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RadhikaG/Crime Data Visualization-Blog Version.ipynb

Created January 24, 2016 20:38
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An IPython notebook for visualizing crime data for India, the United States, France, and the Netherlands.
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Crime Data Visualization-Blog Version.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing Crime Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hi, welcome to my assignment for the Probability and Statistics course at my college. I really just took this as an excuse to learn Pandas and the IPython Notebook in all its glory. So here goes!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing required libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"from pandas import DataFrame, read_csv\n",
"import matplotlib.pyplot as plt\n",
"import pandas\n",
"import matplotlib\n",
"\n",
"%matplotlib inline\n",
"%pylab inline\n",
"\n",
"matplotlib.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib has changed quite a bit from when I first used it in 2011. The new `ggplot` style, I daresay, makes the output plots quite sexy!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading data from CSV files"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"india_women = pandas.read_csv('/home/radhika/PS/viz_project/datasets/india_women.csv')\n",
"india_kidnap = pandas.read_csv('/home/radhika/PS/viz_project/datasets/india_kidnap.csv', parse_dates=True, index_col='YEAR')\n",
"india_murder = pandas.read_csv('/home/radhika/PS/viz_project/datasets/india_murder.csv', parse_dates=True, index_col='YEAR')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"us_violentcrime = pandas.read_csv('/home/radhika/PS/viz_project/datasets/us_violentcrime.csv', parse_dates=True, index_col='Year')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"france_homicide = pandas.read_csv('/home/radhika/PS/viz_project/datasets/france_homicide.csv', parse_dates=True, index_col='Date')\n",
"france_assault = pandas.read_csv('/home/radhika/PS/viz_project/datasets/france_assault.csv', parse_dates=True, index_col='Date')\n",
"france_rape = pandas.read_csv('/home/radhika/PS/viz_project/datasets/france_rape.csv', parse_dates=True, index_col='Date')\n",
"france_kidnap = pandas.read_csv('/home/radhika/PS/viz_project/datasets/france_kidnap.csv', parse_dates=True, index_col='Date')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"neth_homicide = pandas.read_csv('/home/radhika/PS/viz_project/datasets/neth_homicide.csv', parse_dates=True, index_col='Date')\n",
"neth_assault = pandas.read_csv('/home/radhika/PS/viz_project/datasets/neth_assault.csv', parse_dates=True, index_col='Date')\n",
"neth_rape = pandas.read_csv('/home/radhika/PS/viz_project/datasets/neth_rape.csv', parse_dates=True, index_col='Date')\n",
"neth_kidnap = pandas.read_csv('/home/radhika/PS/viz_project/datasets/neth_kidnap.csv', parse_dates=True, index_col='Date')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cleaning up and properly formatting data from CSV files for tabular use"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since I used data from various sources (thanks [data.gov.in](http://data.gov.in) and [Knoema](http://knoema.com)!), all of them were in different tabular formats and had to be parsed separately to be combined together. This was a bit irritating, but `pandas` made the process relatively painless.\n",
"\n",
"I've shown the state of the modified table at every step for the sake of clarity."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATE/UT</th>\n",
" <th>Pupose</th>\n",
" <th>Total No. of cases reported</th>\n",
" <th>Male upto 10 years</th>\n",
" <th>Female upto 10 years</th>\n",
" <th>Male 10-15 years</th>\n",
" <th>Female 10-15 years</th>\n",
" <th>Male 15-18 years</th>\n",
" <th>Female 15-18 years</th>\n",
" <th>Male 18-30 years</th>\n",
" <th>Female 18-30 years</th>\n",
" <th>Male 30-50 years</th>\n",
" <th>Female 30-50 years</th>\n",
" <th>Male above 50 years</th>\n",
" <th>Female above 50 years</th>\n",
" <th>Total Male</th>\n",
" <th>Total Female</th>\n",
" <th>Grand Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>YEAR</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>For Adoption</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>For Begging</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>For Camel racing</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>For Illicit intercourse</td>\n",
" <td>78</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>For marriage</td>\n",
" <td>339</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>73</td>\n",
" <td>1</td>\n",
" <td>164</td>\n",
" <td>6</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>332</td>\n",
" <td>339</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATE/UT Pupose \\\n",
"YEAR \n",
"2001-01-01 Andhra Pradesh For Adoption \n",
"2001-01-01 Andhra Pradesh For Begging \n",
"2001-01-01 Andhra Pradesh For Camel racing \n",
"2001-01-01 Andhra Pradesh For Illicit intercourse \n",
"2001-01-01 Andhra Pradesh For marriage \n",
"\n",
" Total No. of cases reported Male upto 10 years \\\n",
"YEAR \n",
"2001-01-01 8 3 \n",
"2001-01-01 2 0 \n",
"2001-01-01 0 0 \n",
"2001-01-01 78 0 \n",
"2001-01-01 339 0 \n",
"\n",
" Female upto 10 years Male 10-15 years Female 10-15 years \\\n",
"YEAR \n",
"2001-01-01 1 0 0 \n",
"2001-01-01 0 2 0 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 2 0 25 \n",
"2001-01-01 0 0 73 \n",
"\n",
" Male 15-18 years Female 15-18 years Male 18-30 years \\\n",
"YEAR \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 0 24 0 \n",
"2001-01-01 1 164 6 \n",
"\n",
" Female 18-30 years Male 30-50 years Female 30-50 years \\\n",
"YEAR \n",
"2001-01-01 4 0 0 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 25 0 2 \n",
"2001-01-01 91 0 4 \n",
"\n",
" Male above 50 years Female above 50 years Total Male \\\n",
"YEAR \n",
"2001-01-01 0 0 3 \n",
"2001-01-01 0 0 2 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 0 0 0 \n",
"2001-01-01 0 0 7 \n",
"\n",
" Total Female Grand Total \n",
"YEAR \n",
"2001-01-01 5 8 \n",
"2001-01-01 0 2 \n",
"2001-01-01 0 0 \n",
"2001-01-01 78 78 \n",
"2001-01-01 332 339 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"india_kidnap.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Purpose of Kidnapping: \"For Camel Racing\". Wut."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATE/UT</th>\n",
" <th>GENDER</th>\n",
" <th>Upto 10 years</th>\n",
" <th>10-15 years</th>\n",
" <th>15-18 years</th>\n",
" <th>18-30 years</th>\n",
" <th>30-50 years</th>\n",
" <th>Above 50 years</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>YEAR</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>Male</td>\n",
" <td>35</td>\n",
" <td>59</td>\n",
" <td>88</td>\n",
" <td>827</td>\n",
" <td>952</td>\n",
" <td>215</td>\n",
" <td>2176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>Female</td>\n",
" <td>38</td>\n",
" <td>15</td>\n",
" <td>43</td>\n",
" <td>269</td>\n",
" <td>175</td>\n",
" <td>67</td>\n",
" <td>607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>Total</td>\n",
" <td>73</td>\n",
" <td>74</td>\n",
" <td>131</td>\n",
" <td>1096</td>\n",
" <td>1127</td>\n",
" <td>282</td>\n",
" <td>2783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Arunachal Pradesh</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>32</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Arunachal Pradesh</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATE/UT GENDER Upto 10 years 10-15 years \\\n",
