHwG0pS+Da\nJOKQ5p4+14vIP0Tk9yJS0ES2YuLIeO2FwO/jjiGV0HwDLYpYBxwO3Bp3HMA/gTEiMlS0HPocotWT\nFoQl2l8EviUifwd6AI2p7bcB64G/Az8Bnkd7mWTzScMYEQ0aR1QUFIeIVAC/A37qnKtNKg7n3BnA\nQGA/4JNxxiAiRwOHOeceQL8nYZWMFvJZXOycOxr4BPAJEbk0gTg6o1ddzzrnjkVnI9+YQBzpXAjc\nFXcMKYH8OnC0c24wKt7fizsOp1VyX0er554GVhGtnrSkEE8FGEYOrxEYCSzN8dzzwBhgAPBW2vYL\ngf+OO46MbQV52mHGgWYNC5KOI23754Gfx/zd+BqaRf0LWAvsAZaUwGcxM+hnEVYcwPtp26uA1xL8\njo4D3k7i+wkcB/w5bfsngIdK4LvxFeCGQj6TQm6FZtotMiDvck1EOgHXAr9KPe4mIt1T9z8F7HXO\nve20V8l2EZmY8qY+T47JOVHGkeW9CqXoOETkeqC3c252UnGISI/UeIM3C/ZMIPNzijQG59yvnHNV\nzrkR6KDRcudcIdl+sZ9FhYgclNreBZgOvB53HKmXPSgip6Tunwa8mVAcABdReJZdbAzrgSO8vwvw\nKbSBXdxxpL+mD2rZ/KaAOAqjgLPU71Dzfw86qeYLwHeA5egP/IcZZ7S3gTeAx1FfynvuWPTyZiXw\nswTjmI9mdPtS7xN0NLroOFA/vym1/WV0IPKLCcRxMDqSviz1t/kZ0Cnuv0nGPoVUj4TxWXRHKyS8\nz2IBqXkNCXxHh6KX4cuAPwNVScSRev6f6MBdUr/Xr6InrWVootcnoTh+l9r+OjqxMLCWFnqzyTWG\nYRhlhC03ZhiGUUaYaBuGYZQRJtqGYRhlhIm2YRhGGWGibRiGUUaYaBuGYZQRJtpGu0ZE5ojIFW08\nf7aIjIkzJsMoBhNto6NzDnBk0kEYhl9sco3R7hCRfwM+h85424TObNyOzqbrgs7q+xwwHl2weiuw\nDW2vKcAvgH7ATuArzrkVMf8XDCMnJtpGu0JEJqBN6Sei/cBfAv4buN05tyW1zw+ADc65X4jI7cCD\nzrk/pp57Avhfzrl3RGQi8CPn3KlJ/F8MIxt+V2M3jHLhE8B9zrk9wB4ReSC1fWyqKdeBaPvNxzJf\nKLr4wgnAPalGZqCZuWGUDCbaRnsk8/JRgDuAs5xzr4vITODkLK/rBGxxzk2IOD7DKBgbiDTaG88A\n54rIfiLSC10FCKAnsCHVZvWStP3fR5etwjn3PrBKRM73nhSRcfGEbRj+ME/baHeIyDXoogW16IIK\nbwIfAN9NbXsNXaP0iyJyAvBrYDdwPtoi91foyj2dgbudcwWtVWkYUWCibRiGUUaYPWIYhlFGmGgb\nhmGUESbahmEYZYSJtmEYRhlhom0YhlFGmGgbhmGUESbahmEYZYSJtmEYRhnx/wF1qBy/Rd3ZFwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10615bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"1990\":\"1999\"].plot(y='val')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping with resample, not with groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm, looks like something bad might have happened to the housing industry t some point. Maybe we want to see some numbers instead of a graph? To do aggregate statistics on time series in pandas we use a method called `.resample()`, and we're going to tell it **to group the data by year.**\n",
    "\n",
    "When we tell it to group by year, we need to give it a **special code**. I always get mine from this StackOverflow post http://stackoverflow.com/a/17001474 because it's much more convenient than the pandas documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>1963-02-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "1963-01-01       0  42.0     SOLD  New Single-family Houses Sold           0   \n",
       "1963-02-01       0  35.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "1963-01-01   TOTAL  All Houses       K       US  United States  1963-01-01  \n",
       "1963-02-01   TOTAL  All Houses       K       US  United States  1963-02-01  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106296ef0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10627a748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('A').mean().plot(y='val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1063e8320>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10612a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.resample('A').mean()['val'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106506390>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106520588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].resample('A').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106216860>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\no0wwQTcMwygTTNANwzDKhP8PV5TI8+x4hSIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067d7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].resample('2Q').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10689aef0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEPCAYAAABShj9RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLiJSIf4/iMPZqwUUJHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1069524e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].resample('9M').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that it's **December of every year**. That still looks like too much data, though. What if we back out to every decade?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool, right?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphing\n",
    "\n",
    "We can graph these instead of just look at them! Get ready!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphing all on one plot\n",
    "\n",
    "We've done this before, but it's more exciting now - save the first plot as `ax` and pass it to the others as the confusingly- or conveniently-named `ax=ax`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107422f98>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107598dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].plot()\n",
    "df['val'].resample('A').mean().plot()\n",
    "df['val'].resample('5A').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cyclical data (actually using groupby)\n",
    "\n",
    "### What were the top 5 worst months?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_code</th>\n",
       "      <th>cat_desc</th>\n",
       "      <th>cat_indent</th>\n",
       "      <th>dt_code</th>\n",
       "      <th>dt_desc</th>\n",
       "      <th>dt_unit</th>\n",
       "      <th>geo_code</th>\n",
       "      <th>geo_desc</th>\n",
       "      <th>per_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2011-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2011-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SOLD</td>\n",
       "      <td>New Single-family Houses Sold</td>\n",
       "      <td>0</td>\n",
       "      <td>TOTAL</td>\n",
       "      <td>All Houses</td>\n",
       "      <td>K</td>\n",
       "      <td>US</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_adj   val cat_code                       cat_desc  cat_indent  \\\n",
       "date                                                                           \n",
       "2010-11-01       0  20.0     SOLD  New Single-family Houses Sold           0   \n",
       "2011-01-01       0  21.0     SOLD  New Single-family Houses Sold           0   \n",
       "2011-02-01       0  22.0     SOLD  New Single-family Houses Sold           0   \n",
       "2010-08-01       0  23.0     SOLD  New Single-family Houses Sold           0   \n",
       "2010-10-01       0  23.0     SOLD  New Single-family Houses Sold           0   \n",
       "\n",
       "           dt_code     dt_desc dt_unit geo_code       geo_desc    per_name  \n",
       "date                                                                        \n",
       "2010-11-01   TOTAL  All Houses       K       US  United States  2010-11-01  \n",
       "2011-01-01   TOTAL  All Houses       K       US  United States  2011-01-01  \n",
       "2011-02-01   TOTAL  All Houses       K       US  United States  2011-02-01  \n",
       "2010-08-01   TOTAL  All Houses       K       US  United States  2010-08-01  \n",
       "2010-10-01   TOTAL  All Houses       K       US  United States  2010-10-01  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='val').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like there might be a cycle ever year. Maybe houses are sold in the summer and not the winter? To do this we can't use resample - it's for putting time into buckets - we need to **group by the month.**\n",
    "\n",
    "### Getting the month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can't ask for the index column as \"year\" any more, but we can just use `df.index` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "            ...\n",
       "             8,  9, 10, 11, 12,  1,  2,  3,  4,  5],\n",
       "           dtype='int64', name='date', length=641)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the month of each date, it's simply `df.index.month`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing the groupby to view data by month\n",
    "\n",
    "So when we do our groupby, we'll say **hey, we made the groups for you already**. Then we ask for the median number of houses sold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 4, 4, 0, 2, 5, 0, 3, 6, 1,\n",
       "            ...\n",
       "            5, 1, 3, 6, 1, 4, 0, 1, 4, 6],\n",
       "           dtype='int64', name='date', length=641)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_adj</th>\n",
       "      <th>val</th>\n",
       "      <th>cat_indent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>47.685185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>53.592593</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>62.685185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>61.444444</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>61.129630</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>59.188679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>56.735849</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>57.566038</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>52.037736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>51.584906</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>45.471698</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>42.792453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      is_adj        val  cat_indent\n",
       "date                               \n",
       "1          0  47.685185           0\n",
       "2          0  53.592593           0\n",
       "3          0  62.685185           0\n",
       "4          0  61.444444           0\n",
       "5          0  61.129630           0\n",
       "6          0  59.188679           0\n",
       "7          0  56.735849           0\n",
       "8          0  57.566038           0\n",
       "9          0  52.037736           0\n",
       "10         0  51.584906           0\n",
       "11         0  45.471698           0\n",
