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ScienceTony/Examine Earthquake with pandas.ipynb

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Investigation into the number of occurrences over time and location.
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Examine Earthquake with pandas.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Acquisition \n",
"[Tony McDonald](https://twitter.com/ScienceTony)<br>\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'25-07-2019'"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime\n",
"datetime.today().strftime('%d-%m-%Y')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, some data must be found <br>\n",
"https://github.com/socratica/data \n",
"<br>\n",
"<br>\n",
"License: MIT License with Copyright (c) 2019 Socratica https://github.com/socratica/data/blob/master/LICENSE\n",
"<br>\n",
"This license allows me to freely download, use, manipulate and distribute. \n",
"<br>\n",
"This is taken from the [Socratica](https://www.youtube.com/Socratica) SQL youtube video for [SELECT](https://www.youtube.com/watch?v=VkabxQgtGsA&list=PLi01XoE8jYojRqM4qGBF1U90Ee1Ecb5tt&index=7). \n",
"<br>\n",
"This notebook covers\n",
"<ul>\n",
" <li>Importing, quick examination and preparation </li>\n",
" <li>Creating 'Number of Nuclear Explosions By Year' graph</li>\n",
" <li>Creating 'Number of Nuclear Explosions By Decade' graph</li>\n",
" <li>Creating 'Number of Nuclear Explosions By Area' graph</li>\n",
" </ul> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IPython lets me use terminal so i can use this to have a quick look at the dataset. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"! whoami"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"psql (PostgreSQL) 11.2\r\n"
]
}
],
"source": [
"! psql --version "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.7.2\r\n"
]
}
],
"source": [
"! python -V "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.8.0\r\n"
]
}
],
"source": [
"! ipython -V "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chitter with SQL.ipynb\r\n",
"Create A View And Find The Difference Between Nukes and Quakes.ipynb\r\n",
"Examine Earthquake with pandas.ipynb\r\n",
"Examine using SQL magic.ipynb\r\n",
"SQL_Pandas_and_sqldf.ipynb\r\n",
"Untitled.ipynb\r\n",
"\u001b[34mdata-master from YT\u001b[m\u001b[m\r\n",
"\u001b[34mmap-output-files\u001b[m\u001b[m\r\n",
"\u001b[31mserverRestart.command\u001b[m\u001b[m\r\n",
"\u001b[31mserverStart.command\u001b[m\u001b[m\r\n",
"\u001b[31mserverStatus.command\u001b[m\u001b[m\r\n",
"\u001b[31mserverStop.command\u001b[m\u001b[m\r\n",
"\u001b[34msql_data\u001b[m\u001b[m\r\n",
"\u001b[31msql_initial_state.py\u001b[m\u001b[m\r\n"
]
}
],
"source": [
"! ls "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data-master from YT/songs.csv\r\n",
"data-master from YT/earthquake.csv\r\n"
]
}
],
"source": [
"!find 'data-master from YT' -name '*csv'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have 2 files <br>\n",
"<ul>\n",
" <li>data-master from YT/songs.csv</li>\n",
"<li>data-master from YT/earthquake.csv</li> \n",
"</ul>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now i will look at the files with terminal "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 23120 data-master from YT/earthquake.csv\r\n"
]
}
],
"source": [
"! wc -l 'data-master from YT/earthquake.csv'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"earthquake_id,occurred_on,latitude,longitude,depth,magnitude,calculation_method,network_id,place,cause\r",
"\r\n",
"1,1969-01-01 9:07:06,51.096,-179.392,45,5.6,mw,iscgem812771,\"Andreanof Islands, Aleutian Islands, Alaska\",earthquake\r",
"\r\n",
"2,1969-01-02 17:50:48,-56.096,-27.842,80.1,6,mw,iscgemsup812819,South Sandwich Islands region,earthquake\r",
"\r\n",
"3,1969-01-03 3:16:40,37.14,57.899,10,5.5,mw,iscgem812826,Turkmenistan-Iran border region,earthquake\r",
"\r\n",
"4,1969-01-03 13:28:12,51.132,-179.306,15,5.9,mw,iscgem812841,\"Andreanof Islands, Aleutian Islands, Alaska\",earthquake\r",
"\r\n"
]
}
],
"source": [
"! head -n 5 'data-master from YT/earthquake.csv'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" This is a bit messy so i will use pandas"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"from pandas import DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# This gives me a clue to the encoding."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'encoding': 'ascii', 'confidence': 1.0, 'language': ''}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import chardet\n",
"\n",
"#Open the test file and read the contents in as a bytes object\n",
"testfile = open('data-master from YT/earthquake.csv','rb').read()\n",
"\n",
"#Detect the file encoding (this may take some time)\n",
"chardet.detect( testfile )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The output above indicates that the encoding is ascii and it is 100% sure. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the file into a dataframe fo use."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>earthquake_id</th>\n",
" <th>occurred_on</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>depth</th>\n",
" <th>magnitude</th>\n",
" <th>calculation_method</th>\n",
" <th>network_id</th>\n",
" <th>place</th>\n",
" <th>cause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1969-01-01 9:07:06</td>\n",
" <td>51.096</td>\n",
" <td>-179.392</td>\n",
" <td>45.0</td>\n",
" <td>5.6</td>\n",
" <td>mw</td>\n",
" <td>iscgem812771</td>\n",
" <td>Andreanof Islands, Aleutian Islands, Alaska</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1969-01-02 17:50:48</td>\n",
" <td>-56.096</td>\n",
" <td>-27.842</td>\n",
" <td>80.1</td>\n",
" <td>6.0</td>\n",
" <td>mw</td>\n",
" <td>iscgemsup812819</td>\n",
" <td>South Sandwich Islands region</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1969-01-03 3:16:40</td>\n",
" <td>37.140</td>\n",
" <td>57.899</td>\n",
" <td>10.0</td>\n",
" <td>5.5</td>\n",
" <td>mw</td>\n",
" <td>iscgem812826</td>\n",
" <td>Turkmenistan-Iran border region</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" earthquake_id occurred_on latitude longitude depth magnitude \\\n",
"0 1 1969-01-01 9:07:06 51.096 -179.392 45.0 5.6 \n",
"1 2 1969-01-02 17:50:48 -56.096 -27.842 80.1 6.0 \n",
"2 3 1969-01-03 3:16:40 37.140 57.899 10.0 5.5 \n",
"\n",
" calculation_method network_id \\\n",
"0 mw iscgem812771 \n",
"1 mw iscgemsup812819 \n",
"2 mw iscgem812826 \n",
"\n",
" place cause \n",
"0 Andreanof Islands, Aleutian Islands, Alaska earthquake \n",
"1 South Sandwich Islands region earthquake \n",
"2 Turkmenistan-Iran border region earthquake "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read the file as a .csv file with the encoding given...\n",
"earthquake_df = pd.read_csv('data-master from YT/earthquake.csv', encoding = 'ascii')\n",
"# ... and show us the first three rows:\n",
"earthquake_df[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How clean is the data?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Earthquake file is clean of Nan\n"
]
}
],
"source": [
"\n",
"file = earthquake_df.isnull().values.any() # returns True if data has Nans\n",
"if file == False:\n",
" print('Earthquake file is clean of Nan')\n",
"else:\n",
" print('Earthquake file needs some cleaning\\n')\n",
" \n",
"#print('Earthquake file', earthquake_df.isnull().values.any())\n",
"# df = earthquake_df[earthquake_df.isnull().any(axis=1)]\n",
"# print (df)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"earthquake_id int64\n",
"occurred_on object\n",
"latitude float64\n",
"longitude float64\n",
"depth float64\n",
"magnitude float64\n",
"calculation_method object\n",
"network_id object\n",
"place object\n",
"cause object\n",
"dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23119\n",
"unique 23110\n",
"top 2004-01-20 8:11:59\n",
"freq 2\n",
"Name: occurred_on, dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df['occurred_on'].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## I might need to clean the occurred-on column in the code above since i don't really need the exact time. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis and Presentation "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23119.000000\n",
"mean 71.165256\n",
"std 124.327891\n",
"min -1.100000\n",
"25% 12.950000\n",
"50% 33.000000\n",
"75% 53.890000\n",
"max 700.000000\n",
"Name: depth, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This will be interesting for another notebook, but doesn't help me answer \n",
"# the question i currently have\n",
"earthquake_df['depth'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23119.000000\n",
"mean 5.874432\n",
"std 0.421113\n",
"min 5.500000\n",
"25% 5.600000\n",
"50% 5.700000\n",
"75% 6.000000\n",
"max 9.100000\n",
"Name: magnitude, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df['magnitude'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23119\n",
"unique 2979\n",
"top Vanuatu\n",
"freq 699\n",
"Name: place, dtype: object"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df['place'].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## I find this interesting so i will look into it a bit more. Earthquake is listed as one cause, but what are the other two? "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23119\n",
"unique 3\n",
"top earthquake\n",
"freq 22942\n",
"Name: cause, dtype: object"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df['cause'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['earthquake', 'nuclear explosion', 'explosion'], dtype=object)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df['cause'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find the earliest nuke event"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>cause</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>247</th>\n",
" <td>nuclear explosion</td>\n",
" <td>52km NNE of Beatty, NV</td>\n",
" <td>1969-09-16 14:30:01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause place occurred_on\n",
"247 nuclear explosion 52km NNE of Beatty, NV 1969-09-16 14:30:01"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ne = DataFrame(earthquake_df[earthquake_df.cause == 'nuclear explosion'][['cause', 'place', 'occurred_on']])\n",
"#DataFrame(plot_df[plot_df.Gender == 'M']['In constituency since'].value_counts()).sort_values(by=['occurred_on'])\n",
"ne.sort_values(by='occurred_on')[:1]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead 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>cause</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1482</th>\n",
" <td>explosion</td>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>1973-02-16 5:02:58</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause place occurred_on\n",
"1482 explosion eastern Kazakhstan 1973-02-16 5:02:58"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ee = DataFrame(earthquake_df[earthquake_df.cause == 'explosion'][['cause', 'place', 'occurred_on']])\n",
"#DataFrame(plot_df[plot_df.Gender == 'M']['In constituency since'].value_counts()).sort_values(by=['occurred_on'])\n",
"ee.sort_values(by='occurred_on')[:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find the most recent nuke event"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>cause</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22482</th>\n",
" <td>nuclear explosion</td>\n",
" <td>21km ENE of Sungjibaegam, North Korea</td>\n",
" <td>2017-09-03 3:30:02</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause place \\\n",
"22482 nuclear explosion 21km ENE of Sungjibaegam, North Korea \n",
"\n",
" occurred_on \n",
"22482 2017-09-03 3:30:02 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ne.sort_values(by='occurred_on', ascending=False)[:1]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>cause</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6379</th>\n",
" <td>explosion</td>\n",
" <td>Tuamotu Archipelago, French Polynesia region</td>\n",
" <td>1985-05-08 20:27:59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause place \\\n",
"6379 explosion Tuamotu Archipelago, French Polynesia region \n",
"\n",
" occurred_on \n",
"6379 1985-05-08 20:27:59 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ee.sort_values(by='occurred_on', ascending=False)[:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<ul>\n",
" <li>Most recent nuke: North Korea 2017-09-03 3:30:02</li>\n",
" <li>Fist nuke: 52km NNE of Beatty, NV \t1969-09-16 14:30:01 </li>\n",
"</ul>\n",
"NV is Navada, USA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I will now examine the different causes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nuclear explosion"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>Number of occurences</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>nuclear explosion</th>\n",
" <td>173</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of occurences\n",
"nuclear explosion 173"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nukeCauseCount_df = DataFrame(earthquake_df[earthquake_df['cause'] == 'nuclear explosion']['cause'].value_counts())\n",
"nukeCauseCount_df.columns=[\"Number of occurences\"]\n",
"nukeCauseCount_df"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>cause</th>\n",
" <th>magnitude</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>247</th>\n",
" <td>nuclear explosion</td>\n",
" <td>5.82</td>\n",
" <td>52km NNE of Beatty, NV</td>\n",
" <td>1969-09-16 14:30:01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>nuclear explosion</td>\n",
" <td>5.54</td>\n",
" <td>48km NNE of Beatty, NV</td>\n",
" <td>1970-03-26 19:00:01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1591</th>\n",
" <td>nuclear explosion</td>\n",
" <td>5.64</td>\n",
" <td>52km NE of Beatty, NV</td>\n",
" <td>1973-06-06 13:00:01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1665</th>\n",
" <td>nuclear explosion</td>\n",
" <td>6.30</td>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>1973-07-23 1:22:58</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause magnitude place \\\n",
"247 nuclear explosion 5.82 52km NNE of Beatty, NV \n",
"399 nuclear explosion 5.54 48km NNE of Beatty, NV \n",
"1591 nuclear explosion 5.64 52km NE of Beatty, NV \n",
"1665 nuclear explosion 6.30 eastern Kazakhstan \n",
"\n",
" occurred_on \n",
"247 1969-09-16 14:30:01 \n",
"399 1970-03-26 19:00:01 \n",
"1591 1973-06-06 13:00:01 \n",