"YEAR \n",
"2001-01-01 Andhra Pradesh Male 35 59 \n",
"2001-01-01 Andhra Pradesh Female 38 15 \n",
"2001-01-01 Andhra Pradesh Total 73 74 \n",
"2001-01-01 Arunachal Pradesh Male 0 0 \n",
"2001-01-01 Arunachal Pradesh Female 0 0 \n",
"\n",
" 15-18 years 18-30 years 30-50 years Above 50 years Total \n",
"YEAR \n",
"2001-01-01 88 827 952 215 2176 \n",
"2001-01-01 43 269 175 67 607 \n",
"2001-01-01 131 1096 1127 282 2783 \n",
"2001-01-01 0 31 32 4 67 \n",
"2001-01-01 0 10 4 2 16 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"india_murder.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATE/UT</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>YEAR</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Andhra Pradesh</td>\n",
" <td>2783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Arunachal Pradesh</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Assam</td>\n",
" <td>1356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Bihar</td>\n",
" <td>3643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>Chhattisgarh</td>\n",
" <td>1657</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATE/UT Total\n",
"YEAR \n",
"2001-01-01 Andhra Pradesh 2783\n",
"2001-01-01 Arunachal Pradesh 83\n",
"2001-01-01 Assam 1356\n",
"2001-01-01 Bihar 3643\n",
"2001-01-01 Chhattisgarh 1657"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_murder = india_murder[india_murder['GENDER'] == 'Total']\n",
"state_murder = state_murder[[0, 8]]\n",
"state_murder.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Grand Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>YEAR</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>22996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>22173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>20021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>23309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>23133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>24292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>28030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>30595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>34304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>39148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>45239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>48219</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Grand Total\n",
"YEAR \n",
"2001-01-01 22996\n",
"2002-01-01 22173\n",
"2003-01-01 20021\n",
"2004-01-01 23309\n",
"2005-01-01 23133\n",
"2006-01-01 24292\n",
"2007-01-01 28030\n",
"2008-01-01 30595\n",
"2009-01-01 34304\n",
"2010-01-01 39148\n",
"2011-01-01 45239\n",
"2012-01-01 48219"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_kidnap = india_kidnap[india_kidnap['Pupose'] == 'Total']\n",
"state_kidnap = state_kidnap[[0, 17]]\n",
"india_kidnap = state_kidnap.groupby(level=0).sum()\n",
"india_kidnap"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Total</th>\n",
" </tr>\n",
" <tr>\n",
" <th>YEAR</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>38636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>38033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>33821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>34915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>34419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>33808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>33428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>33727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>33159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>33908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>35123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>35122</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Total\n",
"YEAR \n",
"2001-01-01 38636\n",
"2002-01-01 38033\n",
"2003-01-01 33821\n",
"2004-01-01 34915\n",
"2005-01-01 34419\n",
"2006-01-01 33808\n",
"2007-01-01 33428\n",
"2008-01-01 33727\n",
"2009-01-01 33159\n",
"2010-01-01 33908\n",
"2011-01-01 35123\n",
"2012-01-01 35122"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"india_murder = state_murder.groupby(level=0).sum()\n",
"india_murder"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STATE/UT</th>\n",
" <th>CRIME HEAD</th>\n",
" <th>2001</th>\n",
" <th>2002</th>\n",
" <th>2003</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ANDHRA PRADESH</td>\n",
" <td>RAPE</td>\n",
" <td>1150</td>\n",
" <td>1340</td>\n",
" <td>1237</td>\n",
" <td>1443</td>\n",
" <td>1415</td>\n",
" <td>1360</td>\n",
" <td>1436</td>\n",
" <td>1531</td>\n",
" <td>1487</td>\n",
" <td>1761</td>\n",
" <td>1758</td>\n",
" <td>1664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ARUNACHAL PRADESH</td>\n",
" <td>RAPE</td>\n",
" <td>51</td>\n",
" <td>61</td>\n",
" <td>35</td>\n",
" <td>56</td>\n",
" <td>38</td>\n",
" <td>40</td>\n",
" <td>57</td>\n",
" <td>37</td>\n",
" <td>60</td>\n",
" <td>49</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ASSAM</td>\n",
" <td>RAPE</td>\n",
" <td>928</td>\n",
" <td>1019</td>\n",
" <td>1188</td>\n",
" <td>1233</td>\n",
" <td>1406</td>\n",
" <td>1290</td>\n",
" <td>1477</td>\n",
" <td>1445</td>\n",
" <td>1644</td>\n",
" <td>1629</td>\n",
" <td>1470</td>\n",
" <td>1626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BIHAR</td>\n",
" <td>RAPE</td>\n",
" <td>1400</td>\n",
" <td>1304</td>\n",
" <td>1120</td>\n",
" <td>1157</td>\n",
" <td>1455</td>\n",
" <td>1451</td>\n",
" <td>1816</td>\n",
" <td>1464</td>\n",
" <td>1086</td>\n",
" <td>892</td>\n",
" <td>1185</td>\n",
" <td>1327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CHHATTISGARH</td>\n",
" <td>RAPE</td>\n",
" <td>1134</td>\n",
" <td>1214</td>\n",
" <td>1020</td>\n",
" <td>1144</td>\n",
" <td>1107</td>\n",
" <td>1211</td>\n",
" <td>1146</td>\n",
" <td>1108</td>\n",
" <td>1128</td>\n",
" <td>1198</td>\n",
" <td>1257</td>\n",
" <td>1214</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATE/UT CRIME HEAD 2001 2002 2003 2004 2005 2006 2007 \\\n",
"0 ANDHRA PRADESH RAPE 1150 1340 1237 1443 1415 1360 1436 \n",
"1 ARUNACHAL PRADESH RAPE 51 61 35 56 38 40 57 \n",
"2 ASSAM RAPE 928 1019 1188 1233 1406 1290 1477 \n",
"3 BIHAR RAPE 1400 1304 1120 1157 1455 1451 1816 \n",
"4 CHHATTISGARH RAPE 1134 1214 1020 1144 1107 1211 1146 \n",
"\n",
" 2008 2009 2010 2011 2012 \n",
"0 1531 1487 1761 1758 1664 \n",
"1 37 60 49 47 47 \n",
"2 1445 1644 1629 1470 1626 \n",
"3 1464 1086 892 1185 1327 \n",
"4 1108 1128 1198 1257 1214 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state_rape = india_women[india_women['CRIME HEAD'] == 'RAPE']\n",
"state_rape.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"YEAR\n",
"2001-01-01 20446\n",
"2002-01-01 21236\n",
"2003-01-01 19994\n",
"2004-01-01 22489\n",
"2005-01-01 23212\n",
"2006-01-01 23792\n",
"2007-01-01 25363\n",
"2008-01-01 25036\n",
"2009-01-01 25845\n",
"2010-01-01 27074\n",
"2011-01-01 28878\n",
"2012-01-01 31117\n",
"Name: 37, dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"india_rape = state_rape.loc[37][2:]\n",
"india_rape.index = india_murder.index\n",
"india_rape.columns = ['Total']\n",
"india_rape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Population</th>\n",
" <th>Violent crime total</th>\n",
" <th>Murder and nonnegligent Manslaughter</th>\n",
" <th>Forcible rape</th>\n",
" <th>Robbery</th>\n",
" <th>Aggravated assault</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1960-01-01</th>\n",
" <td>179323175</td>\n",
" <td>288460</td>\n",
" <td>9110</td>\n",
" <td>17190</td>\n",
" <td>107840</td>\n",
