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Pandas Play Pen.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Play Pen\n",
"\n",
"### Part of Track-2 - Section-3: Pandas\n",
"#### Supported by the Blog Post: 'The Essence of Pandas\"\n",
"\n",
"Let's work with a real dataset and flex our Pandas muscle\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### This is how it will go:\n",
"\n",
"After working through the **'minsuk'** notebook, we now have some experience with basic Pandas manipulations. We can take the feature-rich **Census** dataset and carve it up, slice through it and pull out any number of aggregated insights we choose. Without even getting into visualization at this point, we will be learning alot about this dataset just by grouping, selecting and comparing data. We should also become familiar with the features of this dataset.\n",
"\n",
"Hopefully, this puts us in a great position to make use of it to learn **visualization** in Track-4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What we will cover in this notebook:\n",
"\n",
"1. Reading a data file into a Pandas dataframe\n",
"2. Exploring the dataset\n",
"3. Hygiene (Cleaning)\n",
"4. Selecting using a number of techniques\n",
"5. Basic Analysis\n",
"6. Simple Visualizations"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pickle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's read in the csv file"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {},
"outputs": [],
"source": [
"# read in the csv data from Kaggle/datasets/census\n",
"# Replace census_path with your path to the data file\n",
"# Retrieve \n",
"data_state_abbrev_path = \"/Volumes/LaCie/datasets/census/US/\"\n",
"census_path = \"/Volumes/LaCie/datasets/census/us-census-demographic-data/\"\n",
"census = pd.read_csv(census_path + \"acs2015_census_tract_data.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {},
"outputs": [],
"source": [
"# Add new column for state abbreviation\n",
"# census['State Abbreviation'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CensusTract</th>\n",
" <th>State</th>\n",
" <th>County</th>\n",
" <th>TotalPop</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" <th>Black</th>\n",
" <th>Native</th>\n",
" <th>...</th>\n",
" <th>Walk</th>\n",
" <th>OtherTransp</th>\n",
" <th>WorkAtHome</th>\n",
" <th>MeanCommute</th>\n",
" <th>Employed</th>\n",
" <th>PrivateWork</th>\n",
" <th>PublicWork</th>\n",
" <th>SelfEmployed</th>\n",
" <th>FamilyWork</th>\n",
" <th>Unemployment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1001020100</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>1948</td>\n",
" <td>940</td>\n",
" <td>1008</td>\n",
" <td>0.9</td>\n",
" <td>87.4</td>\n",
" <td>7.7</td>\n",
" <td>0.3</td>\n",
" <td>...</td>\n",
" <td>0.5</td>\n",
" <td>2.3</td>\n",
" <td>2.1</td>\n",
" <td>25.0</td>\n",
" <td>943</td>\n",
" <td>77.1</td>\n",
" <td>18.3</td>\n",
" <td>4.6</td>\n",
" <td>0.0</td>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1001020200</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>2156</td>\n",
" <td>1059</td>\n",
" <td>1097</td>\n",
" <td>0.8</td>\n",
" <td>40.4</td>\n",
" <td>53.3</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.7</td>\n",
" <td>0.0</td>\n",
" <td>23.4</td>\n",
" <td>753</td>\n",
" <td>77.0</td>\n",
" <td>16.9</td>\n",
" <td>6.1</td>\n",
" <td>0.0</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1001020300</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>2968</td>\n",
" <td>1364</td>\n",
" <td>1604</td>\n",
" <td>0.0</td>\n",
" <td>74.5</td>\n",
" <td>18.6</td>\n",
" <td>0.5</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.5</td>\n",
" <td>19.6</td>\n",
" <td>1373</td>\n",
" <td>64.1</td>\n",
" <td>23.6</td>\n",
" <td>12.3</td>\n",
" <td>0.0</td>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1001020400</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>4423</td>\n",
" <td>2172</td>\n",
" <td>2251</td>\n",
" <td>10.5</td>\n",
" <td>82.8</td>\n",
" <td>3.7</td>\n",
" <td>1.6</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>2.6</td>\n",
" <td>1.6</td>\n",
" <td>25.3</td>\n",
" <td>1782</td>\n",
" <td>75.7</td>\n",
" <td>21.2</td>\n",
" <td>3.1</td>\n",
" <td>0.0</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1001020500</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>10763</td>\n",
" <td>4922</td>\n",
" <td>5841</td>\n",
" <td>0.7</td>\n",
" <td>68.5</td>\n",
" <td>24.8</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.6</td>\n",
" <td>0.9</td>\n",
" <td>24.8</td>\n",
" <td>5037</td>\n",
" <td>67.1</td>\n",
" <td>27.6</td>\n",
" <td>5.3</td>\n",
" <td>0.0</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" CensusTract State County TotalPop Men Women Hispanic White \\\n",
"0 1001020100 Alabama Autauga 1948 940 1008 0.9 87.4 \n",
"1 1001020200 Alabama Autauga 2156 1059 1097 0.8 40.4 \n",
"2 1001020300 Alabama Autauga 2968 1364 1604 0.0 74.5 \n",
"3 1001020400 Alabama Autauga 4423 2172 2251 10.5 82.8 \n",
"4 1001020500 Alabama Autauga 10763 4922 5841 0.7 68.5 \n",
"\n",
" Black Native ... Walk OtherTransp WorkAtHome MeanCommute \\\n",
"0 7.7 0.3 ... 0.5 2.3 2.1 25.0 \n",
"1 53.3 0.0 ... 0.0 0.7 0.0 23.4 \n",
"2 18.6 0.5 ... 0.0 0.0 2.5 19.6 \n",
"3 3.7 1.6 ... 0.0 2.6 1.6 25.3 \n",
"4 24.8 0.0 ... 0.0 0.6 0.9 24.8 \n",
"\n",
" Employed PrivateWork PublicWork SelfEmployed FamilyWork Unemployment \n",
"0 943 77.1 18.3 4.6 0.0 5.4 \n",
"1 753 77.0 16.9 6.1 0.0 13.3 \n",
"2 1373 64.1 23.6 12.3 0.0 6.2 \n",
"3 1782 75.7 21.2 3.1 0.0 10.8 \n",
"4 5037 67.1 27.6 5.3 0.0 4.2 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 272,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Start examining "
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['CensusTract', 'State', 'County', 'TotalPop', 'Men', 'Women',\n",
" 'Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific', 'Citizen',\n",
" 'Income', 'IncomeErr', 'IncomePerCap', 'IncomePerCapErr', 'Poverty',\n",
" 'ChildPoverty', 'Professional', 'Service', 'Office', 'Construction',\n",
" 'Production', 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp',\n",
" 'WorkAtHome', 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork',\n",
" 'SelfEmployed', 'FamilyWork', 'Unemployment'],\n",
" dtype='object')"
]
},
"execution_count": 273,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.columns"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CensusTract</th>\n",
" <th>State</th>\n",
" <th>County</th>\n",
" <th>TotalPop</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" <th>Black</th>\n",
" <th>Native</th>\n",
" <th>...</th>\n",
" <th>Walk</th>\n",
" <th>OtherTransp</th>\n",
" <th>WorkAtHome</th>\n",
" <th>MeanCommute</th>\n",
" <th>Employed</th>\n",
" <th>PrivateWork</th>\n",
" <th>PublicWork</th>\n",
" <th>SelfEmployed</th>\n",
" <th>FamilyWork</th>\n",
" <th>Unemployment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1001020100</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>1948</td>\n",
" <td>940</td>\n",
" <td>1008</td>\n",
" <td>0.9</td>\n",
" <td>87.4</td>\n",
" <td>7.7</td>\n",
" <td>0.3</td>\n",
" <td>...</td>\n",
" <td>0.5</td>\n",
" <td>2.3</td>\n",
" <td>2.1</td>\n",
" <td>25.0</td>\n",
" <td>943</td>\n",
" <td>77.1</td>\n",
" <td>18.3</td>\n",
" <td>4.6</td>\n",
" <td>0.0</td>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1001020200</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>2156</td>\n",
" <td>1059</td>\n",
" <td>1097</td>\n",
" <td>0.8</td>\n",
" <td>40.4</td>\n",
" <td>53.3</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.7</td>\n",
" <td>0.0</td>\n",
" <td>23.4</td>\n",
" <td>753</td>\n",
" <td>77.0</td>\n",
" <td>16.9</td>\n",
" <td>6.1</td>\n",
" <td>0.0</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1001020300</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>2968</td>\n",
" <td>1364</td>\n",
" <td>1604</td>\n",
" <td>0.0</td>\n",
" <td>74.5</td>\n",
" <td>18.6</td>\n",
" <td>0.5</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.5</td>\n",
" <td>19.6</td>\n",
" <td>1373</td>\n",
" <td>64.1</td>\n",
" <td>23.6</td>\n",
" <td>12.3</td>\n",
" <td>0.0</td>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1001020400</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>4423</td>\n",
" <td>2172</td>\n",
" <td>2251</td>\n",
" <td>10.5</td>\n",
" <td>82.8</td>\n",
" <td>3.7</td>\n",
" <td>1.6</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>2.6</td>\n",
" <td>1.6</td>\n",
" <td>25.3</td>\n",
" <td>1782</td>\n",
" <td>75.7</td>\n",
" <td>21.2</td>\n",
" <td>3.1</td>\n",
" <td>0.0</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1001020500</td>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>10763</td>\n",
" <td>4922</td>\n",
" <td>5841</td>\n",
" <td>0.7</td>\n",
" <td>68.5</td>\n",
" <td>24.8</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.6</td>\n",
" <td>0.9</td>\n",
" <td>24.8</td>\n",
" <td>5037</td>\n",
" <td>67.1</td>\n",
" <td>27.6</td>\n",
" <td>5.3</td>\n",
" <td>0.0</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" CensusTract State County TotalPop Men Women Hispanic White \\\n",
"0 1001020100 Alabama Autauga 1948 940 1008 0.9 87.4 \n",
"1 1001020200 Alabama Autauga 2156 1059 1097 0.8 40.4 \n",
"2 1001020300 Alabama Autauga 2968 1364 1604 0.0 74.5 \n",
"3 1001020400 Alabama Autauga 4423 2172 2251 10.5 82.8 \n",
"4 1001020500 Alabama Autauga 10763 4922 5841 0.7 68.5 \n",
"\n",
" Black Native ... Walk OtherTransp WorkAtHome MeanCommute \\\n",
"0 7.7 0.3 ... 0.5 2.3 2.1 25.0 \n",
"1 53.3 0.0 ... 0.0 0.7 0.0 23.4 \n",
"2 18.6 0.5 ... 0.0 0.0 2.5 19.6 \n",
"3 3.7 1.6 ... 0.0 2.6 1.6 25.3 \n",
"4 24.8 0.0 ... 0.0 0.6 0.9 24.8 \n",
"\n",
" Employed PrivateWork PublicWork SelfEmployed FamilyWork Unemployment \n",
"0 943 77.1 18.3 4.6 0.0 5.4 \n",
"1 753 77.0 16.9 6.1 0.0 13.3 \n",
"2 1373 64.1 23.6 12.3 0.0 6.2 \n",
"3 1782 75.7 21.2 3.1 0.0 10.8 \n",
"4 5037 67.1 27.6 5.3 0.0 4.2 \n",
"\n",
"[5 rows x 37 columns]"
]
},
"execution_count": 274,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.head()"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(74001, 37)"
]
},
"execution_count": 275,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.shape"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 37 columns in the Census dataset\n"
]
},
{
"data": {
"text/plain": [
"Index(['CensusTract', 'State', 'County', 'TotalPop', 'Men', 'Women',\n",
" 'Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific', 'Citizen',\n",
" 'Income', 'IncomeErr', 'IncomePerCap', 'IncomePerCapErr', 'Poverty',\n",
" 'ChildPoverty', 'Professional', 'Service', 'Office', 'Construction',\n",
" 'Production', 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp',\n",
" 'WorkAtHome', 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork',\n",
" 'SelfEmployed', 'FamilyWork', 'Unemployment'],\n",
" dtype='object')"
]
},
"execution_count": 276,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"There are %d columns in the Census dataset\" %len(census.columns))\n",
"census.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploration"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Population of the U.S. = 320,098,094\n"
]
}
],
"source": [
"# Print the total population with the ','s in the right places\n",
"print(\"Total Population of the U.S. = \"+\"{:,}\".format(census[\"TotalPop\"].sum())) # could have been max(), mean() etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### There are a couple of ways to extract stats from the dataframe:\n",