"1665 1973-07-23 1:22:58 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nukeCause_df = DataFrame(earthquake_df[earthquake_df['cause'] == 'nuclear explosion'])\n",
"nukeCause_df[['cause', 'magnitude', 'place', 'occurred_on']][:4]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explosion"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>Number of occurences</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>explosion</th>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of occurences\n",
"explosion 4"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"explosionCauseCount_df = DataFrame(earthquake_df[earthquake_df['cause'] == 'explosion']['cause'].value_counts())\n",
"explosionCauseCount_df.columns=[\"Number of occurences\"]\n",
"explosionCauseCount_df"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>cause</th>\n",
" <th>magnitude</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1482</th>\n",
" <td>explosion</td>\n",
" <td>5.6</td>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>1973-02-16 5:02:58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2460</th>\n",
" <td>explosion</td>\n",
" <td>6.4</td>\n",
" <td>Novaya Zemlya, Russia</td>\n",
" <td>1975-08-23 8:59:58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6106</th>\n",
" <td>explosion</td>\n",
" <td>5.7</td>\n",
" <td>Tuamotu Archipelago, French Polynesia region</td>\n",
" <td>1984-11-02 20:44:59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6379</th>\n",
" <td>explosion</td>\n",
" <td>5.7</td>\n",
" <td>Tuamotu Archipelago, French Polynesia region</td>\n",
" <td>1985-05-08 20:27:59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause magnitude place \\\n",
"1482 explosion 5.6 eastern Kazakhstan \n",
"2460 explosion 6.4 Novaya Zemlya, Russia \n",
"6106 explosion 5.7 Tuamotu Archipelago, French Polynesia region \n",
"6379 explosion 5.7 Tuamotu Archipelago, French Polynesia region \n",
"\n",
" occurred_on \n",
"1482 1973-02-16 5:02:58 \n",
"2460 1975-08-23 8:59:58 \n",
"6106 1984-11-02 20:44:59 \n",
"6379 1985-05-08 20:27:59 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"explosionCause_df = DataFrame(earthquake_df[earthquake_df['cause'] == 'explosion'])\n",
"explosionCause_df[['cause', 'magnitude', 'place', 'occurred_on']]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Earthquake"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>Number of occurences</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>earthquake</th>\n",
" <td>22942</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Number of occurences\n",
"earthquake 22942"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthCause_df = DataFrame(earthquake_df[earthquake_df['cause'] == 'earthquake']['cause'].value_counts())\n",
"earthCause_df.columns=[\"Number of occurences\"]\n",
"earthCause_df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>cause</th>\n",
" <th>magnitude</th>\n",
" <th>place</th>\n",
" <th>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>earthquake</td>\n",
" <td>5.6</td>\n",
" <td>Andreanof Islands, Aleutian Islands, Alaska</td>\n",
" <td>1969-01-01 9:07:06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>earthquake</td>\n",
" <td>6.0</td>\n",
" <td>South Sandwich Islands region</td>\n",
" <td>1969-01-02 17:50:48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>earthquake</td>\n",
" <td>5.5</td>\n",
" <td>Turkmenistan-Iran border region</td>\n",
" <td>1969-01-03 3:16:40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>earthquake</td>\n",
" <td>5.9</td>\n",
" <td>Andreanof Islands, Aleutian Islands, Alaska</td>\n",
" <td>1969-01-03 13:28:12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause magnitude place \\\n",
"0 earthquake 5.6 Andreanof Islands, Aleutian Islands, Alaska \n",
"1 earthquake 6.0 South Sandwich Islands region \n",
"2 earthquake 5.5 Turkmenistan-Iran border region \n",
"3 earthquake 5.9 Andreanof Islands, Aleutian Islands, Alaska \n",
"\n",
" occurred_on \n",
"0 1969-01-01 9:07:06 \n",
"1 1969-01-02 17:50:48 \n",
"2 1969-01-03 3:16:40 \n",
"3 1969-01-03 13:28:12 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthCause_df = DataFrame(earthquake_df[earthquake_df['cause'] == 'earthquake'])\n",
"earthCause_df[['cause', 'magnitude', 'place', 'occurred_on']][:4]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a DataFrame with just explosion and nuclear explosion in the cause column"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(23119, 10)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"booleans = []\n",
"for cause in earthquake_df.cause:\n",
" if (cause == 'nuclear explosion' or cause == 'explosion'):\n",
" booleans.append(True)\n",
" else:\n",
" booleans.append(False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### This simple check should return True "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[True]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"booleans[1482:1483]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23119"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(booleans)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"nuke_bool = pd.Series(booleans)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"dtype: bool"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nuke_bool.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>earthquake_id</th>\n",
" <th>occurred_on</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>depth</th>\n",
" <th>magnitude</th>\n",
" <th>calculation_method</th>\n",
" <th>network_id</th>\n",
" <th>place</th>\n",
" <th>cause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>247</th>\n",
" <td>248</td>\n",
" <td>1969-09-16 14:30:01</td>\n",
" <td>37.314167</td>\n",
" <td>-116.460667</td>\n",
" <td>1.2</td>\n",
" <td>5.82</td>\n",
" <td>ml</td>\n",
" <td>ci3326197</td>\n",
" <td>52km NNE of Beatty, NV</td>\n",
" <td>nuclear explosion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>400</td>\n",
" <td>1970-03-26 19:00:01</td>\n",
" <td>37.300500</td>\n",
" <td>-116.534167</td>\n",
" <td>1.2</td>\n",
" <td>5.54</td>\n",
" <td>ml</td>\n",
" <td>ci3325031</td>\n",
" <td>48km NNE of Beatty, NV</td>\n",
" <td>nuclear explosion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1482</th>\n",
" <td>1483</td>\n",
" <td>1973-02-16 5:02:58</td>\n",
" <td>49.835000</td>\n",
" <td>78.232000</td>\n",
" <td>0.0</td>\n",
" <td>5.60</td>\n",
" <td>mb</td>\n",
" <td>usp00000jc</td>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>explosion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1591</th>\n",
" <td>1592</td>\n",
" <td>1973-06-06 13:00:01</td>\n",
" <td>37.245000</td>\n",
" <td>-116.346000</td>\n",
" <td>1.1</td>\n",
" <td>5.64</td>\n",
" <td>ml</td>\n",
" <td>ci3319623</td>\n",
" <td>52km NE of Beatty, NV</td>\n",
" <td>nuclear explosion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1665</th>\n",
" <td>1666</td>\n",
" <td>1973-07-23 1:22:58</td>\n",
" <td>49.986000</td>\n",
" <td>78.853000</td>\n",
" <td>0.0</td>\n",
" <td>6.30</td>\n",
" <td>mb</td>\n",
" <td>usp00002tp</td>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>nuclear explosion</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" earthquake_id occurred_on latitude longitude depth \\\n",
"247 248 1969-09-16 14:30:01 37.314167 -116.460667 1.2 \n",
"399 400 1970-03-26 19:00:01 37.300500 -116.534167 1.2 \n",
"1482 1483 1973-02-16 5:02:58 49.835000 78.232000 0.0 \n",
"1591 1592 1973-06-06 13:00:01 37.245000 -116.346000 1.1 \n",
"1665 1666 1973-07-23 1:22:58 49.986000 78.853000 0.0 \n",
"\n",
" magnitude calculation_method network_id place \\\n",
"247 5.82 ml ci3326197 52km NNE of Beatty, NV \n",
"399 5.54 ml ci3325031 48km NNE of Beatty, NV \n",
"1482 5.60 mb usp00000jc eastern Kazakhstan \n",
"1591 5.64 ml ci3319623 52km NE of Beatty, NV \n",
"1665 6.30 mb usp00002tp eastern Kazakhstan \n",
"\n",
" cause \n",
"247 nuclear explosion \n",
"399 nuclear explosion \n",
"1482 explosion \n",
"1591 nuclear explosion \n",
"1665 nuclear explosion "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_df = earthquake_df[nuke_bool]\n",
"complete_nuke_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(177, 10)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Everything looks good. Now create a DataFrame consisting of the numbers of explosions each year."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"247 1969-09-16 14:30:01\n",
"399 1970-03-26 19:00:01\n",
"Name: occurred_on, dtype: object"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_df['occurred_on'][:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The occurred_on object will need to be changed because i just want the year."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"earthquake_id int64\n",
"occurred_on object\n",
"latitude float64\n",
"longitude float64\n",
"depth float64\n",
"magnitude float64\n",
"calculation_method object\n",
"network_id object\n",
"place object\n",
"cause object\n",
"dtype: object"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tonymcdonald/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n"
]
}
],
"source": [
"# I can ignore this error message. \n",
"complete_nuke_df['Year'] = complete_nuke_df['occurred_on'].str[:4]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"247 1969\n",
"399 1970\n",
"1482 1973\n",
"Name: Year, dtype: object"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# It has worked! \n",
"complete_nuke_df['Year'][:3]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>cause</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1969</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1974</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1975</th>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause\n",
"Year \n",
"1969 1\n",
"1970 1\n",
"1973 7\n",
"1974 6\n",
"1975 10"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_count_df = DataFrame(complete_nuke_df.groupby(['Year']).count()['cause'])\n",
"complete_nuke_count_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rename the column heading"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"complete_nuke_count_df = complete_nuke_count_df.rename(columns={\"cause\": \"Nuclear Explosions\"})"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>Nuclear Explosions</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1969</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Nuclear Explosions\n",
"Year \n",
"1969 1\n",
"1970 1\n",
"1973 7"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_count_df[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Great! Now i can plot the result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot the results"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"complete_nuke_count_df.plot.bar(title = 'Nuclear Explosions By Year\\n',\n",
" figsize=(10,5), grid=False, color='orange',legend=False)\n",
"plt.ylabel('\\nNuclear Explosions')\n",
"plt.xlabel('\\nYear. *Years not listed had no nuclear explosions') \n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Next plot is to look at the number of explosion by decade"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"sixties = complete_nuke_df['Year'].where((complete_nuke_df['Year'] >= '1960') & (complete_nuke_df['Year'] < '1970')).count()\n",
"seventies = complete_nuke_df['Year'].where((complete_nuke_df['Year'] >= '1970') & (complete_nuke_df['Year'] < '1980')).count()\n",
"eighties = complete_nuke_df['Year'].where((complete_nuke_df['Year'] >= '1980') & (complete_nuke_df['Year'] < '1990')).count()\n",
"nighties = complete_nuke_df['Year'].where((complete_nuke_df['Year'] >= '1990') & (complete_nuke_df['Year'] < '2000')).count()\n",
"noughties = complete_nuke_df['Year'].where((complete_nuke_df['Year'] >= '2000') & (complete_nuke_df['Year'] < '2010')).count()\n",
"tens = complete_nuke_df['Year'].where((complete_nuke_df['Year'] >= '2010') & (complete_nuke_df['Year'] < '2020')).count()\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>Decade</th>\n",
" <th>Number Of Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1960s</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1970s</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1980s</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1990s</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2000s</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2010s</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Decade Number Of Nuclear Explosions\n",
"0 1960s 1\n",
"1 1970s 70\n",
"2 1980s 93\n",
"3 1990s 12\n",
"4 2000s 0\n",
"5 2010s 1"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {'Decade':['1960s', '1970s', '1980s', '1990s', '2000s', '2010s'], \n",
" ' Number Of Nuclear Explosions':[sixties, seventies, eighties, nighties, noughties, tens]} \n",
"decadePlot= pd.DataFrame(data) \n",
"\n",
"decadePlot"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = decadePlot.plot.bar(x='Decade', title='Number Of Nuclear Explosions by Decade\\n', \n",
" legend=False, color='m', figsize=(7,6),\n",
" rot=0)\n",
"plt.ylabel('\\nNuclear Explosions')\n",
"\n",
"# create a list to collect the plt.patches data\n",
"totals = []\n",
"\n",
"# find the values and append to list\n",
"for i in ax.patches:\n",
" totals.append(i.get_height())\n",
"\n",
"# set individual bar lables using above list\n",
"total = sum(totals)\n",
"\n",
"# set individual bar lables using above list\n",
"for i in ax.patches:\n",
" # get_x pulls left or right; get_height pushes up or down\n",
" ax.text(i.get_x()-.03, i.get_height()+ 1.8, \\\n",
" str(round((i.get_height()/total)*100, 2))+'%', fontsize=10,\n",
" color='b')\n",
"plt.yticks(np.arange(0, 110, step=10))\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The 1970s and 80s account for 92.09 % of all the nuclear explosions\n"
]
}
],
"source": [
"sum1 = 39.55 + 52.54\n",
"print(\"The 1970s and 80s account for\",sum1,\"% of all the nuclear explosions\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First and last entries of dataset. "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1969-01 Is the earliest entry to the dataset\n"
]
}
],
"source": [
"print(earthquake_df['occurred_on'].min()[:7],\"Is the earliest entry to the dataset\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-12 Is the last entry to the dataset\n"
]
}
],
"source": [
"print(earthquake_df['occurred_on'].max()[:7], \"Is the last entry to the dataset\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a new dataframe showing numbers of nuclear explosions for each area."
