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Assignment09-PartA-PandasSQL-Answer-Fall2017
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Assignment09-PartA-PandasSQL-Answer-Fall2017.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part A"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shivraj Kanungo, Programming for Analytics (DNSC 6211)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import warnings\n",
"warnings.filterwarnings(action='once')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import the required datatime package"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime\n",
"from datetime import timedelta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the following files:\n",
"\n",
"- airlines.csv\n",
"- airports.csv\n",
"- flights.csv\n",
"- planes.csv\n",
"- weather.csv"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"airlines = pd.read_csv('airlines.csv')\n",
"airports = pd.read_csv('airports.csv')\n",
"flights = pd.read_csv('flights.csv')\n",
"planes = pd.read_csv('planes.csv')\n",
"weather = pd.read_csv('weather.csv')"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>carrier</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>9E</td>\n",
" <td>Endeavor Air Inc.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>AA</td>\n",
" <td>American Airlines Inc.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>AS</td>\n",
" <td>Alaska Airlines Inc.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>B6</td>\n",
" <td>JetBlue Airways</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>DL</td>\n",
" <td>Delta Air Lines Inc.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carrier name\n",
"0 1 9E Endeavor Air Inc.\n",
"1 2 AA American Airlines Inc.\n",
"2 3 AS Alaska Airlines Inc.\n",
"3 4 B6 JetBlue Airways\n",
"4 5 DL Delta Air Lines Inc."
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"airlines.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Answer to Part A (i)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The airlines files has 16 rows\n",
"The airports files has 1458 rows\n",
"The flights files has 336776 rows\n",
"The planes files has 3322 rows\n",
"The weather files has 26130 rows\n"
]
}
],
"source": [
"print(\"The airlines files has {:d} rows\".format(airlines.shape[0]))\n",
"print(\"The airports files has {:d} rows\".format(airports.shape[0]))\n",
"print(\"The flights files has {:d} rows\".format(flights.shape[0]))\n",
"print(\"The planes files has {:d} rows\".format(planes.shape[0]))\n",
"print(\"The weather files has {:d} rows\".format(weather.shape[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## For the answer to Part A (ii). I will use the following steps:\n",
"\n",
"- I want to create file with arrival times and destinations *only*\n",
"- To do that I will perform a series of ata manipulations shown below\n",
"- The essential idea is to get data in form that can be ingested by the datetime function\n",
"- Once I have the data, the rest is easy\n",
" - identify the five busiest airportw based on the number of arrivals per day\n",
" - create five separate datasets for each of the five airports\n",
"- Finally, provide the answers in the format requested"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Start by investigating the relevant dataset"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>dep_time</th>\n",
" <th>sched_dep_time</th>\n",
" <th>dep_delay</th>\n",
" <th>arr_time</th>\n",
" <th>sched_arr_time</th>\n",
" <th>arr_delay</th>\n",
" <th>carrier</th>\n",
" <th>flight</th>\n",
" <th>tailnum</th>\n",
" <th>origin</th>\n",
" <th>dest</th>\n",
" <th>air_time</th>\n",
" <th>distance</th>\n",
" <th>hour</th>\n",
" <th>minute</th>\n",
" <th>time_hour</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>517.0</td>\n",
" <td>515</td>\n",
" <td>2.0</td>\n",
" <td>830.0</td>\n",
" <td>819</td>\n",
" <td>11.0</td>\n",
" <td>UA</td>\n",
" <td>1545</td>\n",
" <td>N14228</td>\n",
" <td>EWR</td>\n",
" <td>IAH</td>\n",
" <td>227.0</td>\n",
" <td>1400</td>\n",
" <td>5</td>\n",
" <td>15</td>\n",
" <td>2013-01-01 05:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>533.0</td>\n",
" <td>529</td>\n",
" <td>4.0</td>\n",
" <td>850.0</td>\n",
" <td>830</td>\n",
" <td>20.0</td>\n",
" <td>UA</td>\n",
" <td>1714</td>\n",
" <td>N24211</td>\n",
" <td>LGA</td>\n",
" <td>IAH</td>\n",
" <td>227.0</td>\n",
" <td>1416</td>\n",
" <td>5</td>\n",
" <td>29</td>\n",
" <td>2013-01-01 05:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>542.0</td>\n",
" <td>540</td>\n",
" <td>2.0</td>\n",
" <td>923.0</td>\n",
" <td>850</td>\n",
" <td>33.0</td>\n",
" <td>AA</td>\n",
" <td>1141</td>\n",
" <td>N619AA</td>\n",
" <td>JFK</td>\n",
" <td>MIA</td>\n",
" <td>160.0</td>\n",
" <td>1089</td>\n",
" <td>5</td>\n",
" <td>40</td>\n",
" <td>2013-01-01 05:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>544.0</td>\n",
" <td>545</td>\n",
" <td>-1.0</td>\n",
" <td>1004.0</td>\n",
" <td>1022</td>\n",
" <td>-18.0</td>\n",
" <td>B6</td>\n",
" <td>725</td>\n",
" <td>N804JB</td>\n",
" <td>JFK</td>\n",
" <td>BQN</td>\n",
" <td>183.0</td>\n",
" <td>1576</td>\n",
" <td>5</td>\n",
" <td>45</td>\n",
" <td>2013-01-01 05:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>554.0</td>\n",
" <td>600</td>\n",
" <td>-6.0</td>\n",
" <td>812.0</td>\n",
" <td>837</td>\n",
" <td>-25.0</td>\n",
" <td>DL</td>\n",
" <td>461</td>\n",
" <td>N668DN</td>\n",
" <td>LGA</td>\n",
" <td>ATL</td>\n",
" <td>116.0</td>\n",
" <td>762</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>2013-01-01 06:00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 year month day dep_time sched_dep_time dep_delay \\\n",
"0 1 2013 1 1 517.0 515 2.0 \n",
"1 2 2013 1 1 533.0 529 4.0 \n",
"2 3 2013 1 1 542.0 540 2.0 \n",
"3 4 2013 1 1 544.0 545 -1.0 \n",
"4 5 2013 1 1 554.0 600 -6.0 \n",
"\n",
" arr_time sched_arr_time arr_delay carrier flight tailnum origin dest \\\n",
"0 830.0 819 11.0 UA 1545 N14228 EWR IAH \n",
"1 850.0 830 20.0 UA 1714 N24211 LGA IAH \n",
"2 923.0 850 33.0 AA 1141 N619AA JFK MIA \n",
"3 1004.0 1022 -18.0 B6 725 N804JB JFK BQN \n",
"4 812.0 837 -25.0 DL 461 N668DN LGA ATL \n",
"\n",
" air_time distance hour minute time_hour \n",
"0 227.0 1400 5 15 2013-01-01 05:00:00 \n",
"1 227.0 1416 5 29 2013-01-01 05:00:00 \n",
"2 160.0 1089 5 40 2013-01-01 05:00:00 \n",
"3 183.0 1576 5 45 2013-01-01 05:00:00 \n",
"4 116.0 762 6 0 2013-01-01 06:00:00 "
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Keep the required fields only"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>830.0</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>850.0</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>923.0</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1004.0</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>812.0</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest\n",
"0 2013 1 1 830.0 IAH\n",
"1 2013 1 1 850.0 IAH\n",
"2 2013 1 1 923.0 MIA\n",
"3 2013 1 1 1004.0 BQN\n",
"4 2013 1 1 812.0 ATL"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights = flights[['year', 'month', 'day', 'arr_time', 'dest']]\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Identify if there are any missing values or NaNs in the dataset"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"year 0\n",
"month 0\n",
"day 0\n",
"arr_time 8713\n",
"dest 0\n",
"dtype: int64"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.isnull().sum() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clearly the only NaNs are in the arr_time column, So delete those rows"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"year 0\n",
"month 0\n",
"day 0\n",
"arr_time 0\n",
"dest 0\n",
"dtype: int64"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights = flights[pd.notnull(flights['arr_time'])]\n",
"flights.isnull().sum() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The next three steps are required to\n",
"- convert arr_time to int\n",
"- then to string\n",
"- then to pad the left by a zero\n",
"\n",
"to ensure that there are two numbers for hours and two numbers for minutes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Convert arr_time to int"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>830</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>850</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>923</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>812</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest\n",
"0 2013 1 1 830 IAH\n",
"1 2013 1 1 850 IAH\n",
"2 2013 1 1 923 MIA\n",
"3 2013 1 1 1004 BQN\n",
"4 2013 1 1 812 ATL"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.arr_time = flights.arr_time.astype(int)\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Convert arr_time to str"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>830</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>850</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>923</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>812</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest\n",
"0 2013 1 1 830 IAH\n",
"1 2013 1 1 850 IAH\n",
"2 2013 1 1 923 MIA\n",
"3 2013 1 1 1004 BQN\n",
"4 2013 1 1 812 ATL"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.arr_time = flights.arr_time.astype(str)\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pad arr_time on the left with zero nd ensure a 4 character string\n",
"- first two characters for hour\n",
"- last two characters for minutes"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest\n",
"0 2013 1 1 0830 IAH\n",
"1 2013 1 1 0850 IAH\n",
"2 2013 1 1 0923 MIA\n",
"3 2013 1 1 1004 BQN\n",
"4 2013 1 1 0812 ATL"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights['arr_time'] = flights['arr_time'].str.pad(4, side='left', fillchar='0')\n",
"#flights.arr_time = flights.zfill(flights.arr_time)\n",
"flights.head()"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>1</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>1</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>1</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>1</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>1</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest\n",
"0 2013 01 1 0830 IAH\n",
"1 2013 01 1 0850 IAH\n",
"2 2013 01 1 0923 MIA\n",
"3 2013 01 1 1004 BQN\n",
"4 2013 01 1 0812 ATL"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.month = flights.month.astype(str)\n",
"flights['month'] = flights['month'].str.pad(2, side='left', fillchar='0')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now ensure that month is of two characters length, pad with 0, if needed"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest\n",
"0 2013 01 01 0830 IAH\n",
"1 2013 01 01 0850 IAH\n",
"2 2013 01 01 0923 MIA\n",
"3 2013 01 01 1004 BQN\n",
"4 2013 01 01 0812 ATL"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.day = flights.day.astype(str)\n",
"flights['day'] = flights['day'].str.pad(2, side='left', fillchar='0')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now ensure that day is of two characters length, pad with 0, if needed"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" <th>hour</th>\n",
" <th>mins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" <td>09</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" <td>10</td>\n",
" <td>04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" <td>08</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest hour mins\n",
"0 2013 01 01 0830 IAH 08 30\n",
"1 2013 01 01 0850 IAH 08 50\n",
"2 2013 01 01 0923 MIA 09 23\n",
"3 2013 01 01 1004 BQN 10 04\n",
"4 2013 01 01 0812 ATL 08 12"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights['hour'] = flights.arr_time.str[:2]\n",
"flights['mins'] = flights.arr_time.str[2:]\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Concatenate year, month, and day into a separte string variable called ymd. Note that this is done in two steps"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" <th>hour</th>\n",
" <th>mins</th>\n",
" <th>ym</th>\n",
" <th>ymd</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>30</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>50</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" <td>09</td>\n",
" <td>23</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" <td>10</td>\n",
" <td>04</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" <td>08</td>\n",
" <td>12</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest hour mins ym ymd\n",
"0 2013 01 01 0830 IAH 08 30 2013-01 2013-01-01\n",
"1 2013 01 01 0850 IAH 08 50 2013-01 2013-01-01\n",
"2 2013 01 01 0923 MIA 09 23 2013-01 2013-01-01\n",
"3 2013 01 01 1004 BQN 10 04 2013-01 2013-01-01\n",
"4 2013 01 01 0812 ATL 08 12 2013-01 2013-01-01"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights['ym'] = flights.year.astype(str).str.cat(flights.month.astype(str), sep='-')\n",
"flights['ymd'] = flights.ym.astype(str).str.cat(flights.day.astype(str), sep='-')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Concatenate hour and mins into a separte string variable called hm"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" <th>hour</th>\n",
" <th>mins</th>\n",
" <th>ym</th>\n",
" <th>ymd</th>\n",
" <th>hm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>30</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>50</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" <td>09</td>\n",
" <td>23</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>09:23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" <td>10</td>\n",
" <td>04</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>10:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" <td>08</td>\n",
" <td>12</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest hour mins ym ymd hm\n",
"0 2013 01 01 0830 IAH 08 30 2013-01 2013-01-01 08:30\n",
"1 2013 01 01 0850 IAH 08 50 2013-01 2013-01-01 08:50\n",
"2 2013 01 01 0923 MIA 09 23 2013-01 2013-01-01 09:23\n",
"3 2013 01 01 1004 BQN 10 04 2013-01 2013-01-01 10:04\n",
"4 2013 01 01 0812 ATL 08 12 2013-01 2013-01-01 08:12"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights['hm'] = flights.hour.astype(str).str.cat(flights.mins.astype(str), sep=':')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now, concatenate ymd and hm into the variable called arr_date_time"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" <th>hour</th>\n",
" <th>mins</th>\n",
" <th>ym</th>\n",
" <th>ymd</th>\n",
" <th>hm</th>\n",
" <th>arr_date_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>30</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:30</td>\n",
" <td>2013-01-01 08:30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>50</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:50</td>\n",
" <td>2013-01-01 08:50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" <td>09</td>\n",
" <td>23</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>09:23</td>\n",
" <td>2013-01-01 09:23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" <td>10</td>\n",
" <td>04</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>10:04</td>\n",
" <td>2013-01-01 10:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" <td>08</td>\n",
" <td>12</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:12</td>\n",
" <td>2013-01-01 08:12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest hour mins ym ymd hm \\\n",
"0 2013 01 01 0830 IAH 08 30 2013-01 2013-01-01 08:30 \n",
"1 2013 01 01 0850 IAH 08 50 2013-01 2013-01-01 08:50 \n",
"2 2013 01 01 0923 MIA 09 23 2013-01 2013-01-01 09:23 \n",
"3 2013 01 01 1004 BQN 10 04 2013-01 2013-01-01 10:04 \n",
"4 2013 01 01 0812 ATL 08 12 2013-01 2013-01-01 08:12 \n",
"\n",
" arr_date_time \n",
"0 2013-01-01 08:30 \n",
"1 2013-01-01 08:50 \n",
"2 2013-01-01 09:23 \n",
"3 2013-01-01 10:04 \n",
"4 2013-01-01 08:12 "
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights['arr_date_time'] = flights.ymd.astype(str).str.cat(flights.hm.astype(str), sep=' ')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Finally, create a variable called arrtime by converting the arr_date_time into the date format. Make sure that you coerce the formation of this column; otherwise errors will result."
