Capstone_Project_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building up Data Pipelines using Apache Airflow \n",
    "### Data Engineering Capstone Project\n",
    "\n",
    "#### Project Summary\n",
    "The main goal of the capstone project is to create a Data Lake in S3 and a DWH in Redshift using Data Pipeline orchestrating tool - Apache Airflow\n",
    "\n",
    "The project follows the follow steps:\n",
    "* Step 1: Scope the Project and Gather Data\n",
    "* Step 2: Explore and Assess the Data\n",
    "* Step 3: Define the Data Model\n",
    "* Step 4: Run ETL to Model the Data\n",
    "* Step 5: Complete Project Write Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do all imports and installs here\n",
    "import pandas as pd\n",
    "import re\n",
    "import os\n",
    "import configparser\n",
    "import boto3\n",
    "from io import StringIO\n",
    "from pyspark.sql import SparkSession\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "config = configparser.ConfigParser()\n",
    "config.read('./dwh.cfg')\n",
    "\n",
    "AWS_ACCESS_KEY = config.get('AWS', 'AWS_KEY_ID')\n",
    "AWS_SECRET = config.get('AWS','AWS_SECRET')\n",
    "AWS_REGION = config.get('AWS','REGION')\n",
    "S3_BUCKET = config.get('S3','RAW_DATA_BUCKET')\n",
    "\n",
    "os.environ[\"AWS_ACCESS_KEY_ID\"]= AWS_ACCESS_KEY\n",
    "os.environ[\"AWS_SECRET_ACCESS_KEY\"]= AWS_SECRET\n",
    "\n",
    "spark = SparkSession.builder.\\\n",
    "config(\"spark.jars.packages\",\"saurfang:spark-sas7bdat:2.0.0-s_2.11,org.apache.hadoop:hadoop-aws:2.7.2\").enableHiveSupport().getOrCreate()\n",
    "\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import from_unixtime, unix_timestamp, to_date, expr,\\\n",
    "                                  date_add,udf,col,avg,mean,year,month,split,lit\n",
    "from pyspark.sql.functions import isnan, when, count, col\n",
    "from pyspark.sql.types import StringType, DateType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Project Scope and Data Gathering\n",
    "\n",
    "The scope of the project is to create a Data Lake which could be accessible for the Data Scientists and a DWH which\n",
    "could be accessed by Data Analysts who are interested in providing deeper insights into US immigration. Main focus areas include\n",
    "the issued visa types and the immigrant profiles associated with it.\n",
    "\n",
    "**Data Sources from Udacity**\n",
    "- **I94 Immigration Data**: This data comes from the US National Tourism and Trade Office found [here](https://travel.trade.gov/research/reports/i94/historical/2016.html). Each report contains international visitor arrival statistics by world regions and select countries (including top 20), type of visa, mode of transportation, age groups, states visited (first intended address only), and the top ports of entry (for select countries)\n",
    "- **U.S. City Demographics Data**: This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. Dataset comes from OpenSoft found [here](https://public.opendatasoft.com/explore/dataset/us-cities-demographics/export/).\n",
    "- **Airport Codes**: This is a simple table of airport codes and corresponding cities. The airport codes may refer to either IATA airport code, a three-letter code which is used in passenger reservation, ticketing and baggage-handling systems, or the ICAO airport code which is a four letter code used by ATC systems and for airports that do not have an IATA airport code (from wikipedia). It comes from [here](https://datahub.io/core/airport-codes#data)\n",
    "**External Data Sources**\n",
    "I have enriched the dataset using the following data sources to make the analysis more useful and more informative\n",
    "\n",
    "- **Port of Entry/Nationality Codes**: This dataset contains information about various of port of entry codes which would be used to join the data in the I94 immigration data. The nationality codes contain the country abbreviations [Port Codes; Nationality Codes And Port of Entry codes](https://fam.state.gov/fam/09FAM/09FAM010205.html)\n",
    "- **US Visa Types**: This data is extracted from the US DHS and Wikipedia which would give information on various visa types offered by US [US Non-immigrant Visa Types](https://www.dhs.gov/immigration-statistics/nonimmigrant/NonimmigrantCOA) and [US Immigrant Visa Types](https://en.m.wikipedia.org/wiki/Visa_policy_of_the_United_States#Classes_of_visas)\n",
    "- **Airline Codes**: This data source consists of airlines IATA abbreviations and the country of origin [Airline Codes](https://www.iata.org/en/about/members/airline-list?page=30&search=&ordering=Alphabetical)\n",
    "\n",
    "In the following sections I will explore various data sets needed for the project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Exploring the I94 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data here\n",
    "df_i94_sample = pd.read_csv('./data/immigration_data_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>cicid</th>\n",
       "      <th>i94yr</th>\n",
       "      <th>i94mon</th>\n",
       "      <th>i94cit</th>\n",
       "      <th>i94res</th>\n",
       "      <th>i94port</th>\n",
       "      <th>arrdate</th>\n",
       "      <th>i94mode</th>\n",
       "      <th>i94addr</th>\n",
       "      <th>depdate</th>\n",
       "      <th>i94bir</th>\n",
       "      <th>i94visa</th>\n",
       "      <th>count</th>\n",
       "      <th>dtadfile</th>\n",
       "      <th>visapost</th>\n",
       "      <th>occup</th>\n",
       "      <th>entdepa</th>\n",
       "      <th>entdepd</th>\n",
       "      <th>entdepu</th>\n",
       "      <th>matflag</th>\n",
       "      <th>biryear</th>\n",
       "      <th>dtaddto</th>\n",
       "      <th>gender</th>\n",
       "      <th>insnum</th>\n",
       "      <th>airline</th>\n",
       "      <th>admnum</th>\n",
       "      <th>fltno</th>\n",
       "      <th>visatype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2027561</td>\n",
       "      <td>4084316.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>HHW</td>\n",
       "      <td>20566.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>HI</td>\n",
       "      <td>20573.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20160422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>07202016</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JL</td>\n",
       "      <td>5.658267e+10</td>\n",
       "      <td>00782</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2171295</td>\n",
       "      <td>4422636.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>MCA</td>\n",
       "      <td>20567.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TX</td>\n",
       "      <td>20568.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20160423</td>\n",
       "      <td>MTR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>10222016</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*GA</td>\n",
       "      <td>9.436200e+10</td>\n",
       "      <td>XBLNG</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>589494</td>\n",
       "      <td>1195600.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>OGG</td>\n",
       "      <td>20551.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>FL</td>\n",
       "      <td>20571.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20160407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>07052016</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LH</td>\n",
       "      <td>5.578047e+10</td>\n",
       "      <td>00464</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2631158</td>\n",
       "      <td>5291768.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>LOS</td>\n",
       "      <td>20572.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>20581.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20160428</td>\n",
       "      <td>DOH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>O</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>10272016</td>\n",
       "      <td>M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>QR</td>\n",
       "      <td>9.478970e+10</td>\n",
       "      <td>00739</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3032257</td>\n",
       "      <td>985523.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>CHM</td>\n",
       "      <td>20550.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>20553.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20160406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Z</td>\n",
       "      <td>K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>07042016</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.232257e+10</td>\n",
       "      <td>LAND</td>\n",
       "      <td>WT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      cicid   i94yr  i94mon  i94cit  i94res i94port  arrdate  \\\n",
       "0     2027561  4084316.0  2016.0     4.0   209.0   209.0     HHW  20566.0   \n",
       "1     2171295  4422636.0  2016.0     4.0   582.0   582.0     MCA  20567.0   \n",
       "2      589494  1195600.0  2016.0     4.0   148.0   112.0     OGG  20551.0   \n",
       "3     2631158  5291768.0  2016.0     4.0   297.0   297.0     LOS  20572.0   \n",
       "4     3032257   985523.0  2016.0     4.0   111.0   111.0     CHM  20550.0   \n",
       "\n",
       "   i94mode i94addr  depdate  i94bir  i94visa  count  dtadfile visapost occup  \\\n",
       "0      1.0      HI  20573.0    61.0      2.0    1.0  20160422      NaN   NaN   \n",
       "1      1.0      TX  20568.0    26.0      2.0    1.0  20160423      MTR   NaN   \n",
       "2      1.0      FL  20571.0    76.0      2.0    1.0  20160407      NaN   NaN   \n",
       "3      1.0      CA  20581.0    25.0      2.0    1.0  20160428      DOH   NaN   \n",
       "4      3.0      NY  20553.0    19.0      2.0    1.0  20160406      NaN   NaN   \n",
       "\n",
       "  entdepa entdepd  entdepu matflag  biryear   dtaddto gender  insnum airline  \\\n",
       "0       G       O      NaN       M   1955.0  07202016      F     NaN      JL   \n",
       "1       G       R      NaN       M   1990.0  10222016      M     NaN     *GA   \n",
       "2       G       O      NaN       M   1940.0  07052016      M     NaN      LH   \n",
       "3       G       O      NaN       M   1991.0  10272016      M     NaN      QR   \n",
       "4       Z       K      NaN       M   1997.0  07042016      F     NaN     NaN   \n",
       "\n",
       "         admnum  fltno visatype  \n",
       "0  5.658267e+10  00782       WT  \n",
       "1  9.436200e+10  XBLNG       B2  \n",
       "2  5.578047e+10  00464       WT  \n",
       "3  9.478970e+10  00739       B2  \n",
       "4  4.232257e+10   LAND       WT  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i94_sample.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:** \n",
    "- The columns in the immigration data are encoded. Therefore these columns could be Foreign Keys in the Fact table\n",
    "- To create the corresponding Dimension Tables the `SAS_Label_Descriptions` file will be used to extract them into `csv` files which will be stored in `meta_data` folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 Extracting meta data from i94 immigration data label Descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Extracting Raw Data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the Label Description file and extracting information which can form dimensional tables later\n",
    "with open('./data/I94_SAS_Labels_Descriptions.SAS') as i94_descritpion:\n",
    "    i94_label_content = i94_descritpion.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = re.search(r'value i94cntyl.*? ;', i94_label_content, re.DOTALL).group()\n",
    "text=text.split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94cit_i94res = pd.DataFrame(columns=['i94_code','country_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94cit_i94res['i94_code'] = [code.split('=')[0].strip() for code in text[1:]]\n",
    "i94cit_i94res['country_name'] = [code.split('=')[1].rstrip(' ;').replace(\"'\",\"\") for code in text[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i94_code</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>582</td>\n",
       "      <td>MEXICO Air Sea, and Not Reported (I-94, no l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>236</td>\n",
       "      <td>AFGHANISTAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>ALBANIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>316</td>\n",
       "      <td>ALGERIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>ANDORRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>324</td>\n",
       "      <td>ANGOLA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>529</td>\n",
       "      <td>ANGUILLA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>518</td>\n",
       "      <td>ANTIGUA-BARBUDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>687</td>\n",
       "      <td>ARGENTINA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151</td>\n",
       "      <td>ARMENIA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  i94_code                                       country_name\n",
       "0      582    MEXICO Air Sea, and Not Reported (I-94, no l...\n",
       "1      236                                        AFGHANISTAN\n",
       "2      101                                            ALBANIA\n",
       "3      316                                            ALGERIA\n",
       "4      102                                            ANDORRA\n",
       "5      324                                             ANGOLA\n",
       "6      529                                           ANGUILLA\n",
       "7      518                                    ANTIGUA-BARBUDA\n",
       "8      687                                         ARGENTINA \n",
       "9      151                                            ARMENIA"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i94cit_i94res.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:** This data can be used to map the country codes to their respective names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = re.search(r'i94prtl.*?;', i94_label_content, re.DOTALL).group()\n",
    "text=text.split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94port_i94code = pd.DataFrame(columns=['i94_port_code','i94_airport_location','i94_airport_state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94port_i94code['i94_port_code'] = [re.search('[A-Z](?=)\\w+', code).group() for code in text[1:-1]]\n",
    "i94port_i94code['i94_airport_location'] = [re.findall('\\'(.*?),', code.split('\\t=\\t')[1])[0] if len(re.findall('\\'(.*?),', code.split('\\t=\\t')[1]))>0 else code.split('\\t=\\t')[1].strip(\" '\") \n",
    "                                           for code in text[1:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94port_i94code['i94_airport_state'] = [code.split('\\t=\\t')[1].strip(\" '\") for code in text[1:-1]]\n",
    "i94port_i94code['i94_airport_state'] = i94port_i94code['i94_airport_state'].str.split(', ')\n",
    "i94port_i94code['i94_airport_state'] = [code[-1] for code in i94port_i94code['i94_airport_state']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for code in i94port_i94code.i94_airport_state:\n",
    "    if len(code)>2 and 'Collapsed' not in code and 'No PORT Code' not in code:\n",
    "        tmp = code.split(' ')\n",
    "        if len(tmp)>1:\n",
    "            if len(tmp[0])==2 and '(BPS)' in tmp[1] or '#ARPT' in tmp[1]:\n",
    "                i94port_i94code.loc[i94port_i94code.i94_airport_state==code,'i94_airport_state'] = tmp[0]\n",
    "\n",
    "            if len(tmp[1]) == 2 and len(tmp[0])!=2:\n",
    "                i94port_i94code.loc[i94port_i94code.i94_airport_state==code,'i94_airport_state'] = tmp[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i94_port_code</th>\n",
       "      <th>i94_airport_location</th>\n",
       "      <th>i94_airport_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALC</td>\n",
       "      <td>ALCAN</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANC</td>\n",
       "      <td>ANCHORAGE</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAR</td>\n",
       "      <td>BAKER AAF - BAKER ISLAND</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAC</td>\n",
       "      <td>DALTONS CACHE</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIZ</td>\n",
       "      <td>DEW STATION PT LAY DEW</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DTH</td>\n",
       "      <td>DUTCH HARBOR</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EGL</td>\n",
       "      <td>EAGLE</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FRB</td>\n",
       "      <td>FAIRBANKS</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HOM</td>\n",
       "      <td>HOMER</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HYD</td>\n",
       "      <td>HYDER</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  i94_port_code      i94_airport_location i94_airport_state\n",
       "0           ALC                     ALCAN                AK\n",
       "1           ANC                 ANCHORAGE                AK\n",
       "2           BAR  BAKER AAF - BAKER ISLAND                AK\n",
       "3           DAC             DALTONS CACHE                AK\n",
       "4           PIZ    DEW STATION PT LAY DEW                AK\n",
       "5           DTH              DUTCH HARBOR                AK\n",
       "6           EGL                     EAGLE                AK\n",
       "7           FRB                 FAIRBANKS                AK\n",
       "8           HOM                     HOMER                AK\n",
       "9           HYD                     HYDER                AK"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i94port_i94code.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "- This table can help in mapping the `i94port` from the immigration data and its `airport_location` and `state_code` in US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94port_i94code.to_csv('i94_meta_data/i94port_i94code.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = re.search(r'value i94addrl.*? ;', i94_label_content, re.DOTALL).group()\n",
    "text=text.split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_state_code = pd.DataFrame(columns=['i94_state_code','i94_state_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_state_code['i94_state_code'] = [code.split(\"'='\")[0].strip(\"\\t'\") for code in text[1:]]\n",
    "i94_state_code['i94_state_name'] = [code.split(\"'='\")[1].strip(\" ';\") for code in text[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_state_code.to_csv('i94_meta_data/i94_state_code.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Combining all the above steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {}\n",
    "key_name=''\n",
    "for line in i94_label_content.split(\"\\n\"):\n",
    "    line = re.sub(r\"\\s+\", \" \", line)\n",
    "    if '/*' in line and '-' in line:\n",
    "        line = line.strip('/*')\n",
    "        key_name = line.split('-')[0].strip()\n",
    "        data_dict[key_name] = []\n",
    "    if '=' in line and key_name!='':\n",
    "        data_dict[key_name].append([item.strip(';').strip(\" \").replace('\\'', '').lstrip().rstrip() for item in line.split('=')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in data_dict:\n",
    "    if len(data_dict[key])>0:\n",
    "        if 'CIT' in key and 'RES' in key:\n",
    "            i94cit_i94res = pd.DataFrame(data_dict[key],columns=['i94_country_code','country_name'])\n",
    "            i94cit_i94res.loc[i94cit_i94res.country_name.str.contains('MEXICO'),'country_name'] = 'MEXICO'\n",
    "            i94cit_i94res.to_csv('i94_meta_data/i94cit_i94res.csv',index=False)\n",
    "        if 'PORT' in key:\n",
    "            i94port_i94code = pd.DataFrame(data_dict[key],columns=['i94_port_code','i94_airport_location'])\n",
    "            i94port_i94code[['port_city', 'port_state']] = i94port_i94code['i94_airport_location'].str.rsplit(',', 1, expand=True)\n",
    "            i94port_i94code.loc[i94port_i94code.port_city == 'MARIPOSA AZ','port_state'] = 'AZ'\n",
    "            i94port_i94code.loc[i94port_i94code.port_city == 'MARIPOSA AZ','port_city'] = 'MARIPOSA'\n",
    "            i94port_i94code.loc[i94port_i94code.port_city == 'WASHINGTON DC','port_state'] = 'DC'\n",
    "            i94port_i94code.drop(['i94_airport_location'], axis=1, inplace=True)\n",
    "            i94port_i94code.to_csv('i94_meta_data/i94port_i94code.csv',index=False)\n",
    "        if 'MODE' in key:\n",
    "            i94mode = pd.DataFrame(data_dict[key],columns=['i94_mode_code','i94_mode'])\n",
    "            i94mode.to_csv('i94_meta_data/i94mode.csv',index=False)\n",
    "        if 'ADDR' in key:\n",
    "            i94addr = pd.DataFrame(data_dict[key],columns=['i94_state_code','i94_state_name'])\n",
    "            i94addr.to_csv('i94_meta_data/i94addr.csv',index=False)\n",
    "        if 'VISA' in key:\n",
    "            i94visa = pd.DataFrame(data_dict[key],columns=['i94_visa_code','i94_visa'])\n",
