[Notebook1]_exploratory_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 1: Introduction to Exploratory Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the first notebook! In this notebook, we start exploring our [Kiva](https://www.kiva.org/) dataset. The goal of exploratory analysis is to summarize the main characteristics of a data set, with the belief that it may lead to new hypotheses that inform algorithm choice and experimentation. Exploratory analysis happens before formal modelling commences, and is extremely important for helping inform or sharpen your hypothesis.\n",
    "\n",
    "So what is a hypothesis? At the initial stage a [hypothesis](http://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html) is an educational guess that can be tested. The main goal of exploratory analysis is helping us arrive at better hypotheses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import and Merge Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the beginning of our notebooks we always import all the libraries we will use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2847: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ggplot import *\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import dateutil.parser\n",
    "import seaborn\n",
    "pd.options.display.mpl_style = 'default'\n",
    "\n",
    "# the matplotlib inline command is important, it tells jupyter notebook to show the output of the cell for charts\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are using Jupyter notebook to run your code. it is an incredibly versatile tool and we can tailor the configurations to suit our preferences. For example, in the cell below we tell Jupyter to output the results of all commands in a cell (the default is only to output the results of the last command). For more interesting and incredibly useful Jupyter tricks take a look at [this](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/) blog post. Using Jupyter shortcuts will help you work faster. Some valuable shortcuts have been put together in [this](http://www.math.umassd.edu/~ahausknecht/aohWebsiteSpring2017/examples/pythonExamples/downloads/docs/JuypterKeyboardShortcutsV1Sp2016.pdf) great pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# the command below means that the output of multiple commands in a cell will be output at once.\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a pandas command to tailor how the data is presented in the command below. There are 76 columns in our final Kiva dataset. The default panda settings hide some of the columns. We make all columns visible by setting the display to 80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# the command below tells jupyter to display up to 80 columns, this keeps everything visible\n",
    "pd.set_option('display.max_columns', 80)\n",
    "pd.set_option('expand_frame_repr', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use three main data sets: loans_full.csv, loans_details.csv, loans_partner_details.csv. These are all datasets KIVA makes publically available through its API. Wondering what an API is and how we pulled this data? Don't worry, you can learn all about it in the introduction to the KIVA APIs jupyter notebook we put together. It is available on the Delta Analytics GitHub.\n",
    "\n",
    "Let's start by talking a little about each data set:\n",
    "\n",
    "[loans_full](http://build.kiva.org/api#GET*|loans|search): contains all successful and expired loans made by KIVA in Kenya. KIVA does not make public loans that have defaulted in order to protect the privacy of those users.\n",
    "\n",
    "[loans_details](http://build.kiva.org/api#GET*|loans|:ids): contains many of the coumns as loans full. However, it also contains a comprehensive description of the lender.\n",
    "\n",
    "[loans_partner_details](http://build.kiva.org/api#GET*|partners|:ids): detailed performance metrics associated with each partner. Includes name of partner, rating of partner, amount lent to partner so far.\n",
    "\n",
    "\n",
    "When we import all out data sets we will also import a data dictionary with definitions of each field. We talk about the data dictionary more below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the path below, you should change this path to whatever folder you save the data you download from the google drive [here](https://drive.google.com/drive/folders/0BzVUQjOmkeDIYnhCalBkRVRNY1k?usp=sharing) into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/anthonymiyoro/Documents/code/kivaMachineLearning'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_path = '/Users/anthonymiyoro/Documents/intro_to_machine_learning/data_copy'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import all three data sets in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans_full=pd.read_csv(data_path+'/loans_full.csv', low_memory=False)\n",
    "loans_details=pd.read_csv(data_path+'/loans_details.csv', low_memory=False)\n",
    "loan_partner_details=pd.read_csv(data_path+'/loans_partner_details.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also import our data dictionary which contains the definition for each of the fields in the final data set we will use. Reference the data dictionary for important context about the data. It may be easier to open it as a csv file in your desktop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kiva_data_dictionary=pd.read_csv(data_path+'/kiva_data_dictionary.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_data_set</th>\n",
       "      <th>field_name</th>\n",
       "      <th>field_name_final_dataset</th>\n",
       "      <th>description</th>\n",
       "      <th>additional_notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>id</td>\n",