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>day</th>\n",
" <th>arr_time</th>\n",
" <th>dest</th>\n",
" <th>hour</th>\n",
" <th>mins</th>\n",
" <th>ym</th>\n",
" <th>ymd</th>\n",
" <th>hm</th>\n",
" <th>arr_date_time</th>\n",
" <th>arrtime</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0830</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>30</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:30</td>\n",
" <td>2013-01-01 08:30</td>\n",
" <td>2013-01-01 08:30:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0850</td>\n",
" <td>IAH</td>\n",
" <td>08</td>\n",
" <td>50</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:50</td>\n",
" <td>2013-01-01 08:50</td>\n",
" <td>2013-01-01 08:50:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0923</td>\n",
" <td>MIA</td>\n",
" <td>09</td>\n",
" <td>23</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>09:23</td>\n",
" <td>2013-01-01 09:23</td>\n",
" <td>2013-01-01 09:23:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>1004</td>\n",
" <td>BQN</td>\n",
" <td>10</td>\n",
" <td>04</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>10:04</td>\n",
" <td>2013-01-01 10:04</td>\n",
" <td>2013-01-01 10:04:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013</td>\n",
" <td>01</td>\n",
" <td>01</td>\n",
" <td>0812</td>\n",
" <td>ATL</td>\n",
" <td>08</td>\n",
" <td>12</td>\n",
" <td>2013-01</td>\n",
" <td>2013-01-01</td>\n",
" <td>08:12</td>\n",
" <td>2013-01-01 08:12</td>\n",
" <td>2013-01-01 08:12:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month day arr_time dest hour mins ym ymd hm \\\n",
"0 2013 01 01 0830 IAH 08 30 2013-01 2013-01-01 08:30 \n",
"1 2013 01 01 0850 IAH 08 50 2013-01 2013-01-01 08:50 \n",
"2 2013 01 01 0923 MIA 09 23 2013-01 2013-01-01 09:23 \n",
"3 2013 01 01 1004 BQN 10 04 2013-01 2013-01-01 10:04 \n",
"4 2013 01 01 0812 ATL 08 12 2013-01 2013-01-01 08:12 \n",
"\n",
" arr_date_time arrtime \n",
"0 2013-01-01 08:30 2013-01-01 08:30:00 \n",
"1 2013-01-01 08:50 2013-01-01 08:50:00 \n",
"2 2013-01-01 09:23 2013-01-01 09:23:00 \n",
"3 2013-01-01 10:04 2013-01-01 10:04:00 \n",
"4 2013-01-01 08:12 2013-01-01 08:12:00 "
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights['arrtime'] = pd.to_datetime(flights['arr_date_time'], format ='%Y-%m-%d %H:%M', errors='coerce')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now we keep only the variables that are required to answer the two remaining questions "
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>arrtime</th>\n",
" <th>dest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013-01-01 08:30:00</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013-01-01 08:50:00</td>\n",
" <td>IAH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013-01-01 09:23:00</td>\n",
" <td>MIA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013-01-01 10:04:00</td>\n",
" <td>BQN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013-01-01 08:12:00</td>\n",
" <td>ATL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" arrtime dest\n",
"0 2013-01-01 08:30:00 IAH\n",
"1 2013-01-01 08:50:00 IAH\n",
"2 2013-01-01 09:23:00 MIA\n",
"3 2013-01-01 10:04:00 BQN\n",
"4 2013-01-01 08:12:00 ATL"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights = flights[['arrtime', 'dest']]\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We identify how many malformed instances of the arrtime variable there are"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"arrtime 150\n",
"dest 0\n",
"dtype: int64"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.isnull().sum() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We delete those malformed instances to obtain a clean dataset"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"arrtime 0\n",
"dest 0\n",
"dtype: int64"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights = flights[pd.notnull(flights['arrtime'])]\n",
"flights.isnull().sum() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now we are ready to identify the busiest airports in our list based on arrivals. First step is to list the unique destination airports"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['IAH', 'MIA', 'BQN', 'ATL', 'ORD', 'FLL', 'IAD', 'MCO', 'PBI',\n",
" 'TPA', 'LAX', 'SFO', 'DFW', 'BOS', 'LAS', 'MSP', 'DTW', 'RSW',\n",
" 'SJU', 'PHX', 'BWI', 'CLT', 'BUF', 'DEN', 'SNA', 'MSY', 'SLC',\n",
" 'XNA', 'MKE', 'SEA', 'ROC', 'SYR', 'SRQ', 'RDU', 'CMH', 'JAX',\n",
" 'CHS', 'MEM', 'PIT', 'SAN', 'DCA', 'CLE', 'STL', 'MYR', 'JAC',\n",
" 'MDW', 'HNL', 'BNA', 'AUS', 'BTV', 'PHL', 'STT', 'EGE', 'AVL',\n",
" 'PWM', 'IND', 'SAV', 'CAK', 'HOU', 'LGB', 'DAY', 'ALB', 'BDL',\n",
" 'MHT', 'MSN', 'GSO', 'CVG', 'BUR', 'RIC', 'GSP', 'GRR', 'MCI',\n",
" 'ORF', 'SAT', 'SDF', 'PDX', 'SJC', 'OMA', 'CRW', 'OAK', 'SMF',\n",
" 'TUL', 'TYS', 'PVD', 'DSM', 'PSE', 'BHM', 'OKC', 'CAE', 'HDN',\n",
" 'BZN', 'MTJ', 'EYW', 'PSP', 'ACK', 'BGR', 'ABQ', 'ILM', 'MVY',\n",
" 'SBN', 'LEX', 'CHO', 'TVC', 'ANC'], dtype=object)"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights.dest.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Count the number of arrivals at airports and sort them\n",
"The top five values give you the busiest airports, w.r.t. number of arrivals per day "
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ATL 16871\n",
"ORD 16607\n",
"LAX 16047\n",
"BOS 15020\n",
"MCO 13973\n",
"dtype: int64"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(flights['dest'].values, sort=True).head(5)\n",
"# if you want to see all values, remove the head() option from this command"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create five different datasets for the top five (busiest) airports"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ATL = flights[flights['dest'] == 'ATL']\n",
"ORD = flights[flights['dest'] == 'ORD']\n",
"LAX = flights[flights['dest'] == 'LAX']\n",
"BOS = flights[flights['dest'] == 'BOS']\n",
"MCO = flights[flights['dest'] == 'MCO']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### I will show detailes steps for ATL for obtaining interarrival times in minutes\n",
"- Compute inter-arrival time for each airport file after sorting each file by date and arrival time\n",
"- Convert that time to seconds\n",
"- Convert seconds into minutes (by dividing by 60)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use the diff function to obtain interarrival time in time format"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>arrtime</th>\n",
" <th>dest</th>\n",
" <th>iat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013-01-01 08:12:00</td>\n",
" <td>ATL</td>\n",
" <td>NaT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2013-01-01 08:33:00</td>\n",
" <td>ATL</td>\n",
" <td>00:21:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2013-01-01 08:37:00</td>\n",
" <td>ATL</td>\n",
" <td>00:04:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2013-01-01 08:37:00</td>\n",
" <td>ATL</td>\n",
" <td>00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2013-01-01 09:44:00</td>\n",
" <td>ATL</td>\n",
" <td>01:07:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" arrtime dest iat\n",
"4 2013-01-01 08:12:00 ATL NaT\n",
"29 2013-01-01 08:33:00 ATL 00:21:00\n",
"18 2013-01-01 08:37:00 ATL 00:04:00\n",
"23 2013-01-01 08:37:00 ATL 00:00:00\n",
"62 2013-01-01 09:44:00 ATL 01:07:00"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iatATL = ATL.sort_values(by=['arrtime'])\n",
"iatATL['iat'] = iatATL['arrtime'].diff()\n",
"iatATL.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Convert time into seconds"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>arrtime</th>\n",
" <th>dest</th>\n",
" <th>iat</th>\n",
" <th>iat_seconds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013-01-01 08:12:00</td>\n",
" <td>ATL</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2013-01-01 08:33:00</td>\n",
" <td>ATL</td>\n",
" <td>00:21:00</td>\n",
" <td>1260.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2013-01-01 08:37:00</td>\n",
" <td>ATL</td>\n",
" <td>00:04:00</td>\n",
" <td>240.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2013-01-01 08:37:00</td>\n",
" <td>ATL</td>\n",
" <td>00:00:00</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2013-01-01 09:44:00</td>\n",
" <td>ATL</td>\n",
" <td>01:07:00</td>\n",
" <td>4020.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" arrtime dest iat iat_seconds\n",
"4 2013-01-01 08:12:00 ATL NaT NaN\n",
"29 2013-01-01 08:33:00 ATL 00:21:00 1260.0\n",
"18 2013-01-01 08:37:00 ATL 00:04:00 240.0\n",
"23 2013-01-01 08:37:00 ATL 00:00:00 0.0\n",
"62 2013-01-01 09:44:00 ATL 01:07:00 4020.0"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get arrival difference time in seconds\n",
"iatATL[\"iat_seconds\"] = iatATL['iat'].dt.total_seconds()\n",
"iatATL.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Convert seconds into minutes"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>arrtime</th>\n",
" <th>dest</th>\n",
" <th>iat</th>\n",
" <th>iat_seconds</th>\n",
" <th>iat_mins</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013-01-01 08:12:00</td>\n",
" <td>ATL</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2013-01-01 08:33:00</td>\n",