    "            i94visa.to_csv('i94_meta_data/i94visa.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['I94YR', 'I94MON', 'I94CIT & I94RES', 'I94PORT', 'I94MODE', 'I94ADDR', 'I94BIR', 'I94VISA', 'COUNT', 'DTADFILE', 'VISAPOST', 'OCCUP', 'ENTDEPA', 'ENTDEPD', 'ENTDEPU', 'MATFLAG', 'BIRYEAR', 'DTADDTO', 'GENDER', 'INSNUM', 'AIRLINE', 'ADMNUM', 'FLTNO', 'VISATYPE'])"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i94_country_code</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>582</td>\n",
       "      <td>MEXICO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>236</td>\n",
       "      <td>AFGHANISTAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>ALBANIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>316</td>\n",
       "      <td>ALGERIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>ANDORRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  i94_country_code country_name\n",
       "0              582       MEXICO\n",
       "1              236  AFGHANISTAN\n",
       "2              101      ALBANIA\n",
       "3              316      ALGERIA\n",
       "4              102      ANDORRA"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i94cit_i94res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Exploring the SAS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i94_apr16_sub.sas7bdat\ti94_jan16_sub.sas7bdat\ti94_may16_sub.sas7bdat\n",
      "i94_aug16_sub.sas7bdat\ti94_jul16_sub.sas7bdat\ti94_nov16_sub.sas7bdat\n",
      "i94_dec16_sub.sas7bdat\ti94_jun16_sub.sas7bdat\ti94_oct16_sub.sas7bdat\n",
      "i94_feb16_sub.sas7bdat\ti94_mar16_sub.sas7bdat\ti94_sep16_sub.sas7bdat\n"
     ]
    }
   ],
   "source": [
    "!ls ../../data/18-83510-I94-Data-2016/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "def to_datetime(x):\n",
    "    try:\n",
    "        start = datetime(1960, 1, 1)\n",
    "        return start + timedelta(days=int(x))\n",
    "    except:\n",
    "        return None\n",
    "udf_to_datetime_sas = udf(lambda x: to_datetime(x), DateType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_datetime_frm_str(x):\n",
    "    try:\n",
    "        if x != 'D/S':\n",
    "            return datetime.strptime(x, '%m%d%Y')\n",
    "        else:\n",
    "            return None\n",
    "    except:\n",
    "        return None\n",
    "udf_to_datetime_frm_str = udf(lambda x: to_datetime_frm_str(x), DateType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_i94 =spark.read.format('com.github.saurfang.sas.spark').option(\"dateFormat\", \"yyyyMMdd\").option(\"inferSchema\", \"true\").\\\n",
    "                 load('../../data/18-83510-I94-Data-2016/i94_jan16_sub.sas7bdat').withColumn(\"arrival_date\", lit(udf_to_datetime_sas(\"arrdate\")))\\\n",
    "                .withColumn(\"departure_date\", lit(udf_to_datetime_sas(\"depdate\")))\\\n",
    "                .withColumn(\"departure_deadline\", lit(udf_to_datetime_frm_str(\"dtaddto\"))) \\\n",
    "                .withColumn(\"month_year\", lit('jan'+'_'+'16'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 32\n",
      "Number of Rows: 2847924\n"
     ]
    }
   ],
   "source": [
    "# Printing shape of the dataset\n",
    "print(\"Number of Columns: {}\".format(len(df_i94.columns)))\n",
    "print(\"Number of Rows: {}\".format(df_i94.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cicid</th>\n",
       "      <th>i94yr</th>\n",
       "      <th>i94mon</th>\n",
       "      <th>i94cit</th>\n",
       "      <th>i94res</th>\n",
       "      <th>i94port</th>\n",
       "      <th>arrdate</th>\n",
       "      <th>i94mode</th>\n",
       "      <th>i94addr</th>\n",
       "      <th>depdate</th>\n",
       "      <th>i94bir</th>\n",
       "      <th>i94visa</th>\n",
       "      <th>count</th>\n",
       "      <th>dtadfile</th>\n",
       "      <th>visapost</th>\n",
       "      <th>occup</th>\n",
       "      <th>entdepa</th>\n",
       "      <th>entdepd</th>\n",
       "      <th>entdepu</th>\n",
       "      <th>matflag</th>\n",
       "      <th>biryear</th>\n",
       "      <th>dtaddto</th>\n",
       "      <th>gender</th>\n",
       "      <th>insnum</th>\n",
       "      <th>airline</th>\n",
       "      <th>admnum</th>\n",
       "      <th>fltno</th>\n",
       "      <th>visatype</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>departure_deadline</th>\n",
       "      <th>month_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20465.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>D/S</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>346608285.0</td>\n",
       "      <td>424</td>\n",
       "      <td>F1</td>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20465.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>D/S</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>346627585.0</td>\n",
       "      <td>424</td>\n",
       "      <td>F1</td>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20469.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>20480.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>07152016</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>AF</td>\n",
       "      <td>381092385.0</td>\n",
       "      <td>338</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20469.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>20499.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>07152016</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>AF</td>\n",
       "      <td>381087885.0</td>\n",
       "      <td>338</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>2016-02-15</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20469.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>20499.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>07152016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>AF</td>\n",
       "      <td>381078685.0</td>\n",
       "      <td>338</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>2016-02-15</td>\n",
       "      <td>2016-07-15</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20474.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>07202016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>406155985.0</td>\n",
       "      <td>424</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-21</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-07-20</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20477.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>20524.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>D/S</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>417363085.0</td>\n",
       "      <td>424</td>\n",
       "      <td>F1</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>None</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>07262016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>TK</td>\n",
       "      <td>428558285.0</td>\n",
       "      <td>81</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-07-26</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>20480.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>07262016</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>TK</td>\n",
       "      <td>428561085.0</td>\n",
       "      <td>81</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-07-26</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>CHI</td>\n",
       "      <td>20473.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IL</td>\n",
       "      <td>20482.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>07192016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>BA</td>\n",
       "      <td>401779785.0</td>\n",
       "      <td>295</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>2016-01-29</td>\n",
       "      <td>2016-07-19</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>CHI</td>\n",
       "      <td>20478.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>TN</td>\n",
       "      <td>20495.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>07242016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>OS</td>\n",
       "      <td>420642985.0</td>\n",
       "      <td>65</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>2016-07-24</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>TUC</td>\n",
       "      <td>20476.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>07222016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>412742085.0</td>\n",
       "      <td>442</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-23</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>TUC</td>\n",
       "      <td>20476.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>07222016</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>412735785.0</td>\n",
       "      <td>442</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-23</td>\n",
       "      <td>None</td>\n",
       "      <td>2016-07-22</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>TUC</td>\n",
       "      <td>20476.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MI</td>\n",
       "      <td>20508.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>02252016</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>412708185.0</td>\n",
       "      <td>442</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-23</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>TUC</td>\n",
       "      <td>20476.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MI</td>\n",
       "      <td>20508.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>O</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>02252016</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>412705485.0</td>\n",
       "      <td>442</td>\n",
       "      <td>B2</td>\n",
       "      <td>2016-01-23</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cicid   i94yr  i94mon  i94cit  i94res i94port  arrdate  i94mode i94addr  \\\n",
       "0     7.0  2016.0     1.0   101.0   101.0     BOS  20465.0      1.0      MA   \n",
       "1     8.0  2016.0     1.0   101.0   101.0     BOS  20465.0      1.0      MA   \n",
       "2     9.0  2016.0     1.0   101.0   101.0     BOS  20469.0      1.0      CT   \n",
       "3    10.0  2016.0     1.0   101.0   101.0     BOS  20469.0      1.0      CT   \n",
       "4    11.0  2016.0     1.0   101.0   101.0     BOS  20469.0      1.0      CT   \n",
       "5    12.0  2016.0     1.0   101.0   101.0     BOS  20474.0      1.0      MA   \n",
       "6    15.0  2016.0     1.0   101.0   101.0     BOS  20477.0      1.0      MA   \n",
       "7    17.0  2016.0     1.0   101.0   101.0     BOS  20480.0      1.0      MA   \n",
       "8    18.0  2016.0     1.0   101.0   101.0     BOS  20480.0      1.0      MA   \n",
       "9    20.0  2016.0     1.0   101.0   101.0     CHI  20473.0      1.0      IL   \n",
       "10   21.0  2016.0     1.0   101.0   101.0     CHI  20478.0      1.0      TN   \n",
       "11   22.0  2016.0     1.0   101.0   101.0     TUC  20476.0      1.0    None   \n",
       "12   23.0  2016.0     1.0   101.0   101.0     TUC  20476.0      1.0    None   \n",
       "13   24.0  2016.0     1.0   101.0   101.0     TUC  20476.0      1.0      MI   \n",
       "14   25.0  2016.0     1.0   101.0   101.0     TUC  20476.0      1.0      MI   \n",
       "\n",
       "    depdate  i94bir  i94visa  count dtadfile visapost occup entdepa entdepd  \\\n",
       "0       NaN    20.0      3.0    1.0     None     None  None       T    None   \n",
       "1       NaN    20.0      3.0    1.0     None     None  None       T    None   \n",
       "2   20480.0    17.0      2.0    1.0     None     None  None       T       N   \n",
       "3   20499.0    45.0      2.0    1.0     None     None  None       T       N   \n",
       "4   20499.0    12.0      2.0    1.0     None     None  None       T       N   \n",
       "5       NaN    33.0      2.0    1.0     None     None  None       T    None   \n",
       "6   20524.0    28.0      3.0    1.0     None     None  None       T       O   \n",
       "7       NaN    78.0      2.0    1.0     None     None  None       T    None   \n",
       "8       NaN    70.0      2.0    1.0     None     None  None       T    None   \n",
       "9   20482.0    28.0      2.0    1.0     None     None  None       T       O   \n",
       "10  20495.0    28.0      2.0    1.0     None     None  None       T       N   \n",
       "11      NaN    62.0      2.0    1.0     None     None  None       T    None   \n",
       "12      NaN    54.0      2.0    1.0     None     None  None       T    None   \n",
       "13  20508.0    64.0      2.0    1.0     None     None  None       T       O   \n",
       "14  20508.0    53.0      2.0    1.0     None     None  None       T       O   \n",
       "\n",
       "   entdepu matflag  biryear   dtaddto gender insnum airline       admnum  \\\n",
       "0     None    None   1996.0       D/S      M   None      LH  346608285.0   \n",
       "1     None    None   1996.0       D/S      M   None      LH  346627585.0   \n",
       "2     None       M   1999.0  07152016      F   None      AF  381092385.0   \n",
       "3     None       M   1971.0  07152016      F   None      AF  381087885.0   \n",
       "4     None       M   2004.0  07152016      M   None      AF  381078685.0   \n",
       "5     None    None   1983.0  07202016      M   None      LH  406155985.0   \n",
       "6     None       M   1988.0       D/S      F   None      LH  417363085.0   \n",
       "7     None    None   1938.0  07262016      M   None      TK  428558285.0   \n",
       "8     None    None   1946.0  07262016      F   None      TK  428561085.0   \n",
       "9     None       M   1988.0  07192016      M   None      BA  401779785.0   \n",
       "10    None       M   1988.0  07242016      M   None      OS  420642985.0   \n",
       "11    None    None   1954.0  07222016      M   None      LH  412742085.0   \n",
       "12    None    None   1962.0  07222016      F   None      LH  412735785.0   \n",
       "13    None       M   1952.0  02252016      M   None      LH  412708185.0   \n",
       "14    None       M   1963.0  02252016      F   None      LH  412705485.0   \n",
       "\n",
       "   fltno visatype arrival_date departure_date departure_deadline month_year  \n",
       "0    424       F1   2016-01-12           None               None     jan_16  \n",
       "1    424       F1   2016-01-12           None               None     jan_16  \n",
       "2    338       B2   2016-01-16     2016-01-27         2016-07-15     jan_16  \n",
       "3    338       B2   2016-01-16     2016-02-15         2016-07-15     jan_16  \n",
       "4    338       B2   2016-01-16     2016-02-15         2016-07-15     jan_16  \n",
       "5    424       B2   2016-01-21           None         2016-07-20     jan_16  \n",
       "6    424       F1   2016-01-24     2016-03-11               None     jan_16  \n",
       "7     81       B2   2016-01-27           None         2016-07-26     jan_16  \n",
       "8     81       B2   2016-01-27           None         2016-07-26     jan_16  \n",
       "9    295       B2   2016-01-20     2016-01-29         2016-07-19     jan_16  \n",
       "10    65       B2   2016-01-25     2016-02-11         2016-07-24     jan_16  \n",
       "11   442       B2   2016-01-23           None         2016-07-22     jan_16  \n",
       "12   442       B2   2016-01-23           None         2016-07-22     jan_16  \n",
       "13   442       B2   2016-01-23     2016-02-24         2016-02-25     jan_16  \n",
       "14   442       B2   2016-01-23     2016-02-24         2016-02-25     jan_16  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i94.limit(15).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- cicid: double (nullable = true)\n",
      " |-- i94yr: double (nullable = true)\n",
      " |-- i94mon: double (nullable = true)\n",
      " |-- i94cit: double (nullable = true)\n",
      " |-- i94res: double (nullable = true)\n",
      " |-- i94port: string (nullable = true)\n",
      " |-- arrdate: double (nullable = true)\n",
      " |-- i94mode: double (nullable = true)\n",
      " |-- i94addr: string (nullable = true)\n",
      " |-- depdate: double (nullable = true)\n",
      " |-- i94bir: double (nullable = true)\n",
      " |-- i94visa: double (nullable = true)\n",
      " |-- count: double (nullable = true)\n",
      " |-- dtadfile: string (nullable = true)\n",
      " |-- visapost: string (nullable = true)\n",
      " |-- occup: string (nullable = true)\n",
      " |-- entdepa: string (nullable = true)\n",
      " |-- entdepd: string (nullable = true)\n",
      " |-- entdepu: string (nullable = true)\n",
      " |-- matflag: string (nullable = true)\n",
      " |-- biryear: double (nullable = true)\n",
      " |-- dtaddto: string (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- insnum: string (nullable = true)\n",
      " |-- airline: string (nullable = true)\n",
      " |-- admnum: double (nullable = true)\n",
      " |-- fltno: string (nullable = true)\n",
      " |-- visatype: string (nullable = true)\n",
      " |-- arrival_date: date (nullable = true)\n",
      " |-- departure_date: date (nullable = true)\n",
      " |-- departure_deadline: null (nullable = true)\n",
      " |-- month_year: string (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_i94.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_i94 = df_i94.drop('validres','delete_days','delete_mexl','delete_dup','delete_recdup','depdate','delete_visa','arrdate','dtadfile','dtaddto' ,'occup', 'entdepa', 'entdepd', 'entdepu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_cast_select_exprs(sas_cols, schema_cols):\n",
    "    if sas_cols != '':\n",
    "        exprs = [\"{} AS {}\".format(dfc,sc) for dfc, sc in zip(sas_cols, schema_cols)]\n",
    "    else:\n",
    "        raise ValueError('Cannot create Select Expression without proper header')\n",
    "    return exprs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sas_columns = ['cast(cicid as int)','cast(i94yr as int)','cast(i94mon as int)','cast(i94cit as int)',\n",
    "\t\t\t   'cast(i94res as int)','i94port','arrival_date','cast(i94mode as int)',\n",
    "\t\t\t   'i94addr','departure_date','departure_deadline','cast(i94bir as int)','cast(i94visa as int)',\n",
    "\t\t\t   'cast(count as int)','visapost','matflag','cast(biryear as int)',\n",
    "\t\t\t   'gender','insnum','airline','cast(admnum as float)','fltno','visatype',\"month_year\"]\n",
    "\n",
    "schema_columns = ['cicid','entry_year','entry_month','country_id','res_id','port_id','arrival_date',\n",
    "\t\t\t\t  'mode_id','state_code','departure_date','departure_deadline','age','visa_reason_id','count','visa_post',\n",
    "\t\t\t\t  'matched_flag','birth_year','gender','ins_num','airline_abbr','admission_num','flight_no','visa_type','month_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- cicid: integer (nullable = true)\n",
      " |-- entry_year: integer (nullable = true)\n",
      " |-- entry_month: integer (nullable = true)\n",
      " |-- country_id: integer (nullable = true)\n",
      " |-- res_id: integer (nullable = true)\n",
      " |-- port_id: string (nullable = true)\n",
      " |-- arrival_date: date (nullable = true)\n",
      " |-- mode_id: integer (nullable = true)\n",
      " |-- state_code: string (nullable = true)\n",
      " |-- departure_date: date (nullable = true)\n",
      " |-- departure_deadline: null (nullable = true)\n",
      " |-- age: integer (nullable = true)\n",
      " |-- visa_reason_id: integer (nullable = true)\n",
      " |-- count: integer (nullable = true)\n",
      " |-- visa_post: string (nullable = true)\n",
      " |-- matched_flag: string (nullable = true)\n",
      " |-- birth_year: integer (nullable = true)\n",
      " |-- gender: string (nullable = true)\n",
      " |-- ins_num: string (nullable = true)\n",
      " |-- airline_abbr: string (nullable = true)\n",
      " |-- admission_num: float (nullable = true)\n",
      " |-- flight_no: string (nullable = true)\n",
      " |-- visa_type: string (nullable = true)\n",
      " |-- month_year: string (nullable = false)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_i94=df_i94.selectExpr(create_cast_select_exprs(sas_columns,schema_columns))\n",
    "df_i94.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cicid</th>\n",
       "      <th>entry_year</th>\n",
       "      <th>entry_month</th>\n",
       "      <th>country_id</th>\n",
       "      <th>res_id</th>\n",
       "      <th>port_id</th>\n",
       "      <th>arrival_date</th>\n",
       "      <th>mode_id</th>\n",
       "      <th>state_code</th>\n",
       "      <th>departure_date</th>\n",
       "      <th>departure_deadline</th>\n",
       "      <th>age</th>\n",
       "      <th>visa_reason_id</th>\n",
       "      <th>count</th>\n",
       "      <th>visa_post</th>\n",
       "      <th>matched_flag</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>ins_num</th>\n",
       "      <th>airline_abbr</th>\n",
       "      <th>admission_num</th>\n",
       "      <th>flight_no</th>\n",
       "      <th>visa_type</th>\n",
       "      <th>month_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>MA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1996</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>346608288.0</td>\n",
       "      <td>424</td>\n",
       "      <td>F1</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>MA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1996</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>LH</td>\n",
       "      <td>346627584.0</td>\n",