" <td>154320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961-01-01</th>\n",
" <td>182992000</td>\n",
" <td>289390</td>\n",
" <td>8740</td>\n",
" <td>17220</td>\n",
" <td>106670</td>\n",
" <td>156760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-01-01</th>\n",
" <td>185771000</td>\n",
" <td>301510</td>\n",
" <td>8530</td>\n",
" <td>17550</td>\n",
" <td>110860</td>\n",
" <td>164570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1963-01-01</th>\n",
" <td>188483000</td>\n",
" <td>316970</td>\n",
" <td>8640</td>\n",
" <td>17650</td>\n",
" <td>116470</td>\n",
" <td>174210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1964-01-01</th>\n",
" <td>191141000</td>\n",
" <td>364220</td>\n",
" <td>9360</td>\n",
" <td>21420</td>\n",
" <td>130390</td>\n",
" <td>203050</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Population Violent crime total \\\n",
"Year \n",
"1960-01-01 179323175 288460 \n",
"1961-01-01 182992000 289390 \n",
"1962-01-01 185771000 301510 \n",
"1963-01-01 188483000 316970 \n",
"1964-01-01 191141000 364220 \n",
"\n",
" Murder and nonnegligent Manslaughter Forcible rape Robbery \\\n",
"Year \n",
"1960-01-01 9110 17190 107840 \n",
"1961-01-01 8740 17220 106670 \n",
"1962-01-01 8530 17550 110860 \n",
"1963-01-01 8640 17650 116470 \n",
"1964-01-01 9360 21420 130390 \n",
"\n",
" Aggravated assault \n",
"Year \n",
"1960-01-01 154320 \n",
"1961-01-01 156760 \n",
"1962-01-01 164570 \n",
"1963-01-01 174210 \n",
"1964-01-01 203050 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"us_violentcrime.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>location</th>\n",
" <th>variable</th>\n",
" <th>Unit</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>Netherlands</td>\n",
" <td>Assault at the national level, count</td>\n",
" <td>Number</td>\n",
" <td>53500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>Netherlands</td>\n",
" <td>Assault at the national level, count</td>\n",
" <td>Number</td>\n",
" <td>55300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>Netherlands</td>\n",
" <td>Assault at the national level, count</td>\n",
" <td>Number</td>\n",
" <td>67060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>Netherlands</td>\n",
" <td>Assault at the national level, count</td>\n",
" <td>Number</td>\n",
" <td>69185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>Netherlands</td>\n",
" <td>Assault at the national level, count</td>\n",
" <td>Number</td>\n",
" <td>71570</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" location variable Unit Value\n",
"Date \n",
"2003-01-01 Netherlands Assault at the national level, count Number 53500\n",
"2004-01-01 Netherlands Assault at the national level, count Number 55300\n",
"2005-01-01 Netherlands Assault at the national level, count Number 67060\n",
"2006-01-01 Netherlands Assault at the national level, count Number 69185\n",
"2007-01-01 Netherlands Assault at the national level, count Number 71570"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"france_homicide.head()\n",
"neth_assault.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"france_assault = france_assault[[3]]\n",
"france_homicide = france_homicide[[3]]\n",
"france_kidnap = france_kidnap[[3]]\n",
"france_rape = france_rape[[3]]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"neth_assault = neth_assault[[3]]\n",
"neth_homicide = neth_homicide[[3]]\n",
"neth_kidnap = neth_kidnap[[3]]\n",
"neth_rape = neth_rape[[3]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final merging of data and formation of tables"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Murder</th>\n",
" <th>Kidnapping and Abduction</th>\n",
" <th>Rape</th>\n",
" </tr>\n",
" <tr>\n",
" <th>YEAR</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>38636</td>\n",
" <td>22996</td>\n",
" <td>20446</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>38033</td>\n",
" <td>22173</td>\n",
" <td>21236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>33821</td>\n",
" <td>20021</td>\n",
" <td>19994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>34915</td>\n",
" <td>23309</td>\n",
" <td>22489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>34419</td>\n",
" <td>23133</td>\n",
" <td>23212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>33808</td>\n",
" <td>24292</td>\n",
" <td>23792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>33428</td>\n",
" <td>28030</td>\n",
" <td>25363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>33727</td>\n",
" <td>30595</td>\n",
" <td>25036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>33159</td>\n",
" <td>34304</td>\n",
" <td>25845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>33908</td>\n",
" <td>39148</td>\n",
" <td>27074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>35123</td>\n",
" <td>45239</td>\n",
" <td>28878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>35122</td>\n",
" <td>48219</td>\n",
" <td>31117</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Murder Kidnapping and Abduction Rape\n",
"YEAR \n",
"2001-01-01 38636 22996 20446\n",
"2002-01-01 38033 22173 21236\n",
"2003-01-01 33821 20021 19994\n",
"2004-01-01 34915 23309 22489\n",
"2005-01-01 34419 23133 23212\n",
"2006-01-01 33808 24292 23792\n",
"2007-01-01 33428 28030 25363\n",
"2008-01-01 33727 30595 25036\n",
"2009-01-01 33159 34304 25845\n",
"2010-01-01 33908 39148 27074\n",
"2011-01-01 35123 45239 28878\n",
"2012-01-01 35122 48219 31117"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"india_violentcrime = pandas.concat([india_murder, india_kidnap, india_rape], axis=1)\n",
"india_violentcrime.columns = ['Murder', 'Kidnapping and Abduction', 'Rape']\n",
"india_violentcrime"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Homicide</th>\n",
" <th>Assault</th>\n",
" <th>Kidnapping and Abduction</th>\n",
" <th>Rape</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>180</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>202</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>195</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>202</td>\n",
" <td>53500</td>\n",
" <td>NaN</td>\n",
" <td>1700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>191</td>\n",
" <td>55300</td>\n",
" <td>NaN</td>\n",
" <td>1800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>174</td>\n",
" <td>67060</td>\n",
" <td>910</td>\n",
" <td>2505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>128</td>\n",
" <td>69185</td>\n",
" <td>850</td>\n",
" <td>2405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>143</td>\n",
" <td>71570</td>\n",
" <td>780</td>\n",
" <td>2135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>150</td>\n",
" <td>69005</td>\n",
" <td>760</td>\n",
" <td>1945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>154</td>\n",
" <td>65690</td>\n",
" <td>625</td>\n",
" <td>1920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>144</td>\n",
" <td>60280</td>\n",
" <td>645</td>\n",
" <td>1660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>143</td>\n",
" <td>59425</td>\n",
" <td>560</td>\n",
" <td>1605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>145</td>\n",
" <td>56650</td>\n",
" <td>580</td>\n",
" <td>1490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>125</td>\n",
" <td>52135</td>\n",
" <td>520</td>\n",
" <td>1320</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Homicide Assault Kidnapping and Abduction Rape\n",
"Date \n",
"2000-01-01 180 NaN NaN NaN\n",
"2001-01-01 202 NaN NaN NaN\n",
"2002-01-01 195 NaN NaN NaN\n",
"2003-01-01 202 53500 NaN 1700\n",
"2004-01-01 191 55300 NaN 1800\n",
