Pandas-Pivot-Tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For the examples in this section, we'll use the database of passengers on the *Titanic*, available through the Seaborn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables by Hand\n",
    "\n",
    "- Start by grouping according to gender, survival status, or some combination thereof. You might be tempted to apply a ``GroupBy`` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        survived\n",
       "sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('sex')[['survived']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- If we look at survival by both sex & class, we might: *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Two-dimensional ``GroupBy`` operations are common enough that Pandas includes a convenience routine, ``pivot_table``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Table Syntax\n",
    "- Here is the equivalent to the preceding operation using the ``pivot_table``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "class      First    Second     Third\n",
       "sex                                 \n",
       "female  0.968085  0.921053  0.500000\n",
       "male    0.368852  0.157407  0.135447"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-level pivot tables\n",
    "\n",
    "- Pivot tables can be specified with multiple levels, and via a number of options. For example, we might be interested in looking at age as a third dimension. We'll bin the age using the __``pd.cut``__ function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.423729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 80]</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.133663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class               First    Second     Third\n",
       "sex    age                                   \n",
       "female (0, 18]   0.909091  1.000000  0.511628\n",
       "       (18, 80]  0.972973  0.900000  0.423729\n",
       "male   (0, 18]   0.800000  0.600000  0.215686\n",
       "       (18, 80]  0.375000  0.071429  0.133663"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = pd.cut(titanic['age'], [0, 18, 80])\n",
    "titanic.pivot_table('survived', ['sex', age], 'class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Repeat the strategy when working with columns; let's add info on the fare paid using __``pd.qcut``__ to automatically compute quantiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fare            (-0.001, 14.454]                     (14.454, 512.329]  \\\n",
       "class                      First    Second     Third             First   \n",
       "sex    age                                                               \n",
       "female (0, 18]               NaN  1.000000  0.714286          0.909091   \n",
       "       (18, 80]              NaN  0.880000  0.444444          0.972973   \n",
       "male   (0, 18]               NaN  0.000000  0.260870          0.800000   \n",
       "       (18, 80]              0.0  0.098039  0.125000          0.391304   \n",
       "\n",
       "fare                                 \n",
       "class              Second     Third  \n",
       "sex    age                           \n",
       "female (0, 18]   1.000000  0.318182  \n",
       "       (18, 80]  0.914286  0.391304  \n",
       "male   (0, 18]   0.818182  0.178571  \n",
       "       (18, 80]  0.030303  0.192308  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.qcut(titanic['fare'], 2)\n",
    "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional pivot table options\n",
    "\n",
    "The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows:\n",
    "\n",
    "```python\n",
    "# call signature as of Pandas 0.18\n",
    "DataFrame.pivot_table(data, values=None, index=None, columns=None,\n",
    "                      aggfunc='mean', fill_value=None, margins=False,\n",
    "                      dropna=True, margins_name='All')\n",
    "```\n",
    "\n",
    "- Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here.\n",
    "\n",
    "- ``aggfunc`` controls what type of aggregation is used (mean = default).\n",
    "The aggregation can be a string (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.). It can also be specified as a dictionary mapping a column to any of the above desired options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
       "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</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>female</th>\n",
       "      <td>106.125798</td>\n",
       "      <td>21.970121</td>\n",
       "      <td>16.118810</td>\n",
       "      <td>91</td>\n",
       "      <td>70</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>67.226127</td>\n",
       "      <td>19.741782</td>\n",
       "      <td>12.661633</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              fare                       survived             \n",
       "class        First     Second      Third    First Second Third\n",
       "sex                                                           \n",
       "female  106.125798  21.970121  16.118810       91     70    72\n",