       "      <td>424</td>\n",
       "      <td>F1</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>1</td>\n",
       "      <td>CT</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>None</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1999</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>AF</td>\n",
       "      <td>381092384.0</td>\n",
       "      <td>338</td>\n",
       "      <td>B2</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>1</td>\n",
       "      <td>CT</td>\n",
       "      <td>2016-02-15</td>\n",
       "      <td>None</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>1971</td>\n",
       "      <td>F</td>\n",
       "      <td>None</td>\n",
       "      <td>AF</td>\n",
       "      <td>381087872.0</td>\n",
       "      <td>338</td>\n",
       "      <td>B2</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2016-01-16</td>\n",
       "      <td>1</td>\n",
       "      <td>CT</td>\n",
       "      <td>2016-02-15</td>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>2004</td>\n",
       "      <td>M</td>\n",
       "      <td>None</td>\n",
       "      <td>AF</td>\n",
       "      <td>381078688.0</td>\n",
       "      <td>338</td>\n",
       "      <td>B2</td>\n",
       "      <td>jan_16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cicid  entry_year  entry_month  country_id  res_id port_id arrival_date  \\\n",
       "0      7        2016            1         101     101     BOS   2016-01-12   \n",
       "1      8        2016            1         101     101     BOS   2016-01-12   \n",
       "2      9        2016            1         101     101     BOS   2016-01-16   \n",
       "3     10        2016            1         101     101     BOS   2016-01-16   \n",
       "4     11        2016            1         101     101     BOS   2016-01-16   \n",
       "\n",
       "   mode_id state_code departure_date departure_deadline  age  visa_reason_id  \\\n",
       "0        1         MA           None               None   20               3   \n",
       "1        1         MA           None               None   20               3   \n",
       "2        1         CT     2016-01-27               None   17               2   \n",
       "3        1         CT     2016-02-15               None   45               2   \n",
       "4        1         CT     2016-02-15               None   12               2   \n",
       "\n",
       "   count visa_post matched_flag  birth_year gender ins_num airline_abbr  \\\n",
       "0      1      None         None        1996      M    None           LH   \n",
       "1      1      None         None        1996      M    None           LH   \n",
       "2      1      None            M        1999      F    None           AF   \n",
       "3      1      None            M        1971      F    None           AF   \n",
       "4      1      None            M        2004      M    None           AF   \n",
       "\n",
       "   admission_num flight_no visa_type month_year  \n",
       "0    346608288.0       424        F1     jan_16  \n",
       "1    346627584.0       424        F1     jan_16  \n",
       "2    381092384.0       338        B2     jan_16  \n",
       "3    381087872.0       338        B2     jan_16  \n",
       "4    381078688.0       338        B2     jan_16  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_i94.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3a://dend-capstone-data/i94_raw_data/apr16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/sep16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/nov16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/mar16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/jun16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/aug16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/may16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/jan16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/oct16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/jul16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/feb16_sasdata.parquet\n",
      "s3a://dend-capstone-data/i94_raw_data/dec16_sasdata.parquet\n",
      "CPU times: user 323 ms, sys: 92.8 ms, total: 416 ms\n",
      "Wall time: 39min 56s\n"
     ]
    }
   ],
   "source": [
    "# storing the data in S3 for later use\n",
    "%%time\n",
    "for file in os.listdir('../../data/18-83510-I94-Data-2016/'):\n",
    "    path='../../data/18-83510-I94-Data-2016/{}'.format(file)\n",
    "    df_i94 = spark.read.format('com.github.saurfang.sas.spark').option(\"inferSchema\", \"true\").option(\"dateFormat\", \"yyyyMMdd\").load(path)\\\n",
    "             .withColumn(\"arrival_date\", udf_to_datetime_sas(\"arrdate\")) \\\n",
    "             .withColumn(\"departure_date\", udf_to_datetime_sas(\"depdate\")).withColumn(\"departure_deadline\", udf_to_datetimefrstr(\"dtaddto\"))\n",
    "    df_i94=df_i94.drop('validres','delete_days','delete_mexl','delete_dup','delete_recdup','delete_visa','arrdate','dtadfile', 'occup', 'entdepa', 'entdepd', 'entdepu')\n",
    "    bucket_path = r\"s3a://{}/i94_raw_data/{}_sasdata.parquet\".format(S3_BUCKET,file.strip('.sas7bdat').split('_')[1])\n",
    "    print(bucket_path)\n",
    "    df_i94.write.parquet(bucket_path, \"overwrite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../data/18-83510-I94-Data-2016/i94_apr16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_sep16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_nov16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_mar16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_jun16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_aug16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_may16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_jan16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_oct16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_jul16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_feb16_sub.sas7bdat\n",
      "../../data/18-83510-I94-Data-2016/i94_dec16_sub.sas7bdat\n",
      "CPU times: user 41.2 ms, sys: 8.2 ms, total: 49.4 ms\n",
      "Wall time: 1.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for file in os.listdir('../../data/18-83510-I94-Data-2016/'):\n",
    "    path='../../data/18-83510-I94-Data-2016/{}'.format(file)\n",
    "    print(path)\n",
    "    df_i94 = spark.read.format('com.github.saurfang.sas.spark').option(\"inferSchema\", \"true\").option(\"dateFormat\", \"yyyyMMdd\").load(path)\n",
    "    df_i94=df_i94.drop('validres','delete_days','delete_mexl','delete_dup','delete_recdup','delete_visa','arrdate','dtadfile', 'occup', 'entdepa', 'entdepd', 'entdepu')\n",
    "    df_i94_total = df_i94.union(df_i94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 28\n",
      "Number of Rows: 6865980\n"
     ]
    }
   ],
   "source": [
    "# Printing shape of the combined dataset shape\n",
    "print(\"Number of Columns: {}\".format(len(df_i94_total.columns)))\n",
    "print(\"Number of Rows: {}\".format(df_i94_total.count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:** From the dataset and the SAS_Label description file:\n",
    "- The column `cicid` is like uuid to me\n",
    "- The columns `validres, delete_days, delete_mexl, delete_dup, delete_recdup, delete_visa` are additional columns and can be removed by inferring from the sample csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4 Exploring the Airport Codes Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark = {} # using this dictionary for plotting in section 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import *\n",
    "def parse_latitude(x):\n",
    "    y = x.strip().split(',')\n",
    "    return float(y[1])\n",
    "\n",
    "\n",
    "def parse_longitude(x):\n",
    "    y = x.strip().split(',')\n",
    "    return float(y[0])\n",
    "\n",
    "\n",
    "def port_of_entry(x):\n",
    "    return x.strip().split(', ')[0]\n",
    "\n",
    "\n",
    "def parse_state_code(x):\n",
    "    return x.strip().split('-')[-1]\n",
    "\n",
    "\n",
    "def parse_country_code(x):\n",
    "    return x.strip().split('-')[0]\n",
    "\n",
    "\n",
    "udf_parse_port_of_entry = udf(lambda x: port_of_entry(x), StringType())\n",
    "udf_parse_latitude = udf(lambda x: parse_latitude(x), FloatType())\n",
    "udf_parse_longitude = udf(lambda x: parse_longitude(x), FloatType())\n",
    "udf_parse_state_code = udf(lambda x: parse_state_code(x), StringType())\n",
    "udf_parse_country_code = udf(lambda x: parse_country_code(x), StringType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airport = spark.read.format('csv').options(header='true', inferSchema='true').load('./data/airport-codes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ident</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>elevation_ft</th>\n",
       "      <th>continent</th>\n",
       "      <th>iso_country</th>\n",
       "      <th>iso_region</th>\n",
       "      <th>municipality</th>\n",
       "      <th>gps_code</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>local_code</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>nearest_city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00A</td>\n",
       "      <td>heliport</td>\n",
       "      <td>Total Rf Heliport</td>\n",
       "      <td>11.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-PA</td>\n",
       "      <td>Bensalem</td>\n",
       "      <td>00A</td>\n",
       "      <td>None</td>\n",
       "      <td>00A</td>\n",
       "      <td>-74.93360137939453, 40.07080078125</td>\n",
       "      <td>Beverly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00AA</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Aero B Ranch Airport</td>\n",
       "      <td>3435.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-KS</td>\n",
       "      <td>Leoti</td>\n",
       "      <td>00AA</td>\n",
       "      <td>None</td>\n",
       "      <td>00AA</td>\n",
       "      <td>-101.473911, 38.704022</td>\n",
       "      <td>Leoti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00AK</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Lowell Field</td>\n",
       "      <td>450.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-AK</td>\n",
       "      <td>Anchor Point</td>\n",
       "      <td>00AK</td>\n",
       "      <td>None</td>\n",
       "      <td>00AK</td>\n",
       "      <td>-151.695999146, 59.94919968</td>\n",
       "      <td>Anchor Point</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00AL</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Epps Airpark</td>\n",
       "      <td>820.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-AL</td>\n",
       "      <td>Harvest</td>\n",
       "      <td>00AL</td>\n",
       "      <td>None</td>\n",
       "      <td>00AL</td>\n",
       "      <td>-86.77030181884766, 34.86479949951172</td>\n",
       "      <td>Harvest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00AR</td>\n",
       "      <td>closed</td>\n",
       "      <td>Newport Hospital &amp; Clinic Heliport</td>\n",
       "      <td>237.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-AR</td>\n",
       "      <td>Newport</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-91.254898, 35.6087</td>\n",
       "      <td>Newport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00AS</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Fulton Airport</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-OK</td>\n",
       "      <td>Alex</td>\n",
       "      <td>00AS</td>\n",
       "      <td>None</td>\n",
       "      <td>00AS</td>\n",
       "      <td>-97.8180194, 34.9428028</td>\n",
       "      <td>Ninnekah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00AZ</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Cordes Airport</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-AZ</td>\n",
       "      <td>Cordes</td>\n",
       "      <td>00AZ</td>\n",
       "      <td>None</td>\n",
       "      <td>00AZ</td>\n",
       "      <td>-112.16500091552734, 34.305599212646484</td>\n",
       "      <td>Spring Valley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00CA</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Goldstone /Gts/ Airport</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-CA</td>\n",
       "      <td>Barstow</td>\n",
       "      <td>00CA</td>\n",
       "      <td>None</td>\n",
       "      <td>00CA</td>\n",
       "      <td>-116.888000488, 35.350498199499995</td>\n",
       "      <td>Fort Irwin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00CL</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Williams Ag Airport</td>\n",
       "      <td>87.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-CA</td>\n",
       "      <td>Biggs</td>\n",
       "      <td>00CL</td>\n",
       "      <td>None</td>\n",
       "      <td>00CL</td>\n",
       "      <td>-121.763427, 39.427188</td>\n",
       "      <td>Biggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00CN</td>\n",
       "      <td>heliport</td>\n",
       "      <td>Kitchen Creek Helibase Heliport</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>None</td>\n",
       "      <td>US</td>\n",
       "      <td>US-CA</td>\n",
       "      <td>Pine Valley</td>\n",
       "      <td>00CN</td>\n",
       "      <td>None</td>\n",
       "      <td>00CN</td>\n",
       "      <td>-116.4597417, 32.7273736</td>\n",
       "      <td>Pine Valley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ident           type                                name  elevation_ft  \\\n",
       "0   00A       heliport                   Total Rf Heliport          11.0   \n",
       "1  00AA  small_airport                Aero B Ranch Airport        3435.0   \n",
       "2  00AK  small_airport                        Lowell Field         450.0   \n",
       "3  00AL  small_airport                        Epps Airpark         820.0   \n",
       "4  00AR         closed  Newport Hospital & Clinic Heliport         237.0   \n",
       "5  00AS  small_airport                      Fulton Airport        1100.0   \n",
       "6  00AZ  small_airport                      Cordes Airport        3810.0   \n",
       "7  00CA  small_airport             Goldstone /Gts/ Airport        3038.0   \n",
       "8  00CL  small_airport                 Williams Ag Airport          87.0   \n",
       "9  00CN       heliport     Kitchen Creek Helibase Heliport        3350.0   \n",
       "\n",
       "  continent iso_country iso_region  municipality gps_code iata_code  \\\n",
       "0      None          US      US-PA      Bensalem      00A      None   \n",
       "1      None          US      US-KS         Leoti     00AA      None   \n",
       "2      None          US      US-AK  Anchor Point     00AK      None   \n",
       "3      None          US      US-AL       Harvest     00AL      None   \n",
       "4      None          US      US-AR       Newport     None      None   \n",
       "5      None          US      US-OK          Alex     00AS      None   \n",
       "6      None          US      US-AZ        Cordes     00AZ      None   \n",
       "7      None          US      US-CA       Barstow     00CA      None   \n",
       "8      None          US      US-CA         Biggs     00CL      None   \n",
       "9      None          US      US-CA   Pine Valley     00CN      None   \n",
       "\n",
       "  local_code                              coordinates   nearest_city  \n",
       "0        00A       -74.93360137939453, 40.07080078125        Beverly  \n",
       "1       00AA                   -101.473911, 38.704022          Leoti  \n",
       "2       00AK              -151.695999146, 59.94919968   Anchor Point  \n",
       "3       00AL    -86.77030181884766, 34.86479949951172        Harvest  \n",
       "4       None                      -91.254898, 35.6087        Newport  \n",
       "5       00AS                  -97.8180194, 34.9428028       Ninnekah  \n",
       "6       00AZ  -112.16500091552734, 34.305599212646484  Spring Valley  \n",
       "7       00CA       -116.888000488, 35.350498199499995     Fort Irwin  \n",
       "8       00CL                   -121.763427, 39.427188          Biggs  \n",
       "9       00CN                 -116.4597417, 32.7273736    Pine Valley  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airport.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- ident: string (nullable = true)\n",
      " |-- type: string (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- elevation_ft: double (nullable = true)\n",
      " |-- continent: string (nullable = true)\n",
      " |-- iso_country: string (nullable = true)\n",
      " |-- iso_region: string (nullable = true)\n",
      " |-- municipality: string (nullable = true)\n",
      " |-- gps_code: string (nullable = true)\n",
      " |-- iata_code: string (nullable = true)\n",
      " |-- local_code: string (nullable = true)\n",
      " |-- coordinates: string (nullable = true)\n",
      " |-- nearest_city: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_airport.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airport = df_airport.withColumn(\"airport_latitude\", udf_parse_latitude(\"coordinates\"))\\\n",
    ".withColumn(\"airport_longitude\", udf_parse_longitude(\"coordinates\"))\\\n",
    ".withColumn(\"country\", udf_parse_country_code(\"iso_region\"))\\\n",
    ".withColumn(\"state_code\", udf_parse_state_code(\"iso_region\"))\\\n",
    ".withColumnRenamed(\"ident\", \"icao_code\")\\\n",
    ".withColumnRenamed(\"nearest_city\", \"nearest_city\")\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>icao_code</th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>elevation_ft</th>\n",
       "      <th>continent</th>\n",
       "      <th>municipality</th>\n",
       "      <th>iata_code</th>\n",
       "      <th>nearest_city</th>\n",
       "      <th>airport_latitude</th>\n",
       "      <th>airport_longitude</th>\n",
       "      <th>country</th>\n",
       "      <th>state_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00A</td>\n",
       "      <td>heliport</td>\n",
       "      <td>Total Rf Heliport</td>\n",
       "      <td>11.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Bensalem</td>\n",
       "      <td>None</td>\n",
       "      <td>Beverly</td>\n",
       "      <td>40.070801</td>\n",
       "      <td>-74.933601</td>\n",
       "      <td>US</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00AA</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Aero B Ranch Airport</td>\n",
       "      <td>3435.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Leoti</td>\n",
       "      <td>None</td>\n",
       "      <td>Leoti</td>\n",
       "      <td>38.704021</td>\n",
       "      <td>-101.473907</td>\n",
       "      <td>US</td>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00AK</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Lowell Field</td>\n",
       "      <td>450.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Anchor Point</td>\n",
       "      <td>None</td>\n",
       "      <td>Anchor Point</td>\n",
       "      <td>59.949200</td>\n",
       "      <td>-151.695999</td>\n",
       "      <td>US</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00AL</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Epps Airpark</td>\n",
       "      <td>820.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Harvest</td>\n",
       "      <td>None</td>\n",
       "      <td>Harvest</td>\n",
       "      <td>34.864799</td>\n",
       "      <td>-86.770302</td>\n",
       "      <td>US</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00AR</td>\n",
       "      <td>closed</td>\n",
       "      <td>Newport Hospital &amp; Clinic Heliport</td>\n",
       "      <td>237.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Newport</td>\n",
       "      <td>None</td>\n",
       "      <td>Newport</td>\n",
       "      <td>35.608700</td>\n",
       "      <td>-91.254898</td>\n",
       "      <td>US</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00AS</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Fulton Airport</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Alex</td>\n",
       "      <td>None</td>\n",
       "      <td>Ninnekah</td>\n",
       "      <td>34.942802</td>\n",
       "      <td>-97.818016</td>\n",
       "      <td>US</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00AZ</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Cordes Airport</td>\n",
       "      <td>3810.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Cordes</td>\n",
       "      <td>None</td>\n",
       "      <td>Spring Valley</td>\n",
       "      <td>34.305599</td>\n",
       "      <td>-112.165001</td>\n",
       "      <td>US</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00CA</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Goldstone /Gts/ Airport</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Barstow</td>\n",
       "      <td>None</td>\n",
       "      <td>Fort Irwin</td>\n",
       "      <td>35.350498</td>\n",
       "      <td>-116.888000</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00CL</td>\n",
       "      <td>small_airport</td>\n",
       "      <td>Williams Ag Airport</td>\n",