" <td>ATL</td>\n",
" <td>00:21:00</td>\n",
" <td>1260.0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2013-01-01 08:37:00</td>\n",
" <td>ATL</td>\n",
" <td>00:04:00</td>\n",
" <td>240.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2013-01-01 08:37:00</td>\n",
" <td>ATL</td>\n",
" <td>00:00:00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2013-01-01 09:44:00</td>\n",
" <td>ATL</td>\n",
" <td>01:07:00</td>\n",
" <td>4020.0</td>\n",
" <td>67.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" arrtime dest iat iat_seconds iat_mins\n",
"4 2013-01-01 08:12:00 ATL NaT NaN NaN\n",
"29 2013-01-01 08:33:00 ATL 00:21:00 1260.0 21.0\n",
"18 2013-01-01 08:37:00 ATL 00:04:00 240.0 4.0\n",
"23 2013-01-01 08:37:00 ATL 00:00:00 0.0 0.0\n",
"62 2013-01-01 09:44:00 ATL 01:07:00 4020.0 67.0"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get arrival difference time in minutes\n",
"iatATL[\"iat_mins\"] = iatATL['iat_seconds']/60\n",
"iatATL.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Obtain descriptive statistics for ATL"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>iat</th>\n",
" <td>16870</td>\n",
" <td>0 days 00:31:07.102548</td>\n",
" <td>0 days 01:14:48.856903</td>\n",
" <td>0 days 00:00:00</td>\n",
" <td>0 days 00:07:00</td>\n",
" <td>0 days 00:16:00</td>\n",
" <td>0 days 00:30:00</td>\n",
" <td>0 days 21:56:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_seconds</th>\n",
" <td>16870</td>\n",
" <td>1867.1</td>\n",
" <td>4488.86</td>\n",
" <td>0</td>\n",
" <td>420</td>\n",
" <td>960</td>\n",
" <td>1800</td>\n",
" <td>78960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_mins</th>\n",
" <td>16870</td>\n",
" <td>31.1184</td>\n",
" <td>74.8143</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>30</td>\n",
" <td>1316</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std \\\n",
"iat 16870 0 days 00:31:07.102548 0 days 01:14:48.856903 \n",
"iat_seconds 16870 1867.1 4488.86 \n",
"iat_mins 16870 31.1184 74.8143 \n",
"\n",
" min 25% 50% \\\n",
"iat 0 days 00:00:00 0 days 00:07:00 0 days 00:16:00 \n",
"iat_seconds 0 420 960 \n",
"iat_mins 0 7 16 \n",
"\n",
" 75% max \n",
"iat 0 days 00:30:00 0 days 21:56:00 \n",
"iat_seconds 1800 78960 \n",
"iat_mins 30 1316 "
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iatATL.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ATL: Mean = 31.118 and SD = 74.814\n"
]
}
],
"source": [
"print ('ATL: Mean = {0:.3f} and SD = {1:.3f}'.format(float(iatATL[['iat_mins']].mean()), \n",
" float(iatATL[['iat_mins']].std())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now to plotting using seaborn"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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HfQHxkt0Punt1VctP1Nn/pGTfy4hXKIV4gb1r3f1s4lU/z5rH8Yi0TKEvIboS\n+I1k+YvPA32zfP4udz/k7q8k9/86+e8rxL85TPdwstrkc8C65LH7ge1m9m1gj7s/PMsaROZEoS8h\neox4tL6buM2TmuXzJ2rvtLB661iy31Qv1d1vBzYTr8v+p2Z2wyxrEJkThb6EZh1wGnFL5yfAx4FM\nsq1EmxYhNLO/Awru/mfEC6upvSNt0cmrbIrU8w7wP4Gfm9kh4Emg18xWAX9LvGLim+5+xyLX8VXg\nHjMrEX/t5LWL/H4igGbviIgERSN9kWmSb036Sr1t7n5Gm8sRWVAa6YuIBEQnckVEAqLQFxEJiEJf\nRCQgCn0RkYAo9EVEAvL/AZ/SASTwMlxeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x26996421dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"iatATL = iatATL.dropna()\n",
"sns.distplot(iatATL['iat_mins']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot again using the less extreme data"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2698941c438>"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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B4dLM0R/r0gsWkEpn2Pma7s4VkdJQ6AMDQ/5Sx1XR0jbH2gs0xCMipTXlg9HNLALcC6wF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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x26989414860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"iatATLreduced = iatATL[iatATL['iat_mins'] <= 200]\n",
"sns.distplot(iatATLreduced['iat_mins'])"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>iat</th>\n",
" <td>16606</td>\n",
" <td>0 days 00:31:37.266048</td>\n",
" <td>0 days 01:14:01.643826</td>\n",
" <td>0 days 00:00:00</td>\n",
" <td>0 days 00:07:00</td>\n",
" <td>0 days 00:16:00</td>\n",
" <td>0 days 00:30:00</td>\n",
" <td>0 days 20:06:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_seconds</th>\n",
" <td>16606</td>\n",
" <td>1897.27</td>\n",
" <td>4441.64</td>\n",
" <td>0</td>\n",
" <td>420</td>\n",
" <td>960</td>\n",
" <td>1800</td>\n",
" <td>72360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_mins</th>\n",
" <td>16606</td>\n",
" <td>31.6211</td>\n",
" <td>74.0274</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>30</td>\n",
" <td>1206</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std \\\n",
"iat 16606 0 days 00:31:37.266048 0 days 01:14:01.643826 \n",
"iat_seconds 16606 1897.27 4441.64 \n",
"iat_mins 16606 31.6211 74.0274 \n",
"\n",
" min 25% 50% \\\n",
"iat 0 days 00:00:00 0 days 00:07:00 0 days 00:16:00 \n",
"iat_seconds 0 420 960 \n",
"iat_mins 0 7 16 \n",
"\n",
" 75% max \n",
"iat 0 days 00:30:00 0 days 20:06:00 \n",
"iat_seconds 1800 72360 \n",
"iat_mins 30 1206 "
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iatORD = ORD.sort_values(by=['arrtime'])\n",
"iatORD['iat'] = iatORD['arrtime'].diff()\n",
"\n",
"# Get arrival difference time in seconds\n",
"iatORD[\"iat_seconds\"] = iatORD['iat'].dt.total_seconds()\n",
"\n",
"# Get arrival difference time in minutes\n",
"iatORD[\"iat_mins\"] = iatORD['iat_seconds']/60\n",
"\n",
"iatORD.describe().transpose()\n",
"iatORD = iatORD.dropna()\n",
"\n",
"iatORD.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kX6nGlCvduwyDiEg7hBX6DWbvgE7mikg4Agn95rN3QNM2RSQcQYT+QsswgEJfRMIRROgv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\n+f/KCQ9ZZcZOJAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x26994ac4cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"sns.distplot(iatORD['iat_mins']);"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2698ebec128>"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PAvmZv77jnHuwnAc0mf4hjd4Zq7kxyrUXLeZXm/fz65cP8L53Lac9Vl/tYolI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\nSIAo9EVEAkShLyISIAp9EZEA+f8wcDU/JXWZEQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2698eafeeb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"iatORDreduced = iatORD[iatORD['iat_mins'] <= 200]\n",
"sns.distplot(iatORDreduced['iat_mins'])"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>iat</th>\n",
" <td>16046</td>\n",
" <td>0 days 00:32:45.218746</td>\n",
" <td>0 days 01:10:49.087066</td>\n",
" <td>0 days 00:00:00</td>\n",
" <td>0 days 00:07:00</td>\n",
" <td>0 days 00:17:00</td>\n",
" <td>0 days 00:34:00</td>\n",
" <td>0 days 11:37:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_seconds</th>\n",
" <td>16046</td>\n",
" <td>1965.22</td>\n",
" <td>4249.09</td>\n",
" <td>0</td>\n",
" <td>420</td>\n",
" <td>1020</td>\n",
" <td>2040</td>\n",
" <td>41820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_mins</th>\n",
" <td>16046</td>\n",
" <td>32.7536</td>\n",
" <td>70.8181</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>17</td>\n",
" <td>34</td>\n",
" <td>697</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std \\\n",
"iat 16046 0 days 00:32:45.218746 0 days 01:10:49.087066 \n",
"iat_seconds 16046 1965.22 4249.09 \n",
"iat_mins 16046 32.7536 70.8181 \n",
"\n",
" min 25% 50% \\\n",
"iat 0 days 00:00:00 0 days 00:07:00 0 days 00:17:00 \n",
"iat_seconds 0 420 1020 \n",
"iat_mins 0 7 17 \n",
"\n",
" 75% max \n",
"iat 0 days 00:34:00 0 days 11:37:00 \n",
"iat_seconds 2040 41820 \n",
"iat_mins 34 697 "
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iatLAX = LAX.sort_values(by=['arrtime'])\n",
"iatLAX['iat'] = iatLAX['arrtime'].diff()\n",
"\n",
"# Get arrival difference time in seconds\n",
"iatLAX[\"iat_seconds\"] = iatLAX['iat'].dt.total_seconds()\n",
"\n",
"# Get arrival difference time in minutes\n",
"iatLAX[\"iat_mins\"] = iatLAX['iat_seconds']/60\n",
"\n",
"iatLAX.describe().transpose()\n",
"iatLAX = iatLAX.dropna()\n",
"\n",
"iatLAX.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