       "male     67.226127  19.741782  12.661633       45     17    47"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table(index='sex', columns='class',\n",
    "                    aggfunc={'survived':sum, 'fare':'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Notice that we've omitted the ``values`` keyword; when specifying a mapping for ``aggfunc``, this is determined automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- Find the totals for each grouping via the ``margins`` keyword:"
   ]
  },
  {
   "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>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.968085</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.368852</td>\n",
       "      <td>0.157407</td>\n",
       "      <td>0.135447</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class      First    Second     Third       All\n",
       "sex                                           \n",
       "female  0.968085  0.921053  0.500000  0.742038\n",
       "male    0.368852  0.157407  0.135447  0.188908\n",
       "All     0.629630  0.472826  0.242363  0.383838"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived', index='sex', columns='class', margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This displays class-agnostic survival rate by gender, gender-agnostic survival rate by class, and overall survival rate of 38%. The margin label can be specified with the ``margins_name`` keyword, which defaults to ``\"All\"``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Birthrate Data\n",
    "\n",
    "- Download US birth data from the Centers for Disease Control (CDC) at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# shell command to download the data:\n",
    "# !curl -O https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month day gender  births\n",
       "0  1969      1   1      F    4046\n",
       "1  1969      1   1      M    4440\n",
       "2  1969      1   2      F    4454\n",
       "3  1969      1   2      M    4548\n",
       "4  1969      1   3      F    4548"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births = pd.read_csv('data/births.csv')\n",
    "births.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Add a decade column & review male and female births as a function of decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>1753634</td>\n",
       "      <td>1846572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>16263075</td>\n",
       "      <td>17121550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>18310351</td>\n",
       "      <td>19243452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>19479454</td>\n",
       "      <td>20420553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>18229309</td>\n",
       "      <td>19106428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender         F         M\n",
       "decade                    \n",
       "1960     1753634   1846572\n",
       "1970    16263075  17121550\n",
       "1980    18310351  19243452\n",
       "1990    19479454  20420553\n",
       "2000    18229309  19106428"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births['decade'] = 10 * (births['year'] // 10)\n",
    "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Male births outnumber female births in every decade. Use the built-in plotting tools in Pandas to visualize the total number of births by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fIKMsCwBrpRWjuwQR5nVPbT22tspgNLLnRCaZeeWMHuBOB/uWu+4NJp7/+7//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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93ba352a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()  # use Seaborn styles\n",
    "births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()\n",
    "plt.ylabel('total births per year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Iit appears that over the past 50 years male births have outnumbered female births by around 5%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further data exploration\n",
    "\n",
    "- There are more interesting features we can pull out of this dataset using the Pandas tools. Start by cleaning the data: removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th). One easy way to remove these all at once is to cut outliers; we'll do this via a sigma-clipping operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
    "mu = quartiles[1]\n",
    "sig = 0.74 * (quartiles[2] - quartiles[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This final line is an estimate of the sample mean, where the 0.74 comes from the interquartile range of a Gaussian distribution.\n",
    "\n",
    "- Now we can use the ``query()`` method to filter out rows with births outside these values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Next we set the ``day`` column to integers; previously it had been a string because some columns in the dataset contained the value ``'null'``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set 'day' column to integer; it originally was a string due to nulls\n",
    "births['day'] = births['day'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Combine the day, month, and year to create a Date index. This allows us to quickly compute the weekday corresponding to each row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "births.index = pd.to_datetime(\n",