]
},
{
"cell_type": "code",
"execution_count": 56,
"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>cause</th>\n",
" </tr>\n",
" <tr>\n",
" <th>place</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>21km ENE of Sungjibaegam, North Korea</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47km NNE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48km NNE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51km NNE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52km NE of Beatty, NV</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52km NNE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54km NE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55km NE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57km NE of Beatty, NV</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Novaya Zemlya, Russia</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Russia-Kazakhstan border region</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tuamotu Archipelago, French Polynesia region</th>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ural Mountains region, Russia</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eastern Kazakhstan</th>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>northwestern Russia</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>northwestern Siberia, Russia</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>southern Xinjiang, China</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>southwestern Sakha, Russia</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cause\n",
"place \n",
"21km ENE of Sungjibaegam, North Korea 1\n",
"47km NNE of Beatty, NV 1\n",
"48km NNE of Beatty, NV 1\n",
"51km NNE of Beatty, NV 1\n",
"52km NE of Beatty, NV 2\n",
"52km NNE of Beatty, NV 1\n",
"54km NE of Beatty, NV 1\n",
"55km NE of Beatty, NV 1\n",
"57km NE of Beatty, NV 1\n",
"Novaya Zemlya, Russia 24\n",
"Russia-Kazakhstan border region 6\n",
"Tuamotu Archipelago, French Polynesia region 30\n",
"Ural Mountains region, Russia 1\n",
"eastern Kazakhstan 93\n",
"northwestern Russia 1\n",
"northwestern Siberia, Russia 1\n",
"southern Xinjiang, China 10\n",
"southwestern Sakha, Russia 1"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_place_count_df = DataFrame(complete_nuke_df.groupby(['place']).count()['cause'])\n",
"complete_nuke_place_count_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I will rename the rows like Beatty,NV because this is obviously in the same location or near enough. "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"complete_nuke_place_count_df = complete_nuke_place_count_df.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"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>place</th>\n",
" <th>cause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21km ENE of Sungjibaegam, North Korea</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>47km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>48km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>51km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" place cause\n",
"0 21km ENE of Sungjibaegam, North Korea 1\n",
"1 47km NNE of Beatty, NV 1\n",
"2 48km NNE of Beatty, NV 1\n",
"3 51km NNE of Beatty, NV 1"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_place_count_df[:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now i will pull out all the NV rows and store them in their own database"
]
},
{
"cell_type": "code",
"execution_count": 59,
"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>place</th>\n",
" <th>cause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>47km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>48km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>51km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>52km NE of Beatty, NV</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>52km NNE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>54km NE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>55km NE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>57km NE of Beatty, NV</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" place cause\n",
"1 47km NNE of Beatty, NV 1\n",
"2 48km NNE of Beatty, NV 1\n",
"3 51km NNE of Beatty, NV 1\n",
"4 52km NE of Beatty, NV 2\n",
"5 52km NNE of Beatty, NV 1\n",
"6 54km NE of Beatty, NV 1\n",
"7 55km NE of Beatty, NV 1\n",
"8 57km NE of Beatty, NV 1"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NV_df = complete_nuke_place_count_df.loc[complete_nuke_place_count_df['place'].str.contains(\"of Beatty, NV\")]\n",
"\n",
"NV_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# This will remove the NVs from the dataframe"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"complete_nuke_place_count_df.drop(complete_nuke_place_count_df.index[NV_df.index], axis=0, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"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>place</th>\n",
" <th>cause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21km ENE of Sungjibaegam, North Korea</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Novaya Zemlya, Russia</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Russia-Kazakhstan border region</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Tuamotu Archipelago, French Polynesia region</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Ural Mountains region, Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>northwestern Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>northwestern Siberia, Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>southern Xinjiang, China</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>southwestern Sakha, Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" place cause\n",
"0 21km ENE of Sungjibaegam, North Korea 1\n",
"9 Novaya Zemlya, Russia 24\n",
"10 Russia-Kazakhstan border region 6\n",
"11 Tuamotu Archipelago, French Polynesia region 30\n",
"12 Ural Mountains region, Russia 1\n",
"13 eastern Kazakhstan 93\n",
"14 northwestern Russia 1\n",
"15 northwestern Siberia, Russia 1\n",
"16 southern Xinjiang, China 10\n",
"17 southwestern Sakha, Russia 1"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_place_count_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rename the columns "
]
},
{
"cell_type": "code",
"execution_count": 62,
"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>Place</th>\n",
" <th>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21km ENE of Sungjibaegam, North Korea</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Novaya Zemlya, Russia</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Place Nuclear Explosions\n",
"0 21km ENE of Sungjibaegam, North Korea 1\n",
"9 Novaya Zemlya, Russia 24"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_place_count_df = complete_nuke_place_count_df.rename(columns={\"place\": \"Place\", \"cause\": \"Nuclear Explosions\"})\n",
"\n",
"\n",
"complete_nuke_place_count_df[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now i sum the NV nuke explosions"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# remeber that this df has not changed the column headers, so it still has cause \n",
"NV_sum = NV_df['cause'].sum()\n",
"NV_sum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now append the above sum for Beatty, NV to the dataframe "
]
},
{
"cell_type": "code",
"execution_count": 64,
"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>Place</th>\n",