"2005-01-01 174 67060 910 2505\n",
"2006-01-01 128 69185 850 2405\n",
"2007-01-01 143 71570 780 2135\n",
"2008-01-01 150 69005 760 1945\n",
"2009-01-01 154 65690 625 1920\n",
"2010-01-01 144 60280 645 1660\n",
"2011-01-01 143 59425 560 1605\n",
"2012-01-01 145 56650 580 1490\n",
"2013-01-01 125 52135 520 1320"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neth_violentcrime = pandas.concat([neth_homicide, neth_assault, neth_kidnap, neth_rape], axis=1)\n",
"neth_violentcrime.columns = ['Homicide', 'Assault', 'Kidnapping and Abduction', 'Rape']\n",
"neth_violentcrime"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Homicide</th>\n",
" <th>Assault</th>\n",
" <th>Kidnapping and Abduction</th>\n",
" <th>Rape</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>1051</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>1047</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>1119</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>987</td>\n",
" <td>135003</td>\n",
" <td>2031</td>\n",
" <td>10408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>990</td>\n",
" <td>137679</td>\n",
" <td>2143</td>\n",
" <td>10506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>976</td>\n",
" <td>148651</td>\n",
" <td>2012</td>\n",
" <td>9993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>879</td>\n",
" <td>164359</td>\n",
" <td>2288</td>\n",
" <td>9784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>993</td>\n",
" <td>175886</td>\n",
" <td>2109</td>\n",
" <td>10132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>1021</td>\n",
" <td>187937</td>\n",
" <td>2074</td>\n",
" <td>10277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>819</td>\n",
" <td>193405</td>\n",
" <td>2085</td>\n",
" <td>9842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>796</td>\n",
" <td>192906</td>\n",
" <td>2092</td>\n",
" <td>10108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>856</td>\n",
" <td>192263</td>\n",
" <td>2234</td>\n",
" <td>10406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>784</td>\n",
" <td>193081</td>\n",
" <td>2117</td>\n",
" <td>10885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>777</td>\n",
" <td>192643</td>\n",
" <td>2266</td>\n",
" <td>11171</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Homicide Assault Kidnapping and Abduction Rape\n",
"Date \n",
"2000-01-01 1051 NaN NaN NaN\n",
"2001-01-01 1047 NaN NaN NaN\n",
"2002-01-01 1119 NaN NaN NaN\n",
"2003-01-01 987 135003 2031 10408\n",
"2004-01-01 990 137679 2143 10506\n",
"2005-01-01 976 148651 2012 9993\n",
"2006-01-01 879 164359 2288 9784\n",
"2007-01-01 993 175886 2109 10132\n",
"2008-01-01 1021 187937 2074 10277\n",
"2009-01-01 819 193405 2085 9842\n",
"2010-01-01 796 192906 2092 10108\n",
"2011-01-01 856 192263 2234 10406\n",
"2012-01-01 784 193081 2117 10885\n",
"2013-01-01 777 192643 2266 11171"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"france_violentcrime = pandas.concat([france_homicide, france_assault, france_kidnap, france_rape], axis=1)\n",
"france_violentcrime.columns = ['Homicide', 'Assault', 'Kidnapping and Abduction', 'Rape']\n",
"france_violentcrime"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Murder and nonnegligent Manslaughter</th>\n",
" <th>Forcible rape</th>\n",
" <th>Aggravated assault</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>15586</td>\n",
" <td>90178</td>\n",
" <td>911706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>16037</td>\n",
" <td>90863</td>\n",
" <td>909023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>16229</td>\n",
" <td>95235</td>\n",
" <td>891407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>16528</td>\n",
" <td>93883</td>\n",
" <td>859030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>16148</td>\n",
" <td>95089</td>\n",
" <td>847381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>16740</td>\n",
" <td>94347</td>\n",
" <td>862220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>17309</td>\n",
" <td>94472</td>\n",
" <td>874096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>17128</td>\n",
" <td>92160</td>\n",
" <td>866358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>16465</td>\n",
" <td>90750</td>\n",
" <td>843683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>15399</td>\n",
" <td>89241</td>\n",
" <td>812514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>14722</td>\n",
" <td>85593</td>\n",
" <td>781844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>14661</td>\n",
" <td>84175</td>\n",
" <td>752423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>14827</td>\n",
" <td>84376</td>\n",
" <td>760739</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Murder and nonnegligent Manslaughter Forcible rape \\\n",
"Year \n",
"2000-01-01 15586 90178 \n",
"2001-01-01 16037 90863 \n",
"2002-01-01 16229 95235 \n",
"2003-01-01 16528 93883 \n",
"2004-01-01 16148 95089 \n",
"2005-01-01 16740 94347 \n",
"2006-01-01 17309 94472 \n",
"2007-01-01 17128 92160 \n",
"2008-01-01 16465 90750 \n",
"2009-01-01 15399 89241 \n",
"2010-01-01 14722 85593 \n",
"2011-01-01 14661 84175 \n",
"2012-01-01 14827 84376 \n",
"\n",
" Aggravated assault \n",
"Year \n",
"2000-01-01 911706 \n",
"2001-01-01 909023 \n",
"2002-01-01 891407 \n",
"2003-01-01 859030 \n",
"2004-01-01 847381 \n",
"2005-01-01 862220 \n",
"2006-01-01 874096 \n",
"2007-01-01 866358 \n",
"2008-01-01 843683 \n",
"2009-01-01 812514 \n",
"2010-01-01 781844 \n",
"2011-01-01 752423 \n",
"2012-01-01 760739 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"us_violentcrime['Violent crime total'] -= us_violentcrime['Robbery']\n",
"del us_violentcrime['Robbery']\n",
"del us_violentcrime['Population']\n",
"del us_violentcrime['Violent crime total']\n",
"us_violentcrime = us_violentcrime.loc['2000-01-01':]\n",
"us_violentcrime"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>US</th>\n",
" <th>India</th>\n",
" <th>France</th>\n",
" <th>Netherlands</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>90178</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
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" <td>20446</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>95235</td>\n",
" <td>21236</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>93883</td>\n",
" <td>19994</td>\n",
" <td>10408</td>\n",
" <td>1700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>95089</td>\n",
" <td>22489</td>\n",
" <td>10506</td>\n",
" <td>1800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>94347</td>\n",
" <td>23212</td>\n",
" <td>9993</td>\n",
" <td>2505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>94472</td>\n",
" <td>23792</td>\n",
" <td>9784</td>\n",
" <td>2405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>92160</td>\n",
" <td>25363</td>\n",
" <td>10132</td>\n",
" <td>2135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>90750</td>\n",
" <td>25036</td>\n",
" <td>10277</td>\n",
" <td>1945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>89241</td>\n",
" <td>25845</td>\n",
" <td>9842</td>\n",
" <td>1920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>85593</td>\n",
" <td>27074</td>\n",
" <td>10108</td>\n",
" <td>1660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>84175</td>\n",
" <td>28878</td>\n",
" <td>10406</td>\n",
" <td>1605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>84376</td>\n",
" <td>31117</td>\n",
" <td>10885</td>\n",
" <td>1490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11171</td>\n",
" <td>1320</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" US India France Netherlands\n",