    "    10000 * births.year + \n",
    "    100 * births.month + \n",
    "    births.day, \n",
    "    format='%Y%m%d')\n",
    "\n",
    "births['dayofweek'] = births.index.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Using this we can plot births by weekday for several decades:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wy+7zHD1TxNEzRfSN8mFKQni7jR+JOA/dpClUp6ZQsna1KCjtmCYFpa6uDnk7\nrsF+KxhqTWw8mMXGg9nUGs1oVe7MToxgeF+x3O5lBEGgT6QPvSN0nLxQyi+7zpN6tpjUs8X0jtAx\nJSGcmOD2WylTpG3xiIrGo2s3DMePUZt1AUVomLNNErkFmhSUxMREpkyZwr333ktYWMce5DqjmaQj\nOazbd4HqWhNqTzemDYtgVL8gsQpiIwiCQM9wHT3DdZzKKuWX3ZkcP1/C8fMl9Ajz5s6EcLqFejvb\nTJF2gG7iZHJPn6Jk7WoCH5nvbHPanJtJX28ymXjzzX9x+nQ6ZrOZ8eMncf/9toVO+/bt4cMP38Fi\nsTB58jTuv//PbdaHJgXll19+4ccff+TPf/4zUVFR3HfffYwaNcqlA2c3S73JzLbki6zZe4GKaiOe\nchkzR0Yyun8wCnfRK9hcuoV6szDUmzM5ZfzaICwnL5TSNUTLnQnh9Ajz7lCvG5HWxbNXb+ShYVQd\nPoQxLw/3qzJkdAYmTpzCzJmzefXVF+3HFi169Zr09d999x8efvhRtmzZTH29kW+++ZHa2lr++Me7\nGDNmHP7+et57bxHvv/8x/v565sx5gGHDRhAREdkmfWjSf+Pj48O8efPYvHkzd999N//85z8ZPXo0\nX375JXV1dW1ho8MwmS1sS87lmU/28f3mM9TVm5kyNJy3Hh3CpCHhopjcIjHBWp6aHcfzD/Snb5QP\np7PLeOeHZF7/9jDHzhWL+w06KPUmM2dyyjDWm2/pekEQ0E2cBFYrJevXtrJ1rs/NpK8XBKipqcVk\nMlFXV4tM5oZSqeTkyTSCg0MICgrGzc2NMWPuYNeu7W3Wh2Z9YtbU1LBq1Sq+++47QkNDueuuu9i/\nfz8PP/ww33zzjaNtbHUsFit70/L4Zfd5CstqcZdJGH9bKBNuC0Xt6TpJ6to7UYEa/npXLJl5Ffy6\nO5OjZ4p4/6cUIrqomZIQQWyUjzhjaeeUV9WRcraYlIwi0jJLMNZb6BudxfxpvX63dHVjqOIH4KYP\noGLvbnzunIbbVZnK24rCpT9QeejgDc9dkEowm69PB9UU6gED8bvrnpu+rrH09bffPoZdu7Yzbdp4\namtrefzxp/Dy0lBYWIC//5X09X5+/pw4cfym271VmhSUf/3rX2zYsIHExETeffdd+6bCKVOmMH78\neIcb2JpYrFYOnyrk553nuFRsQCoRGB0fzKShYWjF1O0OIzzAi8dn9iUrv5LVezI5dKqQ/12WSqhe\nxZShEfTr6ttpll+3d6xWKzmF1SSfKSQ5o5jzlyrs5wJ0nigVMlIzilj8cxrzpve+6UUsgkSCbsJE\n8r/+ktJNG/CffW9rd6Fd0Vi8TH+CAAAgAElEQVT6+hMnjiORSPn55/VUVlYwb94cBgwYxI0m/235\npa1JQenSpQtr1669bioGtJvZidVqJSWjmJU7z5FdUIVEEBgR24XJQ8Px1bhWcZ+OTKhezbzpfcgp\nrGL1nkwOnizg45XHCPZTMiUhgv7d/ERhcUHqTRZOZZWSnFFESkYRxRU2V7dEEOgeqiU22pe4aF/0\nOk/qTRYW/5JG8ulCvlhzkocn90Qiubkx9Ro8lOJVP1O+fSs+k6YgVbVtRga/u+5pdDbhKunrN23a\nwG23DUEmk+HtraNPn1jS00+i1+spKLiSvr6wsABfX782s7dJQZkzZ06j55qTpNGZWK1WTlwoZeWO\nc5y7WIEADOml585hEW2W6FDkeoL9VDwytTdTh1Wzes8F9p3IY/HPxwn0VTJ5SBgTR4gpXZxNhcFI\naobNlXU8s4Q6oy0u4imXcVtPPbHRPvSJ9EGpuLbgk5tMwvN/HsSzH+9i/4l8FO5SHhjX7aa+JQsy\nGd53jKPwx+8pTdqE79Tprdq39kRj6ev1ej1Hjhxi3LiJ1NbWcuLEce6++w+Eh0eQnZ3NxYu5+Pn5\ns3nzRl566dU2s7fJ1Cvp6em89NJLpKenYzQa7cdPnjzpcONawu4j2azccY5T2WUA9O/mx7RhER0i\n/1RHS/+QX2pgzZ4L7Dmeh8VqRauWE9nFi+ggDVFBXoQHqG/JH++quOL4Wa1WcouqSckoIjmjiHO5\nFVz+YPD39iCuYRYSHaxp0o3l56fmQnYJb313lKyCKsbfFspdo6JuSlQsdXWc+/sCsFiJfOsdlykT\n7Mixe+ml50hOPkxZWRk6nQ8PPTQXg6GGFSuWAjBy5O088shjCIKAwWDg9df/SWbmecDKxIlT+MMf\nHgBg795dfPjhe1gsZiZNupM//emhZtvQ0tQrTQrKPffcwxNPPMEbb7zB559/zn//+1+USqVL13V/\n6dO9HDlVAEDfKB+mD48kLKDj1J9wxQ+k1qCwrIZ1+7NIPVtMSUWt/bhUIhAWoCYq0CYw0UGaNi2f\n3Nq4yviZzBZOZZeRfMbmyioqt/3PBQFigjTExthEJEDneVNicLl/FdVG3vzvEfJKDEwfEcmUoeE3\nZV/xr6soXrUS37tmoxs34aaudRSuMnaOwuG5vIxGI0OGDMFqteLv78+TTz7J/fff79KCcuRUAT3C\nvJk+IpJoBxS3EnEMfloPHhjXDV9fFafOFnH2YjkZOeWcvVjOhbxKzl2sYNMh23O91XKigjREB3oR\nFawhTK8Wsxg0g6qaelLPFpGcUUza+WJq6myuLA+5lIHd/YmL9qVPlA8qD7cm7tQ0Xkp3nr4njje+\nPcLKHefwcJcyZkBIs6/X3j6akvXrbGWCE8cgcWu5TSKOpUlBuZxCXqPRkJ6ejl6vJzc31+GGtYT/\nvDye+lpj008UcUkEQcBHo8BHo2BQD9sSSGO9mcy8Ss7mlpORW87ZixUcSi/gULptJiqTSggPUNvd\nZFFBGnHlHjZX1qVig92VlZFbbl8J5KdVkNCnC3HRvnQN0TpEkHVeCp6+N443vz3Cd5vPoHCXMaxv\nl2ZdK1Wp0I4aRemG9VTs3Y12xKhWt0+kdWlSUCZOnEhpaSlz587l3nvvxWKx8D//8z9tYdsto1XL\nKRQFpUPh7iala4iWriG2/GBWq5XC8lrO5pbbRebcxQoycsvt1/h4KYgO1hAVaBOYEH9Vp5jFmMwW\nzmSXkdwQVC8oqwFAAKKCNMRG+xAX40egz825sm4VvbcnC+6JY9F/j/DVupMo3KUM6N68BT3eY8dR\nlrSZ0nVr0SQMbzdlgjsrN1UPpb6+nrq6OlRtvIzvVujofk6xf9dTZzRz/lLFVa6yCqpq6u3n3WUS\nwrt42eIwgRqigjR4Kdt+I6sjxq+6tp5jZ4tJziji2LkSaupMAMjdpfSO0NldWV5tsHG3sf6dv1TB\nW98fxWSy8MSsvvSO9GnW/fK/+ZryHdsImPsIXoOcWya4M7z3WkKjgrJ9++9v1x85cmSLGnY0HX3Q\nxf41jdVqpaC0xuYiyy0nI7eC3KKqazZ/+Ws97IH+qCANQX5Kh5cmaK3+5ZUY7AH1MznlWBo65uOl\nsJUYiPGhW4g3brK2nZX9Xv/SL5Ty/tIUBOCp2XH2GefvYSwoIPP5vyMPDib0xVecml2hM7z3WkKj\nLq/PP/8csAXljx07RteuXQE4ffo0cXFxzRKUxMRElEolEokEqVTKihUrKCsr48knnyQ3N5egoCA+\n+OADNBoNVquV1157je3bt6NQKHjzzTfp1asXACtXrmTx4sUAPProo0yf3nnXpYs0H0EQ0Os80es8\nSehj89vX1Jk4d6miwVVm+703LZ+9abbNYHI3KZGBtllMVMMspjUC1K2B2WIhI6ec5AxbUD2/xADY\nXFmRgV72DYZBfkqXTWnTPcybedN689GKY3y4LIW/3Rvf5ApMd39/1AMHUXlgP9XHUlH1jW0ja0Vu\nlkYF5T//+Q8ACxcu5LnnniM21jaIqampLFu2rNkNLFmyBN1V+Xg+/fRThgwZwty5c/n000/59NNP\nWbhwITt27CAzM5ONGzeSkpLCyy+/zNKlSykrK+Ojjz5i+fLlCILAjBkzSExMRKMRV2+J3Dwechm9\nwnX0Cre9Ji1WK3nFBpvAXLTNYk5eKOXkhVL7NQE6T3ugPzpIQ6Cvss129Btq6zl2roSUjCKOnSum\nutbmynJ3k9CvYVlv32hfNE5w3d0qsdG+PDylJ5+sSuPdH5N55r54An2Vv3uNbsJkKg/sp3TdGlFQ\nXJgmg/Jnz561iwlA3759efnll2+5waSkJLtYTZs2jfvvv5+FCxeSlJTEtGnTbBUB4+KoqKigoKCA\nAwcOkJCQgFZrmxonJCSwc+dOJk+efMs2iIhcRiIIBPoqCfRVMjw2ELB9iF8O8J/NLefcpQp2H8tj\n97E8wLbENrLLFYGJDPTCU9F6s5iCUoM9oH46uwyzxebK8lbLGdRDT2y0Lz3CtO16s+egHnpq6kws\nWX+Kd344yrN/7I+ftvHNi/KQa8sEe8R0bUNrRZpLk4Iik8lYtWoVU6dOBWz1UWSy5qd1f+ihhxAE\ngdmzZzN79myKi4vtKVv8/f0pKSkBID8/n4Cr6h8EBASQn59/3XG9Xm/PuCki4gg8FW70jvSxB40t\nFisXi6rJuFhud5WlZZaSlmmbxQhAF18l0Ve5yQJ8PJs9i7FYrGTkltuX9l4qNtjPRXRR211ZIf4q\nl3Vl3Qoj44KoNZr5cUsG7/xwlGfu64+3uvGl3roJk+1lgoOeeKoNLRVpLk0qwxtvvMHChQv5xz/+\ngSAIxMTEsGjRombd/Pvvv0ev11NcXMyDDz5IZGTjRV5utDZAEIRGjzdFS4NLro7Yv7ZFr/eiX68r\n+ycqqo2culBC+oVS0jNLOJ1Vyo6ianakXAJA5eFGtzBvuofr6B7mTddQ72tmMUq1giOnCjiQlseh\nkwVUGmzL3N3dpNzWK4CBPQMY2FPfbjMCNHf8/jipF4JUyg+bTvHh8lTemDes8ZV3fvGU9+xBxbFU\nPCqLUEVGtKLFzcfVXpuuRJOCEhUVxYoVK6iqqgK4qSXDer1tU5qPjw9jx44lNTUVHx8fCgoK8Pf3\np6CgwB5fCQgIIC8vz35tXl4e/v7+BAQEcODAAfvx/Px8Bg0a1GTbHX0lhtg/5xPupyTcT8n4AcGY\nLRZyC6uvbLzMreBwegGHGzZeCgIE+aqIDPSiwlDPsbNFdleWVuXOyLhAYqN96RnmbS83ba6rp7Cw\nvtH2XZWbHb+x8YEUlVaz+VAOz//fLhbe2w8P+Y0/mtRjJ1Bx4iTnvvuJLv9vXmuZ3Gzay2vzVnF4\n6pXL3OzeE4PBgMViQaVSYTAY2L17N/PmzSMxMZGff/6ZuXPn8vPPPzN69GjAtiLs22+/ZdKkSaSk\npKBWq/H392fYsGG89957lJfbNqzt2rWLp54Sp7siroVUIiFUryZUr+b2+GDANos5m1ve4CqrIPNS\nBTmFti9mYXp1wwZDX8L06g7lyrpZBEHgntEx1NaZ2XXsEh8uS+Wpu2Ptwno1nr37IA8JpfLQQXym\n5eOu19/gjiLOwmE1bouLi5k/fz4AZrOZyZMnM2LECPr06cNf//pXli1bRpcuXfjwww8B276W7du3\nM3bsWDw8PHj99dcB0Gq1zJs3j1mzZgEwf/58e4BeRMSV8VK606+rH/262upRmMwWLhZVEx7ijcVo\ncrJ1roVEEPjzhO7UGk0cOlXI//18nMdm9Lkus4GtTPBkLn3yf5RuWIv+gQedZLHIjbipnfLtiY4+\nLRX7134R+9c4JrOF/12eyvFzJQzo7s8jd/a6rkCX1WIh8x/PYiouJvyNt3Hz9m4Ns5tFZxi7ltDk\nFtrk5OQWNSAiIiLSXGRSCfOn96FrsIZD6QUsWZ9+3cIcQSJBN24iVpOJso3rnWSpyI1oUlBeeukl\nZsyYwbJly6irq2sLm0RERDoxcjcpT9wVS1iAmp2pl/hxS8Z1oqIeMhSpVkvZjm2YGxYMiTifJgVl\n1apVPPfcc+zZs4fRo0ezaNEisrOz28I2ERGRToqHXMZTd8cS6Ktk48Fsftmdec15iZsbujvGY62r\no2zLZucYKXIdzcoaN2DAAN577z0+++wz1q1bx7hx43jkkUc4e/aso+0TERHppKg93VkwOw5fjYJV\nu86z8UDWNec1I0YhUSopTdqEpba2kbuItCXNEpRDhw7x5JNPMn/+fGbNmsXWrVuZMGEC8+a1/Tpw\nERGRzoO3Ws7T9/ZDq3Lnhy0Z7Ei5aD8nUSjwHj0WS3U15Tt+Pzu6SNvQpKBMmTKFd955h9GjR7Nx\n40Yee+wx9Ho9U6dOJTg4uC1sFBER6cT4az1YcE8/VB5uLFmXzoGTV1IvaRPHIMjllG5aj6W+/W0C\n7Wg0KSivv/46P/zwA5MnT74uh9cXX3zhMMNERERELhPkq+Sp2bEo5FI++/UEqWeLgIYywSNGYSot\npXLvHidbKdKkoPTp04ft27ezaNEiFi1a1GThLRERERFHEB7gxROzYpFKBD5eeZxTWbbknNo7xoNU\nSsmGtVgtFidb2blpUlDef/993nnnHTQaDV5eXrz33nt88MEHbWGbyA3IrbrE7qyDVBg77uYqEZHG\n6BqiZf6MPlgsVj5clsr5SxW4eXujSRhGfX4+VYcPOdvETk2TO+XHjRvHypUr8fT0BGw5uqZPn86G\nDRvaxMBbpSPtZrVarZwqzWBz1nZOlpwGQEAgQhNGrF8v+vr2wt/T18lWth6dYTey2L+WcSi9gMWr\njuMpl/H3++Lxt1ST+cIzyINDCH3xnw7LjdYZxq4lNJnLy9/fHw+PK4Vv5HK5vZ6JiGMxW8wcKUgl\nKWs72VW21S0x2kj6h/TmUPYxzpZlcq48k5UZawhUBtDXrxexvr0IUQd16mSDIh2fAd39+bOxO1+t\nTefdH5J59o/xqAcMpPLgAQxpx1D27utsEzsljQrK5VhJjx49mDNnjr2O+6pVq4iPj28b6zoptaZa\n9lw8wJbsXZTWlSEgEO/flzGhIwnzCsHPT81wv2FUGqs4VnSS1KLjnCw5w/rMJNZnJuEt19rFJVob\ngVTSfiv7iYg0xvC+gdTWmfk+6Qzv/JDM06PGwsEDlKxdIwqKk2jU5XX//fc3fpEg8M033zjMqNag\nPU5Ly+rK2Za9m10X91FjqsVd4saQwIEkhgzH18PH/rwbTbtrTXWcLDlNSmEax4tPUmOqAcBT5kFv\n3x7E+vWmh64rcqnr1x7vDG4FsX+txy+7z/PzzvN08fHk4cpdGE8cJ+Tvz+MRE9PqbXWGsWsJYrZh\nF+BSdT6bs7ZzMO8oZqsZtZuKkcEJDA8ejMpNed3zm3pRmy1mzpSdI6UwjdSiNMrqbLVk3CQyuuu6\nEuvbiz6+PVG5X39vV6AzvGnF/rUeVquVn7ZmsOFANv0VFYw9/jPKvrEE/c+Trd5WZxi7luCweigi\nv4/VauVM2Tk2Z20nrTgdAH9PX8aEjGRQQDxuUrcm7tA4UomU7roYuutiuLvrVLIqc0gtTCO5KI1j\nRSc4VnQCAYFobYTdNebjoWutromItCmCIHD37dHU1JnZkQL9NF0gNYW67GzkISHONq9TIQpKG2O2\nmEkuPMbmrB1kVeYAEKkJZ0zoSPr49kAiNCsbTrMRBIEwrxDCvEKYEjWefEMhqQ0zl4yy85wpO8fy\nM78SrAq0i0uQqosY1BdpVwiCwAPjulFrNLGlugd3l1+iaO1qgv7fo842rVMhCkobUWc2svfiQbZk\n76S4tgQBgTi/3owOHUmkJqzN7NB7+jE2bBRjw0ZRXlfJsaI0UorSOF2SQU7VRdae34SPwrtBXHoT\npQ1vdZETEXEEEonAnMk9+bjORH7xUfwPHqB26nQUAQHONq3TcFMxFKPRSHl5OX5+fs1uwGw2M3Pm\nTPR6PZ988gl79+7lrbfewmKx4OnpyZtvvklYWBhGo5G//e1vpKWlodVqef/99+25wj755BOWLVuG\nRCLhhRdeYPjw4U226yp+zgpjJduzd7Mjdy8GUw1uEhmDuwwkMWQY/p7N/z9ejSP8uDWmWk4Up5NS\nmEZacTq1ZlvtG5Wb0hbU9+1Fd11X3FvgimsuncFPLfbPcRjrzSz9aCkD09ZzKTyW4c//FUkrzbid\n3TdH09IYivTll19++fee8OSTT5KQkIDVauXOO+/kiy++QCKRNHvp8JIlSzCZTBiNRqZMmcKcOXP4\n8MMPmTdvHnV1daxZs4YxY8bwww8/UFVVxVdffYVSqeTbb79l/PjxZGRk8NFHH7Fq1SpGjx7Nk08+\nyX333YdE8vvfmg0GY7P/CY4gr7qAVWfX8W36Uk6XncVd6s7Y0JE82OsP9NfHorxBsL25KJXyVu+f\nm0RGoCqAfv59SQwdQZQmHLnUnYKaIs6WZ3K4IIWt2TvJrszFZDHhrdA6TFwc0T9XQuyfY5FKJUTH\nxpC7dSeaomy2CSH07No6blxn983RKJXyFl3fpMvr/PnzqNVq1q9fz2233cazzz7L3XffzUMPPdTk\nzfPy8ti2bRuPPPIIX3/9tf14VUOFtaqqKvsmyS1btvDYY48Btt35r7zyClarlaSkJCZNmoS7uzsh\nISGEhYWRmppKv379bqW/DsVqtXK2PJPNWds5VnQCAF8PH0aHjGBwl/64t4Mlu2ATl54+3ejp043Z\n1ulcqMgmpTCNlKLjJBfafiSChBhtpD3u4q3QOttsERE7np5ygqfdSfkP/6FuRxIrtd7MGBHpbLM6\nPE0KislkAuDgwYOMHDkSDw+PJmcHl3n99ddZuHAh1dXV9mOvvfYac+fORS6Xo1Kp+OmnnwDIz8+n\nS5cuNqNkMtRqNaWlpeTn5xMbG2u/Xq/Xk5+fjythsVpIKUxjc9Z2MitsRYDCvUIZEzqSWL9e7ToG\nIREkRGjCiNCEMS16InnV+Q3iksap0gxOlWaw9PQqQtVBxPr1pq9vL7oo9WJQX8Tp+I0cQeW6X4mv\nOMNHO0/hIZcy4ba2i1d2RpoUlKioKP7yl79w7tw5FixYQG0zK6Nt3boVnU5H79692b9/v/34119/\nzaeffkpsbCyff/45b7zxBq+99tp1NaPBtnKjseNN0VJfYHOoMxnZdn4vq08nkV9VCMCAwL7c2X0s\n3XyjHPqh2hb9a6zdPuHR/JGplBjKOJibwsHcFNIKTpFVmcuv5zYQoPJjYFAsA4Pi6OoT0ewvIL9t\npyPTUftnacj26yr9M82YSuZXSxhed5alW+X4+aiYMCS8Rfd0lb65Ik0KyqJFi9i1axfdunXD09OT\n/Px8FixY0OSNjxw5wpYtW9ixYwd1dXVUVVUxd+5czp07Z59xTJw4kTlz5gAQEBDApUuXCAgIwGQy\nUVlZiVarJSAggLy8PPt98/Pzm5VLzJGBs0pjFTty9rAjdy9V9dXIJDISAgcxOmQEeqXNtqKiKoe1\n7zqBQSnx2njitfEYutaQVpxOSuFx0kpO8eupzfx6ajNqdxV9fXvS17cX3XQxuEmaXljoOv1zDB2l\nfzWmGrIrL5JVmUN2ZS7ZlbkUGIqY3WcKw/2GOds8AGT9ByP5cRkDy05yWNeTxctSMBnrGdzz1lZ+\ndZSxawyHb2xUKBSMGTMGo9FITU0NXl5eDBw4sMkbL1iwwC48+/fv58svv+Tjjz8mISGB8+fPExER\nwe7du4mKigIgMTGRlStX0q9fPzZs2MDgwYMRBIHExEQWLFjAgw8+SH5+PpmZmfTt65w8PQWGQpKy\nd7L/0iHqLSY8ZR6MDx/NyOCheLl37m8tnm4eDAzox8CAftSb6zlVmmHfqb/74gF2XzyAXOpOL5/u\nxPr2opdvdzxkHk3fWMQlMNQbyGoQjezKXLIqcyisKb7mOQqpAoVMwY/HfsUvTk93XeunPrlZJAoP\ntKPHUPLrKuaHVvHuBS2f/3oShZuMuJiOk6HbVWhy2fDGjRt59dVXKSy0uXSsViuCIHDy5MlmN3JZ\nUD755