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OMzo2k+xzpc+89EH25XJ1HUt1VdcGMLdQYGxqgZn5AkN9nZwy2MXE9AI/fGGS\n0cl5BnqyDPRkKZVhoVAkiiK6OzOLf67zC0Xm80XmFoos5IvM5YtEqRSFfJGOTIruzgwDvVl6ch1M\nziwwNr3A7HyB+YUiqVTEKTu62DmQY3RynheOzzA9lydK/v3u6O1kqK+T2fkix8ZnGZ2cZ2auwOxC\ngUw6RS6bJpfNkMumSacjxqcWGJuaZ26hSL4QT3XuyXWQy6YZm5pfvNGx3nLZdFxrfyf93Vm6chk6\n0immZ/NMzxXo7e7g1MEuenIdSW1Fcp0ZenMd9HRl6M510JVNL355pKII4v8RRVH832Y6zfHx2S2p\nv56q69zR27klv+3XEvoXA3cCuPtDZra7qu0c4LC7jwKY2f3AHuDCVT5zPvCN5OevAZeyBaF/YnyO\nY+NzXPym1usld+cy7DnvlbzujAEOPz+O/3iMH/90ih//dGq7S5MWl4oiOrMpspk0+UKJmbkC+cIs\npXLl6XLxF1J/T5Z0KqKcfFHNzBUY6O2kr6uDrs5M3GFIp+ju6qBYKC725DPpFOmkd18ux52vUqlM\nqUz8z1LcGZmdLzA1m2d2vkC+WGIhX4q/tE7MbPOfUHM5+1UDfOL959d9v7UkYj8wXvW6aGYZdy+s\n0DYJDKz2GSBy9/KybVc1PNx3Ur/3DQ/3ceDGK1/2/nuHm2tsX0Sk0Wr53WECqE7LVBL4K7X1AWNr\nfKa0wrYiItIgtYT+QeBygGR8/vGqtqeAs81syMyyxEM7D67xmcfM7JLk58uAb272BEREpHbR8gtY\ny1XNxHkT8bWRDwJvAXrd/fNVs3dSxLN3/nKlz7j702b2c8BeIEv8hfEb7q6ni4iINMi6oS8iIu1D\nd/+IiAREoS8iEhCFvohIQFrrzqWTsN4yEtvJzN4O/LG7X7LaEhVm9hvA/yBe5uIz7n5Hg2vsAPYB\nZwGdwGeAJ5utVjNLE08SsKSua4G5Zquzqt5TgEeAdyd1NGudjxJPwQZ4DvjDJq71E8B/IJ4ocgvx\njaBNVauZ/Rrwa8nLHHAe8Q2wn21UnSH09BeXkQA+TrwkxLYzs48BXyD+Fw9LS1S8g3jG05Vmdhrw\nW8TLWrwH+CMz62xwqe8Hjid1vRf4iyat9QoAd78IuJ44nJqxzsoX6f8CKusCNGudOeIbKi9J/v/B\nJq71EuDfJTW8EzijGWt191srf57EX/q/RTz7sWF1hhD6L1lGAti99uYN8yzwy1Wvly9R8YvA24CD\n7j7v7uOfAfYjAAAD7klEQVTAYeJpsI30VeBTyc8Rca+j6Wp19/8LXJO8PJP4xr+mqzPxp8BfA0eT\n181a57lAt5ndbWZfT+65adZa30N8P9B+4ABwRxPXSrI0zRvc/fONrjOE0F9tSYht5e7/AFQ/JX2l\nJSpWW+aiYdx9yt0nzawPuI24F92stRbM7EvA54D/04x1Jr/ej7j7XVVvN12diRniL6j3EA+XNeWf\naWIXcYfuP7NUa6pJawX4PeAPkp8b+mcaQuivtYxEM1lpiYrVlrloKDM7A7gH+Ft3/zJNXKu7/ypQ\nuQmwa4V6trvOXwfebWb3Eo/n/m/glBXq2e46Ab4P/J27l939+8BxoHrZ2maq9Thwl7svuLsTX8+p\nDsmmqdXMdgDm7vckbzX071MIob/WMhLNZKUlKh4G3mFmOTMbIF7V9IlGFmVmpwJ3A7/r7vuatVYz\n+0ByIQ/iHmoJ+E6z1enue9z9ncmY7iHgvwFfa7Y6E79Ocg3MzF5B3Pu8u0lrvR94r5lFSa09wL80\naa17gH+pet3Qv0/bPszRAPuJe1YPsLSMRDP6KLA3WcPoKeA2dy+a2Z8T/0eQAj7p7nMNruv3gEHg\nU2ZWGdv/MPDnTVbrPwJfNLP7gA7gI0ltzfhnulyz/rv/G+DWZMn0MvGXwLFmrNXd7zCzPcRhmQI+\nRDzbqOlqJZ5h9oOq1w39969lGEREAhLC8I6IiCQU+iIiAVHoi4gERKEvIhIQhb6ISEAU+hIMM9tt\nZl9Yo/0KM7uuDsd5hZn902b3I7IVNGVTJGFmvw/g7r+/vZWIbB2FvgQjuevx94FPE6/C2U1849nH\ngO8BX082/YS7f3GVffwa8EvAK4FXES+J+2rgF4iXArgMOA24193PMrNbiddQOT/Z/g/c/Ytm9i7g\nT4hvehoFfsXdj9X1hEVWoOEdCdH/JH6uwluA/w7c4O5PEq98+derBX6VtxEvM/0O4mUKvubulRUQ\n37PC9mck215BvIAZxAvXXevuu4lXhXzLJs5HpGYKfQnR+4E3JstKfBTo3eDnD7r7hLv/KHldWUfl\nR8S/OSx3d7KK4hPAUPLe7cB+M/sL4Cl3v3uDNYicFIW+hOibxL31R4iHeaINfn6h+kUNq7bOJdst\njqW6+83AJcTrpP+JmX1ygzWInBSFvoRmiHjp5Rvc/Z+AS4F00lagQYsQmtm3gD53/yxwMxrekQYJ\nYZVNkWongP8HfM/MJoAHiZ8O1QPcB3zJzF50989tcR2/R7yCZYH4sYnXbvHxRADN3hERCYp6+iLL\nmNl/BT6xUpu7n9fgckTqSj19EZGA6EKuiEhAFPoiIgFR6IuIBEShLyISEIW+iEhA/j+oH1VIUbJN\n+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2698eaedf98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"sns.distplot(iatLAX['iat_mins']);"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2698ea590f0>"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IzELeqGdmIeAuYDMwCnzEOdeeU34DcBuQBHY45+6ebhszuwz4PjCRHP6ac+6e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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x26989569e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"iatLAXreduced = iatLAX[iatLAX['iat_mins'] <= 200]\n",
"sns.distplot(iatLAXreduced['iat_mins'])"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>iat</th>\n",
" <td>15019</td>\n",
" <td>0 days 00:34:57.599041</td>\n",
" <td>0 days 01:07:35.818239</td>\n",
" <td>0 days 00:00:00</td>\n",
" <td>0 days 00:08:00</td>\n",
" <td>0 days 00:19:00</td>\n",
" <td>0 days 00:38:00</td>\n",
" <td>1 days 10:03:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_seconds</th>\n",
" <td>15019</td>\n",
" <td>2097.6</td>\n",
" <td>4055.82</td>\n",
" <td>0</td>\n",
" <td>480</td>\n",
" <td>1140</td>\n",
" <td>2280</td>\n",
" <td>122580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_mins</th>\n",
" <td>15019</td>\n",
" <td>34.96</td>\n",
" <td>67.597</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>19</td>\n",
" <td>38</td>\n",
" <td>2043</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std \\\n",
"iat 15019 0 days 00:34:57.599041 0 days 01:07:35.818239 \n",
"iat_seconds 15019 2097.6 4055.82 \n",
"iat_mins 15019 34.96 67.597 \n",
"\n",
" min 25% 50% \\\n",
"iat 0 days 00:00:00 0 days 00:08:00 0 days 00:19:00 \n",
"iat_seconds 0 480 1140 \n",
"iat_mins 0 8 19 \n",
"\n",
" 75% max \n",
"iat 0 days 00:38:00 1 days 10:03:00 \n",
"iat_seconds 2280 122580 \n",
"iat_mins 38 2043 "
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iatBOS = BOS.sort_values(by=['arrtime'])\n",
"iatBOS['iat'] = iatBOS['arrtime'].diff()\n",
"\n",
"# Get arrival difference time in seconds\n",
"iatBOS[\"iat_seconds\"] = iatBOS['iat'].dt.total_seconds()\n",
"\n",
"# Get arrival difference time in minutes\n",
"iatBOS[\"iat_mins\"] = iatBOS['iat_seconds']/60\n",
"\n",
"iatBOS.describe().transpose()\n",
"iatBOS = iatBOS.dropna()\n",
"\n",
"iatBOS.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ovwO4I8E59bLfb/LeV4CvJGjvrKtWQ7KZiQFSviNLqaKRgYikS+ofOqvWwiN7EkH0vIGm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\noDAQERHg/wN+1wjgHFg4+gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x269894ed390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"sns.distplot(iatBOS['iat_mins']);"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2698d2b0eb8>"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AR4F9/ulfds59s5xfaDpyNyCV3rkwsWiY3/2Fy/jCN57nH3/UTVuijmsuWVjt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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2698d1e8c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"iatBOSreduced = iatBOS[iatBOS['iat_mins'] <= 200]\n",
"sns.distplot(iatBOSreduced['iat_mins'])"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>iat</th>\n",
" <td>13972</td>\n",
" <td>0 days 00:37:36.836530</td>\n",
" <td>0 days 01:14:23.796204</td>\n",
" <td>0 days 00:00:00</td>\n",
" <td>0 days 00:09:00</td>\n",
" <td>0 days 00:21:00</td>\n",
" <td>0 days 00:40:00</td>\n",
" <td>0 days 19:27:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_seconds</th>\n",
" <td>13972</td>\n",
" <td>2256.84</td>\n",
" <td>4463.8</td>\n",
" <td>0</td>\n",
" <td>540</td>\n",
" <td>1260</td>\n",
" <td>2400</td>\n",
" <td>70020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>iat_mins</th>\n",
" <td>13972</td>\n",
" <td>37.6139</td>\n",
" <td>74.3966</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>21</td>\n",
" <td>40</td>\n",
" <td>1167</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std \\\n",
"iat 13972 0 days 00:37:36.836530 0 days 01:14:23.796204 \n",
"iat_seconds 13972 2256.84 4463.8 \n",
"iat_mins 13972 37.6139 74.3966 \n",
"\n",
" min 25% 50% \\\n",
"iat 0 days 00:00:00 0 days 00:09:00 0 days 00:21:00 \n",
"iat_seconds 0 540 1260 \n",
"iat_mins 0 9 21 \n",
"\n",
" 75% max \n",
"iat 0 days 00:40:00 0 days 19:27:00 \n",
"iat_seconds 2400 70020 \n",
"iat_mins 40 1167 "
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iatMCO = MCO.sort_values(by=['arrtime'])\n",
"iatMCO['iat'] = iatMCO['arrtime'].diff()\n",
"\n",
"# Get arrival difference time in seconds\n",
"iatMCO[\"iat_seconds\"] = iatMCO['iat'].dt.total_seconds()\n",
"\n",
"# Get arrival difference time in minutes\n",
"iatMCO[\"iat_mins\"] = iatMCO['iat_seconds']/60\n",
"\n",
"iatMCO.describe().transpose()\n",
"iatMCO = iatMCO.dropna()\n",
"\n",
"iatMCO.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
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nrMVy2j+TDWzQpWbM7C2kI/5d7v6L7OmO9WfDl0BWaA/pqPNOZpem2Aw+CGwH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x26985646198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"sns.distplot(iatMCO['iat_mins']);"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2698416be10>"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AR4F9/ulfds59s5xfaDpyNyCV3rkwsWiY3/2Fy/jCN57nH3/UTVuijmsuWVjt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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x269918f40b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"iatMCOreduced = iatMCO[iatMCO['iat_mins'] <= 200]\n",
"sns.distplot(iatBOSreduced['iat_mins'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Answer Part A (ii)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ATL: Mean = 31.118 and SD = 74.814\n",
"ORD: Mean = 31.621 and SD = 74.027\n",
"LAX: Mean = 32.754 and SD = 70.818\n",
"BOS: Mean = 34.960 and SD = 67.597\n",
"MCO: Mean = 37.614 and SD = 74.397\n"
]
}
],
"source": [
"print ('ATL: Mean = {0:.3f} and SD = {1:.3f}'.format(float(iatATL[['iat_mins']].mean()), \n",
" float(iatATL[['iat_mins']].std())))\n",
"print ('ORD: Mean = {0:.3f} and SD = {1:.3f}'.format(float(iatORD[['iat_mins']].mean()), \n",
" float(iatORD[['iat_mins']].std())))\n",
"print ('LAX: Mean = {0:.3f} and SD = {1:.3f}'.format(float(iatLAX[['iat_mins']].mean()), \n",
" float(iatLAX[['iat_mins']].std())))\n",
"print ('BOS: Mean = {0:.3f} and SD = {1:.3f}'.format(float(iatBOS[['iat_mins']].mean()), \n",
" float(iatBOS[['iat_mins']].std())))\n",
"print ('MCO: Mean = {0:.3f} and SD = {1:.3f}'.format(float(iatMCO[['iat_mins']].mean()), \n",
" float(iatMCO[['iat_mins']].std())))"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2698e766908>"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TNJ2gY/USQlz9JPTn0G2XMNEa1glWeebOxVLJBpG4y0RmB8l3DY5VQka6K4yf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x269841b4d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"warnings.filterwarnings('ignore')\n",
"sns.distplot(iatATLreduced['iat_mins'])\n",
"sns.distplot(iatORDreduced['iat_mins'])\n",
"sns.distplot(iatLAXreduced['iat_mins'])\n",
"sns.distplot(iatBOSreduced['iat_mins'])\n",