       "      <td>87.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Biggs</td>\n",
       "      <td>None</td>\n",
       "      <td>Biggs</td>\n",
       "      <td>39.427189</td>\n",
       "      <td>-121.763428</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00CN</td>\n",
       "      <td>heliport</td>\n",
       "      <td>Kitchen Creek Helibase Heliport</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Pine Valley</td>\n",
       "      <td>None</td>\n",
       "      <td>Pine Valley</td>\n",
       "      <td>32.727375</td>\n",
       "      <td>-116.459740</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  icao_code           type                                name  elevation_ft  \\\n",
       "0       00A       heliport                   Total Rf Heliport          11.0   \n",
       "1      00AA  small_airport                Aero B Ranch Airport        3435.0   \n",
       "2      00AK  small_airport                        Lowell Field         450.0   \n",
       "3      00AL  small_airport                        Epps Airpark         820.0   \n",
       "4      00AR         closed  Newport Hospital & Clinic Heliport         237.0   \n",
       "5      00AS  small_airport                      Fulton Airport        1100.0   \n",
       "6      00AZ  small_airport                      Cordes Airport        3810.0   \n",
       "7      00CA  small_airport             Goldstone /Gts/ Airport        3038.0   \n",
       "8      00CL  small_airport                 Williams Ag Airport          87.0   \n",
       "9      00CN       heliport     Kitchen Creek Helibase Heliport        3350.0   \n",
       "\n",
       "  continent  municipality iata_code   nearest_city  airport_latitude  \\\n",
       "0      None      Bensalem      None        Beverly         40.070801   \n",
       "1      None         Leoti      None          Leoti         38.704021   \n",
       "2      None  Anchor Point      None   Anchor Point         59.949200   \n",
       "3      None       Harvest      None        Harvest         34.864799   \n",
       "4      None       Newport      None        Newport         35.608700   \n",
       "5      None          Alex      None       Ninnekah         34.942802   \n",
       "6      None        Cordes      None  Spring Valley         34.305599   \n",
       "7      None       Barstow      None     Fort Irwin         35.350498   \n",
       "8      None         Biggs      None          Biggs         39.427189   \n",
       "9      None   Pine Valley      None    Pine Valley         32.727375   \n",
       "\n",
       "   airport_longitude country state_code  \n",
       "0         -74.933601      US         PA  \n",
       "1        -101.473907      US         KS  \n",
       "2        -151.695999      US         AK  \n",
       "3         -86.770302      US         AL  \n",
       "4         -91.254898      US         AR  \n",
       "5         -97.818016      US         OK  \n",
       "6        -112.165001      US         AZ  \n",
       "7        -116.888000      US         CA  \n",
       "8        -121.763428      US         CA  \n",
       "9        -116.459740      US         CA  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airport=df_airport.drop(\"coordinates\", \"gps_code\", \"local_code\",\"iso_region\", \"iso_country\")\n",
    "df_airport.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 12\n",
      "Number of Rows: 55075\n"
     ]
    }
   ],
   "source": [
    "# Printing Shape of the Dataset\n",
    "print(\"Number of Columns: {}\".format(len(df_airport.columns)))\n",
    "print(\"Number of Rows: {}\".format(df_airport.count()))\n",
    "\n",
    "df_airport=df_airport.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- icao_code: string (nullable = true)\n",
      " |-- type: string (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- elevation_ft: double (nullable = true)\n",
      " |-- continent: string (nullable = true)\n",
      " |-- municipality: string (nullable = true)\n",
      " |-- iata_code: string (nullable = true)\n",
      " |-- nearest_city: string (nullable = true)\n",
      " |-- airport_latitude: float (nullable = true)\n",
      " |-- airport_longitude: float (nullable = true)\n",
      " |-- country: string (nullable = true)\n",
      " |-- state_code: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_airport.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------+-----+-----+------------+---------+------------+---------+------------+----------------+-----------------+-------+----------+\n",
      "|summary|icao_code| type| name|elevation_ft|continent|municipality|iata_code|nearest_city|airport_latitude|airport_longitude|country|state_code|\n",
      "+-------+---------+-----+-----+------------+---------+------------+---------+------------+----------------+-----------------+-------+----------+\n",
      "|  count|    55075|55075|55075|       48069|    27356|       49399|     9189|       55075|           55075|            55075|  55075|     55075|\n",
      "+-------+---------+-----+-----+------------+---------+------------+---------+------------+----------------+-----------------+-------+----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_airport.summary(\"count\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark['airport-codes'] = df_airport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.5 Exploring the US cities Demographics Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demographics = spark.read.format('csv').options(header='true', inferSchema='true',delimiter=';').load('./data/us-cities-demographics.csv')\\\n",
    "                .withColumnRenamed(\"State\", \"state\")\\\n",
    "                .withColumnRenamed(\"State Code\", \"state_code\")\\\n",
    "                .withColumnRenamed(\"City\", \"city\")\\\n",
    "                .withColumnRenamed(\"Median Age\", \"median_age\")\\\n",
    "                .withColumnRenamed(\"Male Population\", \"male_population\")\\\n",
    "                .withColumnRenamed(\"Female Population\", \"female_population\")\\\n",
    "                .withColumnRenamed(\"Total Population\", \"total_population\")\\\n",
    "                .withColumnRenamed(\"Number of Veterans\", \"num_of_veterans\")\\\n",
    "                .withColumnRenamed(\"Foreign-born\", \"foreign_born\")\\\n",
    "                .withColumnRenamed(\"Average Household Size\", \"avg_household_size\")\\\n",
    "                .withColumnRenamed(\"Race\", \"predominant_race\")\\\n",
    "                .withColumnRenamed(\"Count\", \"count\")\\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 12\n",
      "Number of Rows: 2891\n"
     ]
    }
   ],
   "source": [
    "# Printing Shape of the Dataset\n",
    "print(\"Number of Columns: {}\".format(len(df_demographics.columns)))\n",
    "print(\"Number of Rows: {}\".format(df_demographics.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demographics=df_demographics.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>median_age</th>\n",
       "      <th>male_population</th>\n",
       "      <th>female_population</th>\n",
       "      <th>total_population</th>\n",
       "      <th>num_of_veterans</th>\n",
       "      <th>foreign_born</th>\n",
       "      <th>avg_household_size</th>\n",
       "      <th>state_code</th>\n",
       "      <th>predominant_race</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lewisville</td>\n",
       "      <td>Texas</td>\n",
       "      <td>31.6</td>\n",
       "      <td>52776</td>\n",
       "      <td>52032</td>\n",
       "      <td>104808</td>\n",
       "      <td>4211</td>\n",
       "      <td>24865</td>\n",
       "      <td>2.78</td>\n",
       "      <td>TX</td>\n",
       "      <td>American Indian and Alaska Native</td>\n",
       "      <td>1371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pleasanton</td>\n",
       "      <td>California</td>\n",
       "      <td>41.3</td>\n",
       "      <td>36662</td>\n",
       "      <td>42857</td>\n",
       "      <td>79519</td>\n",
       "      <td>2424</td>\n",
       "      <td>23208</td>\n",
       "      <td>2.87</td>\n",
       "      <td>CA</td>\n",
       "      <td>Hispanic or Latino</td>\n",
       "      <td>7949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Homestead</td>\n",
       "      <td>Florida</td>\n",
       "      <td>31.4</td>\n",
       "      <td>31401</td>\n",
       "      <td>35099</td>\n",
       "      <td>66500</td>\n",
       "      <td>2287</td>\n",
       "      <td>24408</td>\n",
       "      <td>3.48</td>\n",
       "      <td>FL</td>\n",
       "      <td>Hispanic or Latino</td>\n",
       "      <td>41910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Santa Maria</td>\n",
       "      <td>California</td>\n",
       "      <td>30.1</td>\n",
       "      <td>53320</td>\n",
       "      <td>51762</td>\n",
       "      <td>105082</td>\n",
       "      <td>2876</td>\n",
       "      <td>39192</td>\n",
       "      <td>3.83</td>\n",
       "      <td>CA</td>\n",
       "      <td>Asian</td>\n",
       "      <td>5058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Salem</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>35.4</td>\n",
       "      <td>80839</td>\n",
       "      <td>83704</td>\n",
       "      <td>164543</td>\n",
       "      <td>9685</td>\n",
       "      <td>19668</td>\n",
       "      <td>2.69</td>\n",
       "      <td>OR</td>\n",
       "      <td>White</td>\n",
       "      <td>148650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Savannah</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>30.3</td>\n",
       "      <td>69389</td>\n",
       "      <td>76295</td>\n",
       "      <td>145684</td>\n",
       "      <td>9717</td>\n",
       "      <td>10355</td>\n",
       "      <td>2.57</td>\n",
       "      <td>GA</td>\n",
       "      <td>American Indian and Alaska Native</td>\n",
       "      <td>2116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chesapeake</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>36.7</td>\n",
       "      <td>114964</td>\n",
       "      <td>120465</td>\n",
       "      <td>235429</td>\n",
       "      <td>29772</td>\n",
       "      <td>13966</td>\n",
       "      <td>2.73</td>\n",
       "      <td>VA</td>\n",
       "      <td>White</td>\n",
       "      <td>150414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Harlingen</td>\n",
       "      <td>Texas</td>\n",
       "      <td>30.1</td>\n",
       "      <td>32404</td>\n",
       "      <td>33365</td>\n",
       "      <td>65769</td>\n",
       "      <td>2761</td>\n",
       "      <td>10391</td>\n",
       "      <td>3.00</td>\n",
       "      <td>TX</td>\n",
       "      <td>White</td>\n",
       "      <td>61844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Allen</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>33.5</td>\n",
       "      <td>60626</td>\n",
       "      <td>59581</td>\n",
       "      <td>120207</td>\n",
       "      <td>5691</td>\n",
       "      <td>19652</td>\n",
       "      <td>2.67</td>\n",
       "      <td>PA</td>\n",
       "      <td>Asian</td>\n",
       "      <td>2670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bay</td>\n",
       "      <td>Texas</td>\n",
       "      <td>32.9</td>\n",
       "      <td>37977</td>\n",
       "      <td>37508</td>\n",
       "      <td>75485</td>\n",
       "      <td>3478</td>\n",
       "      <td>13192</td>\n",
       "      <td>2.59</td>\n",
       "      <td>TX</td>\n",
       "      <td>White</td>\n",
       "      <td>48797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city         state  median_age  male_population  female_population  \\\n",
       "0   Lewisville         Texas        31.6            52776              52032   \n",
       "1   Pleasanton    California        41.3            36662              42857   \n",
       "2    Homestead       Florida        31.4            31401              35099   \n",
       "3  Santa Maria    California        30.1            53320              51762   \n",
       "4        Salem        Oregon        35.4            80839              83704   \n",
       "5     Savannah       Georgia        30.3            69389              76295   \n",
       "6   Chesapeake      Virginia        36.7           114964             120465   \n",
       "7    Harlingen         Texas        30.1            32404              33365   \n",
       "8        Allen  Pennsylvania        33.5            60626              59581   \n",
       "9          Bay         Texas        32.9            37977              37508   \n",
       "\n",
       "   total_population  num_of_veterans  foreign_born  avg_household_size  \\\n",
       "0            104808             4211         24865                2.78   \n",
       "1             79519             2424         23208                2.87   \n",
       "2             66500             2287         24408                3.48   \n",
       "3            105082             2876         39192                3.83   \n",
       "4            164543             9685         19668                2.69   \n",
       "5            145684             9717         10355                2.57   \n",
       "6            235429            29772         13966                2.73   \n",
       "7             65769             2761         10391                3.00   \n",
       "8            120207             5691         19652                2.67   \n",
       "9             75485             3478         13192                2.59   \n",
       "\n",
       "  state_code                   predominant_race   count  \n",
       "0         TX  American Indian and Alaska Native    1371  \n",
       "1         CA                 Hispanic or Latino    7949  \n",
       "2         FL                 Hispanic or Latino   41910  \n",
       "3         CA                              Asian    5058  \n",
       "4         OR                              White  148650  \n",
       "5         GA  American Indian and Alaska Native    2116  \n",
       "6         VA                              White  150414  \n",
       "7         TX                              White   61844  \n",
       "8         PA                              Asian    2670  \n",
       "9         TX                              White   48797  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demographics.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- city: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- median_age: double (nullable = true)\n",
      " |-- male_population: integer (nullable = true)\n",
      " |-- female_population: integer (nullable = true)\n",
      " |-- total_population: integer (nullable = true)\n",
      " |-- num_of_veterans: integer (nullable = true)\n",
      " |-- foreign_born: integer (nullable = true)\n",
      " |-- avg_household_size: double (nullable = true)\n",
      " |-- state_code: string (nullable = true)\n",
      " |-- predominant_race: string (nullable = true)\n",
      " |-- count: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_demographics.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 12\n",
      "Number of Rows: 2891\n"
     ]
    }
   ],
   "source": [
    "# Printing Shape of the Dataset after dropping duplicates\n",
    "print(\"Number of Columns: {}\".format(len(df_demographics.columns)))\n",
    "print(\"Number of Rows: {}\".format(df_demographics.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----+-----+----------+---------------+-----------------+----------------+---------------+------------+------------------+----------+----------------+-----+\n",
      "|summary|city|state|median_age|male_population|female_population|total_population|num_of_veterans|foreign_born|avg_household_size|State Code|predominant_race|count|\n",
      "+-------+----+-----+----------+---------------+-----------------+----------------+---------------+------------+------------------+----------+----------------+-----+\n",
      "|  count|2891| 2891|      2891|           2888|             2888|            2891|           2878|        2878|              2875|      2891|            2891| 2891|\n",
      "+-------+----+-----+----------+---------------+-----------------+----------------+---------------+------------+------------------+----------+----------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_demographics.summary(\"count\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-------+----------+---------------+-----------------+----------------+---------------+------------+------------------+----------+--------------------+-----+\n",
      "|        city|  state|median_age|male_population|female_population|total_population|num_of_veterans|foreign_born|avg_household_size|state_code|    predominant_race|count|\n",
      "+------------+-------+----------+---------------+-----------------+----------------+---------------+------------+------------------+----------+--------------------+-----+\n",
      "|The Villages|Florida|      70.5|           null|             null|           72590|          15231|        4034|              null|        FL|               White|72211|\n",
      "|The Villages|Florida|      70.5|           null|             null|           72590|          15231|        4034|              null|        FL|Black or African-...|  331|\n",
      "|The Villages|Florida|      70.5|           null|             null|           72590|          15231|        4034|              null|        FL|  Hispanic or Latino| 1066|\n",
      "+------------+-------+----------+---------------+-----------------+----------------+---------------+------------+------------------+----------+--------------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_demographics.where(df_demographics.male_population.isNull()).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark['us-cities-demographics'] = df_demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.6 Exploring I94 labels data\n",
    "\n",
    "**checking i94 meta_data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i94_state_code</th>\n",
       "      <th>i94_state_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>ALABAMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>ALASKA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>ARIZONA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>ARKANSAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  i94_state_code i94_state_name\n",
       "0             AL        ALABAMA\n",
       "1             AK         ALASKA\n",
       "2             AZ        ARIZONA\n",
       "3             AR       ARKANSAS\n",
       "4             CA     CALIFORNIA"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i94_addr = spark.read.format('csv').load('i94_meta_data/i94addr.csv', header=True, inferSchema=True)\n",
    "i94_addr.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+----------+\n",
      "|summary|state_code|state_name|\n",
      "+-------+----------+----------+\n",
      "|  count|        55|        55|\n",
      "+-------+----------+----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_addr_df = i94_addr.selectExpr(\"i94_state_code as state_code\",\"i94_state_name as state_name\")\n",
    "i94_addr_df.summary(\"count\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i94_country_code</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>582</td>\n",
       "      <td>MEXICO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>236</td>\n",
       "      <td>AFGHANISTAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>ALBANIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>316</td>\n",
       "      <td>ALGERIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102</td>\n",
       "      <td>ANDORRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i94_country_code country_name\n",
       "0               582       MEXICO\n",
       "1               236  AFGHANISTAN\n",
       "2               101      ALBANIA\n",
       "3               316      ALGERIA\n",
       "4               102      ANDORRA"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i94_cit = spark.read.format('csv').load('i94_meta_data/i94cit_i94res.csv', header=True, inferSchema=True)\n",
    "i94_cit.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+-------+\n",
      "|summary|country_id|country|\n",
      "+-------+----------+-------+\n",
      "|  count|       289|    289|\n",
      "+-------+----------+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_cit_df = i94_cit.selectExpr(\"i94_country_code as country_id\",\"country_name as country\")\n",
    "i94_cit_df.summary(\"count\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_mode = spark.read.format('csv').load('i94_meta_data/i94mode.csv', header=True, inferSchema=True)\n",
    "i94_mode_df = i94_mode.selectExpr(\"i94_mode_code as mode_id\",\"i94_mode as transportation_mode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------------------+\n",
      "|mode_id|transportation_mode|\n",
      "+-------+-------------------+\n",
      "|      1|                Air|\n",
      "|      2|                Sea|\n",
      "|      3|               Land|\n",
      "|      9|       Not reported|\n",
      "+-------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_mode_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i94_port_code</th>\n",