BM2bdrE//7v/yIIAhqNhtdff52QkBDq6upYuHAhJ0+eRKPR8P777xPSsMt18eLFLF++HKlU\nynPPPcfIkSObbLM1v0WcK7/A5qztpBamYcWKj8KbxJARDAkc6JTcWO3pW5LFauFc+QVSC9NIKTxO\nUW0JAFJBSlfvKGL9bGlgtHKN/Zr21L9bwdX7V1VfTXbFFeHIqsyluGHcLuMp8yBEHUSoOpgQdSAh\n6mB8PXRkVebw3pHFeMo8eHbgk2jkzv+iZa6q4tzfnkKqVGGe/zzvLj+GxQJP3tWXHuE3lyHC1ceu\npTg8l9fYsWNZtGgRcXFxt+QLdxYtHXSL1cKxohNsztrBufJMAELVwYwJHUmcX2+nZvBtry9qq9XK\nxeo8m7gUpZFdmWs/F+4VSqxvL2L9etE7PKpd9q+5uNL4VRqrGmYeOfYZSElt6TXPUbp5NghHkF1E\nfBTejcYID5QcYEnyMrp6R/N43ByXWJRS8OP3lG3agP7PfyEnqDcfLktBKpHw9L1xRAVqmr5BA640\ndo7A4YIya9Ysli1b1qJGnMGtDrrRXM+BvMMkZe+gwGCrW93bpzujQ0cSo410idVLHeVFXVxTSmpR\nGqmFaWSUn8ditQV0vRUavOXe6BRafBQ6fBTe6Dy88VF4463wblYcxpVx1viV11U2CEeOPfZxOXHo\nZVRuSkLVwYSqgwjxCiZEFYROob2p172vr4rXtnxMalEaEyPGMilibGt35aapLynh/LMLcfP1I/xf\nr3PkTDGLfz6Oh1zK3/4QT4i/qln36SjvvcZwmKDU1NjSn3/zzTeo1WomTpyIXH5l08vVRbdckZsd\n9Kr6anbm7GVbzm5boF2QMjAgnsSQ4QSqXCt1Q0d8UVfVV5NWlE5KURoXDZcoNpTaBea3aNzV6BQ6\nfDy80SlsPz6KK4LTFrv5W4Kjx89qtVJurCCrIqfBbWWbgZT/pmy0l7vaJhwNs49QdRBauabFX5r8\n/NRkXsznzYMfUlpbxuNxD9NNF92ie7YGeV9/ScWuHXR5ZD7qAQPZfewSX6w5iZfSnWfvi0ev82zy\nHh3xvXc1DhOU7t27X7ds9/LfNxtDcQbNHfSimmK2ZO9k78WDGC31eMgUDA8awqjgBDRyLwdbeWt0\nhhd1Xn4ZZXUVlNSWUFxbSkltqe13je13aV1Zo4Lj5a62i4xO4d0gPDp8FFp0Cm+nbzBtzfGzWq2U\n1pXZhePyiqtK47WrDLVyzVUuK5vbylGv78v9O1+exXtH/g+lm6dLxFOMeXlk/uNZ5CGhhP7jZQRB\nIOlwDv/ddBofLznP3NcfH43id+/RGd57LaFR30F6enqLbuzqZFZksTlrB8kFx7BixVuuZUrocIZ2\nGYhC9vsvKhHHI5VI8fGwicGN1gpZrBbK6yoori2luKaEkqtEp7i2lOzKXPsm09+idlOh87h2ZmMT\nHh06hbfLFiGzWq2U1JZeIxzZlblU1Vdf8zxvubahFHQwoV42EXHGKsQITSjToyayPGM1X5/43unx\nFPeAAFT9B1B16CCGtOMoe/dhdP9gao0mlm8/xzs/JvPMffFolK45/u2BJp3Rr732Gs8//3yTx9oD\nFquFtOJ0NmdtJ6PsPADBqkDGhI4k3r+vWCq3HSERJHgrtHgrtERrI647f1lwSmrLKK61CU5xTald\neHIrL3KhIvuG91a5Ka/McK4RHh06hbZNvnBYrVaKakquEY7sylyqTYZrnuej8CZaG9ngurL9qN2b\nFw9oC24PGc7psnMcKzrB+swkJjo5nqKbOJmqQwcpWbsaZe8+AEwaEo6hzsS6fVm892Myf/tDP5QK\n13abuipNCsqhQ4euO3bw4EGHGOMo6i0mDuYdISlrB3mGAgB66LoyJnQk3byjXSLQLtK6XC04UYRf\nd95itVBhrLxGaK641kq4WJ1nr1fzW5Runte61H4Tz/G4ScGxWC0U1hSTXZFDVlWubcluVS41pmuz\nUvh6+NBNF33Vct0glG5N+/2diSAI3N/jbt48+CFrz28mShPh1HiKIjQMz959MBw/Rs3ZDDyibLbM\nGhlFbZ2ZrUdz+WBpCgtmx6Fwb9+LP5xBozGUdevWsW7dOvbt28eQIUPsx6uqqqiuruaHH35oMyNv\nhcLCSgz1Bnbk7mN7zm4qjJVIBAkD9f0YHTqCIFUXZ5t4y3QGP66z+2exWqg0Vl+J4dSUNsx0yhqE\np4R6i+mG13rKPOxiY1udprtKgLTIVFaSL5yy7/PIqbxoLxdwGX9PX0JUQYR62VZcBauC8HRz7YUw\nl7nR+LlSPMVw+hQ5b72BMjaOoMf/aj9usVr5fPUJ9qXl0yPMm7/e1Rc32bVeC1d4bToSh8VQIiIi\nGDVqFMeOHWPUqFH24yqV6hqBcUUKq4tZdno9uy8dwGg2opDKGR06gtuDh4lZcUWahUSQoJGr0cjV\nRNygAJrVaqWyvuq6GW51iTMAACAASURBVM5l0ck3FJJddbHJdgQE/D396NMQLA9RBxGsDrrpWY6r\nE6EJZVrURFZkrGbJie95zInxFI+YriiioqlOSaYuNwd5kK3ukkQQ+MvEHtQZzRw9U8S/V6Xx6LTe\nyKTO30fTXmhUULp3705MTAwnTpxg+vTpbWlTi3l8zYtYrBa0cg2TIsaSEDhITPMh0qoIgoCXuxov\ndzXhXqHXnbdarVTVV1/rSmsQHp3KC393vU08VIEoZC2rQdFeSAwZzpmysxwrOunUeIogCOgmTubi\n//cBJWvX0OXh/2c/J5NKeGRqLz5YmsrRM0V8tfYkD03u2WoFujo6v+sklEqlnDp1qq1saTUGBPWl\nu7ob/fWxyNr5JjiR9okgCKjdVajdVYR5hVxzrqO7TRrDFk+ZzRsHPmDt+c1EayPo6u2ceIqybyzu\nQcFUHtiHz7TpuPtdSTjrJpPy+Mw+vPtjMnvT8lG4y/jjHV3FWGszaLJiY2lpKUePHiUsLAxBEDCZ\nTJhMJtzcXHcVxNDQAXhLfFwi5YMj6AxV48T+tV9+r3/uUjciNKHsyzvEieJTDAqIRy5t+xmaIAhI\nPD2pOnwIq8mEKjbumvMyqYT+3fw4fq6ElLPFmMxWeobrOsXYtYQmP3Hffvtt3n77bRISEoiPj6df\nv37NLv8rIiIi8lsiNGFMi5pIhbGSJWk/NLpB1dGoBwzEzc+Pit07MZWXXXdeqXDjqdlx6HWerN13\ngTV7M9vcxvZGk4KSnp5u/zl58qT9t4iIiMitkhgynD6+PUgvPcOGzP+/vTuPi6rcHzj+mRkY2TcF\nxhVcEHfFUDFwFxAVQcXsl1lmmzera6m3a8vNLW8l125XreyalpWm5C4uqIBIKaRsghuyKSiDyo7K\nen5/cJ0iRVRmYEaf9+vl6+WcOec836+D8+Wc5zzPE94sMcgUCmzHjEWqqqLgYNhd97E2VzJvaj/s\nrFqw9Ug6odHpTRylYXk07wkJgqDXbven2LawITTjIOcLLjRLHFZPeqKwtqYoMpzqsrK77tPS2oT5\nT7thZa7kq+2n2BaVTlV181xV6bt6C8rzzz8PgIeHB4MHD9b8uf1aEAShMcyNzZjZaxoymYz1KZso\nrmj6BxXkxkpsvX2puXWLwojD9e7naGfGvKn9cLAzY8+vmXzyYxx5hTebMFLDUO/Axry8PBwcHMjJ\nybnb27Rt21angTXWo/wUzaP+lJDIz7A9aH6HLh5h+4VQutm6MLvfi03+ME3NrZuk/20uMrmCjp8E\nI29Rf8e0mYUJn208ScxpNSZKBdN9XBncS79mI2+Mxg5srPcpL3NzcwCsrKwwNTUlLy+Pqqoq2rVr\nh42N/g8OfNSfxBD5GS6RX10drZy4VJrD6fxzKGQKXGw76TC6O8mMjKkpL+dG8ikUVtaYdupc7742\n1qZ0a2eFg60piWnXiT2Th7rgBt2d7DA2MvweBJ0/5XXixAlGjx7NG2+8wezZsxk9ejRxcXH33UB1\ndTWBgYG8+mrt4CFJkvjss8/w9fXFz8+PDRs2aLYvXboUb29v/P39SUlJ0Zxj+/bt+Pj44OPjw/bt\n2x80R0EQ9Fjd/pQwzhekNXkMtqO8kSmVFITtQ6q6+5Q6t8lkMp7s1ZpFLwygUxsrjqeoWbg+lgs5\nRfc87nHQYEFZvHgxwcHBHDhwgLCwMIKDg2lg6EodGzZsoHPn3yv+tm3buHLlimausHHjxgEQFRVF\nZmYmYWFhLFmyRNNGYWEhq1atYsuWLYSEhLBq1SqKisQHJwiPkrr9KRubvD9FYWmJ9dBhVOXnUxxz\n7L6OcbA14+/T+jP+SSeuF93i4x/i2PVLBjU191wE95HWYEFp0aIFAwYM0Lx2d3fHxOT+5hnKzc0l\nMjKSoKAgzbZNmzYxe/Zszfr0LVu2BODw4cMEBgYik8no168fxcXF5OXlER0djaenJzY2NlhbW+Pp\n6cnRo0cfKElBEPRfJ2snAjr7Ndv4FFufMaBQkL8vFKnm/to2UsiZNLQzf3vGDWsLJTuOZvDpxjiu\nF91q+OBHUIPzkri7u7Nr1y4mTJgAwO7duxk6dOh9nXzZsmXMnz+fsj88jnfp0iX27t3LwYMHsbOz\n4/3338fZ2Rm1Wo1K9XvnlkqlQq1W37Hd0dERtVrdYNuN7VzSdyI/wybyu7uprcaSdeMicZdPEX31\nFyb3HKvlyO7B3pKy4cPIOxyOPO00rZ68+9Osd8vN3t6Sft1VrApJ5JekyyxcH8vsKf0Y0k+/H17S\ntnoLioeHh2bJ3/Xr1/P+++8DUFFRga2tLa+//vo9TxwREYGdnR29evUiJiZGs72iooIWLVqwbds2\nwsLCePfdd9m4cSN3e9jsz0sQ/3F7Q8RTNIZL5GfYGpvf1M6TyLh+iS3Je2ht3AYX2/o7ybXNbIQ3\nhEeQ+dPP1HTpecd3TUO5zfRzpWtbK348dJ5Pvz/Brwk5POPtYjBrq+hs+vqtW7c26sRxcXGEh4cT\nFRVFeXk5paWlzJs3D0dHR3x8fADw9vZmwYIFQO0VSW5urub43NxcHBwcUKlUxMbGarar1WoGDhzY\nqNgEQdBfFsbmzOw1jc/ivmR9ykYWDHyryVahVKpaY9H/CUpPnuDG6RTMe/Z6oONlMhlD+rahSztr\nvt51muhTVzifXcirE3rSsbWVjqLWH/X2obRt2/aefxoyd+5coqKiCA8PZ8WKFXh4eBAcHMzo0aM5\nfvw4ALGxsTg7OwMwcuRIduzYgSRJJCQkYGlpiYODA15eXkRHR1NUVERRURHR0dF4eXlpJ3tBEPTS\n7f6UoooSvk3Z1KT9KXZjxwOQvy/0oc/RuqU57z33BH6DOpBXcJNl358k9FjmI99h3+TXYa+88grz\n5s3ju+++w8zMjI8++giAYcOGceTIEby9vTE1NWXZsmUA2NjY8Nprr2k69mfPnm0Q42AEQWicke2H\nkFqQRvL1s4RlRTDGeVSTtGvi5IxZz17cSEnmZnraPcel3IuRQs6UEV3o0dGOtXtOs/VIOikZ+bw0\nvgd2Vo/WAmq31TtS3tCJe9SGS+Rn2LSZX2llGf+M/TdF5cX81e2VJutPuXH2DNnBn2Dez422r/9V\ns/1hcyu5UcG3+84Sn3oNcxMjZvh14wlXh4YPbGKN7UMx/KGdgiA8siyMzXnxD/N9lVSUNkm7pq7d\nMOnUmbKEeMpzsht9PkszJa9P6s10X1cqq2pYvT2Z7/afpbyiWgvR6o8GF9gCOHbsGL/++iuJiYmc\nOnWKU6dO0adPnyYI7+GJqS0Ml8jPsGk7P1sTG4zlRiReSyan9Arujv10vnqiTCZDYWlFSWwMNbdu\nYtnfHWhcbjKZjI6trXDras+F7CKS0q4Td/4qXdpaY2OhH8tAN3bqlQb7UP7+97+TnJxMjx49UCgU\njWpMEAThYYzqMJQLhen/60+JZIzzSJ23+fsywTG0CpiEsb29Vs7btpU57z/nztYjaYT9domlG04Q\nNLwz3gPaG/za9Q0WlPj4ePbs2aPXS/4KgvBok8vkTO9Rux79nvQDdLZ21vkkkjK5HDu/seSu/Zr8\nsH04TntOa+c2NpLz9CgXena045vQM2wOv0ByRj4vjuuuN1crD6PBPpQ/jlIXBEFoLnX7UzY2SX+K\n5YBBGLeypzj6KFU6mEOwd6eWLJ45kD6dW5KSkc8/vokl4cI1rbfTVBrsQ0lISGDdunUUFxdz+vRp\n0YeiB8Q9eMMm8nt4Td2fIpPLkSmMKEuIB4UCe3c3refWQqlgUA9HLEyNSUy7zrGUXEpuVNCtgy0K\nRdM+N6Xz6esrKiro0KED58+fJzk5WfNHEAShOYzqMJSeLbtxJv88B7Midd6elZcXCisriiIOU1V6\n92WCG0smkzHavT0fPO9Om1bmhMflsOS7E2TnNc1TbdoixqEYIDGOwbCJ/BqvtKKMf/5WOz5lTv9Z\ndLHpqNP28vft5drWLXR49hlMhvvotK2Kymq2RFwgPC4HI4Wcp0Z0ZtQT7XT+ZBs00TiU9PR09u7d\ny44dOzR/BEEQmouF0pyZPWv7U9Yl/6jz/hTr4SOQm5lxZfceqkt125bSWMGzPq68ObkPJkoFGw+l\n8vnPSRSX6f9t0gb7UDZs2MDy5cvZu3cvhYWFbNy4kbKyMs109vpK3KM2XCI/w9ZU+dk1YX+K3Lh2\nmeCyU0kUHj5IZZ4ahbUNRja2OmtT1dKMwb1UZOeVkpyRz68pubSzN8fB1kwn7UHj+1AaLCjvvfce\nISEhHDhwgE2bNjFq1CiSk5Px9fVtVMO6Jv7DGi6Rn2Fryvw6Wncgq+QSZ/LPYyw30umtL9MuLljZ\n21J2MZub585SfDSKssQEQIZSpUJmpP2pEU2URnj0VGHawojEC9f4NTmXm+VVuHawRSHXfiHTeae8\nUqnEzMyMmpoaJEmia9euXLx4sVGNCoIgaINcJuf57k9j08KaPRlhXCjM0FlbMiMj2gZOwPmjj2n7\n1jws+j9BefYl8r7/lvR5c1D/uEEr07T8mVwmw3dgB95/zh2VnZlmMOTla7p5QKAxGrxCub1aY2Ji\nIgkJCaSnp5OYmMgzzzzTRCE+HPEboOES+Rm2ps5PqVDiZNWemNyTnMk/z0BVf1oolDppy9y8BTdu\nVqJ0cMBywCCsvIaiMDOj4nION8+coSgynLLTKciMjDB2VCHT4uwiNhYt8OrdmtKblSSlXSc66QoW\npsY4qSy1dttN57e8evfujYWFBR4eHkRGRpKRkcG7776LvZamIdAV8R/WcIn8DFtz5GdnYouRXEHi\n1WQul+bqrD/lz7kpTE0xc+2GzShvTJycqL5xg5vnzlIad5KiI5FUl5RgbO+AwtxcK+0bKeT069KK\ndvbmnEq/zolzV7mUV0oPZ1taGDe+eDW2oIjHhg2QeOzUsIn8dKNGquHLpPWcvn6OgE5++DiP0Hob\n95NbRV4eRVGRFEcfpbq0dl+znr2wGT4C8z79tHbVkl98i7V7TnP2YiE2FkpeGt+DHs52jTpnYx8b\nbrCgZGZmsmDBAtRqNeHh4aSkpBAeHs4bb7zRqIZ1TfyHNVwiP8PWnPndHp9SXFHCX91e1Xon/YPk\nVlNZSWncCYoiI7iZeh4AI1tbrIcMw2rIMIxtbRsdT02NxL6YLHYczaCmRmLMoA5MHNoJo4ccYa/z\ncSgLFy7kL3/5C5aWtQ11796d/fv3N6pRQRAEXbBQmvNCz9r+3fUpGymtaL6Oa7mxMVaDBtP+nXdx\nWrgE6xGjqLl5k+u7dpDxzlwur15JWUoyUs3DL28sl8sYN9iZd6c/gb2tKftiLvLR9yfJzb+hxUwe\nIJ6GdigpKWHo0KGa+5FyufyBZh6urq4mMDCQV199tc72JUuW4ObmpnldUVHBnDlz8Pb2ZsqUKWRn\n//60xJo1a/D29sbX15ejR4/ed9uCIDx+uth0xL+jL4XlRXx35qcmXY++Pi3atcdx2nQ6Bf8bh+dm\n0KJde0rjT5LzWTCZ7y8g/8A+qkse/qquY2srPpwxAM/eKrJyS1i0/jeOJl6mqXs0GiwoCoWCyspK\nTUFRq9XI5fd/ObVhwwY6d667bOepU6coLi6usy0kJAQrKysOHjzIjBkzCA4OBuDChQuEhoYSGhrK\n2rVrWbRoEdXVj9YqZ4IgaNdop2H0sHPl9PVzHLp4pLnD0ZCbmGAzdDgdPlhI+3f/gdWTXlQV5HMt\nZDPp89/iyto13ExNfahCYNrCiBfH9WBWQE/kchnr953ly50plN2q1EEmd9dgZXjmmWd4/fXXKSgo\nYOXKlTzzzDPMnDnzvk6em5tLZGQkQUFBmm3V1dV8+umnzJ8/v86+4eHhTJw4EQBfX1+OHTuGJEkc\nPnyYcePGoVQqad++PU5OTiQlJT1IjoIgPGbkMjnP9ZiKTQtrdqcf0On4lIchk8kw7dQJ1cyX6LT8\nM+yn/h9GrVpRcvwYlz75iKyFH1AYcZjqmzcf+NwDuzuyaOYAXNpZc+JsHh+ui+XcxQIdZHGnBod2\nBgYG0q5dOyIiIrh58yaffPIJ7u7u93XyZcuWMX/+fMrKfr+P+cMPPzBq1CgcHBzq7KtWq2ndunVt\nUEZGWFpaUlBQgFqtpm/fvpr9HB0dUavVDbbd2M4lfSfyM2wivyaIAUveVr7EwojP+O7MJj71fQ+r\nFhaNP6+2c7O3RNUxCOn/JlN0Kpnc/QfIPx5L3o/fc21rCPbDhqIa44NFp/t/wMDe3pLlb7YiJDyV\nTWHnWL4pnimjuvK0j+tDd9jfj/uaK8Dd3f2+i8htERER2NnZ0atXL2JiYoDaorF//36+//77O/a/\n2yWeTCard3tDxFM0hkvkZ9j0Kb+WOOLf0Zed6ftYcXQtf+nzAnLZw3+h6jy31s60fOFVrCdOpSg6\niqKoI6gPhKE+EIZJp85YDxuB5YCByJX3N3BzVL82ONmb8/WuFDYfOs9vp3N5ZUJPHGxM77p/Y4tl\ngwUlPT2dr776iosXL1JVVaXZ/vPPP9/zuLi4OMLDw4mKiqK8vJzS0lLGjx+PUqnEx6d2+uebN2/i\n7e3NwYMHUalUXLlyBZVKRVVVFSUlJdjY2KBSqcjNzdWcV61W33F1IwiCUJ/RTsNILUzX9Kf4OGl/\nfIq2GdnY0HL8BOzGjqfsVFLtCPzkU9xKT+Pq5k1YeXphM2wEyvtYUbdLW2sWvjCQH8LOcfy0moXr\nYpnu48rgXtpfjbfBcSj+/v4EBATQs2dPFH8YkDNw4MD7biQmJoZ169axZs2aOtvd3NyIj48H4Mcf\nf+TcuXMsXryY0NBQwsLC+Pzzz0lNTWXu3Ln8/PPPqNVqZsyYQVhYWJ1Y7kZffkPSBX36DVAXRH6G\nTR/zK6ko5Z+x/6akspQ5brPobOP8UOdpztwqr16l6OgRio5GUV1S+1CTabfu2AwfiUU/t/uanPJY\nci7fh53jVkU1Hj0cedbHFTOT34/T+RWKkZERL730UqMauR9BQUHMnz8fb29vrK2t+eyzzwBwcXHB\nz8+PsWPHolAo+Mc//tFgMREEQfgjS6UFM3tN499xX7Eu5UcWDJiDhVI706E0FWN7e1pNCqLlhEBK\n405SGBnOzbNnuHn2DApra6yHDMV66HCM7VrWe47BvVR0bmfN17tSOH5azYWcIl7x70mXdtZaibHB\nK5QVK1bg7u7O0KFDtdJgU9G335C0SR9/A9QmkZ9h0+f8DmSGsyt9Pz1bdmNWnxkP3J+ib7mVX75M\n0ZEIin+NpubmTZDJMO/TF5vhIzHr2QtZPUM8qqpr2PVLJqG/ZiKTyZjg6cy4J51QOTausDRYUI4d\nO8Zrr72GXC5HqVQiSRIymYxjx441qmFd06cPXdv07Yda20R+hk2f86uRavgicR1n8s8T2Hks3k7D\nH+h4fc2tpryckt9iKIyMoDyz9hFpo1atsBk6HCuvoRhZWd31uHMXC/jvntPkF5fj0s6aFW8Nb1Qc\nDRYUb29v5s6dS8+ePesMaGzbtm2jGtY1ffzQtUVff6i1ReRn2PQ9v8b0p+h7bgC3MjMojIygJPY4\nUkUFKBRYPuGO9fCRmLp0veMp2bJblXy3/xwnzuax+18BjWq7wYISFBTU4BNd+kjfP/TGMIQf6sYQ\n+Rk2Q8gvtSCdz+PXYN3CigUD52BhfH/9KYaQ223VN8ooPvYrRZERVFy5DICyTRush43AavCTKMx+\nz1mSJI6l5BIwomuj2mxwPZT8/HxSU1Np3749kiRRVVVFVVXVA83n1RzEehOGS+Rn2Awhv5amtshl\nCpKupZBbpuYJx773Nb7NEHK7TW6sxLRTZ6xHjMSsew+kykpupqZyIymRwsOHqLx6FSMbW4xsbJDJ\nZLR