"2000-01-01 90178 NaN NaN NaN\n",
"2001-01-01 90863 20446 NaN NaN\n",
"2002-01-01 95235 21236 NaN NaN\n",
"2003-01-01 93883 19994 10408 1700\n",
"2004-01-01 95089 22489 10506 1800\n",
"2005-01-01 94347 23212 9993 2505\n",
"2006-01-01 94472 23792 9784 2405\n",
"2007-01-01 92160 25363 10132 2135\n",
"2008-01-01 90750 25036 10277 1945\n",
"2009-01-01 89241 25845 9842 1920\n",
"2010-01-01 85593 27074 10108 1660\n",
"2011-01-01 84175 28878 10406 1605\n",
"2012-01-01 84376 31117 10885 1490\n",
"2013-01-01 NaN NaN 11171 1320"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rape_stats = pandas.concat([us_violentcrime['Forcible rape'], india_rape, france_rape, neth_rape], axis=1)\n",
"rape_stats.columns = ['US', 'India', 'France', 'Netherlands']\n",
"rape_stats"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>US</th>\n",
" <th>India</th>\n",
" <th>France</th>\n",
" <th>Netherlands</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
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" <td>1051</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
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" <td>38636</td>\n",
" <td>1047</td>\n",
" <td>202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
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" <td>38033</td>\n",
" <td>1119</td>\n",
" <td>195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>16528</td>\n",
" <td>33821</td>\n",
" <td>987</td>\n",
" <td>202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>16148</td>\n",
" <td>34915</td>\n",
" <td>990</td>\n",
" <td>191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>16740</td>\n",
" <td>34419</td>\n",
" <td>976</td>\n",
" <td>174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>17309</td>\n",
" <td>33808</td>\n",
" <td>879</td>\n",
" <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>17128</td>\n",
" <td>33428</td>\n",
" <td>993</td>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>16465</td>\n",
" <td>33727</td>\n",
" <td>1021</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>15399</td>\n",
" <td>33159</td>\n",
" <td>819</td>\n",
" <td>154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>14722</td>\n",
" <td>33908</td>\n",
" <td>796</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>14661</td>\n",
" <td>35123</td>\n",
" <td>856</td>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>14827</td>\n",
" <td>35122</td>\n",
" <td>784</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>777</td>\n",
" <td>125</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" US India France Netherlands\n",
"2000-01-01 15586 NaN 1051 180\n",
"2001-01-01 16037 38636 1047 202\n",
"2002-01-01 16229 38033 1119 195\n",
"2003-01-01 16528 33821 987 202\n",
"2004-01-01 16148 34915 990 191\n",
"2005-01-01 16740 34419 976 174\n",
"2006-01-01 17309 33808 879 128\n",
"2007-01-01 17128 33428 993 143\n",
"2008-01-01 16465 33727 1021 150\n",
"2009-01-01 15399 33159 819 154\n",
"2010-01-01 14722 33908 796 144\n",
"2011-01-01 14661 35123 856 143\n",
"2012-01-01 14827 35122 784 145\n",
"2013-01-01 NaN NaN 777 125"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"homicide_stats = pandas.concat([us_violentcrime['Murder and nonnegligent Manslaughter'], india_murder, france_homicide, neth_homicide], axis=1)\n",
"homicide_stats.columns = ['US', 'India', 'France', 'Netherlands']\n",
"homicide_stats"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>US</th>\n",
" <th>France</th>\n",
" <th>Netherlands</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
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" <td>NaN</td>\n",
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" <td>859030</td>\n",
" <td>135003</td>\n",
" <td>53500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>847381</td>\n",
" <td>137679</td>\n",
" <td>55300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>862220</td>\n",
" <td>148651</td>\n",
" <td>67060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>874096</td>\n",
" <td>164359</td>\n",
" <td>69185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>866358</td>\n",
" <td>175886</td>\n",
" <td>71570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>843683</td>\n",
" <td>187937</td>\n",
" <td>69005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>812514</td>\n",
" <td>193405</td>\n",
" <td>65690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>781844</td>\n",
" <td>192906</td>\n",
" <td>60280</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>752423</td>\n",
" <td>192263</td>\n",
" <td>59425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>760739</td>\n",
" <td>193081</td>\n",
" <td>56650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>NaN</td>\n",
" <td>192643</td>\n",
" <td>52135</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" US France Netherlands\n",
"2000-01-01 911706 NaN NaN\n",
"2001-01-01 909023 NaN NaN\n",
"2002-01-01 891407 NaN NaN\n",
"2003-01-01 859030 135003 53500\n",
"2004-01-01 847381 137679 55300\n",
"2005-01-01 862220 148651 67060\n",
"2006-01-01 874096 164359 69185\n",
"2007-01-01 866358 175886 71570\n",
"2008-01-01 843683 187937 69005\n",
"2009-01-01 812514 193405 65690\n",
"2010-01-01 781844 192906 60280\n",
"2011-01-01 752423 192263 59425\n",
"2012-01-01 760739 193081 56650\n",
"2013-01-01 NaN 192643 52135"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assault_stats = pandas.concat([us_violentcrime['Aggravated assault'], france_assault, neth_assault], axis=1)\n",
"assault_stats.columns = ['US', 'France', 'Netherlands']\n",
"assault_stats"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>India</th>\n",
" <th>France</th>\n",
" <th>Netherlands</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2001-01-01</th>\n",
" <td>22996</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2002-01-01</th>\n",
" <td>22173</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2003-01-01</th>\n",
" <td>20021</td>\n",
" <td>2031</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004-01-01</th>\n",
" <td>23309</td>\n",
" <td>2143</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01</th>\n",
" <td>23133</td>\n",
" <td>2012</td>\n",
" <td>910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01</th>\n",
" <td>24292</td>\n",
" <td>2288</td>\n",
" <td>850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01</th>\n",
" <td>28030</td>\n",
" <td>2109</td>\n",
" <td>780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01</th>\n",
" <td>30595</td>\n",
" <td>2074</td>\n",
" <td>760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-01-01</th>\n",
" <td>34304</td>\n",
" <td>2085</td>\n",
" <td>625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-01-01</th>\n",
" <td>39148</td>\n",
" <td>2092</td>\n",
" <td>645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>45239</td>\n",
" <td>2234</td>\n",
" <td>560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01</th>\n",
" <td>48219</td>\n",
" <td>2117</td>\n",
" <td>580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>NaN</td>\n",
" <td>2266</td>\n",
" <td>520</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" India France Netherlands\n",
"2001-01-01 22996 NaN NaN\n",
"2002-01-01 22173 NaN NaN\n",
"2003-01-01 20021 2031 NaN\n",
"2004-01-01 23309 2143 NaN\n",
"2005-01-01 23133 2012 910\n",
"2006-01-01 24292 2288 850\n",
"2007-01-01 28030 2109 780\n",
"2008-01-01 30595 2074 760\n",
"2009-01-01 34304 2085 625\n",
"2010-01-01 39148 2092 645\n",
"2011-01-01 45239 2234 560\n",
"2012-01-01 48219 2117 580\n",