"\n",
"1. Dump the df by slicing\n",
"2. Select out with conditional - x = loc[df[\"State\"] == \"StateName\", \"second column_name\"].mean()\n",
"3. Create functions that return the answers you want - stat_data_df(state) and get_state_stat(df,stat,func) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1: Slicing into DF"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here are rows 5-15 of the State column: State TotalPop\n",
"5 Alabama 3851\n",
"6 Alabama 2761\n",
"7 Alabama 3187\n",
"8 Alabama 10915\n",
"9 Alabama 5668\n",
"10 Alabama 3286\n",
"11 Alabama 3295\n",
"12 Alabama 3829\n",
"13 Alabama 2869\n",
"14 Alabama 7455\n"
]
}
],
"source": [
"my_slice = census[[\"State\",\"TotalPop\"]][5:15]\n",
"print(\"Here are rows 5-15 of the State column:\", my_slice)"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Population of California in Thousands 38421464\n"
]
}
],
"source": [
"cali = census[census.State == \"California\"]\n",
"# sum 'TotalPop' column of just the first \n",
"print(\"Total Population of California in Thousands\",cali[\"TotalPop\"].sum())"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Population of California's Santa Clara County 1868149\n"
]
}
],
"source": [
"sc_cty = cali[cali.County == \"Santa Clara\"]\n",
"print(\"Total Population of California's Santa Clara County\",sc_cty[\"TotalPop\"].sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2: Using Loc"
]
},
{
"cell_type": "code",
"execution_count": 282,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total Population of California = 38,421,464\n"
]
}
],
"source": [
"# select using .loc\n",
"# Print statement plus .format with selection by using .loc for \"State\" and two more selectors - Ca, TotalPop\n",
"print(\"Total Population of California = \"+\"{:,}\".format(census.loc[census[\"State\"] == \"California\",\"TotalPop\"].sum()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3: Functions to carve out what you want\n",
"\n",
"We are creating these functions to do some housekeeping tasks that would be repetitive otherwise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### state_data_df Returns a df just for one state"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {},
"outputs": [],
"source": [
"# Utility function to return the data for one state in its own df\n",
"# May simplify processing if we are just interested in a single state\n",
"def state_data_df(state):\n",
" df_state = census[census[\"State\"]==str(state)]\n",
" return df_state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### get_state_stat(df,stat,func) Returns a single stat for one state with func applied"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {},
"outputs": [],
"source": [
"# Function to extract and return a stat from the state with function applied\n",
"def get_state_stat(df,stat,func):\n",
" if func == 'sum':\n",
" state_stat = round(df[stat].sum(),2)\n",
" elif func == 'mean':\n",
" state_stat = round(df[stat].mean(),2)\n",
" elif func == 'max':\n",
" state_stat = round(df[stat].max(),2)\n",
" elif func == 'min':\n",
" state_stat = round(df[stat].min(),2) \n",
" return(state_stat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pop_comparison(state) prints a comparison of a state's population as a % of US. total population"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {},
"outputs": [],
"source": [
"# function prints the Total us pop, Pop of the state that is passed and a % of the U.S. for that state\n",
"def pop_comparison(state):\n",
" us_pop = census[\"TotalPop\"].sum()\n",
" data_state = census[census[\"State\"]==str(state)]\n",
" state_pop = data_state[\"TotalPop\"].sum()\n",
" print(\"Population\\n\")\n",
" print(\"Total U.S. = \"+\"{:,}\".format(us_pop))\n",
" print(\"Population of %s = \"%state +\"{:,}\".format(state_pop))\n",
" print(\"Which is %.2f %% of the total U.S. Population\"%((state_pop/us_pop)*100))"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {},
"outputs": [],
"source": [
"# Scrape individual states into their own DF's to simplify things\n",
"maine_df = state_data_df(\"Maine\")\n",
"cali_df = state_data_df(\"California\")\n",
"newyork_df = state_data_df(\"New York\")\n",
"florida_df = state_data_df(\"Florida\")\n",
"texas_df = state_data_df(\"Texas\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Call the get_state_stat(df,stat,func) to return the answers to this query"
]
},
{
"cell_type": "code",
"execution_count": 287,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maine Men Mean: 1,815.87\n",
"Cali Men Mean: 2,369.01\n",
"New York Men Mean: 1,940.18\n",
"Florida Men Mean: 2,261.49\n",
"Total Texas Income sum : $ 290,491,643.00\n"
]
}
],
"source": [
"# get some stats from selected states\n",
"stat_val = get_state_stat(maine_df,\"Men\", \"mean\")\n",
"print(\"Maine Men Mean: \" +\"{:,.2f}\".format(stat_val))\n",
"stat_val = get_state_stat(cali_df,\"Men\", \"mean\")\n",
"print(\"Cali Men Mean: \" +\"{:,.2f}\".format(stat_val))\n",
"stat_val = get_state_stat(newyork_df,\"Men\", \"mean\")\n",
"print(\"New York Men Mean: \" +\"{:,.2f}\".format(stat_val))\n",
"stat_val = get_state_stat(florida_df,\"Men\", \"mean\")\n",
"print(\"Florida Men Mean: \" +\"{:,.2f}\".format(stat_val))\n",
"stat_val = get_state_stat(texas_df,\"Income\",\"sum\")\n",
"print(\"Total Texas Income sum : $ \" +\"{:,.2f}\".format(stat_val))"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Maine Men Mean: 1,815.87\n",
"Maine Wommen Mean: 1,896.70\n",
"Maine Income Mean: 49,181.97\n",
"Maine WFH Mean: 1,934.40\n",
"Cali WFH Mean: 43,960.20\n",
"Maine Mean WFH as % of Cali WFH : 4.40 %\n"
]
}
],
"source": [
"# Look at one state - Maine\n",
"maine_men = get_state_stat(maine_df,\"Men\",\"mean\")\n",
"print(\"Maine Men Mean: \" +\"{:,.2f}\".format(maine_men))\n",
"maine_women = get_state_stat(maine_df,\"Women\",\"mean\")\n",
"print(\"Maine Wommen Mean: \" +\"{:,.2f}\".format(maine_women))\n",
"maine_income = get_state_stat(maine_df,\"Income\",\"mean\")\n",
"print(\"Maine Income Mean: \" +\"{:,.2f}\".format(maine_income))\n",
"maine_wfh = get_state_stat(maine_df,\"WorkAtHome\",\"sum\")\n",
"print(\"Maine WFH Mean: \" +\"{:,.2f}\".format(maine_wfh))\n",
"cali_wfh = get_state_stat(cali_df,\"WorkAtHome\",\"sum\")\n",
"print(\"Cali WFH Mean: \" +\"{:,.2f}\".format(cali_wfh))\n",
"print(\"Maine Mean WFH as % of Cali WFH : \"+\"{:,.2f}\".format((maine_wfh/cali_wfh)*100)+\" %\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Call pop_comparison for five states\n",
"\n",
"pop_comparison(\"California\")\n",
"print(\"\\n\")\n",
"pop_comparison(\"Massachusetts\")\n",
"print(\"\\n\")\n",
"pop_comparison(\"New York\")\n",
"print(\"\\n\")\n",
"pop_comparison(\"Florida\")\n",
"print(\"\\n\")\n",
"pop_comparison(\"Maine\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleanup time\n",
"\n",
"### Sort frame, drop nan values and non-essential columns\n",
"\n",
"### What we will do in this section:\n",
"\n",
"1. Drop nans and one of the non-essential columns\n",
"2. Sort"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['CensusTract', 'State', 'County', 'TotalPop', 'Men', 'Women',\n",
" 'Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific', 'Citizen',\n",
" 'Income', 'IncomeErr', 'IncomePerCap', 'IncomePerCapErr', 'Poverty',\n",
" 'ChildPoverty', 'Professional', 'Service', 'Office', 'Construction',\n",
" 'Production', 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp',\n",
" 'WorkAtHome', 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork',\n",
" 'SelfEmployed', 'FamilyWork', 'Unemployment'],\n",
" dtype='object')"
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.keys() # Similar to df.columns"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>County</th>\n",
" <th>TotalPop</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" <th>Black</th>\n",
" <th>Native</th>\n",
" <th>Asian</th>\n",
" <th>...</th>\n",
" <th>Walk</th>\n",
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" <th>MeanCommute</th>\n",
" <th>Employed</th>\n",
" <th>PrivateWork</th>\n",
" <th>PublicWork</th>\n",
" <th>SelfEmployed</th>\n",
" <th>FamilyWork</th>\n",
" <th>Unemployment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>1948</td>\n",
" <td>940</td>\n",
" <td>1008</td>\n",
" <td>0.9</td>\n",
" <td>87.4</td>\n",
" <td>7.7</td>\n",
" <td>0.3</td>\n",
" <td>0.6</td>\n",
" <td>...</td>\n",
" <td>0.5</td>\n",
" <td>2.3</td>\n",
" <td>2.1</td>\n",
" <td>25.0</td>\n",
" <td>943</td>\n",
" <td>77.1</td>\n",
" <td>18.3</td>\n",
" <td>4.6</td>\n",
" <td>0.0</td>\n",
" <td>5.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>2156</td>\n",
" <td>1059</td>\n",
" <td>1097</td>\n",
" <td>0.8</td>\n",
" <td>40.4</td>\n",
" <td>53.3</td>\n",
" <td>0.0</td>\n",
" <td>2.3</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.7</td>\n",
" <td>0.0</td>\n",
" <td>23.4</td>\n",
" <td>753</td>\n",
" <td>77.0</td>\n",
" <td>16.9</td>\n",
" <td>6.1</td>\n",
" <td>0.0</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>2968</td>\n",
" <td>1364</td>\n",
" <td>1604</td>\n",
" <td>0.0</td>\n",
" <td>74.5</td>\n",
" <td>18.6</td>\n",
" <td>0.5</td>\n",
" <td>1.4</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.5</td>\n",
" <td>19.6</td>\n",
" <td>1373</td>\n",
" <td>64.1</td>\n",
" <td>23.6</td>\n",
" <td>12.3</td>\n",
" <td>0.0</td>\n",
" <td>6.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>4423</td>\n",
" <td>2172</td>\n",
" <td>2251</td>\n",
" <td>10.5</td>\n",
" <td>82.8</td>\n",
" <td>3.7</td>\n",
" <td>1.6</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>2.6</td>\n",
" <td>1.6</td>\n",
" <td>25.3</td>\n",
" <td>1782</td>\n",
" <td>75.7</td>\n",
" <td>21.2</td>\n",
" <td>3.1</td>\n",
" <td>0.0</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>10763</td>\n",
" <td>4922</td>\n",
" <td>5841</td>\n",
" <td>0.7</td>\n",
" <td>68.5</td>\n",
" <td>24.8</td>\n",
" <td>0.0</td>\n",
" <td>3.8</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.6</td>\n",
" <td>0.9</td>\n",
" <td>24.8</td>\n",
" <td>5037</td>\n",
" <td>67.1</td>\n",
" <td>27.6</td>\n",
" <td>5.3</td>\n",
" <td>0.0</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 36 columns</p>\n",
"</div>"
],
"text/plain": [
" State County TotalPop Men Women Hispanic White Black Native \\\n",
"0 Alabama Autauga 1948 940 1008 0.9 87.4 7.7 0.3 \n",
"1 Alabama Autauga 2156 1059 1097 0.8 40.4 53.3 0.0 \n",
"2 Alabama Autauga 2968 1364 1604 0.0 74.5 18.6 0.5 \n",
"3 Alabama Autauga 4423 2172 2251 10.5 82.8 3.7 1.6 \n",
"4 Alabama Autauga 10763 4922 5841 0.7 68.5 24.8 0.0 \n",
"\n",
" Asian ... Walk OtherTransp WorkAtHome MeanCommute Employed \\\n",
"0 0.6 ... 0.5 2.3 2.1 25.0 943 \n",
"1 2.3 ... 0.0 0.7 0.0 23.4 753 \n",
"2 1.4 ... 0.0 0.0 2.5 19.6 1373 \n",
"3 0.0 ... 0.0 2.6 1.6 25.3 1782 \n",
"4 3.8 ... 0.0 0.6 0.9 24.8 5037 \n",
"\n",
" PrivateWork PublicWork SelfEmployed FamilyWork Unemployment \n",
"0 77.1 18.3 4.6 0.0 5.4 \n",
"1 77.0 16.9 6.1 0.0 13.3 \n",
"2 64.1 23.6 12.3 0.0 6.2 \n",
"3 75.7 21.2 3.1 0.0 10.8 \n",
"4 67.1 27.6 5.3 0.0 4.2 \n",
"\n",
"[5 rows x 36 columns]"
]
},
"execution_count": 291,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census = census.drop(['CensusTract'],axis=1)\n",
"census.head()"
]
},
{
"cell_type": "code",
"execution_count": 292,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(72727, 36)"
]
},
"execution_count": 292,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census = census.dropna()\n",