3sNT9eijdunX7fef/DTSUyWScOXOmUQ3rmqH8FvEwDOm3pIch8jNshpLfw/SnGEpu9akqLqY4\nOorCqEiqrl0DoIVzR2yGj8BywCAc27Vq1PnFAlsGyNB/qBsi8jNshpTfH/tT3uo/i07Wzvfc35By\nuxeppoYbKckUHomgLDEBJAm5qSmDf/qhUefV3aQugiAIes5SacELPZ9BkiS+Sf6R0srmWz+lKcnk\ncsx796Ht63+l48fB2I33R3af07nciygogiA81lxsOzG+kw+F5UV8f3qzXqyf0pSMW7akVeBkOn3y\nr0afSxQUQRAeez5OI+hm60Ly9bOEX3o8F/G7n6lbGiIKiiAIjz25TM6Mnv+HtdKSnWn7SC/KbO6Q\nDJIoKIIgCNTtT1mXvPGx6U/RJlFQBEEQ/sfFtjPjO/lQUF74WPanNJYoKIIgCH8g+lMenigogiAI\nf3Bnf0pWc4dkMERBEQRB+BNLpQUzNP0pP1JWeaO5QzIIoqAIgiDcRVfbzozrWNufsuH0Zh7RSUW0\nShQUQRCEevg63+5POcPhS1HNHY7e03lBqa6uJjAwkFdffRWAuXPn4uvry/jx41mwYAGVlZVA7fTJ\nS5cuxdvbG39/f1JSUjTn2L59Oz4+Pvj4+LB9+3ZdhywIggDc2Z9y/lp6c4ek13ReUDZs2EDnzp01\nrydMmMD+/fvZvXs35eXlhISEABAVFUVmZiZhYWEsWbKE27PqFxYWsmrVKrZs2UJISAirVq2iqKhI\n12ELgiAAdftTPju2ltwydXOHpLd0WlByc3OJjIwkKChIs23YsGHIZDJkMhl9+vRBra79cA4fPkxg\nYCAymYx+/fpRXFxMXl4e0dHReHp6YmNjg7W1NZ6enhw9Kh7lEwSh6XT93/iU6zcKWBqzgh/OhFBw\nq7C5w9I7jZ+85R6WLVvG/PnzKSu7c8RpZWUlO3fu5L333gNArVajUqk076tUKtRq9R3bHR0dNUXo\nXuztLbWQgf4S+Rk2kZ/hebZVAN3adOSnpJ0cu/IbJ9Tx+HYZRmCPMVi1sGju8PSCzgpKREQEdnZ2\n9OrVi5iYmDveX7RoEe7u7ri7uwPc9QmK2wt63W17Qx6FNQvq86isyVAfkZ9he5TzG9C2Lx2Mnfkt\nN549GWHsOX+YQ2nRjO4wjBHth2Bi1LgVD5tbY38R0FlBiYuLIzw8nKioKMrLyyktLWXevHkEBwez\natUq8vPzWbVqlWZ/lUpFbm6u5nVubi4ODg6oVCpiY2M129VqNQMHDtRV2IIgCPckl8kZ1PoJ+jv2\nJTrnOPszD7MnI4wj2b8yxnkUnm0HYSzX6c0fvaWzPpS5c+cSFRVFeHg4K1aswMPDg+DgYEJCQoiO\njmbFihXI5b83P3LkSHbs2IEkSSQkJGBpaYmDgwNeXl5ER0dTVFREUVER0dHReHl56SpsQRCE+2Is\nN2JEey8WDX6HcR29qaypJCR1J0uOLyfmysnHch6wJi+jH374IW3atGHq1KkAeHt78/rrrzNs2DCO\nHDmCt7c3pqamLFu2DAAbGxtee+01Tcf+7NmzsbGxaeqwBUEQ7srEyISxHb0Z0nYwYVkRRGX/yoYz\nmzl08Qj+nXzp3arHfd2mfxSINeUN0KN8jxpEfobuUc7vfnLLv1VAaMZBYq6cREKio5UTAZ39cLHt\n1ERRPrzG9qGIkfKCIAhaZGdiy/TuT/H+oLfpZ9+LjOIs/h3/FasTv+FSyeXmDk+nHs+eI0EQBB1T\nmTvycu/nyCi6yK60fZy+fo7T18/h7tiPcR19cDBr1dwhap0oKIIgCDrU0boDb7q9wtmCVHam7eOE\nOoG4vCQ82wzCz3kU1i2smjtErREFRRAEQcdkMhnd7briatuFhKvJ7E7bz9GcYxy/coIR7b3w7jAc\nM2PT5g6z0URBEQRBaCJymZz+Dn3o26onx6+cIDTjIGFZEUTnHMfHaQTD2j2JUqFs7jAfmigogiAI\nTUwhV+DZdhADVP05kv0LYVkR7EjbS8SlaMZ2HM3g1gNQyBXNHeYDE095CYIgNBOlwhhvp+EsGvx3\nxjiN5GbVTTad28aSmGBOqhMMbnCkuEIRBEFoZmbGpvh3HsPQdp7szzxM9OXjrEvZSPusSCZ09qO7\nXVeDGBwpCoogCIKesG5hyVTXQEa2H0JoRhgn1AmsTvwGF5tOBHT2o6O1U3OHeE/ilpcgCIKesTdr\nyYye/8ffB/yVXi27kVqYTvDJ1Xyd9B2XS3MbPkEzEVcogiAIeqqdZRv+0ncmFwoz2Jm2j8RrKSRd\nO80g1ROM7ehNS1Pb5g6xDlFQBEEQ9FwXm4683f8vpFw/y860fRzPPcEJdTxD2g3G12kklkr9WOBL\nFBRBEAQDIJPJ6NWqOz1aunJCncCe9DAiLkXz6+VYRrUfysgOQzE1MmnWGEVBEQRBMCBymZyBqv70\nd+jDL5dj2ZdxiL2Zh4jKOYav80iGtPHAWGHcLLGJgiIIgmCAjORGDGv3JINUTxCZHc3BrCNsTd1N\n+MWjjOvkwyBVf+Sypn3uSjzlJQiCYMBMjFowxnkUi558h1EdhlJSWcoPZ7bwUexnJF5NpimXvBJX\nKIIgCI8AC2NzJnUZz4h2XuzNOMSxK7/x9akNOFt1IKCzH11tO+s8Bp1foVRXVxMYGMirr74KwKVL\nl5gyZQo+Pj7MmTOHiooKACoqKpgzZw7e3t5MmTKF7OxszTnWrFmDt7c3vr6+HD16VNchC4IgGCxb\nExumdQ/i/UFzcbPvTWbxRT6PX8OqhLVcLM5u+ASNoPOCsmHDBjp3/r0yBgcHM2PGDMLCwrCysuLn\nn38GICQkBCsrKw4ePMiMGTMIDg4G4MKFC4SGhhIaGsratWtZtGgR1dXVug5bEATBoKnMHXip93T+\n5v4G3WxdOJN/nk9O/Idvkn9AfeOqTtrUaUHJzc0lMjKSoKAgACRJ4vjx4/j6+gIwceJEDh8+DEB4\neDgTJ04EwNfXl2PHjiFJEocPH2bcuHEolUrat2+Pk5MTSUlJugxbEAThkeFk1Z433F7mjX4v42TZ\nnri8JJbG/IuNZ7dSWF6k1bZ02oeybNky5s+fT1lZGQAFBQVYWVlhZFTbrEqlQq1WA6BWq2ndunVt\nUEZGWFpaUlBQQtIKhQAADptJREFUgFqtpm/fvppzOjo6ao65F3t7S22no1dEfoZN5Ge4DDU3e/v+\neHV1IzYngZ+SdvHL5Rhi1XH4uYwgsJsPFi3MG92GzgpKREQEdnZ29OrVi5iYmHr3uz2D5t2eRJDJ\nZPVub8jVqyUPEK1hsbe3FPkZMJGf4XoUcuvUogvvPPFXYnLjCM0IY9fZMA5eiMK7w3CeHRDQqHPr\nrKDExcURHh5OVFQU5eXllJaW8tFHH1FcXExVVRVGRkbk5ubi4OAA1F6tXLlyBZVKRVVVFSUlJdjY\n2KBSqcjN/X0yNLVarTlGEARBeHAKuYIn2wxggGM/onKOcSArnF3p+xtdUHTWhzJ37lyioqIIDw9n\nxYoVeHh48K9//YtBgwZx4MABALZv387IkSMBGDlyJNu3bwfgwIEDeHh4IJPJGDlyJKGhoVRUVHDp\n0iUyMzPp06ePrsIWBEF4bBgrjBnVYSiLBr+Dn/PoRp+vyQc2zp8/n/Xr1+Pt7U1hYSFTpkwBICgo\niMLCQry9vVm/fj3z5s0DwMXFBT8/P8aOHctLL73EP/7xDxQKw1saUxAEQV+ZGpkyvpNPo88jk5py\nGGUTMvT7nPfyKNzHvReRn2F7lPN7lHODxj9wIKZeEQRBELRCFBRBEARBK0RBEQRBELRCFBRBEARB\nK0RBEQRBELRCFBRBEARBK0RBEQRBELTikR2HIgiCIDQtcYUiCIIgaIUoKIIgCIJWiIIiCIIgaIUo\nKIIgCIJWiIIiCIIgaIUoKIIgCIJWiIIiCIIgaIXOlgDWFVdXVyZMmMDy5csBqKqqwsvLi759+7Jm\nzZpmju7hFBQUMGPGDACuXbuGXC7Hzs4OgJCQEJRKZTNGpx3Lli2jTZs2mjxffPFFVCoVH330EQAf\nf/wxjo6OvPDCCw2ea+XKlZiZmfHiiy/qMuQG1fe55eTk4ODgwN69e5s1Pl3r3r07Xbt21bxevXo1\n7dq1q7OPWq3mo48+4j//+U9Th9coX375JXv27EEulyOXy1m8eDF9+/a9677btm3D09MTR0fHJo7y\nwT1IXg/D4AqKmZkZqamp3Lp1CxMTE3755ReD+CDvxdbWlp07dwL682WpbW5ubuzfv58ZM2ZQU1ND\nQUEBpaWlmvfj4+N59913mzHCB1ff55adnc2sWbMe+rxVVVUYGen/f00TExNN/ndTVVWFo6OjwRWT\n+Ph4IiMj2b59O0qlkvz8fCorK+vdf/v27bi4uOj999CD5vUwDPKW19ChQ4mMjAQgNDSUcePGad4r\nLCzktddew9/fn6eeeoqzZ88Ctf/hFyxYwPTp0xk1ahQbNmxojtAfSFZWFgEBAZrXX3/9NV988QUA\nmZmZvPjii0yaNIlp06aRkZEB1P57jB8/ngkTJjB9+vRmiftu+vfvT3x8PACpqam4uLhgbm5OUVER\nFRUVpKWl0b17d9auXcvkyZPx9/ev80X05Zdf4uvry4wZMzS56rPq6mref/99xo0bx8yZM7l16xYA\n06dP59SpUwDk5+czcuRIoPa33DfffJNZs2Yxc+ZM8vLymDZtGgEBAYwfP54TJ040Wy4P4s95ZGdn\nM378+OYO64FcvXoVW1tbzZ0BOzs7HB0dWbVqFZMnT2b8+PF88MEHSJLE/v37SU5OZt68eQQEBGg+\nZ31UX14jR44kPz8fgFOnTmm+Nx7mO9MgC8rYsWPZu3cv5eXlnDt3rs4l28qVK+nRowe7d+/mrbfe\n4p133tG8l5GRwTfffENISAirV6/WenVuSh988AEffvgh27Zt4+2332bJkiUArFq1im+//ZZdu3ax\nevXqZo7yd46OjhgZGXH58mXi4+Pp168fffr0ISEhgVOnTuHq6kpsbCxZWVn8/PPP7Ny5k5SUFH77\n7TeSk5PZu3cvO3bsYNWqVZovZH2WlZXFtGnTCA0NxdLSkgMHDjR4TEJCAh9//DEbNmxgz549eHl5\nsXPnTnbu3Em3bt2aIOoHc+vWLQICAggICGD27Nma7