       "12         0  42.792453           0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=df.index.month).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10710cef0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFAueiMiiWPBERBbFgicisqj/B7efuAnClKzbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107050860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(by=df.index.month).mean().plot(y='val')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10713e630>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRf0/hRUPf9NzGAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ac3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].groupby(by=df.index.month).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More details\n",
    "\n",
    "You can also use **max** and **min** and all of your other aggregate friends with `.resample`. For example, what's the **largest number of houses sold in a given year?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2005-12-31    1283.0\n",
       "2004-12-31    1203.0\n",
       "2003-12-31    1088.0\n",
       "Name: val, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['val'].resample('A').sum().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the fewest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2016-12-31    241.0\n",
       "2011-12-31    305.0\n",
       "2010-12-31    322.0\n",
       "Name: val, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['val'].resample('A').sum().sort_values().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we're feeling crazy, why don't we plot the **average, max and min for each year?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1082cb7f0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nojl4DQ0NjX6K5uA1NDQ0+in/HwzITH27CosJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1081649e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].resample('A').agg(['mean','min','max']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1082c34e0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FNX6x7+zvWRTNiG9kgCJCRggdBQCqPSmBL2oNEWK\ngChYUFEUrxelCBfBe0GKiD8JXoKIIIgC0ktIICQgJCQhgZC26dt35/fHkiXL9pJ+Ps/DA5k5c+Y9\nzOads2+laJqmQSAQCIQ2B6O5BSAQCARC40AUPIFAILRRiIInEAiENgpR8AQCgdBGIQqeQCAQ2ihE\nwRMIBEIbxaqC37RpE1599VUsXrzY6Nz+/fsxefJk1NbW6o9t3boVCxYswJIlS5CXl+dSYQkEAoFg\nO1YVfGJiIt5//32j4+Xl5cjIyICPj4/+WFpaGoqLi7F+/XrMmjULmzdvdq20LYjMzMzmFqFZac/r\nJ2tvn7TGtVtV8NHR0RAKhUbHd+zYgZdeesng2MWLFzFo0CAAQKdOnSCVSlFZWekiUVsWrfFhu5L2\nvH6y9vZJa1y7Qzb4S5cuwdvbG6GhoQbHJRIJvL299T+LxWJIJBLnJLRAc/6Hl5SUNNu9geb/sDXn\n+snam4/2/LlvjWu3W8ErlUqkpKQgKSnJpvEURdktlK0058MuLS1ttnsDzf+L3pzrJ2tvPtrz5741\nrp2ypRZNaWkpVq5ciVWrVuHOnTv49NNPweVyQdM0JBIJxGIx/vnPf2L37t2Ii4tD//79AQBvvPEG\nPv74Y3h6epoUtqHAtr4wCAQCgWBIcnKy/t+xsbGIjY0FALBsuZimadS/B0JDQw2cp/PmzcPKlSvh\n5uaGhIQEHD58GP3798fNmzchFApNKvdHhajn3r179q2qGRGJRKipqWluMZqN9rx+snay9pZEYGCg\n2Q2yVQW/bt06ZGVloaamBnPmzEFSUhISExP15xuaYHr06IG0tDTMnz8fPB4Pc+bMcYH4BAKBQHAE\nm0w0TQXZwbce2vP6ydrJ2lsSgYGBZs+RTFYCgUBooxAFTyAQCG0UouAJBAKhjUIUPIFAILRRiIIn\nEAiENgpR8AQCgdBGIQqeQCAQ2ihEwRMIBEIbhSh4AoFAaKMQBU8gEAhtFKLgCQQCoY1CFDyBQCC0\nUYiCJxAIhDYKUfAEAoHQRiEKnkAgENooRMETCARCG4UoeAKBQGijEAVPIBAIbRSi4AkEAqGNQhQ8\ngUBo8VCVlXBftgzQaJpblFYFUfAEAqFlo1JBPGsW3L79Fsy8vOaWplVBFDyBQGi50DQ8li4FzedD\nPnQo2DduNLdErQqi4AkEQotF+J//gJOejoqvv4YqLg7s69ebW6RWBVHwBAKhRcL77Te4bd6M8u3b\nQbu5QRUTAxZR8HZBFDyBQGhxsK9ehceSJZBs3QptUBAAQB0TQ3bwdkIUPIFAaFEw7t2DePp0VK1c\nCdXjj+uPq8PDwSgpAVVX14zStS6IgicQCC0GSiqF97RpqJsxA/KRIw1PslhQd+oEFnG02gxR8AQC\nocXA++UXaHx9UTt3rsnzxExjHyxrAzZt2oTLly/Dw8MDq1atAgB8//33SE1NBYvFgp+fH+bOnQuB\nQAAASElJwbFjx8BkMjFt2jQ83uArFoFAIFiCk5YGxaBBAEWZPK8iCt4urO7gExMT8f777xsc69at\nG1avXo0vv/wSAQEB2LdvHwCgsLAQZ8+exdq1a/Hee+9hy5YtoGm6cSQnEAhtDnZ6OpTdu5s9r4qO\nJpE0dmBVwUdHR0MoFBoc69atGxgM3aWdOnVCeXk5AODSpUvo378/mEwmfH19ERAQgOzs7EYQm0Ag\ntDlkMrCys6GKjTU7RP3YY7pkJ7JxtAmnbfDHjh1D9wdvXIlEAh8fH/05sVgMiUTi7C0IBEI7gH3t\nGtSdOgF8vtkxWm9v0FwuGPfuNaFkrRenFPzevXvBZDIxcOBAADBpjqHM2NIIBAKhIZz0dKji462O\nI3Z427HqZDXH8ePHkZaWhmXLlumPeXt7o6ysTP9zeXk5vLy8TF6fmZmJzMxM/c9JSUkQiUSOitPk\ncDicViWvq2nP6ydrb5y1865dg3roUKvzM7p1g9vt2+A08TNoyc89OTlZ/+/Y2FjEPjBz2aTgaZo2\n2J2np6dj//79WL58Odhstv54QkIC1q9fj9GjR0MikeD+/fuIiooyOWdDIeqpqamxfUXNjEgkalXy\nupr2vH6y9sZZO//iRdS8/jrUVuZXR0aCe/x4kz+DlvrcRSIRkpKSTJ6zquDXrVuHrKws1NTUYM6c\nOUhKSkJKSgrUajVWrFgBQOdofeWVVxAcHIx+/fph0aJFYLFYeOWVV4iJhkAgWIUhkYAhkUAdGWl1\nrComBm6bNjWBVK0fim5BcYz3WpHjpKW+zZuK9rx+snbXr537559w++YblDcwNZhFoUDAY4+hKCsL\n4HJdLos5WupzDwwMNHuOZLISCIRmh5OWZjH+3QAuF+rQULBu3WpcodoARMETCIRmh52eDpWtCh4P\nImlITRqrEAVPIBCaF5oGOy0NShtCJOshNWlsgyh4AoHQrDDz8wEeD1p/f5uvIc0/bIMoeAKB0Kxw\nrNSfMQXZwdsGUfAEAqFZYael2WV/BwBNYCAouRyMB3WwCKYhCp5AIDQrHDvt7wAAiiJmGhsgCp5A\nIDQfKhVYWVlQdetm96XETGMdouAJBEKzwb5xA5qwMNBubnZfS4qOWYcoeAKB0GywL1+23zzzANL8\nwzpEwRMIhGbD1hLBplBHR+uyWTUaF0vVdiAKnkAgNBvWWvRZgnZzg9bXF8zcXBdL1XYgCp5AIDQL\nVE0NmIWFUHfp4vAcxA5vGaLgCQRCs8C+ckXXf7VBTwl7UUdHEwVvAaLgCQRCs8Cxs8CYKVQxMWCR\nomNmIQqeQCA0C/YWGDMFMdFYhih4AoHQLHDS06Hq0cOpOTTh4WCUloJqgY04WgJEwRMIhCaHUVQE\nqFTQBAc7NxGTCXXnzsRMYwai4AkEQpPDSUvTxb+7oGezulMnsLOzXSBV24MoeAKB0OQ4Ev9+rfya\nyePq8HAw8/JcIFXbgyh4AoHQ5HDS06F6/HGbx58rOodn9j6DMlmZ0TlNWBhYd+64Urw2A1HwBAKh\naaFpsK9ds6uC5KrUVeAyubguMY6YUYeF6bpCEYwgCp5AIDQpzPx8aIVCaH18bBp/6u4pFNUV4blO\nz+GGxNiZqgkPB4soeJOwmlsAAoHQvmBfvWrz7p2maaxKXYU3e7wJqVqK9JJ0ozFasRhQq0FVVoL2\n9HS1uK0asoMnEAhNCjsjA6quXW0ae6LwBCoUFRgfOR7R4mjcqDARDklR0ISGEju8CYiCJxAITQrH\nxh18w907k8FEtFc0blbchJbWGo0lkTSmIQqeQCA0HTSt28HboOD/KPgDUrUUYzqOAQCIOCKIeWLk\nVxvb2zVhYcQObwKi4AkEQpPBzM8HLRBYdbDW797f6vkWGNRDNRUtjiaRNHZAFDyBQGgy2FevQmlD\n/Pvh/MPQ0lqMCB9hcDxaHG06kiYsDCxiojHCahTNpk2bcPnyZXh4eGDVqlUAgNraWnz11VcoLS2F\nr68vFi1aBIFAAADYunUr0tPTweVyMW/ePISHhzfqAggEQuvBFgerltZiVeoqvJ3wtsHuHQAeEz+G\nA7kHjK5Rh4WBSZysRljdwScmJuL99983OLZv3z507doV69atQ2xsLFJSUgAAaWlpKC4uxvr16zFr\n1ixs3ry5caQmEAitElscrL/m/goOg4OnQp8yOhftZWYHHxQEZmkpoFC4TNa2gFUFHx0dDaFQaHDs\n0qVLGDRoEABg8ODBuHTpEgDg4sWL+uOdOnWCVCpFZWWlq2UmEAitERscrBqtBmtS12BxwmJQJgqR\ndfTsiHu19yBTywxPsFjQBAaCWVDgaqlbNQ7Z4KuqquD5IKHA09MTVVVVAACJRAJvb2/9OLFYDIlE\n4gIxCQRCa8cWB+uR/CMQcoRIDE40eZ7NYCPCIwI3K24anVOTSBojGj2T1dRbGAAyMzORmZmp/zkp\nKQkikaixxXEZHA6nVcnratrz+snaHVs769YtaHv0sHj9z3k/45X4V+Du7m52TFffrsiT5mGgaKDB\ncUZUFIT374PTSM+mJT/35ORk/b9jY2MRGxsLwEEF7+npicrKSv3fHh4eAHQ79vLycv248vJyeHl5\nmZyjoRD11LSiriwikahVyetq2vP6ydodW7vowgWoYmJQa+b6KkUVjucfx8r+Ky3eI0oUhbR7aRgX\nNs7guDYwEMybNxvt2bTU5y4SiZCUlGTynE0mGpqmQdO0/ueePXvi+PHjAIDjx48jISEBAJCQkIAT\nJ04AAG7evAmhUKg35RAIhPYN5+pViyWCD+YexBNBT8CD62FxHnMlC0iykzFWd/Dr1q1DVlYWampq\nMGfOHCQlJWH8+PFYu3Ytjh07Bh8fH7z55psAgB49eiAtLQ3z588Hj8fDnDlzGn0BBAKhFWBDieC9\n2XsxPXa61alixDEmI2lIspMxVhX8woULTR7/8MMPTR6fOXOmcxIRCIQ2B/POHdB8vlkHa1FdEbIk\nWRgSMsTqXAHCACg1SpTJyuDDfzifpj4WXqsFGCSHEyCZrAQCoQlgX7kCpYXd+885P2N42HDwWDyr\nc1EUZbJkAS0UgnZzA6OkxGl52wpEwRMIhEbHWgZrSnYKJkRNsHk+iyULiJlGD1HwBAKh0bGUwXqr\n4hbKZGXoF9DP5vnMZbSqw8JI2eAGEAVPIBAaFysO1r3ZezE2ciyYDKbNU8aIY0xH0pD2fQYQBU8g\nEBoVvYO1QwejczRNY1/OPkyMmmjXnF3EXXCz4iY0Wo3BcRJJYwhR8AQCwTnkcrCuG9dor4d99apZ\nB2tqSSo4TA7ivOPsuqU7x13X/KPGUJkTG7whRMETCASnEK1diw6jRoH9oOjgo7CvXjXrYE3JTsH4\nyPFmS5pYwpSjlezgDSEKnkAgOAyzoADC779H1aefQvzqqyZrsptzsKq0Kvxy+xe7omcaYkrBazt0\nACWXg2qBJQWaA6LgCQSCw7h/9hlqZ86EdMoU1M6fD/HUqaCqqx8OsOBg/avwL4S5hyHcPdyhe8d4\nxeB6xSOmIYrSJTyRXTwAouAJBIKDcC5eBOfSJdTNng0AqJsxA8r+/eE1ezagVgN44GDl8Uw6WB1x\nrjbEXCy8OjSU2OEfQBQ8gUCwH60W7suXo/rdd0E/aNcJAFXLlwMMBjw+/FC3ezfjYJWqpDh65yjG\ndBzjsAiRnpEmm38QR+tDiIInEAh2w9+3D9BqIZv4yA6cxULFpk3gXLgA4bffmu3gdDj/MHr69jSo\nJWMv5pp/kGSnhxAFTyAQ7IKSySD6/HNUf/yxyaJetEgEyY4dcNu4EYL//c+kgk/JTsH4qPFOy2Kq\nsiRJdnoIUfAEAsEuhN98A1WPHlD27m12jCY4GJItW0DJ5UY14CVyCS7cv4DhYcOdliXay7joGAmV\nfEijt+wjEAhtB6qoCMItW1B66JDVsaoePXD/yhWAZahmDtw+gMSQRLhx3JyWJ1ocjZPXThoc0wQF\ngVlcDKhUAJvt9D1aM2QHT2gbyGTwS0gAZDLrYwkOw/3kE9RNmQJNaKhtF7CM95D7cvY5HPv+KCYj\naTgcaPz8wCwsdMk9WjNEwRPaBKzcXDCLisBu0Mid4FrYGRlg/v47aufPd3iOu7V38XfF3xgcPNgl\nMgUKA6HQKFAmKzM4TiJpdBAFT2gTsHJyAACc9PRmlqQNolZDuHUrxP/4BxQrVoAWiRye6uecnzEq\nYhQ4TI5LRKMoCjHiGNN2eBJJQxQ8oW3Ays6Gxt8f7LS05halTcE5exYdhg8H79AhlO/ZA/Xzzzs1\nX33tGVfSybMTcipzDI5pwsPBMlE2ob1BnKyENgHr9m1IJ04E/+DB5halTcC4exceK1aAnZqK6mXL\nIB81CnCgIFhDblbchEQhQR//Pi6SUkeERwRuV902OKYODQU/NdWl92mNkB08oU3Ays6G/OmnwSgr\nAyWRNLc4rRetFm4bN6LDM89AHRmJ0hMnIB892mnlDuicq+M6jrOrsUc9KhVw8CAPNG18rqNHR+RW\n5xocU5NYeABEwRPaAjQNVk4O1J07Q9WtG7HDO4pCAc8FC8D77TeUHTyImsWLQfP5Lpmapmnsy3Ys\neqa2lsK0aWK8+qoYBQXGL4eOHh2NdvD6gmOm3gjtCKLg2ys0DXZGBtzWrWv1dmvG/fugBQLQHh5Q\ndu8ONlHwdkNVVcH7xRdByeUo273b9jBIG0krTQOLwbK7sUdxMQPPPuuNoCANnnlGhkuXjJ2zoaJQ\nFNUVQalR6o/RIhFoHg+M0lKnZW/NEAXfjqDq6sD77Td4LFkCv5494TV3LrhnzkD47bfNLZpTsLKz\noY6MBACouncHp5W/sJoaxt278Jk4EaroaFT85z+Ai3btDdmXvc/uxh63brEwbpwPRo6UY+XKKvTp\no0RqqrGC5zA5CBAG4E6NoVOVlA0mCr7d4PHOO/Dr0QPC7duh7twZZT/9hJKTJ1G5ejW4J04AGo31\nSVoorJwcvYJXxsfrvpG086/mtsK6fh0+48dDOmkSqj/5BGDabx+3hlqrxv7b+zEucpzN11y4wMFz\nz3njzTdrsHBhLSgKSEhQ4tIl05mppsw0ahILT6Jo2gVqNQQ//YT7Fy+CFosNTmmCg6H19ta1Veve\nvZkEdI6GCl4bEABwuWDeuQNNWFgzS9ay4Zw6Ba+5c1H16aeQj7Nd+drLmXtnECgMRKRnpE3jDxzg\nYelSD2zYUIknn1Toj8fFqXD7Ngt1dRSEQsMXuKlIGpLsRHbw7QJmQQE0HToYKfd6FImJ4B4/3rRC\nuRBWTg7UUVH6n4kd3goyGdzWroXX3Lmo+OabRlXuAJCSY3vlyL/+4uKjjzzwww/lBsodALhcIDZW\nhbQ04128uR08MdEQ2jwNd7imUCQmgvfnn00okWtpaIMHAFV8PDiXLzejRC0Umgbv0CH4JiaCfeMG\nyg4dgrJ//0a9pUwtw+G8wxjbcaxN4zdtEuLdd6sRF6c2eT4hQWXS0WpKwWsDAsC8f99+odsQTplo\nDhw4gGPHjoGiKISGhmLu3LmQSCRYt24damtrERERgfnz54PZCHY9gu08qgAfRdG7N7xu3gQlkZjd\n5bdUKJkMzPJyaEJC9MeU3bvD/YsvmlGqlgfr1i14fPghGCUlqFy1CsqBA5vkvn8W/Ik4nzj4C/2t\njs3OZiEri43t283nMSQkKLFrl8DoeKRHJHKrDGPhNX5+YJSU2C90G8LhHbxEIsFvv/2GlStXYtWq\nVdBoNDh16hR27dqF0aNHY926dRAKhfizFe8M2wqs27ctKnjweFD26QPuyZPmx7RQmDk5UIeFGTgH\nVd26gZWZqcuOaedQNTVwX74c3hMnQj5sGEoPH24y5Q7oomcmRNoW+759uwD/+IcUXK75MT176iJp\ntFrD4wHCAFQqKlGnqtMf0/j66soGt2OcMtFotVrI5XJoNBoolUqIxWJkZmaiTx9dKvKgQYNw4cIF\nlwhKcBxWdraBjdoU8iFDwDt2rIkkch2mzE+0SARNSAjYN4wbMrc3PN5/H8y7d1F67BjqXnmlSeuj\n/y35G2eLzmJExAirY2tqKKSkCPDSS3UWx3XooIWXlxbZ2YbGBwbFQLh7uEFGK+3pCUqhANWOS0g7\nrODFYjFGjx6NuXPnYvbs2RAIBIiIiIBQKATjQRsvb29vVFRUuExYgmNYs8EDgGLwYJ2j9dGtUQvH\n3NpU3bu3+gQuZ2GUloJ39CgqV66E1sfx3qeOoNFq8NbJt/B2wtvw5HpaHf/TT3wMGKBAYKD1z1/P\nnkrzdvjKBnZ4ioLG1xeMdryLd9gGX1dXh0uXLmHjxo0QCARYs2YN0kz8QplLbMjMzERmg9rdSUlJ\nEDlRhrSp4XA4rUPeigowFAoIo6Is1xPp2hWUuzs88vKgfaTFmilayvp5+flQP/WUkSyMvn0hTE0F\nsxFkbClrtwZn40aox4+HmwuzUm1d+8bLGyFgCzCn9xwwKMv7SK0W2LFDgPXrFTbNPXAgA6mpQrz2\nmqFvL7pDNO7K7xrMQQUGQlRTA40LnldLfu7Jycn6f8fGxiI2NhaAEwo+IyMDvr6+cHPTtd3q3bs3\nbt68ibq6Omi1WjAYDJSXl8PLy8vk9Q2FqKempsZRcZockUjUKuRlX7kCbseOqKmttTqWGjQI2gMH\nUNuxo9WxLWX93Bs3UDttGlSPyMKOiYHnpk2NImNLWbtFVCr4ffstynfuhNqFstqy9jvVd7Dy7Er8\nPPZn1NVaNrkAwIkTXLBYPHTtWglbRI2LY2HDBi8jOYL5wTh977TBcZaPD+S5uZB37Wp9Yiu01Ocu\nEomQlJRk8pzDJhofHx/cunULSqUSNE0jIyMDwcHBiI2Nxblz5wAAJ06cQEJCgqO3ILgAaxE0DWl1\n8fBarVkHsio6GsyCAlAt8BeyKeAdOgR1eDjUjz3WpPelaRrvnHoHc7rNsTmxads2IaZPr7O5YGWX\nLmqUlDAhkRiqrwiPCKOqkho/v3btaHVYwUdFRaFv37545513sGTJEtA0jWHDhmHKlCk4cOAAFi5c\niNraWgwZMsSV8hLsxGoETQOU/fqBfe0aqOrqRpbKNTCKikC7uYF2dzc+yWZDHRsL9tWrTS9YC0C4\nbRvqpk1r8vsm30pGhaICr3V7zabx+flMXLrExsSJtjtCmUwgPl6F1FRDh7HJWPh2HirpVBz8pEmT\nMGnSJINjvr6++Oc//+mUUATXwcrOhmyCbWFqNJ8PZa9e4J48qWvw0MKx5jxWxseDk5YG5YABTShV\n88O6dg2sO3cgHz68Se9bIi3BZ+c/ww8jfwCLYZtq2bFDiKQkGfh8+2oH6erScPDUUw+zXb153tDS\nWkjkEoh5unwOja8vuH//bdfcbQmSydrGsSWCpiH6aJpWgFUF36NHuyxZINy+HXUvv9ykIZEA8MGZ\nD/BC9As2lwSWySgkJ/Mxdap1O/2jJCQYV5akKMpoF6/19wezHe/giYJvy6jVYBUUQB0RYfMl8vqy\nBa2gGiPbSny/6sEOvj1BSSTgHzwI6ZQpTXrfQ7mHcF1yHYu6L7L5mr17+UhIUCEszP5Kpt27K3Hl\nCtsol+1RBd/ewySJgm/DMO/cgcbXF+DxbL5GExkJms0GqxV8rbW2g9eEhgJKJRhFRU0oVfMi2L0b\n8mHDmjTuvVxWjg/OfIBVT6wCj2XbZ42mHzpXHcHDg0ZoqAZZWYbfUiLcIwxKFhAnK6HNYksGqxEU\npTPTtIKsVqvroyjdLr69mGk0Ggh37EDdjBlNdsurpVcxct9IvBjzIvoE2N5M+9w5DpRK4IknFNYH\nm8FUwtOjO/j2ns1KFHwbhnX7NtQ2xLQ/SmsoW0DV1YFRUQFNUJDFce2pdDDv6FFofXygio9vkvv9\n+PePmPLbFHzY50Ms6mG7aQYAvvtOiGnTpGA4oYHqHa0NMYqkaefZrETBt2Ec2sEDUPbvD3Z6Oigb\nkqOaC9bt2zrfgpVKparu3Vtf6WCNxqFCacJt21A3fXojCGSIQqPA2yffxsYrG7F39F6M7jjarusr\nKigcO8bFxIlSp+Qw1eGpPhZeSz8seaD182u3jlbS0akNw8rJgWziRLuvo93cdKaNM2egePrpRpDM\neVg5OTZ9O1E+/rguFl6jaZR2dI2B+/LloNRqVNkRbsy6dQusGzcgG22bsr1bexerUldBxBHBj+8H\nP6EffAW+8OPr/vbkeposM1JYU4gpv0yBv9Afv47/FSKO/an7P//Mx+DBCnh6OufIj4jQQC6ncPcu\nA0FBOoUu4oggYotwv+4+At0CATxwtLbTuvBEwbdhHN3BAw+jaVqsgrdxbbRYDK2Pj+6F0LlzE0jm\nHFRNDQQ//QSawUDV8uU2hzoKt23TRc5YqrXbgDWpa6DWqhEkDEKJrAQ3Km6gWFqMEmkJiqXFUGgU\n6MDvoFP6Ap3SF/PE+OHvHzDzsZmY+/hcuxpoNyQ5WYAlS5zPMK7v05qaykFQkFx/vH4Xr1fw7ThU\nkij4NgolkYBSqaDt0MGh6xWJiRBOnaoLd3DwF7kxYeXkQD5smE1j5UOHQrh5M6q+/LKRpXIeQXIy\nFE8+CWZhIbinTkGRmGj1GkomA//nn1Hy++823SO3KheH8w/j1ORTZis9ytQylEhL9Aq/RFqCYlkx\nto7cinhPx238N26wUFzMNGrH5yj1HZ7Gjn2o4Ovt8AMCdQlu2nZsgycKvolg3r0LqrYW6i5dmuR+\n+j6lDipndZcuoNRqMHNyoHHwW0BjwsrOhnr2bJvG1ixZgg7DhoH7559QtOTSGVothNu3o3L1arDT\n08Hfv98mBc87eBDKHj2gDQy06TZrL6/FzLiZFsv48ll8hLmHIczdsHG5swW3kpMFeO45qcusZQkJ\nSnzyiWGpCqNYeD8/cG/edM0NWxnEydpEuH/8MXwmTgQzN9f6YBdgq43aLBTVcqNptFowc3NtXh8t\nEqFy9Wp4LlkCqgX3J+D+9Ze+XIRs9GjwjhwBFNZ3uoLduyF9pGSIObIrs3Gs8Bhmxs10Vly7Ual0\nyU2TJjnnXG1It25K/P03CzLZw42MUTZrO3ayEgXfBDDu3gX3zBnUzJ8P75dfbhIlo9/BO0FLLVvA\nvHcPtIcHaDtqcysHDoRs5Eh4LFvWiJI5h3DrVl0UDEVBGxgIVefO4J44YfEaZkEBWFlZkNvoK1lz\neQ1mdZ0Fd46JAm2NzLFjXISGahAVZX/mqjn4fCA6Wo0rVx76Kkzt4NuriYYo+CZAuHMnpM8+i7rZ\nsyEfOhTiWbMApbJR72lPmWBzKJ54ApyLF1tckoij305qli4FJy0NvIMHG0Eq52Dm5YGdlgbp+PH6\nY7KxY8H/5ReL1/F/+gnyceNsyla+IbmB0/dOY0Zs0yVCNSQ5WYDJk123e68nJkaFmzcfWptDRaG4\nW3sXaq0awINsVrKDJzQKcjkE//d/qJs6FQBQ/eGHoIVCeCxd2qj1Xlyxg6fd3aGKiwPn7FkXSeUa\nHI0Oovl+IQY2AAAgAElEQVR8VKxdC4/33wejrKwRJHMc4Y4dkD7/vG5L+gD5qFHgHT0KmHvBarUQ\nJCdDaqbZw6Osvrwac7rNgZAtdIXIdlFezsDp01yMGeP6zUJwsAaFhQ+N+jwWD758XxTUFAB4kM0q\nl5v/f2zDEAXfyPB/+QWquDho6nfTTCYqvv4anKtX4bZpU+PcVKUCq7AQ6vBwp6dSJCa2uLIF9lbI\nbIiqVy9In3sOHu+912IKqlFSKfh79kD68ssGx7W+vlDFxZn1g3DOnwctEEDVrZvVe1wrv4ZL9y9h\n6mNTXSKzvaSk8DFsmBzu7q7/Pw8J0aCgwDBexMBM8yCbtT3u4omCb0xo2mR2IS0Uonz7dgi//bZR\nzAXM/HxoAgJsjom2hDwxscU5Wp2J7weAmrfeAisnB/yUFBdK5Tj8vXuh7N0bmpAQo3OysWPB37/f\n5HV656oNkVJrUtdgXvw88Fl8q2Mbg927BUhKcr15BqhX8IZhOUaOVl/fdll0jIRJNiLsy5fBqKw0\nGeqmDQyEZNs2iKdMgSYoCCobGl3bfF8TNupvrn6DoroiCNlCuLHdIGAL4MZ2g5AlxMCggWYzEtWx\nsaDq6sDMy4PGBd8IXIEzO3gAAI+Hyq++gvjFF6Ho1w/agADXCWcvDzYBVR9/bPK0fNQouH/2GSip\nFLRAoD9O1daCd/gwqt9/3+otrpRewZWyK9g4ZKOrpLaLa9dYqKqiMGBA4/idgoPVBiYaQKfgs6uy\n9T9r/PzaZTYr2cE3IsLt23W2dzNBv6pu3VC1ciW8Zs/WtZZ3EY/a36UqKVanrkagMBBsBhsSuQR/\nS/7GicIT2HBlAz49/6n5ySgKikGDWkw0DVVbC6qy0mqRMWuounWDdOpUeHzwgYskcwzO2bOARgPl\nwIEmz2vFYih79AD36FGD47xff4Wib1+bEtlWpa7C/Pj5NpfydTXJyQJMmiRzqrCYJfz8tKiuZlgM\nlWyvjlai4BsJRkkJeH/8AenkyRbHyUeOBM3ng5Oa6rJ7PxpBk1aahse8H8Nr3V7Doh6L8EGfD/D5\nwM/x78R/Y/Owzfg191fI1OYdUC3JTMO6fRuaiAi4QlvUzJsHdlYWOKdPu0Ayx9D3TrVgZjEVTSPY\nvRsyG5yrqcWpuCG5gRe6vOCsqA6hVOrs766MfX8UBgMIDDR0tEZ4RBjFwrfHUEmi4BsJwa5dkI0e\nDdrTfLZgPbIxY8AzY2d1hEd38BfuX0Bvv94mxwa6BaKbTzccyT9idj7Fk0+Cc+4cIJebHdMk0DQE\n338PZY8erpmPx0P10qXw+PhjXTGyJqY+P0JmJUlJPnw4uCdP6qt7MnNzwcrOhnzoUIvXSeQSLDy+\nEO/1fg9cpvP+GEc4epSHzp3VCA9v3P/fkBC1gR0+2C0YZbIy/cZF005t8ETBNwYqFYTff29z6VbZ\nmDHgHzjgMiXz6A7+wv0L6OXfy+z4pM5J2HNzj9nztJcX1F26gHPhgkvkcxThli3gpKai+sMPXTan\nfPRoaN3cIEhOdtmctiL87jtIn30WtNBy2CLt6Qll7966zFYAgj17IBs/HuBwzF4jV8sx48gMjIwY\niYlR9lcUtQelEjh9moOTJ43/bNsmbDTnakMedbQyGUyEikKRV50H4EFv1nao4ImTtRHgHTwIdUQE\n1DExNo3XREVB6+MDzvnzUPbv79S9GRIJQNP6lm1qrRqXSy7j6yFfm71mePhwfHDmAxTVFSFAaNrh\nWG+mUT75pFPyOQr399/htmkTyvbvtyuD1SoUheqPPoJ4xgzIxowB7ebmurktodFAsGcPym18sdRH\n08jGjQN/zx5Itm83O1ZLa7HoxCL4C/3xbq93XSSwaaqqKMycKUZlJQNisbEfydNTi9GjG/+b36Ox\n8IDODp9blYsYcYzOyUps8ARXINy2ze62aZbC4eyBlZ2ti6B5YNO9LrmOILcgiHlis9fwWXyMihiF\nvbf2mh2jSExsNkcrKzMTnm+9BcmWLdAEB7t8flV8PBQDB8Lta/MvQVfDOX8eWh8fm8M95c88A865\nc+AdOgStWAx1bKzZsSsvrsS9unv4atBXYFCN9yt+9y4DEyb44LHHVDh8uBTJyeVGfzZvroBQ2Pj5\nBqGhlmPhiYmG4BJY166BefeuzbVB6pGNHauLiVernbv/I/b38/fPo5efefNMPZM6TcKeW3tAm0n+\nUXXrBkZZGZh37zoln70wioshnjYNVStWQOUq27sJqt99F8Lvvmuy9fH374ds7Fibx9MiERQDBsDj\n3XctOle/v/49DuQewLantzVq1ExWFgvjxnXA889L8ckn1c3eSyU4WG0UCx/hEYGcqhwAOjNje8xm\nJQrexQi3b9dlJLLss35pwsKgCQ4G98wZp+5vyv7e29+0g7UhCX4JUGlVSC8107+UwWiUZtyC7dt1\nc5pw4FIyGcTTp0M6ZQrkdihDR9AGBqJu+nSI7Oii5DBqNXgHD0I2Zoxdl8nGjgWjthayCRNMnj9W\ncAyrUldh5/CdFr+xOcvJkxw8/7w3li2rwqxZdY12H3swlewU4R6B/Op83Q8UBU2HDu0uVJIoeBfD\nO3bMrp1ZQ2RjxzodTdNwB0/TNC7ev4g+/ta73VMUhaTOSUi+ad4m7GoFT9XWwuOTT+C2fj384+Ph\nNX06BN9/D0ZREaDVwnPBAqijolC7cKHL7mmJ2rlzwT13DmwXhqyagnvmDDQhIdCEhVkf3AD58OEo\n37kTWrGx8s4sz8TC4wuxedhmdPRwoky0FX78kYXXX/fCf/9bYdBko7np0EGL2loGpNKH4abh7uF6\nJyvwoGxwOzPTEAXvQhhFRYBSCU1oqEPXy8eMAf/QIacqTTbcwedV54HJYCLIzbakoOc6PYf9t/dD\nrjb9i6sYPFj3DcNFlTCZeXlQR0SgPCUFxWfOQD52LDhnz8J32DD49usHRnk5Kr/8ssk6StECAarf\neUcXNtmIdWp4+/fbvXsHAHC5UD7xhNFhjVaDWUdn4dP+n1qMlnKWnTsFWLGCiz17ytG3b+NWQ7UX\nBgMICjJ0tPoL/VGlqIJUpYviaY9lg4mCdyGc9HSo4uMdVkiaoCCoIyPBPXnSMQGUSjDv3YP6wc7w\nQrHOPGNr78wgtyDEecfh9zumW79pvb2hjohwWVIWKy9PXxCNFoshmzABlV9/jftXrqBi0yZIduyw\nuZ6Oltbi6J2jZn0ItiJ77jlArQb/55+dmscsSiV4v/3mmII3w+H8w/Dh+2Bc5DiXzfkopaUMrFwp\nQkqKFJ07O+cnaixCQw3t8AyKYRAqqSE7ePuQSqVYs2YNFi1ahDfffBO3bt1CbW0tVqxYgYULF+Kz\nzz6DVNr4MbAtBXZamtNJOM5E07CvXNGl8D9QihfvXzSb4GQOq2YaF1aXZJmrb8NiQdWjh13hkN9d\n/w5TD0/FHwV/OCcUg4Hqjz+G+4oVYJSXOzeXCbgnT0ITGQmtk6UWGrLl2ha8EveKy+YzxZdfijBp\nkgydOrWMCpymCA42tsOHezw002h9fdtdqKRTCn7btm3o3r071q5diy+//BJBQUHYt28funbtinXr\n1iE2NhYpdlTs45w754w4zQ4nLU23g3cC2ejR4P3+u/1ZowoFPN97DzULFugPnb9/3iYHa0NGhI9A\nanEqiqWmdzrywYNdVraAmZsLdUSE0/PkVuVi1aVV+KD3B/jXxX9Bo3UuYUzZpw+kzz4Lr5kzbWqZ\nZw98R80zZrhaehWFtYUYET7CZXM+SlYWC4cP8/DGG473Ym0KTJUNDncP1ztaNX5+YLazgmMOK3iZ\nTIYbN24g8UGlRCaTCYFAgEuXLmHQoEEAgMGDB+PixYs2z+m+fLlLi241KRoN2FevQulkVUitvz9U\nMTHgWWnV9iiiNWugDg3VmRgAlMnKUCYrQxcv+5p8C9gCjAgfgZRs0y9mVffuYN67B6qoyK55TdHQ\nROMoGq0Gi04swsLuCzG722y4sd2QkuN8GeCad96B1tcXnosXu84eL5eD9/vvkI0e7Zr5AGy+thkz\nYmeAxbAvaisjg43Ro31QUWHZfEfTwPLlHli0qAYeHi139w4YlysAdAo+t1rXB1nr70+iaGyluLgY\nIpEIGzduxDvvvIP//Oc/UCgUqKqqgueD+iuenp6orq62fVIWC/yffnJUpGaFlZ0NrY8PaBMRDvYi\nGzMGPCut2hrCvnwZgt27UbVypd7+f/H+RST4JYDJsD9Aud5MY9KezWJB8cQTYP7hpCkED0w0Tu7g\nN1/bDBaDhZlxM0FRFJb2XoovL30JhcbJnTeDgcp168C6fRtuX33l3FwP4J04AVVMDLT+/i6Z737d\nffxZ8Cee7/K83dfu2cNHWRkDr74qtugz//13LoqLGXjxxZZvajWVzRrhHvHQBu/r2+6crA6XKtBq\ntcjNzcXMmTMRGRmJ7du3Y9++fTZfn5mZiczMTP3PSUlJUH3xBTxefhms558HrNTnaG44HA5EDWzE\nrBs3QPfqZXCsIXtu7EGNsgYzulnPcKUmTwb/iy+gYbEMWriZRCqFcNEiKFavhrBBDfg0SRqeCHvC\nrDyWGOo2FIqTCmRLs9HD34RPYcQIcP74A6IXX7R7bj11dWBUVkLQubPDlSGvl13H11e+xvEpx+Hh\n7qGTXTQUcZlx2H17N+b1mOe4fAAgEkG5Zw/chgwBOyYG6gdFwR599rbCO3QImkmTHLrWFF9d/QqT\noichxMe4UYgltFrg4EEBfv5Zho8/5mDZMh9s2KAwig1QKoHPPhPi88/l8PLSyezo2puCxx6jUFjI\nMpAvNjAWd07d0R2LjASrpMRh+Vvy2pMblLyIjY1F7INMZ4cVvFgshre3NyIfhOT17dsX+/btg6en\nJyorK/V/e3h4mLy+oRD1VMXEgOrdG/jiC9QsXuyoaE2CSCRCTc1Dm6TH2bOQxsWhrsbYTnmn+g4W\n/6Fbz/Cg4Waba+jh88Hu2hWqn3+GfNQoi0PdP/oI8rg4VA4dCjS49+mC01jWZ5mBjPbwXNRz2J6+\nHZ0GdDI6x+jbF35Ll6KmosLuhK56WNevgxcSgpo6xxJlVFoVXj34Kt5JeAfeDG+DdS7uvhjPH3we\n48PGw53j7tD8egQCSLduhffkyaj19obqwUvc7v9XmQzCw4dR/v770Dr4TAymU8uw9cpWpIxJsVuW\nc+c48PTkISioCl99RWHCBB+sXElj3rxag3FbtggRFKREv36V+o+WQ2tvIng8oK5OgPv3a/XlETzg\ngRJpCUorSsFjc+EmlaKmpMT6xskELXXtIpEISWaymx020Xh6esLb2xv37t0DAGRkZCA4OBg9e/bE\n8Qc1S44fP46EhAS75q1ZuhTCbdvAeDBva4GdlgZl9+5Gx7W0Fm/99RbmPT4Pg4IH4bus72yaz5Zo\nGs6ZM+AfOICqFSsMjktVUvxd8Tce7+C4P2BMxzE4kn/EpJlG6+cHbUgIOGlpDs/PctLBuiF9A8Q8\nMaZETzE6FyOOQWJwIr65+o3D8zdE/dhjug5Qs2aBeeeOQ3Pw/vwTqm7dbGrQYQsp2SmI7xCPSE/7\nO1vt38/H2LG6lH2hkMb27eXYulWIgwcfljaoqKCwfr0bli2rbqo0BKehKONIGhaDhUBhoK4Bdzvs\nzepUFM306dPx73//G0uWLEF+fj4mTpyI8ePHIyMjAwsXLkRGRgbGjx9v15yaoCDUvfwy3D//3OI4\n9sWLoCoqnBHfdchkYGVnQ2WiANR317+DXCPHrK6zMD9+PjZf22yxuYZ+yhEjwD1xAqwbN0w6+aja\nWni++SYq//Uv0F5eBudSS1IR6x3rVC2SSI9IaGgN7tSYVmjqp55yKlzSbIikDWSUZWBb5jasenKV\n2Rj/xT0XY0fWDpRIXfPLrBg6FLXz50M8dSpgj1/pAfbWnrEETdO60Miu9odGqtXAr7/y9AoeAAID\ntdi2TYJ33vHAlStsAMDatSKMHClHdHTLjHk3h8mSBR4RDx2t7UzBO1UuODw8HJ+bUMQfOlmvu3be\nPPg++STYaWlQPbIrZhYUwH35cnD/+guKAQNQsXVrk2U6moN97RrUnTvrviM2IL86H6surcK+sfvA\nZDARLY5GD98e+L8b/4cZcZZt8bRYrFMoL70EsFiQDx0KxdChUPTrB/B4cP/0UygGDIDiqaeMrrW1\nPIElKIpC34C+OFt0FmHuxin1mmHDwH33XdS8/bZD8zPz8ky+EK2h0CjwxvE38FHfj8yWNgaAYFEw\nJnWahK/SvsI/B7imvkzdjBlgX74MztdfA6+/bvN1VF0duCdOoNLKpsVWTt47CQoUngg0zmq1xtmz\