"census.shape"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {},
"outputs": [
{
"data": {
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" <td>Alabama</td>\n",
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" <td>1097</td>\n",
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" <td>13.3</td>\n",
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" <th>2</th>\n",
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" <td>Autauga</td>\n",
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" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>4423</td>\n",
" <td>2172</td>\n",
" <td>2251</td>\n",
" <td>10.5</td>\n",
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" <td>3.7</td>\n",
" <td>1.6</td>\n",
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" <td>75.7</td>\n",
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" <td>3.1</td>\n",
" <td>0.0</td>\n",
" <td>10.8</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>Autauga</td>\n",
" <td>10763</td>\n",
" <td>4922</td>\n",
" <td>5841</td>\n",
" <td>0.7</td>\n",
" <td>68.5</td>\n",
" <td>24.8</td>\n",
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" <td>0.9</td>\n",
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" <td>67.1</td>\n",
" <td>27.6</td>\n",
" <td>5.3</td>\n",
" <td>0.0</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 36 columns</p>\n",
"</div>"
],
"text/plain": [
" State County TotalPop Men Women Hispanic White Black Native \\\n",
"0 Alabama Autauga 1948 940 1008 0.9 87.4 7.7 0.3 \n",
"1 Alabama Autauga 2156 1059 1097 0.8 40.4 53.3 0.0 \n",
"2 Alabama Autauga 2968 1364 1604 0.0 74.5 18.6 0.5 \n",
"3 Alabama Autauga 4423 2172 2251 10.5 82.8 3.7 1.6 \n",
"4 Alabama Autauga 10763 4922 5841 0.7 68.5 24.8 0.0 \n",
"\n",
" Asian ... Walk OtherTransp WorkAtHome MeanCommute Employed \\\n",
"0 0.6 ... 0.5 2.3 2.1 25.0 943 \n",
"1 2.3 ... 0.0 0.7 0.0 23.4 753 \n",
"2 1.4 ... 0.0 0.0 2.5 19.6 1373 \n",
"3 0.0 ... 0.0 2.6 1.6 25.3 1782 \n",
"4 3.8 ... 0.0 0.6 0.9 24.8 5037 \n",
"\n",
" PrivateWork PublicWork SelfEmployed FamilyWork Unemployment \n",
"0 77.1 18.3 4.6 0.0 5.4 \n",
"1 77.0 16.9 6.1 0.0 13.3 \n",
"2 64.1 23.6 12.3 0.0 6.2 \n",
"3 75.7 21.2 3.1 0.0 10.8 \n",
"4 67.1 27.6 5.3 0.0 4.2 \n",
"\n",
"[5 rows x 36 columns]"
]
},
"execution_count": 293,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.head()"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['State', 'County', 'TotalPop', 'Men', 'Women', 'Hispanic', 'White',\n",
" 'Black', 'Native', 'Asian', 'Pacific', 'Citizen', 'Income', 'IncomeErr',\n",
" 'IncomePerCap', 'IncomePerCapErr', 'Poverty', 'ChildPoverty',\n",
" 'Professional', 'Service', 'Office', 'Construction', 'Production',\n",
" 'Drive', 'Carpool', 'Transit', 'Walk', 'OtherTransp', 'WorkAtHome',\n",
" 'MeanCommute', 'Employed', 'PrivateWork', 'PublicWork', 'SelfEmployed',\n",
" 'FamilyWork', 'Unemployment'],\n",
" dtype='object')"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Groupby and sort so we can make a plot with Seaborn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a percentages list of columns that are numercial\n",
"round everything and put it into a percentage range"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {},
"outputs": [],
"source": [
"percentages = ['Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific','Poverty', 'ChildPoverty', \n",
" 'Professional', 'Service', 'Office','Construction','Production','Drive','Carpool','Transit',\n",
" 'Walk','OtherTransp','WorkAtHome', 'PrivateWork', 'PublicWork', 'SelfEmployed', 'FamilyWork', \n",
" 'Unemployment']\n",
"for i in percentages:\n",
" census[i] = round(census['TotalPop'] * census[i] / 100) "
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {},
"outputs": [],
"source": [
"# It will only have 52 rows, one for each state\n",
"census = census.groupby('State', as_index=False).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot with Seaborn\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {},
"outputs": [],
"source": [
"# Let's perform a simple bar plot of the total population by state"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51]), <a list of 52 Text xticklabel objects>)"
]
},
"execution_count": 299,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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AG4A/E4b6VRoLLKw6thAYneVchQKUSqOyZB20isV2lmTIM9zeJxEREZFGhDrW0vSEVXlUxxrYCoX8r1E3qDKzlwhD+1K5+5ox6cxsA0Ig9Riwd7KiYKUFQPWVNxp4Peb1RUREREREWimtp+rovjyZmW1PCKguAo5y91oB22PAwVXHpgE/zXLOnh7o6nojS9ZBK0trSHf30mH3PgGMGbMSxWLazgJv1929jAUL3mxSiUREhg7dY2WoUR1raOroGJu7typtTtXlMS9iZiMi0rwDuAn4vrufVyfpnUDBzA4Gzies/rcpcF2dPCKZFIttFHoWMb9zZlT6cROmUiyu3ORSiYgMDcViG0uAWV3Vo/prm1IazYgGgzARkYEgek6VmU0CjiAsJNGeHC4AKwGbABNSXuJAYBXgNDM7reL4j4AuwlDAjd19sZntTOjNOhF4BtjN3WdXv6BIX5jfOZO//O6IqLRbfux0VunYqMklEhEZOmZ1LeS4e56ISnvC9tPYoJRpCrWISL9qZKGKS4AtgBuBLyc/vxPYFjg0LbO7HwIcUifJqRVpH01eV0REREREZEBrpI/9g8A+7v4N4B/Aj939I8B04P3NKJyIiIiIiMhA10hQtRLwZPL/jwObJ///Y8IGviIiIiIiIsNOI0HVP1kePD0OvC/5/9Fk3ENKRERERERksGtkTtWZwBVm1g78AnjYzHqArYD7mlE4ERERERGRgS66p8rdfwp8GHjc3R34JGHFvz8BX2pO8URERERERAa26KDKzI4C/urufwNw91vcfQ/gu8l/IiIiIiIiw07d4X9mtgZhbymAk4A7zWxuVbL/AA4CDuv74omIiIiIiAxsaXOq3g9cC/QkP99H2PC32hV9WCYREREREZFBo25Q5e6/MrN3EoYJ/hPYGphTkaQHeN3dX2leEUVERERERAau1DlV7j7T3Z8CRrj7n4F/EfasGgW8rIBKRERERESGs0b2qeoxs1OALuDvwCPAHDM7P1lmXUREREREZNhpJKg6GdgP2B+Ykvy3P7AbcEzfF01ERERERGTga2Tz332AL7v7TRXHnjWzLuBC4Pi+LJiIiIiIiMhg0EhP1SqExSqqPQVM6pviiIiIiIiIDC6NBFUPAl+rcfzrwP/1TXFEREREREQGl0aG/x0J3GFmOwJ/So5tDbwL+Ggfl0tERERERGRQqNtTZWbHmtlogGQ59c2BuwmB1NrArcA0d7+/2QUVEREREREZiNJ6qo4DLgIWArj7E8DBzS6UiIiIiIjIYJEWVBWacVIz2xK4yd0n9/L7DwK3AW9UHD7D3U9qRnlERERERESyiplTtbaZrZyWyN2fS0tjZgXC3lZnpiTdDLjW3T8bUT4REREREZF+ExNUPZDy+wLQA7RHvNYJwC6EjYSPrpNuc+ChiNcTERERERHpVzFB1QeAuX10vovc/dhkBcF6NgMmmdnXCUHbL4Cj3f3NRk9YKECpNKrxkg5ixWI7SzLkGW7vE4S/O0ue4fheiYg0SvdYGWpCHWtpw3l0TQ9shT6Y8JQWVPUAT7j7K/lPBe7+YloaMysCzwPXAT8B1gSuTcpyWF+UQ0REREREpK/0y0IV9bh7N/ChikNPmdkpwBlkCKp6eqCr6430hENIltaQ7u6lw+59Ar1XIiLNpHusDDW6poemjo6xuXur6u5TBVzJ21fgazozW8vMzjSzkRWHRwKLWlkOERERERGRGHV7qtz9S60qSIW5wN7AQjM7EZhCWNTix/1QFhERERERkbrSeqpawsz2NrPXAdx9EbAzsD0hwLqHMKfq7P4roYiIiIiISG0xq//1OXe/Cxhf8fNVwFUVPz8E7NjygomIiIiIiDRoQPRUiYiIiIiIDFYKqkRERERERHJQUCUiIiIiIpKDgioREREREZEcFFSJiIiIiIjk0C+r/4lI/xgzZiWKxcbaUrq7l7FgwZtNKpGIiPQF3d9F+peCKpFhpFhso4dFzO18Oip9x4R3UCyu3ORSiYhIXiGgaqOra2lU+lKpnaJqgSJ9Rl8nkWFmbufT3HjbYVFpP7HTdCZO2LjJJRIRkb7Q1bWUe+7pikq7/fYlSqVCk0skMnxoTpWIiIiIiEgOCqpERERERERyUFAlIiIiIiKSg4IqERERERGRHLRQhYiIiEgFLU8uIo1SUCUiIiJSoVhsYyltvNS1JCr9GqURWp5cZJjTLUBERESkyktdS7jkvjlRab+y7UTWLrU3uUQiMpBpTpWIiIiIiEgOCqpERERERERy6Jfhf2a2JXCTu0/u5ffrApcDWwGvAAe5++9aWEQREREREZEoLe2pMrOCmR0A3AqMrJP0GuARoAP4MnCNmU1tQRFFREREREQa0urhfycAXwdO7i2BmW0AbAEc6+6L3f0O4AZg/9YUUUREREREJF6rh/9d5O7HmtmOddJsBDzn7gsqjj0BbJnlhIUClEqjsmQdtIrFduIWgX17nuH2PkH4u7PkKZVG0d7eRqFQaChvT08PS5cua/icfSXP3ysi0qjBes8J5V7acJ7+LHcoc3fDefr7vR5sQh1rcF0bkq7B6lxNLQ2q3P3FiGRjgYVVxxYCo/u+RCLZFQoFupcsoKtzZlT60oSpFEeMaXKpRLIZjI0EMrTpmhSRwWQg7lO1AKgO50cDr2d5sZ4e6Op6I3ehBpMsrSHd3UuH3fsE+d6rUmkUXZ0zue/3h0fl23bnMyh1bNSv77OuDelNqTSKxXQzs2t2VPqppUmMpKhrQ+rKe49dQoFnIq+x9UujGEFPn1yTg/FeORjLPBjpfR6aOjrG5u6tGohB1WPAumY2yt3LV+C05LiIiDTJzK7ZHHnvtVFpT9tuL6aV1mhyiWS4e6brDY6/Z1ZU2uO3n8K7Sis3uUQiIrUNuH2q3N2Bh4FTzGwlM/sA8Eng6v4tmYiIiIiIyIoGRFBlZnubWeXwvj2BDQl7VF0G7O/uj/ZL4UREREREROrol+F/7n4XML7i56uAqyp+/hewc+tLJiIiMvSMGbMSxWJj7ajd3ctYsODNJpVIRGRoGYhzqkRERKQPFYttLKGHWV2vRaWfUlqFEQ0GYSIiw5mCKhERkWFgVtdrHH33X6PSnrzD5mxQGtfkEomIDB1qhhIREREREclBPVUiIiIiIkOU5lS2hoIqEREREZEhqlhso9gD3bPjNiAuThoFmlPZMAVVIiIiIiJDWPfsN3j1lx6VdtVPGUwe1eQSDT0KQ0VERERERHJQUCUiIiIiIpKDgioREREREZEcFFSJiIiIiIjkoIUqRERERERkBVqOPZ6CKhGRIUIPPxER6UvLl2NfEJd+0phhuxy7gqomUgVHhhJdzwNfsdjGYrqZ2fVyVPqppdUYWdRjQESGFz3PGtM9ewGv/vrvUWlX3ePdMHlMk0s0MOlp2kQhuu+me+7suPQdk0AVHBmgisU2eljE7Fefjko/adV3UCyu3ORSSbWZXS9zxH1XRaU9fdu9mVZaq8klEhEZWIrFNtp72lg8uzsq/chJRdWYJZUukSbrnjubeTdcG5V2/K57wcQ1mlwikexmv/o0v7r9sKi0e35oOpNX3bjJJRIREWnc4tndvHDdq1Fp19p9VdonD88hbRJPQZWIiAxKGsIjIiIDhYIqEZEBRIFCvDCHbBmzujqj0k8pTWDkMJ1ALSIymAzGZ2FLgyozew9wEbApMBPYz90fqJFuP+BioPKdOdDdr2xJQUVE+kmx2MbiwhJmdr0UlX5qaQ1GFkc0uVQD16yuTo685+aotKdt/1GsNLHJJRIRkbzCugQ9dM+ZH5d+4rh+X3WwZUGVmY0ErgdmANsDewK3mtl67l79jm0GnOXuR7SqfCIiA8XMrpc44r7LotKevu0BTBu3bpNLVN9gbFEUEZGBrXvOfOZd979RacfvvhVMKjW5RPW1sqdqR2CEu89Ifr7GzL4JfAa4tCrt5sA5LSybJFQ5EpFGhWF4S5nZNScq/dTSREYW25tcKhERkdZpZVC1EfB41bEngHdXHjCzdsLwwC+a2dnAQuAy4Ax372n0pIUClEqjspU4p2KxnbjFOt+eJ295i8V2lmQ8b7HYTs+SBbw5Z1ZUvpUmTqG48piGA7GBopihYlf5XmXN21+G2987GPXXZ5Q37xNzX+bIe38Tle+07XZjk441++RelyXPcLwm++u9Gqz3nHDupf1y7qxCmRurdfR3mftLsdjOUpY1nKd8XS4ZhNdG1vpof9ZlW3neQiFTtrefP/9LRBtLCJAqLQRGVx2bBDwIXAnsAWxIGDY4H7ggy4nb29soNPhu9fT0sHRpY1+4oeLNObN4/rqjo9KuvfvJrLzGJk0ukYiIiIjIwNXKoGoBUB0+jgZerzzg7v8Gdqg49JCZnUeYg9VwUNXTA0uXLqPYs4zuOXH7ERQnrsrSQhtdXW80erq3yRItd3cv7dfz9leZ+8twe6+G29+bR38Nhe2vz2gwXhvD7ZrMYzB+Rv35+Q7Ga2swlnm43Wf7y2D8e1t93o6Osbl7q1oZVD0GHFx1bBrw08oDZrYx8Gl3P67i8EhgUZ6Td895la4bbotKW9p1J5jUked0IjLIhVX4FjGza2ZU+qmlqYwsrtzkUomIDB3FYhttPW0s7Iwb6DV6QpGiNgOSAaqVl+adQMHMDgbOJ/Q8bQpcV5VuHvBdM3seuBx4L/At4JstLKuIDAF5W0Fnds3ksD/GLUI6/f2nM23cRg2XUURkOFvY2c0TN8+LSjvto+NZuWNwzuGWoa9lQZW7LzaznQn7VJ0IPAPs5u6zzewoYG9339jdXzCzXYHpwA+BOcBJ7v7LVpVVRIaGYrGNbhbxfGRv09qlqRTV2yQiIiINamknqrs/Cmxb4/ipwKkVP98BbNHCoonIEPV810x+eO9hUWkP3m4665fU2yQi/UPbmogMXhqZKiIiItJH8gRGxWIbPbTR2RU3x2hCSXOMRAYKfRWlz6iFTUREhrtisY1ltDE7MjCaVBUYdXZ1c/O9cXOMPrrdeDpK+ecY6fktkp+CKukzxWIb7T2LeCNy4+BRE6eA5q+IiMgQM7urm2vv64xKu9e2E1itDwKjPIrFNgq0MX9e3Ka248a3q4dMpIq+EtKn3pgzi6duOCoq7Tt3PZWRkzZscolEREQkzfx5S/nLnV1Rabf8QIlVxufc1EdkiFFQNUCpK16GEl3P0htdGyIiMhQoqBqgisU2ij1LWDLn31HpR0xcHYojmlwqkWzCHINFvDzv6aj0q41/x1tLm6vSPbQVi20sZikzu+ZGpZ9a6mBksb3JpWoeXc8iw5e+/0ObgqoBbMmcf9N5/eVRaSd8cn8Kk9ZpcolEsnt53tNcfUfc0uaf/+B01hi/MRAq3UtZxEvz4vaaWmO89poabGZ2zeXIe34blfa07XdhWmlyk0vUPCGIXMasrrhhVlNKJUY2WAkTkYGpWGyjuKxA9+zFceknjQR9/wcNBVUiMuC9NG8ml98dF5Dtv8N01h6vvaZk4JrV1cVRd98XlfbUHbbFSqs2uUQi0irdsxfT+YsXo9JO+MyasJpGIQ0WCn9FRERERERyUE+ViIiI9ErzQERE0imoEhERacBwCzKKxTaWALO6Xo9KP6U0lhGaByIy7A27e2V/F0BERGQwWb7YxLyo9FNK4wf9YhOzul7nmHseiUp70vabskFpbJNLJCIDXVjJuofu2XENMsVJYwf1whwKqkQGmeHW8iMyEM3qmseRd98Vlfa0HXbEShOaWyARkQGoe/brzLvub1Fpx+++GUxepcklah4FVSL9IE9gFPItorMzbs+nCRPeoSXGW0yBrzSDrisRkYFLQZVIPygHRvMiA6PxVYFRZ+fT3HzLoVF5P/qRHzBhwsZZiikZFYttLC4sZmbX81Hpp5bWZmRxZJNLJYNdGHbYw6yu+VHpp5TGDfphhyIig4WCKpF+Mq/zae64OW7vpQ9+dDrjFRgNKjO7nufw+2dEpT1jm+8wbdzUJpdIKg3WXp9ZXfP5/t3/G5X2lB22wkqlJpdIhrvB+l0S6WsKqmRA0E1ZRFpp+WITr0aln1JaVb0+IjUUi2200cbrr3ZHpR+7apFiH9Q+VW+QgaalQZWZvQe4CNgUmAns5+4PZE3XCvrStkax2EYP7NgxAAAgAElEQVRbzyIWzJ0VlX5Mx5S3hsPpMxKRLGZ1vcqRd/8hKu1pO3wYK3U0uUTSV7I8F0DPhqxef7Wbh27rikr7HzuVGL1q/gaKYrGN9p423pgbF8yN6igO6q4E1XXiZXmvCoX8523Z5WVmI4HrgRnA9sCewK1mtp67z280XauE5SCX0j13blz6jg4otje5VEPTgrmzeOymI6PSbvTx0xg1cUMgfEaFnkW83jkzKu/YCVO1cIOIyBAW9tYq8GzX4ug865VGan+tQeaNud3Muilya4OPj2fkxMH7+RaLbRSXFeievSgu/aSVB/Xy5HmEuvsyuufEBfrFiSUgf929lTH7jsAIdy9PMrjGzL4JfAa4NEO6lumeO5euG34blba06y4wcXKTSyTVXu+cyUO/jQvI/mOX0xjTsVGTSyQiIv3p2a7FnHjvv6PTH7vd6ryzNKKJJRLJp3v2IjqvjRvRM2GvKbDaSk0u0cDVPaeLeb+5Nyrt+N22Y8SaE3Ofs5Uh7EbA41XHngDenTGdiIiIiIhIvyv09PS05ERmdjSwpbvvWnHsXGC0ux/QaLpIy3p6epaPkoz9W2sNrOy3vMsi89aIj4dZ3p7IvAXlHZR5l0XmbVNelkXec9pq3HOUV3l7zxuVlbYaj7KBnrcZ5x6UVY4ceYl9r5V3GOYdHBd0oVDoIUeHUyuH/y0ARlUdGw28njFdjGWFUDsLc7HyzELrt7w5xngOs7wF5R3SeduVt4G82e85yqu8vefNnHVQ5s2bf1BWOfK8X8qrvL3mHRQX9DggsmW/tlYGVY8BB1cdmwb8NGO6GIN4nRcRERERERkMWhl03AkUzOxg4HzCqn6bAtdlTCciIiIiItLvWrZQhbsvBnYmBEmdwPeB3dx9tpkdZWb/SEvXqrKKiIiIiIjEatlCFSIiIiIiIkPR8NwVTEREREREpI8oqBIREREREclBQZWIiIiIiEgOCqpERERERERyUFAlIiIiIiKSg4IqERERERGRHBRUiYiIiIiI5KCgSkREREREJIdifxeg1cxsc2BNoJAcGgls4u