       "      <th>port_city</th>\n",
       "      <th>port_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALC</td>\n",
       "      <td>ALCAN</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANC</td>\n",
       "      <td>ANCHORAGE</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAR</td>\n",
       "      <td>BAKER AAF - BAKER ISLAND</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAC</td>\n",
       "      <td>DALTONS CACHE</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIZ</td>\n",
       "      <td>DEW STATION PT LAY DEW</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  i94_port_code                 port_city port_state\n",
       "0           ALC                     ALCAN         AK\n",
       "1           ANC                 ANCHORAGE         AK\n",
       "2           BAR  BAKER AAF - BAKER ISLAND         AK\n",
       "3           DAC             DALTONS CACHE         AK\n",
       "4           PIZ    DEW STATION PT LAY DEW         AK"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i94_port = spark.read.format('csv').load('i94_meta_data/i94port_i94code.csv', header=True, inferSchema=True)\n",
    "i94_port.limit(5).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------+----------+\n",
      "|i94_port_code|      port_city|port_state|\n",
      "+-------------+---------------+----------+\n",
      "|          LIA|    LITTLE ROCK|  AR (BPS)|\n",
      "|          SLS|        SALINAS|  CA (BPS)|\n",
      "|          SLI|SAN LUIS OBISPO|  CA (BPS)|\n",
      "|          STO|       STOCKTON|  CA (BPS)|\n",
      "|          LAK|   LAKE CHARLES|  LA (BPS)|\n",
      "|          RGM|       RANGELEY|  ME (BPS)|\n",
      "|          EUR|         EUREKA|  MT (BPS)|\n",
      "|          ALA|     ALAMAGORDO|  NM (BPS)|\n",
      "|          DNM|         DEMING|  NM (BPS)|\n",
      "|          LOB|      LORDSBURG|  NM (BPS)|\n",
      "|          BBP|  BIG BEND PARK|  TX (BPS)|\n",
      "|          MRS|          MARFA|  TX (BPS)|\n",
      "|          CUR|         CURLEW|  WA (BPS)|\n",
      "+-------------+---------------+----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_port.filter(i94_port.port_state.contains('BPS')).show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------+----------+\n",
      "|i94_port_code|port_city|port_state|\n",
      "+-------------+---------+----------+\n",
      "|          ASE|    ASPEN|  CO #ARPT|\n",
      "|          APF|   NAPLES|  FL #ARPT|\n",
      "|          CAE| COLUMBIA|  SC #ARPT|\n",
      "|          HEF| MANASSAS|  VA #ARPT|\n",
      "+-------------+---------+----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_port.filter(i94_port.port_state.contains('#ARPT')).show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_state_code(x):\n",
    "    if x is not None and 'BPS' in x:\n",
    "        return x.strip().split('(BPS)')[0].strip()\n",
    "    elif x is not None and 'ARPT' in x:\n",
    "        return x.strip().split('#ARPT')[0].strip()\n",
    "    elif x is not None and 'SECTOR HQ' in x:\n",
    "        return x.strip().split('(BP - SECTOR HQ)')[0].strip()\n",
    "    elif x is not None and 'INTL' in x:\n",
    "        return x.strip().split('#INTL')[0].strip()\n",
    "    else:\n",
    "        return x\n",
    "udf_parse_state_code = udf(lambda x: parse_state_code(x), StringType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_port=i94_port.withColumn(\"port_state_cleaned\", udf_parse_state_code(\"port_state\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------+----------+------------------+\n",
      "|i94_port_code|      port_city|port_state|port_state_cleaned|\n",
      "+-------------+---------------+----------+------------------+\n",
      "|          LIA|    LITTLE ROCK|  AR (BPS)|                AR|\n",
      "|          SLS|        SALINAS|  CA (BPS)|                CA|\n",
      "|          SLI|SAN LUIS OBISPO|  CA (BPS)|                CA|\n",
      "|          STO|       STOCKTON|  CA (BPS)|                CA|\n",
      "|          LAK|   LAKE CHARLES|  LA (BPS)|                LA|\n",
      "|          RGM|       RANGELEY|  ME (BPS)|                ME|\n",
      "|          EUR|         EUREKA|  MT (BPS)|                MT|\n",
      "|          ALA|     ALAMAGORDO|  NM (BPS)|                NM|\n",
      "|          DNM|         DEMING|  NM (BPS)|                NM|\n",
      "|          LOB|      LORDSBURG|  NM (BPS)|                NM|\n",
      "|          BBP|  BIG BEND PARK|  TX (BPS)|                TX|\n",
      "|          MRS|          MARFA|  TX (BPS)|                TX|\n",
      "|          CUR|         CURLEW|  WA (BPS)|                WA|\n",
      "+-------------+---------------+----------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_port.filter(i94_port.port_state.contains('BPS')).show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------+----------+------------------+\n",
      "|i94_port_code|port_city|port_state|port_state_cleaned|\n",
      "+-------------+---------+----------+------------------+\n",
      "|          ASE|    ASPEN|  CO #ARPT|                CO|\n",
      "|          APF|   NAPLES|  FL #ARPT|                FL|\n",
      "|          CAE| COLUMBIA|  SC #ARPT|                SC|\n",
      "|          HEF| MANASSAS|  VA #ARPT|                VA|\n",
      "+-------------+---------+----------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_port.filter(i94_port.port_state.contains('ARPT')).show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_port_df = i94_port.selectExpr(\"i94_port_code as port_code\",\"port_city as city\",\"port_state_cleaned as state_code_or_country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+--------------------+---------------------+\n",
      "|port_code|                city|state_code_or_country|\n",
      "+---------+--------------------+---------------------+\n",
      "|      ALC|               ALCAN|                   AK|\n",
      "|      ANC|           ANCHORAGE|                   AK|\n",
      "|      BAR|BAKER AAF - BAKER...|                   AK|\n",
      "|      DAC|       DALTONS CACHE|                   AK|\n",
      "|      PIZ|DEW STATION PT LA...|                   AK|\n",
      "|      DTH|        DUTCH HARBOR|                   AK|\n",
      "|      EGL|               EAGLE|                   AK|\n",
      "|      FRB|           FAIRBANKS|                   AK|\n",
      "|      HOM|               HOMER|                   AK|\n",
      "|      HYD|               HYDER|                   AK|\n",
      "|      JUN|              JUNEAU|                   AK|\n",
      "|      5KE|           KETCHIKAN|                   AK|\n",
      "|      KET|           KETCHIKAN|                   AK|\n",
      "|      MOS|MOSES POINT INTER...|                   AK|\n",
      "|      NIK|             NIKISKI|                   AK|\n",
      "|      NOM|                 NOM|                   AK|\n",
      "|      PKC|         POKER CREEK|                   AK|\n",
      "|      ORI|      PORT LIONS SPB|                   AK|\n",
      "|      SKA|             SKAGWAY|                   AK|\n",
      "|      SNP|     ST. PAUL ISLAND|                   AK|\n",
      "+---------+--------------------+---------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_port_df.show(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark['port-of-entry-codes'] = i94_port_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+--------+\n",
      "|i94_visa_code|i94_visa|\n",
      "+-------------+--------+\n",
      "|            1|Business|\n",
      "|            2|Pleasure|\n",
      "|            3| Student|\n",
      "+-------------+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_visa = spark.read.format('csv').load('i94_meta_data/i94visa.csv', header=True, inferSchema=True)\n",
    "i94_visa.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "i94_visa_df = i94_visa.selectExpr(\"i94_visa_code as visa_code\",\"i94_visa as visa_purpose\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+------------+\n",
      "|visa_code|visa_purpose|\n",
      "+---------+------------+\n",
      "|        1|    Business|\n",
      "|        2|    Pleasure|\n",
      "|        3|     Student|\n",
      "+---------+------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "i94_visa_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+--------------------+\n",
      "|visa-type|         description|\n",
      "+---------+--------------------+\n",
      "|      CW1|CNMI-only transit...|\n",
      "|      CW2|Spouses and child...|\n",
      "|      H1B|Temporary workers...|\n",
      "|     H1B1|Chile and Singapo...|\n",
      "|      H1C|Registered nurses...|\n",
      "|      H2A|Agricultural workers|\n",
      "|      H2B|Nonagricultural w...|\n",
      "|      H2R|Returning H2B wor...|\n",
      "|       H3|            Trainees|\n",
      "|       H4|Spouses and child...|\n",
      "+---------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "visa = spark.read.format('csv').load('./data/visa-type.csv', header=True, inferSchema=True)\n",
    "visa.show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visa_type</th>\n",
       "      <th>visa_type_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CW1</td>\n",
       "      <td>CNMI-only transitional workers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CW2</td>\n",
       "      <td>Spouses and children of CW1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>H1B</td>\n",
       "      <td>Temporary workers in specialty occupations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H1B1</td>\n",
       "      <td>Chile and Singapore Free Trade Agreement aliens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>H1C</td>\n",
       "      <td>Registered nurses participating in the Nursing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H2A</td>\n",
       "      <td>Agricultural workers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>H2B</td>\n",
       "      <td>Nonagricultural workers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H2R</td>\n",
       "      <td>Returning H2B workers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>H3</td>\n",
       "      <td>Trainees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>H4</td>\n",
       "      <td>Spouses and children of H1, H2, or H3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  visa_type                              visa_type_description\n",
       "0       CW1                     CNMI-only transitional workers\n",
       "1       CW2                        Spouses and children of CW1\n",
       "2       H1B         Temporary workers in specialty occupations\n",
       "3      H1B1    Chile and Singapore Free Trade Agreement aliens\n",
       "4       H1C  Registered nurses participating in the Nursing...\n",
       "5       H2A                               Agricultural workers\n",
       "6       H2B                            Nonagricultural workers\n",
       "7       H2R                              Returning H2B workers\n",
       "8        H3                                           Trainees\n",
       "9        H4              Spouses and children of H1, H2, or H3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_df = visa.withColumnRenamed(\"visa-type\", \"visa_type\").withColumnRenamed(\"description\", \"visa_type_description\")\n",
    "visa_df.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark['visa-type']=visa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+\n",
      "|                Post|                Code|\n",
      "+--------------------+--------------------+\n",
      "|             Abidjan|                 ABJ|\n",
      "|Abu Dhabi (Beirut...|BRF (used for IV ...|\n",
      "|               Accra|                 ACC|\n",
      "|          Alexandria|                 ALX|\n",
      "|              Almaty|                 ATA|\n",
      "+--------------------+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "visa_port = spark.read.format('csv').load('./data/visa-issuing-ports.csv', header=True, inferSchema=True)\n",
    "visa_port.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "visa_port_df = visa_port.selectExpr(\"Post as port_of_issue\",\"Code as visa_post_code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>port_of_issue</th>\n",
       "      <th>visa_post_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abidjan</td>\n",
       "      <td>ABJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abu Dhabi (Beirut Files)</td>\n",
       "      <td>BRF (used for IV only)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Accra</td>\n",
       "      <td>ACC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alexandria</td>\n",
       "      <td>ALX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almaty</td>\n",
       "      <td>ATA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>AMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antananarivo</td>\n",
       "      <td>ANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apia</td>\n",
       "      <td>APA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Asmara</td>\n",
       "      <td>ASM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Athens</td>\n",
       "      <td>ATH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              port_of_issue          visa_post_code\n",
       "0                   Abidjan                     ABJ\n",
       "1  Abu Dhabi (Beirut Files)  BRF (used for IV only)\n",
       "2                     Accra                     ACC\n",
       "3                Alexandria                     ALX\n",
       "4                    Almaty                     ATA\n",
       "5                 Amsterdam                     AMS\n",
       "6              Antananarivo                     ANT\n",
       "7                      Apia                     APA\n",
       "8                    Asmara                     ASM\n",
       "9                    Athens                     ATH"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa_port_df.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark['visa-issue-ports']=visa_port_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+---------------+\n",
      "|Code|       Location|\n",
      "+----+---------------+\n",
      "| ABE|   Aberdeen, WA|\n",
      "| ABQ|Albuquerque, NM|\n",
      "| ADT|Amistad Dam, TX|\n",
      "| ALP|     Alpena, MI|\n",
      "| AGN|    Algonac, MI|\n",
      "+----+---------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "port_of_entry = spark.read.format('csv').load('./data/port-of-entry-codes.csv', header=True, inferSchema=True)\n",
    "port_of_entry.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: I have used the port of entry data from I94 SAS labels**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7 Exploring Airlines data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "airlines = spark.read.format('csv')\\\n",
    "    .option(\"delimiter\", \"\\t\")\\\n",
    "    .load('./data/airlines-codes.csv', header=True, inferSchema=True)\\\n",
    "    .withColumnRenamed(\"AIRLINE NAME\", \"airline_name\")\\\n",
    "    .withColumnRenamed(\"IATA DESIGNATOR\", \"airline_iata_code\")\\\n",
    "    .withColumnRenamed(\"3 DIGIT CODE\", \"airline_3_digit_code\")\\\n",
    "    .withColumnRenamed(\"ICAO CODE\", \"airline_icao_code\")\\\n",
    "    .withColumnRenamed(\"COUNTRY / TERRITORY\", \"origin_country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airline_name</th>\n",
       "      <th>airline_iata_code</th>\n",
       "      <th>airline_3_digit_code</th>\n",
       "      <th>airline_icao_code</th>\n",
       "      <th>origin_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aegean Airlines</td>\n",
       "      <td>A3</td>\n",
       "      <td>390</td>\n",
       "      <td>AEE</td>\n",
       "      <td>Greece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aer Lingus</td>\n",
       "      <td>EI</td>\n",
       "      <td>053</td>\n",
       "      <td>EIN</td>\n",
       "      <td>Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aero Republica</td>\n",
       "      <td>P5</td>\n",
       "      <td>845</td>\n",
       "      <td>RPB</td>\n",
       "      <td>Colombia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aeroflot</td>\n",
       "      <td>SU</td>\n",
       "      <td>555</td>\n",
       "      <td>AFL</td>\n",
       "      <td>Russian Federation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aerolineas Argentinas</td>\n",
       "      <td>AR</td>\n",
       "      <td>044</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Aeromexico</td>\n",
       "      <td>AM</td>\n",
       "      <td>139</td>\n",
       "      <td>AMX</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Africa World Airlines</td>\n",
       "      <td>AW</td>\n",
       "      <td>394</td>\n",
       "      <td>AFW</td>\n",
       "      <td>Ghana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Air Algérie</td>\n",
       "      <td>AH</td>\n",
       "      <td>124</td>\n",
       "      <td>DAH</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Air Arabia</td>\n",
       "      <td>G9</td>\n",
       "      <td>514</td>\n",
       "      <td>ABY</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Air Astana</td>\n",
       "      <td>KC</td>\n",
       "      <td>465</td>\n",
       "      <td>KZR</td>\n",
       "      <td>Kazakhstan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            airline_name airline_iata_code airline_3_digit_code  \\\n",
       "0        Aegean Airlines                A3                  390   \n",
       "1             Aer Lingus                EI                  053   \n",
       "2         Aero Republica                P5                  845   \n",
       "3               Aeroflot                SU                  555   \n",
       "4  Aerolineas Argentinas                AR                  044   \n",
       "5             Aeromexico                AM                  139   \n",
       "6  Africa World Airlines                AW                  394   \n",
       "7            Air Algérie                AH                  124   \n",
       "8             Air Arabia                G9                  514   \n",
       "9             Air Astana                KC                  465   \n",
       "\n",
       "  airline_icao_code        origin_country  \n",
       "0               AEE                Greece  \n",
       "1               EIN               Ireland  \n",
       "2               RPB              Colombia  \n",
       "3               AFL    Russian Federation  \n",
       "4               ARG             Argentina  \n",
       "5               AMX                Mexico  \n",
       "6               AFW                 Ghana  \n",
       "7               DAH               Algeria  \n",
       "8               ABY  United Arab Emirates  \n",
       "9               KZR            Kazakhstan  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.limit(10).toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns: 5\n",
      "Number of Rows: 298\n"
     ]
    }
   ],
   "source": [
    "# Printing Shape of the Dataset after dropping duplicates\n",
    "airlines=airlines.drop_duplicates(['airline_iata_code'])\n",
    "print(\"Number of Columns: {}\".format(len(airlines.columns)))\n",
    "print(\"Number of Rows: {}\".format(airlines.count()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------+-----------------+--------------------+-----------------+--------------+\n",
      "|summary|airline_name|airline_iata_code|airline_3_digit_code|airline_icao_code|origin_country|\n",
      "+-------+------------+-----------------+--------------------+-----------------+--------------+\n",
      "|  count|         298|              298|                 293|              298|           298|\n",
      "+-------+------------+-----------------+--------------------+-----------------+--------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "airlines.summary(\"count\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_spark['airlines-codes'] = airlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Explore and Assess the Data\n",
    "#### Explore the Data \n",
    "Exploring data quality issues, like missing values, duplicate data, etc.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(15,8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cb1721f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cb1f07f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABVD3+Zs+enZEjR6axsTGTJk3aYHtra2suuuiiNDY25uSTT85LL72UJGlra8vf//3f57jjjsvRRx+d733ve9UeFQAAYKtV1fhrb2/PhAkTcvvtt2f69Ol54IEHMm/evPX2uffee7PDDjvkoYceytixYzNx4sQkyU9/+tO0trZm2rRpuf/++/N//+//rYQhAAAA701V46+5uTm77rprBg4cmPr6+owaNSozZ85cb59Zs2alqakpSTJy5Mg8+uijKZfLKZVKWbVqVdauXZvVq1enR48e2W677ao5LgAAwFarrpof3tLSkoaGhspyv3790tzcvME+/fv3/9MwdXXZfvvts2zZsowcOTIzZ87M8OHDs3r16owfPz59+vTZ6PftuOOHUlfXvfN/EAAAgPdh5523r/UIFVWNv3K5vMG6UqnUoX2am5vTrVu3/OIXv8gbb7yR0047LYccckgGDhz4rt+3bNnKDz40AABAJ1m6dMVm/b6NxWZVL/tsaGjIkiVLKsstLS3p27fvBvssXrw4SbJ27dqsWLEiffr0yQMPPJBDDz00PXr0yF/8xV/kgAMOyNNPP13NcQEAALZaVY2//fbbLwsWLMiiRYvS2tqa6dOnZ8SIEevtM2LEiEyZMiVJMmPGjBx00EEplUrp379/5syZk3K5nJUrV+app57KbrvtVs1xAQAAtlpVjb+6urpcddVVOfvss3PMMcfk6KOPzqBBg3LLLbdUHvwyZsyYLF++PI2NjZk8eXIuvfTSJMnpp5+et956K8cee2zGjBmTE088MXvttVc1xwUAANhqlcrvdNNdF7W5r6cFAOgqHrvkglqPAIU05IZbN+v31eyePwAAALYM4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKADxBwAAUADiDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAogA7H3+TJk7NixYokyWWXXZbPf/7zefjhh6s2GAAAAJ2nw/F3//33Z/vtt8+vfvWrvPbaa7n22mtz4403VnM2AAAAOkmH46979+5Jkjlz5uS4447LAQcckHK5XLXBAAAA6Dwdjr+ePXvmtttuy7Rp0zJs2LCUy+W0tbVVczYAAAA6SYfj77rrrstrr72Wyy+/PDvvvHMWLVqU4447rpqzAQAA0EnqOrpjc3NzrrzyysryRz/60TQ0NFRlKAAAADpXh8/8/e///b87tA4AAIAtzybP/D399NNpbm7OsmXLcvfdd1fWv/nmm+75AwAA6CI2GX8tLS2ZO3duVq1alblz51bWb7vttrnuuuuqOhwAAACdY5Pxd+SRR+bII4/Mww8/nOHDh2+OmQAAAOhkHX7gy/Dhw/Poo49m4cKFWbt2bWX96aefXpXBAAAA6Dwdjr8rrrgic+fOzSc/+cnKC98BAADoGjocf0888UQeeOCB9OjRo5rzAAAAUAUdftWDd/oBAAB0XR0+8/exj30sY8eOzZFHHpn6+vrKevf8AQAAbPk6HH+tra356Ec/mueff76a8wAAAFAFHY4/7/QDAADoujocf3ffffc7rnfZJwAAwJavw/E3d+7cyt/XrFmTOXPm5FOf+pT4AwAA6ALe92Wfr776aq699tpOHwgAAIDO1+FXPfx3ffv2zYIFCzpxFAAAAKrlfd3zVy6X8/TTT2eHHXaoylAAAAB0rvd1z1/37t2z++67Z/z48VUZCgAAgM7lVQ8AAAAF0OH4W7VqVb7zne/kl7/8ZUqlUoYNG5Zx48alV69e1ZwPAACATtDhB778z//5P/Pqq6/mq1/9asaPH59XX301EyZMqOZsAAAAdJIOn/l7+umnM23atMryAQcckNGjR1dlKAAAADrXe3rVw8qVKyt/X7VqVacPAwAAQHV0+Mzfcccdl1NOOSWjRo1KqVTK9OnTc/zxx1dzNgAAADpJh+Pv3HPPzV577ZVHH3005XI5l156aQ477LBqzgYAAEAn6XD8Jclhhx0m+AAAALqgDt/z98UvfjGvv/56ZXn58uU5/fTTN3nc7NmzM3LkyDQ2NmbSpEkbbG9tbc1FF12UxsbGnHzyyXnppZcq25577rnKpabHHXdc1qxZ09FxAQAA+DMdPvO3cuXK9O7du7Lcp0+fvPnmmxs9pr29PRMmTMjkyZPTr1+/jBkzJiNGjMgee+xR2efee+/NDjvskIceeijTp0/PxIkTc/PNN2ft2rW57LLL8k//9E/Za6+9smzZstTVvacTlQAAAPz/Onzmb926des97fOtt95Ke3v7Ro9pbm7OrrvumoEDB6a+vj6jRo3KzJkz19tn1qxZaWpqSpKMHDmyck/hI488kj333DN77bVXkmTHHXdM9+7dO/yDAQAA8F86fCrt2GOPzZlnnpkvfvGLSZJ77rlnk+/5a2lpSUNDQ2W5X79+aW5u3mCf/v37/2mYurpsv/32WbZsWebPn59SqZSzzjorr732Wo455picc845Hf7BAAAA+C8djr//8T/+R/r27ZtZs2alXC7n1FNPzQknnLDRY8rl8gbrSqVSh/Zpb2/P448/nh/+8Ifp1atXxo4dm3333TcHH3zwu37fjjt+KHV1zg4CAABbhp133r7WI1S8p5vompqaKpdo/ndf+9rX8rWvfW29dQ0NDVmyZElluaWlJX379t1gn8WLF6ehoSFr167NihUr0qdPnzQ0NGTo0KHZaaedkvzpSaPPPPPMRuNv2bKV77oNAABgc1u6dMVm/b6NxWaH7/nblKeeemqDdfvtt18WLFiQRYsWpbW1NdOnT8+IESPW22fEiBGZMmVKkmTGjBk56KCDUiqVMnz48Pz2t7/NqlWrsnbt2jz22GPrPSgGAACAjqvq4zPr6upy1VVX5eyzz057e3tOOumkDBo0KLfcckv23XffHHHEERkzZkwuu+yyNDY2pnfv3rnpppuSJL17987YsWMzZsyYlEqlHHbYYfnc5z5XzXEBAAC2WqXyO9109z40NTVVzuDVyuY+pQoA0FU8dskFtR4BCmnIDbdu1u/bLJd9AgAAsOXqtPjr1k1HAgAAbKk2ec/fqlWrNrq9V69eSZL77ruvcyYCAACg020y/gYPHpxSqbTe+/jeXi6VSnn22WerOiAAAAAf3Cbj77nnntsccwAAAFBFbtQDAAAogE2e+Xv7pev/3duXfT766KNVGQwAAIDOs8n48yAXAACArm+T8TdgwIDNMQcAAABVtMn4e9u7Xf7psk8AAIAtX4fj788v/1yzZk2mTZuWuroOHw4AAEANdfhpnwMGDKj82W233XLhhRdmzpw51ZwNAACATvK+X/WwaNGivPzyy505CwAAAFXyvu75W7duXdauXZsrr7yyaoMBAADQed7XPX91dXX58Ic/nO7du1dlKAAAADpXh+Pv7Vc+tLa2pr29Pa2trUmSXr16VWcyAAAAOk2H4+9nP/tZrrnmmixdujRJUi6XUyqV8uyzz1ZtOAAAADpHh+Pvn/7pn3LzzTfn05/+dLp1e9/PiQEAAKAGOhx/vXv3zgEHHFDNWQAAAKiSDp/Ca2xszPe///0sX748q1atqvwBAABgy9fhM3833XRTkmTChAkplUru+QMAAOhCOhx/zz333Ea3v/baa9lpp50+8EAAAAB0vk57cstZZ53VWR8FAABAJ+u0+CuXy531UQAAAHSyTou/UqnUWR8FAABAJ/PCPgAAgAJw2ScAAEABvOf4e+655/KlL30pJ510Un7xi19U1jc2NnbqYAAAAHSeTb7qob29Pd27d68sf/e73811112XUqmUcePG5dBDD02S/M3f/E31pgQAAOAD2eSZvy9/+cv5f//v/1WWy+VyunfvXnnROwAAAFu+TZ75+/a3v53rr78+P/rRj3LppZdm3LhxufTSS7Nq1apceumlm2NGAAAAPqBNxl+fPn1y7bXX5rHHHst5552XL3zhC7n77rs3x2wAAAB0kk1e9rlu3br8/Oc/T1tbW+68884sWrQo48aNy8KFCzfHfAAAAHSCTZ75u+iii7Lddttl9erV+dnPfpavfe1rWbBgQb7+9a9n//3396AXAACALmCT8ff73/8+P/7xj5Mkxx9/fJLkYx/7WL73ve9l6tSp1Z0OAACATrHJ+Ntll11y5ZVXZvXq1dl///3X2zZ69OiqDQYAAEDn2WT83XLLLXn44YdTV1eXYcOGbY6ZAAAA6GSbjL+6urp87nOf2wyjAAAAUC2bfNonAAAAXZ/4AwAAKADxBwAAUADiDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKICqx9/s2bMzcuTINDY2ZtKkSRtsb21tzUUXXZTGxsacfPLJeemll9bb/sorr2Tw4MG54447qj0qAADAVquq8dfe3p4JEybk9ttvz/Tp0/PAAw9k3rx56+1z7733ZocddshDDz2UsWPHZuLEiettv+6663LooYdWc0wAAICtXlXjr7m5ObvuumsGDhyY+vr6jBo1KjNnzlxvn1mzZqWpqSlJMnLkyDz66KMpl8tJkn//93/PLrvskkGDBlVzTAAAgK1eVeOvpaUlDQ0NleV+/fqlpaVlg3369++fJKmrq8v222+fZcuWZeXKlflf/+t/5fzzz6/miAAAAIVQV80Pf/sM3p8rlUod2udb3/pWvvzlL2fbbbft8PftuOOHUlfX/b0PCgAAUAU777x9rUeoqGr8NTQ0ZMmSJZXllpaW9O3bd4N9Fi9enIaGhqxduzYrVqxInz598tRTT2XGjBmZOHFi3njjjXTr1i3bbLNN/uqv/updv2/ZspVV+1kAAADeq6VLV2zW79tYbFY1/vbbb78sWLAgixYtSr9+/TJ9+vTccMMN6+0zYsSITJkyJYMHD86MGTNy0EEHpVQq5fvf/35ln29961v50Ic+tNHwAwAA4N1VNf7q6upy1VVX5eyzz057e3tOOumkDBo0KLfcckv23XffHHHEERkzZkwuu+yyNDY2pnfv3rnpppuqORIAAEAhlcrvdNNdF7W5T6kCAHQVj11yQa1HgEIacsOtm/X7NnbZZ9Vf8g4AAEDtiT8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKADxBwAAUADiDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKADxBwAAUADiDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKADxBwAAUADiDwAAoACqHn+zZ8/OyJEj09jYmEmTJm2wvbW1NRdddFEaGxtz8skn56WXXkqSPPLIIznxxBNz3HHH5cQTT8yjjz5a7VEBAAC2WlWNv/b29kyYMCG33357pk+fngceeCDz5s1bb5977703O+ywQx566KGMHTs2EydOTJLsuOOOue222zJh30+FAAAY6klEQVRt2rR84xvfyOWXX17NUQEAALZqVY2/5ubm7Lrrrhk4cGDq6+szatSozJw5c719Zs2alaampiTJyJEj8+ijj6ZcLueTn/xk+vXrlyQZNGhQWltb09raWs1xAQAAtlp11fzwlpaWNDQ0VJb79euX5ubmDfbp37//n4apq8v222+fZcuWZaeddqrsM2PGjOy9996pr6/f6PftuOOHUlfXvRN/AgAAgPdv5523r/UIFVWNv3K5vMG6Uqn0nvb53e9+l4kTJ+bOO+/c5PctW7byfUwJAABQHUuXrtis37ex2KzqZZ8NDQ1ZsmRJZbmlpSV9+/bdYJ/FixcnSdauXZsVK1akT58+SZIlS5bk/PPPzze/+c189KMfreaoAAAAW7Wqxt9+++2XBQsWZNGiRWltbc306dMzYsSI9fYZMWJEpkyZkuRPl3cedNBBKZVKeeONN3Luuefm4osvzoEHHljNMQEAALZ6VY2/urq6XHXVVTn77LNzzDHH5Oijj86gQYNyyy23VB78MmbMmCxfvjyNjY2ZPHlyLr300iTJ//k//ycLFy7Md77znRx//PE5/vjj88c//rGa4wIAAGy1SuV3uumui9rc19MCAHQVj11yQa1HgEIacsOtm/X7anbPHwAAAFsG8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKADxBwAAUADiDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFID4AwAAKADxBwAAUADiDwAAoADEHwAAQAGIPwAAgAIQfwAAAAUg/gAAAApA/AEAABSA+AMAACgA8QcAAFAA4g8AAKAAxB8AAEABiD8AAIACEH8AAAAFIP4AAAAKQPwBAAAUgPgDAAAoAPEHAABQAOIPAACgAMQfAABAAYg/AACAAhB/AAAABSD+AAAACkD8AQAAFEDV42/27NkZOXJkGhsbM2nSpA22t7a25qKLLkpjY2NOPvnkvPTSS5Vt3/ve99LY2JiRI0fmF7/4RbVHBQAA2GpVNf7a29szYcKE3H777Zk+fXoeeOCBzJs3b7197r333uywww556KGHMnbs2EycODFJMm/evEyfPj3Tp0/P7bffnquvvjrt7e3VHBcAAGCrVdX4a25uzq677pqBAwemvr4+o0aNysyZM9fbZ9asWWlqakqSjBw5Mo8++mjK5XJmzpyZUaNGpb6+PgMHDsyuu+6a5ubmao4LAACw1aqr5oe3tLSkoaGhstyvX78NAq6lpSX9+/f/0zB1ddl+++2zbNmytLS05FOf+tR6x7a0tGz0+3beeftOnB4AYOtxzF2Taz0CUGNVPfNXLpc3WFcqlTq0T0eOBQAAoGOqGn8NDQ1ZsmRJZbmlpSV9+/bdYJ/FixcnSdauXZsVK1akT58+HToWAACAjqlq/O23335ZsGBBFi1alNbW1kyfPj0jRoxYb58RI0ZkypQpSZIZM2bkoIMOSqlUyogRIzJ9+vS0trZm0aJFWbBgQfbff/9qjgsAALDVquo9f3V1dbnqqqty9tlnp729PSeddFIGDRqUW265Jfvuu2+OOOKIjBkzJpdddlkaGxvTu3fv3HTTTUmSQYMG5eijj84xxxyT7t2756qrrkr37t2rOS4AAMBWq1R+p5vrAAAA2KpU/SXvAAAA1J74AwAAKADxBwAAUADiDwAAoADEH4WyatWq/PM//3P+4R/+IUmyYMGC/Md//EeNpwK6sr/927/Nf/7nf2bdunW1HgXYyixfvrzWI7CVEX8Uyvjx41NfX58nn3wySdLQ0JCbb765xlMBXdkXv/jFTJs2LUcddVQmTpyYF154odYjAVuJL3zhC7ngggvy85//PB7QT2cQfxTKwoULc84556Su7k+vuOzZs6f/mAIfyCGHHJIbbrghU6ZMyYABA3LmmWfm1FNPzX333Ze2trZajwd0YTNmzMgpp5ySH//4x2lsbMyNN96Y+fPn13osujDxR6HU19dn9erVKZVKSf4Ug/X19TWeCujqli1blvvvvz/33ntv9t5775xxxhn5zW9+kzPPPLPWowFdWKlUyrBhw3LjjTfmmmuuyZQpU3LyySfnr/7qr/LEE0/Uejy6IC95p1AeeeSR3HbbbZk3b16GDRuWJ554Itddd10++9nP1no0oIs6//zz8+KLL+b4449PU1NT+vbtW9l24okn5v7776/hdEBXtmzZskydOjU//vGP8+EPfzhjxozJiBEj8uyzz+bCCy/MrFmzaj0iXYz4o3CWLVuWp556KuVyOZ/61Key00471XokoAv7+c9/nr/8y79cb11ra6urCoAPbOTIkRk9enROOumkNDQ0rLdt0qRJOffcc2s0GV2V+KMQnnnmmY1u32effTbTJMDWpqmpKVOmTNnkOoD3or29Pddff33Gjx9f61HYitTVegDYHL7xjW8k+dO/xs+dOzd77rlnkuS3v/1t9t9//9xzzz21HA/ogpYuXZqWlpasXr06v/nNbyoPj3rzzTezatWqGk8HdHXdu3fPc889V+sx2MqIPwrhX//1X5Mkf/d3f5cJEyZU4u/555/PnXfeWcvRgC7q4Ycfzv33358lS5bkuuuuq6zfdtttc/HFF9dwMmBrsffee2fcuHH5/Oc/nw996EOV9UcddVQNp6IrE38UyosvvlgJvyT5xCc+kWeffbaGEwFdVVNTU5qamjJjxoyMHDmy1uMAW6HXX389O+64Y+bMmbPeevHH+yX+KJTdd989V155ZUaPHp1SqZSpU6dm9913r/VYQBf04x//OMcff3xefvnlTJ48eYPtX/nKV2owFbA1GTNmTA488MD11j3++OM1moatgff8USjXXXddBg0alLvuuiv/8i//kj322GO9y7UAOurt+/pWrlyZt956a4M/AB/UNddc06F10FHO/FEo22yzTU477bQcfPDBKZVK+fjHP54ePXrUeiygCzr11FOTJIsWLcqVV16ZHXbYIcmfLtN6+yFTAO/HE088kSeeeCKvvfbaelcWvPnmm2lvb6/hZHR14o9CmTNnTq644ooMGDAg5XI5ixcvzje/+c0MGTKk1qMBXdRvf/vbSvglSe/evd1LDHwgbW1tWblyZdrb29e7kmC77bbLrbfeWsPJ6OrEH4XyzW9+M3fccUd22223JMn8+fNzySWX5P7776/xZEBXtW7durz++uvp3bt3kmT58uX+ZR74QIYOHZqhQ4emqakpAwYMqPU4bEXEH4XS1tZWCb8k+fjHP562trYaTgR0dWeeeWZOPfXUjBw5MqVSKQ8++GDGjRtX67GArUBra2v+8R//MS+//HLWrl1bWX/XXXfVcCq6slL57bfSQgGMHz8+pVIpxx9/fJJk2rRpaW9v99AX4AOZN29efvWrX6VcLufggw/OHnvsUeuRgK3A6NGjc+qpp2bfffdNt27/9ZzGfffdt4ZT0ZWJPwqltbU1d999dx5//PGUy+UMGTIkp512Wurr62s9GgDAek488US3ptCpxB+FsnLlymyzzTbp3r17kqS9vT2tra3p1atXjScDAFjft771rey0005pbGxc7x+q+/TpU8Op6MrEH4XyhS98IZMnT862226bJHnrrbdy1lln5d/+7d9qPBkAwPpGjBixwbpSqZSZM2fWYBq2Bh74QqGsWbOmEn5Jsu2221Ze1AwAsCWZNWtWrUdgKyP+KJRevXrlmWeeyT777JMkmTt3bnr27FnjqQAANvSjH/3oHdefcMIJm3kSthbij0L56le/mgsvvDB9+/ZNkixdujQ33XRTjacCANjQ008/Xfn7mjVr8uijj2afffYRf7xv7vmjcNra2jJ//vyUy+Xstttu6dGjR2XbI488kmHDhtVwOgCAd7ZixYpcdtll+e53v1vrUeiium16F9i69OjRI5/4xCey5557rhd+STJx4sQaTQUAsHE9e/bM73//+1qPQRfmsk/4M06EAwBbinHjxlX+vm7durzwwgs5+uijazgRXZ34gz9TKpVqPQIAQJLkzDPPrPy9e/fuGTBgQBoaGmo4EV2dyz4BAGALNHTo0Oy2225566238sYbb2xwuwq8V+KPQmltbd3ougEDBmzOcQAA3tVPfvKTnHzyyfnpT3+aBx98sPJ3eL887ZNCaWpqypQpUza5DgCg1kaPHp3JkyfnL/7iL5Ikr732WsaOHZupU6fWeDK6Kvf8UQhLly5NS0tLVq9end/85jeVB7u8+eabWbVqVY2nAwDYULlcroRfkvTp08fD6fhAxB+F8PDDD+f+++/PkiVL8o1vfKPyH87tttsuF198cY2nAwDY0PDhw3PWWWdl1KhRSf50Geihhx5a46noylz2SWGsW7cuDzzwQEaPHl3rUQAAOuRnP/tZHn/88ZTL5QwZMiSNjY21HokuTPxRKKeffnruvvvuWo8BALBJixYtSt++fbPNNtskSVavXp0//OEP2WWXXWo8GV2Vp31SKIccckjuuOOOLF68OMuXL6/8AQDY0lx44YXrvYO4W7duufDCC2s4EV2de/4olPvuuy9J1jv7VyqVMnPmzFqNBADwjtrb21NfX19Zrq+vT1tbWw0noqsTfxTKrFmzaj0CAECH7LTTTpk5c2aOOOKIJMm///u/Z8cdd6zxVHRl7vmjUNra2nLPPffk17/+dZJk6NChOeWUU9KjR48aTwYAsL6FCxfm0ksvzauvvpokaWhoyPXXX5+PfvSjNZ6Mrkr8UShXXnll1q5dmxNOOCFJMnXq1HTr1i1f//rXazwZAMA7e+utt1Iul7Pddtutt37KlClpamqq0VR0RS77pFCefvrpTJ06tbJ88MEHe/UDALBF23bbbd9x/V133SX+eE887ZNC6d69exYuXFhZXrRoUbp3717DiQAA3h8X8PFeOfNHoVx++eU544wzMnDgwCTJyy+/nGuvvbbGUwEAvHd//hoI6Ahn/iiUAw44IKecckpKpVJKpVJOOeWUDB48uNZjAQC8Z8788V6JPwrl8ssvz0svvZTzzjsv5513Xl566aVcdtlltR4LAGA969aty09+8pON7nPAAQdspmnYWnjaJ4UyevTo9R748m7rAABq7fTTT8/dd99d6zHYijjzR6F88pOfzJNPPllZfuqpp/yrGQCwRTrkkENyxx13ZPHixVm+fHnlD7xfzvxRKEcffXTmz5+fj3zkI0mSV155Jbvvvnu6dfvTv4NMmzatluMBAFSMGDFig3WlUikzZ86swTRsDcQfhfLyyy9vdPuAAQM20yQAALB5iT8AANgCtbW15Z577smvf/3rJMnQoUNzyimnpEePHjWejK5K/AEAwBboyiuvzNq1a3PCCSckSaZOnZpu3brl61//eo0no6vykncAANgCPf300+s9kfzggw/O6NGjazgRXZ2nfQIAwBaoe/fuWbhwYWV50aJF6d69ew0noqtz5g8AALZAl19+ec4444wMHDgw5XI5r7zySq699tpaj0UX5p4/AADYQrW2tubFF19Mkuy2226pr6+vbHvkkUcybNiwWo1GFyT+AACgC2pqasqUKVNqPQZdiHv+AACgC3IOh/dK/AEAQBdUKpVqPQJdjPgDAAAoAPEHAABd0IABA2o9Al2MB74AAMAWaNWqVbnzzjuzePHiXHPNNVmwYEHmz5+fww8/vNaj0UU58wcAAFug8ePHp76+Pk8++WSSpKGhITfffHONp6IrE38AALAFWrhwYc4555zU1dUlSXr27OkJn3wg4g8AALZA9fX1Wb16deWpngsXLlzvJe/wXrnnDwAAtkCPPPJIbrvttsybNy/Dhg3LE088keuuuy6f/exnaz0aXZT4AwCALdSyZcvy1FNPpVwu51Of+lR22mmnWo9EFyb+AABgC/LMM89sdPs+++yzmSZhayP+AABgC/KlL30pSdLa2pq5c+dmzz33TJL89re/zf7775977rmnluPRhdXVegAAAOC//Ou//muS5O/+7u8yYcKESvw9//zzufPOO2s5Gl2cp30CAMAW6MUXX6yEX5J84hOfyLPPPlvDiejqnPkDAIAt0O67754rr7wyo0ePTqlUytSpU7P77rvXeiy6MPf8AQDAFmjNmjW555578thjjyVJhgwZki9+8YvZZpttajwZXZX4AwCALVRra2vmz5+fUqmUj3/84+nRo0etR6ILc9knAABsgebMmZMrrrgiAwYMSLlczuLFi/PNb34zQ4YMqfVodFHO/AEAwBboxBNPzMSJE7PbbrslSebPn59LLrkk999/f40no6vytE8AANgCtbW1VcIvST7+8Y+nra2thhPR1bnsEwAAtkD77rtvvvrVr+b4449PkkybNi377rtvjaeiK3PZJwAAbIFaW1tz99135/HHH0+5XM6QIUNy2mmnpb6+vtaj0UWJPwAA2AKtXLky22yzTbp3754kaW9vT2tra3r16lXjyeiq3PMHAABboLFjx2b16tWV5dWrV+crX/lKDSeiqxN/AACwBVqzZk223XbbyvK2226bVatW1XAiujrxBwAAW6BevXrlmWeeqSzPnTs3PXv2rOFEdHXu+QMAgC1Qc3NzLr744vTt2zdJsnTp0tx0002e+Mn7Jv4AAGAL1dbWlvnz56dcLme33XZLjx49KtseeeSRDBs2rIbT0dWIPwAA6IKampoyZcqUWo9BF+KePwAA6IKcw+G9En8AANAFlUqlWo9AFyP+AAAACkD8AQBAFzRgwIBaj0AX44EvAACwBVq1alXuvPPOLF68ONdcc00WLFiQ+fPn5/DDD6/1aHRRzvwBAMAWaPz48amvr8+TTz6ZJGloaMjNN99c46noysQfAABsgRYuXJhzzjkndXV1SZKePXt6wicfiPgDAIAtUH19fVavXl15qufChQtTX19f46noytzzB/9fe/fvUuXbxwH87a8CkygTWhqEIITCyIbKAgv6A0oKggqbIxprCKIli7bSoFVaXJvCtn5QBoG1NIjSkNOBOBknRcvOs/lFnhYfeLjv8z2v13RzTe/1zf35XBcAQAm9efMmT548ydzcXI4fP56ZmZmMjo7m6NGjRUejQSl/AABQUtVqNZ8+fUq9Xs/BgwfT3d1ddCQamLFPAAAooZGRkezcuTMnT57MqVOn0t3dnZGRkaJj0cDaiw4AAAD8Y2VlJcvLy6lWq1lcXFy/5KVWq6VSqRScjkam/AEAQIlMTk5mYmIilUolw8PD6+Wvq6srFy9eLDgdjczOHwAAlNDTp09z+fLlomPwL6L8AQBASc3OzmZubi6rq6vrZ2fOnCkwEY3M2CcAAJTQ+Ph43r9/n/n5+QwNDeXVq1c5fPiw8sf/zG2fAABQQlNTU5mYmEhPT0/u3buXZ8+ebfgDCJul/AEAQAlt3bo1ra2taW9vT61Wy65du/L169eiY9HAjH0CAEAJHThwID9+/Mj58+czPDyczs7O9Pf3Fx2LBubCFwAAKLmFhYXUarX09fUVHYUGZuwTAABKaGRkZP17z5496evr23AGm2XsEwAASmRlZSXLy8upVqtZXFxcf+S9VqulUqkUnI5GpvwBAECJTE5OZmJiIpVKJcPDw6nX62lpacm2bdty6dKlouPRwOz8AQBACY2Pj+fKlSvp6urK48eP8/nz51y9ejX79+8vOhoNys4fAACU0NTUVLq6uvLhw4e8ffs2Z8+ezZ07d4qORQNT/gAAoITa2tqSJC9fvsyFCxdy+vTp/Pr1q+BUNDLlDwAASmj37t25fft2nj9/nqGhoayurubPnz9Fx6KB2fkDAIASWl5ezuvXr7Nv37709vamUqlkdnY2J06cKDoaDUr5AwAAaALGPgEAAJqA8gcAANAElD8A+D9aWFjIkSNHio4BAMofAABAM2gvOgAANJqZmZk8ePAgP3/+TJLcuHEj27dvz927d7O0tJTOzs7cunUr/f39BScFgH8ofwCwCd+/f8+1a9cyNjaWgYGBrK2tpVqt5ty5cxkdHc3g4GDevXuX69ev58WLF0XHBYB1xj4BYBM+fvyYvXv3ZmBgIEnS1taWb9++paOjI4ODg0mSY8eOpaOjI1++fCkyKgBsoPwBwCb87Xncer2elpaW/zr/2xkAFEX5A4BNOHToUObn5zMzM5MkWVtbS09PT1ZXVzM9PZ0kmZ6ezu/fv9Pb21tgUgDYyM4fAGzCjh07MjY2lvv372dpaSmtra25efNmHj16tOHCl4cPH2bLli1FxwWAdS31v82vAAAA8K9i7BMAAKAJKH8AAABNQPkDAABoAsofAABAE1D+AAAAmoDyBwAA0ASUPwAAgCbwH62jNGWhdxJEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cb1eee278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cb06e7080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_files = ['airlines-codes','airport-codes','port-of-entry-codes','us-cities-demographics','visa-issue-ports','visa-type']\n",
    "\n",
    "for file in data_files:\n",
    "    df = data_spark[file]\n",
    "    null_count=df.select([count(when(col(c).isNull(), c)).alias(c) for c in df.columns]).toPandas().T.reset_index()\n",
    "    null_count.columns = ['col','null_counts']\n",
    "    null_count['%_null_counts']=null_count['null_counts']/(df.count())\n",
    "    if (null_count['%_null_counts']>0).any():\n",
    "        plt.figure(figsize=(15,8))\n",
    "        sns.barplot(x='col',y='%_null_counts',data=null_count)\n",
    "        plt.title(file)\n",
    "        plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "- In demographics data the % percentage of null values in the data set is very small, these rows can be dropped\n",
    "- In airlines data the `airline_3_digit_code` columns has a very few number of null values, not needed to drop rows there. Can be a choice\n",
    "- In airports data the `iata_code` column has high number of null values. this column can be dropped\n",
    "- In port of entry codes data the `state_code_or_country` column has some null values, but this is because the data is not having any codes provided"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning Steps\n",
    "All the necessary steps I have provided in the above sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Project Architecture and DWH Data Model\n",
    "The following architecture is used in the project\n",
    "![Project_Architecture](./AWS_Help/architechture.png)\n",
    "#### 3.1 Conceptual Data Model\n",
    "I used a Star schema to build the DWH in redshift and is as follows:\n",
    "![Schema](./AWS_Help/data_model.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Mapping Out Data Pipelines\n",
    "To create the Data Lake and DWH the following steps are to be followed:\n",
    "\n",
    "1. Create AWS account and fill the `dwh.cfg` file with necessary details\n",
    "2. Use the data in the `data` folder and upload them into S3 bucket using the `create_resources.py` file\n",
    "3. Copy the `dag` , `emr_bootstrap` folder and `plugins` folder to your `AIRFLOW` directory\n",
    "4. Once the data is in place run the `capstone_meta_data_dag` to fill up a new S3 bucket which is used as a **`staging area`**\n",
    "5. Run the `capstone_immigration_dag` to fill up the data lake in S3 with the SAS data\n",
    "6. Run the `capstone_dwh_dag` to fill up the DWH in redshift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Run Pipelines to Model the Data \n",
    "#### 4.1 Create the data model\n",
    "Build the data pipelines to create the data model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: All the codes are available in the dags folder** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.1 capstone_meta_data_dag \n",
    "\n",
    "**Note:** Run when the data is refreshed not needed to run every day, can be monthly or by trigger\n",
    "**Action:** Uses the Data stored in a S3 bucket and performs all the regex operations for the I94 Labels data and other transforms needed for other data and stores the data in parquet format in a different S3 bucket which is used as staging area\n",
    "\n",
    "![meta_data_dag](./AWS_Help/metadata_dag.png)\n",
    "\n",
    "\n",
    "#### 4.1.2 capstone_immigration_dag \n",
    "\n",
    "**Note:** Run when the data is added monthly can be daily if the data is provided daily\n",
    "**Action:** Uses the SAS Data stored in a S3 bucket and performs all the transforms needed for the data and stores it in parquet format in a different S3 bucket which is used as staging area\n",
    "\n",
    "![meta_data_dag](./AWS_Help/immigration_data_transform.png)\n",
    "\n",
    "\n",
    "#### 4.1.3 capstone_DWH_dag \n",
    "\n",
    "**Note:** Run when the data is added monthly can be daily if the data is provided daily\n",
    "**Action:** Uses the SAS Data and other meta data stored in a staging S3 bucket and performs copy operations to AWS redshift and the data model is contrained accordingly\n",
    "\n",
    "![meta_data_dag](./AWS_Help/dwh_dag.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 Data Quality Checks\n",
    "\n",
    "From the data model it can be observed that the DWH tables have key constraints defined accordingly with data type and primary key when needed\n",
    "\n",
    "Data Quality checks were included:\n",
    " * Integrity constraints on the relational database (e.g., unique key, data type, etc.)\n",
    " * Unit tests for the scripts to ensure they are doing the right thing\n",
    " * Source/Count checks to ensure completeness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 Data dictionary \n",
    "I have created the data dictionary for the DWH Tables to provide column descriptions [here](./Data_Dictionary.md)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Connecting to DWH and Obtaining Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas-redshift\r\n",
      "  Downloading pandas_redshift-2.0.3-py3-none-any.whl (6.2 kB)\r\n",
      "Requirement already satisfied: psycopg2-binary in /home/harisyam/anaconda3/lib/python3.7/site-packages (from pandas-redshift) (2.8.5)\r\n",
      "Requirement already satisfied: boto3 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from pandas-redshift) (1.13.24)\r\n",
      "Requirement already satisfied: pandas in /home/harisyam/anaconda3/lib/python3.7/site-packages (from pandas-redshift) (1.0.3)\r\n",
      "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from boto3->pandas-redshift) (0.3.3)\r\n",
      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from boto3->pandas-redshift) (0.10.0)\r\n",
      "Requirement already satisfied: botocore<1.17.0,>=1.16.24 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from boto3->pandas-redshift) (1.16.24)\r\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from pandas->pandas-redshift) (2019.3)\r\n",
      "Requirement already satisfied: numpy>=1.13.3 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from pandas->pandas-redshift) (1.18.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from pandas->pandas-redshift) (2.8.1)\r\n",
      "Requirement already satisfied: urllib3<1.26,>=1.20; python_version != \"3.4\" in /home/harisyam/anaconda3/lib/python3.7/site-packages (from botocore<1.17.0,>=1.16.24->boto3->pandas-redshift) (1.25.8)\r\n",
      "Collecting docutils<0.16,>=0.10\r\n",
      "  Using cached docutils-0.15.2-py3-none-any.whl (547 kB)\r\n",
      "Requirement already satisfied: six>=1.5 in /home/harisyam/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas->pandas-redshift) (1.14.0)\r\n",
      "\u001b[31mERROR: apache-airflow 1.10.10 has requirement jinja2<2.11.0,>=2.10.1, but you'll have jinja2 2.11.2 which is incompatible.\u001b[0m\r\n",
      "\u001b[31mERROR: apache-airflow 1.10.10 has requirement pandas<1.0.0,>=0.17.1, but you'll have pandas 1.0.3 which is incompatible.\u001b[0m\r\n",
      "\u001b[31mERROR: apache-airflow 1.10.10 has requirement werkzeug<1.0.0, but you'll have werkzeug 1.0.1 which is incompatible.\u001b[0m\r\n",
      "Installing collected packages: pandas-redshift, docutils\r\n",
      "  Attempting uninstall: docutils\r\n",
      "    Found existing installation: docutils 0.16\r\n",
      "    Uninstalling docutils-0.16:\r\n",
      "      Successfully uninstalled docutils-0.16\r\n",
      "Successfully installed docutils-0.15.2 pandas-redshift-2.0.3\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas-redshift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas_redshift as pr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import configparser\n",
    "\n",
    "config = configparser.ConfigParser()\n",
    "config.read_file(open('dwh.cfg'))\n",
    "\n",
    "\n",
    "\n",
    "pr.connect_to_redshift(dbname = config.get('DWH', 'DB_NAME'),\n",
    "                        host = config.get('DWH', 'HOST'),\n",
    "                        port = config.get('DWH', 'DB_PORT'),\n",
    "                        user = config.get('DWH', 'DB_USER'),\n",