"sns.distplot(iatMCOreduced['iat_mins'])"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 0.04)"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pTLJ2kOOn8uUCZuLCtQOpFV3LZdh3aHBO+iZJkiRVjYyVUvt9vUWK0yxzVbVy\noJdlS9OJ2b2+iSVJkuYpk6wdJDuTdWlf14yu7+kucuHa/tQxSwZIkiRpro1m3pbq7ZlZ3ApQKBRy\ni7fuPegEAUmSND+ZZO0gxwdnX5O1Khuo7jt0molSaYrWkiRJ0syNjGeSrN0zT7ICbMnUZT1y8gxD\nZ8Zn3S9JkqR2McnaQXLlAmZYkxXyta3GJ8rsPzLUUL8kSZKkWmPZcgE9s/vfig2r++npTl+775Bv\nYkmSpPln5lm8GQohvAZ4MxCAIeCrwDtijLunef1FwHXA1cBaYAdwY4zx45O07QfeArwKeBxwHPgK\ncF2M8eFJ2g8AbwNeCWwF9gM3A38YY2xpZnJsfILBzKf2s5nJurSvm/WrlnDo+Jmzx/YcPM3m9cvq\nXCVJkqRaxrD1ZRdXnU25AICuYoFN6wbYvf/U2WP7Dg9y6dZVDfVPkiSp1Zo6kzWEcANwE7AEuBG4\nFXgF8N0QwiXTuH4bcAeVgPPrwIeBAeBjIYT3Z9r2AF8CbgAGk+d9E/gl4J4QwhWZ9r3AF4B3AQ8D\nHwR2A78D/EsIoW92P/XsHD89mjs204WvqrIlA/YcPE25XJ7VvSRJkhYbY9jzGx1Pz2TtneVMVoDN\n69IlAx47MkSpZOwqSZLml6bNZA0hXA78HnA7cHWMcTQ5fgvwWeBDwM+d5zYfADYBL4wxfim5/t3A\n14C3hhA+E2P8XtL2N4Argb+MMb6uph8vohKIfgB4Ts2935Ds/0mM8e017T9IZdbCG4E/m8WPXtc/\n3bGLUqnM4FC6NMDBY+lJB13FQu7VqenaumE5399x+Oz+8MgER06cqXOFJEmSwBh2unILX82yJivA\npkySdWy8xOETw2xY3T/FFZIkSZ2nmTNZ35Rsr6sGpwAxxn+g8un8i0IIm6e6OJkBcA3w7Wpwmlw/\nTCXwLQCvr7nkMuAIcH3tfWKMXwSOAc/MPOLNwEi2PfBOKq+EveE8P9+cGh5JB6r9S7opFAqzutfK\nZb2sGOhNHdtz0NpWkiRJ02AMOw2juZqss0+y9i/pZvXy9ATcfYddU0CSJM0vzUyyXgWMUwlGs26l\nEmA+Z5JzVduTNrdOcu52YDR5BgAxxt+MMa6LMe6sbRhCuBBYBTxWc2wblXpXd8YYT9W2jzGeBr4D\nXBpC2FKnf3Mqu4rqbOqx1sqWDNh7aLCh+0mSJC0SxrDTkK/J2tj/Vmxal561+qixqyRJmmeakmRN\nakVtA/bnOufTAAAgAElEQVTEGEcmaVINIp9Y5zaXJdsHsydijGPAHuCS5FmT9WF5COG/Af9EJdB9\nz3TuPYP+zamhkXSSdbb1WKu2rE+/dnXs1AjHTk32TyFJkiQwhp2JXLmABmayQr5kwJGTZzgzOj5F\na0mSpM7TrJqsa6gEhUenOH8i2dZbNnRtsq13jyKwAjhceyKE8NPAl2sOvS3GeNMM732+/s1aoQgD\n/elXosYm0q9crVjWl2szE9uW9NLbsy/1KtfOA6d53rY1s75nMxSLhbPbNWsGztN6cXBM0hyPPMck\nzzHJc0zyHJO86pjoLGPYOmpj2LHxbOy6pKHY9ZK+Hrq7HmW8JiY+cmqUy7ZWflc77XfWvydpjkee\nY5LnmOQ5JnmOSZ5jktepMWyzkqzVT+anmjpZPb6kSfcYBt4HrKayMMGfhBC2Am+OMZbnqH+zVqBA\nITOHOFsuYGBpT0P/0RSLBbZuXM5De0+cPfb9eJCffubFs75nMxUKBbq6OvOXpF0ckzTHI88xyXNM\n8hyTPMdEdRjD1lGNYUvlMqOZJOvSvq4GY9cutmxYxq7HTp49tvfAaZ6YTBDo6mpmlbPZ8+9JmuOR\n55jkOSZ5jkmeY5LnmHS+ZiVZh5PtpK9BAdWPueutxjTre8QYvwF8AyCE8DtUampdmxz7+znq36yV\nKVNOx6UMDo+l9vv7uimVyg0956JMkvXuHYc4MzJOT3fnBKnFYoFCoUC5XG74510oHJM0xyPPMclz\nTPIckzzHJK86JjrLGLaOagx7ZiT/Gn9Pd1fDv1dbM0nWRw6cYmKiRKFQYCLz1le7+fckzfHIc0zy\nHJM8xyTPMclzTPI6NYZtVpL1BFBi6leVVta0m0r1Nah69ygDJ6c4D0CM8UgI4fepBKbXJNvp3Pt8\n/Zu1cgkGh9ITELJJ1q5Cvs1MrVuRfmVreGScO+/dx5Mu7pySAWvWDNDVVaBUKnP0qAscgGOS5Xjk\nOSZ5jkmeY5LnmORVx0RnGcPWUY1hTw6O5s5NjI8zONRYInTdynzsuufASdauWNJxv7P+PUlzPPIc\nkzzHJM8xyXNM8hyTvE6NYZsypTHGOEql8P5FIYSeSZo8PtneX+c2/5Fpe1Zyz62VR8VSCKEQQnhO\nCOHnp7hXdRGA9ee79wz6N2fGJ0qTvHLVeP57aV83a1ek3xa7d+eRhu8rSZK0EBnDTs/oeHrRq2Kh\nQNcc1EZb3t/L8v70sD96yP+ZlCRJ80Mz3xu/jcqrTM+a5NzVVD7B/9c6138jaXPVJOd+Krn37QBJ\njar/BfzvEMIFk7T/8WS7I2m/j8qqrP8lhJCqGhxCWAY8HXgwxnigTv/mzPAkr1z1L5mbScab16eL\nIt/7kElWSZKkOm7DGLau2oVVAXp7inP2yt6mdenY9dHDJlklSdL80Mwk66eS7XtDCEurB0MIL6YS\nYH4+xrh3qouTc18FrgwhXFNz/VLghmT3xppL/prKz/OnIYRiTfvHAddTefXrkzXtPwn0J+dqXZ8c\nv5EWyS561d1VmLO6qVsySdbHjgxx8PjwFK0lSZIWPWPY8xgZS89k7evpmrN7b84kWQ8eH87NnJUk\nSepEzarJSozxjhDCjcAbgXtCCJ8DtgAvAw4Av1VtG0LYDmwH7o4xfq7mNm8C7gD+LoRwC7CXSk2q\nS4H3xRjvrmn7B1RmDLwaeEoI4WvABuDFwFLg2hjjPTXtPwC8FHhLCOEK4N+AZwJXAt8CPjIHwzAt\nQ5mZrHNRKqBq7colLOnt4szoueD0Bw8d4er/tGXOniFJkrRQGMOe39gkM1nnysY1/RQLBUrlysIe\n5TLsPzI0Z/eXJElqlmYvM39t8jVCJdi8ErgZ+K8xxp017bYD76YSfJ4VY9wBPINKof/nUwl2B4Ff\nBd6eaXsS+EkqMwSWJc99EZVXtp4TY/z/Mu1HqAS0f0alftVbgc3Ae4EXJudbIlsuoH8Ok6yFQiH3\n2tUPrMsqSZJUjzFsHSOZmaW9cziTtae7yIY1S1PHLBkgSZLmg6bNZIWzdaY+nHzVa/ce4D1TnHsA\nePk0nzcIvDP5mk77k8BvJ19tk02yLp2jeqxVm9cPsPPRcwvY/mj3MUbGJub01S5JkqSFwhi2vlxN\n1jkqc1W1ad1AavbqvkODlMvlOav7KkmS1AzNnsmqacjWZJ3LmaxQCVRrQ9Kx8RLxkWNz+gxJkiQt\nDqNjzZvJCvm6rINnxjl04sycPkOSJGmumWTtAMMj6UB1LmuyQmUxgvWr069d3fuQJQMkSZI0c9kk\n61y/HbVqWS9LetP33PHI8Tl9hiRJ0lwzydoBsgtfzfVMVqiUDKh170NHKCcLCkiSJEnTNTLevIWv\noLKmwMbMBIG4x7ewJElSZzPJ2gGaXZMVYEsmyXr4xBkOHhue8+dIkiRpYcuVC+ie+zr/G9b0p/aj\nM1klSVKHM8naZmPjJcYyswGaMZN11bI+Vi7rTR27b9fROX+OJEmSFrbcwldzPJMV4II16Zmsh0+c\n4ehJ67JKkqTOZZK1zbKzWGHua7JC5bWrJ29bkzp2/y5fu5IkSdLMNHvhK6hMEMgmb+MeZ7NKkqTO\nZZK1zbL1WHu6ivR0N+ef5ckXr07t/2j3MSZKpSlaS5IkSWnlcpnRzFtYfU2YyVooFNiwOl0yYIdJ\nVkmS1MFMsrbZ8Jnm12OtevLF6ZmswyPj7HrsVNOeJ0mSpIUlm2CF5tRkBfKLX1mXVZIkdTCTrG2W\nncnajHqsVauX97F5XXoBrPutyypJkqRpypYKgOaUCwDYmFn8av/RIU4MjjblWZIkSY1qXkZP05Kt\nybq0rzlBKsBtd+9jxUAv+w4Pnj327R/uZ/lAb67t9is2N60fkiRJmp+yi14VCtDdVWjKs9Ys76On\nq8jYxLln7thznP/8xA1NeZ4kSVIjnMnaZrmZrE0sFwBw4dr0jIBDx4cZm+S1L0mSJClrJDOTta+n\ni0KhOUnWYrHA+kzJgB2WDJAkSR3KJGub5WqyNrFcAFReuyrWxMGlMhw4OtTUZ0qSJGlhyH4439uk\nBVurcnVZ9xxr6vMkSZJmyyRrm2VnsjY7ydrTXWT9qnSw+tgRk6ySJEk6v+xM1mbVY63K1mXde2iQ\n08NjTX2mJEnSbJhkbbMzo+lAtZkLX1VlSwY8dmRwipaSJEnSOdmFr5qdZF27cgldxXQ5ggf2WDJA\nkiR1HpOsbVQql/OvXDU5UAW4cN1Aav/46VGGMmULJEmSpKzswle9Pc3934muYiH3FlY0ySpJkjqQ\nSdY2Gp9kwameJte1Ali7YknuOc5mlSRJ0vmMjmdmsnY3f4LAxjUmWSVJUuczydpGo5MkWZu9eABU\nVmq9YE22ZIB1WSVJklTfSGYma1+TZ7ICbFydjlsfOXCK4RHfwpIkSZ3FJGsbjWVmAkBrZrICXLgu\nX5e1XC635NmSJEman1pdkxVg3aoldHedq8taLsMDe080/bmSJEkzYZK1jbI1rXq6ixQKhSlaz61N\na9N1WYdHJjhxerQlz5YkSdL81OqarADdXUUuuXBF6tgOSwZIkqQOY5K1jbKLXrVqFivA8v4eBpZ0\np449al1WSZIk1dGOmqwA4aJVqf2451hLnitJkjRdJlnbKFuTtRX1WKsKhQIXrkvPZn30sElWSZIk\nTW0kUy6grwXlAgAu25pOsu567FSuL5IkSe1kkrWNsjMBelo0E6BqcybJeuDoMOMT+cW4JEmSpHK5\nzFgbygUAPGHzSoo1ZbUmSmUe2mddVkmS1DlMsrZRu4LUqgvX9lNbAnaiVObA0eGW9kGSJEnzw+h4\niewyqa1Y+ApgSW832y5YnjoWH7EuqyRJ6hwmWdsoWy6glTVZoRIUr1u5NHXMkgGSJEmazMho/vX8\nVk4SyNZldfErSZLUSUyyttFYbuGA1v9zbF6fLhmwzySrJEmSJpGtgVooQE9X6+LXbF3Whx49mYun\nJUmS2sUkaxuN5WaytrYmK8CmTF3Wk4OjnBoabXk/JEmS1NlGx7ITBLoo1NaearLLtqyk9mnjEyUe\nfuxUy54vSZJUj0nWNsqWC2jHTNa1K/pyq8JaMkCSJElZ2XIBrV5PoH9JD1s3LEsdi48ca2kfJEmS\npmKStY1yM1lbHKgCFAoFNq3rTx3bd3io5f2QJElSZ8uWC2jVole1LrMuqyRJ6lDd7e7AYjbZK1ft\nsHn9QOpVq/1HBhmfKNHdwhpbkiRJ6my5mawtfAvrtrv3AflJCnHPcb72/b0Ui+cKCWy/YnPL+iVJ\nklRlFq2NskFiO8oFQL4u6/hEmQf2nmhLXyRJktSZOmEm68Y1S1P74xNljpw80/J+SJIkZZlkbaNs\nTdaeNiVZl/R2s3bFktSxH+480pa+SJIkqTNlZ7L2taHU1ZLeblYu600dO3DUUleSJKn9TLK2Sblc\nztdkbVOSFWDT+vRs1h/sPNqmnkiSJKkTjYyNp/bbVepq4+r0egIHjg23pR+SJEm1TLK2ydhEKXes\nXYEqwObM4ld7D53m2KmRNvVGkiRJnSZXk7UNM1khXzLg4LFhSuVyW/oiSZJUZZK1TcbG8knWnjYF\nqgDrVi7NzaT94cOWDJAkSVJFJ9RkhfxM1rHxkpMDJElS25lkbZNsPVZob7mAYrHAprXpgPWHlgyQ\nJElSIptk7WtTkrV/STfL+3tSx6zLKkmS2q272Q8IIbwGeDMQgCHgq8A7Yoy7p3n9RcB1wNXAWmAH\ncGOM8eOTtO0B3gr8InApUALuAz4aY/zkJO1vBl4+xaPvizH+2HT6OBtj4+kgtburQLFQaNbjpmXT\n+gF2Hzh9dv++h48yPlGiu8tcvCRJWlyMYfM6pVwAwMY1/ZwaOnF2/+CxYZ58cdu6I0mS1Nwkawjh\nBuD3gPuBG4GLgFcAzw8hPD3G+PB5rt8GfBtYD9wM7AdeDHwshPDEGONv17TtAr4EPBeIwCeAXuBn\ngU8kz3t95hFXAMeAP5/k8Qdn+OPOSHYmazvrsVZtXpde/GpoZJwde47z5IvXtKlHkiRJrWcMm1cu\nl/PlAtoYv25cvZQH955Lsh44Oky5XKbQ5kkLkiRp8WpakjWEcDmV4PR24OoY42hy/Bbgs8CHgJ87\nz20+AGwCXhhj/FJy/buBrwFvDSF8Jsb4vaTtr1AJTr8IvKTmeW9L+vDrIYSbY4xfT473U5kp8C8x\nxvfMzU89fdmarO2sx1rVv6SHtSuWcOTkmbPH7tpx2CSrJElaNIxhJzc2XiK7tlS7Z7LWGhmb4Pjp\nUVYv72tTjyRJ0mLXzMjoTcn2umqwCBBj/Afgm8CLQgibp7o4mQFwDfDtanCaXD9MJfAtALWf6ldf\nmXpH5nkngD9Jdn+2pv1Tqfz898zw55oT+Zms7U+yAmzduCy1f9eDhyi7WqskSVo8jGEnkZ3FCu2r\nyQqwbGkPA0vS80UeOzLYpt5IkiQ1N8l6FTBOJRjNupVKgPmcOtdvT9rcOsm524HR5BlVNwHXA/8x\nSfvqcqPLa45dkWzbkmTN1mTt6YByAQBbN6STrEdPjvBITZ1WSZKkBc4YdrKOjOaTrO1ctBXgwkyp\nq0cPu/iVJElqn6aUCwgh9ALbgF0xxpFJmuxMtk+sc5vLku2D2RMxxrEQwh7gkhBCb4xxNMb46Tr3\nemmyvbfmWDVA3RZC+HqyX6AS/P5BjPHf69yvYZ06k3XVsl6W9/dwamjs7LHv7zjEtguW17lKkiRp\n/jOGnVquHmtPse31TzetG8jUZR1ifKJU5wpJkqTmaVZN1jVUgr2jU5yvRkOr6txjbbKtd48isAI4\nPNVNQgg/A/xC0v4zNacuT7bvAv4R+DjwFOBngOeFEF4SY/xinf7NWqEIZdJBaf+SHgb6O6OG1OM2\nr+SeB84N6b07j/A/rnlqU55VLBbObtesGThP68XBMUlzPPIckzzHJM8xyXNM8qpjorOMYaeQncna\n19Pd9tj1CVu6+Nbdj1ItbDVRKnNyaLwlv9/+PUlzPPIckzzHJM8xyXNM8hyTvE6NYZuVZO1NtpPN\nAKg9vqSZ9wghPBu4Jdl9Q4zxSM3pISozDF4aY7yn5poXUFl44KYQwiUxxpN1+jgrBQq5cgF9vV0d\n8x9JNsm6e/8pDh4bzr2SNZcKhQJdXZ3x83cKxyTN8chzTPIckzzHJM8xUR3GsFN1OjOTdUkHxK5L\nl/SwYU0/B46eKxOw5+Bpurpa94aYf0/SHI88xyTPMclzTPIckzzHpPM1K8k6nGx7pzhf/di7XrHP\nhu4RQvjvwN8CS4G3xxhvrj0fY3zuZNfFGL8cQrgZeBXwwuQec6pMOTcboKe7SKnUGQtMbVjdz5K+\nLs6MnOvjX33xPi6/dH2u7fOfsa2hZxWLBQqFAuVyuWN+/nZzTNIcjzzHJM8xyXNM8hyTvOqY6Cxj\n2ClkY9fenq6O+D3aumFZKsn6yIGTTLSgZIB/T9IcjzzHJM8xyXNM8hyTPMckr1Nj2GYlWU8AJaZ+\nlWplTbupVF+xqnePMpD7lD6E8FvA+5Lda2OMH67b27w7qQSoj5/hddNSLsHwyHjmYJnBoakmPLTe\nlvXLUjWuHtp7nCdsXpFrd/RoY6u4rlkzQFdXgVKp3PC9FgrHJM3xyHNM8hyTPMckzzHJq46JzjKG\nncLIaDp27S7SEbHr+pXpCcHHTo7w4K4jrFlRb7Jx4/x7kuZ45DkmeY5JnmOS55jkOSZ5nRrDNuVd\nmhjjKJWFAS4KIfRM0qQa+N1f5zbVFVZzQWJyz62VR8VSzfFCCOHPgfdTWbn1FyYLTkMIy0MIzwgh\nXJ49l6i+Fz88xfmGjWUWvmr36qxZF21Ylto/eGyYM5ngWpIkaSExhp1afuGrrrl+xKysXbkkt4Ds\nfQ9PVQ5XkiSpeZqZ2buNymtSz5rk3NVUPsH/1zrXfyNpc9Uk534qufftmeP/L3AtcAS4Ksb42Snu\n/STgDuBvpjj/7GR7Z53+NWS0QwPVqgvW9tNd86lAGdh70E9MJEnSgncbxrA5ZyYpF9AJisUCF67t\nTx37oUlWSZLUBs1Msn4q2b43hLC0ejCE8GIqAebnY4x7p7o4OfdV4MoQwjU11y8Fbkh2b6w5/mrg\njVRe37oyxnhHnb59F3gIeEoI4XW1J0IIrwWeD3yPfAA8Zzp9Jmt3V5FNmYWuHjlYr/yYJEnSgmAM\nO4n8TNbOiV2zMev9u45as06SJLVcs2qyEmO8I4RwI5Wg8Z4QwueALcDLgAPAb1XbhhC2A9uBu2OM\nn6u5zZuofFr/dyGEW4C9wDXApcD7Yox3J9d3A3+UXHM38AshhF+YpFv3xhg/G2MsJYHoV4BPhhBe\nQuW1ryuA5wL7gVfFGJsSnZXL5VySNfuaUyfYumEZjxw4l1h97PAgY+OljksIS5IkzRVj2MllF77q\n6+6MmayQT7IOnhnn4f0nefymlVNcIUmSNPealmRNXEulLtXrqQSbR4CbgXfFGHfWtNsOvBu4CTgb\noMYYd4QQngFcT+WT+SXADuBXOTfLAOApVOpbAVyZfE3mb4DPJve+PYTwE8C7qLzO9TwqgelHgOti\njPtn9RNPw9h4iWzk24mJyy3rl1EoQDnp7ESpzGNHBrlo4/L2dkySJKm5jGEzRsfTSdaeDprJOrC0\nh5UDvZwYHD177L6dR02ySpKklmpqkjX5FP3DyVe9du8B3jPFuQeAl5/n+nuAGS8rFmP8EfDKmV7X\nqGw9VoDeDpoNUNXX28XG1f3sPzp09tieA6dNskqSpAXNGDZvbKyzS11tWjeQSrL+8OGj/NxPXtLG\nHkmSpMWms6KjRWI0UyoAOi9Qrdq6cVlqf8+h09a4kiRJWmRy6wl0dVbsmi0ZsPPRkwydGWtTbyRJ\n0mLUWdHRIpGdydrdVaBYnPEkhpbYuiGdZB0dK3Hw2HCbeiNJkqR2yCZZuzssybpxzdJUPF0ql7l/\n17E29kiSJC02nRUdLRLZ1Vl7OrBUQNWypT2sWdGXOrbn4OkpWkuSJGkhKpXTbzJ12ltY3V1FNq5e\nmjr2w4ePtqk3kiRpMeqs6GiRyM4E6O2wIDUrO5v1kQOnKJctGSBJkrRYddpMVsiXDPjhw0eMWSVJ\nUst0XnS0CGTLBXTaTICsizJ1WQfPjHPs1EibeiNJkqR26+7uvFJXmzNJ1qMnR3wDS5IktUxnZ/cW\nqGy5gN6ezv5nWLWsj2VLe1LHDFglSZIWr06cybpyWW8uZr37wcNt6o0kSVpsOi86WgRGxzKrs3Zw\nTVaAQqEwSckAk6ySJEmLUXdXgWKh82ayFgoFtmxIz2a9xySrJElqEZOsbZAtF9DpNVkhXzLg2KkR\nTg+Ntak3kiRJapdOnMVatWV9OmZ9+LFTHD9tmStJktR8nRshLWCj4/OrJivA+lVL6etJz7i1ZIAk\nSdLi08lJ1o1r+lnal45Znc0qSZJaoXMjpAUsWy6gt6ezywUAFIv5168eOXiqTb2RJElSu3TyBIGu\nYoEfu2Rt6tg9Dx5pU28kSdJi0rkR0gKWLRfQyYFqrWxd1oNHhzk9bMkASZKkxaS7q/Pqsda64gnr\nUvv37zqaW3hWkiRprs2P7N4CMx9rsgJsWjdAV/FcUF3G168kSZIWm04uFwDw1MevpXZdrtHxEj/a\ndax9HZIkSYtCZ0dIC9RIplzAfJnJ2t1VZNO6dMmAux4wySpJkrSYdHrsumxpD5duWZU6drcTAyRJ\nUpN1doS0QGUXvurt7vyarFUXbUyXDPjhziO+fiVJkrSI9HT4TFbIlwy456HDlMrlNvVGkiQtBp0f\nIS0w5XJ53tZkBdi8fhm1VbhGx0vcv+to2/ojSZKk1uqeB7Hr5U9IL3514vQou/e7aKskSWqezo+Q\nFpjxiRLZD9F7e+bPP8OS3i42rF6aOnbXDl+/kiRJWiw6vSYrwIVrB9i4pj917G7LXEmSpCbqbncH\nFpvRTD1WmF8zWQG2blzGgWPDZ/fvfvAwpVKZYrGzV5qVJElS4zo9dr3t7n0ArF3Rx4GjQ2eP3/6D\nx1i9oi/VdvsVm1vaN0mStHB1doS0AE1Wv3Q+1WQF2LohXZf19PAYD+470abeSJIkqZW6u+bHB+tb\nMjHrsVMjDA6Ptak3kiRpoTPJ2mLZeqxdxcK8mwG6vL+X1cvTswC+v+NQm3ojSZKkVur0maxVG1Yt\nzZXl2nPwdJt6I0mSFrr5ESEtINkk63yqx1orO5v1rgcOUXbFVkmSpAVvPtRkBSgWC2xeN5A69sgB\nk6ySJKk55keEtICMZGqy9syzUgFVF21MJ1kPHT/DvkODbeqNJEmSWqVnniRZAS7auDy1f+DoEMMj\n423qjSRJWsjmT4S0QORmss6T162yVi/vY2BJet207z9gyQBJkqSFrnsexa+b1w+kasiWgUcOnGpf\nhyRJ0oI1fyKkBSKbZJ0vNa2yCoUCWzOzWe/acbhNvZEkSVKrzKeZrN1dRbasT8esu/dbMkCSJM29\n+RMhLRALZSYrwEUb0q9f7T5wiqMnz7SpN5IkSWqF+VKTtWrbBZYMkCRJzTe/IqQFYGQ8U5O1Z37W\nZAXYsHpprmTAXQ84m1WSJGkhm29vYlkyQJIktcL8ipAWgIU0k7VYLHD5E9aljn1/h3VZJUmSFrLu\n7sL5G3WQyUoG7NpvklWSJM2t+Zvhm6cWSk3Wqqdduj61Hx85zuCZsTb1RpIkSc0238oFAFx8Ybpk\nwMGjw5YMkCRJc2r+RUjzXH4m6/wtFwDwY5esSSWKS+Uy9z54pI09kiRJUrN0FQsUC/NrJivApnWW\nDJAkSc1lkrXFRsYyNVnn+UzWvt4unnLxmtSxux6wZIAkSdJCNF9jV0sGSJKkZpufUdI8lpvJ2jP/\n/wmedmm6LusPdh5lbHxiitaSJEmar+ZjqYCqyUoGnDg90qbeSJKkhWb+Rknz1EKryQpw+aXrqH1r\nbGRsgvt3HWtfhyRJktQU8zl2naxkwPdctFWSJM2R+RslzVOjmXIB870mK8CK/l4u3bwydcySAZIk\nSQtPbZJyvpmsZMB3/+Ngm3ojSZIWGpOsLVYql1P783k2QK2nXbY+tX/3A4cplcpTtJYkSdJ8NJ/L\nBUC+ZEDcc9ySAZIkaU7M7yhpAVgINVkhX5f15NAYDz16ok29kSRJUjPM9wkCuZIBZbjT2aySJGkO\nzO8oaQHoWQDlAgA2rO5ny/qB1LG7Hjjcpt5IkiSpGeb7TNburiIXbUzPZv3O/Qfa1BtJkrSQzO8o\naZ7rKhboKs7fulZZV1yaLhnw/R2HKJctGSBJkrRQzPeZrACXZEoG7Hz0JAePDbWpN5IkaaHobvYD\nQgivAd4MBGAI+Crwjhjj7mlefxFwHXA1sBbYAdwYY/z4JG17gLcCvwhcCpSA+4CPxhg/OUn7AeBt\nwCuBrcB+4GbgD2OMTY+0FkKQWuvHL1vHF7+96+z+wWPDPHpkiM3rBqa+SJIkqQMZw05uvs9kBbhw\n7QB9PV2MjE2cPfad+w/ws8+6pI29kiRJ811To6QQwg3ATcAS4EbgVuAVwHdDCOeNYkII24A7gFcB\nXwc+DAwAHwshvD/Ttgv4EvDHQC/wCeDTwBbgEyGEj2ba9wJfAN4FPAx8ENgN/A7wLyGE/5+9O4+O\n+67v/f/6zmi075tly5Ytbx8v8RLH2QlxFghbIL1lK/eclva0pf1xgNveHvi1UKCUUG4pl0svKS3c\n0psfAULKEtaEBBMnMbGTeFEcO/HHtmzLtmzJlmTtu2Z+f8xInq++M+N19B1pno9zfObM57vMW+/E\nk3fe+nw/n7wr+6kvXe4ca7IunleiihJ32vYeOudTNAAAAFeGGja5uTBJIBBwPBtg7XytnSewAADA\nVUlblWSM2SDpbyRtl3S9tfYT1trfk/QeSdWSvnoJt/mKpAWSHrDW/r619uOS1kvaKekvjDE3xJ37\nh5LulfRzSeuttR+x1n5I0mpJ+yX9qTHmrrjz/0zSXZL+0Vp7n7X2r621W2Jx3Srpw1f6s1+qUGhu\nrGIOF0YAACAASURBVMc6yXEcbZq2ZMDewzRZAQDA7EENm1r8plGz2fQm65nOQZ082+9TNAAAYC5I\n56+iPxp7/Zy1dnRy0Fr7Y0nPSXqHMaY+2cWxGQAPSHrBWvvLuOuHFC18HUkfirvkfbHXT077vB5J\n/xh7e3/c+R+TNCLp89M++lOKPhL2Zxf7Aa/WXJgJMN31K6td74+d6dP5vhGfogEAALhs1LAphObA\ncgGSVFteoKJ898ppbIAFAACuRjqrpLsljStajE63VdEC864ExyZtiZ2zNcGx7ZJGY58x6WFFi82D\nCc6f7PKVSFPF71JJL1lr++JPtNb2S3pR0gpjzMIU8V21uVKkxlu5qFyFee6CtYnZrAAAYPaghk0h\nZ45MEnAcR0vml7rGXny9XWGWDAAAAFcoLRtfxdaKWizpuLU20TTGo7HXVSluszL2emT6AWvtmDHm\npKRGY0yutXbUWvtIinu9O/a672L3jovvrlh8p1Lc96oU5OeoqDDtS7+mVWWld1OrG9fM07N7W6fe\n7zvWpd+91yS8PhBwpl4T3SsbkRM38uFFTrzIiRc58SInXpM5QRQ17MUVF+bN+vp10tqlVTpwrGvq\nfVfviNp7R7S2seqi1/J94kY+vMiJFznxIide5MSLnHhlag2bliarpEpFf4PfleR4T+y1PMU9Jqub\nVPcISCqV1JHsJsaYtym6hlaPpO9exr0vFt9Vyw0FM/ZfjEsVTDAb99Z1C1xN1lebO9U/NKay4uQF\nueM4Cs6RNb6uFXLiRj68yIkXOfEiJ17kBClQw15E3hyoXydVlxeooa5EJ9ouTAre3nRa65fXpLjK\nje8TN/LhRU68yIkXOfEiJ17kJPOlq8maG3tNthjn5Hh+Ou9hjHmjpMdib//MWtt5DeO7aqFgQOHw\n7H4kaWIi7BnbuLJaeaGgRsYmJEnhcES/feW07rtlsefcQMCR4ziKRCKzPhfXCjlxIx9e5MSLnHiR\nEy9y4jWZE0yhhr2InKAzp/7+3LGxXt958sJKDdtfadUf3b/2onsn8H3iRj68yIkXOfEiJ17kxIuc\neGVqDZuuJutQ7DU3yfHJKY2ptvC8qnsYY94l6XuSCiR9wlr76DWO76pFIhENDM7uTaG6ugYSjq9f\nVqWXD56der9t90ndOG1TLCm63EAwVqwnu1e2ISdu5MOLnHiREy9y4kVOvCZzginUsBcxNjYx6+vX\neOuWVLre9w2Oafuek9qw3Fu3xuP7xI18eJETL3LiRU68yIkXOfHK1Bo2XSvX90gKK/mjSmVx5yUz\n+RhUqntEJPVOP2CM+UtJP1K00PyItfYfp51yKfe+WHxXbS5ufDXpptW1rvcHT5xXz8BokrMBAAAy\nAjXsRYRyMu9/aK5GbXmBli2YtgHWa+0+RQMAAGaztHT5rLWjii6832CMCSU4ZVns9bUUt5l8bmfZ\n9AOxey6KfpQNx407xph/lvRlRXdufY+19muXc+/LiO+qXewxpNls3dIq5YWCU+8jEWm3PZviCgAA\nAH9Rw15czhycJHDzmnmu901HOjQ27l0SCwAAIJV0VknbFH2U6fYEx+5R9Df4v01x/bOxc+5OcOyO\n2L23Txv/35I+IqlT0t3W2h8lurG1tlXRXVlvNsa4tmYzxhRLuknSEWttWn+NPZebrLmhoDaucD9m\n9fLrNFkBAEDG2yZq2KTmYpN186paxc/PHR6d0IHjyfYWAwAASCydVdK3Yq9fMMYUTA4aY35H0QLz\np9baU8kujh17StKdxpgH4q4vkPRg7O1DceP/VdKHFX086k5r7Y6LxPfvkgolfX7a+Odj4w95rrjG\n5mKRGu+mVe4lAw6d7Nb5vrmzhhcAAJiTqGGTCAYcBQJza7kASSovztOyhWWuMZ7AAgAAlytdG1/J\nWrvDGPOQokXjK8aYxyUtlPReSe2S/nLyXGPMFklbJDVZax+Pu81HJe2Q9ANjzGOSTkl6QNIKSV+y\n1jbFrs+R9A+xa5okvccY854EYe2LmxnwFUnvlvTfjDEbJe2UdKukOyU9L+nrV5WASzCXZ7JK0nVL\nK1WQF9TQyISk6JSO3fas7t28yN/AAAAAkqCGTS4Umnu167amVklSebF7L7GXD55V4/xSV1N5y8b6\nGY0NAADMLmlrssZ8RNG1oz6kaLHZKelRSZ+21h6NO2+LpM9IeljSVIFqrT1kjLlF0d/M3ycpX9Ih\nSX+sC7MMJGmtoutbSdEC884k8XxH0c0EZK0dMcbcHfvc9yhanJ6U9AVJX7TWpn3K5VxvsoZygtq4\nvEY7DrRNjb10kCYrAADIeNSwCYSCwYufNEs1zCvRroPnpt6PjoXV1jWoBdVFKa4CAAC4IK1NVmtt\nRNLXYn9SnfdZSZ9NcuywpPdd5PpXJF32s0vW2l5J/z32Z8bN9SarJN24utbVZD1yqkddvcOqLM33\nMSoAAIDkqGETm8u1a3FBSNVl+eroGZ4aa2nro8kKAAAu2dytlGaB0Bxfk1WSrmusVEGeu5e/6yBr\nXAEAAMw2c7nJKkkN84pd70+e7Vc4EvEpGgAAMNvM7Uopw831QlWKbu61aWW1a+xlmqwAAACzzlyv\nXRfXlbjeD49O6GzXkE/RAACA2WZuV0oZLDBHd2dN5MZV81zvm0/3qqOHghUAAGA2metN1pLCXFWU\n5LnGWtr7fIoGAADMNune+ApJZMNSAZPWLKlQUX6OBobHp8a+/5sjWttYqaLCPAUCjsLhiAYGL+zT\nwO6tAAAAmWWuN1ml6GzW830XatIT7X26aXWtHCc7JkcAAIArN/crpQyVDUXqpOiSATWuseNtzAoA\nAACYTUI5Qb9DSLvF09ZlHRqZ0LlunsACAAAXlz2dvgyTTU1WSbpptXvJgM6eYfUNjvoUDQAAAC5X\nNtSvZcV5KivOdY21tPX7FA0AAJhN5n6llKFysmi5AElatbhceSH37IcWZrMCAADMGtmy3NXiee4N\nsE609ykSifgUDQAAmC2yo1LKQNkwEyBeMBDQ4jr341csGQAAADB7ZEv9Or1mHRgeV2fvsE/RAACA\n2SI7KqUMlC1FarzFde5ZAV29I+ruH0lyNgAAADJJttSv5cV5KikMucZOsGQAAAC4iOyolDJQtjxu\nFW9eRaHyc91LBjSf6vYpGgAAAFyObGmyOo6jhulLBpylyQoAAFLLjkopA2VLkRovEHA8s1mbW3t8\nigYAAACXI5QTvPhJc0TDPPeSAb0DozrdMeBTNAAAYDbIvk5fhsjJwiar5F0yoLNnWOdZ4woAACDj\nZdMkgeqyfBXk5bjG9hw651M0AABgNsieSinDhIKO3yH4oraiQAV57lkQR1gyAAAAIONlU5M1umSA\nezYrTVYAAJBK9lRKGSabitR4AcfR4mlrXB2myQoAAJDxsq1+XVTrbrIeb+tTZw9PYAEAgMSyq1LK\nINm0ptV0S+a7m6zne0fUxZIBAAAAGS3bNm6tqyxUbsj9M+85zGxWAACQWHZVShkk22YCxKspL1Dh\ntDWuWDIAAAAgs2XbJIFAwNHCGvds1r0sGQAAAJLI3k6fz7JtJkA8x3E8G2AdO93rUzQAAAC4FNk4\nSWD6uqz2ZLf6Bkd9igYAAGSy7KuUMkROFhap8RbXuQvWrt5hClYAAIAMFQw4CgSyb+PWBdVFCsb9\n3JGI1HSkw8eIAABApsruTp+PsnkmqyRVlxcoP9f9yNmpswM+RQMAAIBUsnEWqyTlBAOqrylyje09\nRJMVAAB4ZWe1lAGytVCdFHAcT8F68my/T9EAAAAglWxbjzXeolr3E1j7j3VpaHjcp2gAAECmyu5O\nn4+yvckqeQvW9vODGhmb8CkaAAAAJJPNtevC2mI5cSsljE+EtefQWf8CAgAAGSl7qyWf5QSzb02r\n6eZXede4On2OJQMAAAAyTTY3WfNCQdVVFrrGdu5v8ykaAACQqbK3WvJRKCcgx6HJGsoJqL7GPZuV\nJQMAAAAyT24WN1klqWGeu2bdfbBdozyBBQAA4mR3teSTbJ4JMN2S+aWu960dAwqHIz5FAwAAgESy\neU1WSVpUW6L4KRKDw+Paa1kyAAAAXEC3zwc0WS9YPK3JOjYeVvv5QZ+iAQAAQCLZXr8W5udoxcIy\n19jzTad9igYAAGSi7K6WfJKb5TMB4hUXhFRbUeAaO3WWdVkBAAAySbY3WSXpxtXzXO9feu0Mm7YC\nAIApVEs+oEh1WzLfPSvg5Nl+RSIsGQAAAJApqF+lzaZG8dsqDI1MaPfBdv8CAgAAGYVqyQcUqW6N\nC9xLBvQPjam1g9msAAAAmYL6VSorzpNZVO4a2/4KSwYAAIAoqiUf5IZYLiBeVVm+igtCrrGmwx0+\nRQMAAIDpsn3jq0k3TVsyYNfr7RoZZckAAABAk9UXzARwcxxHS6ZtgNV0hCYrAABApqB+jbrB1CgQ\nt2bA6FhYrzRTtwIAACnH7wCy0Vza+GpbU+s1uc+S+aXaf7Rz6v3R073q6R9RWXHeNbk/AAAArhxN\n1qiSwlytXlKhA8e6psZeev2sZ4YrAADIPlRLPqBI9VpQXaRQ0J2XV5o7k5wNAACAmUT9esGNq2pd\n7/c1d2poZNynaAAAQKZgJqsPQiGK1OmCwYAW1BSppa1vaqzpcIfeuGGBj1EBAABAYk1W6cITXCOj\nEwo4UjgSHR+fCOt7Ww9radxmrls21vsRIgAA8BHdPh/MpeUCrqVFtUWu9weOd2lkjI0EAAAA/MZM\n1gvycoNaWFviGjt+ptenaAAAQKagWvIBRWpi9dXFittHQGPjYb1+/Lx/AQEAAEAS9et0yxeWu96f\n7hjQKJMDAADIamlfLsAY8/uSPibJSBqU9JSkT1prWy7x+gZJn5N0j6QqSYckPWSt/eYlXPs/JH1c\nUsha61koyRjzqKT3Jbn8gLX2ukuJ8XJRpCaWlxtUbXmB2s8PTY01HTmnjSuqfYwKAABkI2pYt1zq\nV5clC0oV2OsoHFszIByRTrT3a/nCMp8jAwAAfklrk9UY86Ckv5H0mqSHJDVIer+k+4wxN1lrj13k\n+sWSXpBUI+lRSW2SfkfSN4wxq6y1/z3FtX8g6a8uEuJGSecl/XOCY2cvcu0VY7mA5BbVFk9rsnYq\nHIkoED/FFQAAII2oYb1Yk9UtLxRUw7wS1zIBR8/00mQFACCLpa3JaozZoGhxul3SPdba0dj4Y5J+\nJOmrkt55kdt8RdICSW+31v4ydv1nJP1G0l8YY75rrd097XODkj4r6ZOSknbmjDGFklZI+rW19rOX\n+/NdDWayJrewtli77Lmp970Dozp2plfLFlCwAgCA9KOGTSyH+tVjZUO5q8na1jmo/qExFReEfIwK\nAAD4JZ3V0kdjr5+bLE4lyVr7Y0nPSXqHMSbptpuxGQAPSHphsjiNXT+kaOHrSPrQtGtuk7RX0qck\n7ZLUkSK+dYr+/K9cxs90TeSGKFKTKS3KVVlRrmus6XCqf4wAAADXFDVsAsEATxVN17igzFPXN7f2\n+BQNAADwWzq7fXdLGle0GJ1uq6IF5l0prt8SO2drgmPbJY3GPiPeeyUtlfRpSbdLGkhx/42x1xlv\nsvK4VWoLa4td75uO0GQFAAAzhhoWlyQnGPBsgNXc2qtIJOJTRAAAwE9pWS7AGJMrabGk49bakQSn\nHI29rkpxm5Wx1yPTD1hrx4wxJyU1GmNy42YZfF/Sg9ZGnzc3xqQKc7JAXWyMeSb23lG0+P07a+3L\nqS6+Grm5AQWdtO85Nis4gQuvRYV5kqQVDRU6cKxr6pzWcwMajUh1VUV+hDjjArGZIoGAo8rK7PiZ\nUyEfXuTEi5x4kRMvcuIVYHaiCzVsavH1WrabrGFXN1bqtbi6tX9oTD2D41n3HcP3qxc58SInXuTE\ni5x4kROvTK1h09Xpq1S02OtKcnzyOZryJMel6C6susg9ApJKFXukylq74zJi3BB7/bSkn0j6pqS1\nkt4m6U3GmN+11v78Mu53SQKOlBMIyGEjJxdHzlSxOr+6SKVFueodmHpCT7sPntU737jMp+j84TiO\ngkH+PZlEPrzIiRc58SInXuQEKVDDphBfryFqXkWhKkrzdL73Qk/enjivYDA7E8X3qxc58SInXuTE\ni5x4kZPMl64m6+SimolmAMSP56f5HqkMKjrD4N3W2qnHrYwxb5X0c0kPG2MarbW9yW5wJUI5QcmR\nwmEeI5KiswAcOYoookj4wvjm1fP0m10np97vPNCmt9/e6EOEMy8QcOQ4jiKRCP+eiHwkQk68yIkX\nOfEiJ16TOcEUatgUptdr2WyyhpUjrWqo1I79Z6aONZ/qVv/AqArys+fJNb5fvciJFznxIide5MSL\nnHhlag2brv/yD8Vec5Mcn3zOqD/N90jKWntvkvEnjDGPSvqApLdL+t6V3D+Z3FBAkbA0MJis7s4u\nRYV5cgLy5GT1onJXk/W1o506dbpbhflzf7fWysoiBYOOwuGIurpSLcmWHciHFznxIide5MSLnHhN\n5gRTqGFToIa9IL6GXVhTKMeRJpdiHZ+I6Fc7jumNGxb4G+QM4vvVi5x4kRMvcuJFTrzIiVem1rDp\neo6lR1JYyR+lKos7L5nJR6xS3SMi6Zr/ll7SS7HXa/58OpteXZq1jRXKiXvMaiIc0atHkz11BwAA\ncE1Qw+KyFeTlqL7avUbe9lfPJDkbAADMVWlpssYW8T8qqcEYk2jq4WTh91qK2xycdu6U2D0XRT/K\nXvZDS8aYEmPMLcaYDUlOmayShpIcv2K5Odm5PtPlys/N0ZolFa6x3fasT9EAAIBsQA2LK7V8YZnr\n/ZFTPWrvGvQpGgAA4Id0dvy2KfqY1O0Jjt2j6G/wf5vi+mdj59yd4NgdsXtvv8LYVkvaIek7SY6/\nMfb6UpLjVywUosl6qTauqHa9bzrSoYHhMZ+iAQAAWWKbqGFxmepripUXcj+xxmxWAACySzo7ft+K\nvX7BGFMwOWiM+R1FC8yfWmtPJbs4duwpSXcaYx6Iu75A0oOxtw9dYWy7JDVLWmuM+aP4A8aYD0q6\nT9JuXXkBnFQuywVckm1NrRoZnVAgbiHj8YmIvvPUIW1rap36AwAAcI1Rw+KyBQOOli4odY29sL+N\nDUoAAMgiadvy0lq7wxjzkKQPS3rFGPO4pIWS3iupXdJfTp5rjNkiaYukJmvt43G3+aiiv63/gTHm\nMUmnJD0gaYWkL1lrm64wtnCsEP2VpH83xvyuoo99bZR0r6Q2SR+w1l7zqijEcgGXLC83qIW1RTrR\nfmFfiObTPVrZkGyJMwAAgKtDDYsrtXxhqV5vOT/1/nzfiF450qHrV9b4GBUAAJgp6e74fST2Z0TR\nYvNOSY9Kus1aezTuvC2SPqNo8TnFWntI0i2Sfqjob+Y/LGlA0h9L+sTVBGat3S5pcyyezZI+JslI\n+rqk62Offc2FQsxkvRzL6t3rW53rHlbvwKhP0QAAgCxBDYvLVlGSr6rSPNfYUy+f9CkaAAAw05xI\nhF90z6TPfnNH5KY1dRoYHPE7lIxQVJinQMBROBxJmJNwOKIfbGvW8OjE1Ni6ZVW6PrZe65aN9TMW\n60yprCxSMBjQxERYXV0DfofjO/LhRU68yIkXOfEiJ16xnDgXPxPZ7skdxyPJ6rVslKyGbW7t0W9f\nbXOd++kPbtaSutLpt5hT+H71Iide5MSLnHiREy9y4pWpNSzPrs8wlgu4PIGAo8b57qL0aGuP+OUA\nAAAAMs2S+aUqyHM/ucZsVgAAsgMdvxnGxleXb2m9u8k6MDyu9q4hn6IBAAAAEgsGHK1qqHCNvfz6\nWXX1DvsUEQAAmCk0WWdYKETKL1dlSZ7Ki3NdY82ne3yKBgAAAEhuxaJy5cY9vTYRjmjrnlM+RgQA\nAGYCHb8ZxkzWy+c4jmcDrJa2Po2Nh32KCAAAAEgsPzeo29fNd409u/e0hkfHfYoIAADMBJqsM4w1\nWa9M4/xSxa9oPD4R0Yn2Pt/iAQAAAJJ5042LXO8HR8Y9G2IBAIC5hY7fDKPJemUK83O0oLrINXb0\ndK9P0QAAAADJ1VUWasOyKtfY0y+fVDjM5q0AAMxVOX4HkG1yQywXcKWW1peqtWNg6v2ZzkG1dQ2q\nrrLQx6gAAAAAt21NrZpXWSg1d06Nne0e0iNPWzXMK3Gdu2Vj/UyHBwAA0oBplTMsl5msV2xRbbFn\nJvAvdhz3JRYAAAAglXmVBaooyXONHTh23qdoAABAutHxm2EhZrJesZxgQGZRuWtsx/52nese8iki\nAAAAIDHHcbRmSYVr7Fz3kNq7Bn2KCAAApBNN1hnGmqxXZ01jhYKBC1tghSMRPbGzxceIAAAAgMSW\nzC9VYb57hbZ9cUsIAACAuYOO3wwLOM7FT0JS+bk5WjltNuv2V8+oq3fYp4gAAACAxIIBR2sbK11j\nZzoH1dHDk1gAAMw1NFkx66xtrFQgbjbr+ERET754wseIAAAAgMRWLCxTfq57ybBXm7t8igYAAKQL\nTVbMOoX5OVqxsMw19uwrp9XTP+JTRAAAAEBiOcGAZ23Wk2f7db6P2hUAgLmEJitmpbWNlYqbzKqx\n8bB+9dJJ/wICAAAAkljZUK7caXsz7D/K2qwAAMwlNFkxKxUXhLS03j2b9Zm9reobHPUpIgAAACCx\n3JygVi12z2Y9fqZPvQPUrgAAzBU5Fz8FyEzrllaqubVHkUj0/cjYhL7xswO6wdQmPH/LxvoZjA4A\nAAC4YNXiCr12vEvjE9HiNSLpwLEuvfP2Rn8DAwAA1wQzWTFrlRTmqnF+qWvs9ePdzGYFAABAxsnP\nDWrlonLXWHNrj7p6h32KCAAAXEs0WTGrrV9W5VqbNRyJaLc9519AAAAAQBJrllQqEFe8hiPSr3ef\n8jEiAABwrdBkxaxWWpTrWd/qRHu/2roGfYoIAAAASKwwP0fLp+0r8FzTaQ2PjvsUEQAAuFZosmLW\nW7+sSnmhoGts18GzCk8u1goAAABkiDVL3BMEBkfG9cL+Np+iAQAA1wpNVsx6uaGgNqyoco119Y6o\nubXXp4gAAACAxEqLcrWwpsg19vSuU0wQAABglqPJijlh5cJylRXnusb2HjqnsfGwTxEBAAAAia2e\nNpu1vWtQ+492+RQNAAC4FmiyYk4IBBzduKrWNTY8OqH9Rzt9iggAAABIrK6yUOXTJgg8veukT9EA\nAIBrgSYr5owF1UWqn/bo1YHj59U/OOZTRAAAAICX4zie2awHjnWp9Vy/TxEBAICrRZMVc8pmUyPH\nufA+HI5o96Fz/gUEAAAAJNA4v9Szeeuvd5/yKRoAAHC1aLJiTikrzpNpKHeNtbT16ez5QZ8iAgAA\nALxyggGtXFTmGnthf5v6h3gKCwCA2YgmK+acDcuqlRty/6v98uvn2LEVAAAAGcU0VCgYuPAY1th4\nWM82tfoYEQAAuFI0WTHn5OUGtWFZtWuss3dYO/a3+RQRAAAA4FWYn6MbV7s3b/3NnlaNT4R9iggA\nAFwpmqyYk0xDuUqL3Du2/vDZZo2MTvgUEQAAAOD1ps2LXO/P942o6XCHT9EAAIArRZMVc1Ig4Giz\nqXGNdfeP6okXW3yKCAAAAPBqnF+q5fXutVm3sgEWAACzDk1WzFn1NUWaX1XoGnvixRPq7Bn2KSIA\nAADA6+4b6l3v7clunTrb71M0AADgStBkxZzlOI5uXFUrJ25sbDysHz9/1LeYAAAAgOk2m1rPUle/\n2cNsVgAAZpMcvwMA0qm8JE8rFpXr0MnuqbEd+9v05hsXqWFeiY+RAQAAANK2plZJ0pK6Eu1r7pwa\n3/7qGdVVFSo3FJwa27Kx3nM9AADIDMxkxZy3YXmVcoIX5rNGJP3g2Wb/AgIAAACmWbmoTE7cI1jj\nExEdae3xLyAAAHBZaLJizivIy9F1jZWusf1Hu/T68S6fIgIAAADcCvNDniet7IluRSIRnyICAACX\nI+3LBRhjfl/SxyQZSYOSnpL0SWvtJW3zboxpkPQ5SfdIqpJ0SNJD1tpvXsK1/0PSxyWFrLXjCY4X\nxY7/nqRFktokPSrp7621g5cSH2aH1UsqdexMn3oGRqfGHtvWrL/9gwoF4qcMAAAAiBoW/ljVUK6W\ntr6p932DYzrdMaj6miIfowIAAJcirTNZjTEPSnpYUr6khyRtlfR+SbuMMY2XcP1iSTskfUDSM5K+\nJqlI0jeMMV++yLV/IOmvUhzPlfQzSZ+WdEzS/5LUIun/lfRrY0zexeLD7BHKCWj14grXWEtbnx5+\n8qC2NbW6/gAAgOxGDQu/1FYUqLzYvQHWwRPnfYoGAABcjrQ1WY0xGyT9jaTtkq631n7CWvt7kt4j\nqVrSVy/hNl+RtEDSA9ba37fWflzSekk7Jf2FMeaGBJ8bNMb8vaT/UOqf788k3SXpH62191lr/9pa\nuyUW162SPnyJPypmieULyzy7tu491KGJMI9gAQCAKGpY+MlxHK2aNjGg9dyA+gZHk1wBAAAyRTpn\nsn409vo5a+1UVWCt/bGk5yS9wxiTdHvM2AyAByS9YK39Zdz1Q4oWvo6kD0275jZJeyV9StIuSR0p\n4vuYpBFJn582/ilFHwn7s1Q/HGafQMDRppXVrrH+oTEdOtHtU0QAACADUcPCV43zSxXKcf9v2sEW\n6lUAADJdOpusd0saV7QYnW6rogXmXSmu3xI7Z2uCY9sljcY+I957JS1V9PGp2yUNJLpxrPhdKukl\na21f/DFrbb+kFyWtMMYsTBEfZqFFtcWqKc93je1r7tTo+IRPEQEAgAxDDQtfhXICWl5f5ho7fKpb\nw6PUqwAAZLK0bHwVWytqsaTj1tqRBKccjb2uSnGblbHXI9MPWGvHjDEnJTUaY3LjZhl8X9KD1tpz\nsTgu+95x8d0Vi+9UihiviBOQigpZLkuK5mLydaZycvuGej3+bPPU+5GxCR062aub19ZJkior/d1Y\nIBBwpl79jiUTkA8vcuJFTrzIiRc58ZrMCaKoYVOjhr0g3TXsplW1OthyXpOLWo1PRNR8ujdjZXfr\nKQAAIABJREFUv7v4fvUiJ17kxIuceJETL3Lilak1bFqarJIqFf0NfleS4z2x1/IU96iKvaa6R0BS\nqWKPVFlrd1xifJdy74vFd8UcOVOFGaJmMif1NcVqXFCqY6d7p8b2HTmn9curVVQQUjCYGf9wHMdR\nMJiZXxx+IB9e5MSLnHiREy9yghSoYVOghvVKV07KS/K1oqHctazV/uZOjYxNqDA/dO0/8Brh+9WL\nnHiREy9y4kVOvMhJ5ktXk3Vyd6FEMwDix/OTHL9W9/Dj3hcVUUSRcDruPPs4gWhxOtM5uXlNnY6f\n7nXNDnjxQJu2bFqoiQl//+EEAo4cx1EkElGYTbnIRwLkxIuceJETL3LiNZkTTKGGTYEa9oKZqGGv\nX1nrarKOjE3o59uP6nfvWpGeD7wKfL96kRMvcuJFTrzIiRc58crUGjZdTdah2GtukuOTz9T0p/ke\nftz7oiJhaWAwWW2cXYoK8+QEZj4nuTmOli8s0+FTPVNjB493acXCUnV1JVwGbcZUVhYpGHQUDkd8\njyUTkA8vcuJFTrzIiRc58ZrMCaZQw6ZADXvBTNSweTmOGuYV60T7hX+cjz/brNvWzFNeKJiWz7xS\nfL96kRMvcuJFTrzIiRc58crUGjZdD/z0SAor+aNKZXHnJTP5GFSqe0Qk9SY5nsql3FtKHR9muQ3L\nq5UT95cyImnvoVSb+QIAgDmOGhYZZd3SKtf7vsExPffKaZ+iAQAAqaSlyRpbxP+opAZjTKJFg5bF\nXl9LcZuD086dErvnouhH2St5QCfpvS8jPsxyhfk5Wr2k0jV28my/Dp/qTnIFAACYy6hhkWmqyvK1\noNq9ycmTL57QuM/LWwEAAK90Ll2/TdFHmW5PcOweRX+D/9sU1z8bO+fuBMfuiN17+5UEZq1tVXRX\n1puNMa6qxRhTLOkmSUeste1Xcn/MHmsbKzyPWz32zBFFIqxzAgBAltomalhkkHXL3JMCzveN6IX9\nbT5FAwAAkklnk/VbsdcvGGMKJgeNMb+jaIH5U2vtqWQXx449JelOY8wDcdcXSHow9vahq4jv3yUV\nSvr8tPHPx8av5t6YJXJzglq/3P0YVnNrr/awbAAAANmKGhYZZV5FoeZVFLjGfrmjRRNhZrMCAJBJ\n0rXxlay1O4wxD0n6sKRXjDGPS1oo6b2S2iX95eS5xpgtkrZIarLWPh53m49K2iHpB8aYxySdkvSA\npBWSvmStbbqKEL8i6d2S/psxZqOknZJulXSnpOclff0q7o1ZZOWich1sOa++wbGpsR8916zrV1Qr\nEMi8hZQBAED6UMMiE61bVqX2XRd6+2e7h7R93xndubHex6gAAEC8dM5klaSPxP6MKFps3inpUUm3\nWWuPxp23RdJnFC0+p1hrD0m6RdIPJd2naLE7IOmPJX3iagKz1o4o+hjX/1R0/aq/kFQv6QuS3h47\njiwQDDjauKLaNXamc5DHsAAAyF7UsMgo86sKVVWW7xr78XNHNTQy7lNEAABgOoe1J2fWkzuOR8Lh\niAYGqX8lqagwT4GAI79zEolE9IsdLerqvRBDVWmevvCntyiUE0xx5bVXWVmkYDCgiYmwuroGZvSz\nMxH58CInXuTEi5x4kROvWE54bAMXRQ3r5kcNe7pjQL/e5V6p4m23LNa7tyTbB23m8P3qRU68yIkX\nOfEiJ17kxCtTa9h0z2QFZgXHcXT9ihrXWGfviJ7Z0+pTRAAAAMAFC6qLtGGZey+Bp14+qY7uIZ8i\nAgAA8WiyAjELqgs1r9K9qcDPd7TwGBYAAAAywnvvXq74iTvjE2H957ZmHyMCAACTaLICMY7jaNNK\n92zW/qEx/eqlEz5FBAAAAFwwv6pId13v3uzq5YNndfhUt08RAQCASTRZgTg15QW6ftomWL966aR6\nB0Z9iggAAAC44J1vaFRRfo5r7NGthxVmrw0AAHxFkxWY5r/cuUxO3PLJI2MT+tkLx32LBwAAAJhU\nXBDSO29vdI0dO9OnnQfafIoIAABIUs7FTwGyS311kW6/br62v3pmamzb3lbde8NCzass9DEyAAAA\nZLNtTdFNWYNBR6WFIfUOjk0d+87Th9Q/NKbcUFCStGVjfcJ7AACA9GAmK5DAu97QqJzghb8eE+GI\nfsCmAgAAAMgAgYCjG1bVusaGRia093CHTxEBAABmsgLTTM4QMA3lOnCsa2p896Fz+v5vDrtmszJD\nAAAAAH5YWFOkBdVFOt0xMDVmT3Rr6YJS1ZQX+BgZAADZiZmsQBLrllYqL/a41aRd9pwibCoAAAAA\nnzmOo5vX1CoYcFzjOw+0KxymXgUAYKbRZAWSyA0FtWF5lWuss2dYx8/0+RQRAAAAcEFJYa6nXj3f\nN6LXW877FBEAANmLJiuQwspF5SotDLnG9hw6p4mJsE8RAQAAABesWVKp8uJc19grRzrU0T3kU0QA\nAGQnmqxACoGAo02mxjU2MDzO7AAAAABkhEDA0a1r61xj4xMRPfL0IZa5AgBgBtFkBS5iUW2x5lW4\nNw949WiXhkbGfYoIAAAAuKCmokArF5W5xvY1d+ql18/6FBEAANmHJitwEY7j6IZVta6xsfGw9thz\nPkUEAAAAuG1aWaP8XPemrY88ZXW+b8SniAAAyC40WYFLUF2Wr6ULSl1jzad7dZBlAwAAAJABckNB\n3bTaPTFgYHhc//HL11k2AACAGUCTFbhEm1bWKJTj/ivz7aesxtkECwAAABlgcV2JFteVuMb2H+vS\nM3tbfYoIAIDsQZMVuESF+Tm6fkW1a+xM56CefPGETxEBAAAAFziOo1vWzFNBnnvZgMd+c0RtXYM+\nRQUAQHagyQpchpUN5aoqzXON/eyF4zrbPeRTRAAAAMAFeblB3XbdfNfY6HhY3/zZa5oI8wQWAADp\nQpMVuAwBx9Eta+vkxI2NjYf1yFOWta4AAACQEeprinTXpnrX2LEzvfrFjhafIgIAYO6jyQpcpqqy\nfJmGctfY/qNd2mXP+RQRAAAA4PbeLcs1r6LANfbT7cdlT7BxKwAA6UCTFbgCG1dUe9a6euQpq96B\nUZ8iAgAAAC7Iyw3qj+9fo4Bz4RmscCSif/3JAXX3j/gYGQAAcxNNVuAK5IaCunFVrWusb3BMDz95\nkGUDAAAAkBGWLSjTO9+wxDXWMzCqf318v8YnWJ8VAIBriSYrcIUW15Vo4/Jq19jewx3avu+MTxEB\nAAAAbu+4bYmuW1rpGjt0qkc/fLbZp4gAAJibaLICV8hxHH3wratUUhhyjX9362Gd7R7yKSoAAABA\n2tbUqm1NrXruldNas6RSRfk5ruO/eumkHn7yoLY1tfoUIQAAcwtNVuAqlBbl6oNvXeUaGxmd0L//\n/DWFwywbAAAAAP/l5wZ15/ULXOuzStILr7app589BQAAuBZosgJX6foVNbpj/XzX2OFTPXrypRM+\nRQQAAAC4VZcV6KbV7j0FxibCemZvqwaHx3yKCgCAuYMmK3ANvP+eFaouy3eN/fi5ozp2pteniAAA\nAAC3FYvKtHRBqWusd2BUX//JAU2E2QgLAICrQZMVuAYK8nL0J/evUfwTWBPhiP7lx/vVP8TMAAAA\nAPjPcRzdsnaeKkryXOMHjnXp0a1HfIoKAIC5gSYrcI2sWFiut92y2DXW2Tus//Pz1xSOsD4rAAAA\n/JcTDOiuTfXKzw26xrfuPqVte9kECwCAK0WTFbiG3vWGRq1cWOYa29fcqSd2tvgUEQAAAOBWXBDS\nXdfXezbC+s7Th/R6y3mfogIAYHajyQpcQznBgD70rutUWhhyjf/ouaMUrAAAAMgYNRUFuvW6ea6x\n6HJXr6qta9CnqAAAmL1osgLXWEVJnj70rutc67NGItK//fSAuvtH/AsMAAAAiLOsvkxrGytdYwPD\n4/rKY03qHRj1KSoAAGYnmqxAGqxeXKEH7ljqGusdGNW/snMrAAAAMsimldXauLzaNXaue1j//MN9\nGhmb8CkqAABmH5qsQJq8/dbFWre0yjV26GS3fvTcUZ8iAgAAANwcx9GfvnONFs8rcY0fPd2rb/z0\ngMJhNnAFAOBS0GQF0iTgOPqT+9eoqjTPNf7EzhPae/icT1EBAAAAbvm5OfrYe9Z76ta9hzv0va2H\nFYnQaAUA4GJy0v0Bxpjfl/QxSUbSoKSnJH3SWntJ260bYxokfU7SPZKqJB2S9JC19ptJzn+7pL+W\ntE7ShKTnJf2ttXZfgnMflfS+JB99wFp73aXECCRTXBDSnz+wTv/wyG5NxM0C+Pefv67P/GGxasoL\nfIwOAAAkQw2LbLKtqVWSdPv6+Xpi5wmNjV9Y3mrr7lPq7h+ZWrt1y8Z6X2IEACDTpXUmqzHmQUkP\nS8qX9JCkrZLeL2mXMabxEq5fLGmHpA9IekbS1yQVSfqGMebLCc7/E0k/l7RQ0jcl/VjSmyTtNMbc\nlOAjNko6L+nvEvz5l8v5WYFkli4o1fvvWeEaGxwZ17/8eL/GxlnnCgCATEMNi2xVXpynu66vV8Bx\nj++259Tc2uNPUAAAzBJpm8lqjNkg6W8kbZd0j7V2NDb+mKQfSfqqpHde5DZfkbRA0tuttb+MXf8Z\nSb+R9BfGmO9aa3fHxudJ+mdJhyXdaK3tiY1/Q9Jzkr5pjNlorY3ExgslrZD0a2vtZ6/ZDw4kEAhI\nS+pKdLytb2qspb1PX/5+k25ZW+c697/cvXKmwwMAADHUsMh2dVWFum3dfG3fd8Y1/sL+NuWFgj5F\nBQBA5kvnTNaPxl4/N1mcSpK19seKFozvMMYkfdYkNgPgAUkvTBanseuHFC18HUkfirvkTxWdbfCl\nyeI0dv6Lkh6VtF7SrXHnr1P053/lin464DI4jqNbr6tTaVGua/zQyR4dPsWsAAAAMgg1LLLe0gWl\n2rSy2jUWiUjPNp3W4VPdPkUFAEBmS2eT9W5J44oWo9NtVbTAvCvF9Vti52xNcGy7pNHYZ8R/3uS9\nE31e/DlS9DEriQIVMySUE9CdGxcoOO35qxcPtOtc95BPUQEAgGmoYQFJaxsrtWZJhWtsIhzRV/9z\nn06d7fcpKgAAMldalgswxuRKWizpuLV2JMEpR2Ovq1LcZvKZ6SPTD1hrx4wxJyU1GmNyY7MMVipa\nECfajCDR500WqIuNMc/E3juKFr9/Z619OUVsgKQLmwRcqoqSPN163Txt39c2NRaORLRt72m9/dbF\nKsxP+150AAAgCWpY4ALHcXSDqdHI6ISaT/dOjQ+OjOvLjzXpr//rJtVWFPoYIQAAmSVdHZ1KRYu9\nriTHJx+FKk9xj6rYa6p7BCSVSuqInd9jrU20k1Ciz9sQe/20pJ8ousnAWklvk/QmY8zvWmt/niK+\nK+YEpKLCvHTcetZxAhdesyUn65bXqndwXPuOdEyNDY2M6/l9Z/SuO5YqEJvpGgg4qqws8ivMjEE+\nvMiJFznxIide5MQrMH13G1DDppBN9drFZFMNe+9NizW+87ha4vYW6Okf1Ze//4oe/PPbVFtRyPdr\nAuTEi5x4kRMvcuJFTrwytYZNV5N1cuHJRDMA4sfzr+E9ci/z8wYVnWHwbmvt1ONWxpi3Krq768PG\nmEZrba+uMUfOVGGGqGzLye3rF6izZ1it5y48atXeNajt+07r/jcukxSdPRAMZuYXhx/Ihxc58SIn\nXuTEi5wgBWrYFLKtXrsU2ZCTQMDRW25dop8+f1RnOgamxs91D+lv/22HvvjhN6i6vEAS36+JkBMv\ncuJFTrzIiRc5yXzparJOLjCZm+T45K97Uy3mc7n3GLqcz7PW3pvoRGvtE8aYRyV9QNLbJX0vRYxX\nJKKIIuFrfdfZyQlEi9NszMmbbmrQD585rL7Bsamx14516WfPH9X9dyxVJBJROBzxMcLMEAg4chyH\nfMQhJ17kxIuceJETr8mcYAo1bArZWK8lk201bMBx9NZbluinzzero2d4ary9a1Cf/Ppv9eCf367q\n8gK+X+Pw3xwvcuJFTrzIiRc58crUGjZdTdYeSWElf5SqLO68ZCYfsUp1j4ikyd/Sd0mqNcY41trp\n/9ZdyufFe0nRAnXZJZ5/WSJhaWAw2YSF7FJUmCcnkL05uXPjAj2x84Qm4r4ov/mTV1VdXqCb1sxT\nV9dAiquzQ2VlkYJBR+FwhHzEkBMvcuJFTrzIiddkTjCFGjaFbK3XEsnWGvbuG+r1wqttOnXuwnfo\n6Y4B/e2/vaAv/D+3q7Qwl+/XGP6b40VOvMiJFznxIidemVrDpuXhltgi/kclNRhjQglOmSz8Xktx\nm4PTzp0Su+ei6EfZcNz5ubHxlJ9njCkxxtxijNmQ4FxJmlzkgi3fkVaVpfm6bV2daywSkf7pkV2y\nLed9igoAgOxEDQuklp+bo796//WaX+Xe8OrU2X598usvqCtulisAANkmnSsIbVO0YLw9wbF7FP0N\n/m9TXP9s7Jy7Exy7I3bv7dM+T0nOvyf2Onn+akk7JH0nyWe/Mfb6Uor4gGuicX6pNiyvco2Njof1\n4P99Se1dgz5FBQBA1tomalggqT2Hz+n2dfNVUuj+PcTJ9j597CvP6mcvHNO2plZta2r1KUIAAPyR\nzibrt2KvXzDGFEwOGmN+R9EC86fW2lPJLo4de0rSncaYB+KuL5D0YOztQ3GXPKLo5gCfMsZUxZ1/\ns6T3SWqy1k4WxLskNUtaa4z5o/jPNcZ8UNJ9knbLXQADabN+WZWW15e5xnoHRvWVx15R78CoT1EB\nAJCVqGGBiyjMz9Gbb1qk4gJ3o7VvcFRPvnhC5/uyZwkFAAAmOZFI+hbNNcZ8TdKHJR2W9LikhZLe\nK6lD0m3W2qOx87ZI2qJoEfl43PUrFf1tfZmkxySdkvSApBWSvmSt/fi0z/srSV+SdFrS9yWVKrou\nVVjSXdbal+POfYOkX0kqlPRLRR/D2ijpXkltku601h66ZsmIeXLH8Ug4HMmqtZtSKSrMUyAQXVsk\n23MSDkf0mz2tOt3hXmOlqjRfb7pxoXJDQdf4lo31Mxmeb6JrrQQ0MRFm/ZkYcuJFTrzIiRc58Yrl\nJPMWtPIZNawXNawbNWxU/9CYnn75pGsjV0nKDQV0zw0L9Z4ty32KzH/8N8eLnHiREy9y4kVOvDK1\nhk3nTFZJ+kjsz4ikj0q6U9KjiitOY7ZI+oyixeeUWIF4i6QfKvqb+Q9LGpD0x5I+Mf3DrLX/pGhB\nelrSn0u6X9LTkm6PL05j526XtDkWz2ZJH5NkJH1d0vXpKE6BVAIBR3duXKDq8gLXeGfvsLbuPqWx\n8SzYuhYAgMxADQtcguKCkN5yc4OqyvJd46NjYT398kntP9bpU2QAAMy8tM5khRezANyYBeDlBAL6\n0bZm9Q26lwmYV1Ggu29YqFBO9HcjzGTNXuTEi5x4kRMvcuKVqbMAkHmoYd2oYd1ycnL0xI5jOtPp\n3k8g4Dh6/z3Ldc8NC+U42fVVw39zvMiJFznxIide5MQrU2vYdM9kBXCZCvNDeucdS1WYl+Mabz8/\npGf2tGp8ghmtAAAAyBx5uUHdf8dSLaotdo2HIxF999eH9e1fWWpYAMCcR5MVyEDlJXm6/46lys91\nr8Pa1jWoZ/a0aoIiFQAAABkklBPUW29boiV1JZ5j25pO639+v0n9Q2MJrgQAYG6gyQpkqMqpDa/c\nf03PdA7q17tOaXCYIhUAAACZIxgI6I4N87V+WZXn2MET3fr7h19WS1ufD5EBAJB+NFmBDFZRkq83\nbV6k3Bz3X9X280P64nf26Hwfa4ABAAAgcziOo40rqnXHhvlTewlMOtc9rAe/vUvP7Dkl9gYBAMw1\nNFmBDFdVlq97Ny/0FKmnzg3oC9/erTOdLHwNAACAzNI4v1RvunGhCvLcy1+NT0T07acO6XMP79JT\nL5/QtqZWnyIEAODaoskKzALV5QW676ZFniK1s3dY//DIHjW39vgUGQAAAJBYdVmB3n7rYlWX5XuO\ntbT16Rc7WtTZM+xDZAAAXHs0WYFZorI0X2+5uUElhSHXeP/QmL74nT16+uWTPHYFAACAjFKYH9J9\nNzdozZIKz7G+wTE9sfOEtu5m+QAAwOxHkxWYRUoKc/WWmxtUVeqeDTARjuh7Ww/raz96VQNsiAUA\nAIAMEgw42ryqVndtqvds6hqORPSdpw/p64/v1+DwuE8RAgBw9WiyArNMQV6O3nzTIl3XWOk5tvdw\nhz77rZfVfJrlAwAAAJBZFtUW6x23LUm4fMAue05/939f0vG2Xh8iAwDg6tFkBWahUE5AH3vPer39\n1sWeY529w/riI3v04+eOanwi7EN0AAAAQGLFBSG9JcnyAee6h/WFb+9mGSwAwKxEkxWYpZ7fd0ZV\nZfm654aFygu5N8SaCEf0sxeO6+Nff0E/eLbZpwgBAAAAr0Bs+YC7EywfMD4RXQbrqz/Yp97BUZ8i\nBADg8tFkBWa5+poi3X/7YtVWFHiOdfeP6omdLfrBtmaNjU/4EB0AAACQ2MLaYt1/2xItry/zHNvX\n3KnPfOslvd5y3ofIAAC4fDRZgTmgMD+kN9+4SOuXVclx3MciEemXO1v09w/v0smz/f4ECAAAACRQ\nVBDSxz9wvd56S4PnWE//qP7pe3v1w2eZMAAAyHw0WYE5IhBwtHFFtd5262JVlOR5jp86N6C/f/hl\nPfFii8Jh1rgCAABAZsgJBvSeLcv1l+/boNLCkOtYRNIvdrToM996WYdOdvsTIAAAl4AmKzDHVJXm\n6223LtaG5d5ZreMTEf3nM8360vf2qqNnyJ8AAQAAgDjbmlq1ralVHT3Duu/mBs2vKvSc09Y1qC9+\nZ4/+v19ZDQ6P+xAlAACp0WQF5qBgwNGG5dV6x22JZ7Xak936zLde0ssHz/oQHQAAAJBYQV6O7t28\nUJtWVnsmDEjStr2t+tT/2aln9rZqdIwlBAAAmSPH7wAApE9FSb7edmuDmg536sCxLtexoZEJff3x\n/fr1wjLduLpWOcGAtmys9ylSAAAAIMpxHF23tEr1NUV6YX+7OnuGXce7+0f17V9Z/eT5o7p38yLd\ntaleRfmhJHcDAGBmMJMVmOOCgYBuMDV6802LVJTv/b3K4VM9+uWOFnX3jfgQHQAAAJBYRUm+3npL\ngzavqlFO0DuttXdwTD967qj+6l9e0LefsrInzrP3AADAN8xkBbJEXWWh7r99iV58rV3HzvS5jnX3\nj+oXO1pUXBDS3TcsVCDRs1kAAADADAs4jtYsqVRDbYl2vtam0x2DnnNGRif0zJ5WPbOnVWVFudpk\narTZ1MosKlcgQF0LAJgZNFmBLJIbCuoN6+drQXWRXnytXeMTF37TPxGO6Lu/PqyXXj+rD751lRZU\nF/kYKQAAAHBBcWFI99ywUPMqCvXEzhYdOH4+4Xk9A6NTDdfSwpA2mVptNjUyDeUKBniQEwCQPjRZ\ngSzjOI6W1Zepuixfz71yRuenLRNwpLVHn/2Pl/TO2xv1lpsblBOkGAUAAID/HMfR2e4h3bCqVkvm\nl+rAsS61tPUp2QIBvYNj2ra3Vdv2tqq4IKRNK2t0g6nR6sUV1LgAgGuOJiuQpcqK8/S2Wxq0y56T\nPdHtOjY+EdGPnjuqna+164E3NGqTqWEJAQAAAGSMqrJ8vXHjAvUNjurY6V4db+tTd/9o0vP7h8b0\n3Cun9dwrp1WQF9SGZdXatLJG65ZWKS83OIORAwDmKpqsQBYLBgO6ec08LZ5Xoh0H2tQ3OOY6frpj\nQP/y+H4tqi3WA3c0auPyajk0WwEAAJAhSgpztX55tdYvr1ZP/6ha2vvU0tbneVor3tDIhHa+1q6d\nr7UrlBPQdY2V2rSyRhuWV6u4IDSD0QMA5hKarABUVxXdFKurZ0S/evmEItOeuTp5tl//+4evakld\nid580yJtNrU8YgUAAICMUlacq/XFVVq/rEq9A6NqaetTS3ufunqTN1zHxsPae7hDew93KOA4Mg3l\n2rC8WuuWVqquspAJBgCAS0aTFYAkKScY0HvvXq4bV9fq4ScO6sTZfs85x9v69I2fvqZHi47ozg0L\ntOX6elWU5PkQLQAAAJBcaVGu1i2r0rplVeobjDZcT7T3q6NnOOk14UhEr7ec1+st5/XoVqm6LF/r\nl1Vp9eJKLV1QSt0LAEiJJisAl8b5pfr0H96o3facHn/+qM50DnrO6R0Y1c9eOK5f7GjRhuVVuu26\nOq1fVq1QDrNbAQAAkFlKCnN13dIqXbe0SoPDYzpxtl8n2/vV1jXoeYIrXkfPsH6zp1W/2dMqSaoo\nyVPj/FI1zi/R0gVlWlJXooI8/pcaABDFfxEAeAQcRzeuqtUNK2v04uvt+sn2Yzp7fshzXjgSmXq8\nqig/Rzetnqdbr6vTsgWlPFoFAACAjFOYH9KqhgqtaqjQyOiETp3r14lYw3VsPJzy2vN9Izrfd057\nDp2TJDmS5lcXqXF+iZbVl2n14grVlhdQBwNAlqLJCiCpQMDRrWvrdNPqWu051KGtu0/p0MnuhOcO\nDI/rmb2temZvq2orCnTbdXW6dW2dasoLZjhqAAAA4OLycoNaVl+mZfVlGhsP63THgFo7BtR6rl9D\nIxMXvT6i6EaxpzsG9NtX2yRJVaV5Wr24UpvX1mn98hpVlrLEAABkC5qsAKZsa2pNefyWtfNkGspl\nT5zX0dO9Gp9I/HzV2fNDevz5Y3r8+WNaubBMt62br82mVoX5fOUAAAAg84RyAlpcV6LFdSWKRCI6\n3zei1nMDOtM1qM6e4YvOcp3U2Tui7a+e0fZXz0iSqsvztXR+qZbVl2npglItqCpiiQEAmKP4dgdw\nWSpK8nTL2jptWlmjlvY+HW3tVXuCpQQmHTrVo0OnevTIU4d0/Ypq3XZdndY2VionyPqtAAAAyDyO\n46iyNF+Vpflat6xKkUhEPQOj6uwZVkfPsDq6h9XVN5xyPddJHd3R8196/ezUWFlRruoqC1VXVai6\nykLNqyzU/MpCVZfnKxigRgaA2YomK4Arkvv/s3fncZZU5eH/P7d7enYYmEF2QUR4QFEsClJ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7qyyyhGIfwpxQiCnwCPBo4Bfgg8MTPbPblVkiRJC5QxrCRJkqaqmyNZAV5R/mwHXkkR/J1PXXBa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\n+BjDSpIkaUoq1aoXumdZdWho2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GmtO2btOzdRsrkBAACUEknWgC0sJDQ3n3CNrbaSVZK2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VVI0p8uVt3XuaW+9az5Y9pzJQMxERGW40yCoiPo7jUD2+hBsureaaCycxdlTyZQSaWtp5/OV9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0d+s4OTptRx2ZKTuOSsGYyrqRjizCVJkiRJ0mhmkVXSiJTdTmAiC2dP5MDhVja+doAN2xr7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x26983765ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"# Effectively using matplotlib -- http://pbpython.com/effective-matplotlib.html\n",
"sns.set_context(\"notebook\", font_scale=0.75, rc={\"lines.linewidth\": 1.5})\n",
"\n",
"fig, axs = plt.subplots(nrows=3,ncols=2, dpi=200)\n",
"fig.subplots_adjust(left=None, bottom=None, right=None, top=1.5, wspace=0.25, hspace=0.35)\n",
"# https://stackoverflow.com/questions/6541123/improve-subplot-size-spacing-with-many-subplots-in-matplotlib\n",
"# https://stackoverflow.com/questions/15858192/how-to-set-xlim-and-ylim-for-a-subplot-in-matplotlib\n",
"#fig.tight_layout() \n",
"\n",
"sns.distplot(iatATLreduced['iat_mins'], ax=axs[0,0])\n",
"axs[0,0].set(title=\"ATL\")\n",
"axs[0,0].set_xlim([0, 200]); axs[0,0].set_ylim([0, 0.04])\n",
"sns.distplot(iatORDreduced['iat_mins'], ax=axs[0,1])\n",
"axs[0,1].set(title=\"ORD\")\n",
"axs[0,1].set_xlim([0, 200]); axs[0,1].set_ylim([0, 0.04])\n",
"sns.distplot(iatLAXreduced['iat_mins'], ax=axs[1,0])\n",
"axs[1,0].set(title=\"LAX\")\n",
"axs[1,0].set_xlim([0, 200]); axs[1,0].set_ylim([0, 0.04])\n",
"sns.distplot(iatBOSreduced['iat_mins'], ax=axs[1,1]) \n",
"axs[1,1].set(title=\"BOS\")\n",
"axs[1,1].set_xlim([0, 200]); axs[1,1].set_ylim([0, 0.04])\n",
"sns.distplot(iatMCOreduced['iat_mins'], ax=axs[2,0])\n",
"axs[2,0].set(title=\"MCO\")\n",
"axs[2,0].set_xlim([0, 200]); axs[2,0].set_ylim([0, 0.04])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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