7HZ3y90e6+sJl5zWw7d7/XzFYHTgQ6gZPcfUGW8w43eT4jEZGByMzGAXsB6wI/ADZ397sj8j0IXAlc4+6zM5y3HdgSWAd4Gbjf3bsj8u3q7jdUHVsXuNTdP9JoOfIys/Xd/ZlWn3cwSK6tXVj+Gf/W3ef0b6nqM7PVgMmsWLe7ot8KJW8zHD6jYRVUmdnpwMFAF9BO6KkbB9wWkffdwKmsGJCtB6zSxLzTgc8SHpwXA+OBxcCPgH0jyt0GjGHFi/iutLxZmdn17v7JGsfvcfftI1+jDfgA4T26GljL3Z9OyZP5fe4L/fFeD0dmtp67P2tmY4BvExoZLnH3ZSn5Nnb3f7SkkCueO1MFuL8l73F75TF3nx+Rr6GHp5l9zN1/Z2a79vaa1RXyga6ZjTlm9h/ArcBTwLuBnwG/N7MD3P3qlOznAJ8DTjezu4CfA79x9zcizrsecBPhOn4RWAuYbWY7ufvMlOw/MbN93P2m5LUOBE4n7vnbDuxG7QbRfVPyfpDwN1fnLf+XKmsgmeTN1ZCb9TtY5/XqBpPJc/RWYB7wDDAFODv5jP/WS55XgZ5653X3CSnlWhs4nBXfq43cfWpK3m8DZ7J89FUhKc8jwBX18uZlZiu7+6LkGtkT6HT3PzT4GqOAEZXH6n3GeetYfd25EKO/PqOk4aaWxcCr7v5mX55vWAVVhCBkG0LF9wB3/6KZnUZ4+Ke5BJhJuMm8C7iZUKE7ucl5dwe2NrOxwM7AxsBLwLNpGc3sM4RArDqgmAOslpK3DTgU2I/lD5KfA8e7+9Ia6dcj/E0AHzGzs6uSlIAN08qcvNYU4HeEgLcE/BF4KGnpvLVO1jzvcy5Z3mszu8/dtzWz/6OXB5K7bxZx7j5pVTSz9xNuMo83mrcvxFRCzex7wNGExoUfAf8JLAM2AA5JOcWfzOwpwnV8tbv/u4Gy5XngN1wBNrPrSK+k7JFy3g7ga+5+ipm9LzlvJ7CPu3tK3k8CFwCrVxwuPwTba2ZanjfLw3M64Tt/Ti+/7wFqBlVm9jV3v8jMvtVbmdz93JQyrwJ8hdoVjR1T8uZpNMt6XZ0HfMfdrzazV939KTP7BHA+oRGqV+7+M+BnZjaREOjvD5xnZjcCP3P3O+pkPwe4HTjU3ZeY2UhCI8F5hHtQPbsC15nZscDewPrAF939Nyn5INzbP0G4n64MzAc2JeVvTfwQ+D0hSNgC+ClwDOH7kCpPIJmzITfzdzDJnzWYnAGc6e5nVbzW95LjvVXYd0v+3Rn4IHAKMAtYG/g+cGdaeYGfEP6uOcAawF+B/0d4D9J8F9gDWEIIbA4l/O2vROQtP7+/npz3Q8CxwLfcfVFKvs8D5wITCQ0E+wA9Zna2u58Rcd6PA5cn+ctqfsZ9VcfKeU1uDZwNvLO6fGlBM/k/o4/0ct6693bgaVZ8FpUtSxqW/p+7vxRTjjTDLaha2d0fNLMJQLmyehKhspNmU2BHwoPyYnefYWb3EL4QaV+ePHk73P2FpAX3eXd/0sxGEDcf7iRCBXQB4UZxZnK+mBvc0cBnCDeXZ4B3EG6OPcmxt0l6DpYQAtQ2YNWqJG8mrxfjR4TK13RCq88TZrYf4aZVL6jK8z7nqliR7b0uPyzOIaUCXafMDbcqVuT9CHCuu5uZHUn4XJeZ2UHu/uOUvP1SCSUE+duY2UrAp4GtgReAf5AeVE0mVOw+B5xkZvcRKla/juhRyPPAz1IBfij59x2EyupPCZWUtYAvAddFnPciYKyZFQjfqZuB14ALCRWfek4BLiUEoEsizlWp4Yenu2+S/DulwXNB+EwvIjRA1dJDqPjUcwWhQjKbUDl5hlBBvDji/Hkac7JeV5sA1yT/3wPg7reb2ZoR5yRJPyepUEwkVNx3Bt6bNODt30tP6jbAp919SfIai83scCC1gcLd7zezjwG3EK7vDd399cji7kboKZoMHOzunzazgwj3+zTvBN5LCIp2cfffmNmjwI2EQCFNnkByX7I35Ob5DkL2YPK9wH9VHZtBjed+WflaMbMrgS3dvfxdf8TM/kL4vI9OOe/WhGfCusBZ7v4dM7uBuO/ReHe/MekhP93d55nZd4CHCfefXiXX0UGEv/FU4A1C49fZwDdSznsY8Pmkl+oAQuD/InAXEXWO5JznAv9D6DnpVR/WsfYl+zV5HuGzPIbGr8k8n9EFwBeTtJXnjbm3H05oDDgUeI7Q+HwK8CThu3UE4X7b2/OjIcMtqHrWzDZw93+a2aSkYrgUGBuRdw7hw5xJ0hrg7n8zs/WbnPcRMzuF8PC4PumVOAl4ICLvmu5+vpmtBXzD3R8ys32B+wgBSj37Ah9y91nJz382sz8D99DLzdXdDwcws0fc/YcR5evN+4Bd3b3HzMoVhqvN7MKUfHneZ8hXsWr4vS73UuQcT5ylVbHsROCMpFfy24QK8IvAr4C6QRX9Vwld3d3/YWY7EXrVHk7Kv3JaxqTV8X+A/zGzEqEyfghwYdIzdJm739NL9jwP/IYrwO5+AkDynfsvd/9r+XdmdhVwVcR5tyK8v6sD/wF8hFDB6ozIux5h3uYKvdIRMj88AZJetc8m5f4Xoffk772ld/ePJf97BnCHu9etnPTiw4TezrWA49x9NzPbC/hyRN48jTlZr6tnCd/vu8oHzGxL4kYwTCG8v58DphJ6AA8DbnH3pWb2ZeC/k3JVe4MQ/FWeZw3CddXb+aZXHXoA2AE42cwWA7j7YWnldvenzWw2odIPIZD+flo+Qu/9CEKF6l3Jaz3VQACaOZAkX0Nunu8gZA8mOwkjYh6pOLYx4X1MUyKMHKg0EhgdkbfL3eeb2ZPJ+XD3O8zMIvI+b2aru/u/zWztJPB9jTCiIc23gI8lDdYnu/vcpJfwEdKDqnXd/VYz24bwd9+f1FtKEeeFcK88JTJtX9Wx8lyTBrw/4z02z2e0B7Cduz+UmnJF3yb0/JfXIHgqaaR/3N2PNrOvE+o9fWK4BVUXAX+0MBznV4QW/sWE4WVp/gqcBRxFuDj2IjxgYlra8uTdn9DiNJMQzLyHcKPcNyLvi2Y2LunpmpK0WP8bmBSRtwQ8X3XseSIqr8DT1svcCI+bFzGbcFN9uHzAzKaR/hDL8z5DvopVw++19c3wv4ZbFSu8091/bGF89RiWV6piro/+qoQ+ZWZfBT5OGELXTgiMoudKmdl7CZXKTwGjCEHevwhzPq5391o9Xnke+JkrwITA9eGqY08S3rs0KxOuq48CjySVhQ5Ci2aa24GdCAFvozI/PJOGiB8BvyYE3esR7tlfcPfrU7JfRe1AIMYid3/ZzF4j3GMBfklcT2Sexpys19WxwA1m9jNgpaThbX9CBTHNk4Qe9LOBX9boLbqdcM3U8hPgRjM7kRCkrE/ogbiyzvnWqPp5NuG97Ygoa9nTZra1u//JzEYnAfsS4p5H9xFapA8AHksanRYRFyRAhkCyQp6G3DzfQcgeTJ4D/M7MziH8zesTrqvq4LiWa4HfJj0fLxC+v0cRrps0j5rZIcn55yeNK4uAmLlrvwDuMbPtCMPYrkny9toYU2FVwigAWD5y4lUihlgCL5nZhwjDWW9LAqo9CfeuGLeZ2W4eNwT2Le7+w+QeUWs4XFodK881+SChF++vaQlryPMZLQUey3BOCPeI1QnDAMvWYHmg305KL2EjhlVQ5e4XmtlDhIfgtwnDVMYRKuJpvg1cBkwgdCdeR/iwvhKR91uECmPDeT0szlAZoNxHeg9E2c2Em+Mngf8l3KwWsfwGUs/9wKlmdkRS0S4Sukz/FJG3el7EqoQv7P30Mi+iyllJuWcAI5KW0+8Sup7ryfMZQb6KVZb3uvy6MUNQepOnVfHVJFj9HHB78jlvT5izl6a/KqHfJHzGXYQAbgfgQEIvW11mdgJheMSawPWEVsjbPFngwsJwwLupPYwwzwM/TwX4L4T5LkcmPT4TCUNLYxa5uJkwT2kaMN3MphKGAN0YkXdBUuYHqLqWPGUuF/kenscBH3X3e8sHLMwNuYDwmdVzH/BFM7vaG18gws3sU+7+SzNbZmbvJDxoYypWeRpzMl1X7n5D8r7sRwjWJwN7Vb5vdazn7i/Uee2Z9P59OpFQbzg7OeezhGuq18YQd/8igJl9CfhFhs+G5PVvM7ONCb3o9xMqWjEBx0GEgGAk8B3C9TiO8B2MkSWQLMvTkJvnOwgZg0l3P8/MFhGGW5U/48PdPaZ3/CBCL+s5hIrsi4T3L6Y35nuEoOw64ISk/BDRQOjuJ5jZPwnv2TcIo0PGEYbSprkXOM3MDmN54+YhwJ8j8h5BaAB6DfiwmX2A8H2o+zyy5XNmJxBGTvyRqhEE9T5jMzuO8L68xIrD4dLqWHmuyUeBO8zsJla8JusOv6/zGe0Tcd4zgYvM7FSqhpF7+qItFxDuHecROgbWIdQZLrSwovbPSX+2RBtWQRVA0tI1ljAm9UcN5PsXYfgMwAtJ1+lKxA07er5WXo9YEt1yTJAn3KS+S3j4HEhokR9HXG/CwYQv2wFm9lJy/hcIPQR1eY15EWZ2MGEYQip3v9TM5hIeBs8R5s6c7O4/T8lX8zOKeZ/LL5GjYtXwe10x/K/mw9nMYoZM5GlVPIVQ0V0MfNDCEIbfExeE9ksl1N3/QujpAsDMXnH39SPOCWE43Mn0PofqGeALveTN88DPUwHenxCszk0qOisnr/G5iLxfJjy8rnb3y81sQ+AO4io4/4xMt4KcFZwJrFiZuYe4Xo1phDkNF5vZfCp6fz19EvUxhEUU/kYYGfAg4bscU5HM05iT6boysyuAg9w9bXjSCpLe9L1ZPjF/B0KwdKCnrACYNLxcCJzq7gvM7D3API9bCe9MQoWzYe7+qyS4eMndjzGzxwjXVExgM83dv5r8/2zCdYKZ7Rx5+upA8rnkvKlzZnI25Gb+DiYqg8mDCUM6S8QFk38HPunur5rZjsT1buNhJbVDiRjmWyPv30k+G2CWmd0LrOJef1EdCEPe3f3MikNfTY6fTPpcrm8RFiLZDxhnZs8Sgs+P1c0VynyjmU0oD9E0s+cIUwG6UrJWDmOLmeNebX9C41Pq4hLVcl6TYwlBJKw4pyvm3P9d8eNXe024ohMJo2n2Zfl9PWrRluR59C9Cb+JahFEpx3iYUrIpoeEvz3SVtyn09GSaHz8oWVjh5Ue8/WIoAD3unrai1Znu/r2qY58GznP3uivpJWkzrVxiZrfRy0Rmd0+7WeRiYUGM7Vj+IPlL5MOz1mu1AbPdPbVylLQITq8xLKW39LlX/0peZwdC5WYLwgTkkwgVq6vd/aCYsmRhdRZucPfUpeCTnrxyq+JzwJWRrYokPR+L3P11MxsPTHL3JyPyZX6vkoaCywkPsWlUVEI9ZX6Z5VsgYwN3/2fVsXbg++5+Yr28NV5rTSIf+H3BwjyY1YEX3P25yDznuvsK3wkz+1m596AZalRwysdPTrtnWVjRqkhYFODN5L5xLDCh1t9SlXeH3n7ncfs3jSZ8F5aZ2VZAyd1vSctX43WKJA1u7j63wbxR15WZvQKs4xmWA7Ywv+2rhIrEdJbPq3rU3b+Wknd3Qsvutu7+f8lrHQ98zt1/n5L3csL96WpCD0Zl0BvVe2WRy4sn3+vy9hb/IlSmyveLHsJQ1MfdPWbIU24VDblviWhhr8w/mTCHNMuCFQ0xs68RnkfbeZi/ug8hIP5ebw2AFXlHEeonteo6aQsJYRY/pC15T7ZKfvxvwpDuQkWSEqGeFPMMbUteaz3Ctfknj5g3lOT7BLWfRwek5c/KzOYAq3n2+XYtY30wzcHCyoc1uXvMMPqWGW49VWcRHgC/ZcXJlGl2NrNl7n5Y0mV4EWEfpaPSMlq+lUsanshsZle5+95WZ1nm3rqWrfZcqIWEFaI+ZmZZ94vZk9BFHuNAQqttrL5Y/Qt3vzup8C9KhkA8QErFKs97XSHXUvDufilhlagoVmNPIKuYwmFmG6Z9xlneqwojfflmn432KF5B9gUy7jCzHd39KXhrr44fE1YcTA2qaj3wzczS3isz24BwPdeqLMQsmV953kkWhm30VtFYmzCpF+DLFubqVFc0PhFxzoZ6yKsqOCeYmdc477dJbzX+GGGu3pfM7EVCQ0EJeM3M3upFrNXzFBM4pSgCGyYV8oXAQjPb1N0fqZepusHN3bvNbA/CcOXetlPodT+u5Pdp19WvCXNXfsWKAUra/flAQiv302Z2urt3JsHSo0DdoIpQ2f6Eu/9fcq4ZZvYI4f5aN6giPAPGEYZ4NtTabI0vLz4ZcJYHVrXmP/025Zy57+31GnJ7KXdl3pUJge8+hCB9iZldC3y1XhBqZqe5+5G24pLbleWuF+AcDuzgyZ5+7n5l0oP7G9J7Bn8CbEvoTW8oALTGh7TNJyxUMpnQKFf9jH+T+J6+0YRn77qE4dH/SRhimuZiQr1jLuEzeh3YiOULE9XV6H22wuXAcUlDVdR8IDN7zt3XtTp7iqX16FuDW+0kMk9zMLNV3P01why3TPI+fxs13IKqlYALM0b3OxLGZU4lLEd8J2FJ2JhVQ/KsXJJlIvOjyb9ZzleeD9VOaN3rJLT0rUG4eT1CypjdGl/akYT3Pra352rgcjP7BStWGFao4Hiy+pe7fyDy9avLu2mNYxBXsSq/1w+TcVl0cizcYGbvIDw4prDi5oG93TAy7wlUJVMllLDn2HPJOa539z8TNzcJ8i2QcTohsNqFMMzv24RlzVOH8GV44Fe6jDAU7mKaX9F4kbBa2WTC9VBd6XuTMC8tTaNLffdVBaeRISFvk7Oy8BXC92Glql/F7AmUpcEt73223ChRvWpezPU4ntBjBMsrcl3EbdOxNhULriTuSo6n2TwiTW8aWl7c3V+yMCR5NGEoZ/W53/T0feryPEfL8jTknk6ooH+A5duanEa4f9f7DpcXhGl4eFZiAlC9V+Hjka/3YWCz2N70Kg0NafOwmuv7AMzs1xGNlzUlz/9bCd+JDQm9Xn8wsy+5e1pwtDuh4XsiYduMzyS9t9tEnj7rlgq7EK6NoywMdX5LnXvd55N/d+vl9zEa2monKU/daQ4pXiA0xMxjxXt77J5tmZ+/WQy3oOoC4NikZTG21wQAd59tYRLiLcD/unvqxPgKeVYuaXgis7uflvzbSG9POe8UAAtzdOYAp/jyifyHkwR2Kaq/tEuBpyIeYmUHJv9Wz8GIad3bnTDfZE3CHKPLvf5mlhAempVf2HLLZnlZ1Ln0sodDxXt9fMo56smzcEN5Q9fLiFzBxvPtCQTkroR2EOZx7AxcaWH52ZsIAdZNKXkzL5DhYcn7RcD/ESpMW5db3CNkHsOelHPNyJ64XOdNvqufATCzH7r7wRnOCQ32kPdVBceX73fzHpIl1d099t5Zfd+ZSLiXXBuR91BCRfVKb3yI84402OCW9z6b57tLmJh/hoWFC8r3vUOJW4To74QGjMqe4X2IeL4lPWOrEFYWLLdy/97dm7LEvy/fK2kigJmtUT5vzLMoz3O0Qp6G3D0JAcrs5OeXLUw5eJQ6QZW7fz3590sZzglhTuPxZnash6GwBUIDwYMReeeTvVdhNGHOZ8PcfQ8L283sQviM/w381uOG354PHObuP7Xl+wjuSni+pQVVBQ9Lsb/M8qX+LyAsYBEj65YKB6b8fgXufl/yb54e/X1pcKudsow9RuX7YJ77XZ7nb8OGW1D1d0IrxNEVHT1151TVGAe6CrCFhf0eyntspHUh5lm5JPMEeQtLYx9C7Ys4rdKzD2Hj4crWtTOJuGEmQ8M+C9yZVIB3J7TG/iItb5I/psV0BUkl4XuEAON2woP4F2Z2uNfZzLZ8Pgsb4E4hzOfosjAO/jRCEJt27jup3Uq+mFBp+r33Ps8pz+phmwATY4cAVLOwQ/q+VHTlu/vtEVkzV0I9zAv4A6E18BxCJe1gwpCCtICs4QUyqoZavUIIwrYBpprZOkmZ0lr3Mz/wCft/VC/pGivPeXesddDMnnX3tCXZMy8hn6eCY2bvIgwzehehMWNiMvRo97RRAbUqCxY2t/0jYcPjeiYBP6m630XJ2eDW0H3WagzdrdLj7mmrOx5EGOLUSdgc+nlCZThtI1sIk9p/Z2bfJLTsr024t6dO6rewct+thF7tZwj32nPM7MMRozgyLy9uYXjx1YStJ94ERlpYbW2PioClXv6JhOdKrdEAac/RzA25hICsepjfQiJ7vCz7/KZvEt7nb1hYpGp1wjMsddgwYbjiL5Khh9V1nbQRDA0PaSuzMC/5VkJj6DOEz+qHZraTu/8tJfu7CT2gsHwfwdssbh+zmWa2pbv/xczGJHWubuKW+oeM99mkjtVO2BC7/Oy+P+ZZnDzzz6b2dZG2oE+erXYa7jHysPgYHjY+Hu3uC5O/+zPAXI+bbpDn+duw4RZUzQCOJFRSYluO8ix3XZZn5ZLMK+IQVlpag9AL0Gi35xxCpayygr0zoTu2rmTI0j6EISovE24yPzCz9d09ZpdxLOxDMpkVJ39eUSfbNwitKG/tWWRmvyQEpWmb2UKoMKxZvqF7WLzhEMLDIW01owcJn+8lLK9sHEB4/54HTjez9dz91Bp5My+5TxgmtB5h75mGWNhP4wrCA+VuwoT135jZV/ztq/TUkrkSamafJwxp+QChFfkewhCCmGCut1Xa6q0MWWuYYzeh8gpxQ6YyP/AJQy3vMLOfs+IytGlz/Ro6b9K7eRbhe7Oxmf26KkmJuGFemZeQz1nBuTTJu7m7L7KwMMEZhO9V6sqjNRTppZe5yq8JQf1lsS/cRw1ujd5nY4bu1g2qPKz+twVhkZnyxPy/eMQiCB5Wz30X4bNYLSnn7yJ7m2YA51Y+A8zsiOT4jil58ywvPoMQNK6RNPKtSXj/ZhBWBEtzFeE+dTON72fTcENuhVuAy8zsIHefY2He4gzCamUxMs1v8rB/kREansqf8Z9jrg+WX5fV+5zFjGDIMqStbAZwpru/tYJd0sg6g/QtaJ4j/K2V2zhsQRiOm2Y64d6+EeFZej/hHnlrRF7IeJ+1sHDDTYQerhcJDRuzk3vszJRznkcYnXMMjdcL82y1k7nHyMJ82nMJdaQfEIYyLjOz88o9ynXkef42bLgFVaO8xqpU9Xjvy12vBSxw95gNADdp5Jw1zrWdu9+bBBrHE5ZXPjni4twamBr5wKt2DHCTmd1KCArWIzz4Ph2R9yvAVhWtDDea2cOEL2RqUGVm3yZUeMuVv3IA+gjhxtWbEYQhdJWeIH5s+SLCTb2y1XQzwnyDNNsD/+UVw8kszAm7zN2/YGHlq9sJE72rNbxwgy1f6fAJ4C4zu5QV97lIu2EcS1g2962ekKTM5xMqAvU0XAmt8HNCT9yPgbMrhhKk8gwLZPQ2VMrMCu4eOw8uzwN/W8J1+f6q4zELqDR0Xnd/xsz+QAgkdmHFjYPfJMwlS5O5h5x8FZwtgJ3KFTgPy3Z/l4h912zFifkjCQFKTO/G2sAlFvYyq37w9hYY9UWDW0P3We+DobuJdZLW9ccIjTqbmNmlvTWSWDJhPOmBXMzyZZXLvx8XMepic8LnUelMQkNnmjzLi+8EvMuT1WTd/UUz25/4DVrfT6gMNtrTBNkacsu+S/j+vWJh2PJKyevEBILQ4PwmM9vYw2p/5TnGnSx/pmxoYZGqur1NWUeZJBoe0lbhvYSeyEoziLtfHU/4Dl5B2EfwBEIdJnW1Qne/1sweJAQ2RxOex+MIjWExervPHpeS7xxCfeJQd19iYYP1HxACprQeZwPen3F0yyGEYL/hrXbI12N0KPCppJdqP8Lf+ALJHmMpefM8fxs23IKqCy0s131io0OWkpaLU939v5Ib8qXAIjP7tKfMA0m6LiuHw7xMGA4zJ+K80wlLha5LaK0dT3ioXUD6pmkvUTVcIZa7/0eig50AACAASURBVLeZ/YMwrnt14AHgYI9YbpvQK1f9t71CfJf4dwkT7Jck5z+UcBN5pVbi5L2F8P78xMwOSR6cEwlfuF5XQapyJnB70qJRruDsTRialmYayyc2lz1JqBDjYdx1aYVcQZaFGypXOvwnYY5SpZgbxvrUnnS+Vko+yFYJLVud8MD/MCEgXEyoLNzu7v8Tce5JyXdqDGEOS6eZtaX1miUt1D8mjJ9/BDjFzN4L7OPL51/0JvMD3zMuoJL1vO5+IYCZ/d3dq3uqYl8jTw95ngrOI4SHYOXeLZsA/6id/G2qG0+WEoaaXBSR9yri9qR6S180uOW5z1rYuLt6SFrq8L8kwD2a8Cw5n7DS2TJCZau3imRfTBjvZMX75Ib0cl+v5PnmNfUQnj2Vw6lXImJYd+IpYBTxq9dWarghtyy5J21jYa7eZOA5j1sYq6zR+U1/InzGvQ3FjPmMs44yyTvXp5MwfK4y6NuYiMYYd7/Owkqj+xOCmnWBz7t76v5RZrarv33o+M+S3ptjSA+Mer3PEupu9WwDfLqi8WmxhbmYMfPWHyQMefxrRNrq8j6V3He2J4xWeY6wQX3Mnpp5eozWSYZFbgt0u/v9AHXqVJWvnef527DhFlR9nLDS2hFmtoDGNoc8C7jTwqTNYwhDvV4hVMTrBlW9DIc5O3I4zO7A1hbm9+xMuFG8RFiEIc0VhPHvF7HiRZy6ultS8Xxby5SFIXzPpGS9jTCH7GAPy/WuSuihir1pjk96t1YDTnf3eRZW1HmY2sPwyg/68k18LzNbRujpKhAClNTlyT2suvckoZV4GqH1abfIm/2fgAvM7ND/z955h9lRlm/4poVeBARCEQTlRYEfWFDpRZoUUZAuGgm9V0Gq1NAEVIp0aQEiHaRIC70qIoi+ikjvEAGlivn98XyzO3v2TD9nN9n97uvKleTszpnZPVO+tz1PON7ZUUD3kEmGdHv6B10JlYUbOnSjcHR+XZF6bUMUpBVReRHas1MtFsYCY03eWNshFbNtgNygqmVBeCq9C8JFKc4snoWuv6QydjLKUp5JgSJS1jlgOWIi1gH/tDr7TW17pcnQeQcUKG+OzsMjiip0ZtbWqDcEoW+g2aGsmb/aCxx0jV9vZpeixex8KLFxd7oS5W1mQrz+YH5egFS4WGiScAv7rnyfNbODUGb7Ffq2pBW2/6FM7/JmNi261y2LgqY/k30NdWJg/BT0PDqJXqPyPchJellnLCuuBMaZ2Y/RInAhpKyXm3Cw3rm1O9Bi8AT6dwMUPUebJHL7JSFMLYTJnO7tBa1eleab3H2W8HftalOdLhPrgNQ3SrreYJrRTc6t3VB7XiEhkflgOJ656T/LlsWvzewSYG9Xu/IXkHjUTJQIqszsrfTPFpLBrUJZ7XgfjXak14EjaW8d0MoT6Hy+nv7rwtxnaOp4b0u9lnjBFQU4TSpGL5m8XjcntL+a7Aoyz/9OPH/rMNyCqjJtL1l80d1XNvXOzg2MCxdRGWWpJu0wc7h64L8NvBAqHtNQbi4i8RxplfYtnCExs9XQjarVPyH5k8eu6IH1emhbmA6VaTcrccwgsYZ53P0VM5s/lLbfpVcqtpWmrTA9uPtvKfAvyeBHSIjjzdTPfCsKvldBN/jNM/ZZW7ghlMO/Q3vzwVEFx3wAcJ2ZbU3vg2glYIOC7ZouQldALTlroD7rB1BbZNFiEOotCBOWQ6Ien4Sf4bUQrBdm90xCBL+g2vXQ2D+t5n6TbTdHC9bzwrFMiQKU6Sj219sO/W5fQlXb+cKf51DWfoSZrefu97XZtskCZzoUWE+JAmWQcAUUtPFaPQ+VZNtMA26UOc6jScKt7n12d2BVd7+r4NjaMU9o81oDGco+Fn53mZ0EHlq50axba+vf1CjAOzBvpyFp9SF9jcoPzrqXBDphWbEfSpzci9Y8H6KE0H4F27XOrSVBTtpEuCioapLIXRrd2++nd053eVQZAPiFmW2WE9hVnm8K5/CnvGVkINxvj3b3vQuOuVKXSSCR+v4uNT9jVyv4B/Q9t/bzbHGoHkKi6Ch3X8fMRqHWvffNbGMvMLRGz6/zgd+b5rf3RF1EmQFVSIhdgc6jWUzzwWlmRvfdPM5Dz+7D6U0UHESxjxgo4Euu4cLRiA4db9NE8H5IifEdYI1w3zyH/pYhaTriX1qVYRVUNcn6oszjSPQB3RMCqsWRQlURjdphzOwotDC/xtTqdgRqEyni26G8XIeTkJnjv9CcwwVowXBh0YYuudrlwu91buBFd29VjMnjMuAuM1sRZSUuRa0abX8WTzlqhyrPApQLOpNtGjt+o5vr6ujnnQ9JQSfD5i8QpNIz9t9EuOFMpMr0BloUvYMe4mOLNnT3W0OWfVP0ILoH2NXdC3ueGy5Cr0RzLicDN3nxLEaaygvCFO+ghXrag2VBymX3Tqbi9eAN/dPq7jfFwcB67v57M9vR5d2zNvqci4Kqv6IW5Z5+dTPbEzB338HMtkf3iK+3bthkgdOk2kQND5UUTQy4myTc6t5nP6B3/qIqT4XPbz3gxpCY2YtyLZZnhYzxHu7+fngGXoSu/bZBlZl9w90fgJ7W1CIlxh68M5YV74dq19ZoEfmqu080STxnVuW9V/Z+KRTczIO6RK718p6TTRK5AFulrxsz+x5q+9rE5Lc3hozArmrFycy+geZ75jXNQH/LJezxDXRezoyCpjyqdpmkpb7HVznelmOfy93PQlXi9Os/cPcLCjY/Ebg3BJSHoo6J11ESKDeocvfnzWwrpDCaXLcHeE4rumvu9RDUPnc6/YP3Dynu6jkcrd9PpPceez4lZtar3mObHm8nKkbu/lszmz3psDCz55HwTGZLboeev5UZVkFVk6wvajV6HM0LfdfMvoIevO2EB1pp0g4zGj14n0YLg6VQkFY0TwUq8X42p00nj8+F/XwGWNfdrzapWl1HxoC2Zcv9zm1mX4bSbYeHmdnfkOLTTqhVYxYKfuZw8z6e/lm4ol7w2o7fKS5Gw8zPU041KE1t4Qb0sP8aurHuGR62u1KspgVACFCOJfX7sXJD500WoXOjoPdrwNpm9gpwX8n2mCYLwlPDNr+kN/O7C+UWeZWvh4SG1cTa+0WLwEQ4JUkWJJWmIjYI26f5Bbpn7YA+/8zKU3qBY2HGKG9nZjbG3X9i/cUm0u9ZVIkcRU0PFRoYcNMs4Vb38z0PqXAdVLWtDJ3zZ6NAblvUerwzqiwU8X9h34+a2RWoK+EXaJGXxXgzO9zbK5/mYv3VK/tRov1vrJlt4e4fAq+Y2ZSmVsBD0PM8b//HoyrrfaiivRRwoJmdWuJ8bCR9jc7H77W8dhXhugoLzVxBIas233QycE14/32RV9U96BpIPusiqnaZYDltfwklqnq3m9kqHubUTWJGZwFJQJjH4u6+qql1byRwSbiGCxOT4Vl0DLpef4WEIh4xs63zAu/QEYOZPeHuZfy/Wrf/BCUxcqvDLcda+x7b8Hg7VTFa02Shkl6rFAZkptnrUaijqU+iocw1XJVhFVTRIOvr7seYFJredbXgzQNs6sXGstCgHSZUDdJByj0Utwz2bI5aeOoYlr6KBqCfQ4vmZEgxz7uhjNxvYVAVStrXpoLB7Use84GoHHy9l1d1w4PjN6oU7ZqX/cjhHmArMxvr7mX7sRMaCTe4TDVfp9d88FeUuNma2QYooEwvnssOnTdZhC5Arxzsy1STg00WhG9TcUHo7seZ2b9Qu0kiFXyku59XtC31roeE2tXEhvv9PQp0T0q99n36KwK24y10PqZVFVejd1g/s8Jn9WaMkkVXWaXOdjTxUGliwN0k4Vbp800tQKdClYM9TW1lPZRYgC6F1FnfC+/5ursvVOJYEzn2ZKD/JyghdKjnt1euhmZFVwe2dPeiIfw0Zc7VIkYCF5nZlkj05Dw0x5qrZGuScd4QBSF/T72+JLKeeNTdc9cO1kz6+jmUSEzfn7YK75O0rWX+Lq36fNPiqKX0fZMPWTJzu5W7l6m6QsUuk0DuPGtJbkQtuN9Ez4Jj0Rz7F0ts+2EIPr+DAt5kNqqMyMcRwGgPLbEmSfTDUBCe2QpvqZnVUOEG/Z6eR/YGRe3Ki6N54HaBQlZXTSfusTubWZ4f583ufnf6C52oGJnZaejcf4y+MvBlArLfoOTV/ZT0eGvCFBMn1m1TnvwID5+Z0Q3uYndfPkS+17l7ZntWavtaqjZh223p2w5zfpl2GGtg4BuyTMuik/0l+vZz57azmdkFKKO9DcpeXY8u+t3cfdG8bXPec1Z3L5QnN7MzkGjDlCgDdC0KMHIlQE0mlp/1cn4a7bZ/DanMfFhjW0eLoolo0Vyldz79Pmnhhk95gZeJmT0E7O7yj3kRScB/DDzt7pnZwbDtE8gI9yJa/Co81VKZse2z9N7QX3D3ecLrE9y9aPblalThapWD/Zy7lzEgTb9XoepfJ2hyPZjZm2RUE73AKLbhfr+IFhZvo3PzYdQSt3ZeFjVsuyE6L25BgckCaHE8ClUF7wDGuHu/BIrJcHc8Wlz8E1UEXkNzpY3sJQqO+Vq0EEx7qBwDLObuuZK/oSLyLGqLvBstyt4HTnf3BUrs+8v0Tbh9sUzCrerna2atKp/98AJhHTN7CyloVpX4xsx2RtLmV6LkzSlISW9rd89UEzPNWp6AFryjc4LrMscwF2r9LXWfD/u+BnU7LBGO++Ci5JeZ3Q8c5G3M0E0tkD9192UL3qP2vS4EJteghXZyDY5Ev8N/o5arbd390oztn0MJp37zTe7erxXPzN7xIFYR/v8Bah++Ne8427zP5ui5PQ29XSYHu/tTJbatW9XDzI5EwidvoORomRldzOxAlHyaGVUGX0ZB2vFeoNxoZnO7e7+OIwuGwDnbPdrm5anpDb5X997xgXbbP4buw7fQEih4/oxiI8zsTGQofQW93R4boSTwv9G6bW9372e1Ym2EVwKFwiumbpbC51bGtu8An/Fy9keNGW6VqtpZ3xpZn/S2a3j7ft/93f2Ygl03MfDtt88K7IoqTyPQjepSdHMcXbShtSjahNemQL/3MhKY24dtlkQy9PsBF5vZ7e7e2g6R5iikOngyLd5SXs6r40rgt6a2ltYgtKjCVsaoty3WTLjhWOCWkLk6Fw1jf0I5b54FkRJc5cUVqoL8DC1CXzCzjdEitEyraW05WDObHt3Ue5IMFkw1i0r51kxBLH097Il8vGalxPUQ3r9WNbHJft39SdMvZ116TV5vcPfCDKxLOXBpNKM0H1LG2s3VX78A8jd7MGPzxd19FasxY2RSzdzR3Y8KWd8LURvdD929SJVyTxREJh4q86HFaBkPld1RBbSOATeo6jmX9Xr8vGNmo0ok3CrdZ7MCJjNbDgUaf2n39RYuR61dY+l/nytq+z0EfRbXhP0uG167m5zMfAhgdgrnwHVm9g9Svk0lknzToYrrD1EQ93F4r+2LgiN3f8/M1kMLwXuBfUp2MhhaLLZjPDlzsilq3+tc3pSLoPN3PhRcXevyC5sTJQsyF97UmG9q4YOqAVU47nRLYtkuk1pVvdT1BhK4WRjJhb+UfM2LvbWOMrNbUFLkL6ZW3q3cvdDA1zVz9nVgR9Te/X2UIMn1TnL3L7V7PSSCjkdrzbbCVoGFkWhM1dZfzOzcjC9lVptSLILalNPqf2eioHkzM/saGoVo51/ZRHjlE+DJ/J8sk7+gKl0MqrrAPSj7ug3wpEmB7wPKzTbVUbVJuNkkX7mF951vOgBlcvKobeCbZCzCTWIBNKBbRood1BqxZ3hgvU6vl0JbrEMKMan3mxotHpNZt+nQwjCPqdCCOxnETGTWS/lrAIkB749bXi9sW/RmvfO1hRvc/QqTAe7L7n6wycxzFsqpAN2GArkyAVgrTRahTeRgz0PSrOOpnmRozXLNga7lrIdMD6HCmiwQCq+HFv5hZsuGauIMYZHzMSXa0hruF+RbNC5cT5ug8zPTKNn6mry+Qksrr2nermhusMmM0RnATCEJcyo6N99FQehqeRuGwHUxYEV6OwIeKnkNfuxtDLhRq3YuTRJuaOFb+fMNlZJfuLuZ2U9QYPM/M9vV3YvO581Rq2Ii4pE+5qL75BLu/nryn5CQOTRUCYuO+StosP5pdF5VSeYcg1q5VqVXhGQMCkh3ydhf66zO1Ej57F9mliiA5nURTImeW+3ux9NR7CUIze51oN/RCFTNvAYZKN/pJTwuqTHf1ELpDgDLEXlKKAqcqWdo+0f62qkkJEJeZZ/9L7n7C6GquRXlvQ83Ccd3PrrfTY3WH9Oj0ZJKuPt/zewI+oopteNG4Jvk3Mtz+C/Z1abZgF3NrG21iXD+tbx2P5pdw2UoPlfOvusKr5yAEuZH098eoGi9tAuaubualuvO3fNmQWsx3IKqdlnfUtUXmmV93keVkwdNZnGJulrrjaAdtQ18zWwOlDVYA2UhRpjZfcCG6QdjBsdSYrGZ4J1RtEmO+xakKvYMGjL/BXBHiQz7T9G8zG1Ud6/vUXuqQ50sW4q522VOLcerxnoNj0E3iulDFSeRgx9Bsbnlf4BrQ1DW6leRW7kJC+t+i1B3zxUkCDSRg10d+HLJymPrMfczEA1ZtkyhCuuMgELlamLe/sru1zQT8gsU+B6Hro3/mdkvPaXq10InTF6bzBh9A3URzIMym2uFYymbVPqGyyRyHiSesL6ZHVnivPwb+rmBnsXNlKhqXKRm2STh5mb2AOpI+I2Xn+c8HDg2HOPuYb8voYVS0X27dgumu79u8j5LMvM93mdZ24Rk06Eo+XIq8BOv3mK9Ebruk+fWq2FB+wQZQRXNZ3UeQJXadt0em6LFZBG173WhUvw75Ne2JKra3mhm23jvHHAeVeebZmhJhvZLjuYERk1EnhIqV/W8gadWgknV9KcoiXsKst74H0pw7FOw+aGoRfJhM9vW5TO1Flq3VA6qAFzenkViQj9Dn+2f6R8o5CafaFZtegI4INxT/xfuPz8hBIFmtg69PpCtrEJ94ZXD0fNkFNUTQYegNfTn6Lsu7Mrs07AKqhpmfZtkfT5x961Mho0PhBLnbZT7UH9NfQPfk1CWbd5Qpp4XPexPRn41eVRuEfGUQgy6+D4JGacFgDeL2jRS/BctUN5CP/OrtM8WtvIxcFWdknhCaGcZRcrnxtv01LehTpYtYRUzq6pK2W7R20rRzeZvqGWyNFYgj2ol1HjQDXIqlLVOXNlLycGi86DMAHFZ/oFEI7JoPNxbs5rYZJg4YV/ge2FRuzU6D19ErVpZQVVjk1dvJuozHTqv1wb+5O5vhuRQ4SLczI5DXnifQZWtT6Fk0qnomm79/oVQW+M0qDrWGrhNSzmhhCYJt3mBjcPx/TxUfC4AflfQovY5dz83BKwzopadT0wzuLm4+7NhMbQqqv6PBebzclYKae+z9SnnffYIqgqvU/Je2o5p6W/I+h451RQvni1bqGCfRwNXhfPi6vD7nQZVMsYA6xQdNA2kr9GzYw93H2uaVX3KzNZHC/8ylhmtKrpjUOCQpaJbqpU5Y19ZvoXzAf/xcvMsjap6Vn/mfVtgpfDM3hR1QryIKs1FQdVI1AoPvc/jZ8hphS0iVNuLEjJnokTVnVRPIDepNm2LxhJ2N805jURB76YheL+EbO+oJsIrTWZxVwXmL3kONmZYBVVhcZGYsM6LLt5zSrRLQD1Vmz64+5Fm9hfgCjM7mAKZ4UBtA19Uofp80nIYsiij0UVfRJMWkenRTWkdtGgZjfrp1/Oc4c0Ed/9WeHgtiyoTY4AvmtlDHpRkMjgK+agcjgKyKrMCmNlGKIi9CN10FkYqT9t53z7xdtTunaeeKmWy6P0RukkehDJE8yNZ83aDsH1oV7kpQW151JZA7DWUbUtvt3PWtilOAi4LlZzWNoDc3nnrL/WfPEQzr2F33zH83cQ/qc9MX4lzqWd/Zra4u5eRi2/HAqFqswLwX3e/N7xn5lxjqED2CJWEjGmdSvlTwMahWnY85R/8NyIF0cWA48xsYXQ9FLaXoXNyWTObCd17FkcP6bYtz6G6viYKvm6g/zn9IX1tMLKonXBztXWfAZxhZp9BQeEZ6Nk8f86mE8Lia3Mk4vOJma1Ejhpcgpl9Fv28s6BF9n3AH0MXRdEcSR3vs78BO5ToNMjjd8DZpvbGN8Ki72RKKNtaTSsVd7/LzHZElewLQnA1N0rqjPbsmcL0e1SWvk6xBFpnQHiWufttVk75E5PC3OWpZOYOed+fFRhVweopfyY0qeo1acGdx+V3uDrwtrs/amo/LmM98QdUKU0/t7ag4L5hfWfBEqZGSY5DKZaBXxhYuiDxkkXtapNr5mwxtDZL5vzuD+8zPTCnZwvI7Iq8VvegRXjFJPRzJwra2u33WVN3zrr0Jrx/6+XaYJMuhBhUdYGj0aLwZ+hkWAg5nc+d0w4D1PdOCvS0+YWs9T9Rf3Re72ny/bUzxuimMh19xQOmpbgtDJplBk5GGbYHAdz9p2b2d5R562cW2o5Q7XkFLZz/hc7VWfK34gi0SPhB6rUqM1WHoAH8noy6mV2GMoNFC+EmWbbKXjWpRe92wKLeO6v3ZHiYPk1GS46ZXewyw6ws3ODN5FGzArGEMvKoSVvp2m22LfqMW1tSP0E33MI5MKshoGBhbiMPL1B3BO43s6fC/sZ6G6WpHJJWlM0Ji08z2xSdG7mYBvvPQWbUCaWuJWvWtrQdur+OdfdzTNLGt1OudXAOl+T3t5Ei5d9DciazRcjdHwvHvKAXt0Rn0TjhFoLHLVCQPw1qv8njqPD+HwGrmVrybqTcTOOpaKF5HPCWu//VZIx7DPrc8qjsfebum5Y4piL2Qq1Cr5rZh+g5dhvFHRfQzEplXKgefgPdz19Fc4K5SrQJJkGJfVASrE9yIusem+JZZJ8yPvV+XyMjSdCG3YFTw33+/AZVwir8DEmbT4F+x6MIyp+oPT6PJlW9Ji24T4cA8NvoPpW01JYRRtgD+F3YfiYzuxMJmLQ+n1ppNwv2IQo2LqG4g+RW1BpdZx46q9q0SYlqE6h9cBZUKZ4DWM/UoVKU+HoybFtZeMUkWvY7dA0/g66nE03jFa3z+61cjsydz0PP7HSyvYw/ViWGW1D1feDr7t7jZWKa37mTArUWqK9qg2Qm0+/zBzNbhoyovJXQ4tHOOPSnBZteCYwzGR0mmZ9jwuu5NMwMfN77+3eMRQ/zQszsHFShmgVdSJcjyd6ifS9d5v1zWIjUAywwHt0AimgyJ9TEi2gatPBNB87zkz9k/ET4+zEq9hVbjit6QtaNqmYg1voetXvosxIU1nc+LYs6Agr/QJnty9GDqs6ifS5kxLsZcKTJJuFC4EovbqfdDy3w3wHWMLPVUKBUtJgDLUR/gRS1Si0gU9RuW3L3D9CiKuFTBDPOEvv9k5kdhXr3rwmf6xH0Dq3nYmZjqGFdUaPNKr3P3VBgsCRKtO2LWv9yB+Td/Twzuw4ptP07VB+X9pSfUg5fB77t7hMteM6Ez6qMCXYT77MmfJnQxkNYcLt7WeGjJgbayTk5vs5Bo66HT6P7RdXr6BA083ohMG04t0ejbptC3P0roaqwBaqEjkDB+gVeTiWyDl9095WthvInqtCMcfc6Vb0mLbi7oDnEt9Gs4KooiM+87s3st+6+rrs/YWaGWmEThdXr3T1XlKfJcywwAd3jHqR/oFB0f58KdQJUrjaZ2aHovHyZ/n5RRUHV44C1WRtSYm13MrLk6OluMQnNnUyxb+saKMG3YsvrZU2HKzHcgqoP6N9y9xY57S1WrGoz0d2/krHtOu5+AzC79W89ghIPIjM7BolqvI0uhilRsFHG0Hc/1Ht7L/qsP0I3+f1L7LdJZuBpM1vf+/pErEm5tkPQz/oj4C6vNh/VdNYmMTu8IvXahqiaUUSTLFsTVcqzgNvM7BSU5VoQldlPytogqcqWCMrbkVSbZgCWQefwP9HNeRl0Xnb8RpXGavbOW7bU//MUS/1XFlBw90VDZnkrdC78ntDKVjbTHRZ0l6GWx1lRNnUv4PSQgT7b3e/K2Pa3ZjZ70iJiZs8DI72cGMJs7l5p3i5F7bYla6ZqNxqd90+H7ZZCi+ky3QS1rSvM7NfIFydJbOS2WbWwCRoIv8wrqH4GFgE2M82sPU+JykvgddQa2fP8CYvvMq3Ku6PM/DYoyXBvOI6izHxTLkazwU9TotLaQpOkVVOWR8dd2VDe3a8NiZCtUVA3F7CxZ8tdt3uPv6Jr4ZBQfT4FqduW6dyoQxPlz52Rt10dmrTgPkDKJNjkV/mZgta6ngV6+GzLCId0kmcpV71vxx3Iz7PfeeTu7xdsOxr5RZVZf7byBlqfPFH0jW34ElpDpjkZndu5dCKZW4VhEVRZb//qpWjw9AB0g50HXcSn5Wyel8makb5zIa0ch3rX+xlkBspE96PQjXlGYBuX4MUYcloHzeyv7r4Y8uPYMrR2fApJqpetTDTJDBwE/MbMbqVXsvObaCC7EHffK1TnvhUWvVCuOpcn3lDmIXIAqjZtjW5aC6GfdYMSx9zTO29m03o1dasmqpQHomBqU3Q+v4gMKzMXWCaj01xy2v9WDe8xFjjNU334piH2rUocc22sYu+8dU7qv5aAgmuG8CGTwtTaKKv/c5PFwgUe5pyKMA3xbobUk6ZHyZLngfPM7BpvowSYVBUt+HilXi/T9nCLmX3H3a8uc3wtNGlbqq1q5xJa6Elemdn97l50r0qobV2B5reqdC704O4rAJjZIqF74U5ghqIAy8xGoYrplShZtSBwn5l934OHVA4/Q+JHJwPTmIzp90YVxqLjTbzP1qNX6bSU95mZLezFSqhZ3ANsZWZjS1Ro221bN2nVlKfQ9Vo5qEoF6zvV3Xmo1n4PVau+jtYjhe/XoEulifLnWOAcU8t9qzhW0WxjoxZcU1v0Dqgj6JsoCN2tZIV8MJgKOM77WvSUxdH9rk5gNAPZ3m1FPIeehY/TBI/fRgAAIABJREFU//Mtqq69hRJB6fNgcUpew+Hz3Zq+AmTnlD/08gyLoIr+/av3tHx9bfQw74e3Gd4MrSy7o7afR7J26u7JXNK33b10f30L07n7I6aZjkTO9Ah0s85iPjNbGdg3LKCTn3su6zVKLbpJNckMXG8aWN0YtQA8jFTxylR8kjacvahenWtt75ozvE+pm4e73xqOe1MUtN6DHmplVLGSYc8dgE+FQP5sJF6R+zDyGqqU1r8NLwkcJob975azcO5Eq856KEBIMw4t9rtJpd5575zUfxMBBULF9XoguTZ+jRZ5RfNJh6NqxryoPWwn4JakPczUDngnOs9baZ1hmwP1+19BtphIMmc3O2odvo+WalyJB2CTtqXaqnbhM9nf3bcLWflxZvYvZCHxQMHmta0raGAaHgLzy4CV0bn4dbQ4XNPdf5+z6aEoY9yTbQ5VjdPQeZKJu59lZm+i8+85dH4d6e4X5W2X2v4/4Zir8kczew5dM9d4CaGHFIuhFqszzOwd+v6O87ymoFnSqhapzpQ7kEfOCfS/joruHbWD9XAMV6GK+qPoXvU9L6GA1qRLxZspf+4c/v5By+uFc5zeYObdzHZF58jJKPh7H7Xjnkh2ADqtFdhetEt0dZCdqF/VA7jJzN6g//2qyEvsHORLd2TZbosUD1OyFbsNP0eJoJ/Tm/DeDV3XuZjEZg4O7/EMqqwfaWYzuXtWwaM2wyKo6kD/aqKYNRr13y6Iyui7ezllrtvN7LM1swrPmtmi7v43M/u0mc2MBuxnytnmLBRITEX7BXSZof5GmQF3/7OZfYBO/jupJjH6IypW58I+WzPhz5qEHP5MjgKQ9Z2peR5VQvp8vURrzhFoYbQNypK9htoRT0VBWrv9tj48+uHuWSpAtUUfvJ7qXyvPolmQdEVsG8q1Sjahcu+8p6T+3T0zCVJAEwEFQkvMZihjvAhaiO9aYtOvIzXHrBmqZ+gf3ALt2x7MbGNS1Zw2pA2S7yhxfO3226RtqbaqHQooXgzV7eNRomwCug6+VrDtr6lvXVHbNBzdH/6CquEvhPvmcWiB1zoDkGZ2pKya5i4UOBfi7ldSYrY2wfJFV6ZAbfBFz5Q50D3yW8D54Zl6PQqwikQMyghwtKVO0qoDtC7WDm35f5lzo3awHvgz8GMvN2eXpkmXCunAy91foVxbaeN1mtefed8Nyf3/PQQLb5rZBmjtkxVUTUFnbC/qcgmq6l2K7o1Vqnpn0d57rQzrolbJA0Jyo4ei5EaTdYe7/zKsJ7dCz5Nngf08ZSScw+5olrJHEdnMbkTJxRhUNcUqzmOExdNuaOHyMJpH+iVwlLuXUZaBZuXWX6G2jqXRSfA7NBt1X+bOlCHZy8zedfci48osmmQG5kSBRdXsa0Kd6lwW85EfgEJ+22BZ9cDvA19z+YFNdA2PjyLbCA+UNU1Ygv69xhPJkFZt0idsOap/qfcvqkbsiVpp96F3lmtuij25mtLEL+7PZrY97cUIcrOK3iKg4Br0zq3amtlsqPVmS7SovxllUK+v0B46DVp0tm15ClXQq0q+F+gekvlATT/4zGx+pA73npktBfyrTeIi630eIaeKn0MTVbul0fm3EPqMT0UCLseX2La2dYU3U2hdDUnff2hBNAIldX5SsN05wAlmtm/YdkrUdp2pUpqTWX8f+KO754kJNBZdcQ2/3wrcGp4r26L7yNYUVyLaVpOtwGvKzKZDLfQro7nJ59Bz/KIK12BlGp4TCUmwniSLkjVL7jlpveMO45ApfD/57oKFd+UuFTObQPEzpaiiWGte1syWQOd++jN+BFVf/1q0TxQcJc/pZL8TyD8nP/CGNhsNaVLVayKdv3Pxt/TFGqgNt/A4UmeeYGarUMK7MDA3/SXuH0eJqY4zrIIqq+dl8ARSdvuqu3t4nzrRbbtya6bIRYK7n25mf0RDfruj9qdZaKmmZFBG1Sxrv62Zgeconxk4hXrZ14Q61bl2wcII5I5eJIe+M2rtasIIehX4khvz/5CRcVvc/UvJv00qaV/K+t4O88fib8kntEp+DrWozIMe4Nd6My+aMjTpnT8PGTuOp6QYgZk95+6fyVs05CwWXkHKe78BfkqvkIpZ+Tbc2ll1669qOAI9hAurzWb2XTSHsgJqH1oVmYFv7u43Fmy7KGpNaRe85raXeH9Vu9kor2oHqm6vBzzkkupdgL7KmFn7bbQINvmsbEav/+H5Xq7d+V0kkPFM6rWR5AigBNYBFgV+ZGYvoXv0rMC7Jm8woN+5mZVZnwv4gZktm5Vc8A6IrpjZFug8WhW1Zt+FZkILpb6thtdUqA6PD1+/CQWGc4Z97m5mq3i9GbpKWH313u+EP/OgasS17l7m3t1OrjtN0cK7TpfKd0ocVy511mcme4tbgauRvPmb6DNeByWjV/bi0Yu7gTEmleTkHr8X/SvBabJ+twNCnaqemd3j7itYjvhaiftzmVb5VpJkce11h5ntgLpCVkTP0QVRUmmfEkHio6jDLL1u37nJ8eQxrIIq6nkZnIV6zhcys7NQpreq4Vq7cmuRyAWgBYq73596aYyZTY0yqm3nwJKFIPBWKvvZhzJZI3evWyaum31NqFydC7ReJJ+gB39RJv9Yd++REzaza909r0WqHTehcvyuwMSQIT0e3fDLUMfEry4dCXxCVW4cqQd0yVbJJvvMkq8ubKVEMv1f9pQRbwm2CH9/l+qf0Qj0gN+R3kpI+mFcpgJ6M/CASdiitQWoSGyitQI7RXht68Ij1wNs/aRlwt1PNrM/oYVtblCFZgn/g2Toywav67j7DZZSSbWUwIaZfaFEy9MlaCE0EtjFpDp2FcVGmsk+lkVVjfQwc5kF/1bonnUVah/+LPB7M9ssaT3N4Rw0Z3cYMJWZrYEC8KJjrjxrk5dZNxkP/4H2s3nJ9k1FVy5CVbFzgRPdPa+K30odr6njgIeAUS4hIQDMbCoUGB6Frs2uYTXVe00zWLuiZ94rSMnyQDM7tURVvem4Q+UulZqL7VbqrM+OBA5291ZBsUtDF8URFAd8u6E21K2RmNGzKFG3Ts42pRUYu0VImm2M7lcnAF8p+BxusF7T7Kr7apJcfMbMpmnS/oeUrFf2MG7j7uebRKeupti2Zl9UHd8Onc8LoudyVxRLh1tQVWceYwcz2xu18OyLFhWzoIHvUu1/3lchrZTIRYrbQ0btqbD9V9BDaWYygioaLATzshgJRdkM6mdfk/fPqs7lBqENLtrWrNMKNd5jT/SgfyW837soS7pFzjaDRWMTXlPP+Wkoi5pQxWi5Emb2+aRa0dI7v0P4+pYoE53HO1QMKN39nvD3+CrbhW0az3KiBc1r9J8JKuOx0Vp9+QQpgJYJdOanvWfb/CW2XQrJSLfaV+TRCaXUvdBi522XhPtCqN3yvKKdm9lGKBt+EZoBXRi42sy2aznf2nEg/UUjVkc/S1FQdQxawB2GnsenoUXCMXkbJYsnM5uJFnPjmkmNt8t+o9cUXUH3itXDn/Fm9hGaS7zN3ccVbFvHa2oNYMl0QBWO/5NQlSilvNmQUVRX7/0+ukcvka7QmgQkrjazRz1H3TX1/Q+jZ9IlXk65D2jcpdKEOl5TXyV7RvR0cpIECe7+vEld9Rv0ek3dn1eBdfe8gKvrWH+D9YsoNlhfF7VwXgWc7hlWHBkk65g6FcljgZNN/qNneMkW8hZmR91Paf5Cibk2d/+9mX0enSfJ+XyDlxBtqcNwC6pqzWOExcGZwJnhIbI9eqA8B1zs7rnD6tZM5OIYFFitizKDu4ftM/ubmywEqZHFaEPd7GsPrdW5vO9t0/bX7v3yenYbVYlC5XCEu3/bpDT3PqpSPOXudQxfu4p3xrfhKFTFvIiKvj41+V1oT+oz9Bx+9z9H12TRQ/8k5Pd0Ii0JkaI2vJoZusY0+aySh5fJ0DFRtpvezKYvsfB+HM28nJF67YfAkyV2/RRaQBeqZqaOdYnwd+U2PDOb2eUVMzOhMhyyuG+hGaCZUUCdxyGoX79Hpcwk7XwKxe3DswGt6oJ3oQxyLi4VxxPpa3hciJltgmbG0uderaSGme2Eqr5FYhHJ99cSXXHNII8Fxoa2zu2QuMc2qH04jzpeUzNm3X/d/SXTzG63qTMfvDOwXWvLq7s/Hj6rn1LOk+wXSPDlWDO7A92rr/YCL6LQUnUCLV0qJhGHg0rsty511mdTZf087v4fM5ux5L5nQK2DyUL7cybriTL3u8GgssG6uy9vMmXeGlnevIGSOBcWPQ9Sa8o6FckFkTjN1sBfzez2sN8bvLzFz4Oo/fwQl0nxFKhbq7AwYWaPoETVpQOxHhtuQVUjLwPoHcA2s71QkLMdGQpg1gGRC3c/JWSNHkW9qct6SsUkY7+1h0Zb+1PDA3QBlOEum2GolX1tcNxd6Y0tg0ky9g60eDsYlakfRAucQ8zsWc+Qc7a+A8RThUxkn6pZ0WJ/EFkQOKI1C9xF7kbeSSu41LwSIYUrwrGUEchIqiCtZf8yC9HWDN2caPGTN9zfmNCq9B3az2SMKth2PZTgmDP1ctmF996oXWQXer3m5iO/JSbhBpQIuoj+SnqFxtBmtqK73x0y1kegxc6ROZWvF1Elu53gTNmfdyHaV+bmKzpe9DseY2Y/cfePrddeIc8rrlBevuB3dSK6v/4WzW424V10jz476xusA6IrZrYCqh6tgaqZD6Bn53V52wXqeE0VLdgGYi6mznywke0FNB51yRQSqlkXmoSjNkaJ3V+aZhYvbEkgzIUqNQCHmZnT9/czK0rodjOoarw+a0PhZ2xmo1EAOn3Ll7rSddEhahmshyBxH5Ox+gbonDg6JJBOL7G2/Br6XX2WFguKnDXlRPRMuMFkjfFDNBpxqpmdCZzj7kVzvrugEYudzOxl1Pn0OrJZKOLnKLlwjJmNp2RyoS7DKqjyBl4Gbd7rXVRePj3n22qLXFhqtgBl1S9HbQQLm4av82RVOzE0OgfK/K+B5plGmDxrNiyK9utmX6l53O3a/sJDYkLJVqepQ5YnuQFP0/L/vN/1EcCd7n5w+P9H7r5aOIZj0fDsZhnbtg4Ut8rfT8o39dvQuXHTAO1vFMpm3xBaq1ZE5+djSMigULa3STteuwxduEHfR/49oClnogfHG8iA+B3g/8jIRrZwMnoAjkPXcGnc/f7QMrEeUk96EWUWy7TwrgA8jURi0pRpKz0OXS+fQT/7bOHYTyP7Pr14+Hth6ledHbVcXZF6bUNyLAJSSaBkTmYHM3sFBbGzoN9BFlktuCOQUuxH5P+upkWLoMZJjTKtZHRGdOVKdL84GbipYpti2mtqD7SgLPKamrJdoir99Qr7r0ud+eApya6uTkeO+FE73P2NcK+aE1WQvwV8KbSOjg73tndQG+tcYR+t596HqDuhFGa2CCk7lTKfdc312QjTHHPWZ1zGe25/etv3B6LrohM0MVhPlDgvBy43s/nQ7/oRitcbZwP3owRyVZ8qwtrxBCQysSwK1J9D97O87f4WbjTL0/s8erDM+q5KcqETDKugChp5GdShichFu+Drv/Sq/mXOGBSVaK1AhjZwErrRzusSJJg3HNPJKFuZ9/7TI+GAStLVrcdd9cZsEoc4Cd2EpwU+NrPfANt7hiR14DX6PkTeaPl/3jzH2vRfPCacQo6CUJNF/iTAf5DB68P0r0aUkUetRCj5b46MTR9BbUdHAEdXaCGoM9ybx9QUeKd1gO+gysBcwJ7uvklYRKxSYtvZ3L30Qgh6W+nC7+kjWryMrIQQScP20u8Cy4YF37dQwPQyOYsFd38+/P1Mg/0eAFxnZlvTO5y/EsrmZlE7edXud2SShr4YBXi591gUZB5iZieEBF+36YToytzuPjHc15cxsyoL7jpeUzOgxFWeCl5X8XrzwQ8gb8N2IlGbokVtIWb2WZSg2BwlHK5F7ZY3u+bKtkWtrfO6LCO+Hra7su493BraqaTXZ2a2R4lqwgMo+ZH39SLmAs4OyeDJhSYG60BPl81WaL00EwUWIYEFgKWqPHPb7HcadO8chdZOZcdC5nX38eHZsBuwuJmdVfZzK5lcaMywCKqsWHyhUNq8Dt5A5MLDbIGZLenFkqBtsRoytCnWAD7vwbA49KCPpq/4RBaVpatbjrvujfkYZEy3Kr3O2WNQhnOXrI3cfaGqx5jiU8miLnBS6n2fDxfsUORvtM9cdm2hElqrNkQqYM/XCBjqDPcm27ZWXUegm3LXK3Xu/g8zex0N6oOy3weW2PQWM/uOu19dYXedaKXDzNaifVKlqP1vDnd/MVTqX3AZck5DTlXBOmBM67II+CpatM6F2s12dffMubCmSaCWbfdA19OvgX1KLCYfRwvig6xXJbGsCW9lOpQAmt1kVroKFe7rZrYcsJi7nxv+PwK1PR7mYdaji8fcGK8wHxw4GnkAvoXalD4J18BWYfuyIgl/R63pJwKXJ8/yFLfRXgHti9Y7p1iVWnYqoZ1zfnffI/x/NuCfZjbG3fNUB1epcYytXIWSwL/uwHt1ldC2d7bXNFgP184GKKD5Jmqz3B/4bclA6QF03ZYJVlv3vUzY72boOXMmsHmZ+2Q4Pw5CnQunAMugtmejQIykSnKh6s/UjmERVJEvvlBK2rwu3lDkAs0mfLbNDbEMdWRoEyaiNoD0fqdFPc5F1JGuTlPX52qjsN+kPfFV00D3E+QEVQ1528zmd/cXANy9R5HRNBPSbd+mAcV6Z0EG9OeyvvNnBwEXm9kp6NoCSrUfVR7uTdGqMvQJEnH4VakfoD7/MAl03G9mM4Rz6mN0bbbFeoVbZgfGhbbdPm17OZnoxq10ZnYaWgA+Rt+kShnFwj+FrOsqwDWhYnYEmknNorExLYBLOKhMtrYPTbLzppnVC1CQv4kXS7AnnIzmtm5H52KV413Y3fNaE7vFqcBfUZa61H3dzFZC8xjpBMp0qLXy5vA7HggVv0pYs7nmu8xsR9RWfEEIrpJnyWh3z/NPSrOgu7+Ys/+n0TOzlRGo/bBOUFXZTsXMtkEL421TL/8btQD+wsxe8xzz3w4wN3CuSVirteuiVXV1sFkfODTc409z953Kbhjuy5uiNdx5wI5l12ippOJ/kGjUlfR/prQNbsxsX1QNWxh1PmyQlwzJYGtgeTObFnV/LYsCsz+TEVSZ2edcqtl1kwu1GBZBlbcxB7Pq0uadOI7SIhfpzdAJlOtrkUEdGdqEK9GC7Meo53UhVAm6Mm+jQGXp6hbq+lxNC7S2+b1H80HuPMYDP0ILv1a2pd7nNinTWI69Ju0MLRPVsmS/RRn6WsO94fsyPX66zLGo4rQ4slK4Fy2i8ypkaeGWO6rsrEOtdBsCK3o5o9JWRqNq79MowFkK3cMy5yq8gTFtJ6pc1M/Ob4SSAvcg6e8qweD0LoW2OvwxJPWuBa6psEhvSp37+qFIKfec5IWQ2d7eJKbwU9RVManRaK7Z3ceZ2bVIPGIkWuzfU3Q+t/C+mR3o7keZTHIvQqIvP3TXjHcGDwGPhlapVm+8IonyOnYqu6CEQs9C2yXbf4mZvU1vBbdbXB7+TPK4+3LWq+B3ufUq+F1QorK4KErqXx1+v1VIkorv0jt3WihnHtgW3efOc/c3K+43YZ5wX10Dzco/ZhIEykwuAm5mT6MA8gpgfGhz7ftN2cmFWgyLoCrBmkmbdwwvJ3KR5qZw8aRvcGVaFuvI0Cbshy6Ee9F58iG6Ke9fYtva0tWBuj5XNwNnm9muoX82MbrrZmBzNBpEngapgL0YjvVHaKC6422lg4l3Ro69zn470cpTe7g33MD3RQ+zHnNY4KfeRQVEd7/CNLf2srsfbGZPova8TMNDTwm3mBQS33L398xsKeBfnqPi2aEg4xPKSa/3I7TbpQ2A73f3lUpsV9eYthNVrjrZ+fNQy1FS7RwZqlbpnynvXnm6mR0OHF5jgTQHqqp9Czg/PBevRwFWKVn1mtS5ry8FrJXxtbJtsAOO9/qILerufcROTIqeB6I20bz3+ID+ipRVOAOYySQ/fSrqWknWHqvlbPc+qg5Cgd1MG+rYqSyUU7m4mXKiPLVJB+yTA95ewW9MaK3NVPBz99Ub7LN2UtHdF627bYqnzGx7JJx0Y7iG9kKVqizmQaMgq6FE73ymOc4bgBvzWrubMCyCKuuAtPkgchb9B1bLtiy2ytDui26YufKVqR72LU2D2/OgaP/Ckm2ITaSrob7P1d6oP/pVM/sQZTFupXjouzYhe7I6CtAPojfofQxY093/2a19RyrTZLj3INQ6cQi983oHos+7cstYFdItGl5sRNuDmX0XXf8rIEuGVZHXx+bufmPGZp0IMk4AfmVmR9M/qVIkNrMwsL+7b2eShB9nZv9CqqOFffxe0Zi2SZUrRZ1gIam87UCv8EOaonvlekgFcn8z+w99qwm5/ksuxaxbgVtNarTbIvWzrQv22bR1sLF/YQulpNwHmdvNbJXQhoSZfQVVnGcGDs/dsjnfQMnUeYClUXD6LwqSkw2r8nXsVN43s5ky1hbTU0Nlrgpm9jlUEW03Azqptf/14PUV/Gpj8lvbMVX9vJDe6memUmoH2BWtg99G96uVkaVJZoUpVP7HhT+E39FqYdsfm9n77m5Z29dlWARVNJA2H2zSrYs1WhYry9C29rCH7OsE1Jd6U5ke9g5UFWr5XIUAefmwMJsLeM7dX2p4LIW4+8PA10NVYD7k6fVMt/cbqYbXHO4NjAK+mQqSHzSzB5HJa8eDqoKKEQAlKkZHA+snmUt3P9nM/oSydm2Dqg4FGYejxM8oehf7ZUUuTgNeDNn148N7TQjHXLjAsRrGtA2qXAmVg4UO3CN3r7uhmW2BAuxVkRLWXShBcFuJzZu0Dta5rz+KPpN2FbR1yJG8T2Nma9PeW6cbrcppjkGB1brovNodJeC6mogJTIeuubWBP7n7myarlMJg1My2RMH+vGgRejiwsxcIqHg9O5Xb0ShEu222pYQogmmucR/af8ZFSobnoMDtfCYfSXWgtoJfE1qrnzehpNKvyK9+NiLcZ3rmqsOc3UJltzfN5i6HzuWV0LVxV4cPExg+QVUTafNBpW7LYqratH34/9vAC0gxKW8R2ZEe9nASL0DfRdREL6FkWOfGbFLe+qK7X+fuT5vZsyhbfnyXMyg9uMQqXhiIfUXKY2afd/e/Q+9cY8vXt3T3iwveZlb6f7YvkN/T3YROVIzmp72h7fx5G3UgyFiixrEmLI2MnBdCmeNT0dD68VkbWAeMaaF6lStFrSRQEzxD/tfK2WVchDoWzgVOrFhNr906WHPBfULYz7bIJ+2T0PqzDmpPL6wymwRtRqHugXRioFvznz24+ylm9gEKDp8Als1qz+oCN6IE6WLAcSHZeAHZ9iBAjxLl9qiN/zg0l7xo+H+7qmo7ddR+5MxjHYaSVPOhtdmrqLq2EUqAlWk5vwj4NFrkV61sfQnJddcRAxtwrLmCXxNqVT/zsBybDpP640/anV/W64+XKVSBKvrfRv5WT6Dr4YfAA936XQ2LoMobSJsPFk1aFlurTYHp0EKtqNrUuIfdzLZDLYCthm6FmWqT19Qo9OCeFc2DPQxclLVAMhk83oWqkdeFl2dBAh0PmNlK7v5E0XFHhiy/Myno9TEINrOp0b1gO+QPlMe9yHl+/7CwmxpdX6U8Y6rSoYrR4yjTe0bqtR9Sct6papBhvTLMTZUhZ0QPw4dcnlkL0FeFtJVOGNPWqnKF965rdl4ba2aXMQ9SaF0dGG9mH6FqwW3uPi5vwzqtg9arGJr3vm2DG3e/2cwOQOf+CJMS3hwoKNzf3X9T9N7oGfqVpEtlIDBZAiS8hpIjywMLh/MZzzaU7xTbISGfse5+TlhT3E6xONbOwNouK4dj3P2t0Er8BBlBFb2iBZ8G1kSS9/9EnRsbAJm2Di5j15XQSMPuyD7hY2R/sVKZaxf9buf1ejLwT4XjnuSDKmug4NchmlQ/32ptTQ4Vr+fRWq8dy4S/y4pipPkbej6fBWzR+vzvFsMiqIKOSJsPNE1aFrulmFQ267svqqyd7xWGqMOiZjxaFNyEgsA5USC3e+hNb5cRORI4wVO+Re4+AVjLpMJ1BMWqdZGhy91IQW8Fl4loIuBwBar+rlviPfZED/ltzOxltJh9EUncdoUOVIz2Bm4ws11QciJpTy3lc1MjyOiEx9UlyDB7JLCLSfXwKvLnbmob0zapcjUJFjpAbbuMkJQbC4wNP/92yLdlG8L8QRY1Wwez7r0jkLLtR+RUjNz9XDO7BLXvfBqZQd8XArwyvIkW+ANJ+lmdXAv/RZW35LWuBlUuoYsTU///C+Xaw2ZD9wvovY7eJscrLpnDMrObgfXc/XfJ10IC4MiCY30cWNMk+DQH8EaVtQMKjKanngz89cBtplnbVkn102q8XzdpouDXCSpVP0Pl/Ap0Hs1iZn9o+ZaZkQBbFqua2d9T+22r4JfBpSjA3xv4Qnhu3utdNnkeNkFVGq8nbT7QNGlZbFJt6kQP+6eRfGbVk/c4JOc6ylOKaqHd43xUGdixzXbfQIu/doxBsvSR4csotFi8wSQqsiKqTD0GLF0mgxWytouFbedCi46HBuLBVrctzeVttSgKGudGQc8NGYkJoHErXWOPK6TodDfwtkvufqGw//OyNmg4n9SkytUoWGhIbbsMM1sBJdXWQM+KB9Dz77q87QKVWwe9jWKomS2BrkGnhJBQmOUpM/OV3scs4Z/nIh+iA2mpoma1HTXF3T8bjmEpJK8+DwoGr/V6VgOVCSMAP6O9AEOemMndwLEmw9XkOt6XclX55eiftLmH1DxMHiFQLl1RSFUE70CzayfQ3z+pKHhdFd0bW2eCJqJW3kkGb6Dg1yEqVT/d/RkzOwStCU+nb7IBlKjPU8GsreDn7luYVHuXR50Picrq71Al9UZ3f6PoB67KFBMnThajRcMSM5sRPXC2Q/NJs6CyfK4Uq0l+fZ49Et60AAAcSklEQVR2C74QoLzm7nNkbLsWCmAye9iLWi7M7FyUSTy76Gds2e4VMvxaTDLw9yYPq5avTQDmaBfEhfLyBHevKg0bGUKEDOg1qDK1CKpeHt2kr9ok2T/e3b/YmaPM3E+7itHF7l7oQRUeKjPSt0VsCXcfn/H9H9AbZIyjTStfyXacSiStg6mFcD+6sQA2s/Q9Iy2q0fOaFwuCpN8vCRZGAFu6e2tmtiOY/Fe+gNqkXnH3ucLrb7t7VitNsu1rqBPgeuCmKr/XcM4nrYPfRIFjqdbB1HvsQa/30D5eIH5Ql/DZpr3t0td6WXuAJvs/HrXw34cChQWRxcapOfNFndz/I8iz7lJaBBjy1hBhtuk6lByZid7Ew7pFQbSZPYCqFmPcfWK4/xyOfOtWbvDjZO2vKKif6O4Ld3q/kb6Y2UJeIM5lZl8NBY0m+0kr+K0JVFLwC0m6DVCb6WfcveOFpWFZqZpcaNCyWLva1KEe9vnDMbdzKP9yznYztguownYvmeQ82/EkypC187pYgb5Sx5FhiLt/bGYbojaC59Otog2YBui4JCt0RnzBzDZF81Qzt3zpDVS5akeTVromHledaB2sTMMqVx8GKlgItNpl7IPmLHLtMgJzhwXvIsAyIfM7Q5ngqmHr4Ej0XFkSmb3+tsSxNqFfAm6gMLPvIxPsJTyI5ITXlwSuNrNH3b2wVbPpYQDLeTXDYNz9xbDe+CoKBF9CVfky7ZY7ouTV3iFJOi+aKSvTYl2ZdknWNCYhlUJM8uA7oPbo76PzeUyTpNtQxKSe+3PqzXL+0/qaUV+IqopFZtTJvmsp+JnZTGgduFLY9v+QWNUZedvVJQZVkwkVWxYbKSZ1oIf9YooH/9tRdAObIuP1U1F7x/fSWXQz+z/gbKRaFBmmhPMg4SDgYpMi2JnJi92ovjSkE+ILR6Cf9z2U3TsBOBa1yrSlYZDRRLGwE62Dg8IgBAvQ1y5jT/T7zrXLSDG7ySh0FdR+83XgLpOA0e/zNqzbOmhmG6Hr7R4yuhE6jQeTazO7xt03aHNMd6GFVjfYGdguHVCFY3rczHZC13S3g6pH0DmZ+5m2krpffkBv6/wXzKzwnuPuj5pU15ZHrVsvAfd4Fw3SIVcE4TmyRRCS79sEiYCdjwLhqYEfoRmtg7tywJMvJ1FzlhO137WTY880o7YGCn6hFXRldJ96Fs1EH4uq6v8pcby1iEHVZIZL3eb08CfrexpXm+r0sKe2PR96FhsLIN+mZ0tsOmXI5GUFT20XfO4+Nqw0/xDaYhJJ1gWBY939V1V/hsiQ4o/0bQMC9YXvFP7dlSpIQ2pXjFLM65J0ng+pRP3RzEahhW3H5b69gWKhuz8f/n6m08fVTQYjWABwCa5sH/77OhocL8upwF/RrM8LLgPz49As1ooF216JFkMnU7J10MzOA36AMsPJXMPI9PeUSWpYBa8pM1uQXi+vtay/JPOsqH2yWxhqi2zH+C7vO+EJNGd0Pf07RvLaD9vNfP0PBShlWunmQ3POC4T9vorOt1ysoteUNRdBSDgUiWs8bGbbhq6YtVAVJAZVfak9y0k9OfYmCn6LoefQTR7MtweCGFQNUTpQbaqNSWLzYpTN/AgFdvcBGxYsOmZAN/SsoCozK+Huh5oEPdai9+e9ueJFGBmCNKm+tFS5WslqoWtMh9rSXjJ5gLxoZp8NGcJX0PXRFbymYmHD1sFBoVPBQsV9/qDoe9w9TykRlBVewGXsntxTTwB+UuIQ6rQO/jD8vQPtJbnLWG1U8ppy92fN7GMkKjMl/SWZP0TS1N1iSrSob/d7mQ4pAXabmVAQDBUkqVvvPeF5fgBqG87FzFZGIgB3o7b75YD9zGwDd7+lYPNKXlPeXAQhYSS91bzkengGrUcifXkVBbzPoQAJd3/KNPNeRB059toKfu6+Xolj6jgxqBrCNKk2NeQk9DCZ191fDRfcz1EmI1PpqelC0mW+e07hN0Yi5WlX5UozKbeq3YQUDzdAbVo/Ry09XZeX9uqKhZ0wOx5oGgcLNdgz9e8lUDWidZ9FQdW7aCH5TOq1kZQz8KzcOtihBEFlryl33w8k6uHuR3TgGKrwAArazmrztU3pkr9dGg8y5x14nzfNbH+0kB5T8O3HAqPd/bLkBTPbLGxXFFRV9ppKWm3N7IkGIgh/QBYw6eB8C2BSawmfFGgyy1nZjNoHScGvCTGoinSDNYDPe3AoD+X00UTBiMhkRieFDAaBfVCG7xM043EmmrvZtts7tooeV01aBweLwTg33P1Lyb/NbEL6/xU4BwW7hwFTmdkaaManKBiDZq2DTWjiNbWbmZ2UPI8GiKOBq0Lr/dVhrnkadH6PoaRXXBPMbHpUSW0nqV5VfXCZ4m/RbtEcaJpxpOZXc2jiNbWFyUOtHyV+1j2QQfxoNPNzJ2rPXLvGcQxJzOxzoYWuySxnLTPqUJW6O/zZz3oV/A5DVhuTVBwzSR1MZMgwEZV60w+xaVFGIxKJDAAhGEkyy/8ys3W6OTBuDRUL67YODmPqVkmPQffiw9Aa4DQUyJaZs2vSOlgZ64zXlCPfsKJKScdw97vMbEfUlnZBCK7mRsc+2t0fHIDDOA+pno2nRVI9DzN7lL7n1gjU6lXm/Hgh7DOtyrYivWbC7fbXCa+p1vbGOZC/0fk5+/2tu6/r7k+Emez