    "                        password = config.get('DWH', 'DB_PASSWORD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UNITED KINGDOM</td>\n",
       "      <td>6019081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JAPAN</td>\n",
       "      <td>4118737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHINA, PRC</td>\n",
       "      <td>3955056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MEXICO</td>\n",
       "      <td>3348981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRANCE</td>\n",
       "      <td>2385333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BRAZIL</td>\n",
       "      <td>2200040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AUSTRALIA</td>\n",
       "      <td>1771403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>1756452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ITALY</td>\n",
       "      <td>1435205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>1165643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country_name    count\n",
       "0  UNITED KINGDOM  6019081\n",
       "1           JAPAN  4118737\n",
       "2      CHINA, PRC  3955056\n",
       "3          MEXICO  3348981\n",
       "4          FRANCE  2385333\n",
       "5          BRAZIL  2200040\n",
       "6       AUSTRALIA  1771403\n",
       "7           INDIA  1756452\n",
       "8           ITALY  1435205\n",
       "9       ARGENTINA  1165643"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of people coming from different countries\n",
    "query = \"\"\"\n",
    "SELECT top 10 SUBSTRING(b.country_name, 0, 15) as country_name, COUNT(cicid) as count\n",
    "FROM immigration a INNER JOIN i94res b ON a.country_id=b.country_id\n",
    "GROUP BY b.country_name\n",
    "ORDER BY COUNT((cicid)) DESC\n",
    "\"\"\"\n",
    "df = pr.redshift_to_pandas(query)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title(\"Top 10 countries with immigrants visiting US\")\n",
    "sns.barplot(x='country_name', y='count', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "    im.gender,\n",
    "    im.visa_type,\n",
    "    count(im.cicid)\n",
    "FROM immigration as im\n",
    "    JOIN visa_type\n",
    "    ON im.visa_type = visa_type.visa_type\n",
    "GROUP BY im.gender, im.visa_type;\n",
    "\"\"\"\n",
    "df = pr.redshift_to_pandas(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Visa Types across Gender in 2016')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.set_index(['visa_type', 'gender']).unstack()[[('count', 'M'),('count', 'F')]]\n",
    "df.columns=['F', 'M']\n",
    "df.plot(kind='bar', figsize=(15,8));\n",
    "plt.title(\"Distribution of Visa Types across Gender in 2016\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query=\"\"\"\n",
    "SELECT a.state_name AS State, airports.count AS airport_count\n",
    "FROM\n",
    "    (SELECT top 10 distinct state_code, count(*)\n",
    "     FROM airport_codes\n",
    "     WHERE country = 'US' AND airport_type='small_airport'\n",
    "     GROUP BY state_code) airports INNER JOIN i94addr a ON airports.state_code=a.state_code\n",
    "ORDER BY airports.count DESC;\n",
    "\"\"\"\n",
    "\n",
    "df = pr.redshift_to_pandas(query)\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title(\"Top 10 states with small airports\")\n",
    "sns.barplot(x='state', y='airport_count', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query=\"\"\"\n",
    "SELECT top 10 distinct airport_type, count(*) AS count_type\n",
    "FROM airport_codes\n",
    "WHERE country = 'US'\n",
    "GROUP BY airport_type\n",
    "ORDER BY count_type DESC;\n",
    "\"\"\"\n",
    "df = pr.redshift_to_pandas(query)\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title(\"Top 10 airport types in US\")\n",
    "sns.barplot(x='airport_type', y='count_type', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1frs7dh07doymTZvW6DHY3fwRsdef4dRTT43Vq1fHggUL9jprxCdXG83Ly9vrdnv27BkLFy6Mo446apfHoaioqGK9oqKi+M53vhN33313PProo/HMM8/EG2+8Ua1Zqmt3j2W3bt1i5syZsXXr1oplL7/8cmzevHmPx/aEE06Il19+eZflL7/8crRp0yYOO+ywKu9bVFQURUVFu5z925uuXbvGs88+W2nZjBkzokWLFnHUUUfVaFufNl27do2XXnqp0j+q7Pw7UZUZM2ZE796948orr4xTTz01OnfuHCtXrqzxDHv6XT/ppJPihz/8YTz99NNx8cUXx1133VXj7QMHH3EJUEPdunWL8847Ly6//PKYOnVqvPbaazF8+PBYvnx5/J//838qrXvHHXfEQw89FEuXLo3rrrsu5s2bFz/4wQ+q3HZKqeJM1t///vfYuHFjzJs3L/72t79VrHPOOedEt27d4uKLL445c+bECy+8EIMHD45+/fpFnz59qtz2T3/603jwwQfj2muvjfnz58fy5cvjqaeeiksvvbRGsdixY8d45513Ys6cObFu3brYtGnTXu9zxRVXxJQpU+Kvf/1rtV4SGxHx0ksvxa233hrLli2Le++9N+6444646qqrIqJmj8HOtn9W6RNPPBFr166NjRs37na9r371q3HaaafFRRddFI8//nisXLkynnvuuZg8efJu18/lcnH00UfHtGnTori4uMqXFo4cOTJKS0vjwgsvjJkzZ8abb74Zzz33XFx33XUxZ86ciIgYNWpUTJkyJZYuXRpLliyJ3//+99GyZcvo0KFDdQ5dtXXs2DGee+65WLVqVaxbty5SSvGDH/wg3n333bjsssti0aJF8eyzz8bgwYNj4MCB0atXryq3NXLkyHjmmWfihhtuiAULFsSSJUvi9ttvj4kTJ1Y8bhER119/fYwaNSqmT58eK1eujPnz58cPfvCDWL58eXzjG9+o0fw/+tGP4oEHHoixY8fGsmXL4oEHHojRo0fHqFGjKr2ctD4aMWJEvPnmmzFixIh4/fXX45lnnombbropIvZ+RvNzn/tczJ07N5544olYvnx53HbbbfH444/XeIaOHTvG1q1b48knn4x169bFhx9+GIsXL47rr78+Zs6cGW+99VbMnDkzXnjhhejates+/ZzAQaYO3ucJUC9U96NIGjduXOXHYDzwwAPpi1/8YmrSpEnq2LFjevDBB/e4z+0XYtn5a+eLarzzzjvpggsuSM2bN08tW7ZMF1988V4/KiKllP7yl7+k/v37p+bNm6fmzZunrl27pquuuqriYie7u4jQzhf12LRpU/rWt76VCgoKdvkokj1drbZLly7ppJNO2uuM27d18803p0suuSQ1b948FRYWph/96EeVLm5S3cdg+wVadnTLLbekdu3apVwut8ePItmwYUMaOnRoatOmTWrcuHHq1KlTGjt2bMVxiB0urpJSSn/6058qPhZjTx9F8sYbb6Rvf/vbFbMfffTRadCgQentt99OKaX04x//OHXt2jU1a9YsHXrooenMM8/c4wVUqrqgz46zpZTS6aefXulCQbNmzUonn3xyatq06S4fRdK3b98afRRJSv94fhUVFaWWLVumU089Nd17772VLqbz5z//OV100UXpqKOOSk2aNElFRUXpi1/84l5/N3Z3HFPa/UeRbL8gTUqfXNDne9/73l5nr+qCPjs+f5YtW7bLxWxWrlyZIiI999xzVc65u4tI/fWvf00Rkd55550q77f9o0gaN26cevTokf7nf/6n0oWBqnqOb968OQ0ePDgddthhqWXLlmnQoEEVV37dbvtHkexod3MOGzYsFRUVVXwUydtvv53OP//8io/nad++fRo6dGjauHHjXo8xcPDLpVQLbwACoJLXX389unTpErNnz46ePXvW9Th17uOPP44jjzwybrrppvje97631/Xbtm0bP/zhD+OHP/zhAZgO6oc///nPcfbZZ8fSpUujc+fOdT0OwC4a7n0VANg327Zti7Vr18avf/3r2LZtW1x66aV1PRLUGxMmTIiePXtG27ZtY+HChfGDH/wg+vXrJyyBTy1xCcB+s2zZsujSpUt06NAh7r333kxX7YTPmhUrVsR//ud/xtq1a6Ndu3bx1a9+NcaMGVPXYwFUyctiAQAAyKx+X0oNAACATwVxCQAAQGbiEgAAgMxc0KeGVq9eXdcjAAAA1In27dtX+T1nLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAILOGdT3AwaTRY9PqeoRPjS3nD6jrEQAAgAPImUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzBoeqB09/vjjMW3atMjlcnHkkUfG8OHD4+9//3v86le/itLS0ujYsWOMGDEiGjZsGFu2bIkJEybEihUrokWLFjFy5Mho3bp1REQ8+uijMW3atGjQoEEMHjw4evToERER8+bNi8mTJ0d5eXmcddZZccEFF0RExNq1a2u8DwAAAGrmgJy5LCkpiaeeeirGjBkTY8eOjfLy8pg1a1b87ne/i3PPPTfGjx8fzZs3j2nTpkVExLRp06J58+bx61//Os4999y4//77IyJi1apVMWvWrPjlL38ZN9xwQ0yaNCnKy8ujvLw8Jk2aFNdff32MGzcuZs6cGatWrYqIqPE+AAAAqLkD9rLY8vLyKCsri23btkVZWVkcdthhsWjRoujTp09ERPTv3z9mz54dERFz5syJ/v37R0REnz59YuHChZFSitmzZ0ffvn2jUaNG0bp162jbtm0sX748li9fHm3bto02bdpEw4YNo2/fvjF79uxIKdV4HwAAANTcAXlZbKtWreK8886LYcOGRePGjePkk0+OTp06RbNmzSIvL69inZKSkoj45ExnYWFhRETk5eVFs2bN4oMPPoiSkpLo3Llzpe1uv8/29bf/97Jly+KDDz6o8T5atmy5n48GAADAweeAxGVpaWnMnj07Jk6cGM2aNYtf/vKXMW/evCrX390ZxFwuV+WZxarW35Pq3mfq1KkxderUiIgYM2ZMFBUVVbnN9/e4x8+WPR0nAADg4HNA4nLBggXRunXrirOCvXv3jiVLlsRHH30U27Zti7y8vCgpKYlWrVpFxCdnHtevXx+FhYWxbdu2+OijjyI/P79i+XY73mfH5evXr4+CgoJo0aJFjfexs4EDB8bAgQMrbq9bt67Kn7NRhmN0sNnTcQIAAOqn9u3bV/m9A/Key6Kioli2bFl8/PHHkVKKBQsWxBFHHBHdunWLF198MSIipk+fHj179oyIiFNPPTWmT58eEREvvvhidOvWLXK5XPTs2TNmzZoVW7ZsibVr10ZxcXEcd9xxceyxx0ZxcXGsXbs2tm7dGrNmzYqePXtGLper8T4AAACouVw6QFexeeihh2LWrFmRl5cXxxxzTAwdOjRKSkp2+ZiQRo0aRVlZWUyYMCFWrlwZ+fn5MXLkyGjTpk1ERDzyyCPx17/+NRo0aBCXXnppnHLKKRERMXfu3Lj33nujvLw8zjzzzLjwwgsjIuLdd9+t8T72ZPXq1VV+r9Fj02rhSB0ctpw/oK5HAAAAatmezlwesLg8WIjL6hGXAABw8Knzl8UCAABwcBOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZNazrAaAqGx8dVtcjfGq0/MYddT0CAADskTOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgs4YHakcffvhh3HnnnfHOO+9ELpeLYcOGRfv27WPcuHHx3nvvxeGHHx5XXXVV5OfnR0opJk+eHK+++mo0adIkhg8fHp06dYqIiOnTp8cjjzwSEREXXnhh9O/fPyIiVqxYERMnToyysrI45ZRTYvDgwZHL5aK0tLTG+wAAAKBmDtiZy8mTJ0ePHj3iV7/6VfziF7+IDh06xJQpU+LEE0+M8ePHx4knnhhTpkyJiIhXX3011qxZE+PHj4/LL7887r777oiIKC0tjYcffjhGjx4do0ePjocffjhKS0sjIuKuu+6KK664IsaPHx9r1qyJefPmRUTUeB8AAADU3AGJy48++ihee+21GDBgQERENGzYMJo3bx6zZ8+Ofv36RUREv379Yvbs2RERMWfOnDjjjDMil8vF8ccfHx9++GFs2LAh5s2bFyeddFLk5+dHfn5+nHTSSTFv3rzYsGFDbNq0KY4//vjI5XJxxhlnVGyrpvsAAACg5g7Iy2LXrl0bLVu2jNtvvz3eeuut6NSpU1x66aXx/vvvR0FBQUREFBQUxMaNGyMioqSkJIqKiiruX1hYGCUlJVFSUhKFhYUVy1u1arXb5dvXj4ga72P7ugAAAFTfAYnLbdu2xcqVK2PIkCHRuXPnmDx5csXLU3cnpbTLslwut9t1c7ncbtffm+ruY+rUqTF16tSIiBgzZkylIN3Z+zWe4uC1p+NUXRtrYY6DRW0cTwAA2J8OSFwWFhZGYWFhdO7cOSIi+vTpE1OmTIlDDz00NmzYEAUFBbFhw4Zo2bJlxfrr1q2ruP/69eujoKAgWrVqFYsXL65YXlJSEl27do3CwsJYv359pfVbtWoVEVHjfexs4MCBMXDgwIrbO95nZ41qdFQObns6TtSc4wkAwKdB+/btq/zeAXnP5WGHHRaFhYWxevXqiIhYsGBBHHHEEdGzZ8949tlnIyLi2WefjV69ekVERM+ePWPGjBmRUoqlS5dGs2bNoqCgIHr06BHz58+P0tLSKC0tjfnz50ePHj2ioKAgDjnkkFi6dGmklGLGjBnRs2fPim3VZB8AAADUXC7ty2tK98Gbb74Zd955Z2zdujVat24dw4cPj5RSjBs3LtatWxdFRUVx9dVXV3xMyKRJk2L+/PnRuHHjGD58eBx77LERETFt2rR49NFHI+KTjyI588wzIyLijTfeiNtvvz3KysqiR48eMWTIkMjlcvHBBx/UeB97sj2Qd6fRY9Nq4UgdHLacPyDzNjY+OqwWJjk4tPzGHXU9AgAA7PHM5QGLy4OFuKwecVm7xCUAAJ8Ge4rLA/KeS6DuzXp6SF2P8KnR9+x76noEAICDzgF5zyUAAAAHN3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAOqsMcQAACAASURBVGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkVu24/NOf/rTb5Y8//nitDQMAAED9VO24/OMf/1ij5QAAAHx2NNzbCgsXLoyIiPLy8or/3u7dd9+NQw45ZP9MBgAAQL2x17i84447IiKirKys4r8jInK5XBx22GExZMiQ/TcdAAAA9cJe43LixIkRETFhwoT4/ve/v98HAgAAoP7Za1xut2NYlpeXV/pegwYuOgsAAPBZVu24XLFiRUyaNCnefvvtKCsrq/S9P/zhD7U+GAAAAPVHteNy4sSJceqpp8awYcOiSZMm+3MmAAAA6plqx+W6devin//5nyOXy+3PeQAAAKiHqv1myV69esX8+fP35ywAAADUU9U+c7lly5a47bbb4oQTTojDDjus0vdcRRYAAOCzrdpxecQRR8QRRxyxP2cBAACgnqp2XH7rW9/an3MAAABQj1U7LhcuXFjl97p3714rwwAAAFA/VTsu77jjjkq3N27cGFu3bo3CwsKYMGFCrQ8GAABA/VGjz7ncUXl5efzxj3+MQw45pNaHAgAAoH6p9keR7HLHBg3iwgsvjMcee6w25wEAAKAe2ue4jIj429/+Fg0aZNoEAAAAB4Fqvyx22LBhlW6XlZVFWVlZXHbZZbU+FAAAAPVLteNyxIgRlW43adIk2rVrF82aNav1oQAAAKhfqh2XXbt2jYhPLuTz/vvvx6GHHuolsQAAAEREDeJy06ZNMWnSpJg1a1Zs27Yt8vLyom/fvjFkyBBnLwEAAD7jqn3q8Z577onNmzfHbbfdFr/73e/itttui7Kysrjnnnv253wAAADUA9WOy3nz5sWIESOiffv20ahRo2jfvn0MHz485s+fvz/nAwAAoB6odlw2btw4Nm7cWGnZxo0bo2HDar+yFgAAgINUtctwwIAB8dOf/jTOPffcOPzww+O9996LJ554Is4666z9OR8AAAD1QLXj8sILL4xWrVrF888/HyUlJdGqVas4//zzY8CAAftzPgAAAOqBasfl5MmT4/TTT4+f/OQnFcuWLFkSv/nNb+LSSy/dH7MBAABQT1T7PZczZ86MY489ttKyTp06xfPPP1/rQwEAAFC/VDsuc7lclJeXV1pWXl4eKaVaHwoAAID6pdpxecIJJ8SDDz5YEZjl5eXx3//933HCCSfst+EAAACoH6r9nsvBgwfHmDFj4oorroiioqJYt25dFBQUxKhRo/bnfAAAANQD1Y7LwsLC+M///M9Yvnx5rF+/PgoLC+O4446LBg2qffITAACAg1S14zIiokGDBnH88cfvr1kAAACop5x2BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADKr0dVisyovL4/rrrsuWrVqFdddd12sXbs2fvWrX0VpaWl07NgxRowYEQ0bNowtW7bEhAkTYsWKFdGiRYsYOXJktG7dOiIiHn300Zg2bVo0aNAgBg8eHD169IiIiHnz5sXkyZOjvLw8zjrrrLjgggsiIvZpHwAAANTMAT1z+eSTT0aHDh0qbv/ud7+Lc889N8aPHx/NmzePadOmRUTEtGnTonnz5vHrX/86zj333Lj//vsjImLVqlUxa9as+OUvfxk33HBDTJo0KcrLy6O8vDwmTZoU119/fYwbNy5mzpwZq1at2qd9AAAAUHMHLC7Xr18fc+fOjbPOOisiIlJKsWjRoujTp09ERPTv3z9mz54dERFz5syJ/v37R0REnz59YuHChZFSitmzZ0ffvn2jUaNG0bp162jbtm0sX748li9fHm3bto02bdpEw4YNo2/fvjF79ux92gcAAAA1d8Di8je/+U1ccsklkcvlIiLigw8+iGbNmkVeXl5ERLRq1SpKSkoiIqKkpCQKCwsjIiIvLy+aNWsWH3zwQaXlO95n5+WFhYVRUlKyT/sAAACg5g7Iey5feeWVOPTQQ6NTp06xaNGiva6/uzOIuVyuyjOLVa2/L/vY2dSpU2Pq1KkRETFmzJgoKiqqcpvv73GPny17Ok7VtbEW5jhY1Mbx5B8cTwCA2ndA4nLJkiUxZ86cePXVV6OsrCw2bdoUv/nNb+Kjjz6Kbdu2RV5eXpSUlESrVq0i4pMzj+vXr4/CwsLYtm1bfPTRR5Gfn1+xfLsd77Pj8vXr10dBQUG0aNGixvvY2cCBA2PgwIEVt9etW1flz9ko22E6qOzpOFFzjmftcjwBAPZN+/btq/zeAXlZ7MUXXxx33nlnTJw4MUaOHBndu3ePK6+8Mrp16xYvvvhiRERMnz49evbsGRERp556akyfPj0iIl588cXo1q1b5HK56NmzZ8yaNSu2bNkSa9eujeLi4jjuuOPi2GOPjeLi4li7dm1s3bo1Zs2aFT179oxcLlfjfQAAAFBzB/SjSHb2ne98J371q1/Fgw8+GB07dowBAwZERMSAAQNiwoQJMWLEiMjPz4+RI0dGRMSRRx4ZX/jCF+Lqq6+OBg0axHe/+91o0OCTPh4yZEjceuutUV5eHmeeeWYceeSR+7QPAAAAai6XXCK1RlavXl3l9xo9Nu0ATvLptuX8AZm3sfHRYbUwycGh5TfuyLyNWU8PqYVJDg59z76nrkcAAKiX6vxlsQAAABzcxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADJrWNcDANRH//X84Loe4VPj8i9OrusRAIBPAWcuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMxcLRaAOnXZS/fU9QifGnf3HlLXIwDAPnPmEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmTWs6wEAgNpz+ay/1vUInxr/1ffMuh4B4DPFmUsAAAAyE5cAAABkJi4BAADIzHsuAQCq8L0XVtT1CJ8aE7/Qqa5HAD7lnLkEAAAgM3EJAABAZuISAACAzLznEgCA/e7Rl/LqeoRPjW/03lbXI8B+4cwlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJk1PBA7WbduXUycODH+/ve/Ry6Xi4EDB8Y555wTpaWlMW7cuHjvvffi8MMPj6uuuiry8/MjpRSTJ0+OV199NZo0aRLDhw+PTp06RUTE9OnT45FHHomIiAsvvDD69+8fERErVqyIiRMnRllZWZxyyikxePDgyOVy+7QPAAAAauaAnLnMy8uLQYMGxbhx4+LWW2+Np59+OlatWhVTpkyJE088McaPHx8nnnhiTJkyJSIiXn311VizZk2MHz8+Lr/88rj77rsjIqK0tDQefvjhGD16dIwePToefvjhKC0tjYiIu+66K6644ooYP358rFmzJubNmxcRUeN9AAAAUHMHJC4LCgoqzgoecsgh0aFDhygpKYnZs2dHv379IiKiX79+MXv27IiImDNnTpxxxhmRy+Xi+OOPjw8//DA2bNgQ8+bNi5NOOiny8/MjPz8/TjrppJg3b15s2LAhNm3aFMcff3zkcrk444wzKrZV030AAABQcwfkZbE7Wrt2baxcuTKOO+64eP/996OgoCAiPgnQjRs3RkRESUlJFBUVVdynsLAwSkpKoqSkJAoLCyuWt2rVarfLt68fETXex/Z1t5s6dWpMnTo1IiLGjBlT6T47e7/mh+OgtafjVF0ba2GOg0VtHE/+wfGsXY5n7XEsa1ftHM8VtbCNg0P24+kf8bfzu87B6oDG5ebNm2Ps2LFx6aWXRrNmzapcL6W0y7JcLrfbdXO53G7X35vq7mPgwIExcODAitvr1q2rcpuNajzFwWtPx4maczxrl+NZuxzP2uNY1i7Hs3ZlP555tTLHwcBzk/qsffv2VX7vgF0tduvWrTF27Nj40pe+FL17946IiEMPPbTipagbNmyIli1bRsQnZxF3/KVbv359FBQURKtWrWL9+vUVy7efaSwsLKy0fP369dGqVat92gcAAAA1d0DiMqUUd955Z3To0CG+/vWvVyzv2bNnPPvssxER8eyzz0avXr0qls+YMSNSSrF06dJo1qxZFBQURI8ePWL+/PlRWloapaWlMX/+/OjRo0cUFBTEIYccEkuXLo2UUsyYMSN69uy5T/sAAACg5g7Iy2KXLFkSM2bMiKOOOiquueaaiIj453/+57jgggti3LhxMW3atCgqKoqrr746IiJOOeWUmDt3blx55ZXRuHHjGD58eERE5Ofnx0UXXRQ/+tGPIiLim9/8ZuTn50dExGWXXRa33357lJWVRY8ePeKUU06JiKjxPgAAAKi5AxKXJ5xwQjz00EO7/d6NN964y7JcLheXXXbZbtcfMGBADBgwYJflxx57bIwdO3aX5S1atKjxPgAAAKiZA/aeSwAAAA5e4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkdkCuFgsAANSed5/0Z/x2bc7ZWtcj8P9z5hIAAIDMxCUAAACZiUsAAAAy82JtAADgMy3voeK6HuFTY9s/tdvn+zpzCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJmJSwAAADITlwAAAGQmLgEAAMhMXAIAAJCZuAQAACAzcQkAAEBm4hIAAIDMxCUAAACZiUsAAAAyE5cAAABkJi4BAADITFwCAACQmbgEAAAgM3EJAABAZuISAACAzMQlAAAAmYlLAAAAMhOXAAAAZCYuAQAAyExcAgAAkJm4BAAAIDNxCQAAQGbiEgAAgMzEJQAAAJk1rOsB6tK8efNi8uTJUV5eHmeddVZccMEFdT0SAABAvfSZPXNZXl4ekyZNiuuvvz7GjRsXM2fOjFWrVtX1WAAAAPXSZzYuly9fHm3bto02bdpEw4YNo2/fvjF79uy6HgsAAKBe+szGZUlJSRQWFlbcLiwsjJKSkjqcCAAAoP7KpZRSXQ9RF1544YWYP39+DB06NCIiZsyYEcuXL48hQ4ZUWm/q1KkxderUiIgYM2bMAZ8TAACgPvjMnrksLCyM9evXV9xev359FBQU7LLewIEDY8yYMfUqLK+77rq6HuGg4njWHseydjmetcvxrD2OZe1yPGuX41m7HM/aczAcy89sXB577LFRXFwca9euja1bt8asWbOiZ8+edT0WAABAvfSZ/SiSvLy8GDJkSNx6661RXl4eZ555Zhx55JF1PRYAAEC9lHfzzTffXNdD1JV27drF1772tTjnnHOiS5cudT1OrerUqVNdj3BQcTxrj2NZuxzP2uV41h7HsnY5nrXL8axdjmftqe/H8jN7QR8AAABqz2f2PZcAAADUHnEJAABAZp/ZC/ocDKZPnx79+/ffZfnWrVtjwoQJMXLkyAM/FETExx9/HHl5edGw4Sf/E7N69eqYO3duHH744dG7d+86nq5+evvtt+NPf/pTrFq1KnK5XHTo0CHOO++8OProo+t6NACAiBCX9dpTTz0VW7dujYEDB1Ys27x5c/ziF7+IoqKiOpyMz7rRo0fH0KFDo127drFmzZq44YYb4ktf+lLMnTs33njjjbj44ovresR6Zfbs2fHb3/42LrjggjjvvPMipRQrVqyIsWPHxqBBg6JXr151PWK989JLL1W6ncvlokWLFnHMMcfEIYccUkdT1W9r166Nd955p+IfP9q0aVPXI0FERCxcuDBWrVoVERFHHHFEdO/evY4nqr/WrVu3x+/7+zOb119/PZ5//vm47LLL6nqUfSYu67Gf/OQnceutt0ZZWVmcc845sXHjxvjZz34W3bt3j+985zt1PV699C//8i+Ry+V2WZ5SilwuF/fee28dTFX/lJaWRrt27SLikzPsp59+egwZMiS2bt0ao0aNEpc19NBDD8WPf/zjaN26dcWyY445Jrp37x6/+MUvxOU+eOWVV3ZZVlpaGm+99VYMGzbMH5818NFHH8Wdd94ZK1asiGOOOSZSSvHWW29Fp06dYujQodGsWbO6HrHeefXVV2PKlCmVguj888+Pz3/+83U8Wf1SUlISt912WzRq1Cg6deoUKaV44YUX4v77749rrrkmWrVqVdcj1js/+9nPIpfLxY7XA83lcrFx48Z4//334w9/+EMdTlc/vfnmm/H888/HCy+8EK1bt47TTjutrkfKRFzWY/n5+fGTn/wkfvazn8WGDRtizpw58eUvfznOOeecuh6t3rrvvvvqeoSDwo6BvmjRojjvvPMiIqJhw4bRoIG3etfU1q1bK4Xldq1bt46tW7fWwUT13/Dhw3e7/L333otx48bF6NGjD/BE9dfkyZPjiCOOiJEjR1b8fqeU4o9//GPcc8898f3vf7+OJ6xfpk6dGlOnTo1LLrmk4iMJVqxYEffff3+UlJRUerUSezZp0qT4yle+sstbiJ599tm4++6749prr62bweqxsWPHVrq9du3aeOyxx2LBggXxjW98o46mqn9Wr14ds2bNipkzZ0Z+fn707ds3Ukpx00031fVomYnLemz7y7rOOuusuO+++6J79+5RWFhYsdx722qutLR0j9/Pz88/QJPUb0cddVTcd9990apVq1izZk2cfPLJERHx4Ycf1vFk9VPDhg1j3bp1u7zc6L333ou8vLw6murgdPjhh8e2bdvqeox6ZcmSJfG9732v0rJcLhff/OY348orr6yjqeqvJ554Im655ZZK/3/TvXv3uP766+PGG28UlzWwatWquOaaa3ZZ3q9fv3jkkUfqYKKDR3FxcTzyyCOxfPny+PrXvx6DBw+uuM4Ce3fVVVfFCSecEKNGjYq2bdtGxCe/+wcDz4J6bMeXdfXs2XOXZeKy5r773e9Gq1atKv5g3/llHxMmTKir0eqVoUOHxpNPPhnvvfde3HDDDdGkSZOI+OT/6LefxaT6vvWtb8Utt9wS3/jGNyrOZLzxxhsxZcoUL4GvZatXr/YHUg35uOzat7t/yGzx/7V371FR1/n/wJ+DiIhcFQlQvKFcirxLKiokZl6WLVNkK/HL10tp5mYZ4irregXxsp71kKW264V11TQpAz22fFWEIdQ0U1NgQVDJRRBlcBqG4TK/P/zxWUbQlWGaNx94Ps7pnM98PonPOA3M6/N5vV9vOzsBSeSttrb2ieefdI2e7tatWzhy5AgKCwvx29/+FvPnz2dHkhEWL14MpVKJVatWYcCAAQgICGg1P0v5G1TGntTWRcabMGECrl27Bm9vbwQEBMDHx6fRNZj0dNXV1Xj99dcbnPf29kaXLl0EJJI3f39/uLi4ICkpCcePHwcAeHh44MMPP0SvXr3EhpOp9evXN3hvq9VqlJWVsY2ziby9vXH48GFMnTrV4Ht6+PBh9OvXT2AyeerYsSMKCgoavLcLCgpgbW0tJpRMDRkyBJ999hkiIiKk751Wq8WePXswaNAgwenkKTIyEs7Ozhg0aBByc3ORm5trcH3WrFmCksmLv78//P39odVqcf78eSQnJ0OlUmHnzp3w9/eXOr7kSKFvLWVyG1VbWwu1Wg17e3sAjz7Unz59GsnJydiyZYvgdPKk1+vx008/QalUIjc3FwMGDMD48eMbXfNGjYuKikJcXBwAYPXq1VixYkWj1+jZ6HQ6aLVa6X1eR6VSoWPHjrCyshKUTL6uXbvW4JydnR3c3Nz45LKJ6gb65Ofno2fPnlAoFFJxNG/ePHTq1El0RFnJysrC1q1bERQUhD59+kChUCAvLw+pqalYuHAhfHx8REeUjerqavzjH/9AamqqtKzg3r17CAwMxFtvvcX3uhFOnz791OuNbZFHz0atVuO7775DRkaGrNdesriUMaVSiR07dsDa2hqurq4IDQ1FfHw8PD09MXXqVKl9jozzyy+/QKlU4uDBg3jzzTe5zqUJlixZgg0bNjQ4buw1/Xfbt2/HwIEDG7S6p6WlISsrC3PnzhWUrHX64x//iDVr1oiOITtFRUUG003r1hFR05WVleHEiRO4ffs2gEffzwkTJsDR0VFwMnnS6XQoKiqCXq+Hq6urtFSDmker1QIAn6iTAd6ykbEjR44gLi4Orq6uuHHjBqKjo7Fo0SLZjzAWSavV4vvvv0dGRgbKy8vh7++PuLg47tvURPVb4x5vPWSbcdNlZWXh3XffbXB+9OjRSExMFJCodftv+7hR41xdXQ0Kyjt37uDo0aOYN2+ewFTy5OjoiLCwMNExWoWHDx8iPT0dP//8M4BHhXpAQADXsDbDt99+i8TERFRWVgJ4VFy+9tprePXVVwUnk4/6W9/VPedTKBSoqalBdXU1Dhw4IDJes7C4lDFLS0vpF3mfPn1axd44os2dOxeurq4ICAiAq6ur1I6Ul5cHgEOSnpVKpUJSUhL0er10DDz6AVpeXi44XevC5hMS7ebNm0hISMCDBw8wbNgwTJgwAZ9//rk0RZKa5vz58ygtLcWECRMAAMuWLZN+br799tsYMWKEyHiyUlhYiNWrV2PAgAHo3bs39Ho9cnNzkZiYiBUrVqBbt26iI8rOl19+iZycHKxcuRLPPfccAODu3bvYtWsX1Go1pk6dKjihPDy+9V1FRQVOnDiBlJQU2X+WZ3EpY/U/tAOPnrrVf81f6k03fPhwKBQK3LlzB3fu3GlwncXlswkODkZFRUWDYwAYO3asqFiyZW9vj9zcXPTt29fgfG5uboN1mPRs6rZsepxer4dOpzNzGnnbvn07xo8fDy8vL1y6dAlRUVEYNWoUfv/733M9sBGOHj2KDz74QHpdVVWF2NhYVFZWYtu2bSwum+DgwYOIiIjAyJEjDc5nZmZi//79+PjjjwUlk68zZ85g48aNBu/t5557Dh999BEiIyNZXDbRL7/8guTkZJw5cwajRo1CbGys7J+qs7iUscc/tD/+mpru8b3a6isrKzNjEnkLDQ194rXWso+TOYWHh2PLli0IDAw02FQ9NTUVixYtEpxOnupv2/S4IUOGmDGJ/FVVVUlDPNzd3fHNN9/g7bff5vYERqqurjZYiuHj4wM7OzvY2dlJbYj0bG7duoXFixc3OD98+HDs379fQKLWobGbRlZWVlz20gTl5eVISkpCRkYGXn75ZWzYsAE2NjaiY5kEi0sZe9oHeDINjUaDzMxMKJVKFBYWYvv27aIjyV5SUhImT54sOoas9O3bFzExMThx4oQ0qa9Hjx6IiYmBg4OD2HAyxa2cTKeqqgr5+flSi7a1tTVu3rwpveZwuaZRq9UGr2fPni0dc1lB0zxt0AyH0BinS5cuuHLlCl588UWD81evXoWTk5OgVPKzYMEC2NvbIygoCB06dMDJkycNrsu5+5DFpcz98MMP+Oqrrwwm9L322msYPHiw4GTypdPp8P333yM9PR35+fmoqKhAZGQkfH19RUejNszBwQHTp08H8OjJRt0USTJO/SUEdezt7eHj48Nth5rIycnJYP2Qo6OjwWs5j9QXoV+/fkhJSWkwofyf//wnPD09BaWSp8eXD9Xh+n/j/e///i82bNgAHx8faauc3NxcZGdnY8mSJaLjyUZISIj0pPfxrkO5PwHmViQylpKSgpSUFMyYMcOgVW7fvn0IDg7m1hlG2Lp1K65fv47+/fsjICAAfn5+WLhwIT755BPR0VqN+fPn49NPPxUdQ1Z27NiBiRMnwsPDAxqNBsuXL4eFhQXUajXCw8MxatQo0RFl59ChQw3OqdVq/PjjjwgNDUVAQICAVESPCqKNGzeiffv26N27N4BHv9urqqoQGRnJ7UiaoLH3eX3sADOOTqdDeno6CgsLodfr4eHhgVGjRnGNtYk0NmNBTvjkUsaSk5OxZs0a2NraSuf8/PywbNkyrFixgsWlEW7fvo1OnTqhW7ducHd3h4WFhezvIIlQf8R2fRyWYpysrCy88847AIBTp07Bzc0NS5YsQVlZGWJiYlhcGuFJHyrVajXWrFnD4rKJVCqVtC+jQqFA9+7d8eqrr7Jt2wgODg5Yu3Ytrl69KnUoDB48GH5+foKTyQ+LR9Nbu3YtoqOjOZzPxAoL4VthagAAIABJREFUC6FUKqFUKmFjY4P169eLjmQ0FpcyV7+wrCP3KVMibdy4ET///DPS09OxZs0aODg4oKKiAmVlZbxb3ASPj9im5rG0/M+P6suXL0vTIvn/pOnZ2tpye5cmysrKwtatWxEUFITAwEDo9Xrk5+dj2bJlWLhwIXx8fERHlCU/Pz+poNRqtUhLS0N6ejr+8Ic/CE4mH4cPH37q9WnTppkpSevBdmLTKSkpkQpKCwsL3Lt3D7GxsbJfmsHiUsY6duyIgoIC9OrVy+B8QUEBF6o3Q7du3RAWFoawsDDk5eVJv8y7dOmCtWvXio4nC1evXpU+FBUXFxv8oDx79iy3dGmiTp064cKFC+jcuTOys7Mxf/58AEBNTQ2fBJvY1atX0alTJ9ExZCUhIQGRkZFSCycADBs2DP7+/tixYwdiYmIEppOv6upqXLx4Eenp6fjxxx/x0ksv4ZVXXhEdS1Y6dOjQ4FxlZSVOnjyJhw8fsrg0gkajeeJWTgC3bHtW0dHR0Gg0GDlyJD766CO4ublhwYIFsi8sARaXsjZz5kxs2LABQUFB0qLqvLw8pKamYuHChaLjtQqenp7w9PTEzJkzcf36ddFxZCMhIQFxcXEAgM2bN0vHAHDkyBH+8mmiuXPnYteuXSgrK0NERIT0xPLKlSsc3mWkxYsXN2jdVqvVcHJyeuqWRNSQRqMxKCzr9OrVi9tjGeHy5ctSQfnCCy9gzJgxyMvL44RjI4SEhEjHFRUVOHbsGE6dOoWRI0caXKNnp9FonrqVE3+/Pxt7e3uUlpZCpVKhvLwcbm5urWYZFotLGfPx8WmwPUH37t2xbt06tssZ6W9/+9tTrz///PNmSiJv9dsKH28xZMth07m7u2P58uUNzg8cOBADBw4UkEj+li5davBaoVDA1taWXR9GUqvVDZZpqNVqvt+NsG7dOvj4+GDNmjXSU4zdu3eLDSVjarUaSUlJSEtLQ2BgIOLi4hpdUkTPpmvXrrzRYQJLliyRtrv74osvUFRUBI1GI/thPgCLS1n7+uuvERISgrCwMNFRWg3ux2Ya9e++PX4nrrXcmTOn/3bTY9asWWZK0np07dr1idc40bhpJk+ejHXr1iE8PNxguum+ffu4p60R1q9fD6VSKRWXAQEBqK2tFR1LlhISEnDu3DkEBwdj8+bNvHlkArxhZDo2NjYYO3Ysxo4di7KyMmRkZGD37t0oLS2V9e8gbkUiY59//jmys7Mxe/ZsDkygFiUiIgK+vr7Q6/XIysqS9gjV6/XIzs7Grl27BCeUl7rOhCcJCgoyS462gsVl0124cAFHjx6Vppt6eHggJCQEQ4cOFZxM3rKysqBUKnH27Fn06tUL/v7+nATfBGFhYbC0tES7du0Mbmzq9XooFArs2bNHYDp5unXrFnr06CG9fvjwIa5fvw5nZ2feoDeRkpKSp94AbelYXMrcjRs3sGvXLri7u2P8+PEGPzz5Jm+6+msDGxMVFWWmJPJ27dq1p15ne3HT6HQ6aLVa2NvbG5xXqVTo2LEj9xYzMRaX1NLU1tbiypUrUCqVbEkkodavX4+33noLPXr0wIMHDxAVFYU+ffrg7t27GDduHLsVnlFWVhbu3r2LwMBAAI/mU6jVagDA1KlTZb31ENtiZa5Pnz743e9+h82bN+Pu3bsGxeWf/vQngcnkKScnB87OzggICJB9z7tILB5Na9euXRg4cGCDQQmXL19GVlYW5s6dKyiZfCUlJTV6Xq/XQ6vVmjmNvOl0OmRkZMDW1hZDhgzB0aNHcf36dTz33HOYOnVqg5si9HSXLl2CVqvF8OHDpXMWFhYoLy/nnrYmoNVqcf78eW7rYqTi4mLpyeWpU6fQv39/vP/++6ioqMAf//hHFpfP6IsvvjBY0nLnzh0sWLAAWq0WiYmJLC5JDJVKhb1796K4uBgrVqxosCUJNd3OnTulSX3p6ekYPHgwAgIC4OHhITqarJw/fx6lpaWYMGECAGDZsmXS3lgzZsww+NBE/11WVhbefffdBudHjx6NxMREAYnk72lTTCdNmmTGJPIXHx8PS0tLaLVafPPNN/Dw8MCECROQlZWFbdu2NRieRE936NChRrtkXnzxRWzcuBH9+/cXkEreuK2L6bRr1046vnr1KoKDgwE82h6PMxWeXUVFBbp37y69dnNzkzoO9+/fLyqWSbC4lLHly5fj9ddfx/vvv883tIlYWFhIEzirqqqgVCqxcuVKTJs2DRMnThQdTzaOHj2KDz74QHpdVVWF2NhYVFZWYtu2bSwuTYgrG4wTGhoqOkKr8fPPP2Pz5s2oqanBvHnzsGrVKgCPphlHRkYKTic/lZWVjT7tdXR0RGVlpYBE8sVtXUyvS5cuOH78OLp06YL8/HxpYrlOp0NNTY3gdPLxyy+/GLz++OOPpeOysjJzxzEpFpcyFhMTw3ajX0FVVRUuXrwIpVKJkpISTJw4kfs2NVF1dTWcnZ2l1z4+PrCzs4OdnR0/HBnB3t6+0fHkubm5/BnQDD/88AO++uorFBYWAni0ldNrr73GvUObyNLy0UeJdu3aoXPnzgbXLCwsRESStaqqKtTU1Bg8IQIe/VzV6XSCUskTt3Uxvfnz5+PgwYO4cuUKFi1ahE6dOgF4tKyIw+Wenbu7Oy5evNjg982FCxfg7u4uKJVpsLiUMX6oNL34+Hjcvn0bgwYNwrRp0wwmotGzq1uUXmf27NnScV17LD278PBwbNmyBYGBgVLbzI0bN5CamopFixYJTidPKSkpSElJwYwZMwy+p/v27cP9+/c5kbMJSktLpe1y6h8DwP3790XFki1/f39s374ds2bNkrbO0Gq12LVrF/z9/QWnkxdu62J6Dg4OeOeddxqc9/Pzk/U6QXOLiIhAbGwsMjMzDbZwysnJkf3wSE6LJaonLCwMHTp0AACOLW+GrVu34vnnn2/wAf2f//wnfvrpJxZERigrK8O3336LW7duAYC0rs3BwUFwMnn68MMPsWbNmgabqT98+BArVqzAli1bBCWTH26VY1o1NTU4cOAATp48KXWA3Lt3D2PHjpW21qCm47YuprF+/foGe1nb2dlJbcf07KqqqpCWliZ1z3h4eMDNzQ3p6emYM2eO4HTGY3EpYwUFBejZsyfXW1KLo1KpsHHjRrRv397gjlxVVRUiIyPh6OgoOCG1dR9++OETC8inXaOmkft+bSLpdDoUFRUBAFxdXbnlkInU1tbi8uXLSEpKQnR0tOg4stPYVmNqtRpnzpyBm5sb3n77bQGp5C0/Px9KpRLfffcdXFxc8NJLL0kDEeWIt79k7LPPPkNxcTF69+4Nb29veHt7o1+/frCxsREdjdo4BwcHrF27FlevXpU2VR88eDBbZoxUNyClMQqFAitWrDBjmtahY8eOKCgoaDBlu6CgQGpFpGeXk5OD+/fvw9fXFw4ODrh58ya++uorZGVlcc/QJjp79myDc//+97+lY84AaJ66wX3bt28XHUWWnrTV2NChQxEVFcXi8hnduXMHGRkZUCqVsLW1xciRI6HX61vFNoIsLmVs/fr1qKysRG5uLrKzs3H8+HHEx8fD0dER3t7esn6kTq0D12CYRnh4eINzOTk5OHr0KNtijTRz5kxs2LABQUFB6NOnDxQKBfLy8pCamoqFCxeKjicrCQkJuHjxInr27Imvv/4agwcPxrfffospU6Zg/vz5ouPJzoULF556ncUltUQc3tU0H374IXx8fBAVFQVXV1cAQHJysuBUpsHiUuY6dOiAF154AZ6enujXrx+ys7ORmpqKS5cuiY5GbdjMmTMbbdeuqalBdXU1Dhw4ICCVfNUNnAEetSR9+eWXqKqqwty5czFo0CCByeTLx8cHMTExOHHihLRmsHv37li3bh3btpvo4sWLiIuLg5WVFdRqNd59911s2rQJbm5uoqPJErfJoJbs8YF9defOnDnDPcGbYPHixVAqlVi1ahUGDBiAgICAVrO1GItLGUtPT0d2djYKCgrQvn17qcBcs2YNPxyRUHv37jV4XVFRgRMnTiAlJYXTDo106dIlfPnll7CyssKUKVP4RNgEHB0dERYWJjqG7FlZWUnrAW1tbeHu7s7CshmSkpKeev03v/mNmZLIX/3JxY/TaDRmTNJ6REVFQaFQSIVQ/YE+7Jh7dv7+/vD394dWq8X58+eRnJwMlUqFnTt3wt/fHwMGDBAd0Wgc6CNj4eHh6NatG1555RX4+vrKfl8can1++eUXJCcn48yZMxg1ahQmT54MOzs70bFk5w9/+APKy8sREhICLy+vBtfrP9mkZ8N1rKYTEREBX19f6fX169cNXst9rL65HTp0SDpOSUlpMNE0NDTU3JFki5OMSU7UajW+++47ZGRkyHrtJYtLGautrUVBQQFycnKQnZ2NO3fuwMnJCV5eXvDy8uKTDRKmvLwcSUlJyMjIwMsvv4yJEydy0FQzrFy58qlToeX8S0iUGzduNDhXfx1rbGysgFTy1Nj0yPqeNACE/rslS5Zgw4YNomO0SpxkbDyVSoUTJ07g9u3bUCgU6N69O1599VXOACAALC5blbKyMmRmZiI5ORnFxcU4ePCg6EjURoWHh8Pe3h5BQUHo2LFjg+ts6zKd6upq7nvXTPXXsU6ZMoXrWJtIo9E88ebRvXv3pL0aqemioqIQFxcnOoascZKxaWVlZWHr1q3SMDS9Xo/8/HxpGJqPj4/oiCQYP5HI2M2bN5GdnS09uayuroaXlxcmTpwIb29v0fGoDQsJCZGetFVUVBhc476szafX6/HTTz8hPT0dFy5cwM6dO0VHkiWuYzWNVatWSQXQ6tWrDVqKN27cyOKIhOEkY9NLSEhAZGSktIc1AAwbNgz+/v7YsWMHYmJiBKajloDFpYxt27YN3t7eGDhwIMLCwtjeQS3G9OnTn3gtNzfXjElal3/9619IT0/HuXPnoFarMXv2bMyYMUN0LFlqbB1r/VZZrmN9dvUboB6fJMnmqKZbvHixdBOuqKgIH3/8scH1TZs2iYglS5xkbHoajcagsKzTq1evBjeTqW1icSljf/rTn9iKRLJQWFgIpVIJpVIJGxsbrF+/XnQkWdm/fz++++47ODs7IyAgANOmTcPSpUs5jKIZOnToABcXF5w9e7bRTeu5jvXZ1e9GeLwzgZ0KTffxxx9DpVKhS5cuBudLSkrQuXNnQankiZOMfx1qtRq2trYNzvFmEgEsLmWNrUjUkpWUlEgFpYWFBe7du4fY2Fi4uLiIjiY7KSkpcHd3x/jx4zF48GBYWVnxQ3szrVy5UnSEVkOlUiEpKQl6vV46Bh49tSwvLxecTn727NmDN998s0E3Unl5OXbv3o2lS5cKSiY/d+/eNfgsVFJSgri4OOj1eigUCk4yNsLkyZOxbt06hIeHS08wb9y4gX379mHy5MmC01FLwOJSxtiKRC1VdHQ0NBoNRo4ciY8++ghubm5YsGABC0sj7dy5Ez/++COUSiV2796NF154ATqdDjU1NWjXrp3oeLLFiYemERwcLLXD1T8GgLFjx4qKJVslJSXo2bNng/Oenp4oKSkRkEi+lixZAgDQ6XT497//jQEDBsDV1VV6mklNN27cODg5OeHgwYO4ffs2AMDDwwNvvPEGhg4dKjgdtQQsLmWMrUjUUtnb26O0tBQqlQrl5eVwc3Pj/5PNYGFhgUGDBmHQoEHQ6XS4ePEiKisrMW/ePPj5+eGDDz4QHVF26k88DAwMlCYeLlu2jBMPm8jOzg4TJkwQHaPV0Ol0Rl2jhry8vHDgwAGcOnUKzs7O0Ov1KC0tRVBQEN58803R8WRryJAhGDJkiOgY1EKxuJQxtiJRS7VkyRJoNBpkZmbiiy++QFFRETQaDXJzc9G3b1/R8WTNysoKw4cPx/Dhw1FRUdHoekH67zjx0HROnTrF4tKEPD09kZKSgnHjxhmcP3nyJAdNNdHf//53aLVaxMfHS9tiaTQaJCQk4O9//zsiIiLEBpShv/3tb0+9PmvWLDMloZaKxaWMsRWJWjIbGxuMHTsWY8eOhUqlQkZGBnbv3o3S0lLuLWaE2tpaqNVq2NvbA3i0v6VSqURycjIH+xiBEw+ppYqIiMCmTZuQnp4uFZN5eXmorq5GZGSk4HTycvHiRfzlL38x6JyxsbHB3LlzsWjRIhaXRqh/g+PQoUMIDQ0VmIZaIhaXMsY3NMmFg4MDJk6ciIkTJ3LNkBGUSiV27NgBa2truLq6IjQ0FPHx8fD09MTChQtFx5MtTjw0jZs3b+J//ud/GpyvG5qyZ88eAanky9HREWvXrsXVq1elNW2DBw/mPqxGUCgUjS7JsLCw4FINI9W/mXns2DHe3KQGWFzK2OHDh596fdq0aWZKQvTsuB9r0x05cgRxcXFwdXXFjRs3EB0djUWLFsHf3190NNnixEPT6dGjBzZs2CA6Rqvj5+fHgrKZunXrhtTUVAQGBhqcP3PmDNzd3QWlaj1YoFNjWFzKWIcOHRqcq6ysxMmTJ/Hw4UMWl0SthKWlJVxdXQE8aklycXFhYdlMnHhI1PrNmTMHmzZtwqlTpwxajHU6HVuMiX4lCj37f1qFiooKHDt2DCdPnsSIESMQEhLCcfpErcS8efPwm9/8RnqdlJRk8Lr+MZG5HTlyBG+88YboGERPVNdirNfr4eHhgRdffFF0JNmaOXOm9MSysrJSetDBNniqw+JS5tRqNZKSkpCWlobAwEBMmjSpwRoiInNLSUnBCy+8ADc3N+j1enz66ac4e/Ysunbtivfee48TD5vo0KFDT73O9ddNx4mHpvPnP/8ZH330EYBH0zlnzJghXVu7di2io6NFRSMiIjNjW6yMJSQk4Ny5cwgODsbmzZthbW0tOhIRAOD48ePSIn+lUombN28iPj4e+fn52L17N1avXi02oMyweDQ9Tjw0nbt370rHV65cMbjGbbGIiNoWFpcylpSUBEtLSxw5cgSJiYnSebYmkGgWFhawtHz04+XChQsIDAyEnZ0d+vfvj3379glOJz/1n/4kJiZiypQpghPJHycemgcHfhARtS0sLmXs4MGDoiMQNcrCwgIPHjxAp06dcPXqVYP1WDqdTmAyear/9CczM5PFpYmxAGqeyspK5OfnQ6/XQ6fTSccA3+9ERG0Ni0siMrnp06dj6dKlqK2txZAhQ+Dh4QEAuHbtGlxcXASnkx8WP9SSOTk5Ye/evQAe7dFYd1z3moiI2g4O9CGiX0VNTQ0qKioMBkxptVoA4PrgJoqIiICvry/0ej2ysrLg6+trcD0qKkpQMvnixEPTycnJgZeXl+gYRETUArC4JKJfhUqlwokTJ3D79m0oFAp0794d48eP55MMI1y7du2p159//nkzJSFqKCoqCnFxcaJjEBFRC8C2WCIyuaysLGzduhVBQUEIDAyEXq9Hfn4+li9fjoULF8LHx0d0RFlh8UgtGe9RExFRHT65JCKTW758OebMmYPevXsbnC8oKMCOHTsQExMjKBkRmVpd2/aTsG2biKjt4JNLIjI5jUbToLAEgF69eqGiokJAIiL6tdjb2yMkJER0DCIiagFYXBLRr0KtVhsM86k7x2YJotbF2tqardtERASAxSUR/QomT56MdevWITw8XHqCeePGDezbtw+TJk0SnE5+tm3b9sRrCoUC8+fPN2MaIkOP30QiIqK2i8UlEZncuHHj4OTkhIMHDxpMi33jjTcwdOhQ0fFkZ/DgwQ3O3bt3D8eOHUNtba2ARET/YWlpieXLl+P555/HwIED4ePjg3bt2omORUREAnCgDxGZVXJyMiZPniw6hmzdvXsXiYmJuH79OiZPnoyxY8fC0pL3CUksnU6Ha9eu4YcffkB2djacnZ0xcOBADBw4EM7OzqLjERGRmbC4JCKzmj9/Pj799FPRMWSnsLAQR44cQUFBAX77299i9OjRfDpELVZxcTF++OEHXLp0CWVlZYiNjRUdiYiIzIC3u4mIWrg///nPyMvLQ0hICCIiImBhYWEwdZdr3kik06dPIygoyOCci4sLgoODcf36daxZs0ZMMCIiMjsWl0RELVxeXh4A4JtvvkFSUhKA/2xcr1AoEB8fLywb0fHjx1FdXY1x48ZJ57RaLTZt2oQuXbqwbZuIqA1hWywRmdzMmTOhUCganNfr9dDpdDhw4ICAVET0a1Cr1Vi3bh1Gjx6NSZMmoby8HLGxsfDz88Pbb78tOh4REZkRi0siIhkqKipCRkYGlEolNm/eLDoOtXEajQaxsbHw8fHB999/j1deeYXbDhERtUEsLomIZOLBgwfIyMhAeno6bt26hddffx0vvfQSevToIToatWFnz54FAFRUVGDv3r3w8/NDQECAdP2ll14SFY2IiMyMxSURUQuXkpICpVKJ+/fvY8SIERgxYgQ2bNiATz75RHQ0Imzbtu2p19977z0zJSEiItG4yp6IqIX761//Ci8vL/z+97+Hp6cnADS6ppVIBBaPRERUh8UlEVELt337dmRmZmLv3r0oKyvDiBEjUFNTIzoWkaS2thZqtRr29vYAgOrqapw+fRrJycnYsmWL4HRERGQubIslIpKR0tJSKJVKKJVK6HQ6DBs2DG+99ZboWNSGKZVK7NixA9bW1nB1dUVoaCji4+Ph6emJqVOnok+fPqIjEhGRmbC4JCKSqTt37kCpVCI0NFR0FGrDFi9ejMjISLi6uuLGjRuIjo7GokWL4O/vLzoaERGZGdtiiYhauNzcXDg7O8PR0REAkJqairNnz8LZ2RnTp08XnI7aOktLS7i6ugIA+vTpAxcXFxaWRERtlIXoAERE9HQ7d+6EpeWje4HXrl3DP/7xD4wZMwY2NjbYvn274HTU1qlUKiQlJUn/aLVag9dERNR2sLgkImrhamtrYWtrCwDIyMhAcHAwhg8fjt/97ncoKioSnI7auuDgYFRUVEj/1H+t1WpFxyMiIjNiWywRUQtXW1uLmpoatGvXDlevXsU777xjcI1IpKet+c3NzTVjEiIiEo3FJRFRCxcQEICVK1fCzs4OVlZW8PX1BQAUFRXBxsZGcDoiQ4WFhdJEYxsbG6xfv150JCIiMhNOiyUikoGcnByUlZWhf//+sLa2BvBoWqxWq+VWDyRcSUmJVFBaWFjg3r17iI2NhYuLi+hoRERkRiwuiYiIyGjR0dHQaDQYOXIkAgIC4ObmhgULFuCTTz4RHY2IiMyMA32IiIjIaPb29qioqIBKpUJ5eTkAQKFQCE5FREQi8MklERERNYtGo0FmZiaUSiWKioqg0WiwfPly9O3bV3Q0IiIyIxaXREREZDIqlQoZGRlQKpUoLS3Fp59+KjoSERGZCYtLIiIiMlpBQQF69uzZaCtsSUkJunbtKiAVERGJwOKSiIiIjLZ06VIUFxejd+/e8Pb2hre3N/r168dtcoiI2iAWl0RERNQslZWVyM3NRXZ2NnJycpCXlwdHR0d4e3tjzpw5ouMREZGZsLgkIiIik9BqtfjXv/6F7OxspKamQq/XIz4+XnQsIiIyExaXREREZLT09HRkZ2ejoKAA7du3h6enJ/r16wcvLy84OjqKjkdERGbE4pKIiIiMFh4ejm7duuGVV16Br68v3N3dRUciIiJBWFwSERGR0Wpra1FQUICcnBxkZ2fjzp07cHJygpeXF7y8vODn5yc6IhERmQmLSyIiIjKZsrIyZGZmIjk5GcXFxTh48KDoSEREZCaWogMQERGRfN28eVOaEpudnY3q6mp4eXlh4sSJ8Pb2Fh2PiIjMiE8uiYiIyGhRUVHw9vaGl5cXvL290bVrV9GRiIhIEBaXREREZDSNRgMbG5tGr927dw/Ozs5mTkRERKJYiA5ARERE8rVq1SrpePXq1QbXNm7caO44REQkEItLIiIiMlr9Bii1Wv3Ea0RE1PqxuCQiIiKjKRSKRo8be01ERK0bp8USERGR0VQqFZKSkqDX66Vj4NFTy/LycsHpiIjInDjQh4iIiIx26NChp14PDQ01UxIiIhKNxSURERERERE1G9tiiYiIyGiHDx9+6vVp06aZKQkREYnG4pKIiIiM1qFDhwbnKisrcfLkSTx8+JDFJRFRG8K2WCIiIjKJiooKHDt2DCdPnsSIESMQEhICBwcH0bGIiMhMWFwSERFRs6jVaiQlJSEtLQ2BgYGYNGkSbG1tRcciIiIzY3FJRERERktISMC5c+cQHByMCRMmwNraWnQkIiIShMUlERERGS0sLAyWlpZo164dFAqFdF6v10OhUGDPnj0C0xERkTmxuCQiIiIiIqJmsxAdgIiIiIiIiOSPxSURERERERE1G4tLIiKiViomJganT58WHYOIiNoIrrkkIiJqgaZPn46tW7fC1dXVJF/v9OnT+L//+z+sWbPGJF+PiIjocXxySURE1ILU1NSIjkBERGQUPrkkIiIykQULFmDcuHE4c+YMysrKMGzYMMyZMwdWVlZISUnB119/DbVaDR8fH8ydOxedO3cG8Ogp5axZs3Ds2DHU1NTA2dkZ169fR4cOHQAA8+fPx8iRI5/4954/fx5ffPEFiouLYW9vj9mzZ2PgwIFYuXIlRo8eDW9vb0RFRaG6uhpWVlZo164doqOjERcXh88++wzt2rUDAGRmZuLLL7/Exo0bf/1vFhERtTqWogMQERG1Junp6Vi+fDmsra0RFxeHI0eOwM/PD/v378fy5cvh4eGBhIQE/OUvf8GqVaukP3f+/HnExMTAysoKVlZWmD59OjZu3Phf22Jzc3MRHx+PxYsXw8/PD2VlZaioqDD4d7p37465c+c2aIu1tbXF5cuXMWjQIABAWloaxowZY8LvBhERtSVsiyUiIjKhV199Fc7OzrC1tcWUKVOgVCqRlpaGl19+GX369EH79u3x1ltvIScnB8XFxdKfmzJlCmxtbWFlZdWkv+/kyZN4+eWX0b9/f1hYWKBz587o1q3bM/3ZwMBApKWlAQDUajVW+Nk2AAACTUlEQVR+/PFHjBo1qkl/PxERUR0Wl0RERCbk7OwsHXft2hX379/HgwcPDM5bW1vD1tYW9+/fl8516dLFqL+vtLTU6KE/Y8aMwYULF6DVapGRkQFfX184OTkZ9bWIiIhYXBIREZnQvXv3DI47d+4MJycng/NarRZqtVpacwkACoXCqL+vS5cuKCoqMurPdu7cGV5eXjh37hzS0tIwevRoo74OERERwOKSiIjIpE6cOIHS0lKo1WokJiZixIgRGDVqFE6dOoWCggJUVVVh//796Nu3L1xcXJ74dRwcHHD37t3/+veNHTsWp0+fxpUrV1BbW4v79+/j559/bvDvOTo64v79+6iurjY4P2bMGHz99de4desW/P39m/4fTERE9P9xoA8REZEJjRo1CmvXrsWDBw8wdOhQTJ06FR06dEBYWBg2b94MtVoNb29vLFq06KlfJzQ0FJ988gl0Oh3eeeedJ06L7du3L9577z3s2bMHxcXFcHBwwOzZsxusu/Tz85MG+1hYWOCvf/0rAMDf3x+ff/45hg0bBmtra9N8E4iIqE3iViREREQmsmDBArz77rvo37+/6ChNsnDhQsydO1d2uYmIqGVhWywREVEblpmZCeDRk00iIqLmYFssERFRC3fkyBEkJiY2OO/r64tly5YZ/XVXrlyJwsJCvP/++7Cw4P1mIiJqHrbFEhERERERUbPxNiURERERERE1G4tLIiIiIiIiajYWl0RERERERNRsLC6JiIiIiIio2VhcEhERERERUbOxuCQiIiIiIqJm+39bzdbcO98a7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query=\"\"\"\n",
    "SELECT TOP 10 b.port_city, b.state_code_or_country, COUNT(cicid) AS count\n",
    "FROM immigration a INNER JOIN i94ports b ON a.port_id=b.port_code\n",
    "GROUP BY b.port_city, b.state_code_or_country\n",
    "ORDER BY COUNT(cicid) DESC;\n",
    "\"\"\"\n",
    "df = pr.redshift_to_pandas(query)\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title(\"Top 10 entry port cities into US for immigrants\")\n",
    "sns.barplot(x='port_city', y='count', data=df);\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas\n",
    "# port city from where people arrived\n",
    "query = \"\"\"\n",
    "SELECT a.airport_longitude, a.airport_latitude\n",
    "FROM airport_codes a;\n",
    "\"\"\"\n",
    "df = pr.redshift_to_pandas(query)\n",
    "\n",
    "query2 = \"\"\"\n",
    "SELECT a.airport_longitude, a.airport_latitude\n",
    "FROM airport_codes a\n",
    "WHERE airport_type='large_airport';\n",
    "\"\"\"\n",
    "df2 = pr.redshift_to_pandas(query2)\n",
    "\n",
    "gdf = geopandas.GeoDataFrame(\n",
    "    df[['airport_longitude','airport_latitude']], \n",
    "    geometry=geopandas.points_from_xy(df.airport_longitude, \n",
    "                                      df.airport_latitude))\n",
    "gdf2 = geopandas.GeoDataFrame(\n",
    "    df2[['airport_longitude','airport_latitude']], \n",
    "    geometry=geopandas.points_from_xy(df2.airport_longitude, \n",
    "                                      df2.airport_latitude))\n",
    "\n",
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "ax = world.plot(color='white', edgecolor='black', figsize=(20,15))\n",
    "gdf.plot(ax=ax, color='green', markersize=0.3)\n",
    "gdf2.plot(ax=ax, color='red', markersize=2.0)\n",
    "\n",
    "plt.title(\"Airport Locations around the World (with large airports in red)\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_month</th>\n",
       "      <th>transportation_mode</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Air</td>\n",
       "      <td>1851856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Air</td>\n",
       "      <td>1043318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Air</td>\n",
       "      <td>739711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Air</td>\n",
       "      <td>655251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Air</td>\n",
       "      <td>654599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Air</td>\n",
       "      <td>586326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>Air</td>\n",
       "      <td>551515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>Air</td>\n",
       "      <td>528591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>Air</td>\n",
       "      <td>464859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Air</td>\n",
       "      <td>435446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Air</td>\n",
       "      <td>411238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Air</td>\n",
       "      <td>402673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>Land</td>\n",
       "      <td>23844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>Land</td>\n",
       "      <td>11065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11</td>\n",
       "      <td>Land</td>\n",
       "      <td>9880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>Land</td>\n",
       "      <td>9225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Land</td>\n",
       "      <td>8842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12</td>\n",
       "      <td>Land</td>\n",
       "      <td>8841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>Land</td>\n",
       "      <td>8674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7</td>\n",
       "      <td>Land</td>\n",
       "      <td>7321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>Land</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9</td>\n",
       "      <td>Land</td>\n",
       "      <td>6828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
       "      <td>Land</td>\n",
       "      <td>6264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6</td>\n",
       "      <td>Land</td>\n",
       "      <td>5787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>3052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>Sea</td>\n",
       "      <td>2792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>Sea</td>\n",
       "      <td>1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>1270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>Sea</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12</td>\n",
       "      <td>Sea</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11</td>\n",
       "      <td>Sea</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>Sea</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>10</td>\n",
       "      <td>Sea</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>Sea</td>\n",
       "      <td>537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>9</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>Sea</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>11</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>8</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>8</td>\n",
       "      <td>Sea</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>7</td>\n",
       "      <td>Not reported</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6</td>\n",
       "      <td>Sea</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>7</td>\n",
       "      <td>Sea</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    entry_month transportation_mode    count\n",
       "0             4                 Air  1851856\n",
       "1             5                 Air  1043318\n",
       "2             7                 Air   739711\n",
       "3             9                 Air   655251\n",
       "4             8                 Air   654599\n",
       "5             6                 Air   586326\n",
       "6            10                 Air   551515\n",
       "7            12                 Air   528591\n",
       "8             3                 Air   464859\n",
       "9             1                 Air   435446\n",
       "10           11                 Air   411238\n",
       "11            2                 Air   402673\n",
       "12            4                Land    23844\n",
       "13            3                Land    11065\n",
       "14           11                Land     9880\n",
       "15           10                Land     9225\n",
       "16            2                Land     8842\n",
       "17           12                Land     8841\n",
       "18            5                Land     8674\n",
       "19            7                Land     7321\n",
       "20            1                Land     7065\n",
       "21            9                Land     6828\n",
       "22            8                Land     6264\n",
       "23            6                Land     5787\n",
       "24            4        Not reported     3052\n",
       "25            4                 Sea     2792\n",
       "26            5                 Sea     1624\n",
       "27            5        Not reported     1270\n",
       "28            9                 Sea     1266\n",
       "29           12                 Sea     1134\n",
       "30           11                 Sea      710\n",
       "31           12        Not reported      610\n",
       "32            3                 Sea      599\n",
       "33           10                 Sea      599\n",
       "34            3        Not reported      546\n",
       "35            1                 Sea      537\n",
       "36            9        Not reported      531\n",
       "37            2                 Sea      524\n",
       "38           10        Not reported      471\n",
       "39           11        Not reported      461\n",
       "40            1        Not reported      416\n",
       "41            8        Not reported      335\n",
       "42            8                 Sea      286\n",
       "43            2        Not reported      279\n",
       "44            6        Not reported      262\n",
       "45            7        Not reported      257\n",
       "46            6                 Sea      204\n",
       "47            7                 Sea      152"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT a.entry_month,b.transportation_mode,COUNT(cicid) AS count\n",
    "FROM immigration a INNER JOIN i94mode b ON a.mode_id=b.mode_id\n",
    "WHERE a.state_code = 'CA'\n",
    "GROUP BY a.entry_month, b.transportation_mode, a.entry_month\n",
    "ORDER BY COUNT(cicid) DESC;\n",
    "\"\"\"\n",
    "df = pr.redshift_to_pandas(query)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "plt.title(\"# of immigrants entering into California by various transport modes\")\n",
    "lplot=sns.lineplot(x=\"entry_month\", y=\"count\", hue=\"transportation_mode\", data=df);\n",
    "plt.ylabel(\"Log Count\")\n",
    "lplot.set(yscale=\"log\")\n",
    "plt.legend(bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0.);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### move files to S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import configparser\n",
    "import boto3\n",
    "import pandas as pd\n",
    "from io import StringIO\n",
    "\n",
    "config = configparser.ConfigParser()\n",
    "config.read('./dwh.cfg')\n",
    "\n",
    "AWS_ACCESS_KEY = config.get('AWS', 'AWS_KEY_ID')\n",
    "AWS_SECRET = config.get('AWS','AWS_SECRET')\n",
    "AWS_REGION = config.get('AWS','REGION')\n",
    "S3_BUCKET = config.get('S3','BUCKET_NAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = boto3.resource('s3',region_name=AWS_REGION,\n",
    "                    aws_access_key_id=AWS_ACCESS_KEY,\n",
    "                    aws_secret_access_key=AWS_SECRET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '19F04C1FF73127C2',\n",
       "  'HostId': 'k3HL5Z2nAKNSyZzF0ZBo8r3FjGu25hPNDcYvJvoR76u3F+Anp/hMHlZq5t+TPiMYXBcrvno/zOk=',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amz-id-2': 'k3HL5Z2nAKNSyZzF0ZBo8r3FjGu25hPNDcYvJvoR76u3F+Anp/hMHlZq5t+TPiMYXBcrvno/zOk=',\n",
       "   'x-amz-request-id': '19F04C1FF73127C2',\n",
       "   'date': 'Mon, 11 May 2020 18:37:02 GMT',\n",
       "   'etag': '\"75285372d53b7508543b5c671383a2fe\"',\n",
       "   'content-length': '0',\n",
       "   'server': 'AmazonS3'},\n",
       "  'RetryAttempts': 0},\n",
       " 'ETag': '\"75285372d53b7508543b5c671383a2fe\"'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_buffer = StringIO()\n",
    "df_airports = pd.read_csv('airport-codes_csv.csv')\n",
    "df_airports.to_csv(csv_buffer)\n",
    "s3.Object(S3_BUCKET,'airport_codes/airport-codes_csv.csv').put(Body=csv_buffer.getvalue())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': 'A7D85AA646A9DBCF',\n",
       "  'HostId': 'twCyiYQxiy9gukylHvfVolx7NdbXHCWm66vW+KiyqMTcanwLyBsQDCHl+BBik3PElJ49rEeIcgw=',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amz-id-2': 'twCyiYQxiy9gukylHvfVolx7NdbXHCWm66vW+KiyqMTcanwLyBsQDCHl+BBik3PElJ49rEeIcgw=',\n",
       "   'x-amz-request-id': 'A7D85AA646A9DBCF',\n",
       "   'date': 'Mon, 11 May 2020 18:37:20 GMT',\n",
       "   'etag': '\"175d3f6963129a4bd9dec03c4caaa452\"',\n",
       "   'content-length': '0',\n",
       "   'server': 'AmazonS3'},\n",
       "  'RetryAttempts': 0},\n",
       " 'ETag': '\"175d3f6963129a4bd9dec03c4caaa452\"'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demographics = pd.read_csv('us-cities-demographics.csv')\n",
    "df_demographics.to_csv(csv_buffer)\n",
    "s3.Object(S3_BUCKET,'us-demographics/us-cities-demographics.csv').put(Body=csv_buffer.getvalue())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_buffer = BytesIO()\n",
    "df_i94.to_parquet(out_buffer, index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}