"2013-01-01 NaN 2266 520"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kidnapping_stats = pandas.concat([india_kidnap, france_kidnap, neth_kidnap], axis=1)\n",
"kidnapping_stats.columns = ['India', 'France', 'Netherlands']\n",
"kidnapping_stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualization and plotting of data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yay for pretty plots!"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa05873e208>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pKioCYPfu3dx44438+9//pmfPnkydOrU2qy5JUh0QQrDpZBaT153G21XL4hFt6RNcO6dm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\nUd97HTUzDWWkvK27JF1LFYJ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N/fpCY9WqVdbH4eHhhIeHX9d6atTG8fDwIDw8nOPHj5Of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\nC/+4e17w2TsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa05877b080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"india_violentcrime.plot(kind='line', title='Violent Crime in India')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This was pretty much expected. It's interesting how the number of murders show a downward trend, but the number of kidnappings and rapes show a dramatic rise."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa0586cca20>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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27MmOHTt8vp5Ro0bx4Ycf+nw9Na1KwSY5OZmGDRvy5ptv8pe//IUlS5aQn59P\nZmYmoaGhAISEhJCZmQlAWloaYWFh+vxhYWGkpaWRnp5eZrrVatXTrVYraWlpAGWuQ9S8nj170rJl\nS33fuAwePJioqChOnz5dSzkTl0PTNDRNA5z7+Ntvv/X5Onfv3k337t3LnWbGjBlERUXx9ddfe6TP\nmTOHqKgoVq9e7cssemwXXytrPadOnSIqKgqHw1Ej+ahOVQo2drudEydOMHjwYF588UUCAwNLXc4q\nubGUUlXPZRlqascL7zRNo3nz5nz66ad62s8//0x+fn6V943NZquu7PlkeVcLX/xeL5emabRq1Yo1\na9boaTabjc8//5wWLVpcVceDqu6f2vw9VCnYhIWFYbVa9ctcN910EydOnCA0NJSMjAwA0tPTCQkJ\nAZw1k9TUVH3+1NRUrFYrVquVlJQUj3TXst3Pll3p4KzNeFuHu4SEBFavXq2/wNm0ry6+jEZjVXZR\njRkxYgRr167Vh9esWcOoUaM8fgwlLwt8/PHH3HPPPfpwVFQUK1eupHfv3volm3/961/ccMMNdOvW\njY8++shjnQUFBTz//PPceOONXH/99fz1r38lPz8fcJ4hd+vWjTfffJOuXbvy1FNPlcrzyZMnuffe\ne+nYsSOdOnVi2rRpXLhwQR/fs2dPlixZwsCBA2nfvj2TJ0+moKBAH19e3koaNWoUL7/8MrGxsURH\nRzN27FiP7/bXX39N//796dChA6NGjeLYsWOXlA9XOZcuXUqXLl244YYb+Pjjjy9pGwG8+eabehk+\n+OCDMi8FlqxxHD58mMGDBxMdHc1jjz3G448/rl9yA9i8eTODBg2iQ4cO3H333fz8888Vlic3N5eH\nHnqIc+fO0bZtW6Kjo0lOTva6PQcNGsS+ffv0Kxpbt26lQ4cOhIeH69+5y9m/aWlp/PGPf6RDhw7E\nxMQwYsQIr/mIi4tj+PDhdOjQgRtuuIG//e1vFBUVAd5rH+6/Abvdzj/+8Q86depEr169ePvtt0tN\nf+rUKa/fGVd+2rdvT9u2bTl48CAAH330Ef369SMmJoYHHnjA46qCt99XSa7/C5V8AR7H0oSEBK/z\nX4oqBZvQ0FAaN27MmTNnADh06BB/+MMf6NatG9u2bQNg+/bt9OjRA4Du3bvrVfHExETMZjOhoaF0\n6dKFQ4cOkZOTQ3Z2NocOHaJLly6EhoYSFBTE0aNHUUqxY8cOj2V5W4e7mJgYRo8erb/A+V+Vuviy\n2+1V2UUngZZJAAAgAElEQVQ15oYbbiArK4tjx45ht9vZuHEjI0eOLDVdRWedX3/9NV988QVbt25l\n69atLF26lI8++oidO3eWuk4+b948Tp48yebNm9m1axdJSUksWLBAH5+SkkJmZibff/89L774otf1\nTZ8+nbi4OLZv386ZM2d49dVXPfL6+eef88EHH/Ddd9/x888/6yctFeXNmw0bNrBgwQJ++OEHCgsL\nWbp0KQC//vorTzzxBM8//zyHDx9mwIABPPzww/rZZ3n5cJUzOzubgwcP8sorr/Dss8/qB9XyttHW\nrVtZtmwZH3/8MTt37uS7776rsAwAhYWFTJgwgTFjxvDTTz8RGxvLpk2b9H37448/8l//9V+8/PLL\nJCQk8OCDDzJu3Dj9IFxWeYKDg1m1ahVNmjQhMTGRX375hYiICK95CAgIYPDgwXpteu3atYwaNUpf\nvktV9+/SpUtp1qwZhw8f5tChQzzzzDNe82EymXj++ef58ccf2bhxIzt37uSdd94pd/u58vf++++z\nbds2Nm/ezFdffcVXX31V6vdR1nfG1XL3yJEjJCYmcsMNN7Bp0yYWL17M8uXLOXz4MDfeeCNTpkzx\nWJ7778sbu93u9fgDeBxLY2Jiyi1jearc9HncuHEsXrwYm81GkyZNmDJlCg6HgwULFrB161a9WTI4\nD0hxcXFMmzaNwMBAJk+eDECDBg0YOXKkvkNHjRqlN2OeOHEib7zxBoWFhXTt2pXrr78egNjYWK/r\nuFrZH72rWpZjXLaxyvOOHDmSNWvWcNNNN9G2bVuaNm1a6WVMnTpVr6V+9tln3HfffbRt2xaAp556\nSj+4KKX44IMP+Oabb/Tpp06dyrRp0/TvkcFg4KmnnsLPzw8/P79S62rRogUtWrQAnLXuRx991CNY\nAUyYMEE/4A0aNEg/oysvb2W577779Kb+w4cPZ/PmzQBs3LiRgQMHcssttwDw+OOPs3z5cvbv389N\nN91Ubj7AecCbOXMmBoOB2267DbPZzK+//sr1119f7jZylaFNmzZ6GVwHsfIcPHgQu93O+PHjARgy\nZIj+uwRYtWoVDz74oJ527733snjxYg4ePEjPnj3LLU9lLgvde++9/Pd//zexsbHs3buXhQsXsnLl\nSn385exfPz8/kpOTOXXqFC1atPB6MgvQqVMn/XNUVBQPPPAAe/bsYeLEiRXm/7PPPmPixIn672Tq\n1Kns2rXLY5qyvjPettN7773HtGnT9CtN06ZNY/HixZw+fZrIyEh9Hd6uAtWkKgebFi1a8M9//rNU\n+nPPPed1+gkTJnhN79+/P/379y+V3qpVK4+zEZcGDRqUuY6r0eUEieqgaRqjRo3innvu4dSpU6Uu\noV2qZs2a6Z+Tk5Pp0qWLPuz6wYDzkmpeXh5DhgzR05RSHpcgrFYr/v7+Za7r/PnzzJ49m++//56c\nnBwcDofe6MQlPDxc/xwYGMi5c+cqzFtZ3M/SAwMDycnJAeDcuXMe82uaRrNmzUhKSiozH+7jGjVq\nhMFw8eJEUFAQOTk5FW6j5ORkjyBxzTXXVFgGV35LTuu+306fPs3atWt5++239bSioqJLLs+l0DSN\nHj16kJqaysKFCxk0aBCBgYEe01Rl/7ryMXnyZF599VXGjh0LwAMPPMATTzxRKh+//vor//jHPzh8\n+DB5eXnYbDaP70V5kpOTPbabt+1f1nfGm99//53Zs2fz/PPPe6QnJSXp3y/39dUWeVyNuGyRkZE0\nb96crVu3ej1BCA4O1v8/Bc6DQUnulxEiIiL0S7SAx/Vnq9VKYGAgW7dupUmTJl7zU9EluxdeeAGj\n0ciWLVsICQnhq6++4m9/+1u581xK3iqradOmHDlyRB9WSnHmzJkq1QzdVbSNSpbB/bOLt20YERHB\n2bNnPdJOnz6t1yKaNWvG9OnTmT59eqXzXNmb+yNHjmTBggUe9wtdKrt/3ddtNpuZPXs2s2fP5pdf\nfmH06NFcf/319O7d22OeZ555hs6dO7NkyRKCg4NZtmwZX3zxBeD8vgPk5eXpV2rcv/OXsv0vJa8u\nkZGRzJgxg9jY2ErNV9PkcTWiWrz66qusXr2aoKCgUuNiYmL44osvyMvL48SJExX+h2D48OGsXr2a\no0ePkpeX53EJxGAwMHbsWObMmaM3Ojl79izbt2+/5Lzm5OQQHByMxWLh7Nmz/Otf/6pwHldtrby8\nVTRvScOGDeM///kPO3fupKioiKVLlxIYGFhhE+CKVLSNXGU4duwYeXl5vPbaa6Xy6y3P3bp1w2g0\n8vbbb2Oz2di0aRM//PCDPv6BBx7gvffeIy4uDqUUubm5fPPNN+WelbuEh4eTnp5e7nMA3fM1fvx4\nPvroI/3ynLvK7l/3sm7evJkTJ06glKJBgwYYjUaP2qNLbm4uZrOZoKAgjh07xrvvvquPCwsLo2nT\npqxduxa73c5HH33k0fhi+PDhrFixgqSkJDIzM3nzzTcvufVuWFgYBoOBkydP6mkPPfQQixcvJjEx\nEYALFy7w2WeflVvm2iDBRlSLa6+91uM6tvuP59FHH8Xf35/rr7+eJ598kpEjR3qML/lD69+/PxMn\nTmT06NH06dOHPn36eEzz7LPP0qJFC4YPH067du24//77OX78eJnLK+nJJ5/k8OHDtGvXjkceeYQ7\n77yz3Hnc/19RUd7Kmt/bslq3bs3ixYt57rnn6Ny5M9988w0rV67EZPJ+waHk/zzKW29526h///6M\nHz+ee++9lz59+tCtWzcA/dJjWevx9/dn+fLlfPjhh3To0IFPPvmEgQMH6vfFOnfuzMsvv8zf/vY3\nYmJi6NOnD2vXri0znyW3RWxsLL169SImJsZrazT36UNDQ0vVNlwuZ/+ePHmS+++/n7Zt23L33Xfz\n8MMP06tXr1LzPPfcc2zYsIHo6Giefvpp7r77bo91vPzyyyxZsoROnTqRmJjocQLxwAMPcOuttzJw\n4ECGDBnCgAEDSgW1sr4zQUFBTJ8+ndjYWDp06EBcXBx33HEHU6ZMYcqUKbRr144BAwZ4nHxdCbUa\nAE1diQ3qfaAyVdUricVikac+C586evQoAwYM4OTJk17P4sszbNgw/vjHP+qtPkXlbdmyhWeeeYa9\ne/fWWh7KOs5U570eqdkIcRX68ssvKSgoICMjg7lz5zJ48OBLCjR79uwhOTkZm83G6tWrOXLkiNcG\nPqJs+fn5/Oc//8Fms3H27Fnmz5/v0ZijvpJgI8RV6P3339dvfPv5+XltWerNr7/+yuDBg+nQoQPL\nli3jf//3fz1adomKKaWYP38+MTEx3HHHHURHR/PnP/+5trPlc3IZ7Qonl9GEEL4ml9GEEELUCxJs\nhBBC+JwEGyGEED4nwUYIIYTPSbARQgjhcxJsxBWtbdu2nDp1CvDsrtib+tY9sxD1iTyIU1RZz549\nSUlJ0Tt40zSNHTt2lNkXSVW4nvfkWv6V8ugNIUTlSLARVaZpGu+88w59+vSp8jJsNluZzwLzpib+\nFma326/4HlKFqGvkMpqodgUFBcyePZtu3brRrVs35syZQ2FhIeC922aHw8GiRYvo3bs30dHRDBky\nRH+UfclLY2lpadx///1ER0czatSoMh/xX1G3yO4+/vhj7r77bv7+97/TsWNH5s+fz2+//VZh18Kv\nv/46/fv3JyYmhieffNKj6+jyukcW4mokwUZcFm81jUWLFhEfH8/mzZvZvHkz8fHxLFy4UB9fstvm\npUuXsnHjRt577z1++eUXXn311VIdYrnWtX79embOnMnhw4fp0KEDU6dO9ZqvirqOLik+Pp4WLVpw\n6NAhpk2bhlKq3K6Fwdl17wcffMCuXbs4fvy4Xsayukd2BVwhrkYSbESVKaWYMGECHTp0oEOHDnqX\nuK6AYLVasVqtPPnkk6xbt06fz73b5sDAQD788EOefvppWrVqBUCHDh1o1KiR13UOHDiQG2+8EX9/\nf/76179y4MCBUh16ubqOnjNnDiEhIZjNZqZOncrGjWX3atqkSRMeeeQRDAYDgYGBtGjRgltuuQU/\nPz+9a+E9e/bo02uaxiOPPMI111xDaGgo06dP17uHdu8eWdM07r33Xvz9/Tl48GDVNrQQ9YDcs6nj\n7n7/SMUTXYJPH2hX6Xk0TeOtt94qdc/m3LlzREVF6cORkZF6t8pQutvmM2fO6L09VrQ+9y50g4OD\nCQ0NLdVd8aV0HV1SyWdAXUrXwu7zREZG6l0Ll9U9svs2EOJqI8GmjqtKkPC1pk2bcurUKdq0aQM4\nD77u3ROXbFHWrFkzTp48Sdu2bctdrqvbZJecnBwyMjJKdX18KV1Hl1QyT5fStbD7/aLTp0/r3Tlf\nTvfIQtRXchlNVLu7776bhQsXkpaWRlpaGgsWLGDkyJFlTj927FheeuklvTven376ifT0dK/Tbtmy\nhX379lFYWMhLL71Et27dPGo1UDNdRyuleOeddzh79izp6eksWrSIu+66C7i87pGFqK8k2Ihq96c/\n/YkuXbowcOBABg4cSOfOnfnTn/6kjy9Zi5g0aRLDhw9n7NixtGvXjqefflpv2VWye9x77rmH+fPn\n07FjRxISEli8eLHX5VbUdbQ7b//fqahrYVdexo4dS+/evWnZsqVexrK6Rxbiaib92VzhpD+bK9NN\nN93EK6+8cln/MRLiSiH92QghhKgXJNgIIYTwOWmNJkQVuP/nRghRManZCCGE8DkJNkIIIXxOgo0Q\nQgifk2AjhBDC5yTYCCGE8DkJNkJUg549e7Jjx47azkaFdu/eTffu3Ws7G+IqJMFGXLZRo