" <th>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>eastern Kazakhstan</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Tuamotu Archipelago, French Polynesia region</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Novaya Zemlya, Russia</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>southern Xinjiang, China</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>National Security Site, NV</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Russia-Kazakhstan border region</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21km ENE of Sungjibaegam, North Korea</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ural Mountains region, Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>northwestern Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>northwestern Siberia, Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>southwestern Sakha, Russia</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Place Nuclear Explosions\n",
"5 eastern Kazakhstan 93\n",
"3 Tuamotu Archipelago, French Polynesia region 30\n",
"1 Novaya Zemlya, Russia 24\n",
"8 southern Xinjiang, China 10\n",
"10 National Security Site, NV 9\n",
"2 Russia-Kazakhstan border region 6\n",
"0 21km ENE of Sungjibaegam, North Korea 1\n",
"4 Ural Mountains region, Russia 1\n",
"6 northwestern Russia 1\n",
"7 northwestern Siberia, Russia 1\n",
"9 southwestern Sakha, Russia 1"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complete_nuke_place_count_df = complete_nuke_place_count_df.append({'Place':'National Security Site, NV', 'Nuclear Explosions': NV_sum}, ignore_index=True).sort_values(by='Nuclear Explosions', ascending=False)\n",
"complete_nuke_place_count_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Now i can plot the table"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# will put the highest number at the top of the graph. \n",
"complete_nuke_place_count_df = complete_nuke_place_count_df.sort_values(by='Nuclear Explosions', ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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DI4BjgGtKB5qDwSuAvSNifeDqkrFV91t2vhFxVUT0jYi+tG9fetrMrEV1ybthO3VKG/hHj07Pb7wR7rsPbr659rJjQ21feQVeey1lx7p3T8udG24I77xT027WrNT/0UfDb36TsnB9+qRrVYIDMmttjwB7S+oEIGnFvMy3PClw+khSZ2CnfL490CEiHgBOIC3pAXxMWnIkIv4HvCZpn9xGkkqS1AvlC2B/YG1JZ+ayDsCb+XhAdUVJxwDLRcQ5hfYdgHfz0uzWpGxXtc9JAeZBkg6obxB5ubNNRNwJnApsmE99eQ+oCb7ey/ds70bP0sysgj79FD7+uOZ42DDo2RMefBDOPTctPdaVsaqr7frrp4zZ1Knp0bUrPPssfPvbNW3POw+OPz7tI5s9OwV8bdqkQK0SvGRprSoinpd0CjBMUhtgLnBMRIySNJ60zPYqMDI3WQ64J2eABJyYy28Drs6b8vcmZaOuzH23y+cnNsN45+TN+I9JmgGcR1qyPAl4tFB1IDBX0oT8fDBwM3CvpLHABNL+s2Lfn0raFXhYUmkWr2hV4Pp8vwB+k3/eAAyWNBvYjJQVmwxMBcYszHzNzFrbjBkpswUwbx4ccADsuCOssUbafF+9vLjppjB4MLz1Fhx+ODzwQN1tG/LWW+kTmYMGpee/+EXqv2NHuPvuZp9ioygiKnNlM1skqVu34PjjKz0MM1vMLW7/l6WkcRFR5pvQGsdLlmZmZmYV5oDMzMzMrMK8h8zMaunTuTNjF7OlBDOzRZ0zZGZmZmYV5oDMzMzMrMIckJmZmZlVmAMyMzMzswpzQGZmZmZWYQ7IzMzMzCrMAZmZmZlZhTkgMzMzM6swB2RmZmZmFeaAzMzMzKzCHJCZmZmZVZgDMjMzM7MKc0BmZmZmVmEOyMzMzMwqrG2lB2Bmi5ZxM2ag88+v9DC+MWLgwEoPwcwWAc6QmZmZmVWYAzIzMzOzCnNAZmZmZlZhLRKQSfo/SRPy4x1JbxaeL9kS16xjHG0k/fortJ8i6a8L0W4NSRPqOHe2pK0XcjyHS7poYdou5PWelPTvwmu3Rwteq62kmY2od7ik/+bxvCDp0EbUb5V7JukhScu1xrXy9bpJur21rmct69BDoVMn6Nmzdvmll8Laa8N668HJJ5dve/HFqd1668FFhXf7oEGw6qpQVZUeDzyQykeOhF69YKON4D//SWUzZ8IOO0BEs0/NzBqhRTb1R8T7QBWApEHAJxFRiV3CbYBfA+c0taGkXsA84P9JWjoiZpep0zYi5jWl34j4XVPHUmH7RkRdwaUARcQXrTymmyPiBEnfBqZIGhoR77XyGBYQETssbNuFfC9NA/Zd2GvaomXAADj2WDjooJqy4cPhnntg0iT41rfg3XcXbDdlClx9NYweDUsuCTvuCLvsAmuumc6feCKUfm7gggvgzjth6lS48sr0/Kyz4Le/BamlZmhm9WnVJcvSzJGkX0s6JR8fJWmMpImS7pC0dC6/SdLlkoZLekVSP0k3SnpR0rWFvg6UNDlntf6Qi88BlsvZlCH1Xb+M/YEhwKPAroU2T+Ys1+PAsZK+LekeSZPy2DfJVdtKulbSc5L+KWmpwnx2z8fTJZ0jabSkZyR9N5d3lnSXpLH53KZl7uVuuc14ScMkdcrlnSQ9IulZSVfk7GTHfO7kfH+mSPp541+5WtddI7cfDDwLrCJpJ0lP52veLmnZwvwG5TFOkrRWLl8uv4aTc/nuhf7Pyffx6eo51SUi3gGmAqtJWknS0NzfU5Jq5RkkdZT0qqS2heevSVoiv6bVr8O/JW2e67SV9OdcPknS4bl81dxmQr4X1fWnF+71vZLG5df/8Dru5XRJp0oaCewhaU2lLNs4SY8X7tea+bUeLeks5Uxi8f0saenCPX1WUr9cfrikv+d+X5b0x8a/2taa+vWDFVesXXbllfDrX6dgDFIGrdQLL8Cmm8Iyy0DbtrDllvCPf9R/rXbtYPZsmDUrHb/yCrz5ZmprZpWxKO0huyMiNoqI3sArwIDCuQ4RsTVwMnAvcC6wLtBHUk9JXYHfA1sDGwDfl7QrKTv2cURURUTh352N8mPgduBWUnBWtHxE9IuIi4DLgYcjohfQB3gh11kbuCgi1gNmA7tT3ocRsTHwF+DPuewS4LyI6JvHcU2Zdo8Dm0bEBsBdwC9y+ZnAgxGxIfAA0AVA0sZAf2BjYDPgaKUsYENuV82SZcdcti5wbb72XNJ93iZfcxJwfKH9jFzvGuCkXDYI+G9ErA/0Bh7L5R2Ax/J74GmgoeXINYDVgVeBs4Bn8uswCLihWDciZgIjgR1z0QHA3yJifnV3+XX4JXBaLjsSeDeXbwQcI2k14EDg3oioyuOfVGZ4B0dEn9zuJEkr1DGNTyPi+xFxB3AVcHRu9xvgslznUuD8PI4ZdfRzHPB5vqc/Af6qmu0BvYG9gV7AgZK61NGHLWJeegmeeAI22SQFS2PGLFinZ094/HF4//0UYD3wAEybVnP+ssvS8uShh8KHH6ay3/wGjjwyLW8eeyz87ncpQ2ZmlbMoBWS9JD0haTKwH7Be4dy9+edk4K2IeD4vkz0PdAc2AR6NiPciYi5wC9BvYQciaTNgekS8CTwMbCKpQ6HKbYXjrUjBFBExLyL+l8v/ExGT8/G4PM5ybs0/bwY2z8fbAoNz9uNuYAXljGHBasCwfL9OouZ+/aB6fBFxH/BxLt8CuDMiZkXEx7nfH9R5E2rsmwPaqhzUALwSEdW/GjYnBWhP5fH2L5nrXfln8R5sSwpkiST/mmB2RPyzTP1S/SWNJ92zw/O4fgD8Nfc5DOhSnakruAY4JB8fAlzfwDi3Bw7J83oG6AisCYwBDpd0OtAzIj4pM8YTJU0kBZZdge/VMZfbIWXsgE2BO/P1LicH06T39535+JY6+inO/zngLWCNfO5fEfFxXnZ/kfTeqUXSkUoZ2bF8Um46Vgnz5qUgatQo+NOf4Mc/XnCP1zrrwK9+Bdttl5Yre/dOmTKAn/0sZb8mTIBVVoFf5H+2VVWlPocPh1dfhS5dUr/77gsHHggz6gr7zazFtPYXw86jdhC4VC6DtDy4U0RMyUs8xWW6OfnnF4Xj6udtgcbueqjv+kX7Az0lTc3Plwf2oCbr8mlJ/XLbYIvjnE/d97pcWwEbR8TntQprb+64HPhDRDwgaVtSlqq6bTnNuTOkOH+RMnI/qaNu9X0o3gNRft7F+dZ3z26OiBNKykrnt8B8I+IxSZcpfahibkS82IhxHh0Rj5T2JWkrYBfgZkl/jIibC+e2Jf2DYNOImC3pSdJ7rZzqeyngvZx1Wxj1vb4Nvhcj4ipShg516+Zt3YuIrl1hzz3Tvq6NN4Y2beC992DllWvXO+yw9IC0D6xr13TcuXNNnSOOgF13rd0uAn7/e7j99pQpO+OMtK/skkvg7LNbbFpmVkZrZ8jeIWUuVlDaU7VL4dyywDuS2pGWk5piFLC10qc725IybI9Vb5Ku3jfUwPXJdZcA9gLWjYjuEdEd2JMFly2rDQeOqm4rafkmjr16U/b+pCU1gH8BxxTGVO6XdAfgTaUo7eBC+ZOkZU4k7QxUf+rvcdI+paUltQd2A57I9UZIKvzV3SRPAVuqZv/bspLWbKDNMODYXF/1LOc1xeOk7Fx1QDQ9IkoDZ4CbSJm168ucK/UQaWm3et/Z2vn+rQ68k4OYG0jL5EUdgA9yMLYeadmyXjlL+LbyJ1mVPiHcO58eTfoHAaT3djnF+a8DrAL8pxFztEXY7rvDo4+m45degs8/h5VWWrBe9Wb/N96Au+6C/fPfVm+/XVPnH/9Y8BOcN96YPgCwwgppubNNm/SYNav552Jm9WvVDFlEfKa04X4Mad/P84XTp5F+8bwBTKHujEK5fqdLOg0YQcoU3BsR9+fT1wKTJI2NiIPquX61rYHXIqKYtB8O3FRH0HIscLWkn5KybT8FPmjs2IFlJI0mZYyqg75jgCslHUJ6jYZTCNCyQcA/gOmk+7ZKLj8duEVSf9IHEmaQ9imNlnQrae4AV0bE5ByAfhf4qAlj/lJEzJB0GGmvWfWepd8CL9fT7AzgCklTSBmbU0n73b6K04DrJU0CPqFmabLUzbluY74u4i+k5b0JOTv5LimQ3Ya0L2xuvtaBJe3uB47MS5YvkpY7G2M/0us+CFiSFDxOJO0P+6ukX5HuU7nX6lLgL3kJey5wUER8Ln9k7mtj//1hxIiUAevaNWWrDj00PXr2TJ+gvPHGlC176y04/PCar7HYa6+0h6xdO7j88hRgQfqajAkTUpvu3eEvf6m53qxZqb9hw9Lzk05K/Sy5JNx6K2bWyhT+0pmKkTSdtAepwe/fakKfSwHzImKepB+QPljQt576VcABEVHHNxwtXiTtB+wQEXUFbIucvBduVkSEpAOBPSJirxa7XrduwfHHN1zRmoX/L0uzxYOkcfX9vm2I/3PxxU934Nac+ZpDytjVKX/HWNnvGVvcSLqS9IGCHRuqu4jZCLhIUhvgQ+rO/pmZ2deUA7IKioiuLdDniyy4p8mAiPhZpcewMCJiBPmLls3MbPHkgMzMaunTuTNjvYxmZtaqFqXvITMzMzP7RnJAZmZmZlZhDsjMzMzMKswBmZmZmVmFOSAzMzMzqzAHZGZmZmYV5oDMzMzMrMIckJmZmZlVmAMyMzMzswpzQGZmZmZWYQ7IzMzMzCrMAZmZmZlZhTkgMzMzM6swB2RmZmZmFda20gMws0XLuBkz0PnnV3oYi7QYOLDSQzCzxYwzZGZmZmYV5oDMzMzMrMIckFmjSApJFxSeD5Q0qIJDqkXS9ZImFB5TJc1opr5HSOrbHH0V+rxB0mt5rBMlbdPM/T8gqWNz9mlmZi3HAZk11hxgT0krVXog5UTEIRFRFRFVwIbAG8DvKjyshvwyj/cEYHBzdhwRO0fEzObs0xZ06KHQqRP07FlTduqp0KsXVFXB9tvDW2+Vb7vEEqlOVRX86Ec15RHwu9/BWmvBOuvAJZek8jvvhPXWgy22gPffT2WvvAL77dcyczOz1uWAzBprHnAVcGLpCUmrS3pE0qT8czVJHXKWqk2us4ykaZLaSTpC0picGbozn1suZ4za5frL5/Zl6zcw1t8C70XENbmvlXO7Mfnx/Vw+SNKNkobla+0p6TxJkyU9WD2WwjwPk3Rh4fkRkv6cj++WNE7Sc5KObOK9fRpYtdDv1OrAV1JfSSPy8ZaFDOD4fM9WkfR4LpsiaYsyfXyVsVk9BgyABx+sXfbLX8KkSTBhAuy6K5x5Zvm2Sy+d6kyYAEOH1pTfcANMmwYvvggvvFATcF1wAYwaBQcdBLfckspOOQXOOqu5Z2VmleCAzJricqC/pA4l5ZcBQyKiF3AzcElEfARMBLbMdX4IPBQRc4G7ImKjiOgNvAAcFhEfAyOAXXL9/YA766pf1wAlbQwcnh/VLgYujIiNgL2AawrnvpevuRtwEzA8ItYHZhfGUu024EeFQO0Q4Pp8fGhE9AH6AsdJ+r+6xljGjsDdjag3EDgmZ9W2yGM8gHRfq4DewIQy7b7K2Kwe/frBiivWLlt++ZrjTz8FqWl9XnklnHYatMl/O3fqlH62aQNz5sCsWdCuHTzxBKyyCqy55sKP38wWHf7aC2u0iPifpCHAcaRgoNpmwJ75+K/Aefn4dmBfYDgpwLoil/eU9HugI9AeeCiXXwOcTApODgGOaKB+LZLa5+sfFhEfFE5tC6yrmt+My0taLh//MyLmSpoMLAFU5zsmA91L5v+ppEeBXSW9ALSLiMn59HGS9sjH3YA1gffLjbPgT5LOAzoBmzZQF2Ak8GdJN5OC1OmSxgDX5SDx7ogoF5A1OLacOUvZs47eevZV/e53MGQIdOgAw4eXr/PZZ9C3L7RtC7/+Ney+eyp/5RW4/Xb4xz9g5ZXTkuWaa8Lpp8MOO0CXLnDTTfDjH8Ntt7XenMysZTlDZk11ESlDtWw9dSL/HArsJGlFoA/waC6/ATg2Z6LOAJYCiIiRQHdJWwJLRMSU+uqXcSkwNCIeKSlvA2xWvccsIlbNGTlIe+OIiC+AuRFRPfYvKP8PlmuAARSyY5K2IgV9m+Us3vh6xlj0S2AN4BTgxkL5PGr+bH7ZT0ScQ8oOO6S9AAAgAElEQVT8LQ2MktQjIh4H+gFvAn+VdFDxAo0dW0RcFRF9I6Iv7ds3YuhWn7PPTsuO/fvDZZeVr/PGGzB2bFp+POGEFIhByoIttVQ6d8QRaZ8awHbbwbhxcO+9cPfdsPPO8O9/w957p3qzZrXO3MysZTggsybJmae/UXvZ8ClSBgygP/BkrvsJMJq0ZHhfRMzPdZYD3s5Znf4llxgC3ErNUmBD9QGQtDdpya7cRv5hwLGFulX1z7JuEfEMKct0QB4nQAfgw4iYJakHhWyXpCF5GbWu/r4g3Z82knbIxVNJASykJdbqvr4XEZMj4lxgLNBD0urAuxFxNXAt6QMNRXWOzVreAQekzfjldOmSfn73u7DVVjB+fHretSvslV/1PfZI+9GKZs2CG2+Eo4+G3/wGrrsO+vSBm29ukSmYWStxQGYL4wKg+GnL44BDJE0CfgIcXzh3O3Bg/lntVOAZ4GHgxZK+bwZWoCbYaah+tbOBlYHRJV9/sXQeX9/8oYPngaMaPdPy/gaMjIgP8/MHgbZ5/mcBowp1ewFv19dZzsr9nrRcCykLeLGkJ4D5haon5I37E0lLxv8EtgImSBpPCt4uLum+vrFZC3j55ZrjoUOhR48F63z4YcqEAbz3HowcCeuum57vvjs8mnPJjz2WPm1ZdN55cPzxaR/Z7Nlpj1qbNs6QmX3dqWaFxqzycqZrt4j4SaXHUhdJ95E+JFC6NFpab3ng2ojYp3VG1jzUrVtw/PENV/wGq/6vk/bfH0aMSEFV585wxhnwwANpKbFNG1h9dRg8GFZdNS1BDh4M11wDTz0FP/1pqvPFF2nJ8rCcc545My11vvEGtG+f2vTunc699RYceSTcd196fscdMGhQ2vZ3991pz5mZVYakcRGx0N9Z6YDMFhmSLgV2AnaOiJcqPZ5SSl+0OhqY+HULsprCAVnD/H9ZmlmprxqQ+VOWtsiIiJ9Xegz1yV+0ulaDFc3MzJrIe8jMzMzMKswZMjOrpU/nzoz1kpyZWatyhszMzMyswhyQmZmZmVWYAzIzMzOzCnNAZmZmZlZhDsjMzMzMKswBmZmZmVmFOSAzMzMzqzAHZGZmZmYV5oDMzMzMrMIckJmZmZlVmAMyMzMzswpzQGZmZmZWYQ7IzMzMzCqsbaUHYGaLlnEzZqDzz6/ItWPgwIpc18ys0pwhMzMzM6swB2RmZmZmFeaAzMzMzKzCHJBZs5O0laTNC89vkLR3K1z3JEnXFp73l3R/Pj5K0kENtO8r6ZJ8/CNJv27ZES9w/Z0kjZX0gqQXJZ2fy8veP0ldJP29NcfYWrp3h/XXh6oq6Nu3fJ0RI9L59daDLbdsuO2vfgW9esFBhXfBX/8KF1/cAhMwM2sib+q3lrAV8Anw1FftSJIARcQXjah+CTBW0veB54DfA9sARMTghhpHxFhgbD4eCgxd2HE3laSewGXALhHxoqS2wJH1tYmIt4AWD3QrZfhwWGml8udmzoSjj4YHH4TVVoN3362/7UcfwVNPwaRJ0L8/TJ4Ma6wBN9yQ+jAzqzRnyOxLkpaVdL+kiZKmSNo3l28jabykyZKuk/StXD5V0kr5uK+kEZK6A0cBJ0qaIGmL3H0/SU9JerWY7ZH0S0ljJE2SdEYu656zRFcAzwLdJH0i6ew8tlGSOpeOPyLmAUcDlwPnAddFxKu5z0GSBubjEZLOlTRa0kvVY8yZvfvy8QBJl+XjH0p6Jt+Df1VfO/d5Xe7vVUnHFeZ1as5yPSzp1upr1+Nk4OyIeLF6LhFxReH8Avcv36cphfHeJelBSS9LOq8wlitz5u256nv8dXfLLbDnnikYA+jUqf76bdrA559DBMyeDe3awZ/+BMcdl47NzCrNAZkV7Qi8FRG9I6In8KCkpYAbgH0jYn1SVvVndXUQEVOBwcCFEVEVEU/kU6sAPwB2Bc4BkLQ9sCawMVAF9JHUL9dfGxgSERtExOvAssCoiOgNPA4cUcf1nwJeALYlBWV1aRsRGwMnAKfXUw/gSWDTiNgAuI0UPFXrAeyQ53C6pHaS+gJ7ARsAewJ1LLrV0hMYV8/5Be5fGVXAvsD6wL6SuuXy30VEX6AXsKWkXo0YT0VJsP320KcPXHXVgudfegk+/BC22irVGTKk/rbLLQd77QUbbADf+Q506ABjxsBuu7XKdMzMGuQlSyuaDJwv6Vzgvoh4QlJv4LWIeCnXuRE4BrioiX3fnZcdny9kt7bPj/H5eXtSgPYG8HpEjCq0/xy4Lx+PA7YrdxFJ7UkBUDtgZWB6HeO5q9BX9wbG3hW4XdIqwJLAa4Vz90fEHGCOpHeBzqTA6Z6ImJ3HdG8D/TdGuftX6pGI+Chf83lgdWAa8GNJR5L+vK8CrAtMKjbM59MSaceOzTDcr2bkSOjSJS1Fbrcd9OgB/frVnJ83D8aNg0ceSRmvzTaDTTeFtdaqu+3JJ6cHwOGHw5lnwjXXwLBhaW/ZKadUZq5mZuAMmRXkoKsPKTD7o6TTANXTZB4176GlGuh+TuFYhZ9/zJm0qohYIyKqN+V/WtJ+bkREPp5P3f+YOAO4CTgbuLAR46mvr2qXApflDOFPqT3X4ryq+6rvntXlOdK9r0u5+1dfnflAW0nfAQYC20REL+B+yrxWEXFVRPSNiL60b9+0kbeALl3Sz06dYI89YPTo2ue7doUdd4Rll017xfr1g4kTG9d2fA7/11orZdb+9jeYMgVefrnl5mNm1hAHZPYlSV2AWRFxE3A+sCHwItBd0hq52k+Ax/LxVGqCiL0KXX0MLNeISz4EHJqzWkhaVVIDu4HqHf/6wC7AucBVwOqSymbSmqgD8GY+PrgR9Z8EfihpqTy3XQpjPFbSsWXa/An4raS1cr02kk76iuMGWJ4U3H6UM2s7NUOfLerTT+Hjj2uOhw2Dnj1r19ltN3jiiZQpmzULnnkG1lmncW1PPTVlx+bOhfnzU1mbNqkfM7NK8ZKlFa0P/EnSF8Bc4GcR8ZmkQ4A78if/xpD2iEHKRl0r6bfAM4V+7gX+Lmk34Od1XSwihklaB3g6fZiST4ADSdmdJsmfxrwSODEiPstlRwNDJFU1oasoUzaINP83gVHAd+rtIGKMpKHAROB10ic3P8qnewAjy7SZJOkE4FZJy+Rx3N+Ecdc1lomSxpMycK+Wu/aiZsaMlNmCFHAdcEDKhg3O77qjjkrB1447pqXGNm3SEmTPnvDqq+XbVrv7bthoo5os2mabpa/I6NULevduvTmamZVSzSqQ2TebpL2AH0VEY7JgDfXVPiI+ycHV48CREfFs/hTnnhHx+Ve9RktRt27B8cdX5Nr+vyzN7OtK0rj8AaqF4gyZGemLYEn7zg5tpi6vkrQuab/WjRHxLEBE7NpM/ZuZ2WLEAZkZzf9FsBFxQHP1ZWZmiz8HZGZWS5/OnRnrpUMzs1blT1mamZmZVZgDMjMzM7MKc0BmZmZmVmEOyMzMzMwqzAGZmZmZWYU5IDMzMzOrMAdkZmZmZhXmgMzMzMyswhyQmZmZmVWYAzIzMzOzCnNAZmZmZlZhDsjMzMzMKswBmZmZmVmFOSAzMzMzq7C2lR6AmS1axs2Ygc4/v9WuFwMHttq1zMwWVc6QmZmZmVWYAzIzMzOzCnNAZmZmZlZhDsi+wSSFpAsKzwdKGtRAm60kbV54fpSkg5p5XN0lTSlT3kbSJZKmSJosaYyk7zTntctc8xpJ6+bj3y5E+99Jek7SJEkTJG3SHP2WXOMGSW9K+lZ+vpKkqfn4NUlrl9S/SNLJX+WaLaF7d1h/faiqgr59FzwfAccdB2usAb16wbPP1pw7+WRYbz1YZ51UJwLmzIEdd4SePeGKK2rqHnkkjB/f4tMxM2sSB2TfbHOAPSWt1IQ2WwFfBmQRMTgihjT3wOqwL9AF6BUR6wN7ADNb6mKSloiIwyPi+VzUpMBJ0mbArsCGEdEL2BaYBvBV+q3DfODQMuW3AfsVxtQG2Bu4vRmu2eyGD4cJE2Ds2AXP/fOf8PLL6XHVVfCzn6Xyp56CkSNh0iSYMgXGjIHHHoOHHoI+fVL5VVeluhMnwhdfwAYbtN6czMwawwHZN9s84CrgxNITkn4o6RlJ4yX9S1JnSd2Bo4ATc7ZnC0mDJA3MbaokjcrZoH9IWiGXj5B0rqTRkl6StEUu7y7pCUnP5sfmpeMosQrwdkR8ARAR0yPiw9zX9pKezv3cIal9Lt9I0lOSJubrLydpgKTLCnO9T9JW+fgTSWdKegbYLI+9r6RzgKXzvG+WdJak4wt9nC3puDLjfS8i5uTxvhcRbxXuyQL95nMH5rFOkPQXSUs0cF8ALsqvS+knp2+lEJAB/YCpEfF6I/pcpNxzDxx0EEiw6aYwcya8/XZ6/tln8PnnKSs2dy507gzt2sHs2TBvXk0fp54KZ55ZuTmYmdXFAZldDvSX1KGk/Elg04jYgJRlOTkipgKDgQsjoioinihpMwT4Vc4GTQZOL5xrGxEbAycUyt8FtouIDUnZr0saGOvfgB/mQOUCSRtAWqIDTgG2zX2NBU6StCQpE3R8RPQmZahmN3CNZYEpEbFJRDxZXRgRvwZm53n3B64FDs7Xb0MKem4u6WsY0C0HoVdI2rL0YqX9Slon34vvR0QVKfPVv4ExA7xBes1+UtL/JOALSb1z0X6kIK0WSUdKGitpLJ980ojLNT8Jtt8+ZbWqM1pFb74J3brVPO/aNZVtthlsvTWsskp67LBDWrrcbjt45x3YZJO0pDl0aOq7S5fWm5OZWWP5e8i+4SLif5KGAMdRO1jpCtwuaRVgSeC1+vrJAV3HiHgsF90I3FGoclf+OQ7ono/bAZdJqg481mpgrNPzfqj/lx+PSNoHWBpYFxgpiTzep4G1SRm1MdVzzWOt7zLzgTvrq5D7mirp/RwUdgbGR8T7JXU+kdQH2ALYmnQ/fx0RN9TT9TZAH2BMHufSpMC1Mf4ADAXuLym/FdhP0nPAbsBpZeZzFSlbirp1i0Zer1mNHJmCpXffTcFUjx7Qr19xjAu2keA//4EXXoDp01PZdtvB44+ntrfcksrmzk2B2tChcNJJ8MYbKdv2ox+1/LzMzBrDAZlBWu56Fri+UHYp8OeIGJqX8wZ9xWvMyT/nU/O+OxGYAfQmZWs/a6iTvPz3T+CfkmYAu5MyUQ9HxP7FupJ6AeWCi3nUzg4vVTj+LCLmNzib5BpgAPBt4Lo6xjsfGAGMkDSZlFW7oZ4+BdwYEb9p5BiK1/qPpAnAj0tO3Uq6R48BkyKisQFeq6rOXHXqBHvsAaNH1w7IunaFadNqnk+fntrcdFNawmzfPpXvtBOMGlW77RVXwMEHw9NPw5JLwu23p8yaAzIzW1R4ydKIiA9Iy4GHFYo7AG/m44ML5R8Dy5Xp4yPgw+r9YaSls8dK65XoQM2esJ8A9e6VkrShpC75uA3QC3gdGAV8X9Ia+dwyktYCXgS6SNooly+X91hNBaqUPrXZDdi4gXFWmyupXeH5P4AdgY2Ah8qMd21JaxaKqvJ46+v3EWBvSZ1yHytKWj0fD5HU0FjPBmp99X1EvAK8D5xDmeXKRcGnn8LHH9ccDxuWPh1Z9KMfwZAhKVM2ahR06JCWKFdbLW3inzcvZcIeeywtWVb78EO4776UEZs1C9q0qdl3Zma2qHCGzKpdABxbeD4IuEPSm6SAp/rrJe4F/i5pN+DnJX0cDAyWtAzwKnBIA9e8ArgzLzsOBz5toH4n4Grlr3cARgOXRcRnkgYAtxbOnRIRL0naF7hU0tKkJdltgZGkJdjJwBRSdrAxrgImSXo2IvpHxOeShgMz68iqtc/X7kjKyv0HOLKhfiWdAgzLQedc4BhSINcLeLu+AUbEc5KeBTYsOXUr8EdSELnImTEjZcUgBVYHHJC+smLw4FR21FGw887wwAPpay+WWQauz/ncvfeGRx9NX5khpXY//GFN32eeCaecks7tsANcfnmqe9RRrTtHM7P6KMptzDCzBuWA6Vlgn4h4uYWvtTxwbUTs05LXgbyH7PjjG67YTPx/WZrZ4kDSuIgo8y2KjeMlS7OFoPSlrv8BHmnpYAzSBxJaIxgzM7PK8JKl2ULIX+r63UqPw8zMFg8OyMyslj6dOzPWy4hmZq3KS5ZmZmZmFeaAzMzMzKzCHJCZmZmZVZgDMjMzM7MKc0BmZmZmVmEOyMzMzMwqzAGZmZmZWYU5IDMzMzOrMAdkZmZmZhXmgMzMzMyswpoUkElatqUGYmZmZvZN1aiATNLmkp4HXsjPe0u6okVHZmZmZvYN0dgM2YXADsD7ABExEejXUoMyMzMz+yZp9JJlREwrKZrfzGMxMzMz+0Zq28h60yRtDoSkJYHjyMuXZrZ4GTdjBjr//GbpKwYObJZ+zMwWd43NkB0FHAOsCkwHqvJzMzMzM/uKGpUhi4j3gP4tPBYzMzOzb6TGfsryRkkdC89XkHRdyw1r8SNpvqQJkqZIurd4P5uh776SLmlimxsk7Z2PV5Q0XtIhzTimqZJWKinrLmlKE/rYKi+VN9eYPmmuvlqiv4W4fhdJf6/kGMzMrHk0dsmyV0TMrH4SER8CG7TMkBZbsyOiKiJ6Ah/QjEu+ETE2Io5bmLaSOgAPAVdFxPXNNaZmshXQbAHZVyGpsfst62ovSXX+eZO0RFP7jIi3ImLvrzKu1vDZZ7DxxtC7N6y3Hpx++oJ1Bg+G9deHqir4wQ/g+edT+eefwyGHpHO9e8OIEal8zhzYcUfo2ROuKHwBz5FHwvjxLT4lM7Nm19iArI2kFaqfSFqRxn8gwBb0NGk/XnUW6L7qE5IukzQgH58j6XlJkySdn8v2yVm2iZIeL+1D0saSnsoZr6ckrV3PONoD/wRuiYgrc/v2kh6R9KykyZJ2y+VH5QzfBEmvSRqey6+UNFbSc5LOKL2ApKUlPSjpiFy0hKSrc/1hkpbO9Y4rzPU2Sd1JexdPzNfcQtIPJT2T5/YvSZ1z20GSrpM0QtKrkuoMTiVdkOf2iKSVc1mVpFH52v+ofq/n/v4g6THgeEnfkfS0pDGSzirp95e5fFL1fcgZwRfyd/Y9C3QraTNV0mmSngT2kfS9fK/GSXpCUo9c73t5fGMknVmdmStmHCUtJen6/JqNl7R1Lh8g6a7c78uSzqvn/dAivvUtePRRmDgRJkyABx+EUaNq1zngAJg8OZ0/+WQ46aRUfvXV6efkyfDww/CLX8AXX8BDD0GfPjBpElx1VaozcWI6t4H/qWhmX0ONDaouAJ4qLI/sA5zdMkNavOVMyDbAtQ3UWxHYA+gREaGaJc7TgB0i4k2VX/Z8EegXEfMkbQv8Adirjsv8GbgmIi4slH0G7BER/1NachwlaWhEDAYGS2oHPJrbAvwuIj7I83pEUq+ImJTPtQduA4ZExJAcZK0J7B8RR0j6Wx7bTcCvge9ExBxJHSNipqTBwCcRUR2MrgBsmu/H4cDJwC/ytXoAWwPLAf+WdGVEzC2Z77LAsxHxC0mnAacDxwJDgJ9HxGOSzszlJ+Q2HSNiy3z9ocCVeS5fZjglbZ/ntTEgYKikfsAbwNrAIRFxdB2vwWcR8YPczyPAURHxsqRNgCuA/wdcDFwcEbdKOqqOfo4BiIj1cyA3TNJa+VwVKaM9J9+bS8t8jU2LkaB9+3Q8d256SLXrLL98zfGnn9acf/552GabdNypE3TsCGPHQrt2MHs2zJtX0+7UU1Omzczs66hRGbKIGALsDcwA3gX2jIi/tuTAFkNLS5pA+nLdFYGHG6j/P1JwdI2kPYFZuXwkcEPOOJVb5uoA3JEzJxcC69VzjUeB3SR1KpQJ+IOkScC/SJm8zoXzFwOPRsS9+fmPJT0LjM/XWrdQ9x7g+vz+qfZaREzIx+OA7vl4EnCzpAOBwq/ZWroCD0maDPyyZG73R8Sc/AGUd0vGXO0L4PZ8fBPwA6Ul244R8Vguv5HaX3p8e+H4+8Ct+bj4/t8+P8aTMmE9SAEawOsRUZIPquV2SJlJ0vLsHfl98hdglVxnM+COfHxLHf38oHpMEfEi8DpQHZA9EhEfRcRnwPPA6qWNJR2ZM51j+aT5t8bNn5+WIzt1gu22g002WbDO5ZfD976XMmSX5B2RvXvDPfekwOu112DcOJg2LfXxzjupn5NPhqFDU8asS5dmH7qZWatoyhfDPgf8jfRL9hNJq7XYqBZPsyOiivTLcElq9pDNo/brsBRARMwjZVzuBHYHHszlRwGnkJa/Jkj6v5LrnAUMz3vVfljdX17OmiDpgULd24ArgQckLZfL+gMrA33yeGcU+hiQx1+9JPcdYCCwTUT0Au6vrpuNBHaSauVD5hSO51OTpd0FuBzoA4xT+T1blwKXRcT6wE9LrlVXv/WJRtT5tBFtBPwx7xGsiog1IqI6A1ravq7+2wAzC31URcQ6jRhfcQx1afDeRMRVEdE3Ivp+mc5qRksskZYjp0+H0aNhSpmPdhxzDLzyCpx7Lvz+96ns0EOha1fo2xdOOAE23xzatk2PW25J+8X22QcuuigtZ550Euy9dwrQzMy+Thr7KcsfSXoZeA14DJhK2ntkTRQRH5G+WHdgXv57HVhX0rdytmYb+DJj0iEiHiAtn1Xl8u9FxDMRcRrwHiX7kkgZsjfz8YDCdQ/Jv+R3LhnPRcAjwD+UvvS3A/BuRMzN+5BWz9ftQwq+DoyIL3Lz5UkBxUd5P9dOJWM5jZQRrPf/PVXa7N4tIoaTliE7kpY7PyYtQZab28H19VmHNqRML8ABwJP59fhQ0ha5/Cek93g5I4H98nHxa2AeAg7NrxmSVi3JOjYoIv4HvCZpn9yHJPXOp0dRs+y8X7n2wOPVY8pLlasB/27KGFpDx46w1VZpH1ld9tsP7r47HbdtCxdemIK5e+6BmTNhzTVr17/iCjj4YHj6aVhySbj99pqAzszs66KxGbKzgE2BlyLiO6SgYWSLjWoxFxHjgYnAfnkvz9/IS3akZS9Igch9eenwMeDEXP6nvHF7CumX8MSS7s8D/ihpJOWXNMuN51fANNKS161AX0ljSb/gX8zVjiUttQ7PmbZr8v9pOh54DriO8u+JE4ClGthMvgRwU16KHA9cmD/Vey+wR77eFsAg0pLeE6RgtKk+BdaTNI60N+vMXH4w6b5OIgW+Z9bR/njgGEljSMEhABExjLSU+HSew9+pHUg2Vn/gMEkTSfd0t1x+AnCSpNGkZcyPyrS9gvSBicmkZdABETGnTL1W99//pkAK0r6vf/0LevSoXefll2uO77+/JuiaNSvtKYO0qb9tW1j3/7d353FelvX+x19vGNQQE/eHCIaKISA4wGB2EpdU9HgSl0jsUOLRtDyeQn/CyZVcjmlHOrmWuRRqndAig6OGmlvYSdkdEBMXyI1MO4QiLiyf3x/X9XXu2ZgZnJkvy/v5eHwfc3+v+76W+/qOMx8/13UPhUXxZcvg3nvh5JPTtR06pP1n77/fdvdjZtYWFNH0qo2kWRFRlX9RDIyItZJmRMT+bT9Es82bpM6kJe+QdBLpoYhjm6q33v316BGMGdMqbcXYsVRXpwzWmjXpKcgTT4Tx49OrqgqGD4cxY1Kg1qkTbLcd3HBD+hMZS5bAkUemQGu33eC22+BThR1w55wDxx0HBx+cgrDhw+G11+Ab34BvfrNVbsHMrFkkzY6IqvWu38yA7HekfUxXAjuSNk0PiYgN4m9EmW3KcnbwBtI+sb8Dp0bEC23WXysHZGZmm4OPG5A1989eHEt64u8c0rLKtjS+rGNmrSgipgP7NXmhmZlttJr7b1kWnxS7vY3GYmZmZrZZWmdAJukd0mP+pUfqS+ubAiIiPtlgRTPbaA3eZRdmeanRzKxdrTMgi4j1eVLMzMzMzFqgqQzZVqR/T7AX6c8y/CT/wVIzMzMzayVN/R2y24EqYD5wNOnftDQzMzOzVtTUpv6++Z+pQdJtwIy2H5KZmZnZ5qWpDNmq0oGXKs3MzMzaRlMZsv0kvZ2PBXwiv/dTlmZmZmatpKmnLJv1byGamZmZ2fpr7j8ubmZmZmZtxAGZmZmZWZk5IDMzMzMrMwdkZmZmZmXmgMzMzMyszByQmZmZmZWZAzIzq2X2G2+gCRPQhAnlHoqZ2WbDAZmZmZlZmTkgMzMzMyszB2RmZmZmZeaAzGqRdIikfyi8nyhpRDv0e4qkbm3Y/oWSnpFULWmepM80cf1jkqoaKF/RSuPpKem9PJaFku6Q1Kk12s7tD5d0Xmu1BzBtGvTuDb16wVVX1T8/cSLstBNUVqbXrbfWnHv5ZRg2DPr0gb59YcmSVD5qFAwYABdcUHPt5ZfDlCmtOXIzsw1fU/+4uG1+DgFWAP/bzv2eAiwAXm9uBUkVEbG6Gdd9FvgCMCgiPpC0I7DF+g60Fb0YEZWSOgIPAScCP2+NhiNiKjC1NdoCWLMGzjoLHnoIuneHIUNg+PAUXBWNHAk33FC//sknw4UXwhFHwIoV0KEDVFenc9XVMHQoLF8OK1fCjBlw8cWtNXIzs42DM2SbCElbS7pP0tOSFkgamcsPkzRX0nxJP5G0ZS5fkgMTJFXljFBP4BvAOTlzMzQ3f5Ck/5X0UilbJumHkobn43sk/SQfnybpP/LxVyTNyG39WFLH/JqYxzhf0jm5zSrg5/naT0gaLOlxSbMlPSBp19zmY5K+K+lxYExu67q646tjV+CtiPgAICLeiojXc3vjJc3M47lZkurMawdJt5fuKZddkef5SUm75LJjJD2V5/p3pfLmiIg1wAxgt9zWKZI+Cmsk3Zszl/XmLp//Vs6yVUuaVLeNjzO2khkzUmZszz1hiy3gpJOan8VauBBWr07BGECXLtC5M3TqBO+9B2vXwocfQseOMH48XHZZS0dnZrbxc0C26TgKeD0i9ouIfYFpkrYCJgIjI6I/KSN6ZmMNRMQS4CbgBxFRGRHT86ldgQNJWabSYtXvgVLAthtQypUcCEyX1AcYCf3Xg0oAACAASURBVHwuIiqBNcAooBLYLSL2zWP6aUT8CpgFjMrXrgauB0ZExGDgJ8AVhaF2jYiDI+L76xhf0YNAD0mLciB5cOHcDRExJM/ZJ3IbJRWkjNWiiLgol20NPBkR++U5OD2XPwEcEBEDgUnAvzcwjgblz+kzwLQmLq03d7n8PGBgRAwgBdR1rffYSl57DXr0qHnfvXsqq2vy5LQEOWIEvPJKKlu0CLp2hRNOgIEDYdy4lHHr0wd23x0GDYITT4QXXoCIdI2Z2ebGAdmmYz5wuKTvSRoaEcuB3sDiiFiUr7kdOGg92v5NRKyNiIVAKbsyHRgqqS+wEHgjZ7E+S1ruPAwYDMyUNC+/3xN4CdhT0vWSjgLebqC/3sC+wEO57kVA98L5u5oxvo9ExIo8ljOAN4G7JJ2STx+as0fzgc8D/QpVfwwsiIhiMPghcG8+ng30zMfdgQdyO+PqtNOYvfL9/Q14OSKqm7i+sbmrJmUXv0IKZutqcmySzpA0S9IsVtTfJhdRv9HauUQ45pi0N6y6Gg4/HEaPTuWrV8P06TBhAsycCS+9lPabAVxzDcybB+eem5YpL7sMrrgiBWi33NLEbJiZbUIckG0ictA1mBSYXSlpPKB1VFlNzee/VRPNf1A4Vu7vNWA7Umbu96QA7URgRUS8k6+7PWfaKiOid0RcEhHLgP2Ax4CzgMLW71p9PFOo2z8ihhXOv9vU+OqKiDUR8VhEfAf4N+CLOTP1Q1Imrj9wS525+F9SwFYsWxXxUXiyhpp9mNeTsm39ga/T9JxC3kMG9AIOKC0BU/uzodTWOubun4AbSZ//bEl194Y2ObaIuDkiqiKiii5d6g20e/eajBfAq69CtzqPYOywA2y5ZTo+/XSYPbum7sCBabmzogKOOw7mzKldd8oUqKqCd9+FBQvg7rvhzjvTnjIzs82BA7JNhNITiisj4mfABGAQ8Cegp6Re+bKvAo/n4yWkX+AAXyw09Q6wTTO7/SNwNjUB2dj8FeBhYISknfP4tpf0qbxvrUNETAYuzuOs2+9zwE5Km/GR1ElSczJODZLUW9LehaJK4M/UBCZvSeoC1N1/dhtwP/DLBoKcurYFSot4owt97y/pjnVVjIilpGXH83PREqAy71/rAeyf26o3d5I6AD0i4lHSUmRXoG5E1eDYWmLIEHj+eVi8OO33mjQpbeovWrq05njq1LQkWaq7bBm8+WZ6/8gjtR8GWLUKrr02LWWuXFmTeSvtLTMz2xz4KctNR3/gaklrgVXAmRHxvqR/oSagmEnaIwZwKXCbpAuApwrt/A/wK0nHAt9sos/pwLCIeEHSn4HtcxkRsVDSRcCDOWhYRcrqvAf8NJdBTRAyEbhJ0nukZc8RwHWStiV9n14DPNPiWUm6ANdL6krKPr0AnBERf5d0CymruIQ0P7VExH/lMdwpadQ6+riENM+vAU8Ce+Ty3Un33JTfAJcoPUjxBLA4j2sBUMon7Ub9uesI/CyPUaT9f3+v82xCY2NrtoqK9PTkkUem/V+nngr9+qVN+FVVKTi77roUiFVUwPbb1yxLduyYlisPOywtfQ4enDJoJTfemJY3O3dO+88ioH9/OProtPfMzGxzoGhoc4iZtQpJVwN3NmN/2AZDPXoEY8YAEGPHlnk0ZmYbB0mzI6Le369sLmfIzNpQRIwr9xjMzGzD5z1kZmZmZmXmDJmZ1TJ4l12Y5aVKM7N25QyZmZmZWZk5IDMzMzMrMwdkZmZmZmXmgMzMzMyszByQmZmZmZWZAzIzMzOzMnNAZmZmZlZmDsjMzMzMyswBmZmZmVmZOSAzMzMzKzMHZGZmZmZl5oDMzMzMrMwckJmZmZmVmQMyMzMzszJzQGZmtcx+4w00YQKaMKHcQzEz22w4IDMzMzMrMwdkZmZmZmXmgMzMzMyszByQbeYkHSepb+H9Y5Kq2qHfsyV1bqO2O0i6TtICSfMlzZS0Rz53v6SuknpKWvAx+/mGpJPXs+4hkpZLmivpT5JadcPWxxlbY6ZNg969oVcvuOqq+ucnToSddoLKyvS69daacy+/DMOGQZ8+0LcvLFmSykeNggED4IILaq69/HKYMqU1R25mtuGrKPcArHwkVQDHAfcCC9u5+7OBnwErm1tBUseIWNOMS0cC3YABEbFWUnfgXYCIODq31bXlQ641loqIuOnjtAFMj4gvSPoEMFfSPRHxh4/ZJgCtMLZa1qyBs86Chx6C7t1hyBAYPjwFV0UjR8INN9Svf/LJcOGFcMQRsGIFdOgA1dXpXHU1DB0Ky5fDypUwYwZcfHFrjt7MbMPnDNlGLGd5npV0i6RnJD2Yf7kjqVLSk5KqJd0jabtc/pik70p6HPg2MBy4WtI8SXvlpr8kaYakRZKG5nr3SxqQj+dKGp+PL5f0tXw8LmejqiVdmsu2lnSfpKdzxmqkpG+RAqZHJT2arxsm6Y+S5kj6paQuuXyJpPGSnsjjekzS9+qOr45dgaURsRYgIl6NiGWF9nbM11VIuj2P91eljJ2kwZIelzRb0gOSdm1g7sZIukTS2Hzu9HzvT0ua3JLsX0S8B8wDdsttfdRufr8gf9b15jKfv0rSwnwfE+q28XHGVjJjRsqM7bknbLEFnHRS87NYCxfC6tUpGAPo0gU6d4ZOneC992DtWvjwQ+jYEcaPh8sua+nozMw2fg7INn57AzdGRD/g78AXc/kdwLcjYgAwH/hOoU7XiDg4Iq4ApgLjIqIyIl7M5ysiYn9SFqtU7/fAUEmfBFYDn8vlBwLTJQ3LY9kfqAQGSzoIOAp4PSL2i4h9gWkRcR3wOnBoRByaA6SLgMMjYhAwC/h/hfG+HxEHRsSkdYyv6G7gmBxkfl/SwEbmrjdwc56jt4F/ldQJuB4YERGDgZ8AVzQwd9+v09avI2JIROwHPAuc1kif9eRgeW/SHK9LvbmUtD1wPNAv38d/NFCvybFJOkPSLEmzWLGiXgOvvQY9etS87949ldU1eXJaghwxAl55JZUtWgRdu8IJJ8DAgTBuXMq49ekDu+8OgwbBiSfCCy9ARLrGzGxz44Bs47c4Iubl49lAT0nbkgKHx3P57cBBhTp3NdHmr4vt5ePpuY0DgfuALjnT0jMingOG5ddcYA6wDynImA8cnrNaQyNieQP9HQD0Bf4gaR4wGvjUOsbb0Pg+EhGvkoKt84G1wMOSDmug31cKS4Q/y/fWG9gXeCiP5SKg+zrGUrKvpOmS5gOjgH6NXFc0VFI18Bfg3oj4SxPXNzSXbwPvA7dKOoGGl4CbHFtE3BwRVRFRRZcu9RqIqN+oVPv9McekvWHV1XD44TB6dCpfvRqmT4cJE2DmTHjppbTfDOCaa2DePDj33LRMedllcMUVKUC75ZYmZsPMbBPigGzj90