16VU7vN7d3yzY53DCw7z6jciM/V13f5QKs5wezKjNbBEz+yZqzTzBS5j5DrSCXxNiUBXpBlcC48zsx+hmuhC6IV+Zt1FdzOwcdx9tZqu5e9ZwcCQyrAjtFfu7+3Zmth5wmZm9jWYbH+jCLjuhWFjb7DhSjpD5PTH8qUqT1sE6JBL7Sfvt5qmvVZHav8nk3/gSqYDB8y0+GuHu48zsWjSgPxK1SN0zgJXX1YEvu3tmQJNBq+DAJ8DfQtKjiKPQdXshUlxbiF6vzSxaZ6EObfn/RHJaxKB9q6OZrQjsl7NZT2U1tByOzdvHMKe2CW9CECK5FAVFpVqHB0vBrwkxqIp0g/1Quf9edI59iPpw9+/S/jYxs52Bq1EpOhKJqALxYhCoOB5JrE9AD8WvdWF/nVAsrNw6GCmHmeVaaUB7Jb0WmrQO1qETXlNn0X62qeuELPz4wdg3SnD0M+/OI9wrrnP3WkGyu18aqnLfRwvw55BKXDsPyWSbjnhNteEBuieZP6wIAmPjwp9WE94fm1kZE95TgL+g1r2yrcODouDXhBhURTqKmS0HLObuW5rZ1ijDcR5wYRd72h9CykTThxt6P7zFwyISGQYsjUw3F0JzFaeiltzju7GzhiqLjc2OhwMtapRTWRsLipxq4HczXh+B2uM+oo2SXgtNWgcr4x3wmvL6Fh+TOyeh6vSJyIC3h3bniJktjtq75gsy2RvVWci6++9QRaES1sxrqlWldQS6lzyds9m0baT2+1Bj9mzIYzVNeKnROjxYCn5NiEFVpGOY2UqoHHwUQLh4JgB/R+0Xa3ZpLmJ95J1wC9kLh0hkODIjUk16yN3fNbMF6DvrOKnQkdbBYUCrGuVjLV/PrAa6+6qtr5nZEsj+wslRZk29R5PWwUpYh7ymrL7Fx+RO0lbXKriQdY6cgIQHzke/9xOQIElpgtjML2jvNdUvsWmd85pq9db6H/I4yhPlmYIKUvPDGWtgwptioFuHB4UYVEU6yaHA7u7eI2sehoi3NzNHi6U1Or1Td38PuNfMvuXudwKY2VzABB8AX65IZBLlEuBB9ODaJWSir6J7rVpN6Ejr4FCnk4qDZrYHSoD9GtgnWHBkfW8nWgcr0UGvqVoWH5M7Nc6VZZEJ7idBQv3JGrs9G8nFH8wAek3VvC4+6JTs/DCgiQlvwkC3Dg8KMaiKdJKlkPluO34FHNjl/T9kZqej7Mm0wMdm9htg+xB4RSLDib2QDO3b7n5byAofg9pxJykmc+n6yYrQBncBsCSwSeL1U0AnWgcr0yGvqWFr8RFatTZGbY8nIK+vrCBlikQd1N0nmNm0NXa5ALBUycoFYV+d8JrCzD6LZjEXQKIgl7n7X3M2yfIejPSntglvitbW4VPRfagrrcODRQyqIgPFQMxFjAG+iFRqnkFD7mOQr8IuA7D/SGSSwd0nmtlN7v6emU2F+uBfqrLgiQwtzGwjJCJ0D7Bk2fa3TrQONqSJ19SwtPgws6XRbNNTKIC+CLjRzLZx93ZKd50IMh5Aqm511EVre02Z2crIDPZu9OxfDnkabeDuWVL6d9c4xmFJExPexONqIFuHB5MYVEU6yaOof/v6Nl9bB5WQu8lGSEI2WSi8amaboP7fGFRFhhVm9n1UPZgdiVNsAfzPzH7p7mNyN44MOczsPGQGewa9i6KR6e8pO7dWpXWwQzTxmhpQi49JiF8Ce7j7WJNR9FNmtj5SYWsXVE3ZInzSTwgl6/xIzbv9BxnpXkl/r6ki0YfKXlMpjkX+X5eljmkzlFRte864e9cNmIcSDUx40x5XNwDjy3hTTa7EoCrSSU4AzjezbYEbQm/2VCigOhMo7MtvyLRAa5vfe2hoNRIZbuwLbByuwa2REuCL6KEYg6rJGDNb2N3zlM3a8cPw9w70zq2lKZxbq9k62Cnqek2lLT6mofsWH5MKS6C2LQi/r9AGPG/G98+ABB/SFau0EEre+ZEERO8i4Yn0a6Wo6TXV861I5CbNOPS5RzpETRPexh5XkxMxqIp0DHe/2cwOQA/dEUHefA7gfWRC2nrT6zQ3A2eb2a7u/kYQqziZetnNSGRyZ4GwiFoB+G+ivNnA9yUy6fBHM3sOmaJe4+4PFm3QdG6tbutgh6jtNRVMQhOLj08hSfXh0AL7LFr8jk9eCOp8beXkm5wfRYIPZjZjzbcu6zX1Alrsp6W9V0SVyUhDmpjwdsjjarIhBlWRjuLu55rZJain+dPAy8B9A6TCtzdSN3vVzD5ElavbGMIKT5FIDi+Z2VrA5oTEgpltSr53S2TyYA60KPkW6g6YFbVdX+Pu7dqvG9HJ1sE61PGaylMsTMn0d1xcYxLiEOBaM7sQeTIdBYymSx0jZrYmqo7d4e6Ppl5fHQXERSa/dbymEo5CynIXokX/QmHb7coefySXjpjwNvC4mmyYYuLE4ZCwiQwnzGxhJMP7nLuX8biIRIYcZrYuanN6BymgzY8qGxsGg87IEMDMPoP8ePYEpnf3jkvPm1lRC/XEbuw3tf9+XlNArteUmd2R8XY9ioXuPl0XDneSwcy+ilp/F0Rtkxe4e8cFGkKHysHAX5F32PoooflzYCfgPHffpuA9Ws+xHq+pMv6WIaj7PuHZj37Weyr+KJEOk+NxdQPlPa4mG2JQFYlEIkMUM5sieWiZ2fTA1O7+7iAfVqQhQSVt1fBnTpTtvQ214zwxmMfWDczsApTV3rXFa+ojdy/diZBSLBwBbOnurWazQwIzmwL4lLsPiLGqmf0T2M7dbwniUKOQuuJXkYDEgLXgh599dnd/c6D2GckmBMuJx9VNNT2uJhtiUBWJRCJDkCBQ8R1gXnqHz0cAS7j7qME6rkhzwkLlfeBc4ER3/+cgH1JXMbOXSXlNhddmAZ5x99lLvsdAKxYOCsHk+0ZgPuDPqJpXu2Wr5D7/7e4zhX9PhQKqu4DvufuECu9TyWsq7OunwH/c/Rgz+yL62edHs3/fHajAMtIeMxuLPK5eRtWpOh5Xkw3RcDESiUSGJmcCpwM7AnugNqDjiLO0Q4F5UMvfjMB4M/u7mZ0RqgRDkcRrKk0prykzG2lmtyC1v03cfeehGlAFTgAuQQqN94f/d5ueBXIwEP4Y2KJiQLUy8DiatZkCzd78wczWyNnsQOB7QFJx/CVSLPwM8CRwdIWfIdIF3H0L1JK5U3jpV8AbZnapmW1lZnMO3tF1nhhURYYMZrZPxutHDvSxRCKTAN9BsyOjgUfcfWlgd2D6QT2qSGPc/TV3H+vuWyNFrrOQT98lg3tkXSPxmvqqmc0VVOwuo8BrKigWPoGsNZYcYAn4wWJZ4AB3fxIFkl8fhGP4wN1frbhN4jX1LXff0d3XQomgPPuHLQkzomFxvgpwpLu/CByOZrsig4y7/8/d73b3/dx9ceDLKOA/DBhS7YAxYxmZrAmy6d8I/z3MzJy+PhuzooXkQQN9bJHIYOPu/zCz14EvhZd+hbK7kcmYIJO/RvizFJKePhq4bjCPq4tU9poabMXCQWSKUC3C3SeY2bQDsM/axsEp6nhNzevufwn/Xh6J8jwc9vdytI+YdKjpcTXZEYOqyOTOO2iROBdqD2mVyP0Q9dFHIsONf5jZsu5+v5nNYGZzo7acIa14Nky4ErgJ+fDd5O7vDPLxdJWaXlONzY4nU6Yo/paO08Q4OKGO19R7ZjZzEN9ZBbg7JcyzAPB2qaOPdI0mHleTIzGoikzWuPsHhPYGM7vS3Tcc5EOKRCYVjgVuCYPr56Is/ydoMR6ZvJnb3Sea2SLAMmZ2JzDDUAuumnhNNTU7nozpRNWoEh36XdfxmroJOMHMLkdy6ntBj4DFkQR/vsig0hGPq8mFqP4XGTKY2dTAPsBYd3/OzLYBZgdOGKpKM5FIHsHD6GV3/9jMNgdmAc4PyYjIZErwbboUZec/RImlu4A13f33g3hoHSV6TVUnKENOJLti1VVPsSZU9ZoK7f+XovN/nLv/KLz+EvIzW9Hdn+/6gUcigRhURYYMZnYKsAywcQiqlkfKR/e5+96De3SRSCTSGczsUuB1NFP0grt/ysz2A9Zz9xUH9+i6y3DxmhrONPWaMrO1UCvge509skgknxhURYYMZvYqsFhaxjUoAj3h7vMM3pFFIgOHmX1S9D2TaqY6Ug4zew1YwN0/NLO33H320PL0prvPNtjH1y2Gi9fUcCJ6TUWGEnGmKjKUmBq1PaT5uM1rkchQ5h/A3MDlSGL79cE9nEgXeBcYCTyTem0kMCQXn0G17wLkvbTJMJFGHy4kXlO7h/8nXlPLIdXeo2kvNBKJTHIM10HOyNDkKuAyM/uymX3azL6EZHevH+TjikQGDHdfFEltv4cGhI8APg/8xd0fc/fH8raPTBacg4b6N0ZCBGsg36YLBvewOs8w9ZoaTkSvqciQIVaqIkOJPZHnwX2o3/4jtNCI81SRYYW7PwQ8ZGZ7Amuj4e+fm9n1aPj73kE9wEhTjgE+QOaZUwOnoQD6mME8qE4zjL2mhhPRayoyZIgzVZEhh5mNQF4mr0fVv0hEmNlX0SzKF+JMVWRyICjZ5THJKtlFyhFmoT/n7u+a2UnAIu7+7fC1BYAH3H2+QT3ISKQksVIVmewxsx3c/VftPE2KvEwikaFMyOpvBmwBLIJMY3cd1IOK1CbPtylhKN3rhrHX1HAiek1FhgwxqIoMBb6NWkO+m/H1icCQWWhEInmY2Wxo8HtL4GvAzagt7Hp3/3Awjy3SmKx7XI9vE/FeF5m82Bd5TV2NvKYuDK8/T/CaGqwDi0SqEtv/IkMGM1sbuN3dPxrsY4lEBgsz+wDNJfwGGAdMaP2eOIcydIi+TZGhSPSaikyOxKAqMmQwszfR0GvMxkeGLS1zKMkNfor0a3EOZWgQfZsikUhk0iEGVZEhg5ldA1wHjI3ZrUgkMlRp8W0aHWXGI5FIZPCJQVVkyGBmjvx4JqL2p56T291nH6zjikQikU4RfJvOBO4BtnH3aO4ciUQikwBRqCIylNhusA8gEolEukX0bYpEIpFJlxhURYYSG7l7O1n1C4E7B+F4IpFIpJP8MPy9Q/jTykQgzstFIpHIIBCDqshkjZnND2wY/rutmT3V8i2zAusP7FFFIpFI54m+TZFIJDLpEoOqyOTOS8DywFzANPT3cfkQ2GWgDyoSiUQikUgkMnyIQhWRIYOZneTuew72cUQikUgkEolEhhcxqIoMKcxsQXd/1sxmBHYH3gTOcvf/FWwaiUQikUgkEonUIrb/RYYMZrYPcBAwG3AqsAzwP8CAvQbx0CKRSCQSiUQiQ5hYqYoMGczsSWBj4ClgArAs8CLwZ3efezCPLRKJRCKRSCQydIlKQpGhxDzu/mdgJWCCuz8GvAVMN7iHFYlEIpFIJBIZysT2v8hQ4ikz2x5YD7jRzKZCbX9/HtzDikQikUgkEokMZWJQFRlK7AqcBbwNbAusDOwMbDSYBxWJRCKRSCQSGdrEmarIkMXMpoyqf5FIJBKJRCKRbhODqshkj5mNcfefmNmJWd/j7lH9LxKJRCKRSCTSFWL7X2QoMFv4+1ODehSRSCQSiUQikWFJrFRFIpFIJBKJRCKRSANipSoyJDCz6YBRSJxiVuA54GHgInf/cBAPLRKJRCKRSCQyxImVqshkj5mNBMYDI4CbgDeBOYE1gX8Dq7j7W4N2gJFIJBKJRCKRIU2sVEWGAscBDwGj3P2T5MXgU3U+cBSw4yAdWyQSiUQikUhkiDPlYB9AJNIB1gD2SgdUAOH/PwbWHpSjikQikUgkEokMC2JQFRkKzOjur7f7gru/BMw+wMcTiUQikUgkEhlGxKAqMhQoGgycYkCOIhKJRCKRSCQyLIkzVZGhwJRmtiTZwVNMHkQikUgkEolEukYMqiJDgRmAP5IdVEWJy0gkEolEIpFI14iS6pFIJBKJ/H97dxOiVRmGcfwPOrmxsGhVggXhBQ1I0KJV0sgsDCWCQkEZMUKoJJJM0NCwog+kRUS1CHKrJWPiR1ChpgUtWkgI5R0SupM+hGKWIi7OmXp7sWg8NnDk/4OzOM+5z/PyLC+e572PJEkdeCxKkiRJkjowVEmSJElSB4YqSZIkSerARhWSpF5KMhd4AXgCuAv4HTgK7Kiqs0lGgA1V9f5/nG9G9ZIkTXOnSpLUV68DG4DngQArgZuBk0kWAGuAV2Yw30zrJUkC3KmSJPXXk8CmqjrS3p9Lsgr4GXj0GubzQ+GSpGtiS3VJUi8l+QU4THNk79LA+D3AKHBgoHwM+Bp4GVgL3AlcBPbS7HQ9CBwfrK+qL5OsA7YDC4EzNEcLjyBJ0gBDlSSpl5JsBd4ALgCfAseAz6rq1yQ3AU8DL9EErIvA5nZsAjgPLAV2A6uBQ1epHwM+Ap4CvgXGgbeBZVX1zeysUpLUB4YqSVJvJXmMJgwtBUaAS8B7NAFqAnirqm5vax8Bpqrq2MD7p4GPq+rVJOuH6k8An1fVawP1HwC3VdXjs7E+SVI/+J8qSVJvVdUkMJlkPvAQsA54jmb36sJQ7cEkY0l2AYuBJTRdA+f8w/SjwANJtg2MjQA/Xs81SJL6z+5/kqTeSbIkybvT91U1VVWHq2oVzZG95Vd5ZyewnyZETQIPA9/9y8/MBbYB9w1cozRdBiVJ+pM7VZKkPpoDbEyyr6pODD37o30+fL59I7C5qnYDJJkHLOKvrn/D9T8Ai6rq7PRAkheBy8Cb12UVkqQbgqFKktQ7VXUqyX5gXxt0jtN8o2qcprvfOHAHMD/JvcBPwG/AiiQngVuAncCtwLx22qmh+l3AniRngC+AZTTdAydmZZGSpN7w+J8kqa/WAO8Am4DTwFc0x/6Wt935jgKn2msFsB64u609AJwDPgTub+f7W31VfQI8S9Ny/XtgC/BMVe39/5cmSeoTu/9JkiRJUgfuVEmSJElSB4YqSZIkSerAUCVJkiRJHRiqJEmSJKkDQ5UkSZIkdWCokiRJkqQODFWSJEmS1IGhSpIkSZI6MFRJkiRJUgdXAP/l0dbqMQtKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1008x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(14,4))\n",
"fig = sns.barplot(x=census['State'], y=census['TotalPop'],data=census)\n",
"fig.axis(ymin=0, ymax=40000000)\n",
"plt.xticks(rotation=90)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* How about some conditional selection based on gender, race and population "
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>TotalPop</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" <th>Black</th>\n",
" <th>Native</th>\n",
" <th>Asian</th>\n",
" <th>Pacific</th>\n",
" <th>...</th>\n",
" <th>Walk</th>\n",
" <th>OtherTransp</th>\n",
" <th>WorkAtHome</th>\n",
" <th>MeanCommute</th>\n",
" <th>Employed</th>\n",
" <th>PrivateWork</th>\n",
" <th>PublicWork</th>\n",
" <th>SelfEmployed</th>\n",
" <th>FamilyWork</th>\n",
" <th>Unemployment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>California</td>\n",
" <td>38221472</td>\n",
" <td>18933999</td>\n",
" <td>19287473</td>\n",
" <td>14692293.0</td>\n",
" <td>14798592.0</td>\n",
" <td>2127149.0</td>\n",
" <td>140367.0</td>\n",
" <td>5176065.0</td>\n",
" <td>138295.0</td>\n",
" <td>...</td>\n",
" <td>996420.0</td>\n",
" <td>942280.0</td>\n",
" <td>1976858.0</td>\n",
" <td>221289.7</td>\n",
" <td>17227654</td>\n",
" <td>29653010.0</td>\n",
" <td>5367271.0</td>\n",
" <td>3133738.0</td>\n",
" <td>67771.0</td>\n",
" <td>3882730.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>New Hampshire</td>\n",
" <td>1324201</td>\n",
" <td>653484</td>\n",
" <td>670717</td>\n",
" <td>42138.0</td>\n",
" <td>1210349.0</td>\n",
" <td>15467.0</td>\n",
" <td>1917.0</td>\n",
" <td>31109.0</td>\n",
" <td>230.0</td>\n",
" <td>...</td>\n",
" <td>40533.0</td>\n",
" <td>16685.0</td>\n",
" <td>79720.0</td>\n",
" <td>7773.3</td>\n",
" <td>698810</td>\n",
" <td>1051932.0</td>\n",
" <td>178401.0</td>\n",
" <td>92202.0</td>\n",
" <td>1569.0</td>\n",
" <td>76731.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Oregon</td>\n",
" <td>3939233</td>\n",
" <td>1948453</td>\n",
" <td>1990780</td>\n",
" <td>485656.0</td>\n",
" <td>3042948.0</td>\n",
" <td>69110.0</td>\n",
" <td>36325.0</td>\n",
" <td>154411.0</td>\n",
" <td>14325.0</td>\n",
" <td>...</td>\n",
" <td>158402.0</td>\n",
" <td>128937.0</td>\n",
" <td>245917.0</td>\n",
" <td>18752.2</td>\n",
" <td>1789807</td>\n",
" <td>3058983.0</td>\n",
" <td>564227.0</td>\n",
" <td>309229.0</td>\n",
" <td>6701.0</td>\n",
" <td>374214.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Vermont</td>\n",
" <td>626604</td>\n",
" <td>308573</td>\n",
" <td>318031</td>\n",
" <td>10701.0</td>\n",
" <td>586539.0</td>\n",
" <td>6739.0</td>\n",
" <td>1857.0</td>\n",
" <td>8672.0</td>\n",
" <td>185.0</td>\n",
" <td>...</td>\n",
" <td>37293.0</td>\n",
" <td>10097.0</td>\n",
" <td>41867.0</td>\n",
" <td>4160.3</td>\n",
" <td>326732</td>\n",
" <td>475152.0</td>\n",
" <td>91692.0</td>\n",
" <td>58318.0</td>\n",
" <td>1452.0</td>\n",
" <td>34751.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Washington</td>\n",
" <td>6977372</td>\n",
" <td>3482455</td>\n",
" <td>3494917</td>\n",
" <td>834539.0</td>\n",
" <td>4939626.0</td>\n",
" <td>243173.0</td>\n",
" <td>80693.0</td>\n",
" <td>529058.0</td>\n",
" <td>42454.0</td>\n",
" <td>...</td>\n",
" <td>228502.0</td>\n",
" <td>141628.0</td>\n",
" <td>377110.0</td>\n",
" <td>37633.5</td>\n",
" <td>3258598</td>\n",
" <td>5383656.0</td>\n",
" <td>1159643.0</td>\n",
" <td>421363.0</td>\n",
" <td>12668.0</td>\n",
" <td>560386.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" State TotalPop Men Women Hispanic White \\\n",
"4 California 38221472 18933999 19287473 14692293.0 14798592.0 \n",
"29 New Hampshire 1324201 653484 670717 42138.0 1210349.0 \n",
"37 Oregon 3939233 1948453 1990780 485656.0 3042948.0 \n",
"46 Vermont 626604 308573 318031 10701.0 586539.0 \n",
"48 Washington 6977372 3482455 3494917 834539.0 4939626.0 \n",
"\n",
" Black Native Asian Pacific ... Walk \\\n",
"4 2127149.0 140367.0 5176065.0 138295.0 ... 996420.0 \n",
"29 15467.0 1917.0 31109.0 230.0 ... 40533.0 \n",
"37 69110.0 36325.0 154411.0 14325.0 ... 158402.0 \n",
"46 6739.0 1857.0 8672.0 185.0 ... 37293.0 \n",
"48 243173.0 80693.0 529058.0 42454.0 ... 228502.0 \n",
"\n",
" OtherTransp WorkAtHome MeanCommute Employed PrivateWork PublicWork \\\n",
"4 942280.0 1976858.0 221289.7 17227654 29653010.0 5367271.0 \n",
"29 16685.0 79720.0 7773.3 698810 1051932.0 178401.0 \n",
"37 128937.0 245917.0 18752.2 1789807 3058983.0 564227.0 \n",