nHAQGaowacHTrpsKXX1Zhxgwx1q6twN69fJw4UeoSeZuSkBBjR2vDkgUaf/921Zu1RWay0m5uqFmy\nRBc2Wb97lckgWr0aPiNGQBUXh+LUVLAKCsDfY75RRVPBMfEiqjfNvB7/OqI8H1Z3nB8/H5uuboJS\nYz3Vu3bePJRcuADJ1q3Q+vvDbd06+MfHQ/zCC+AeP45qM42aLxRfsKmCpDX6BfTD2aKzJs9p+vQB\nKy8PjLIyh+Zm5ebaHUFD0zQ+PPMhOnp2tKmJxYLuC7A/Zz9yq3IdktEUNW++CfZ//wvKDlss9+hR\nKBMSXBJhBQBbMnSJTbZmKDfkl1/4Brv3hgwfLscrr9TiH//wxrx5tfD2bn0hy9Zi4dvaDp7z118W\nz7dIBQ8A0qQkMKRS8H75BbyDB+E7eDBYt26h7PBh1L7xBmiRCBXr1ukyDpu4hO2jsNPToXwkwWlH\n1g4oNUq8GveqwfHuvt0R6RmJvdnma68bQFFQx8Sg9vXXUb5vH4rPnIFs8mRIdu40memp1qqRVpKG\nBD/7fB+mqFfwJsMl2WwoBgxwuEa8Izb4rZlbkVqcirVPrrVJuYl5Yrza9VV8cekLo3NqrRplsjJ9\nnRJb0XTsCM2QIRB+Z5svBXBtclNOZQ6ulF3B+Cj7TJ8AoFIBhw7xMGaM+SS62bPrsGlTBWbMaBlV\nIu3FVNlggx18G6tHI7LSw6DldnRiMlH10UfwfvFFqDt2ROWaNVAOGGAwRB0bi7pXXoHXW2+h/P/+\nr9lMNZz0dNQuWqT/Oa86D6tTV+tNM4+yIH4B3j75NiZ1mmR3nDotFuvatZkhszwTQW5B8OJ5mR1j\nK5EekVBqlCioKUCou3EBNcXgweAeP65PrrIZmQwMiQSawECbLzlecBwb0jdg/7j9cOPY3nXp1bhX\nMTB5IF44+AKqldWokFegQlGBOlUd3DnuYDFYWNp7KSZ1mmTzjli5eDGEo0ejbvp00AKBxbGM8nJw\nT59G5Zo1FseptCqUSktRIivR+w1CRCEIdgs2iLr6NvNbTImeAj7L/iiQU6e4CA/XIDjYfKYvRcHi\nC6ClY8oG/6iC52ZnN4NkrodZWAj2tWsWx7RcBQ9AOWAAypKTdeYPM+F4tXPngnfkCATffQfp1KlN\nLKHuF5hRUaGv4KiltXjrhLFppiH9AvpBzBPjQO4BlxeIsrX+uy1QFKXbxd8/a1rBJyZCtHKlrpes\nHR0fWHfuQB0cbPM12ZXZWHB8AbY8tQUhIvtivgVsAZJHJSOvOg9eXC948bzgxfWCB9cDDIqB9NJ0\nvHfqPez+ezc+H/g5Ont1tjqnNiYGyl69INi1C3Wvvmp+IE3D4733IP3HP0A/Ei5M0zSWnV2Gc0Xn\nUCIrQaW8Et58b/gKfOHL9wUNGoU1hSioLQCXyUWQWxCC3YJxrugc/nzuT7v+D+ppGD3TVjFlogkR\nheBe7T2otCpw/fzajImGv2cPZOPGwVLGUItW8ACg6mXFlsxiofKrr+A9YQIUgwY5HJnhKOz0dKi6\nddMn6+zI2gE1rTYyzTSEoigs6L4A/7r4L4ztONYhW6o5LhRfcGldkr4BfXH23llM7jzZ6JwmKAha\nHx+wr1418kFYwp4Imgp5BaYenoqlvZc6/OKK8owy+7KN7xCPA+MOYOf1nXj2wLN4ocsLeKP7GxCw\nLe/MaxcsgHjaNNS99JKRc70e3v79YN28iYr1643O7bi+A6nFqVgzaA18Bb7w4fmY/DZH0zQqFBUo\nqClAYW0hpsdOh7/Q/kxYhQI4coSHt9+2PwKoNeHtrYVCAdTUUBCJdKZFDpMDP4EfCmsK0amtmGi0\nWgj27EHFpk0WFXyLtcHbgzoqCrULFsBz0SLdbrIJ4aSnG8S/b7m2BZ/0+8Sq6WVoyFAwKIbZ0ryO\nQNO0bgdvZwVJS/QP6I9zReaLwDnSq5WZm2tTDRqVVoXX/ngNT4c97VA6vs3yMJiYFjsNfzz7B+7V\n3kPiT4n4447lUgyqrl2hio2FwEzTbEZxMTyWLUPlV18ZvQBuVdzC6tTV+Hfiv9HVpyv8BH5mPy8U\nRUHME+PxDo9jVMQoPBFkf+QMAJw4wUWXLioEBLQ+x6k9UJSZSJoHVSW1bWQHzzl/HjSPp9tcWqBN\nKHgAqJs5E2AyIdy8uUnv2zCUs1hajEpFJbr6dLV6HUVRmB8/H+vT1ztdw7yeW5W3wGFwECxyXWPq\nKM8oyDQyXaKICeSDB4P3p30mA1Zurq4xuAXqI2a4TC4+6P2BXfM7iq/AFxuGbMCqJ1fh9WOvo1xm\nuVxwzYIFukbdKpXhCZqG59tvQ/rii0bVRZUaJeYfn4+3E952KEnJUSxFz7Q1TJYNfmCH13p5gZJK\nW31vVkFyMqRJSVb9jm1GwYPBQOWaNXD7+muwbt5smnvSNDhpafoImvNFugbXtnayHxk+EtWKapy6\nd8ol4vxw4wdMiDLdr9NRKIpCX/++ZsMllX36gHXzJiiJxOY5rZloKhWV+Nelf+F80XlsHLLRoYJp\nzvBE0BMYEjIEv+RaLvimSkiAJjwc/L2GEVH85GQwi4pQY6LV4OrLq+En8MOL0U7U8bETmQw4epSH\nkSNbr/PUHsyFSuZV5z3szVra+mL866Hq6sA7fBiyidZDhduOggegCQ1FzbvvwmvOHLvilB2FmZ8P\nrUAArZ8fAJ2D054EIyaDiXnx87AhfYPTskhVUuy5tQcvxbzk9FyP0i+wn3kzDZcLZd++dnWhYpop\nE3yt7BoW/7UY/X/sj7s1d7Fz+E7rdXsaiQlRE8yWTG5IzcKFEK1frzcNMgsL4b5iBSrWrQM4HIOx\n54vOI/nvZKx+crVL/S7WOHaMh7g4FXx927Z5ph5TZYMbVpVs7b1ZeQcOQNm7N7S+vlbHtikFDwDS\nf/wDyl694DV3ri4vuxHRt+h7gCMNNiZETkCWJMusCcRWUnJS0Nu/t91RJrbQz998whMAyBMTbW8C\nolCAWVICTbDOjKTQKLA3ey/G/jwW045MQ4goBCcmncCGIRtcamqyl0HBg5BblYs71ZZrzyj79YOm\nQwfwf/lF1yj8zTdRN2sW1DExBuOqldVYcHwBvnjiC/jwfRpTdCPaQ/RMQ6xms/r5tepsVr15xgba\nnIIHRaHq008BtRruZjI9XQX78mW9/b1KUYX8mnyb7O8N4TA5GBUxyqbdojlomsa2zG2Y9tg0h+ew\nRGevzqhT1eFuremEMr2j1YZmLaw7d3Tx7ywWtLQWQ34aguSbyZjTbQ7OPX8OC7svRAeBawpyOQOb\nwcbojqOtNw+hKNS+8Qbc1q+HcPt2UFIpaufMMRr2wekPkBiSiKfCjEtLNCZSKYXjx7kYNap9mGcA\n07Hwoe6hKKgpgEaradWOVmZeHlg3b0I+bJhN49ueggcANhsV33wD7qlTEGzb1mi3aRhBc7H4Ih73\neRwcJsfKVcY8G/Us9mbvddjZerH4IhQahcUIi4sXOcjPd8yWTVEU+gT0MV+2ICwMtJsbWFlZVudi\nNnCw5lblQqPV4MeRP2JExAi7uxI1NhMidWYaa89FMWgQaB4Pos8/R8VXXxnlbOzP2Y+00jQs67Os\n0WS9eJGN//2Pb/RnzRoRevRQQixuH+YZoH4Hb/gM+Cw+vHheKKoratXZrII9eyCbMMHI/GeOlvUb\n5UJoDw9IvvsOPuPGQRMWBsWQIa69gUoFVlaWPkzpwv0L6BPgWIGvBL8EyNVyZJZnIs4nzu7rt2Vu\nw9THppp17tbWUnjxRTF4PBpeXloMHarAsGFyJCQowWbbdo/+Af1x9t5ZPNfJdNaqfMgQ8P78E7Vx\nluVv2KbvWvk1u7/xNCX65yLJRJy3hXVRFKpWrACzqAiaKMN4e4lcgg/Pfogdz+ywGlvvKLdvMzFt\nmjeGDDG9S3/zzZZXw7wx8fLSQqkEqqspuLs/fDlHuOuqSnby8wM3J6cZJXQQrRb8PXsg2brV5kva\nrIIHdE5XyX//C/HMmSjfvdvILuoMjGvXdDvXB52nzt8/j7d6vuXQXBRFYULUBPwv+392K/hiaTFO\nFJ7Avwb+y+yYI0d46NNHie3bJbh6lY2jR3n45BN33LnDwhNPKLB4cTWioiznD/QL6Ict17aYPa8Y\nPBhuGzagtkHTb1Ow8vL0Wb8ZZRkOvdCaCoqiMD5qPFKyUywreACqHj2gMnE8+WYyBgUNQnwH55qx\nW2LFCnfMnVtr1LCjvVIfC19QwERs7EM/nD5U0i+kVTpZOadOgfb0hNrKJqohbdNE0wBVr16oXr4c\n4mnTwHCh3Y2Zmqo3z8jUMmSWZ6Knb0+H55sYNRE/5/wMjda+RK1d13dhbORYeHBNd84CHjrZGAwg\nPl6FxYtrcOhQGf74owTu7lps22a9tktnr86oUlThXq3pRiyKfv3AvnYNVFWVxXkaRtBklGW06B08\noDPT7MvZBy1tv4mDpmnsurGrUSKb6jl9moOsLDZmziTKvSGmzDT1Cl7j6+tSXdBUCPbssdm5Wk+b\nV/AAIJswAdLJkyGeOhXMXNeUjmWmpuojaNJL09HFqwuEbMf7yHby6gRfgS9OF522+RqVVoVdN3ZZ\ndK5WVVE4d46DZ54x/vru769FUpIUly5Zt9MwKAb6BfTDuftmwiX5fCh79QLXSvnS+iqSNE3rTDTe\nLVcJG34AACAASURBVFvBdxF3gZgrtpjNa44zRWfAZrBdUtnTFBoN8PHHHli6tNpctYR2i6lQyfqy\nwVp/fzCLikw20mmpUNXV4B09alPse0PahYIHgNpFiyAbNw4+Y8ZA9PnnoBzsBVoP49IlgwQnVxT4\nmhg10a5omkO5hxDhEYFocbTZMb/9xsOAAQp9XY5H6dpVhZwcFurqrMdl19elMYd81CjwLTVeVyrB\nvH8fmuBg3K29Cx6T1yIiZqxh73OpZ+f1nXgp5qVGi3lPThZAKNS26uqPjYW1bFatl5dNQQEtBf4v\nv0AxYAC0j/QUKJVaTthqdQpeIpfgUO4hfHLuE2RX2lH2k6JQN3s2So8eBfPePfgOGqRTRg68xZl5\neWDcvQt1tE6x2pvgZI5xkeNwOO+wTS39AGB71nZMfcxyBU1rKepcLhAbq0ZamvVdfL+AfjhTdMbs\nedmYMeCeOQNGuekUf2ZBATQBAQCHg4yyDMR629/R6VE0GiA1lY0vvhDhlVe8jKoGuIJxkeNwMO8g\nFBqFzdeUycpwovCETY1JHKG2lsKXX4rw8cetp2dqU2IqVDLMPQx51XmgAciffhq8I0eaRzh70Wgg\n2LnTpHnmo3MfWby0RSt4mqZRLC3GL7d/wfun38fQn4ai34/98P2N73Gr8ha+vfat3XNq/f1R+e9/\no2LTJrht3AjvZ58FKzPT5uupqiqIp06FYvlygMWCWqtGakmqS3bwfgI/PN7hcfyeb70AWVZ5FvKr\n8zE8fLjZMRIJhUuXOBg2zLJiSkhQ4tIl62FX0eJoVCoqUVRXZPI8LRJBPmyYUep+PQ0jaDLKHbe/\nV1dT2L+fh4ULPdG9ux+WLPGESgXk5LBw4YL9YarWCHQLRIw4BscKbO9Bu/vv3RgRPsKib8QZNmxw\nw4ABCsTHN8IbrQ1gqlyBiCOCkC1EsbQY8meeAe/w4WaSzj6EmzeDdnOD4pHY98zyTJy5Z37DBbSw\nKJqv079GYW2h7s+DWtg8Jg89/XqiX0A/TOo8CXHecWAxWCioKcDIfSOxvN9yh2LPlb16ofTQIQh2\n7YL3Cy+gds4c1M2ebbl4j0oF8axZurjnV18FamqQVZ6FAGEAxDzz7djUauDjj90REqLBa69ZNg1N\niJqgy+yMtNygeXvWdrwY8yLYDPM770OH+Bg0SAGh0PK3lIQEJX74wXoIH4NioK9/X5wrOofO/qbr\npksnT4bH8uUm66SzcnP1NWgyyjIwJXqK1Xs+yp9/cjFnjhd691Zi6FA5Fi+uQUiIzjEtFNI4fJiH\nAQOst0O0l/rnYumFWo+W1mLXjV34eojlbjuOUljIxM6dQvz+e+tzFDYVISFqo2xW4KGZxr9XLzAL\nC8G4exfaoKBmkNA2WH//DbcNG1B28KC+JHk9Ky+uxOvxlnsDt6gdvEQhQWevzng55mVsHLoRaVPS\ncO3la9jxzA7M7jYb8R3i9ckwIaIQRHlE4XjhccdvyGRC+vLLKP3tNwh++gnuy5aZLzdM0/B4/33Q\nXC6qP3r4tchaeQKplMKMGWIcO8bD779b94SNCB+Bc0XnIJGbL95VpajCgdsHrCpIW1PUe/ZUIjWV\nY0siKvoFWi5boOzfH1R1NVgmOs0w8/L0bfqulTkWA79tmxD//GcVdu6UYNo0qV65A8Azz8hx5Aiv\nUXxnoyJG4a/Cv1CttF5P/eTdk3DjuDVaaOTnn4swfXodAgPbT/KSvXh60tBqdUEGDdGXLGCxoBg6\ntGWbaVQqeL7xBmrefReaUMOGOxfvX8SNihtWI7RalIL/sM+HmB47HU+FPYUYcYzVQlMTO020vbep\nBbSBgSjbuxfsGzfg9dprJkuJCv/zH3AuX0bFxo0GnYgs2d9LSxl47jlv+PhokZJShsxMtlUlKuKI\nkBiSiF9um65kqNFqsCp1FQaHDIavwHyxodJSBq5eZZtNfmmIr68Wnp5aZGdb/0JnqRE3AIDBgGzS\nJJN10utNNMXSYqi0KgQKbW/ZBwD37zOQmsoxWxUxOloNigKuX3f9F1NPrif6B/bHobxDVsd+f/17\nvBj9YqM4V1NT2Th3jou5c0lYpCUaxsI3JNw9HLnVukg6+TPPgN+CzTRuGzZAKxZDOsVwI0fTND6/\n+Dne6vEWuEyuxTlalIK3l9ERo3G84DhqlM5n6tEeHij//nvQPB58Jk8Go0H5W97hw3DbvBmSHTtA\nuz2MGadpGufvnzep4HNymBg3zgdDhyqwenUlfH21cHfXIi/PerkAc1EbZbIyTPltCq5LrmN53+UW\n5/j1Vx6GDpWDb2Przp49bbPDx4hjUCGvQH5Vvtkx0kmTwE9JAZSGphJWbi40ERH6+Hd7FeD//ifA\nqFEy8Pmmt+gUBTz9tByHDzdOzKAtFSbv193H6XunG8W5StO6sMh33qmGQNB6Qvyai+Bg41j4CPcI\n5FfrPruKQYPATkuzmrvRHLAzMiDctg2Vq1YZmY2PFR5Dubwcz3Z61uo8rVrBe/G80D+wPw7mHXTN\nhFwuKtevh6JvX/iMGwdmfj7YGRnwWLwYkm+/heYRW11OVQ64TK5R1cNLl9h49lkfzJ9fi7feqtE/\nn27dVMjIsB6tMjhkMHKqcgwqGZ4vOo/hKcMR3yEeP4780Wp4ob0NHmx1tDIoBsZGjsXu67vNjtGE\nhUHdpQt4vzdwFqtUYN67B3VIiEMJTjQN7N7NR1KS5TXVm2kag2Ghw5BRloHcSvO5FD/+/SNGdxxt\nV2NwW9m1SwCtFnjuufZTGdIZQkLUuHPHdCQNANBCIZR9+theCbWpUCjguXAhqj/6CNqAAINTWlqL\nlRdX4u2Et22q3dSqFTxge91um2EwULN0KWpnzoTPhAkQT5+Oqs8/N+rMA5g2zxw5wsX06WKsWVOJ\nF16QGpzr2lWFK1esK1E2g40xHccgJScFWlqLjVc2YtYfs7By4Eq82+tdqw/2/n0GbtxgY9Ag28P6\ndAretsI0SZ2TsCtzl8UiXNKkJAMzDbOwEBpfX4DLxbWya/rU/xs3WDbZzC9fZoOmKSQkWHag9u6t\nREEBE/fuuf6jzWfx8WaPNzHsx2HYn7Pf6LxGq8EPf/9gU+YqTes2AnIbQ9jz85lYuVKEtWsrH/W1\nEcxgtmxwVZ7+s9sSo2lEq1dDHRFhMqnpwO0DYFAMjAwfadNcrf6jUr+rul/n2vrO0mnTULlyJf6/\nvTOPi6pe//h7FoYZhs0BKSQVzR3R3HEDRQ0zzR0zb4ren91S05uaZqapuXTNXCrNMhfqlqmlptdc\nSsElF0RAzSUR0NyVfYCBYZjz+2MCIQaYgVEWz/v14qWcOduXc+Y53/Msn0c7bRrZ/fubXcdcgHX+\nfBfWrk0hMLC4cW3VKpdz5ywzooMbDWbblW2MOzCOn6/9zM+DfqZXvV4Wbbtnj4o+fbKxL909V4Rm\nzQzcvy8jOblst0m+aubpe6dLXCf7xRdRnD5dUBIuv3aNvL8CrPkpklqthN69a1s0496yxYHg4Kwy\nc77lcggMzHlks/h/tvwnWwZuYdmZZUw8NJGU7JSCz8JuhlFbVbvMt5O4OBmjR2sYOdKNqVNdy3zA\nGY0wdaorEydm0KTJo+1xUJPw9jYUiyvVUtZCJpUVJDFk9+mD/eHDxdyJlYVdZCQOW7eS9uGHxVwz\nBqOBj858xDsd3rHYvVntDbxKriKofhA/xf1k833n9OlD1siRJX4ecTcCP0+/gt8TE6WkpEjp3Nn8\nzdKqVS6//152oBWgvUd77KR21HOux/b+2/FytDyVqzwNHmQyk05NVFTZbxgSiYRRPqPYdmVbiesI\najXZffsW5MTna9AkZyeTnpNOfef6nD1rh4uLwIIFzqV+v3Q6CXv2qBg6NKvklQrxKN00AO0927N/\nyH7clG703t6b8BvhwMPgaklkZEhYtMiJgQPd6do1h6ioe9y4Iefjj0tPJli/Xk1eHowfX7Hq6ycN\nH59cLl4sPqEqHGg1enhgePZZ7E+UkjhgY2TXruG4apXZn1pTppC2cCHG2sVdsFuvbOUph6fw9/K3\n+FjV3sCDKShpi2waa7itvY1Wr6Wxa+OCZWfOKGjTRl/iK7RGY3mgVSKRcHDYQRZ0XmBVnv+tW1Li\n4mR062a5eyYfS/3wACOaj+Dnaz+XWnWbFRyMw5YtIAgmDRpvb5N7xr0lUomU6GgFwcFZNGhgYNOm\nknV89u1T8txzeovTAnv0yOHMGQXp6Y+uxFMlV7GgywJWBqxkxrEZTAmfwul7pxn47MBi6woCbN+u\nIiDAg3v3ZBw8+IDXX8/EyUlgw4ZkfvhBxfbt5qPhV6/KWbXKkRUrUgsnb4lYQJ06RgwGuH+/6Bey\ncHcnePxuGpf330f+xx9IdLpiPxkTJpj1GGQbslketZxZHWZZlZxQ4Xwyo9HIrFmz0Gg0zJw5k/v3\n77Nq1SoyMjJo0KABb775JrJHfGd29uxMoi6RKylXaFLLfAGOrTl+6zgdn+5Y5I995oxdmT7i/EBr\nw4Zlq0Za2ry7MLt3q+jbN9vSfgBFaN9ez2efWRYc9HT0pK1HW/Ze21tixoi+Uyckej12Z88iT0hA\n37WrSSL4L/97TIwdL72kY+TILIYOdWPYsCw0muL+ii1bHBg50vLZq1ot0LGjnrAwewYOLNnJffas\nHSdOKAgJySy3WFd3r+78OvRX5p2YR0iLkGKa7zqdSYs/M1PC2rXJdOhQtPK0dm0jmzYlExzsRt26\neXTo8PD+MRjg3/92Zfp0LQ0aWKcyKmLycPj4GPj9d7siLlNzBt5t5EjSFi0qvdDRBsiuX8cuKop7\nERFYnOIGfH3pa3zdfWn3lHWKtRWewf/88894Fcou+fbbb+nfvz+rVq1CrVZz6NChih6iTGRSGYMa\nDXqss/h8A1+YyEgF7dqVbeDPnbN9OX0+puyZ8olPtWmj59w5O4v1XIY1HlaqmwaJhKzhw3HYssWU\nA9+gAeeTHmrAx8QoaNMmlyZNDAwYkM2KFcVdFbduyTh/3s6sGmZplOWmycyUMGFCLQ4cUBIY6MGB\nA/blLpByVjizPGA5b7d/u9hnR47YYzTCnj2JxYx7Ps2aGVi1KpXXXqtVpOvWmjWOqNUCo0db5poS\nKY6PTy4XLhR10+QHWvMxNGqEoFRid/78Iz8f9aZN6EaMsMq438u6x2cxnzGz/Uyrj1chA5+UlER0\ndDS9ej0M/v3+++906mTKLAkICCAiIqIih7CYwY0Gs/Nq6brdtzJucT7xPCfvnOTXP3/lp7if+O7y\nd2z4fQP3s6wr+z5x60SRDBq9Hs6ft6NNm9KtozWBVmu5cUPGjRsyunSx3j0D4OIiULduHpcuWXZ+\nQfWDOJd4rsRerQC64cNR7dqF7NYtDPXqmVIk3Xy5fVtKbi4FlajTp2vZuVNVLCi2bZspnmDtDLtP\nn2zCw5UlPqwWLXKmQwc927cnsWRJGosWOfPqqxquXrXt22ZYmD1BQdlluld69sxhyhQto0drSEuT\ncOGCnHXr1CxfLmbNVISWLUsw8NprDxdIJGW6aVTff49daGiFzkWSlYVq2zYyx5QuEFgYQRB499i7\njGw2slTV2JKo0K0TGhrKq68+lEPVarU4Ojoi/euOdHNzIyUlpbRd2AwfjQ8Odg5E3oss9lm6Pp0Z\nR2cQtD2IaUemsTRyKaEXQ9l7bS9R96OIvB/JsP8N416WZV1eztw7w430G0W6EV24YIe3d16Jsrz5\n5LtoLAm0WsvBg/b06pXz95agVmFpwROAUq5kQMMB/Bj7Y4nr5Hl5kevrS56bG+lSPfey7vGs67PE\nxCh47rncgjdijcbIxIkZLFjgXLCtIMC2babsGWt56ikjDRoYOHGi+FiOHLHnl1/smT/fVOASEJDD\nL788oFu3HAYNcmfhQmcyMir+qi4IJgPfo4dlD9yQkCwCAnL41780TJlSi/feS8fLS3TNVAQfH1Ni\nQ2G8nb1JSEsoMhks0cALAk4ffojzf/6DYsWKCmnIq7ZvR9+xI3l161q8ze743cSlxfFWm7fKdcxy\nG/ioqChcXFzw9vYuyCkVBKFYbvSj0sL+O/lt7/7upgm7EUavH0xvGCdePsGBIQfYPmA73/T9hrW9\n1rLMfxlrAtcwtPFQgvcElzmTP/jnQUIOhLC+3/oiQl+RkYoy/e9gXaDVWsLClPToUTFtcGvy4QGG\nNx7O1itbS82Jz3zlFQxNm3Ih6QLNNc2RS+XExNjRpk3Rv9fYsZnExck5csSU3xkRoUChEMqtmGjO\nTZOeLmHaNBeWLUvDxeXhOSsU8PrrmRw8+IAHD6QEBdXGUMGMxLg4OXl5Epo2tXxH77+fjr29QN26\nhjKLukTK5tlnDdy5Iy3ywHZXueOp9iTi7kPvgr5dO6T37yP782FxIXo9rlOmYH/sGA9++QX0euSx\nseU7EUFAvXEjmSEhFm+SpEti7om5fOz/MUp5+YJE5Z7rXb58mcjISKKjo9Hr9eh0OjZt2kRWVhZG\noxGpVEpSUhK1atUyu/2FCxe4UEimNzg4GCen0tPFyuIfrf+B/3/9WfH8CrJys5h9eDZHbhzh876f\n07N+z1K3fc//PewUdry892X2DN+Dh7q4zsvmi5uZc3QOWwdtpZt3N/SFcvvOnlXywgsGi8bQtq1A\nbKwzrVvbLqc5OxtOnbLnq69ycXIqvwsoIEDC8uXKMsehUChwcnIiwDEA+VE5lzIu0alOCZr4r7xC\n7rBhXPl9HW092+Lk5MT58yr+/W99keM4OcGiRbl88IErx45lsX27ktGj83B2Lt99MWSIlCFDVKxc\nKRS8KcyYoeSFF4z0768Ais/unZxgw4Y8unWTcPGiK127Fp9B54+9LI4ft+P5560//x9+yMVoBLm8\nYt+HR4GlY69KNG8u8OefznTq9HDGPqrlKH669hN9mvQpWGZ84QVcDh8md8IESE9HNW4cgkpF9t69\nqB0cEPr3xyU8HH0761tzyo4eRSoI2Pfrh72Fk97JRyYzosUIejTqUeI6WX+93G4tVFTo4+ODj4+p\n10K5Dfwrr7zCK6+8AsDFixfZvXs3kydPZsWKFZw8eZIuXbpw+PBh2rc3366s8Enko9VWTFOmlqQW\njV0bM/vQbHbF7+L5+s9zYPABHBWOFu17gs8EcnJy6LelH1tf3FpEDmDtubVsuLCBrf220tipMXq9\nvmCfggAnTjjw9tvpaLVlv1I3by4QESElKMh23e6PHFHQpIk9dnZaKvJn9PCAzEwVf/xRulqhk5NT\nwfiHNRrGpphNtHBqUeq+I29F0sWzC6mpWqKi1DRpkoZWW3TmHxAAn33mxsqVRnbvljFtWhJabfn8\nWXXqgFxuz8mTOlq2NHDggD3Hjqn45ZfEYsf9O717w44dElq1Kv7HLDz20ti3T8M//pGBVltzOi5Z\nOvaqRIsWUiIicmnR4qGr74VnXmDZqWXM7TAXldwU8MwNDET91Vdk9u6N26uvktOhA2kLF5oUZrVa\nZH37Ip8/H+1rr1l9DrVWr0Y7ejRZGZaJxO27to8zd87w69BfS/x7m2ojarFvn2mCbA6bh29GjRrF\n//73P6ZMmUJGRgaBgYG2PkSpjGg6gn3X9rGqxyoWd11stSbIW23fon/D/gTvCSZRl4hRMPLBqQ/Y\n8scWdgzYQeNajYttc/u2jLw8qFfPMn/powi0hoUp6dmz4oZEIjG5ac6csTzTZ2jjoexJ2FNmJ6oL\nSRfwdffl6lU57u5GatUqbmQlEpObIj8I6uFR/mCFRGJy0+zfryI5Wco777iyfHlqmfr4AEFBugpJ\nD+t0pmYr5alHELEtLVoUD7Q+rX6a52o/x4HrD+WCc/z9sTt3DveBA9ENGkTa4sVFlGPzunVDHh+P\n9J5lsbp8pLduYX/8OLrhwy1aPzUnldm/zeZj/48LHj7mWLzYmbS00k24TXRVW7RoQYsWptmbh4cH\nixcvtsVuy0Vw42CCGwdXyPc/te1UjIKR4D3B+Lj58Kf2T7YP2E4tpXl3U2SkKf/d0kPmV7QKgu3S\nbsPD7VmxItUm+2rfPpfISIXFvT491Z60dm/NgesHzBb6AOgMOq6nX6dJrSbsOGhH27Ylxyt8fXOZ\nPDmDrl0rbhyDgrKZM8eF2Fg5Awfq8POzrCTdx8dAbi5cuSK3yoeez/HjCnx9c3F2FlUfKxsfn1y2\nbi3e0GZY42H8EPtDwT0rqFRkhoRgaNrUfHNrhYLsHj1Q/vILWf8ouWL576i//pqsoUMR1CUX8xVm\n/sn59PXuW6RK/u98+60D+/cr2bXrAeBZ4nrVNgHr2jWZOdl2JBJJhQO7EomE6e2mM6DhAPR5er7v\n932Jxh0sD7Dm4+ZmxMnJdoHWW7dkJCZKadXKNu3brJ3Bg0mAbOuV4hrw+VxMukjjWo1RyBRERSnK\nDJxOn64tUfLBGtq313PnjpRLl+TMmFF2s458Kio9HB5uefaMyKOlRQsDf/whL5Yy29e7L2funSmS\nWKGdNcu8cf8Lq6tes7Nx2LzZ4tTIQzcOcfz2cWZ1mFXiOkePKli61Imvv04yWxhYmGpp4GNj5bzw\nQm02b7bsiVgeJBIJb7V9iy96f1HqaxJYVuD0d2zppslPxbNVvnSrVnouX5abfYCWRJB3EDEPYkrs\n13o+yZT/DqYK1ueeezziTjIZvPuultWrU6ypLQFMBr68mjaHDtnGZSZScdRqgTp1jMTFFXVYqOQq\n+nr3tUqNNqdnTxQREUgyLausVu3aRW6rVuQ9+2yp693JvMOOqzuYeXQmH3X/qETX8tWrciZNqsXa\ntSmWVcNbdJZViKQkKWPGaOjYUc/Jk4+uItRSsrIkxMbK8fW1bvbs62u7ilZbzxZVKlN1pTXnp5Kr\nCG4SzKv7XuXYrWPFPs/XoNHpTOmDPj6Pr1n0yJFZtGxpvZulc2c9CQly7t617muSkCAjK0uCj4+o\n/FhVMFfRCg/dNJYiODujb9sW+/BwC1YuOTXyhvYGW69sZerhqXT5vgu9f+zN/+L/x8wOM/F/xryY\nWHKyyfa9+266xW+31crAZ2fDuHEaXnpJx6JFaZw6pXgk/TetISbGjhYtDFZXWtpqBq/Xw2+/2d4d\nYE3BUz5zO81lSpspvH30bcYeGEtcalzBZ/lNPn7/XUGTJtb/vSoDOzvo0SPbol66hcl/4D6mEhAR\nC2jZsnjBE4Cfpx9p+jQuJF0ws5V5LHXT2J05gzQ9nZy/JZosOrWIF3e+yME/D9LKvRUbnt/A+VfP\ns/759QxrPMzsvnJy4J//rEX//jpGjLD81braGHhBgOnTXXnqqTxmzNDyzDN52NsLxMVVrsSetf73\nfAoHWivCmTMKGjQw4OZm29LY9u317N+vRKu13EpJJBIGNBxA2LAwOjzVgYG7BjL3xFzuZd3jaupV\nmmuaEx1tV+7CpcqgPG4aWxScidiWkmbwUomUoY2GllqN/Xey+/TB/tAhyqqEc/zqK5PvvZDv9MSd\nE/x49UfCh4fzRe8vCPEJoZmmWanCgoIAb7/tSu3aRmbOtC5FtdoY+JUrHbl2Tc6qVSkFf69OnfSc\nOmVFV4tHQHkNvJubEUfHigdaw8Ls6dnT9sG83r2zadTIQECAB1u3qqySVlDKlUxoPYHw4eHo8/T4\nb/WnvnN9VHKV2QrWqkxgYA4REQqLpQtMBWcK/P3FAGtVIt/Am5tQDW08lB1Xd2AwWuZSM3p5kVe3\nLopSdLbsDx7ELjqarL9qhQAy9BlMPTyVD7t9iEapsfjcV61yJC5OzqpV1usSVQsDv3Oniu+/d2DD\nhuQigTI/v8r1wxuNphm0tQHWfGzhpnlUs0WVCpYvT+Wrr5IJDVUzcKA7Z89ad67uKnc+7PYhP730\nE/M7m5qER0cryhRkq0o4OQm0b68nPNyyiUREhD1NmxrM5viLVB4eHkbs7ASzrRwbuTbCy8mLo7eO\nWry/7OefL9FNI0lJwXXGDFKXL0dwfBgs/eDUB3T27Mzz9Z+3+Dg//aTku+/ybZ/191SVN/CnT9sx\nZ44zGzcmFyt66dQph1OnKs/Ax8fLcXY28tRT5XOPmITHyn/+d+9KuX1b9kgNZtu2uezencioUZmM\nGaPh7bddSEqy7rZppmlGd6/uJCVJSU2V0rBh9Qo+WpMuaXqjEt0zVRFzypL5WBtszQ4KQnnggFnx\nMZe5c9G9+CL6rl0LloXfCCfsZhjzOs+z+BiRkXbMmePCpk3J5bYxVdbAG40mqdj/+z8NK1em0qJF\ncaPw7LN56PWSYo11Hxf5BU7lpaIz+MOH7enevWLqkZYglcLLL+s4fPg+KpVAz561uX7d+ghiTIwd\nrVvnVjv52+efz+bQoZKlhwvzqFxmIhWnJD88wEsNX+LQjUNo9Zb5uA3Nm5s6lV26VGS5cs8eFNHR\naGc9zGNPy0lj+tHpLPNfhrPC+e+7MsuNGzJee03D8uXmbZ+lVMmv2vnzdgwa5M6mTWo2bkymVy/z\nXxiJxOSHNycJaw3lDXSWJ/+9MPnSweU9/uPOtTb1T01n7NhM3n/f+thHdLTiseW/2xJPTyP16xuI\niCj9Prt5U0Zysu0KzkRsiznJgnw0Sg1dPLuwJ2GPZTuTSIq5aaSJibi89x4pK1ciFPIlzz0xl6D6\nQRb3Uk1PlzBmjIaJEzPo3btik4UqZeCTk6XMmOHCq69qGDkyi927E2nbtvQvi59fxdw0CQkyAgJq\ns3at9UVT5Q2w5lORQKvBAMeOVU615OuvZxIRIeP0aev+7jExpUsUVGUscdOEhdkTEGC7gjMR21JS\nqmQ+5XbTAAgCLjNnkjV8OLmFBBb3XdtH5L1IZnecbdE+DQZ4441a+PnpGTeu4k3Wq9St2KNHbZRK\ngcOH7zNyZJZFX5ROnfScPFm+TJqoKDuGDHFn+HAd69Y5snev5elwyclw546MZs0q5k8ur5smOlpB\nnTp55fbNVQSVSmDu3BzmzXO2OLtGEEzn3Lp19Zzd5mvLl/a2FR4uumeqMt7eeSQlSUlLM+9eFC3J\nuQAAFAtJREFUDKwXyOXky0X6tZaGvlMn5H/+ifT2bVTbtyNPSEA7bVrB50m6JGYdm8XKgJVFevUa\nDKZqfHM/s2e7ALBgQZpN6igesffWOrZuTbLaYDZrZiAlRcq9e1KrjN2BA/ZMm2ZSF+zTJ4eAgBxG\njdLg5ZVn0Sv26dMynnsut8L+b19fU6C1tObQ5ggLsycwsPKCecHBBlavlrFjh4qhQ8suvLh2TYaD\nQ/kD0pVNs2YGJBK4dElOJzOy93o9HD9uz9KlaY//5EQsQiaD5s0NXLxoZ7YS1F5mz79a/Yt/h/+b\nbf23FWnoYxa5nOzAQNRff43Dd9+R/O23YP9wsvnub+8ytPFQOjzdochmCxc6s2uXCien4t+FOnXy\n+OKLFJvF1arUDL48s2GpFDp00FvlpgkNdWDmTFe++SaZPn1MM65WrXJZujSNsWM1ZlOp/s6pU7IK\nuWfyKe8M3ppWcI8CqRTmzUtnyRJndLqypxr5DbarK6WJjwkCHDyopGFD2xecidiWstw0E1tPxMXe\nhQ9OfmDR/rKDgnD69FMyx44l19e3YPn+a/u5mHyR6e2mF1k/I0PCtm0O7N79gMOHi/9s3pxsUwXS\nKmXgy4vJD1+2m0YQYMkSJ9atc2THjsRiFZUvvJDN//1fBiEhbmRmlm60IiJsY+DbttVz8aKchATL\n/fCJiVKuXZPb5PgVoWNHPe3a6S2KX0RHV68CJ3OYtOVNBj4nx5TFNHeuM127evDeey689pplzRxE\nKo/SMmnAVNn6SY9POHjjoEUiZDmBgWgnTCBj0qSCZVq9ltnHZ7O0+9JirfZ++EFFly45eHk9nolA\njTDwporW0mfwRiNMmeLK8eP2/PRTIt7e5pXYXn89k1at9EycWIs8M6sIginLJypKZpOAoaurwL/+\nlcmiRZalT4Fp9t61aw52tu0ZUi5mz07nq68cuXOn9FvJlEFTfWfwYHqg3bwpY8QIFa1bP83HHzvh\n7m5k3bpkIiPvWe1mE3n8lGXgAVzsXVjXex1zT8zlYtLFUtcVHBzQzp5N4S/jktNL6PlMTzp7di66\nrgCbNqkZO7biwVNLqREGvmXLXG7ckJGSUvKse8cOFbGxcrZuTSr1NVoigcWL08jMlPDBByajm5kp\nYd8+JW+/7UL79k/xxhu1mDpVX6Rpc0UYPz6D8+ftOH68bDdTZqaElSudGDEiq8x1Hwf16uUxalQm\nS5eW/IDS602+6+qePiiXw6JFaQwenMvx4/fZtSuRyZMz8PExiMJi1YSmTXOJj5eRU4Z3s4VbC+Z3\nns/4X8eTlmN5XOX0vdPsu7aP2Z2KZ80cO6ZAJsMmfQ4spUYYeDs7k6ujpLQ9nU7CkiXOLFiQblG5\nr0IB69Ylc+iQPQMGuNO27VNs3KimSRMD27YlcuzYfaZPt91FUirh3XfTmT/f2exbQ2EWLXKmfXt9\nQeygKvDmmxmEh9tz/nzxmdHdu1LWrXPE2zvPolZ5VZ2BA7N5+WUDGo3oa6+OqFRQv34esbFlRzGH\nNBpCYN1ApoRPwSiUfb1z8nKYcWQG8zvPx9XetdjnGzeaZu+PczJQIww8mHRpTpww74dfu1ZNu3Z6\nOnSw3Ci7ugps2ZLEm29qiYy8x5YtSYwfn2mRyH55eOmlbOztTT66kjhyRMEvv9izYEHVytRwchKY\nNk3LvHmmB1RUlB0ffeREUJA7vXp5cOGCnEWLqtY5izy5WOKmyWdOpzmk5KTwSfQnZa675uwa6jvX\np3+D/sU+u3FDRkSEgiFDrOiiYwOqVJpkRfDz0zN/fnE3wZ07Ur76ypG9ex9YvU9PTyOeno9npiyR\nwLx5aYwfr6F//+xis930dAnTp7uybFmazVxDtmTkyCw2bVLTqtXTPPVUHr16ZbNgQTrt2ukfuZSC\niIg1+PiYMmks0VVXyBR80esLXtz5Ik08mtDXq69Zad/YlFg2XNjAvsH7zLYMDQ1VM3y4DgeHx/vd\nrTEz+Nat9cTGyovJuv7nP86MGpVJvXqPZuZtS9q2zaVz5xxWry7ermvePBcCA035+lURmQy++SaJ\nffsecOjQA2bP1tKpk2jcRaoe1szgAZ5WP826PutYfWY13bZ0Y+25taRkpxR8bhSMzDg6g6ltp+Ll\n6FVse51OwpYtKsaMeXzB1XxqjIFXKk055YW7EJ07Z8fhw/a8+Wb1SV+bNUtLaKiaW7cepk0eOGDP\nyZMK5syxvGl0ZeDpaaRu3ar/IBV5ssk38Lt3l16ZXJi2Hm05POownwV+xsWki3Td0pWph6dy7sE5\nvr38LQbBwOjmo81uu3OnijZtckvM3HuU1BgDD/myBSYDLwgwf74z06ZpcXKqei6NkvDyymPMmEyW\nLHECIDlZwjvvmCpua0KQUkSkstFoBEJDk1m1yongYDcuX7bsNVMikdDWoy2f9PyEo8FHaejSkPG/\njmfeiXl81P0jZNLitSyCABs2qG2iK1MeapSB9/N7mA+/d6+StDQpI0dWjXRCa5g4MYMTJ+yJirJj\n9mxXXnpJh59f9S4SEhGpSvj56dm37wEvvqgjONiNuXOdS9SoMYebyo1Jz03i+IjjhA8Pp5mmmdn1\nTp9WkJ0tqbQOXzXKwLdrp+f8eTvS0yUsXOjM3LlpyCq3ZWu5UKsFZsxIZ/x4DRcuyJk5s2q7ZkRE\nqiNyOYSEZBEe/oDsbAkBAR5s3uxgVXtKmVRGXae6JX6+YYOakJDMSlMYrVEGXq0WaNbMwJQprjRu\nbMDfv/rOeocP19Ghg55PPkkt0qZQRETEtmg0RpYuTeObb5L5+msHs9l45eHOHSlHj9oTHFx5XoQa\nl+Pg56dn3To1Bw/er+xTqRBSKaxdm1L2iiIiIjbB1zeXzZuTeOkld5591oHRoytmmEND1QwapKvU\nGGCNM/BDhmTxzDMGGjUSszlERESsw9XVFIAdPNid+vXzypWWfPeulEWLnDl+3J4ff0x8BGdpOeU2\n8ElJSXz22WekpqYilUrp1asX/fr1IyMjg5UrV/LgwQM8PDx46623cHBwKHuHNqJFC0OFehiKiIg8\n2TRoYNJkHz++Ftu2JdG0qWX2JCcHvvrKkc8/VzNqVBZHjtyv9My3cht4mUzGmDFj8Pb2Jjs7m5kz\nZ9K6dWvCwsLw9fVl4MCB7Ny5kx07djBq1ChbnrOIiIjII6VTJz1z5qQTEqJh9+5E3N1Lj7weOmTP\n3LkuNGxoYNeuxEcmaWIt5Q6yurq64u3tDYBSqcTLy4ukpCQiIyMJCAgAoEePHpw+fdomJyoiIiLy\nOBk+XMfgwTrGjdOQbUYJOiNDwt69SsaM0TBnjgvz5qXx9dfJVca4g4188Pfv3+f69es0adKEtLQ0\nXF1NSmqurq6kp4spfiIiItWT6dO1xMfLmTbNldDQPK5dk3HwoJJff7XnzBkF7drpCQrK5ssvswp3\n66syVNjAZ2dns3z5ckJCQlAqLW9aLSIiIlLVkUphxYoUhg93p3lzJQaDA4GB2YwencW6dSk4Olbt\n6vIKGfi8vDw+/vhj/P396dDB1FjW1dWV1NTUgn9dXFzMbnvhwgUuXLhQ8HtwcDB16tSpyOk8dpyc\nnCr7FCqVJ3n84tifLKKiCv+m/uun6rB169aC//v4+ODj42P6RagAn376