       "      <td>id</td>\n",
       "      <td>Unique identifier for a Kiva loan</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>name</td>\n",
       "      <td>name</td>\n",
       "      <td>The name of the borrower or group receiving th...</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>gender</td>\n",
       "      <td>gender</td>\n",
       "      <td>The gender of the primary borrower OR majority...</td>\n",
       "      <td>possible values are male or female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>description</td>\n",
       "      <td>description</td>\n",
       "      <td>The description of the loan profile in English.</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>status</td>\n",
       "      <td>status</td>\n",
       "      <td>All possible loan statuses</td>\n",
       "      <td>The public dataset does not reveal defaulted l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>fundedAmount</td>\n",
       "      <td>fundedAmount</td>\n",
       "      <td>​The amount of the loan that has been purchase...</td>\n",
       "      <td>​The dollar amount in US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>basketAmount</td>\n",
       "      <td>basketAmount</td>\n",
       "      <td>The amount of the unfunded loan that is sittin...</td>\n",
       "      <td>​The dollar amount in US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>image</td>\n",
       "      <td>image</td>\n",
       "      <td>The picture for this loan profile</td>\n",
       "      <td>Image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>activity</td>\n",
       "      <td>activity</td>\n",
       "      <td>The activity is a structured categorization of...</td>\n",
       "      <td>A property of loan which is more descriptive t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>sector</td>\n",
       "      <td>sector</td>\n",
       "      <td>The sector is a more general classification of...</td>\n",
       "      <td>A sector is a broad category for a loan, e.g. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>use</td>\n",
       "      <td>use</td>\n",
       "      <td>Text describing what the loan is to be used fo...</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>location</td>\n",
       "      <td>location</td>\n",
       "      <td>The physical location of the borrower and/or b...</td>\n",
       "      <td>Kiva Geocode = city, state, postalCode, llatit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>partner_id</td>\n",
       "      <td>partner_id</td>\n",
       "      <td>Kiva Id for partner</td>\n",
       "      <td>int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>posted_date</td>\n",
       "      <td>posted_date</td>\n",
       "      <td>When the loan started fundraising on Kiva.</td>\n",
       "      <td>Returns a UTC datetime in ISO8601 format. As a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>planned_expiration_date</td>\n",
       "      <td>planned_expiration_date</td>\n",
       "      <td>When the loan will expire if it is not fully f...</td>\n",
       "      <td>Returns a UTC datetime in ISO8601 format. As a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>loan_amount</td>\n",
       "      <td>loan_amount</td>\n",
       "      <td>The amount of this loan, as shown to lenders</td>\n",
       "      <td>The dollar amount in USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>borrower_count</td>\n",
       "      <td>borrower_count</td>\n",
       "      <td>The number of borrowers participating in the loan</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>lender_count</td>\n",
       "      <td>lender_count</td>\n",
       "      <td>The number of borrowers participating in the loan</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>bonus_credit_eligibility</td>\n",
       "      <td>bonus_credit_eligibility</td>\n",
       "      <td>The loan is eligible for matching funds if a s...</td>\n",
       "      <td>Boolean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>loans_full</td>\n",
       "      <td>tags</td>\n",
       "      <td>tags</td>\n",
       "      <td>A list of tags on this loan</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>id</td>\n",
       "      <td>id</td>\n",
       "      <td>Unique identifier for a Kiva loan</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>name</td>\n",
       "      <td>name</td>\n",
       "      <td>The name of the borrower or group receiving th...</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>gender</td>\n",
       "      <td>gender</td>\n",
       "      <td>The gender of the primary borrower OR majority...</td>\n",
       "      <td>possible values are male or female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>description</td>\n",
       "      <td>description</td>\n",
       "      <td>The description of the loan profile in English...</td>\n",
       "      <td>includes fields: languages:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>status</td>\n",
       "      <td>status</td>\n",
       "      <td>All possible loan statuses</td>\n",
       "      <td>The public dataset does not reveal defaulted l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>fundedAmount</td>\n",
       "      <td>fundedAmount</td>\n",
       "      <td>​The amount of the loan that has been purchase...</td>\n",
       "      <td>​The dollar amount in US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>basketAmount</td>\n",
       "      <td>basketAmount</td>\n",
       "      <td>The amount of the unfunded loan that is sittin...</td>\n",
       "      <td>​The dollar amount in US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>image</td>\n",
       "      <td>image</td>\n",