"46 10097.0 41867.0 4160.3 326732 475152.0 91692.0 \n",
"48 141628.0 377110.0 37633.5 3258598 5383656.0 1159643.0 \n",
"\n",
" SelfEmployed FamilyWork Unemployment \n",
"4 3133738.0 67771.0 3882730.0 \n",
"29 92202.0 1569.0 76731.0 \n",
"37 309229.0 6701.0 374214.0 \n",
"46 58318.0 1452.0 34751.0 \n",
"48 421363.0 12668.0 560386.0 \n",
"\n",
"[5 rows x 35 columns]"
]
},
"execution_count": 300,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census[(census['Men']/census['Women'] < 1) & (census['Asian'] > census['Black']) | (census['TotalPop']>35000000)]"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>TotalPop</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" <th>Black</th>\n",
" <th>Native</th>\n",
" <th>Asian</th>\n",
" <th>Pacific</th>\n",
" <th>...</th>\n",
" <th>Walk</th>\n",
" <th>OtherTransp</th>\n",
" <th>WorkAtHome</th>\n",
" <th>MeanCommute</th>\n",
" <th>Employed</th>\n",
" <th>PrivateWork</th>\n",
" <th>PublicWork</th>\n",
" <th>SelfEmployed</th>\n",
" <th>FamilyWork</th>\n",
" <th>Unemployment</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>District of Columbia</td>\n",
" <td>634444</td>\n",
" <td>299385</td>\n",
" <td>335059</td>\n",
" <td>64875.0</td>\n",
" <td>223893.0</td>\n",
" <td>306993.0</td>\n",
" <td>1243.0</td>\n",
" <td>22145.0</td>\n",
" <td>221.0</td>\n",
" <td>...</td>\n",
" <td>66254.0</td>\n",
" <td>30023.0</td>\n",
" <td>30100.0</td>\n",
" <td>5436.4</td>\n",
" <td>334180</td>\n",
" <td>441979.0</td>\n",
" <td>164087.0</td>\n",
" <td>27664.0</td>\n",
" <td>698.0</td>\n",
" <td>68695.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" State TotalPop Men Women Hispanic White \\\n",
"8 District of Columbia 634444 299385 335059 64875.0 223893.0 \n",
"\n",
" Black Native Asian Pacific ... Walk OtherTransp \\\n",
"8 306993.0 1243.0 22145.0 221.0 ... 66254.0 30023.0 \n",
"\n",
" WorkAtHome MeanCommute Employed PrivateWork PublicWork SelfEmployed \\\n",
"8 30100.0 5436.4 334180 441979.0 164087.0 27664.0 \n",
"\n",
" FamilyWork Unemployment \n",
"8 698.0 68695.0 \n",
"\n",
"[1 rows x 35 columns]"
]
},
"execution_count": 301,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"census[(census['White'] < census['Black'])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# more women than men\n",
"census[(census['Women'] > census['Men'])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# It looks like 42 out of 50 states have more Women than Men\n",
"census[(census[\"Women\"] / census[\"Men\"] >1)]"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {},
"outputs": [],
"source": [
"# Create new column for Poverty Ratio which is Poverty/TotalPop\n",
"census['Pov_Ratio'] = census['Poverty'] / census['TotalPop']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot the Poverty Ratio by state (Poverty/TotalPop)"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51]), <a list of 52 Text xticklabel objects>)"
]
},
"execution_count": 304,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(14,4))\n",
"fig = sns.barplot(x=census['State'], y=census['Pov_Ratio'],data=census)\n",
"fig.axis(ymin=0, ymax=.6)\n",
"plt.xticks(rotation=90)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot the Child_Pov_Ratio by state (ChildPoverty/TotalPop)"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {},
"outputs": [],
"source": [
"# Create new column for Child Poverty Ratio which is ChildPoverty/TotalPop\n",
"census['Child_Pov_Ratio'] = census['ChildPoverty'] / census['TotalPop']"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51]), <a list of 52 Text xticklabel objects>)"
]
},
"execution_count": 306,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(14,4))\n",
"fig = sns.barplot(x=census['State'], y=census['Child_Pov_Ratio'],data=census)\n",
"fig.axis(ymin=0, ymax=.6)\n",
"plt.xticks(rotation=90)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Interesting statistic to examine would be the ratio of child vs. non-child poverty as a % of TotalPop\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {},
"outputs": [],
"source": [
"# import seaborn as sns\n",
"# sns.set(style=\"whitegrid\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create array of abbreviations based on the "
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"whitegrid\")"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(16, 10))\n",
"\n",
"sns.set_color_codes(\"muted\")\n",
"sns.barplot(x=\"State\", y=\"Child_Pov_Ratio\", data=census,\n",
" label=\"Child Poverty Ratio\", color=\"b\")\n",
"\n",
"sns.set_color_codes(\"pastel\")\n",
"sns.barplot(x=\"State\", y=\"Pov_Ratio\", data=census,\n",
" label=\"Total Poverty Ratio\", color=\"b\")\n",
"\n",
"ax.legend(ncol=2, loc=\"upper left\", frameon=True)\n",
"ax.set(xlim=(0, 24), ylabel=\"\",\n",
" xlabel=\"Child Poverty vs. Total Poverty\")\n",
"sns.despine(left=True, bottom=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How about a cleaner looking plot ?\n",
"\n",
"#### Let's use State Abbreviations instead of names so it tightens up the x-axis\n",
"\n",
"We pickled the dictionary containing the key:value of state:abbreviation\n",
"Now we can map it into the dataframe so it will be used as the 'X' axis in the plot"
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {},
"outputs": [],
"source": [
"# Map the us_state_abbrev values to a new column called 'StAbbv'\n",
"# unpickle the state abbreviations from a saved pickle file\n",
"\n",
"with open('state_abbs.pickle', 'rb') as handle:\n",
" us_state_abbrev = pickle.load(handle)"
]
},
{
"cell_type": "code",
"execution_count": 311,
"metadata": {},
"outputs": [],
"source": [
"# This statement will add a new column to the dataframe called 'StAbbv' which will hold the abbreviated state names\n",
"census['StAbbv'] = census[\"State\"].map(us_state_abbrev)"
]
},
{
"cell_type": "code",
"execution_count": 313,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>TotalPop</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Hispanic</th>\n",
" <th>White</th>\n",
" <th>Black</th>\n",
" <th>Native</th>\n",
" <th>Asian</th>\n",
" <th>Pacific</th>\n",
" <th>...</th>\n",
" <th>MeanCommute</th>\n",
" <th>Employed</th>\n",
" <th>PrivateWork</th>\n",
" <th>PublicWork</th>\n",
" <th>SelfEmployed</th>\n",
" <th>FamilyWork</th>\n",
" <th>Unemployment</th>\n",
" <th>Pov_Ratio</th>\n",
" <th>Child_Pov_Ratio</th>\n",
" <th>StAbbv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Virginia</td>\n",
" <td>8202003</td>\n",
" <td>4030052</td>\n",
" <td>4171951</td>\n",
" <td>706305.0</td>\n",
" <td>5203049.0</td>\n",
" <td>1549383.0</td>\n",
" <td>16852.0</td>\n",
" <td>485435.0</td>\n",
" <td>4975.0</td>\n",
" <td>...</td>\n",
" <td>51127.1</td>\n",
" <td>3977156</td>\n",
" <td>6094519.0</td>\n",
" <td>1687052.0</td>\n",
" <td>410302.0</td>\n",
" <td>10354.0</td>\n",
" <td>545816.0</td>\n",
" <td>0.116528</td>\n",
" <td>0.153983</td>\n",
" <td>VA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Washington</td>\n",
" <td>6977372</td>\n",
" <td>3482455</td>\n",
" <td>3494917</td>\n",
" <td>834539.0</td>\n",
" <td>4939626.0</td>\n",
" <td>243173.0</td>\n",
" <td>80693.0</td>\n",
" <td>529058.0</td>\n",
" <td>42454.0</td>\n",
" <td>...</td>\n",
" <td>37633.5</td>\n",
" <td>3258598</td>\n",
" <td>5383656.0</td>\n",
" <td>1159643.0</td>\n",
" <td>421363.0</td>\n",
" <td>12668.0</td>\n",
" <td>560386.0</td>\n",
" <td>0.134609</td>\n",
" <td>0.170959</td>\n",
" <td>WA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>West Virginia</td>\n",
" <td>1851420</td>\n",
" <td>913631</td>\n",
" <td>937789</td>\n",
" <td>25426.0</td>\n",
" <td>1713466.0</td>\n",
" <td>60363.0</td>\n",
" <td>2705.0</td>\n",
" <td>13296.0</td>\n",
" <td>469.0</td>\n",
" <td>...</td>\n",
" <td>12268.1</td>\n",
" <td>751252</td>\n",
" <td>1415982.0</td>\n",
" <td>351294.0</td>\n",
" <td>81958.0</td>\n",
" <td>2157.0</td>\n",
" <td>147254.0</td>\n",
" <td>0.181494</td>\n",
" <td>0.248899</td>\n",
" <td>WV</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Wisconsin</td>\n",
" <td>5726162</td>\n",
" <td>2842827</td>\n",
" <td>2883335</td>\n",
" <td>363768.0</td>\n",
" <td>4718671.0</td>\n",
" <td>352947.0</td>\n",
" <td>45985.0</td>\n",
" <td>140911.0</td>\n",
" <td>1083.0</td>\n",
" <td>...</td>\n",
" <td>30618.8</td>\n",
" <td>2875637</td>\n",
" <td>4716720.0</td>\n",
" <td>702451.0</td>\n",
" <td>296464.0</td>\n",
" <td>10503.0</td>\n",
" <td>373634.0</td>\n",
" <td>0.130826</td>\n",
" <td>0.167541</td>\n",
" <td>WI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Wyoming</td>\n",
" <td>579679</td>\n",
" <td>295561</td>\n",
" <td>284118</td>\n",
" <td>55681.0</td>\n",
" <td>489646.0</td>\n",
" <td>5963.0</td>\n",
" <td>11006.0</td>\n",
" <td>5161.0</td>\n",
" <td>383.0</td>\n",
" <td>...</td>\n",
" <td>2420.7</td>\n",
" <td>293949</td>\n",
" <td>416467.0</td>\n",
" <td>124583.0</td>\n",
" <td>37077.0</td>\n",
" <td>1527.0</td>\n",
" <td>28565.0</td>\n",
" <td>0.116970</td>\n",
" <td>0.148206</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" State TotalPop Men Women Hispanic White Black \\\n",
"47 Virginia 8202003 4030052 4171951 706305.0 5203049.0 1549383.0 \n",
"48 Washington 6977372 3482455 3494917 834539.0 4939626.0 243173.0 \n",
"49 West Virginia 1851420 913631 937789 25426.0 1713466.0 60363.0 \n",
"50 Wisconsin 5726162 2842827 2883335 363768.0 4718671.0 352947.0 \n",
"51 Wyoming 579679 295561 284118 55681.0 489646.0 5963.0 \n",
"\n",
" Native Asian Pacific ... MeanCommute Employed PrivateWork \\\n",
"47 16852.0 485435.0 4975.0 ... 51127.1 3977156 6094519.0 \n",
"48 80693.0 529058.0 42454.0 ... 37633.5 3258598 5383656.0 \n",
"49 2705.0 13296.0 469.0 ... 12268.1 751252 1415982.0 \n",
"50 45985.0 140911.0 1083.0 ... 30618.8 2875637 4716720.0 \n",
"51 11006.0 5161.0 383.0 ... 2420.7 293949 416467.0 \n",
"\n",
" PublicWork SelfEmployed FamilyWork Unemployment Pov_Ratio \\\n",
"47 1687052.0 410302.0 10354.0 545816.0 0.116528 \n",
"48 1159643.0 421363.0 12668.0 560386.0 0.134609 \n",
"49 351294.0 81958.0 2157.0 147254.0 0.181494 \n",
"50 702451.0 296464.0 10503.0 373634.0 0.130826 \n",
"51 124583.0 37077.0 1527.0 28565.0 0.116970 \n",
"\n",
" Child_Pov_Ratio StAbbv \n",
"47 0.153983 VA \n",
"48 0.170959 WA \n",
"49 0.248899 WV \n",
"50 0.167541 WI \n",
"51 0.148206 WY \n",
"\n",
"[5 rows x 38 columns]"
]
},
"execution_count": 313,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check out the new column\n",
"census.tail()"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1296x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Now the cleaner plot with the state abbreviations\n",
"\n",
"# Set up the figure\n",
"f, ax = plt.subplots(figsize=(18, 10))\n",
"sns.set()\n",
"sns.set_context(\"talk\",font_scale=.8)\n",
"sns.set_color_codes(\"muted\")\n",
"\n",
"# Now the two barplots with the two differnt stats combined\n",
"# Child Poverty Ratio\n",
"sns.barplot(x=\"StAbbv\", y=\"Child_Pov_Ratio\", data=census,\n",
" label=\"Child Poverty Ratio\", color=\"b\")\n",
"\n",
"# Total Poverty Ratio\n",
"sns.set_color_codes(\"pastel\")\n",
"sns.barplot(x=\"StAbbv\", y=\"Pov_Ratio\", data=census,\n",
" label=\"Total Poverty Ratio\", color=\"b\").set_title(\"Poverty Statistics by State\")\n",
"\n",
"ax.legend(ncol=2, loc=\"upper left\", frameon=True)\n",
"ax.set(xlim=(0, 51), ylabel=\"\",\n",
" xlabel=\"Child Poverty vs. Total Poverty\")\n",
"sns.despine(left=True, bottom=True)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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