0YRExNT\nZ/trqY4DcF3tsrpnz57s3LmztrMhrgISbMRlOXXqFHv37kXTNL7++mufrcdut/ts2VczTdNqpKtt\nISTYiMuydu1aunXrxr333suaNWs8xqWlpfHwww/Trl07hg4dyosvvsg999yjj9++fTu33HIL7du3\nZ9asWYwcOZIPP/wQqHxXzW+88QaTJk3yWP/s2bOZPXu2vrx+/foRHR3NzTffzKpVqwDIzc3loYce\n4ty5c7Rt25bo6GiSk5NRSvH666/Tu3dvOnbsyOOPP05GRoZHuW+88UY6duzIokWLyt1G33zzDYMH\nD6Zdu3b06NGD+fPn6+Py8/OZNm0aHTt2pEOHDgwdOpSUlBQ9zzfffDPR0dH06tWL9evXA3Dy5Mly\nu6wu2ZX2jBkzeOmll0rla9q0aZw+fZpHHnmEtm3bsmTJknLLIcTlkGAjLsvatWsZMWIEI0aMYPv2\n7fqBEpxPXm7QoAHx8fG89tprrF27Vr/UlJaWxmOPPcazzz5LQkIC1113HQcOHPC4FFWZrppjY2PZ\nsmWL/hh/u93O559/rge3xo0b8+677/LLL78wf/58/v73v/Pjjz8SHBzMqlWraNKkCYmJifzyyy9E\nRESwYsUKvv76a9atW0dcXBwhISE8++yzACQmJjJr1ixef/11Dh48SHp6eqneQt2ZzWYWL17MkSNH\nePfdd3n33XfZtGkTAGvWrCErK4v9+/eTkJDAiy++SGBgILm5ucyZM4dVq1bxyy+/sHHjRmJiYvRl\nVtRltbuyLvEtXryYyMhI3nnnHRITE3n88cfL39lCXIbLCjYOh4Onn36aF154AYDk5GRmzZrF9OnT\nee2117DZbICzl8IFCxYwffp0nn32Wc6fP68vY/369UyfPp0ZM2bwww8/6Onx8fHMmDGD6dOns2HD\nBj29rHWImvf9999z+vRphg8fTqdOnbj22mv1s2+73c6XX37JU089RWBgIG3atOHee+/VL9n85z//\nITo6mjvuuAODwcCECRMIDw/3WH5lumqOjIykU6dOfPnllwDs2rWLoKAgunbtCsCAAQNo3rw54Hxi\nc9++fdm7dy+A18tIq1at4umnn6Zp06b4+fnx5JNP8u9//xu73c6///1vBg0apHdP/fTTT2MwlP1T\n6tWrF9HR0QC0b9+eu+66i++++w4Af39/0tPTOXHiBJqm0bFjRxo0aAA4++U5cuQIeXl5hIeH653L\nVdRltTdyqUzUtst6NtoXX3xBVFQUeXl5gPMHOmzYMG6++WaWLVvGli1bGDx4MFu2bMFisbBo0SJ2\n797N+++/z4wZM/j999/ZvXs38+fPJy0tjf/+7/9m0aJFKKVYsWIFzz33HFarlWeeeYbu3bsTFRVV\n5jquVp99nFHxRJdg+H2hFU9Uwpo1a7j11ltp1KgR4KxdrFmzhkcffZTU1FRsNpvHI8rdOzkr2ZVz\nyfFQ+a6aY2Nj+fTTTxk1ahTr16/3uGS3ZcsW5s+fr3fQlpeXR/v27css26lTp5g4caJHEDEajZw/\nf75U3oOCgvRt4M3BgweZN28eiYmJFBUVUVhYyLBhwwAYOXIkZ86cYcqUKVy4cIERI0bwl7/8heDg\nYP71r3+xZMkS/uu//ovu3bsze/ZsWrdufUldVgtxpalysElNTSUuLo577rmHzz//HICEhARmzJgB\nQN++fVmzZg2DBw9m//79jB49GnC2flmxYgUA+/bto3fv3phMJiIiImjatClHjx4FnF0LR0REANC7\nd2/2799PZGRkmeu4WlUlSFSHvLw8PvvsMxwOh157KCwsJDMzk59//pm2bdtiMpk4c+YMrVq1Ajz7\nFGrSpAmbN2/Wh5VSpS5FVbar5mHDhvH8889z9uxZNm3axMaNGwEoKCjg0UcfZfHixdx+++0YjUYm\nTJign+17u8QUGRnJ/PnzvbZSa9Kkif49dW2LsnoWBZg6dSrjx4/ngw8+wN/fnzlz5pCWlgaAyWRi\n5syZzJw5k99//52HHnqI6667jjFjxtC3b1/69u1LQUEBL774Ik8//TSffPJJhdshKChIPwEE59WA\nsvolqYst6ETdVOXLaO+88w4PPvigfuaXlZWF2WzWh61Wq/6DSktLIywsDHCeHQYHB5OVlUV6erqe\nDhAWFqZ3Jeye7lpWdnZ2mesQNWvTpk0YjUa2bdvG5s2b2bx5M9u2baNnz56sWbMGo9HIkCFDmD9/\nPnl5eRw7dox169bpB7cBAwZw5MgRNm3ahM1mY+XKlR6XV72pqKvmsLAwbr75ZmbOnEnz5s1p3bo1\n4LyMW1RUhNVqxWAwsGXLFo8uosPDw0lPT/foPOqhhx7ihRde4PTp04Dz5MrV2m7o0KF88803evfU\nL7/8Mg6Ho9x8h4SE4O/vT1xcHBs2bNC3w+7du/n555+x2+2YzWZMJhMGg4GUlBQ2bdpEbm4ufn5+\nBAcH69/7irZDTEwM69evx263s3Xr1nIvsTVu3NijMYEQvlKlYHPgwAEaNmxIy5Yt9bNDuSZ8dVm7\ndi1jxoyhWbNmNG7cmMaNGxMeHs4jjzzChg0bcDgczJ07lwsXLtC1a1dmzJhBbGwsfn5+gPNEYenS\npfzP//wPnTp14ujRo3Tu3Bl/f3+gal01g/NS2s6dO4mNjdXTGjRowPPPP8/jjz9OTEwMGzZs4Pbb\nb9fHt27dmtjYWHr16kVMTAzJyclMnDiRwYMHc//99xMdHc1dd91FXFwcAG3btmXu3Lk88cQT3HDD\nDYSGhpbbo+G8efN45ZVXiI6O5rXXXuOuu+7Sx50/f57HHnuMdu3a0b9/f3r16sWoUaNwOBwsW7aM\nbt260bFjR/bu3avfG61oOzz//PNs3ryZDh06sH79eoYMGeKRH/dpp02bxsKFC+nQoQNLly4tZ48L\ncXmq1C30Bx98wI4dOzAYDBQVFZGXl0ePHj344YcfWLZsGQaDgcTERNauXcusWbOYO3cu9957L23b\ntsVutzNp0iRWrFih3/h3HRjmzp3L6NGjUUqxZs0avfXP+vXr0TSN2NhYJkyY4LEO9+lcEhISSEhI\n0IdHjx5dZ7tWNhqNHk1u67K5c+eSkpLCggULSo1zOBz06NGD119/nV69etVC7oS4eoWGhnr9L5vF\nYmH16tX6cExMjEeryMqo0j2bsWPHMnbsWAB++uknNm7cyPTp05k/fz579uzh5ptvZtu2bfr17u7d\nu7N9+3batm3Lnj176NSpk56+cOFChg0bRlpaGklJSbRu3RqHw0FSUhLJyclYrVZ2797Nn/70J72w\n7uvo0aNHqfx52yB1NdhYLJbazkKVHTt2jMLCQtq3b098fDwfffSRRxPd7du3c/311xMYGKhfCrrh\nhhtqK7tCXLXsdrvXY6TFYtHvt1+uaump01Utf/DBB3nttdf46KOPaNmyJbfddhsAt912G4sXL2b6\n9OlYLBY9cERFRdGrVy9mzpyp37TVNA2j0cj48eOZO3cuDoeD2267jaioqHLXIa48OTk5TJkyhXPn\nzhEeHs7jjz/u0ZjjwIEDPPHEExQVFdG2bVtWrFhBQEBALeZYCOErVbqMVhe5t4SqSywWS52tlQkh\n6oayjjPl3YusLHmCgBBCCJ+TYCOEEMLnJNgIIYTwOQk2QgghfK5aWqMJ36qo+bPRaKzX/b1I+eq2\n+ly++ly26ibB5gp3KS3R6nuLNSlf3Vafy1efy1bd5DKaEEIIn5NgI4QQwuck2AghhPA5CTZCCCF8\nToKNEEIIn5NgI4QQwuck2AghhPA5CTZCCCF8ToKNEEIIn5NgI4QQwuck2AghhPA5CTZCCCF8ToKN\nEEIIn5NgI4QQwuck2AghhPA5CTZCCCF8ToKNEEIIn5NgI4QQwuck2AghhPA5CTZCCCF8ToKNEEII\nn5NgI4QQwuck2AghhPA5CTZCCCF8ToKNEEIIn5NgI4QQwuck2AghhPA5CTZCCCF8ToKNEEIIn5Ng\nI4QQwudMVZkpJSWFN954g8zMTDRNY8CAAdx5551kZ2ezYMECUlJSCA8PZ+bMmZjNZgDeeust4uPj\nCQgIYMqUKbRs2RKAbdu2sX79egBGjBhB3759ATh+/DhvvPEGRUVFdO3alXHjxgGUuw4hhBBXpirV\nbEwmEw8//DDz589n7ty5bNq0id9//50NGzbQuXNnFi5cSMeOHdmwYQMABw8e5Ny5cyxatIhJkyax\nfPlywBk41q1bx7x585g3bx5r164lNzcXgGXLljF58mQWLVpEUlIS8fHxAGWuQwghxJWrSsEmNDSU\nFi1aABAYGEhkZCRpaWns379fr5n069ePffv2AXikt2nThpycHDIyMoiPj6dz586YzWbMZjOdOnUi\nLi6O9PR08vPzad26NQC33nor33//fallua9DCCHEleuy79kkJydz8uRJ2rRpQ2ZmJqGhoQCEhISQ\nmZkJQFpaGmFhYfo8YWFhpKWlkZ6eXma61WrV061WK2lpaQBlrkMIIcSVq0r3bFzy8/N59dVXeeSR\nRwgKCvIYp2max7BS6nJW5VXJdbgkJCSQkJCgD48ePRqLxVLt679S+Pv7S/nqMClf3VWfy+ayevVq\n/XNMTAwxMTFVWk6Vg43NZuPVV1/l1ltv5cYbbwScNY2MjAxCQ0NJT08nJCQEcNZMUlNT9XlTU1Ox\nWq1YrVaPoJCamkrHjh09ajKudFcNqKx1uPO2QbKysqpa1CuexWKR8tVhUr66qz6XDZzlGz16dLUs\nq0qX0ZRSLFmyhMjISIYOHaqnd+/enW3btgGwfft2evTooad/++23ACQmJmI2mwkNDaVLly4cOnSI\nnJwcsrOzOXToEF26dCE0NJSgoCCOHj2KUoodO3Z4LMvbOoQQQly5NFWF61tHjhxhzpw5NG/eXL+U\nNXbsWFq3bl1ms+QVK1YQHx9PYGAgkydPplWrVgBs3brVo+lzv379gItNnwsLC+natSvjx48Hqt70\n+cyZM5UtZp1xNZxdSfnqrvpcvvpcNoBmzZpV27KqFGzqIgk2dZeUr26rz+Wrz2WD6g028gQBIYQQ\nPifBRgghhM9JsBFCCOFzEmyEEEL4nAQbIYQQPifBRgghhM9JsBFCCOFzEmyEEEL4nAQbIYQQPifB\nRgghhM9JsBFCCOFzEmyEEEL4nAQbIYQQPifBRgghhM9JsBFCCOFzEmyEEEL4nAQbIYQQPifBRggh\nhM+ZajsD9ZFSiiKHotBe/LI5KLQrCuwOj7QCu6JQT3NQaFOl0gps6uL4EmlFxWl2BRpgMDjPHgya\nhkFzvmua57Dz3fs0xuJxWgXTAzgUOJRCAQ6H892unGV3uL17TKePu/jZoUBxcR6723jXu1a8Tg3Q\nNNe7VmrY4Jys+N1zWMNZzvLm1/R53fZlqX1b/viSKRVPD34mI5pyYNQ0jAYNk8G5nU2GMoY1MBo0\njMVpBgOY9Hmd5b44r3O/6p+Lx2uA0bVPDZ772/ldcn42lvoulPxOlZ7Gtb+EcHfVBJsnPjsOOA9u\nFB/cnJ+cBzmlwFGc4HxXxePcptGnd6Urt/EXp3MoMBrA32jA36jp7wEmrcK0AKOGJcBUKs3f5Bp2\nn14jwGggNMRC5oUsj4O4XQ8E7gd2hQNncCh5oNfTcAsSehC5OL+r7M7A5jq4eDsYuY+/OJ2xeLx+\nQKPEPJrnPAagQYMGZGVn69vYUXwEd+1D9+FS+6uMYb0slNiXCmfm3JQ8dJY6lJaa3jOhomNvYGAQ\nWTm52BwKu0NhVwq7A+ewcqXhMd7mALtDYXMoimyQ43CUntd92uLl2ByqxPekxPfD40Th4rvd9X3x\nMs7z3Vkm931p1PdzcZoeyLydCLnNU4XxRk3Dz1j8Mjh/I34Gg0ea67N/iXR/o4ZJ/2zwmNYgAfSy\nXTXB5i+3RJZzVlv6zBeKD4Ju06E5fzRoF8+AXdM4PztHun4MNSXIz4jN31hj66tpliA/DLb6+1W1\nWCxkZdV2LqpPyQAUbG7AhQtZHidCDkXpYbeTIGda6UB2cRrlthy3ZTmcVxWK7MUvh4OcIjtFBW5p\nxelFdkWhQ2ErvtrgnM97uskAJoOhOHhdDEIBJhNKOfSA57o6UCpIGlw15xI1RoP3gKpp3mqVbrVG\ntyqyKh5wrzWXrFG7p3mrXSu3GdzHP9OsWVW+Al7V319wCc1DA2o7C0JcFVwHR9dpm9nfiCOg7p4M\nqYkfa10AAAzRSURBVOLaoR6g7M4aYqFd4R8YRHZOjkcgLBlMS14+9labdH9XHgG3xDSOi6HAvbKl\n6S/NMwHPirdW6oPro+ZZI/fBufJVE2yEEKIqNE3Dzwh+RiP4eY6zWMxkBTpqJ2N1jLRGE0II4XMS\nbIQQQvicBBshhBA+J8FGCCGEz0mwEUII4XMSbIQQQvicBBshhBA+J8FGCCGEz0mwEUII4XMSbIQQ\nQvicBBshhBA+J8FGCCGEz9XJB3HGx8ezcuVKHA4Ht912G7GxsRXOo06dKH7GdnEnJrh1fqJ/dj2D\n2206r/O4T0cZ0xan6x+9Pd+75HLc09yTyh9fGBiIysstMam3fHrJc1lp7nnz9rxyF1e/DM4BV18M\nF9NKDnvMo+mD+rD7Y2mL+3YoDAxC5ea65U+hHK5t7b5PSu6HkvvLUXo/udJwW4ZDeazrYsc3jrK/\nIx7rrMTygFx/fxyFhRe3zcUNVfpjReNLpXt59C8aaAa3/jWKt73BtQ+0MtJxm89QOq2MZRQGB+Mo\nKCiezrluzVCcB9e75ho2XJzX9dlgKDGN5j3d1SmSZrhYdvf8lsrjJW4DrXRafeogTnk9PlS/Ohds\nHA4HK1as4LnnnsNqtfLMM8/QvXt3oqKiyp9vxXzPA5r7Fwg8D5olP1/KPF6nBa8/fHce6yljXKnl\neI4vMplw2Ox6+sXHjHvLn9sCvOVTc/9cTlrJwFoyKLvSPA68btMWD7s6LfMMjp7DhSYTDrudkgc3\nzduBseTBQ3Ofp8Q+dT+Ilrcsj3Fu05Y62LoOhJVbniEgEAryK3WC4fVEpqxpvB1MSgVpB9hKpLkH\nyZJBWSnPtLKCv0NRZDJCYeHFdIcDh3IU98bmcEu3e0xz8d1RcbpyXBzn7VUyb+WVUTm8p5X8nmoa\nGR4nSOW4pOBU2QBWie9AyWnKzYZbPj7fV8k8la3OBZtjx47RtGlTIiIiAOjduzf79++vMNgY/764\nJrJXK8wWC1n1qfetEhrU8/IFWiwU1ePy1cfvpyoOcJYGl1C2SzrIX2IgUJSISWWczF7CNDVdO6tz\nwSYtLY2wsDB92Gq1cuzYsVrMkRDiaqNpGmhGNKPzJSomDQSEEEL4XJ2r2VitVlJTU/Xh1NRUrFar\nxzQJCQkkJCTow6NHj6ZZNfalfSWyWCy1nQWfkvLVbfW5fPW5bACrV6/WP8fExBATE1Ol5dS5ms11\n111HUlISycnJ2Gw2du/eTffu3T2miYmJYfTo0fprzpw5NZpH951TE6R81UvKV71qsnz1uWxQO+Vz\nP5ZWNdBAHazZGI1Gxo8fz9y5c/WmzxU1DggPD6+h3Dldzg6pCilf9ZLyVa+aLF99LhvU7fLVuWAD\n0LVrV7p27XrJ07tartWUmv5CSPmql5SvetVk+epz2aBul6/OXUaripreQTVNyle3SfnqrvpcNqje\n8mlKXeo/fYQQQoiquSpqNkIIIWqXBBshhBA+VycbCKSkpPDGG2+QmZmJpmkMGDCAO++8k+zsbBYs\nWEBKSgrh4eHMnDkTs9kMwFtvvUV8fDwBAQFMmTKFli1bArBt2zbWr18PwIgRI+jbt2+tlculusp3\n8uRJli9fTl5eHgaDgXvuuYebb765lktXvfsPIDc3lyeffJIbb7yR8ePH11axdNVZvpSUFJYsWUJq\naiqapvHMM8/UeAuokqqzfKtWrSIuLg6Hw0Hnzp0ZN25cbRat0mU7ffo0b775JidPnmTMmDEMHz5c\nX1ZVHhjsa9VVvrKWUy5VB6Wnp6sTJ04opZTKy8tT06dPV6dOnVLvvfee2rBhg1JKqfXr16tVq1Yp\npZQ6cOCAmjdvnlJKqcTERDVr1iyllFJZWVlq6tSpKjs7W2VnZ+ufa1t1le/MmTPq7NmzSiml0tLS\n1KRJk1ROTk4Nl6a06iqfy1tvvaUWLlyoVqxYUXOFKEd1lm/OnDn/397dhTTVx3EA/26P5EzcVo4o\n7EV8iZCcs1p148tFF0UXEUiQkGgvSOW6ikIKurTIpb2ZQV2EQjd1I3RRN5kUFU/mGm6OJWL0auHm\nosV05+w8F9FhmrN05+yl5/u5PGf+d7545m/nnL+/v+R0OiVJkqRQKCRNTk4mMMnslMrn8Xik06dP\nS5FIRBJFUTp16pTkcrkSHyjKfLMFAgFpeHhYun37ttTT0yOPI4qi1NTUJI2NjUnhcFg6fvy49Pbt\n24TnmUmpfLHGmUta3kYzGo3Iz88HAOh0OuTl5cHn8+HFixfylUl1dTX+/fdHx9Lo7cXFxQgGg5iY\nmIDD4YDZbEZ2djays7NRWloKh8ORlEzRlMq3YsUKLF++HACwZMkS6PV6fP36NfGBZlAqHwCMjIwg\nEAjAbDYnPkgMSuV79+4dIpEISktLAQCZmZlYtGhR4gPNoFQ+jUaDcDiMcDiMqakpiKIIo9GYlEw/\nzTebXq9HYWEh/pnRHy26YXBGRobcMDjZlMo32zh+v3/O907L22jRPn/+jNHRURQXFyMQCMgnq8Fg\nQCAQAPBr887c3Fz4fD74/f5Zt6eSePJFf3CHh4chiqJcfFJFPPn0ej26urpgs9ngdDqTcvy/E0++\n8fFxLF68GK2trfjy5QtKS0tRW1sLrTZ1viPGk2/t2rUoKSlBY2MjJEnC9u3bU6qt1J9kiyUdGgbH\nky/WOHNJnbN2AUKhEOx2O+rr65GVlTVt38z22VIazvBWKp/f78eVK1dw5MgRVY5zoeLN9+DBA5SX\nl//SGy9VxJtPFEV4PB7U1dWhpaUFY2Nj6O3tVfOQ5yXefJ8+fcKHDx/Q2dmJzs5ODA4OwuPxqHrM\nf2o+2dKRUvlCoRAuXLiA+vp66HS6OV+btlc2giDAbrejsrISmzdvBvCjIk9MTMBoNMLv98NgMACI\n3bxz6dKl0xp2jo+PY/369YkNEoMS+YAfD8/Pnj2LvXv3oqioKPFBYlAin9frhcfjwf379xEKhSAI\nAnQ6HWpra5OSKZoS+URRRH5+vvxf3FarFa9fv058mFkoka+vrw/FxcXIzMwEAFgsFni9Xqxbty7x\ngaLMJ1ssf9IwOFmUyBc9TkVFhTzOXNLyykaSJHR2diIvLw87d+6Ut2/atEn+5vfo0SNYrVZ5e19f\nHwDA6/UiOzsbRqMRZWVlcDqdCAaD+PbtG5xOJ8rKyhKeZyal8gmCgNbWVlRVVWHLli0JzxGLUvmO\nHTuGjo4OXL16Ffv27UNVVVVKFBql8hUWFiIYDMrP2QYHB7Fq1arEhpmFUvlMJhPcbjcikQgEQcDQ\n0NBv+xyqbb7Zon8u2p80DE4GpfLFGmcuadlBwOPx4MyZM1i9erV8yVdbW4uioqKYUy9v3rwJh8MB\nnU6Hw4cPo6CgAADw8OHDaVOfq6urk5IpmlL5+vr6cO3atWl/oI4ePYo1a9YkJddPSv7+furt7cXI\nyEhKTH1WMp/T6URXVxckSUJBQQEaGxt/eVibaErli0QiuHHjBoaGhqDRaGCxWFBXV5fMaPPONjEx\ngebmZnz//h1arRY6nQ5tbW3Q6XQYGBiYNvV59+7dSc0GKJdvdHR01nEsFkvM907LYkNEROklLW+j\nERFRemGxISIi1bHYEBGR6lhsiIhIdSw2RESkOhYbIiJSHYsNERGpjsWGSEGXLl1CR0fHtG1utxsH\nDhyQO1UT/R+x2BApaP/+/XA4HHIX6qmpKVy/fh11dXWKtM8XRTHuMYiSgR0EiBT27NkzdHd3w263\n4+7du3jz5g1qampw69YtvH//HiaTCQ0NDSgpKQHwo2VST0+PvGzCrl27sG3bNgCAy+XC5cuXsWPH\nDty7dw9msxlNTU3JjEe0IGnb9ZkoVW3duhVPnjxBe3s7vF4vzp07h5MnT8Jms8FiscDpdMJut6O9\nvR05OTkwGAxobm7GsmXL4Ha70dLSgsLCQnnp5EAggGAwiI6ODkQikSSnI1oY3kYjUsHBgwfhcrlQ\nU1ODx48fo7y8XG5SaDabUVBQgJcvXwIANmzYIC8jUFJSArPZjKGhIXksjUaDPXv2ICMjIyVW6iRa\nCF7ZEKnAYDAgJycHK1euxPPnz/H06VP09/fL+0VRlNdOGhgYwJ07d/Dx40dIkoTJyclpnbn1ej0y\nMvhRpfTGM5hIZSaTCZWVlWhsbPxlXzgcht1uh81mg9VqhVarxfnz56etH/I3rAxJxNtoRCrSaDSo\nqKhAf38/Xr16hUgkgqmpKbhcLvh8PgiCAEEQkJOTA41Gg4GBAXkmG9HfhFc2RCrLzc3FiRMn0N3d\njYsXL0Kr1aKoqAiHDh1CVlYWGhoa0NbWBkEQsHHjxpRY0ZFIaZz6TEREquNtNCIiUh2LDRERqY7F\nhoiIVMdiQ0REqmOxISIi1bHYEBGR6lhsiIhIdSw2RESkOhYbIiJS3X8CPZYfk8BfbwAAAABJRU5E\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0586d5710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"us_violentcrime.plot(kind='line', title='Violent Crime in the US')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the number of aggravated assaults, the Americans seem to be an angry race."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa0586266d8>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/P2RnZ/PTenp68uMcHBzg4eHB9zs6OkKlUplc7u3bt9GuXbtq66ZQKKDRaAzq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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa05866fe48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"france_violentcrime.plot(kind='line', title='Violent Crime in France')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa058628b00>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wmd6hQwfy8/PJyckhLi6OgIAANBoNGo2Gbt26ERsbS3Z2NkVFRfj6+gIQGhrK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\nDEREBBUDEREB/g+3PMDLREo6QAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa058580748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"neth_violentcrime.plot(kind='line', title='Violent Crime in the Netherlands')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa05856f128>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RhRuSwBJC1CtHA87aVQPhqoGo4iL45WdU8nZszz8CwU1Kw6sfRLaVQ4eiUhJY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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0584c0eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rape_stats.plot(kind='line', title='Rape Statistics in India, US, France, and the Netherlands')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now this is interesting. Looking at the amount of attention garnered by India and its rape problem, one wouldn't have expected the United States to actually have a *considerably* higher number of rape cases reported. 'Rape cases reported' being the operative phrase of course. It's also interesting to see the steady decline in rapes in the US, and its rise in India. France and the Netherlands seem to be quite benign in this matter, considering how prostitution is legal in the Netherlands."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa0585b8e48>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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g5yoo5yqQk226NU5DqQS0nsbekzmU3LQQXD0Ad9N9F9cm+X7OlnjFTERAXg5w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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa05849dc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"assault_stats.plot(kind='line', title='Assault Statistics in the US, France, and the Netherlands')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, Americans, get a grip on yourselves. On a side note, it's kind of unfortunate how I couldn't find any data on assault in India, although I suspect it would give the Americans a run for their money."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa0584ed5c0>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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zSjhbbm+Uec6+qUujzMcXeCwZ6nQ6XnzxRUpKSnjxxRc5duwY9957L+Hh4VRW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\nMoYEQRCE30PGkCAIgvB7/g+TQZkOZCyYugAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0584424e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"homicide_stats.plot(kind='line', title='Homicide Statistics in India, US, France, and the Netherlands')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interesting again. Gun violence has attracted quite a bit of attention in the US, but turns out India surpasses the US by a considerable margin in terms of the number of homicides committed."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa05834f0b8>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1apTpPaPRyIQJE6hfvz5+fn5s27bNrO6VK1fi5+eHm5sbTZs2Zc2a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\nwpGPwZ4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa05830e4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kidnapping_stats.plot(kind='line', title='Kidnapping and Abduction Statistics in India, France, and the Netherlands')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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