       "      <td>The picture for this loan profile, returns a d...</td>\n",
       "      <td>Image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>activity</td>\n",
       "      <td>activity</td>\n",
       "      <td>The activity is a structured categorization of...</td>\n",
       "      <td>A property of loan which is more descriptive t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>sector</td>\n",
       "      <td>sector</td>\n",
       "      <td>The sector is a more general classification of...</td>\n",
       "      <td>A sector is a broad category for a loan, e.g. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>partner_id</td>\n",
       "      <td>partner_id</td>\n",
       "      <td>Kiva Id for partner</td>\n",
       "      <td>int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>posted_date</td>\n",
       "      <td>posted_date</td>\n",
       "      <td>When the loan started fundraising on Kiva.</td>\n",
       "      <td>Returns a UTC datetime in ISO8601 format. As a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>loan_amount</td>\n",
       "      <td>loan_amount</td>\n",
       "      <td>The amount of this loan, as shown to lenders</td>\n",
       "      <td>The dollar amount in USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>lender_count</td>\n",
       "      <td>lender_count</td>\n",
       "      <td>The number of borrowers participating in the loan</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>bonus_credit_eligibility</td>\n",
       "      <td>bonus_credit_eligibility</td>\n",
       "      <td>The loan is eligible for matching funds if a s...</td>\n",
       "      <td>Boolean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>tags</td>\n",
       "      <td>tags</td>\n",
       "      <td>A list of tags on this loan</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>borrowers</td>\n",
       "      <td>borrowers</td>\n",
       "      <td>Information about the borrowers associated wit...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>loans_detail</td>\n",
       "      <td>terms</td>\n",
       "      <td>terms</td>\n",
       "      <td>Information about terms of the loand, returns ...</td>\n",
       "      <td>\"local_payments\", \"scheduled_paymnets\" and \"pa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>id</td>\n",
       "      <td>partner_id</td>\n",
       "      <td>Unique identifier for a Kiva partner</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>name</td>\n",
       "      <td>partner_name</td>\n",
       "      <td>The name of the microfinance field partner</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"status\":</td>\n",
       "      <td>partner_status</td>\n",
       "      <td>The status of the partner’s relationship with ...</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"rating\":</td>\n",
       "      <td>partner_rating</td>\n",
       "      <td>Field Partner Risk Rating</td>\n",
       "      <td>Float as a string</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"image\"</td>\n",
       "      <td>partner_image</td>\n",
       "      <td>Field Partner logo, returns dictionary with im...</td>\n",
       "      <td>Image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"start_date\"</td>\n",
       "      <td>partner_start_date</td>\n",
       "      <td>When the partner started offering loans on kiva</td>\n",
       "      <td>Returns a UTC datetime in ISO8601 format. As a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"countries\"</td>\n",
       "      <td>partner_countries</td>\n",
       "      <td>The physical location of the borrower and/or b...</td>\n",
       "      <td>Kiva Geocode = city, state, postalCode, llatit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"delinquency_rate\"</td>\n",
       "      <td>partner_delinquency_rate</td>\n",
       "      <td>Kiva defines a partner's delinquency (arrears)...</td>\n",
       "      <td>Float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"default_rate\"</td>\n",
       "      <td>partner_default_rate</td>\n",
       "      <td>the percentage of Kiva loans being paid back b...</td>\n",
       "      <td>Float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"total_amount_raised\"</td>\n",
       "      <td>partner_total_amount_raised</td>\n",
       "      <td>The total of all loans made by the partner on ...</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"loans_posted\"</td>\n",
       "      <td>partner_loans_posted</td>\n",
       "      <td>The number of loans made by the partner on Kiva</td>\n",
       "      <td>Int</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"portfolio_yield\"</td>\n",
       "      <td>partner_portfolio_yield</td>\n",
       "      <td>Kiva uses a calculation called \"portfolio yiel...</td>\n",
       "      <td>Float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"profitability\":</td>\n",
       "      <td>partner_profitability</td>\n",
       "      <td>an indication of a Field Partner's profitability</td>\n",
       "      <td>Float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"social_performance_strengths\"</td>\n",
       "      <td>partner_social_performance_strengths</td>\n",
       "      <td>Returns a list of Social performance types tha...</td>\n",
       "      <td>Array</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"delinquency_rate_note\"</td>\n",
       "      <td>partner_delinquency_rate_note</td>\n",
       "      <td>A Kiva-supplied note that is shown in lieu of ...</td>\n",
       "      <td>is an empty string to be shown user w/o appror...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"default_rate_note\"</td>\n",
       "      <td>partner_default_rate_note</td>\n",