HheA3N2xf4bjPbLLY3E6gChpIyOXOB00lBEYCAK3OmrTIiekXEbRGxCBhMCiauLC111iHgoULdvhFRzOLUHW9D46slIj6IiN9GxDjgu6S9cvUua+C9gGcKY+kfEcPWMZaSicC/RUR/4FJgq0auK5qes1r9gTMlVeby1dT+b3OrfE/15jIiVpOykpPzPU5rpbHV0r17TcYL4NVXoVu32tfssANsuWU6Pv10mD27pu7AgWm5s6ICjjsO5sypXXfKFKiqgnffhQUL4O674c47054yM7PNgQOyTVDOnCwr7K/6KvB4I5e/A2zTjDY/BF4BTgSeJGXMxuavAA8Apxb2fu0maWdJ3YCVEfEzYAIwqIF+nwQ+J6lXrttZ0qebe791SRqU+0VSB2AA8OcGLt1d0mfz8ZeBJ4DngJ1K5ZI6SWpOtmsbYGle8hxVGMvxkq5cV8UcaF1J2tMHsIQ8T5IGAaUnROvNZZ7vbSPiftISbiX1NTi2lhgyBJ5/HhYvTvu9Jk1Km/qLli6tOZ46NS1JluouWwZvvpneP/JI7YcBVq2Ca69NS5krV9Zk3kp7y8zMNgd+ynLTNRq4KS8rvgT8SyPXTQJuyRvtRzTR5nTgsIhYKWk6aSlvOkBEPCipD/BHpd+oK4CvAL1IDw2sBVYBZ+a2bgZ+K2lp3kd2CvALSTnHwkXAopbedLZzvqdSWzOABp7941lgtKQfA88DP4qIDyWNAK7LS78VwDXAM030eTHwFCnwm09NsLkXaVmxKTcBY5X+PMdk4OS8ZDqTmnnoT/253AaYImkrUnbvnBaMrdkqKtLTk0cemfZ/nXoq9OuXNuFXVaXg7LrrUiBWUQHbb1+zLNmxY1quPOywtPQ5eHDKoJXceGNa3uzcOe0/i4D+/eHoo9PeMzOzzYGioc0hZtYqJP0MOCci3iz3WJpLPXoEY8YAEGPHNnG1mZkBSJodEev9dzydITNrQxHxlXKPwczMNnzeQ2ZmZmZWZs6QmVktg3fZhVleqjQza1fOkJmZmZmVmQMyMzMzszJzQGZmZmZWZg7IzMzMzMrMAZmZmZlZmTkgMzMzMyszB2RmZmZmZeaAzMzMzKzMHJCZmZmZlZkDMjMzM7Myc0BmZmZmVmYOyMzMzMzKzAGZmZmZWZk5IDMzMzMrMwdkZlbL7DfeQBMmoAkTyj0UM7PNhgMyMzMzszJzQGZmZmZWZg7IbIMn6ThJfQvvH5NU1Q79ni2pcxu13VPSe5LmSVoo6Q5JnVqx/eGSzmut9szMrG05ILMNmqQK4Digb1PXtoGzgRYFZJI6tuDyFyOiEugPdAdObElf6xIRUyPiqtZqD2DaNOjdG3r1gqsaaHniRNhpJ6isTK9bb6059/LLMGwY9OkDffvCkiWpfNQoGDAALrig5trLL4cpU1pz5GZmGz4HZNamciboWUm3SHpG0oOSPpHPVUp6UlK1pHskbZfLH5P0XUmPA98GhgNX52zSXrnpL0maIWmRpKG53v2SBuTjuZLG5+PLJX0tH4+TNDP3eWku21rSfZKelrRA0khJ3wK6AY9KejRfN0zSHyXNkfRLSV1y+RJJ4yU9kcf1mKTv1R1fYyJiDTAD2C23d4qkGwpzeK+kQyR1lDQxj3G+pHPy+W/lLFu1pEl125B0jKSn8pz8TtIuLf0c16yBs86C3/4WFi6EX/wifa1r5EiYNy+9vva1mvKTT4Zx4+DZZ2HGDNh5Z6iuTueqq2H6dFi+HJYuTeePPbalIzQz27g5ILP2sDdwY0T0A/4OfDGX3wF8OyIGAPOB7xTqdI2IgyPiCmAqMC4iKiPixXy+IiL2J2WxSvV+DwyV9ElgNfC5XH4gMF3SsDyW/YFKYLCkg4CjgNcjYr+I2BeYFhHXAa8Dh0bEoZJ2BC4CDo+IQcAs4P8Vxvt+RBwYEZPWMb4GSdoK+Awwbd3TSCWwW0TsGxH9gZ/m8vOAgXkev9FAvSeAAyJiIDAJ+Pcm+qlnxoyUGdtzT9hiCzjppOZnsRYuhNWr4Ygj0vsuXaBzZ+jUCd57D9auhQ8/hI4dYfx4uOyylo7OzGzj54DM2sPiiJiXj2cDPSVtSwq6Hs/ltwMHFerc1USbvy62l4+n5zYOBO4DuuQ9YD0j4jlgWH7NBeYA+5ACtPnA4TmrNTQiljfQ3wGkZdM/SJoHjAY+tY7xNjS+uvbKbf0NeDkiqpu455eAPSVdL+ko4O1cXg38XNJXSIFoXd2BByTNB8YB/epeIOkMSbMkzWLFinoNvPYa9OhRaLB7Kqtr8uS0BDliBLzySipbtAi6doUTToCBA1OmbM2atHy5++4waBCceCK88AJEpGvMzDY3DsisPXxQOF4DVDSjzrvNbLPY3kygChhKypbNBU4nBUUAAq7MmbbKiOgVEbdFxCJgMCkwu7K01FmHgIcKdftGxGnrGG9D46urtIesF3CApOG5fDW1/9vcCiAilgH7AY8BZwGlXVr/BNyY72F23ndXdD1wQ86qfb3UXlFE3BwRVRFRRZcu9QYaUX/wUu33xxyT9oZVV8Phh8Po0flmVqclyQkTYOZMeOmltN8M4Jpr0vLmuefCxRen7NgVV6QA7ZZb6vdpZrapckBmZZGzUMsK+6u+CjzeyOXvANs0o80PgVdIm+OfJGXMxuavAA8Apxb2fu0maWdJ3YCVEfEzYAIwqIF+nwQ+J6lXrttZ0qebe79NjHspadnx/Fy0BKiU1EFSD9ISK3nZtENETAYuBgZJ6gD0iIhHSUuRXYG6EdW2QCmfNXp9xti9e03GC+DVV6Fbt9rX7LADbLllOj79dJg9u6buwIFpubOiAo47DubMqV13yhSoqoJ334UFC+Duu+HOO2HlyvUZrZnZxqc5mQqztjIauCkvK74E/Esj100Cbskb7Uc00eZ04LCIWClpOmm5bjpARDwoqQ/wR6X0zgrgK6QM1dWS1gKrgDNzWzcDv5W0NO8jOwX4haQcdnARsKilN92I3wCX5AD1CWAxKWO3gLS8CmnT/09zEAYpgOsI/CwvAQv4QUT8XbXTV5cAv5T0Gimw3KOlgxsyBJ5/HhYvht12g0mT4L//u/Y1S5fCrrum46lT05Jkqe6yZfDmm+kpzEceScFXyapVcO21cO+9qY/S0Et7yzq3yR8eMTPbsCgaWosws82WevQIxowBIMaO/aj8/vvh7LPT/q9TT4ULL0yb8KuqYPhwOP/8FIhVVMD228OPfgT77JPqPvRQWpaMgMGD4eab08MBkJYtt9suLXFGwD//c8qSHX00fO977X33ZmbrR9LsiFjvv5HpgMzMamksIDMzs8Z93IDMe8jMzMzMysx7yMyslsG77MIsZ8bMzNqVM2RmZmZmZeaAzMzMzKzMHJCZmZmZlZkDMjMzM7Myc0BmZmZmVmYOyMzMzMzKzAGZmZmZWZk5IDMzMzMrMwdkZmZmZmXmgMzMzMyszByQmZmZmZWZAzIzMzOzMnNAZmZmZlZmDsjMzMzMyswBmZnVMvuNN9CECWjChHIPxcxss+GAzMzMzKzMHJCZmZmZlZkDMjMzM7Myc0C2EZDUU9KCOmWXSBrbwnZOkXRDI+Uh6bBC2fG5bMT6j7zBMXSV9K/NvPZ/W7Pv9SXpVkl9W7nNQyQtlzRX0p8kteqGLUnfkHRya7Y5bRr07g29esFVV9U/P3Ei7LQTVFam16231px7+WUYNgz69IG+fWHJklQ+ahQMGAAXXFBz7eWXw5QprTlyM7MNX0W5B2CtS1JFRKxej6rzgS8DD+f3JwFPt9rAanQF/hX4YVMXRsQ/tGbH6zs3EfG11hxHwfSI+IKkTwBzJd0TEX9ojYYj4qbWaKdkzRo46yx46CHo3h2GDIHhw1NwVTRyJNxQL+SHk0+GCy+EI46AFSugQweork7nqqth6FBYvhxWroQZM+Dii1tz9GZmGz5nyDYBkh6T9F1JjwNjJB0j6amcffmdpF2a0cx0YH9JnSR1AXoB8wp9HJbbmy/pJ5K2zOVLJO2Yj6skPZaPL8nXPSbpJUnfyk1dBewlaZ6kqyV1kfSwpDm57WMLfa7IXw/J7fwqZ5N+Lkn53FWSFkqqbijLlMdxs6QHgTskdcz9zsx1vp6v6yDph5KekXSvpPtL2cHcd1U+/nIe5wJJ3yuOVdIVkp6W9GQz5xyAiHgvz/VuhTF/lP3MffWUtLWk+3IfCySNbGwOim1IOj3f79OSJkvq3NyxlcyYkTJje+4JW2wBJ53U/CzWwoWwenUKxgC6dIHOnaFTJ3jvPVi7Fj78EDp2hPHj4bLLWjo6M7ONnwOyTUfXiDg4Ir4PPAEcEBEDgUnAvzejfgC/A44EjgWmlk5I2gqYCIyMiP6kzOqZzWhzn9ze/sB3JHUCzgNejIjKiBgHvA8cHxGDgEOB75eCrToGAmcDfYE9gc9J2h44HugXEQOA/2hkHIOBYyPin4HTgOURMQQYApwuaQ/gBKAn0B/4GvDZuo1I6gZ8D/g8UAkMkXRcPr018GRE7Af8Hji9GfNTanc7YO9cb12OAl6PiP0iYl9gWjPn4NcRMSSP7dk8By3y2mvQo0fN++7dU1ldkyenJcgRI+CVV1LZokXQtSuccAIMHAjjxqWMW58+sPvuMGgQnHgivPACRKRrzMw2Nw7INg7RjPK7CsfdgQckzQfGAf2a2c8k0lLlScAvCuW9gcURsSi/vx04qBnt3RcRH0TEW8BfgYayRgK+K6maFBDu1sh1MyLi1YhYS8om9QTeJgV0t0o6AVjZyDim5iwUwDDgZEnzgKeAHUjB0IHALyNibUT8BXi0gXaGAI9FxJt56fPn1MzDh8C9+Xh2Hl9Thub7/gtwb+53XeYDh0v6nqShEbGc5s3BvpKm5++HUTTw/SDpDEmzJM1ixYp6DUQD34F1w+Zjjkl7w6qr4fDDYfToVL56NUyfDhMmwMyZ8NJLab8ZwDXXwLx5cO65aZnyssvgiitSgHbLLU3MhpnZJsQB2cbhb8B2dcq2B94qvH+3cHw9cEPOZn0d2Ko5nUTEDGBfYMdC8AUpaGrMamq+j+r280HheA0N71kcBewEDI6ISuCNRsZbr60cFO0PTAaOA6Y1Msbi3Aj4Zs7QVUbEHhHxIOu+x2LdxqyK+Chsaexe65qes1r9gTMlVeby4pxCno/8mQwmBWZXShrfzDmYCPxb/n64lAbmNyJujoiqiKiiS5d6DXTvXpPxAnj1VejWrfY1O+wAW26Zjk8/HWbPrqk7cGBa7qyogOOOgzlzatedMgWqquDdd2HBArj7brjzzrSnzMxsc+CAbCMQESuApcpPQeZlqqNIS5MN2RYoLSiNbmF35wMX1Cn7E9BTUq/8/qvA4/l4CSlIAPhiM9p/B9imzlj/GhGrJB0KfKq5A8173baNiPtJy5mVTVQBeIAU/HTKbXxa0takufxi3ku2C3BIA3WfAg6WtKOkjqSHIB5v4LriGI+XdOW6rsmB1pXAt3PREmBQrj8I2CMfdwNWRsTPgAnAoGbOwTak759OpAC4xYYMgeefh8WL036vSZPSpv6ipUtrjqdOTUuSpbrLlsGbb6b3jzxS+2GAVavg2mvTUubKlTWZt9LeMjOzzYGfstx4nAzcKOn7+f2lEfFiI9deAvxS0mvAk+Rf6M0REb9toOx9Sf+S26wAZgKlp/guBW6TdAEpYGmq/b9J+oPSn/H4LWlP1v9ImkVaivxTc8dKCjSm5D1uAs5pRp1bScuJc/JetTdJmaXJwGHAAmBRvpfldca+VNL5pOVMAfdHRFNb2/ciLSs25SZgbN7PNpmaZdWZeTyQMmlXS1oLrCLt42vOHFyc7+fPpOzaNg1cs04VFenpySOPTPu/Tj0V+vVLm/CrqlJwdt11KRCrqIDtt69ZluzYMS1XHnZYWvocPDhl0EpuvDEtb3bunPafRUD//nD00WnvmZnZ5kDR0OYQs82QpC4RsULSDsAM4HPN2NfVVJs/A86JiDdbZZDtQD16BGPGABBjW/Sn7szMNluSZkdE1frWd4bMrMa9kroCWwCXf9xgDCAivvLxh2VmZps6B2RmWUQcUu4xmJnZ5skBmZnVMniXXZjlpUozs3blpyzNzMzMyswBmZmZmVmZOSAzMzMzKzMHZGZmZmZl5oDMzMzMrMwckJmZmZmVmQMyMzMzszJzQGZmZmZWZg7IzMzMzMrMAZmZmZlZmTkgMzMzMyszB2RmZmZmZeaAzMzMzKzMHJCZmZmZlZkDMjOrZfYbb6AJE9CECeUeipnZZsMBmZmZmVmZOSAzMzMzKzMHZGZmZmZltkkEZJJ6SHpU0rOSnpE0pnDuS7lsraSqQvkpkm5opf4fk/ScpHn59atcfomklZJ2Lly7onC8plBnnqTzWtDnPrnOXEl71Tl3qqT5kqolLZB0bGvcZ50+viHp5Hx8maTD8/ESSTu2dn/tIY99cuH9CEkTW9hGpaSjC+8vkTS2mX3vmI8HS1osaWBL+m5r06ZB797QqxdcdVX98xMnwk47QWVlet16a825l1+GYcOgTx/o2xeWLEnlo0bBgAFwwQU1115+OUyZ0pZ3Yma24ako9wBayWrg3IiYI2kbYLakhyJiIbAAOAH4cRuPYVREzGqg/C3gXODbDZx7LyIq17O/44ApEfGdYqGk7sCFwKCIWC6pC7DTevbRqIi4qXA8vrXbL6MqSf0i4pmWVpRUAVQCVcD969O5pAHAr4CRETG3uf1GxOr16a+51qyBs86Chx6C7t1hyBAYPjwFV0UjR8INDfxvzsknw4UXwhFHwIoV0KEDVFenc9XVMHQoLF8OK1fCjBlw8cVteTdmZhueTSJDFhFLI2JOPn4HeBbYLb9/NiKeW1d9Sf8k6Y+SdpQ0UdKPcsbtJUkHS/pJzr5NXI/h/QQYKWn79ahbyrg8mbNd90jaLmdgzga+JunROlV2Bt4BVgBExIqIWJzbeqyUJcz3uiQfnyLp15KmSXpe0n8W+j9N0qJc95ZSVrGY+clzNqIwhnGSZuRXr3zNMZKeyhm930naJZdvned3Zj53bC7vLOnufN935bqlsf9I0qyc+by0MNYlkr6bP8tZkgZJekDSi5K+0cwpnwBcULdQ0vaSfpPH82QOnErzcLOkB4E7gMtIn/c8SSNz9b55/l6S9K119N0H+A3w1YiY0ZJ+JXWUdHWex2pJX8/XdZH0sKQ5OWu6XtnSGTNSZmzPPWGLLeCkk5qfxVq4EFavTsEYQJcu0LkzdOoE770Ha9fChx9Cx44wfjxcdtn6jNDMbOO2SQRkRZJ6AgOBp5p5/fHAecDREfFWLt4O+DxwDvA/wA+AfkB/SY1ltH6umqXHqwvlK0hB2ZgG6nxCtZcsRzZwzR3AtyNiADAf+E5E3A/cBPwgIg6tc/3TwBvAYkk/lXRME1NQUgmMBPqTAooekroBFwMHAEcA+zSzrbcjYn/gBuCaXPYEcEBEDAQmAf+eyy8EHomIIcChwNWStgb+FViW7/tyYHCh/QsjogoYABxcClKyVyLis8B0YCIwIo+/ub/m7wYGlQLJgkuBuXk8F5A+l5LBwLER8c/AeOCunSW7QwAAEapJREFUiKiMiLvy+X2AI4H9ge9I6tRI31OAf4uIJ9aj39OA5XkehwCnS9oDeB84PiIGkeb3+5JUt2NJZ+QgdhYrVtQ9zWuvQY8eNe+7d09ldU2enJYgR4yAV15JZYsWQdeucMIJMHAgjBuXMm59+sDuu8OgQXDiifDCCxCRrjEz29xsKkuWQMoGAJOBsyPi7WZUOZS0vDSszvX/ExEhaT7wRkTMz+0/A/QE5jXQVmNLlgDXAfMkfb9O+TqXLCVtC3SNiMdz0e3AL9d1QxGxRtJRpF/KhwE/kDQ4Ii5ZVz3g4YhYnvtdCHwK2BF4PCL+L5f/Evh0E+0A/KLw9Qf5uDtwl6RdgS2Axbl8GDBcNfustgJ2Bw4Ers33tEBSdaH9EyWdQfr+3RXoC5TOT81f5wNdcsb0HUnvS+oaEX9vYuxrgKuB84HfFsoPBL6Yx/OIpB3y5wMwNSLeW0eb90XEB8AHkv4K7AK82sB1vyNlPR+IiDUt7HcYMKCQqdwW2Dv3811JBwFrSZnjXYC/FDuOiJuBmwHUo0fUHVjUK4G6Yd0xx8CXvwxbbgk33QSjR8Mjj6Ts2PTpMHduCsBGjkz7zU47Da65pnb9H/8YrrgCnn46ZdROP72BWTIz2wRtMhmynHWYDPw8In7dzGovAdtQP8j4IH9dWzguvW9xEJuDgP8mZX3aXCQzIuJK4CTyL3TSXrvSZ75VnWrF+1xDus96mZTmDqGB4+uBGyKiP/D1Qv8CvpgzSpURsXtEPNtY3znrMxY4LGeN7qtzL63x2d0JHEQKDD/quoHrSvf2bhPtNTS3Dfm3/PWH69GvgG8W5nGPiHgQGEXaQzg4B/9vUP+zb1L37jUZL4BXX4Vu3Wpfs8MOKRiDFEjNnl1Td+DAtNxZUQHHHQdz5tSuO2UKVFXBu+/CggVw991w551pT5mZ2eZgkwjI8hLMbcCzEfFfLaj6Z9KG/zsk9WuTwdX4L1Ig0uyALmeslkkamou+Cjy+jipI6iZpUKGoknSfAEuoWfor7vlqzAzSkuB2ShvWv9hUhWxk4esf8/G2QGmRa3Th2geAb5aW0VTzZOETwIm5rC9pKRXgk6RAZHneh/aPzRzTRyT9aV3nI2IVKbN3dqH496TgBkmHAG81koV9hxTkr4+1wJeB3pJKS6zN7fcB4MzScqikT+el322Bv0bEKkmHkjKfLTZkCDz/PCxenPZ7TZqUNvUXLV1aczx1alqSLNVdtgzefDO9f+SR2g8DrFoF116bljJXrqzJvJX2lpmZbQ42lSXLz5GClfmSSsuJF0TE/XmP2PWkLMF9kuZFxJGlihHxnKRRwC9bsN+qIT+XVFo+eisiDi+ejIi3JN1D2pdW8onCeAGmRUTdP30xGrhJUmdSRu9fmhhHJ2BC3v/1PvAmUNrQPgG4W9JXgUeauqGIeE3Sd0n78V4HFgLLi5c0UnVLSU+RAv4v57JLSHP8GvAksEcuv5y0z6w6B2VLgC+QskS356XKuaQlyeUR8bykucAzpPn4Q1P3UaT0pyWak/m7Dbio8P4S4Kd5PCupHVQWPQqclz/XK1syNoCI+CBvvH9c0hst6PdW0nL6nDyPb5KexP058D+SZpGW2tcZjDamoiI9PXnkkWn/16mnQr9+aRN+VVUKzq67LgViFRWw/fZpWRLSZv0JE+Cww9LS5+DBtZcib7wxLW927pz2n0VA//5w9NFp75mZ2eZA0dDmELNMUpeIWJEzZPcAP4mIeyRdD8yJiJ+2Ub8dgU4R8b7S31l7GPh0RHysnImkLwB7RsR1rTHOTZF69AjGpGdQYmyTf0LNzMwASbPzA2frZVPJkFnbuUTpj75uBTwI/EbS5cBnSNmbttIZeDQvwQk48+MGYwARce/HHpmZmVkrc0Bm6xQRDaVILs6vtuz3HdITsGZmZps8B2RmVsvgXXZhlpcqzcza1SbxlKWZmZnZxswBmZmZmVmZOSAzMzMzKzMHZGZmZmZl5oDMzMzMrMwckJmZmZmVmf9Sv5nVIukd4Lkydb8j8NZm1G85+97c+i1n377nzaPv3hGxvv+Wsf8OmZnV89zH+ec/Pg5Js8rRd7n6LWffm1u/5ezb97x59J3/zeD15iVLMzMzszJzQGZmZmZWZg7IzKyumzfDvn3Pm36/5ezb97x59P2x+vWmfjMzM7Myc4bMzMzMrMwckJnZRyQdJek5SS9IOq+N+/qJpL9KWlAo217SQ5Kez1+3a4N+e0h6VNKzkp6RNKY9+pa0laQZkp7O/V6ay/eQ9FTu9y5JW7Rmv4X+O0qaK+nedu53iaT5kuaVnkJrp8+5q6RfSfpT/qw/20799s73Wnq9Lensdur7nPy9tUDSL/L3XHt9zmNyv89IOjuXtfo9t+TnhpLr8s+zakmD2qDvL+V7Xiupqs715+e+n5N0ZFPtOyAzMyD9wgZuBP4R6At8WVLfNuxyInBUnbLzgIcjYm/g4fy+ta0Gzo2IPsABwFn5Ptu67w+Az0fEfkAlcJSkA4DvAT/I/S4DTmvlfkvGAM8W3rdXvwCHRkRl4U8RtMfnfC0wLSL2AfYj3Xub9xsRz+V7rQQGAyuBe9q6b0m7Ad8CqiJiX6AjcBLt8DlL2hc4HdifNNdfkLQ3bXPPE2n+z41/BPbOrzOAH7VB3wuAE4DfFwvzz5STgH65zg/zz9jGRYRffvnlF8BngQcK788Hzm/jPnsCCwrvnwN2zce7kv4mWlvf9xTgiPbsG+gMzAE+Q/oDlhUNfQat2F930i+qzwP3AmqPfnPbS4Ad65S16VwDnwQWk/dJl+v7CxgG/KGd7nk34BVge9LfGL0XOLKdvr++BNxaeH8x8O9tdc/N/bkB/Bj4ckPXtVbfhfLHSMFw6X2tn5/AA8Bn19W2M2RmVlL6gV7yai5rT7tExFKA/HXntuxMUk9gIPBUe/Sdlw3nAX8FHgJeBP4eEavzJW0159eQfkGuze93aKd+AQJ4UNJsSWfksrae6z2BN4Gf5mXaWyVt3Q791nUS8It83KZ9R8RrwATgZWApsByYTft8zguAgyTtIKkzcDTQg/ab78b6KefPtBb37YDMzErUQNkm+xi2pC7AZODsiHi7PfqMiDWRlrK6k5Z3+jR0WWv2KekLwF8jYnaxuK37LfhcRAwiLR+dJemgNuqnqAIYBPwoIgYC79I2y6KNynu1hgO/bKf+tgOOBfYAugFbk+a8rlb/nCPiWdLS6EPANOBp0taAcivnz7QW9+2AzMxKXiX9X21Jd+D1dh7DG5J2Bchf/9oWnUjqRArGfh4Rv27PvgEi4u+kJY4DgK6SSv+MXVvM+eeA4ZKWAJNIy5bXtEO/AETE6/nrX0l7qfan7ef6VeDViHgqv/8VKUBrt8+YFAzNiYg38vu27vtwYHFEvBkRq4BfA/9A+33Ot0XEoIg4CPg/4Hnab74b66ecP9Na3LcDMjMrmQnsnZ/K2oK03DK1nccwFRidj0eT9ne1KkkCbgOejYj/aq++Je0kqWs+/gTpF+izwKPAiLbqNyLOj4juEdGT9Jk+EhGj2rpfAElbS9qmdEzaU7WANp7riPgL8Iqk3rnoMGBhW/dbx5epWa6kHfp+GThAUuf8PV665zb/nAEk7Zy/7k7a5P4L2m++G+tnKnByftryAGB5aWmzHUwFTpK0paQ9SA8WzFhnjdbe3OeXX35tvC/S3o9FpL1NF7ZxX78g7XVZRfq/ydNIe5seJv3f9cPA9m3Q74GkpYNqYF5+Hd3WfQMDgLm53wXA+Fy+Z/5B/QJpeWvLNpzzQ4B726vf3MfT+fVM6XuqnT7nSmBWnu/fANu1R7+5787A34BtC2Xtcc+XAn/K3193Alu21/cXMJ0UAD4NHNZW99ySnxukZcMb88+z+RQ23bdi38fn4w+AN6j9YNSFue/ngH9sqn3/pX4zMzOzMvOSpZmZmVmZOSAzMzMzKzMHZGZmZmZl5oDMzMzMrMwckJmZmZmVmQMyMzMzszJzQGZmZmZWZg7IzMzMzMrMAZmZmZlZmTkgMzMzMyszB2RmZmZmZeaAzMzMzKzMHJCZmZmZlZkDMjMzM7Myc0BmZmZmVmYOyMzMzMzKzAGZmZmZWZk5IDMzKzNJIen7hfdjJV3SSm1PlDSiNdrK7W0r6Q5JL+bXHZK2LZy/WtIzkq6uU+8USWslDSiULZDUcz3HsWJ976GJdidKWixpXn7973q2c4ike9ejXjdJv1qfPm3j5oDMzKz8PgBOkLRjuQdSJKljA8W3AS9FxF4RsRewGLi1cP7rwKCIGNdA3VeBC1t/pOuvkXscFxGV+fUP7TmeiHg9IlotgLaNhwMyM7PyWw3cDJxT90TdDFcpM5QzMI9LulvSIklXSRolaYak+ZL2KjRzuKTp+bov5PodczZrpqRqSV8vtPuopP8G5tcZSy9gMHB5ofgyoErSXpKmAlsDT0ka2cB93gv0k9S7gftcUTgeIWliPt5F0j2Sns6vegGSpHGF+7i0UP4bSbNzxu6MYl+SLpP0FPDZBsZZj6TrJI3Px0dK+r2kDvnzuanu/Napu30eS7WkJ0tZQkkHFzJxcyVtI6mnpAX5/FaSfpo/z7mSDs3lp0j6taRpkp6X9J+5vGMez4Jcp973k224Kso9ADMzA+BGoLr0y7WZ9gP6AP8HvATcGhH7SxoDfBM4O1/XEzgY2At4NAdWJwPLI2KIpC2BP0h6MF+/P7BvRCyu019fYF5ErCkVRMQaSfOAfhExXNKKiKhsZLxrgf8ELgBGN/MerwMej4jjczarS/GkpGHA3nnMAqZKOigifg+cGhH/J+kTwExJkyPib6SgcUFEjG+kz6slXZSPn4mIUcB5uY3peUxHR8RaSdDw/BZdCsyNiOMkfR64A6gExgJnRcQfJHUB3q9T7yyAiOgvaR/gQUmfzucqgYGk7Opzkq4HdgZ2i4h989x0Xce82gbGGTIzsw1ARLxN+kX9rRZUmxkRSyPiA+BFoBRQzScFCSV3R8TaiHieFLjtAwwDTs7B1FPADqTABmBGA8EYpIAnWlDekP8GDpC0RzOv/zzwI0jBX0Qsr3N+WH7NBeaQ7q10H9+S9DTwJNCjUL4GmLyOPotLlqNy3yuB04GHgBsi4sXC9Q3Nb9GBwJ25nUeAHZT23f0B+C9J3wK6RsTqddT7E/BnoBSQPRwRyyPifWAh8Knc956Srpd0FPD2Ou7RNjDOkJmZbTiuIQUVPy2UrSb/z7NSOmaLwrkPCsdrC+/XUvvne91gKUhB1Dcj4oHiCUmHAO82Mr5ngIGSOkTE2nx9B1Km7tl13dhHHUesVnqA4dsNjKlkq+a0VRoycGVE/LhWYbqPw4HPRsRKSY8V2n2/mOVrgf7A34Budcobmt+6Y6wrIuIqSfcBRwNPSjqc2lmyhuqVFD/7NUBFRCyTtB9wJCm7diJw6jrasA2IM2RmZhuIiPg/4G7gtELxEtK+LYBjgU7r0fSX8n6nvYA9geeAB4AzJXUCkPRpSVs3Mb4XSJmoiwrFFwFz8rnmmkgKlnYqlL0hqU8O8I4vlD8MnJnH2FHSJ+u09QBwal7yQ9JuknYGtgWW5WBsH+CAFoyvHkmfAs4lLRP+o6TPFE43NL9FvwdG5XYOAd6KiLcl7RUR8yPie8As6mfWivU+DezeQNvFMe4IdIiIycDFwKD1ulkrCwdkZmYblu8DxactbwEOljQD+AyNZ6/W5TngceC3wDfyMtetpKWuOXkT+Y9p3qrJacCnJb0g6UXSEtppTdSpJSI+JO3D2rlQfB5p0/8jwNJC+RjgUEnzgdlAvzptPUhaBv1jvuZXwDbANKBCUjXpIYQnWzDEqwub7eflPXa3AWMj4nXS/d4qqZRxa2h+iy4hPfhQDVxFzf65s/MG/KeB93L9oh8CHfN93QWckpenG7Mb8Fhehp4InN+Ce7YyU0Rzl/3NzMysSOlp0Hsjwn87zD4WZ8jMzMzMyswZMjMzM7Myc4bMzMzMrMwckJmZmZmVmQMyMzMzszJzQGZmZmZWZg7IzMzMzMrMAZmZmZlZmf1/Djq42b4P6yoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = complete_nuke_place_count_df.plot.barh(x='Place', title='Number of Nuclear Explosions By Place\\n', \n",
" figsize=(6,6), color=['darkcyan'],\n",
" legend=False)\n",
"\n",
"# create a list to collect the plt.patches data\n",
"totals = []\n",
"\n",
"# find the values and append to list\n",
"for i in ax.patches:\n",
" totals.append(i.get_width())\n",
"\n",
"# set individual bar lables using above list\n",
"total = sum(totals)\n",
"\n",
"# set individual bar lables using above list\n",
"for i in ax.patches:\n",
" # get_width pulls left or right; get_y pushes up or down\n",
" ax.text(i.get_width()+.3, i.get_y()+0.1, \\\n",
" str(round((i.get_width()/total)*100, 2))+'%', fontsize=10,\n",
"color='b')\n",
"\n",
"\n",
"\n",
"plt.xticks(np.arange(0, 120, step=10))\n",
"plt.xlabel('\\nNumber Of Nuclear Explosions')\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[View Full Report](https://www.sciencetony.com/2019/07/nuclear-test-map-for-1969-to-2017.html)<br>\n",
"[Sciencetony.com](https://www.sciencetony.com/)<br>\n",
"[View My LinkedIn CV](https://www.linkedin.com/in/tony-mcdonald-sciencetony/)<br>\n",
"[My Twitter](https://twitter.com/ScienceTony)<br>\n",
"[My YouTube](https://www.youtube.com/Dreamazium)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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