X/MwxB5enpy5coVfH19WbhwIbGxsQA8++yz\nbN26lT179nDr1i0iIiIYM2YMvXr1Ijg4mJ07d2JiYtLM0devvrzu5UG/M/X/uvouunXrRnZ2Nnv2\n7GHYsGF13jt58iQrV64EYPDgwRQWFlJSUrsG9LBhw1AqldjZ2WFnZ8f169dRqVRNHn9jFRcXk5iY\nyBtvvKHZVl1dDdReCbzzzjuMGTMGb2/v5grxrtzc3IiPjyc+Pp4XXngBtVpNXFwclpaWuLm58csv\nv/DLL78QGBgIwI0bN8jMzKSsrIzRo0djamoKoPmtXp+1a9eO7t27A9CzZ09ycnIaPMbT0xMbGxsA\nevfuzbvvvktVVRWjR4/WnEuf1HfL6495GCJzc3O2bdvGiRMniImJ4a233mLu3LmYm5uzdu1abt26\nRWFhIS4uLgbxs3hbfXndy4N+ZxpkQYHaL5VPP/2UDRs2UFhYqNl+t7kuZTIZQJ3ObYVCQVVVle4D\nbQSFQkFNTY3mdXl5OQqFAkmS6ty//6OlS5eSmJhIREQEAQEB7Nq1C2tr66YMu163b3udP38eFxcX\nVCoV69atw8LCgsmTJxMbG8srr7zC008/Xee4b7/9VvMZGoo//6yVl5dr/n77Z7SioqLOMbcLJsCA\nAQP44YcfOHLkCH/729948cUXNYVW3/0xD0OlUCgYNGgQgwYNomvXrmzevJlz586xdetWWrduzcqV\nKzWfqSH5c147duyo8zP555we9DvTIG95AQQFBfHaa6/h6upaZ/uAAQPYtWsXADExMdja2mJhYdEc\nITaavb09eXl5FBUVUV5eruk3sra2xt7enoMHDwJQU1Oj6Su6dOkS/fr1Y86cOVhZWaFWq5sr/Dv0\n79+fiIgIrK2tUSgU2NjYUFJSQkJCAv369cPLy4utW7dSVlYG1D4hdP36dQYMGMDBgwe5desWpaWl\nRERENHMmD69t27YkJycDsH///nr3y8nJoWXLljz11FNMnjyZlJSUpgrxsZeenk5mZqbm9ZkzZ+jY\nsSNQ+yBGWVlZnVuY5ubmmp9ZfXa3vNq0aVPnZzIsLKxRbRjsFYpKpeL555+/Y/vrr7/OggUL8Pf3\nx9TUlI8//rgZotOOFi1aMGvWLIKCgmjfvj1dunTRvPfZZ5+xcOFCVq5cSWVlJRMmTKBbt24sW7aM\nnJwcJEnC09OzzmOdza1r164UFBTU6aTt2rUrZWVl2NnZ4eXlRVpamuYKxczMjOXLl9OzZ0/Gjh1L\nQEAAbdu25YknnmiuFBpt5syZzJkzh127djFo0KB694uNjeWbb77ByMgIMzMzPvnkkyaM8vF248YN\nli5dSnFxMQqFAicnJxYvXoylpSX+/v60bduW3r17a/afOHEiH374ISYmJmzevFlv+1Hqyys9PZ33\n3nuPNWvWNPoRYrEeiiAIgqAVBnvLSxAEQdAvoqAIgiAIWiEKiiAIgqAVoqAIgiAIWiEKiiAIgqAV\noqAIwh+4urrqZEzBoUOH8PPzIzAwkPT0dK2f/7aYmBgmTZqks/MLwr0Y7DgUQTAkP/30E2+++SZ+\nfn7NHYog6Iy4QhEea2FhYYwZM4ann35aM5MzwNy5c5k0aRL+/v7Mnj2boqIiAF5++eU6I9zDwsKY\nOXMmUDsh5PPPP4+/vz8TJ04kKioKqF0L5uTJkwQHBzN9+nR++uknFi1aBEBSUhKurq4kJSUBsHDh\nQjZv3gxAYmIi06dPZ9KkSUyaNEkzUwLAkSNHePrpp5k0aRJTp04lISHhjtyKi4t57rnn+Pbbb7X3\nDyYI9yIJwmPq2rVr0sCBA6W0tDRJkiTp66+/lrp27SqVlpZK169f1+y3YsUKafny5ZIkSdKRI0ek\nZ599VvPec889Jx08eFCSJEkKCgqStmzZIkmSJKWmpkoDBw7UnOfZZ5+VwsPDJUmSpMzMTMnX11eS\nJEn66quvpKlTp0pr1qyRJEmSfHx8pKysLKmoqEgKCAiQ1Gq1JEmSpFarpSFDhkhFRUVSVlaW9NRT\nT0klJSWSJEnS+fPnpWHDhkmSJEnHjx+XJk6cKGVnZ0sTJ06U9u3bp/1/OEGoh7jlJTy2EhIS6NGj\nB506dQJg6tSpBAcHA7Bz5052795NZWUlN27cwNnZGYAhQ4bwz3/+k7S0NKB27rQRI0ZQWlrKmTNn\nmDx5MgBdunShe/fuJCQk3DEjrZOTE+Xl5eTm5nLs2DHefvttvvzyS/z9/amsrKRDhw4cOXKE7Oxs\nXn75Zc1xMpmMrKwskpKSuHjxItOmTdO8V1VVxbVr14DadS+ee+45PvnkE9zd3XXzjycIdyEKivDY\nkuqZdejMmTNs2rSJn376CTs7O3bv3s2WLVuA2i/1adOmsXHjRqC2CCkUinrbqG+WZA8PDyIjI7l+\n/ToDBw5k8eLFREZGaub3kiQJV1dXfvzxxzuOTUpKYsiQIXz66ad3vJeWloa1tTUqlYqoqChRUIQm\nJfpQhMeWm5sbp0+f1szAGhISAtT2PVhYWGBjY0NFRQVbt26tc1xgYCCHDh1i7969TJkyBQALCwu6\nd+/O9u3bgdov9rNnz9Y72Z6Hhwdff/01bm5uQO1MzP/9738ZPHiwJrasrCyOHz+uOSYpKUkz6efR\no0dJTU2t895tSqWSL774grS0NJYuXVpv4RQEbRMFRXhstWzZkiVLljBr1iyefvppzZXGoEGD6NCh\nA35+frz00kv06NGjznEWFhYMGTIET09P7OzsNNuDg4PZtWsX/v7+zJs3j08//bTO+3/k4eFBTk6O\npoDcfu3h4QHULlHwxRdfsHr1aiZMmICfnx+rVq1CkiScnZ1Zvnw57733nua92x35tymVSj7//HOu\nX7/OBx98UGddHUHQFTHbsCA8oKqqKiZMmMDHH39Mnz59mjscQdAb4gpFEB7A4cOH8fb2xtPTUxQT\nQfgTcYUiCIIgaIW4QhEEQRC0QhQUQRAEQStEQREEQRC0QhQUQRAEQStEQREEQRC04v8BiFWJXlca\ntlgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93b8140b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "births.pivot_table('births', index='dayofweek',\n",
    "                    columns='decade', aggfunc='mean').plot()\n",
    "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
    "plt.ylabel('mean births by day');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apparently births are slightly less common on weekends. Note the 1990s and 2000s are missing because the CDC data contains only the month of birth starting in 1989.\n",
    "\n",
    "- Plot the mean number of births by the day of the *year*. First group the data by month and day separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>4009.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4247.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4500.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4571.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       births\n",
       "1 1  4009.225\n",
       "  2  4247.400\n",
       "  3  4500.900\n",
       "  4  4571.350\n",
       "  5  4603.625"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births_by_date = births.pivot_table('births', \n",
    "                                    [births.index.month, births.index.day])\n",
    "births_by_date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The result is a multi-index over months and days. Convert the indexes into a date by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "births_by_date.index = [pd.datetime(2012, month, day)\n",
    "                        for (month, day) in births_by_date.index]\n",
    "births_by_date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We now have a time series reflecting the average number of births by date of the year. ``plot`` method to plot the data. It reveals some interesting trends:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lq4/u0QpV45FUkmG323nggQcACIVC3HrrrVx33XU0Njbi9/vZsmULAKtWreI73/kODQ0N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93b8068cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "births_by_date.plot(ax=ax);"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}