       "      <td>A Kiva-supplied note that is shown in lieu of ...</td>\n",
       "      <td>is an empty string to be shown user w/o appror...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"portfolio_yield_note\"</td>\n",
       "      <td>partner_portfolio_yield_note</td>\n",
       "      <td>A Kiva-supplied note that is shown in lieu of ...</td>\n",
       "      <td>is an empty string to be shown user w/o appror...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"charges_fees_and_interest\"</td>\n",
       "      <td>partner_charges_fees_and_interest</td>\n",
       "      <td>A true/false property for whether the partner ...</td>\n",
       "      <td>Boolean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"average_loan_size_percent_per_capita_income\"</td>\n",
       "      <td>partner_average_loan_size_percent_per_capita_i...</td>\n",
       "      <td>A ratio of the partner's average loan size to ...</td>\n",
       "      <td>Float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"loans_at_risk_rate\"</td>\n",
       "      <td>partner_loans_at_risk_rate</td>\n",
       "      <td>A decimal representing the percentage of the p...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"currency_exchange_loss_rate\"</td>\n",
       "      <td>partner_currency_exchange_loss_rate</td>\n",
       "      <td>Kiva calculates the Currency Exchange Loss Rat...</td>\n",
       "      <td>Float</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>loans_partner_details</td>\n",
       "      <td>\"url\"</td>\n",
       "      <td>partner_url</td>\n",
       "      <td>Partner's web address or link</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        original_data_set                                     field_name  \\\n",
       "0              loans_full                                             id   \n",
       "1              loans_full                                          name    \n",
       "2              loans_full                                        gender    \n",
       "3              loans_full                                   description    \n",
       "4              loans_full                                         status   \n",
       "5              loans_full                                 fundedAmount     \n",
       "6              loans_full                                   basketAmount   \n",
       "7              loans_full                                         image    \n",
       "8              loans_full                                       activity   \n",
       "9              loans_full                                         sector   \n",
       "10             loans_full                                          use     \n",
       "11             loans_full                                       location   \n",
       "12             loans_full                                     partner_id   \n",
       "13             loans_full                                    posted_date   \n",
       "14             loans_full                       planned_expiration_date    \n",
       "15             loans_full                                    loan_amount   \n",
       "16             loans_full                                 borrower_count   \n",
       "17             loans_full                                   lender_count   \n",
       "18             loans_full                       bonus_credit_eligibility   \n",
       "19             loans_full                                           tags   \n",
       "20           loans_detail                                             id   \n",
       "21           loans_detail                                          name    \n",
       "22           loans_detail                                        gender    \n",
       "23           loans_detail                                   description    \n",
       "24           loans_detail                                         status   \n",
       "25           loans_detail                                 fundedAmount     \n",
       "26           loans_detail                                   basketAmount   \n",
       "27           loans_detail                                         image    \n",
       "28           loans_detail                                       activity   \n",
       "29           loans_detail                                         sector   \n",
       "..                    ...                                            ...   \n",
       "32           loans_detail                                     partner_id   \n",
       "33           loans_detail                                    posted_date   \n",
       "34           loans_detail                                    loan_amount   \n",
       "35           loans_detail                                   lender_count   \n",
       "36           loans_detail                       bonus_credit_eligibility   \n",
       "37           loans_detail                                           tags   \n",
       "38           loans_detail                                      borrowers   \n",
       "39           loans_detail                                          terms   \n",
       "40  loans_partner_details                                             id   \n",
       "41  loans_partner_details                                          name    \n",
       "42  loans_partner_details                                      \"status\":   \n",
       "43  loans_partner_details                                      \"rating\":   \n",
       "44  loans_partner_details                                        \"image\"   \n",
       "45  loans_partner_details                                   \"start_date\"   \n",
       "46  loans_partner_details                                    \"countries\"   \n",