qbBp06Yiy7755hthx44d\ngiAIwo4dO4T//ve/FTlEqWzZsuWR7bss5s6dW2nHFoTKHbsgVO74xbFXHk/yfV8dx17uGfzly5c5\nevQo9erVY8aMGUgkEkaOHMmgQYNYsWIFYWFhuLu7M3Xq1Ao/nUqi4ClVCdSuXbvSjg2VO3ao3PGL\nY688nuT7vjqOvdwGvlmzZmzZssXsZ3PmzCnvbq2iMi+2h4dHpR0bKv+LXpnjF8deeTzJ9311HHuN\n0qJ5nFT2F62yeZLHL479yaQ6jl0iCJZK3ouIiIiIVCfEGbyIiIhIDUU08CIiIiI1lBqnJlkRPv/8\nc6KionBxcWHZsmUAXL9+nXXr1pGTk0Pt2rWZPHkySqWSY8eOsWvXLiQSCYIgcP36dZYuXUr9+vWJ\nj49nzZo15Obm0qZNG0JCQip3YBZgzdjz8vJYu3YtCQkJGI1G/P39GTRoEAAxMTFs2rQJQRDo2bNn\nwfKqjDVjNxgMfPnll8THxyOVSgkJCaFFixYA1fK6l0c0cMOGDcTExGBvb8/EiRMLJEvCw8PZsWMH\nAEOGDCmQLKmqWDv227dvs2bNGhISEhg5ciT9+/cv2FeVve9tm6lZvbl06ZKQkJAgTJs2rWDZO++8\nI1y6dEkQBEEICwsTvv/++2LbXb9+XZg0aVLB77NmzRJiY2MFQRCExYsXC9HR0Y/4zCuONWM/evSo\nsHLlSkEQBCEnJ0eYMGGC8ODBAyEvL0+YNGmScP/+fSE3N1eYPn26cPPmzcc/GCuxZuz79u0T1qxZ\nIwiCIKSlpQkzZ84s2KY6XveUlBQhISFBEARB0Ol0wuTJk4WbN28K33zzjbBz505BEIrWs0RFRQmL\nFy8WBEEQrly5Irz77ruCIAiCVqsVJk2aJGRmZgoZGRkF/6/KWDv2tLQ0IS4uTti8ebOwe/fugv1U\n5ftedNEUolmzZqjVRSvU7ty5Q7Nmpn6Lvr6+nDp1qth2v/32G127dgUgNTUVnU5Ho0aNAPD3968W\ngmvWjF0ikZCTk4PRaCQnJwc7OztUKhVXr17F09OT2rVrI5fL6dq1a40Ze0REBAA3b97E19cXAGdn\nZ9RqNXFxcdX2ulsqGhgZGQnA6dOnC5Y3btyYrKwsUlNTOXv2LK1atcLBwQG1Wk2rVq2IiYmplDFZ\nirWCic7OzjRs2BDZ3/qAVuX7XjTwZVC3bt2Cm/vEiRMkJSUVW+f48eN069YNgOTkZNzc3Ao+c3Nz\nIzk5+fGcrI0paex+fn4oFApee+01Jk6cyIABA1Cr1cXGrtFoaszYExNNjRvq16/P6dOnMRqN3L9/\nn/j4eJKSkmrEdS9NNDAtLQ0ofn/nX+Pqfu0rIphYlccuGvgyeOONN9i/fz+zZs0iOzsbubxo2OLq\n1asolUqeeeYZAAQzWacSieXd2qsSJY09NjYWmUzGl19+yWeffcbu3bu5f998i8SaNvbAwEA0Gg2z\nZs0iNDSUpk2bIpVKq/11r4hoYH4cqrryKAQTq8q1F4OsZVCnTh1mz54NmF7bo6Oji3xe2D0Dpplb\n4Vl+UlIStWrVejwna2NKGvtvv/3Gc889h1QqxdnZmaZNmxIfH49GoymY6YJpZlPTxi6VShkzZkzB\nenPmzMHT0xO1Wl1tr7s1ooEajcbsON3c3IqIByYlJdGyZcvHO5ByUBHBxHyq8n0vzuD/hiAIRWYj\n+a9nRqORH3/8kT59+hRZ9+TJk3Tp0qVgmaura4E/WhAEjhw5UnDjVHUsHbu7uzu///47YJr9xMbG\n4uXlRaNGjbh79y4PHjzAYDDw22+/0b59+8c/kHJg6dj1ej05OaY+nefOnUMmk+Hl5VWtr/vnn3/O\nM888Q79+/QqWtWvXjvDwcMCUHZN/Hdu3b8/hw4cBuHLlCmq1GldXV1q3bs358+fJysoiIyOD8+fP\n07p168c+FmuxZuyFKXyvVOX7XqxkLcSqVau4ePEiWq0WFxcXgoOD0el07N+/H4lEQseOHXnllVcK\n1r948SLfffcdCxcuLLKf+Ph4Vq9eXZAuN3bs2Mc9FKuxZuzZ2dmsWbOGW7duAdCzZ8+ClLGYmBg2\nbtyIIAgEBgZWnXSxUrBm7A8ePGDRokVIpVI0Gg2vv/467u7uQPW87pcvX+b999+nXr16SCSSAtHA\nRo0asWLFChITEwtEA/MD0evXrycmJgalUskbb7xBw4YNAZMx3L59OxKJpFqkSVo79tTUVGbNmoVO\np0MikaBUKlmxYgVKpbLK3veigRcRERGpoYguGhEREZEaimjgRURERGooooEXERERqaGIBl5ERESk\nhiIaeBEREZEaimjgRURERGooooEXEQHWrFlTYo9hEZHqimjgRUSsYP78+Rw6dKiyT0NExCJEAy8i\nIiJSQxHFxkSeSBISEli7di13796lTZs2BcszMzP59NNPuXr1KkajkSZNmvDaa6+h0Wj4/vvvuXTp\nErGxsYSGhhIQEMC4ceO4desWGzduJD4+vkDqoHPnzpU4OhERE+IMXuSJw2AwsGzZMgICAti4cSN+\nfn4FzUzytUQ+//xz1qxZg729PevXrwfg5Zdfpnnz5owbN47Q0FDGjRtHTk4OCxcupHv37qxfv54p\nU6awfv16bt68WZlDFBEBRAMv8gQSGxtLXl4e/fr1QyqV4ufnV9CJydHRkY4dO2JnZ4dSqWTw4MFc\nunSpxH2dOXMGDw8PAgICkEgkeHt707FjR06ePPm4hiMiUiKii0bkiSMlJQWNRlNkWb4ipF6vZ9Om\nTZw9e5bMzEwEQSA7OxtBEMw2cUhMTCQ2NraIcqTRaKR79+6PdhAiIhYgGniRJw5XV9diLdUSExN5\n+umn2b17N3fu3GHJkiU4Oztz7do1Zs6cWaKBd3Nzw8fHp6A5iIhIVUJ00Yg8cTRp0gSZTMbevXsx\nGo2cOnWKq1evAqDT6VAoFKhUKjIyMti2bVuRbV1cXIq0J2zXrh23b9/myJEj5OXlYTAYiIuLK9DK\nFxGpTEQ9eJEnkvj4eL744osiWTSenp4EBQWxatUq4uLi0Gg09O/fn3Xr1rF582akUilXrlxh9erV\naLVa/P39CQkJ4c6dO4SGhhZ0c/L29mb06NHUr1+/kkcp8qQjGngRERGRGoroohERERGpoYgGXkRE\nRKSGIhp4ERERkRqKaOBFREREaiiigRcRERGpoYgGXkRERKSGIhp4ERERkRqKaOBFREREaiiigRcR\nERGpofw/cmDBPWAlRVAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10842a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].resample('A').mean().plot(color='green')\n",
    "df['val'].resample('A').min().plot(color='blue')\n",
    "df['val'].resample('A').max().plot(color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `.fill_between` to draw between lines\n",
    "\n",
    "Getting maximums and minimums for years is a very common way of dealing with resampled time series. A fun graph to make is the `fill_between` graph, which colors between two areas.\n",
    "\n",
    "### Step 1: Calculate the maxes and mins and get a list of x values\n",
    "\n",
    "`ax.fill_between` requires three things\n",
    "\n",
    "    - a list of x values (the dates),\n",
    "    - a list of minimum values for those x values,\n",
    "    - and a list of maximum values for those x values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: \n",
      ".resample() is now a deferred operation\n",
      "You called index(...) on this deferred object which materialized it into a series\n",
      "by implicitly taking the mean.  Use .resample(...).mean() instead\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1963-12-31', '1964-12-31', '1965-12-31', '1966-12-31',\n",
       "               '1967-12-31', '1968-12-31', '1969-12-31', '1970-12-31',\n",
       "               '1971-12-31', '1972-12-31', '1973-12-31', '1974-12-31',\n",
       "               '1975-12-31', '1976-12-31', '1977-12-31', '1978-12-31',\n",
       "               '1979-12-31', '1980-12-31', '1981-12-31', '1982-12-31',\n",
       "               '1983-12-31', '1984-12-31', '1985-12-31', '1986-12-31',\n",
       "               '1987-12-31', '1988-12-31', '1989-12-31', '1990-12-31',\n",
       "               '1991-12-31', '1992-12-31', '1993-12-31', '1994-12-31',\n",
       "               '1995-12-31', '1996-12-31', '1997-12-31', '1998-12-31',\n",
       "               '1999-12-31', '2000-12-31', '2001-12-31', '2002-12-31',\n",
       "               '2003-12-31', '2004-12-31', '2005-12-31', '2006-12-31',\n",
       "               '2007-12-31', '2008-12-31', '2009-12-31', '2010-12-31',\n",
       "               '2011-12-31', '2012-12-31', '2013-12-31', '2014-12-31',\n",
       "               '2015-12-31', '2016-12-31'],\n",
       "              dtype='datetime64[ns]', name='date', freq='A-DEC')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is what our x values will look like\n",
    "df['val'].resample('A').index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "1963-12-31    62.0\n",
       "1964-12-31    56.0\n",
       "1965-12-31    58.0\n",
       "1966-12-31    53.0\n",
       "1967-12-31    49.0\n",
       "Freq: A-DEC, Name: val, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_maxes = df['val'].resample('A').max()\n",
    "y_mins = df['val'].resample('A').min()\n",
    "y_maxes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Build the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108527668>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8VdW5+P/POoEQkpzkZCBACBDm4RDGCChIANtrr5cq\n9X5Nq3awttd70aoXO1BqqddfHepUwAFtrVOrtWArqbNWIagoQoRADCDzEAIkBDKTkGSv3x+bnELJ\ncIZ95uf9evkyOefsvZ+VE56ss/Zaz1Jaa40QQoiIYwt2AEIIIfxDErwQQkQoSfBCCBGhJMELIUSE\nkgQvhBARShK8EEJEqB7dveDJJ59k8+bNJCcn8/DDDwOwYcMGXnnlFcrKyrj//vsZOnSo6/WrV69m\n7dq1xMTEcMMNNzBhwgT/RS+EEKJT3fbg58yZw5133nneY4MGDeInP/kJY8eOPe/xsrIyPv30U5Yu\nXcrixYv5wx/+QKROsy8tLQ12CEEVze2XtkencGx7twl+9OjRJCQknPdYZmYm/fv3v+C1RUVFXHLJ\nJcTExJCRkUH//v3Zs2ePddGGkHB8s60Uze2XtkencGy7pWPwJ0+eJD093fV9amoqJ0+etPIS5wnm\nD7yioiJo14bg/7IFs/3S9uCJ5t/7cGy7pQm+o+EYpZSVlzhPMN/sysrKoF0bgv8PPZjtl7YHTzT/\n3odj25U7tWgqKyt54IEHXDdZ291999185zvfcd1kLSgoAGD+/PkA3HvvveTn5zNixIgOgz034Pz8\nfI+DF0IIAatWrXJ97XQ6cTqdgBuzaMDsmbtzszQ3N5dHH32UefPmcfLkSY4dO8bw4cM7fO25QbQr\nLy93J5yQYLfbqaurC3YYQRPN7Ze2S9tDSWZmZqcd5G578MuXL2f79u3U1dWRnJxMfn4+CQkJPPfc\nc9TW1pKQkEB2dja/+MUvAHOa5Jo1a+jRo4fH0yQlwYePaG6/tF3aHkoyMzM7fc6tIZpAkQQfPqK5\n/dJ2aXso6SrBy0pWIYSIUJLghRAiQkmCF0KEBX3ieLBDCDuS4IUQIU9XHsO487/RTY3BDiWsSIIX\nQoQ8/ckaMAyQXrxHJMELIUKaNgz0Jx9An35QKQneE24tdBJCiKDZuQ0S7agRTnTlMfxX/CTySIIX\nQoQ0vf591IyvgNZw/EiwwwkrMkQjhAhZuqEeXfI5aloeKr0fWoZoPCIJXggRsvTGD1HOSagEO/Tp\nCyeOBTuksCIJXggRslzDMwBpfeFEBdowghtUGJEEL4QISbpsP9RWw1izYKHq1QsSEqHaf5sIRRpJ\n8EKIkKTXf4C6ZC7KFvPPB9P7ylx4D0iCF0KEHN3agv5sHeqSy857XPXph5ZxeLdJghdChJ6tmyBz\nECqj//mPp8tiJ09IghdChBzj3Jur55KZNB7pdqHTk08+yebNm0lOTnbtyVpfX8+yZcuorKwkIyOD\nhQsXEh8fD8Czzz5LcXExvXr14pZbbiE7O9uvDRBCRBZdXQV7d6L+e9EFz6n0fhiV7wYhqvDUbQ9+\nzpw53Hnnnec9VlBQQE5ODsuXL8fpdLJ69WoAtmzZwvHjx3n00Ue56aabePrpp/0TtRAiYulP16Km\nXGLOmvlXffrJTVYPdJvgR48eTUJCwnmPFRUVkZeXB8Ds2bMpKioCYNOmTa7HR4wYQWNjI9XV1VbH\nLISIUFpr9MedDM8AJKdAQz26uTmwgYUpr8bga2pqcDgcADgcDmpqagA4efIkaWlprtelpqZy8qTM\nWRVCuGnvToiJgaGjOnxa2WyQniG9eDf5vdiYUh3XfistLaW0tNT1fX5+Pna73d/hWCY2Njas4rVa\nNLdf2u6/tp/e9QVMm0XvpKROX1PfL4teDbX0DPB7EMrv+6pVq1xfO51OnE4n4GWCdzgcVFdXu/6f\nnJwMmD32qqoq1+uqqqpISUnp8BznBtEuFHcs70yo7rAeKNHcfmm7/9retnUjtm98h9YurmGkpNF4\neD+2keP8FkdHQvV9t9vt5Ofnd/icW0M0Wmu01q7vp0yZQmFhIQCFhYXk5uYCkJuby7p16wDYtWsX\nCQkJrqEcIYToim5sgCOHYNjorl+Y3g8qZaqkO7rtwS9fvpzt27dTV1fHggULyM/PZ/78+SxdupS1\na9eSnp7OHXfcAcDkyZPZsmULt956K3FxcSxYsMDvDRBCRIhdJTBsNKpnbJcvU336Yez6IkBBhbdu\nE/ztt9/e4eNLlizp8PEf/OAHvkUkhIhKensx6mxhsS716Ss9eDfJSlYhREjQO7aixriR4NPN1azn\nDhuLjkmCF0IEnT5ZCfW1kDWk29equHiIjTNLCYsuSYIXQgSd3rENNXqCOc/dHX3kRqs7JMELIYJv\nRzG4MzxzlkrvK2WD3SAJXggRVFpr98ff20lNGrdIghdCBFf5IegVh+rTz/1j0vtKXXg3SIIXQgSV\n3lHsWe8d2dnJXZLghRBBpbd7ODwD0oN3kyR4IUTQ6NZW2LMdRo337MCUdKirRrec8U9gEUISvBAi\nePbvgj79UfbOq0d2RMXEmEm+qsJPgUUGSfBCiKDxZvzdpY9swN0dSfBCiKBxu/5MB1S63GjtjiR4\nIURQ6NONUHYAho/17gRSdKxbkuCFEMGx6wsYMhIV28Hm2m4wp0rKEE1XJMELIYLC49Wr/0qmSnZL\nErwQIij09mLUmInenyC9n5QN7oZPm26/9dZbfPDBBwBcdtllXHHFFdTX17Ns2TIqKyvJyMhg4cKF\nxMfHWxKsECIy6OoqqDkFg4d6fQ6VkAg2G9TXgYfTLKOF1z34w4cPs2bNGn7zm9/w0EMPsXnzZo4d\nO0ZBQQE5OTksX74cp9PJ6tWrrYxXCBEB9I5tMHo8yhbj24nO9uJFx7xO8EeOHGHEiBH07NkTm83G\nmDFj2LhxI59//jl5eXkAzJ49m02bNlkWrBAiQuwoRo3xcPVqR/r0RctMmk55neAHDhzIjh07qK+v\np7m5mS1btnDixAmqq6txOBwAOBwOamtrLQtWCBH+/lke2Ifx97NUumz80RWvx+AHDBjAVVddxa9/\n/Wt69+5NdnY2MTHuf9wqLS2ltLTU9X1+fj52u93bcAIuNjY2rOK1WjS3X9ruW9vbjhyiPqYH9mEj\nUUr5dK7mgYNp2/sl8QF4P0L5fV+1apXra6fTidPpBHy8yTpnzhzmzJkDwMsvv0xaWhoOh8PVi6+u\nriY5ObnDY88Nol1dXZ0v4QSU3W4Pq3itFs3tl7b71najaD2MzqG+vt7neHSiA6P8MG0BeD9C9X23\n2+3k5+d3+JxP0yTbh19OnDjBxo0bmTlzJlOmTKGwsBCAwsJCcnNzfbmEECLCmDdYfZj/fq4+fWVn\npy741IN/5JFHqK+vJyYmhh/+8IfEx8czf/58li5dytq1a0lPT+eOO+6wKlYhRJjTbW2wqwT17QXW\nnDC1D9ScRLe2onr4lM4ikk8/kbvvvvuCxxITE1myZIkvpxVCRKpDe8GRhkpOseR0qkdPSE6Fk5WQ\n0d+Sc0YSWckqhAgYc/WqRcMz7dKl6FhnJMELIQJG79xmeYJXaRnok5WWnjNSSIIXQgSEPtNs7uA0\ncpy1J05Nh1MnrD1nhJAEL4QIjD07ICsb1dvi2lQpaXBSEnxHJMELIQJC79yKGm1BeYJ/oVLS0aeq\nLD9vJJAEL4QICL3dmvIEF0iRIZrOSIIXQvidbqiHY0dg6CjrTy4JvlOS4IUQ/vflNhg+GtWzp/Xn\njk8AwzD3eBXnkQQvhPA7vcP66ZHtlFIyk6YTkuCFEH7n8/6r3UlJl5k0HZAEL4TwK32yEhpqIWuI\n366hHGlo6cFfQBK8EMKv9I5tqFHjUTY/ppvUdJCpkheQBC+E8K+dW8GfwzMgM2k6IQleCOE35vZ8\n26zZf7ULKkWGaDoiCV4I4T9HD0OPHtDHz6V8ZYimQ5LghRB+o3dsQ40e7/Peq92SIZoO+bThxxtv\nvMHatWtRSjFo0CBuvvlmTp48yfLly6mvr2fIkCHceuutHm3GLYSIHHpHMeqiS/1/ofhEaGtFn260\nvphZGPO6B3/y5EneeecdHnjgAR5++GHa2tr4+OOPeemll5g3bx7Lly8nISGBNWvWWBmvECJM6NON\nsLvU7+PvcHaxU0ofqJZhmnP5NERjGAZNTU20tbVx5swZUlNTKS0tZdq0aQDk5eWxceNGSwIVQoQX\nvfZNVE4uKsma7fm6JWWDL+D1EE1qairz5s3j5ptvplevXowfP54hQ4aQkJCA7ex817S0NE6dOmVZ\nsEKI8KCbm9Dvv4btJ/cG7JrtM2n8PNofVrxO8A0NDRQVFbFixQri4+P57W9/y5YtWy54XWc3V0pL\nSyktLXV9n5+fj91u9zacgIuNjQ2reK0Wze2Xtnff9qYP36ZtzAQSRjkDEJXpdL8BqIY64vz03oTy\n+75q1SrX106nE6fT/Ll7neBLSkrIyMggMTERgKlTp7Jr1y4aGhowDAObzUZVVRUpKR1/PDs3iHZ1\ndXXehhNwdrs9rOK1WjS3X9reddt1yxmM11Ziu+1XAf05GfF2OLSXFj9dM1Tfd7vdTn5+fofPeT0G\nn56ezu7duzlz5gxaa0pKSsjKysLpdLJhwwYA1q1bR25urreXEEKEIf3x+zB4GGrQ0IBeV6Wmy2Kn\nf+F1D3748OFMnz6dRYsWERMTQ3Z2Nl/5yleYPHkyy5YtY+XKlWRnZzN37lwr4xVChDDd2oJ+52/Y\nbvpp4C8uFSUv4NM8+GuuuYZrrrnmvMcyMjK47777fApKCBGe9IZC6JuJGjY68BdPSZPVrP9CVrIK\nISyh29rQb72C7T++GZwAEuzQ1oJukp2d2kmCF0JYQm/6CBypqFHjgnJ9pRQ4pCbNuSTBCyF8pg0j\nuL33dilpUpPmHJLghRC+27IBesXB2IlBDcOcSSM9+HaS4IUQPtFaY7y5Ett/5Pu/amR3ZCbNeSTB\nCyF8ote8YX4x/qLgBgIyRPMvJMELIbxmrP8A/d5qbAsW+3fPVTeplD4yRHOO4L8jQoiwZGz6CL36\nT9gW/n+oPv2CHY5JevDnkQQvhPCY3roR/fLvsf3vXah+WcEO559kZ6fzSIIXQnhEby/GeOExbLf+\nCpU1JNjhnC/RDi0t6KbTwY4kJPhUqkAIEZ70nh0YS39l9njTM8whlvS+qHTz//Tpi4pPvOC41p0l\nGH94BNv//Bw1ZEQQIu+aubPT2ZIF/UPok0WQSIIXIgrpD99FXXENavLFUHkMfeI4VB7H2LMDKo/B\niQqIscHZhK/69IUkBw3vrsb2gztQIwNX591j7cM0kuAlwQsRbfTpRnTxZ9jueRKV5ID+Ay/YBUlr\nDfV1cOJs8j9xHI6XE3/zYpqGjQlK3O5SKeZiJ9nZSRK8EFFHF30Mo3LM5N4JpRTYk8CehBoy0vV4\nT7udphDc9OI8KWlwqjLYUYQEuckqRJTR69/HNvMrwQ7Df1Kl4Fg7r3vw5eXlLFu2DKUUWmuOHz/O\nN7/5TWbNmsWyZcuorKwkIyODhQsXEh8fb2XMQggv6aNl5nDLuCnBDsVvVEo6xraiYIcRErxO8JmZ\nmTz44IMAGIbBggULmDp1KgUFBeTk5HDVVVdRUFDA6tWruf766y0LWAjhPb3+fdT0OaiYmGCH4j8p\naXBShmjAoiGakpIS+vbtS3p6OkVFReTl5QEwe/ZsNm3aZMUlhBA+0q2t6A1rUTMieHgGIKWPDNGc\nZUmC/+STT5g5cyYANTU1OBzmzRuHw0Ftba0VlxBC+Kp0sznlMdKnDyba4Uwzurkp2JEEnc8JvrW1\nlaKiIqZPn25FPEIIPzE+fj/ye++cu9hJShb4PE2yuLiYoUOHkpSUBJi99urqatf/k5OTOzyutLSU\n0tJS1/f5+fnY7XZfwwmY2NjYsIrXatHc/nBsu1F9ktpdJSTd9ktUfILX5wmXttf36UevpkZ6Whhr\nKLd91apVrq+dTidOp7kQzecE//HHHzNjxgzX91OmTKGwsJD58+dTWFhIbm5uh8edG0S7ulCfX3sO\nu90eVvFaLZrbH45tNz54AzVhGvVtBvgQe7i03Uhy0Fh+GFv2yO5f7KZQbbvdbic/P7/D53waojlz\n5gwlJSVMmzbN9dj8+fMpKSnh9ttvp6SkhPnz5/tyCSGEj7TW6I/fR0Xy3Pd/lZImOzvhYw8+NjaW\nZ5555rzHEhMTWbJkiU9BCesZa9+E6lOQkADxiebH9PhE878BgyN72ly0O7AbWltgRAjXj7FaSh8o\nOxDsKIJOShVEAW20of/6POryq+HUSThyCKOxHhrrofI4auZXUFdeF+wwhZ/oszdXg75fagCplDSM\nElnsJAk+GlQchSQHtiuvveApvesLjFXPgiT4iKSbm9FFH2O769FghxJYsvEHILVoosORgzBgcMfP\nDR0NFUfRtdWBjUkEhN78CQwdiUpND3YogSX1aABJ8FFBlx1EZWV3+Jzq0QNG5aC3bwlsUMLvdFMj\nuuBFbF+NwokOiUnQ3IRubg52JEElCT4K6CMHYEB2p8+rcZOhVBJ8pNGv/hE1ejxq7MRghxJwstjJ\nJAk+Ghw5iMrqZIgGUM5J6NItaMMIYFDCn/SuUvTmDaj8HwQ7lOCRcXhJ8JFONzdBdRVkZHb6GpXe\nFxLscHhfACMT/qLPNGP88XFs192ESrhwX9VooVLS0JLgRUQrPwR9B3Q7z12Nm4z+YnOAghL+pN/4\nC2QNRk2+JNihBFeK3GiVBB/hdNmBTm+wnks5J0mCjwD64F70x+9ju+6/gx1K8KWmR/1qVknwke7I\nwS5vsLqMHAeH96MbG/wekvAP3dqK8fyjqP/3fVRSSrDDCTrlSENXSw9eRDBddgDV2Rz4c6jYXjB8\nNOzcGoCohD/od18FRwrq4jnBDiU0ONLM+09RTBJ8BNNamz34LmbQnEvG4cOXPnoY/f5r2L59S1SV\nJOhSSqqMwQc7AOFHtdWAhuRUt16unFPQpZvNPwwirBgvPYW68lpUWp9ghxI6khzQ2IBubQl2JEEj\nCT6SnV3g5HaPrt8AQMGxMn9GJSymqyrMtQ6zvhbsUEKKssWAPRlqTgU7lKCRBB/BdNlBt8bf2yml\nwnaYRre0YLz6R3RbW7BDCTj9+SeoSdOl5HNHUtKiephGEnwkKzvQeZGxTihnmCb4j95Fv/1XiMIS\nsXrzJzLnvTOOVKg5GewogsancsGNjY089dRTHD58GKUUCxYsoH///ixbtozKykoyMjJYuHAh8fHx\nVsUrPKCPHMSW5+HH9tHj4dll6OZmVK9e/gnMYvpMM/rtv6LmzsP48F1iJk7r/qAIoU+egGNHzPdN\nXEA50tCnqojW284+9eCfe+45Jk2axNKlS3nooYcYMGAABQUF5OTksHz5cpxOJ6tXr7YqVuEBbbSZ\nY+me9uDjE2DQENj1hZ8is55e9w5kj0Rd/T3Y96U5Jh0l9JZPUROmmlVBxYVSonuqpNcJ/vTp0+zc\nuZM5c8w5tzExMcTHx1NUVEReXh4As2fPZtOmTdZEKjxTcRSSU1BxvT0+VDkno0vDY5hGNzeh330V\n21XXonr1Qk3LQ3/0XrDDChhdtB41RYZnOpWcau5iFqW8TvDHjx/HbrezYsUKFi1axO9+9zuam5up\nqanB4XAA4HA4qK2ttSxY4YGuNvnohho3JXwS/No3UcPHorKGAKBmfQ398fvo1tYgR+Z/uroKyg/C\nmOgrB+wulRLdq1m9TvCGYbB//34uv/xyHnjgAXr16kVBQYGVsUUUXVGOPrw/cNdzcwVrhwYOgYZ6\ndOUxa4OymG5qRL9XgDpnK0I1YBD06QvbIv+To96yATX+IlTPnsEOJXRF+WpWrwfuUlNTSUtLY9iw\nYQBMnz6dgoICHA4H1dXVrv8nJyd3eHxpaSmlpaWu7/Pz87Hb7d6GE3CxsbFux6u1pv63K0CD/f+W\n+zkyU8PxI/SccRmxXv5MGyZOpcfeHfQaOqLD5z1pv780vf932sbnkjDKed7jZ/5tPmfWv09i3r/5\n5bqh0HaA+uLP6HXFf9IzgLGEStvdpXsMpqb6JImJiT6v8A3ltq9atcr1tdPpxOk0/014neAdDgdp\naWmUl5eTmZlJSUkJWVlZZGVlUVhYyPz58yksLCQ3N7fD488Nol1dXZ234QSc3W53O169cxtGVSU0\nNlC7f49Zf93P2g7soe3r19Hs5c/UGJlDy+frOTO947omnrTfH3RjA8abr2Bb9JsL4tDOyRh/fJza\nfbtRffpZfu1gtx1A157C2L8bY8homgIYSyi03WM2G3XHj/lcGz9U226328nPz+/wOZ9m0Xz/+9/n\nscce46c//SkHDx7k6quvZv78+ZSUlHD77bdTUlLC/PlRuB/kObTWGK+/jJr3LVTuDPRn6/x/zabT\n5tzfjP5en0ONGAsHdlsYlbX0+6+hcnJR/bIueE7F9kJNnxPRN1v1ls9Q4yabReJE16J4mManuVXZ\n2dncf//9Fzy+ZMkSX04bWXZug+pTqKmzoE8/jBceQ19xjX8LQpUfgn5Zvq1sTO0DZ5rRdTUoe8fD\nbMGiG+rQa9/AtvjhTl+jZl2O8cgv0VdeF5FTCPXn67HN/vdghxEeUtKg+qTXkw7Cmaxk9aN/9t6/\naSbbYaOhtQUO+XdrPH3EsxIFHVFKQdYQCOCNYXfp9/6OmnQxqotPKKr/QOibCVs/C2BkgaHras1P\nV84pwQ4lLChHatTOpJEE7087t0Ftjdl752ytl+mz0RsK/XvdIwfBjV2cuqMGDgnozB936Lpa9Lq3\nUVdc0+1r1ayvYax7JwBRBZYu3oAaOylsVhoHnSN669FIgvcTrTXGay+j5uWfN1SipuWhN33o16JY\n5hTJbN9PNHBoyG3ErQteRE2d5daNajX5Eig7gK4oD0BkgaM3fwJTZgQ7jPARxatZJcH7y85tUFeD\numjWeQ+rflnmZsA7t/nlsuYmHwcsGW9UA7PRZQd8Po9V9J4d6G0bUfO/7dbrVc+eqIvnoD+MnJut\nuqEe9u5E5cjwjLtUciq6OjpXs0be3acQ8M/e+zc7vNHZPkyjnJOsv3h77etkC/bk7D8IKo+iW86g\nesb6fj4f6NZWjBdXoPJ/aNbLcZO69HKMB3+OvvxqaKiFmlPmP/aaU+ZMo35ZqJlfDZtdkHTxZzB6\nvFclKKJWFJcMlgTvD+2996mXdvi0umgmxt//jG5uQvWKs/baZzfZtiJhqZ49ISPTnJUzeLgFwXlP\n/6MAUtJRuZ4NTah+A2DYaIyf/8AsHZucgko2/09yKnrNm7BnB3z75qCtCNWGYQ4hnDiOrjwOJ46D\nUqjMgdB/IGRkumLTn69HTcsLSpxhS6ZJCquYvfc/m713W8fTFFVSCgwbbS41nz7b2usfOYCy4AZr\nO5U1BH1oHyqICV5XHkO/txrbLx7x6g+X7eZfAHR4rJ77HxjPLUM//AtsCxajHO5tb2gFY+Uf0CWf\nQ1UFJNihT1/z3kJ6P2hrxdj4IRw9DCcqID0D+g2E3aWo//pJwGKMCEkOaKhDt7ZG5JTZrkRXawNh\nx1aor+20997OHKZZCxYneMoOwoix1p1v4BBz45Ag0VpjvPQk6t+u9npVald/FFSvOGz/vQj95kqM\n+35iJvkhHZdnsJL+sgRd/Bm2W5dAet8uFyzplhaoKEeXH0ZdPBvVW/ZX8ISKOWfrvijbs1ZuslrM\neG816or8Tnvv7dTEabD3S3SttftFWjEH/lzmVMngzaTRRR9D9UnUV6/y2zWUUtjmfQvbt/4L49G7\nMfy82lhrjfHqH1FXXYfKHNTtalTVsydqwGBsF82UnZu8FaXDNJLgLaS1hv273Lp5qnrFoSZMRW/8\nyLrrt7XBscOQOciyc7b34LXW1p3TTbqxHr3yGWzfvjkgH63V5Iux/fgedMGLGAUv+u9CWzdCc5Nr\nfYQIAMfZ1axRRhK8lU6dgB49UUkOt15u+aKnY0cgOdXSGRYqMQni4s0bfwGmX/2juVvR8DEBu6bK\nysb2i0fQa95A19VYfn5ttGEUvIht/re7/ZQnrKNSonM1qyR4K3m6gnT0eKiuQh8ts+TyumSTf6Ze\nDgx8yQK9dye6eCPq6u8G9LoAyp4EI8eh/bBWQW/8EOJ6w4Splp9bdCFKV7NKgreQLvNsBouKiUFd\nNAv9WaE119+yATVxuiXnOlcgSxbo5maMN1ZiPPZrbNfd5HOJV2+pMRPMG+YW0q0t6L//Gds3vhM2\n8+4jhozBC5+VHfB8k+vpeWavzke6usocohk1zudz/atA3GjVhoHxyQcYSxagy/Zj+8XDQb2hqMZM\nQG8vtvTeg/7oH+ac9lE5lp1TuMfcui/6xuBlmqSFdNkBbJd/w7ODBg0zy/JWHvNpcwpdvBGVMwXV\nww+LdQYOgVees/68Z+md2zBeeRZ69MR2008DOubeqf4DobUVKo/5VFe/nW5uQr+5Ctutv7QgOOEx\nR2pUDtFIgreIbmkxk0H/gR4dp5Ry9RZV3te8v/6WDdhm+WeLOtL7mQtFGuotHzIxfv8Qev8u1NXf\nQ+XOCJmhC9f7smNrl2WJ3aXXvAHDRwd1wVhUOztEo7UOmd+xQPApwd9yyy3Ex8ejlCImJob777+f\n+vp6li1bRmVlJRkZGSxcuJD4+ChYmHGszFyw4k3NljET0Fs3gZcJXjfWw76dsODnXh3fHWWzmTeP\ny/aDhcMLuqEeva0I29IXQ3Pj6DET0Ns2ev2+tNMN9ej3CrD97DcWBSY8pXrHg7LB6QaID859nWDw\nKcErpbjrrrtITPznD6ygoICcnByuuuoqCgoKWL16Nddff73PgYY6T2+wnkuNmYhe+QzaaPNq6pwu\n+RxG5fi1AJXKMm+0Wjp+XFEOffuHZnLn7Dj8Ku/fl3b6vdXmdM/+F24vKAIoJdWcCx9FCd6nm6xa\n6wtuQhUVFZGXZxZDmj17Nps2bfLlEuHDixus7VRKmln86qB3NzL1lk/NlbH+5Iepkvr4EVRGpqXn\ntJJKSTPrmPjQbt1Qh173Durr11oYmfBKFM6k8SnBK6W49957Wbx4MR988AEANTU1OBzmQh+Hw0Ft\nba3vUYZ2BJ0dAAAeQUlEQVQBs8jXEK+PV2Mnordv8fy6Z5ph+1aUn+dVq4FDrZ9Jc/youa1eCDPv\nj3g/XVKXFMEIJyrKaqCEIuVIQ5+Krpk0Pg3R3HPPPa4kfs8995CZ6f4/1tLSUkpLS13f5+fnY7fb\nfQknoGJjY8+Lt6b8EImjndi8bEPL5ItpfusVEj08vuXzL2jKHo49078f//VoJzXHy0nsHYfq0fOC\n9nuj4WQFPSdOJTaE3/eWydNpfnc1ifk3uB7zpO0NpZvpMe1SeoVwGz1hxfseLKf79kedrifOy/hD\nue2rVq1yfe10OnE6nYCPCb69p56UlMRFF13Enj17cDgcVFdXu/6fnJzc4bHnBtGurq7Ol3ACym63\nu+LVdTXopibqY3ujvGyDHjQUY88Oak+c8GivTeOTNTA+NzA/u7QM6nbvQGUNOa/93mo7coi2WV+j\nOYTfdz1wGMau7dRWnXAVBXO37bq1BWNbEW3X3MiZEG6jJ6x434PFiE+Eo4dp8TL+UG273W4nPz+/\nw+e8HqJpbm6mqakJgKamJrZt28agQYOYMmUKhYWFABQWFpKbm+vtJcLH2fF3X6Zfqbh4cw/U3V+4\nfYxua0Nv2+SX1asdUQOHoA9ZMw6vtT57kzXEh2jiEyBrMOzd6fnBu74wd4xKsmB3LeEzc4gmusbg\nve7B19TU8NBDD6GUoq2tjUsvvZQJEyYwbNgwli5dytq1a0lPT+eOO+6wMt6QpI8ctGSTDTV2ojnv\nepyb+23u2QEpaW5tQG0JK2+01lWDLcYsZhbizPnwxWb5Ag/orZv8fm9EeCAl+ipKep3gMzIyeOih\nhy54PDExkSVLlvgUVNgpOwBDRvp8GjV2IsaLK9x+vS72T+2ZzqisIRilr1pzsuNHLVkhGghqzASM\nVc/C1e4fo7VGb91obughQkMEzqLRn6+HzGs6fT6satFordEH92C8+gJtv7oF/cXnwQ4J8G0O/Hmy\nR0BVpVubgGitzeJikwKX4Blk9uCtqM+iK8pRIT484zJ0FBw/gm7wYPz1yEHz/1bW5he+SXJAfS26\ntTXYkVjGePtvXT4f8gleGwZ6706MV57FWPxfGL83PzWoMRPQmz8NcnRmfW+OHoYBvv9DVjExMGoc\neocbZWoP7wObzeu5995QSSnQo4dZ995Xx4+E/Ph7O9WjJwwfCztL3D5Gb92ImjgtqpbFhzoVEwOJ\nyVBbHexQLKEP7TOHOrsQUrVodGMDHD2MPnrY/H/5YXPMt3c8KncGtlvuhKxslFLoI4cwHv91sEOG\nimOQ5DBvklpAjZkA24thWl6Xr9NbPkNNmh74BJKVbb4ng4f6dBp9/ChqSvhsP+cah3czZr11I7b5\n3/ZzVMJjjlRzmCY1PdiR+Ex/9B5qxle7fE1IJXjjZ983Zx30HwiZA7Hlfc2cndJRlcXMgdDSgq44\nakkxKK8dOeDZJh/dUGMnYrzzardFkfSWT7F9e4Fl13WXueDJghutx4+g+g7w/TwBosZOwFj3tluv\n1TWnzE8oI53dv1gEVkpkjMPr5mb0po+w/WpZl68LqQRve/QvZmErN5xXhTGICd6y8fd2fQeAwqzt\n3kntEl1xFOpqzLHhQBs4BP35Jz6dQhsGVB6FvuFxkxWAzMFwuhF94jh0s9hFb9uEGjvJP6WbhU/a\nV7OG+8CZ/vxjGDoKldr1CumQGoN3N7m7nJ1WGEy67ADKwnHwc/9wdXrNdW+bxauCsKenGjjEHP/3\nRXUV9E6wbFgrEJTN5iof3B29bZNsyReq2odowpz+6D1sl3ZfHjykEryn1JjxsHObeaMzWMoOWDpE\nA8CYiegdHSd4452/mZt7XBmk4lV9M6G22ixR7K3j5WEzRfI8bmzjp880w85tqBw31zKIwIqAIRpd\nfggqj0NO94tIwzvBO9LMv8gH9wbl+rqp0bwjb3GyUmMmwK7SC6ZzGe8VoD98F9tP7jXbHgTKFgND\nR9HaxSeM7uiKo2E1/t5OjZmI3rnNHGLqzM5tMGgYKiE0a5ZEu0hYzao/+gfqkrmoHt2PsId1gof2\nKozeJxufHDkE/QdaPlSikhyQlgEHdrkeMz54HV34lpncU4KT3NupcZNpKfahDPTxIxDCZYI7o9L6\nQO8EWks7r/qpt25ETbgogFEJj4T5albdcga9YS1qZtezZ9qFf4J3c1zUH8wbrP6Zh27+4TLbZax9\nE/2Pv2P78T3d3lQJBOWcQuvWjV4veDJ78GE4RAPYvvVfND76a3QHSV4bhnmDdbyMv4esc7buC0d6\n86cwcIjbE0vCPsEzchwc2INubgr8tf0x/n6WWZemGKPwbfS7q83knpbhl2t5LHMguq3N7Il74/gR\nc7ZQGFI5U0j48a8xnvktxsYPz3/y0F6I643qF55tiwrtu541nQ5uHF7SH72HuvRyt18f9glexfU2\nF93sLu3+xRbTRw6gBmT75+TDx8Khvei3X8F2x687XgsQJEopek6civ5is8fH6rY2OFEBIdQeT/UY\nnYPtjl+jX3kOY+2brsf1Vum9hzqlVNjWpNEV5VB+yKPd28I+wQPdTiv0B601lB30Xw++Vy/Uf95g\n9txDcMZJj/EXdThM0a2TlebK31j3a96HIpWVje1n96Pffw3j738+W1zsM6keGQ4cqRCGN1r1R/9A\nXTzHoz2MIyTBB/5Gq66qgNheKHvHG5pYwTZ3XsjuWdojZwrs2W5OC/REGNWg6Y7q0w/bogfQ2zai\n//CIWaNn2OhghyW6oVLS0GHWg9etrehPPkDN7H7u+7kiIsGTPQJOnTCXiAdI28F9AS30FWpsCYnm\np5fd2z06Th8/Gj5VJN2gkhzYfnIfuuaUufgsJvCLz4SHHGnh14PfthH6ZqI6Wd3emYhI8ComBkbm\nBHQ2TdvhfdaWKAhDyjnZ83H4MJ0i2RXVO968T/Ltm4MdinCHI/ymShofvuvRzdV2Pid4wzBYtGgR\nDzzwAAAVFRXceeed3H777Sxbtoy2tsCsMlVjJ5pVGAOk7dA+v42/hws1bjK61LMEH1Z14D2gbDap\nPRMmVEpqWA3R6IN74cghVO4Mj4/1OcG/9dZbDBjwz2lhL730EvPmzWP58uUkJCSwZs0aXy/hlvb5\n8N3Nb9UtLeiaU+ijZei9O9ElRRibPjZXpXqg7dDeqO/BM2iYuYFCVaX7xxwvj7gevAgzYTZEY7yx\nEvW1q1E9Yz0+1qcEX1VVxZYtW7jssstcj33xxRdMm2ZO48nLy2Pjxo2+XMJ9fTPNDTCOlXX4tC75\nnLaF12Pcmo9x920YT9yLsfIP5grRD17DeGaZ24sfdFMjxvGj0M+z8bBIYxbgmuh2L163tpjT0wK1\nh6wQHQmjIRp9eD/s34Vyo7BYR3wqF/zCCy/wne98h8ZGs/dbV1dHYmIitrNVIdPS0jh1KjA3PpVS\nrrIFqv/A854zPnoPXfCiuWHIsDEX1FnXLS0YDyxCr3kTddm8Lq+jDQPjueXEzvwKbR5MV4pY4yaj\nizfALDfGByuPQ2oft2poCOE3ySlwpgldV+PXWXBWMN5Yifq3+V5PK/b6X9rmzZtJTk4mOzub0lJz\nkZHW+oJecGebVpSWlrqOA8jPz8feTZ3t7pyZPJ0z6z8gcf51rnia/vo8LR+9T+L/PUpM5sBOj21b\n+H/U/+pH9B4/mR5d1Fk/vepZWutrSV50Hy3hudrZErGxsdjtdoypM6lb+TSJvXt3m7hbvjxJc+Yg\nEn18n4Otve3RKFLaXj92IrH7vyR2xmXdv/isQLe97fB+6vfuIOm2X5oLOruwatUq19dOpxOn09xs\nxusEv3PnToqKitiyZQtnzpzh9OnTPP/88zQ2NmIYBjabjaqqKlJSUjo8/twg2tXVebCpcQd09kiM\npx+h9uynBv3iE+iyg9gW3U+j3QFdnT8hCXXtTdQv/T9sv1yKik+44CXGpo/Rhe9gu/NhWrTv8YYz\nu91utj+mJzqtL3XbPkeNGNvlMcbBvZCWEfY/N1fbo1CktN0YlcPpok9o9mDlcaDbbqx6Dr5yJfUt\nrdDS+XXtdjv5+fkdPuf1GPx1113Hk08+yeOPP87//u//Mm7cOG677TacTicbNmwAYN26deTmdl+z\n2CrKnmwugd+5FePxX6Nra7D99D5zs2h3js+diXJOQv/x8Qs+ieiDe9B/fgrbLXe6fb5ooca5OV3y\neHnELHIS4U05J6G3F4ds0TF99DB65zbU7Ct8Oo/l8+Cvv/563njjDW6//Xbq6+uZO3eu1Zfokhoz\nAeOJ+1Cpfcxk3CvOs+Pzf4A+Xo4+Z/9NXX0SY8V92L5zM2qQb5tNRyLldG+6pD4emVMkRRjq0x96\n9oQjB4MdSYf0G6tQX7my26GZ7lhyt2vs2LGMHWt+PM/IyOC+++6z4rReUTO/Cun9UHlf63LT6k6P\n7xmL7b9/Zt50HToa+mdhrLgPNfPfUJMv8UPEEWDoKKg4iq6tNmvZd6biqEyRFCFBKXW2F78l5KY7\n62Nl6O1bsH17gc/nioiVrOdS/bKwzf53r5L7P88xAPWt/8L43YPo55aj0jJQ875pXZARRvXoAaNz\n0Nu72AijuRnqayE1PYCRCdE585OnFwXz/Ey/9Qrqsnmo3r7vWRxxCd4qtml5qFHjzM0pbrjdpz8Y\n0UCNmwxd/WOpLIf0vkHZKFyIDo3Kgb1fel4wz490RTm6pAg1t+vp2u6SCcldUN+5BdXWJvO23aCc\nkzEKXkIbBsrWQb9BbrCKEKPiE2DgENhVCuMmBzscAPRbf0XN/g9UfKIl55MefBeUUpLc3aTSMiAx\nydzVqANyg1WEIuWc2OXQYiBoow395RcYf/6duafAV6607NyS4IVl1CVzMZ5Ziu5oZoLUoBEhKFjj\n8LqtDb1jK8aLKzB++n2MlU9Dcgq2xQ+jEqzpvYMM0QgL2b72nxhJKRgP34n6fzegLrnMde9CV5Rj\nu8T9VYNCBMTgYVBzCn2qCpWSFpBL6s2fYPxpBaRloKbMwLboN37b2EcSvLCU7ZK56OzhGE89AF+W\nwPULzLUIx8uhb+htPSiim7LFnN3ycwtqxlf8fj3d0oKx8hlsN/0UNWaC368nQzTCcipzELY7HwEU\nxr0/Ru/ZAWeaITk12KEJcaGxE7ueAWYhvf4fkDkwIMkdJMELP1G94rDd+L+oy6/GWP5/kNFfppqK\nkKSck9A7itGGfzcn0i1n0G/9FduV1/v1OueSIRrhV7YZl6GzR8CJ48EORYgOqdQ+YHfAoX3m/s5+\noj98FwYNRQ3x3zX+lfTghd+pAYNQEy4KdhhCdEo5J/l1No1ubka//TdsV17rt2t0RBK8ECLqqbGT\n/DofXq97C4aNQg0a5rdrdEQSvBBCjBwHB/ehT3u2N7M7dNNp9LursV15neXn7o4keCFE1FO9esHQ\nkebUXovptW+iRuWgBgy2/NzdkQQvhBD4Zxxen25E/+PvqK8Hduy9nSR4IYQAV314K+kPXkc5J6H6\nZ1l6Xnd5PU2ypaWFu+66i9bWVtra2pg+fTrXXHMNFRUVLF++nPr6eoYMGcKtt95KTIyUiBVChLgB\n2dDcZJYIz/B91bVurEd/8Dq2nz/oe2xe8roH37NnT+666y4efPBBHnroIYqLi9m9ezcvvfQS8+bN\nY/ny5SQkJLBmzRor4xVCCL9QSqEumoV+b7Ul59P/eA014aKgVlH1aYimV69egNmbb2trQylFaWkp\n06ZNAyAvL4+NGzf6HqUQQgSAmvdN9JYN6E7KXrtLNzagC99E/Udwd4LzKcEbhsHPfvYzbrrpJsaP\nH0/fvn1JSEjAdnbDh7S0NE6dOmVJoEII4W8qIRF11fUYL/8erbXX59GFb6HG5aL69LMwOs/5VKrA\nZrPx4IMP0tjYyMMPP8yRI0cueE1n9UdKS0spLS11fZ+fn4/dbvclnICKjY0Nq3itFs3tl7ZHdtv1\nv3+D+vX/oNe2z4id+VXX4+62XTc3UbvmDRKX/JaYAP2sVq1a5fra6XTidDoBi2rRxMfHM3bsWHbt\n2kVDQwOGYWCz2aiqqiIlJaXDY84Nol1dXZ0V4QSE3W4Pq3itFs3tl7ZHftt1/g9pfOoBmkaNR8WZ\nm1+723bjg9fRQ0fRmJwGAfhZ2e128vPzO3zO6yGa2tpaGhvNVV9nzpyhpKSErKwsnE4nGzZsAGDd\nunXk5uZ6ewkhhAgKNWw0asx49JuveHScbm0xV61ecY2fIvOM1z346upqnnjiCQzDQGvNJZdcwuTJ\nk8nKymLZsmWsXLmS7Oxs5s6da2W8QggREOrq72HcfSt65lfdngmjNxRC/4EoP1al9ITSvtxJsFh5\neXmwQ3BbtHxU7Uw0t1/aHj1tN95djf6yhJjbftVt27XRhrHkFmzf/RFq1LiAxZiZ2fkfH1nJKoQQ\nnVCXzYPKo+htm7p9rf78E7AnwUhnt68NFEnwQgjRCdWjJ7Zv/hBj5R/QLWc6fZ3WGv3WK9iuuCak\ndi6TBC+EEF1Q46ZA/4GcfuEJdHNTxy/aVgQoyAmtSSWS4IUQohu2796CbqzH+OX/YHz8j/P2b9Va\nY7y1ChVivXeQBC+EEN1SSSkk3LYE24LF6PUfYPx64T8rT+76AurrUFMuDm6QHZBNt4UQwk1q6Chs\nP7sftnyK8dJTkNEfGhtQ//6fKFvoVc2VBC+EEB5QSsHkS7CNvwi97h301o2o6bODHVaHJMELIYQX\nVI+eqMu+Dpd9PdihdErG4IUQIkJJghdCiAglCV4IISKUJHghhIhQkuCFECJCSYIXQogIJQleCCEi\nlNfz4Kuqqnj88ceprq7GZrNx2WWXccUVV1BfX8+yZcuorKwkIyODhQsXEh8fb2XMQggh3OB1go+J\nieF73/se2dnZNDU1sWjRIiZMmMDatWvJycnhqquuoqCggNWrV3P99ddbGbMQQgg3eD1E43A4yM7O\nBiAuLo4BAwZQVVVFUVEReXl5AMyePZtNm7ovlC+EEMJ6lozBV1RUcPDgQUaOHElNTQ0OhwMw/wjU\n1tZacQkhhBAe8jnBNzU18dvf/pYbbriBuLg4K2ISQghhAZ+KjbW1tfHII48wa9YsLrroIsDstVdX\nV7v+n5yc3OGxpaWllJaWur7Pz8/vcvPYUGS324MdQlBFc/ul7dEpVNu+atUq19dOpxOn8+y+sNoH\njz32mH7++efPe+xPf/qTXr16tdZa69WrV+sXX3zRl0t0aeXKlX47d3d+9atfBe3aWge37VoHt/3S\n9uCJ5t/7cGy71z34nTt38tFHHzFo0CB+9rOfoZTi2muvZf78+SxdupS1a9eSnp7OHXfc4fNfp864\n/koFQZ8+fYJ2bQhu2yG47Ze2B080/96HY9u9TvCjR49m5cqVHT63ZMkSb0/rkWC+2RkZGUG7NgT/\nH3ow2y9tD55o/r0Px7bLSlYvBfsfWrBFc/ul7dEpHNuutNY62EEIIYSwnvTghRAiQkmCF0KICCWb\nbp/jySefZPPmzSQnJ/Pwww8DcPDgQZ5++mmam5vp06cPt912G3FxcXz88ce89tprKKXQWnPw4EEe\nfPBBBg8ezL59+1ixYgUtLS1MmjSJG264IbgNc4MnbW9ra+Opp55i//79GIbBrFmzmD9/PgDFxcU8\n//zzaK2ZM2eO6/FQ5knbW1tb+f3vf8++ffuw2WzccMMNjB07FiAs33dvigY+++yzFBcX06tXL265\n5RZXyZLCwkJWr14NwNVXX+0qWRKqPG17eXk5K1asYP/+/Vx77bXMmzfPda6Q/b23dqZmeNuxY4fe\nv3+//vGPf+x67Oc//7nesWOH1lrrtWvX6r/85S8XHHfw4EH9ox/9yPX94sWL9e7du7XWWt933316\ny5Ytfo7cd560/aOPPtLLli3TWmvd3Nysb775Zl1ZWanb2tr0j370I11RUaFbWlr0T37yE11WVhb4\nxnjIk7a/8847esWKFVprrWtqavSiRYtcx4Tj+37q1Cm9f/9+rbXWp0+f1rfddpsuKyvTf/rTn3RB\nQYHW+vz1LJs3b9b33Xef1lrrXbt26V/84hdaa63r6ur0j370I93Q0KDr6+tdX4cyT9teU1Oj9+7d\nq19++WX9+uuvu84Tyr/3MkRzjtGjR5OQkHDeY0ePHmX06NEA5OTk8Nlnn11w3Pr165kxYwYA1dXV\nnD59muHDhwMwa9assCi45knblVI0NzdjGAbNzc307NmT3r17s2fPHvr370+fPn3o0aMHM2bMiJi2\nb9y4EYCysjJycnIASEpKIiEhgb1794bt++5u0cCioiIANm3a5Hp8xIgRNDY2Ul1dzdatWxk/fjzx\n8fEkJCQwfvx4iouLg9Imd3laMDEpKYmhQ4cSExNz3nlC+fdeEnw3Bg4c6Prl/vTTT6mqqrrgNZ98\n8gkzZ84E4OTJk6SlpbmeS0tL4+TJk4EJ1mKdtX369OnExsZy0003ccstt/D1r3+dhISEC9qempoa\nMW0/ceIEAIMHD2bTpk0YhkFFRQX79u2jqqoqIt73rooG1tTUABf+fre/x+H+3vtSMDGU2y4JvhsL\nFizg3XffZfHixTQ1NdGjx/m3Lfbs2UNcXBxZWVkA6A5mnSqlAhKr1Tpr++7du4mJieH3v/89jz/+\nOK+//joVFRUdniPS2j537lxSU1NZvHgxL7zwAqNGjcJms4X9++5L0cD2+1Dhyh8FE0PlvZebrN3I\nzMzkzjvvBMyP7Vu2bDnv+XOHZ8DsuZ3by6+qqiIlJSUwwVqss7avX7+eiRMnYrPZSEpKYtSoUezb\nt4/U1FRXTxfMnk2ktd1ms/G9733P9bolS5bQv39/EhISwvZ996RoYGpqaoftTEtLO694YFVVFePG\njQtsQ7zgS8HEdqH8ey89+H+htT6vN9L+8cwwDP72t7/x1a9+9bzXbtiwgUsuucT1mMPhcI1Ha635\n8MMPXb84oc7dtqenp/PFF18AZu9n9+7dDBgwgOHDh3Ps2DEqKytpbW1l/fr15ObmBr4hXnC37WfO\nnKG5uRmAbdu2ERMTw4ABA8L6fX/yySfJysriiiuucD02ZcoUCgsLAXN2TPv7mJuby7p16wDYtWsX\nCQkJOBwOJkyYQElJCY2NjdTX11NSUsKECRMC3hZPedL2c537uxLKv/eykvUcy5cvZ/v27dTV1ZGc\nnEx+fj6nT5/m3XffRSnF1KlTue6661yv3759O3/+85+55557zjvPvn37eOKJJ1zT5b7//e8Huike\n86TtTU1NrFixgiNHjgAwZ84c15Sx4uJinnvuObTWzJ07N3Smi3XBk7ZXVlZy7733YrPZSE1N5X/+\n539IT08HwvN937lzJ3fddReDBg1CKeUqGjh8+HCWLl3KiRMnXEUD229EP/PMMxQXFxMXF8eCBQsY\nOnQoYCbDV199FaVUWEyT9LTt1dXVLF68mNOnT6OUIi4ujqVLlxIXFxeyv/eS4IUQIkLJEI0QQkQo\nSfBCCBGhJMELIUSEkgQvhBARShK8EEJEKEnwQggRoSTBCwGsWLGi0z2GhQhXkuCF8MDdd9/NmjVr\ngh2GEG6RBC+EEBFKio2JqLR//36eeuopjh07xqRJk1yPNzQ08Nhjj7Fnzx4Mw2DkyJHcdNNNpKam\n8pe//IUdO3awe/duXnjhBfLy8rjxxhs5cuQIzz33HPv27XOVOrj44ouD2DohTNKDF1GntbWVhx9+\nmLy8PJ577jmmT5/u2sykvZbIk08+yYoVK+jVqxfPPPMMAN/61rcYM2YMN954Iy+88AI33ngjzc3N\n3HPPPVx66aU888wz3H777TzzzDOUlZUFs4lCAJLgRRTavXs3bW1tXHHFFdhsNqZPn+7aiSkxMZGp\nU6fSs2dP4uLi+MY3vsGOHTs6Pdfnn39ORkYGeXl5KKXIzs5m6tSpbNiwIVDNEaJTMkQjos6pU6dI\nTU0977H2ipBnzpzh+eefZ+vWrTQ0NKC1pqmpCa11h5s4nDhxgt27d59XOdIwDC699FL/NkIIN0iC\nF1HH4XBcsKXaiRMn6NevH6+//jpHjx7l/vvvJykpiQMHDrBo0aJOE3xaWhpOp9O1OYgQoUSGaETU\nGTlyJDExMbz99tsYhsFnn33Gnj17ADh9+jSxsbH07t2b+vp6XnnllfOOTU5OPm97wilTplBeXs6H\nH35IW1sbra2t7N2711UrX4hgknrwIirt27eP3/3ud+fNounfvz+XX345y5cvZ+/evaSmpjJv3jye\nfvppXn75ZWw2G7t27eKJJ56grq6OWbNmccMNN3D06FFeeOEF125O2dnZfPe732Xw4MFBbqWIdpLg\nhRAiQskQjRBCRChJ8EIIEaEkwQshRISSBC+EEBFKErwQQkQoSfBCCBGhJMELIUSEkgQvhBARShK8\nEEJEqP8fHWFbsn7krg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1070a7400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['val'].resample('A').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Add the fill\n",
    "\n",
    "`ax.fill_between` requires three things\n",
    "\n",
    "    - a list of x values\n",
    "    - a list of minimum values\n",
    "    - and a list of maximum values\n",
    "\n",
    "We have all of those, so let's draw it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: \n",
      ".resample() is now a deferred operation\n",
      "You called index(...) on this deferred object which materialized it into a series\n",
      "by implicitly taking the mean.  Use .resample(...).mean() instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x109244eb8>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEWCAYAAABsY4yMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDMMwcer/AXLhRjDsti4zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1082d20f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['val'].resample('A').mean().plot()\n",
    "\n",
    "x_values = df['val'].resample('A').index\n",
    "y_maxes = df['val'].resample('A').max()\n",
    "y_mins = df['val'].resample('A').min()\n",
    "\n",
    "ax.fill_between(x_values, y_maxes, y_mins, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}