       "47  loans_partner_details                             \"delinquency_rate\"   \n",
       "48  loans_partner_details                                 \"default_rate\"   \n",
       "49  loans_partner_details                          \"total_amount_raised\"   \n",
       "50  loans_partner_details                                 \"loans_posted\"   \n",
       "51  loans_partner_details                              \"portfolio_yield\"   \n",
       "52  loans_partner_details                               \"profitability\":   \n",
       "53  loans_partner_details                 \"social_performance_strengths\"   \n",
       "54  loans_partner_details                        \"delinquency_rate_note\"   \n",
       "55  loans_partner_details                            \"default_rate_note\"   \n",
       "56  loans_partner_details                         \"portfolio_yield_note\"   \n",
       "57  loans_partner_details                    \"charges_fees_and_interest\"   \n",
       "58  loans_partner_details  \"average_loan_size_percent_per_capita_income\"   \n",
       "59  loans_partner_details                           \"loans_at_risk_rate\"   \n",
       "60  loans_partner_details                  \"currency_exchange_loss_rate\"   \n",
       "61  loans_partner_details                                          \"url\"   \n",
       "\n",
       "                             field_name_final_dataset  \\\n",
       "0                                                  id   \n",
       "1                                               name    \n",
       "2                                             gender    \n",
       "3                                        description    \n",
       "4                                              status   \n",
       "5                                      fundedAmount     \n",
       "6                                        basketAmount   \n",
       "7                                              image    \n",
       "8                                            activity   \n",
       "9                                              sector   \n",
       "10                                              use     \n",
       "11                                           location   \n",
       "12                                         partner_id   \n",
       "13                                        posted_date   \n",
       "14                           planned_expiration_date    \n",
       "15                                        loan_amount   \n",
       "16                                     borrower_count   \n",
       "17                                       lender_count   \n",
       "18                           bonus_credit_eligibility   \n",
       "19                                               tags   \n",
       "20                                                 id   \n",
       "21                                              name    \n",
       "22                                            gender    \n",
       "23                                       description    \n",
       "24                                             status   \n",
       "25                                     fundedAmount     \n",
       "26                                       basketAmount   \n",
       "27                                             image    \n",
       "28                                           activity   \n",
       "29                                             sector   \n",
       "..                                                ...   \n",
       "32                                         partner_id   \n",
       "33                                        posted_date   \n",
       "34                                        loan_amount   \n",
       "35                                       lender_count   \n",
       "36                           bonus_credit_eligibility   \n",
       "37                                               tags   \n",
       "38                                          borrowers   \n",
       "39                                              terms   \n",
       "40                                         partner_id   \n",
       "41                                       partner_name   \n",
       "42                                     partner_status   \n",
       "43                                     partner_rating   \n",
       "44                                      partner_image   \n",
       "45                                 partner_start_date   \n",
       "46                                  partner_countries   \n",
       "47                           partner_delinquency_rate   \n",
       "48                               partner_default_rate   \n",
       "49                        partner_total_amount_raised   \n",
       "50                               partner_loans_posted   \n",
       "51                            partner_portfolio_yield   \n",
       "52                              partner_profitability   \n",
       "53               partner_social_performance_strengths   \n",
       "54                      partner_delinquency_rate_note   \n",
       "55                          partner_default_rate_note   \n",
       "56                       partner_portfolio_yield_note   \n",
       "57                  partner_charges_fees_and_interest   \n",
       "58  partner_average_loan_size_percent_per_capita_i...   \n",
       "59                         partner_loans_at_risk_rate   \n",
       "60                partner_currency_exchange_loss_rate   \n",
       "61                                        partner_url   \n",
       "\n",
       "                                          description  \\\n",
       "0                   Unique identifier for a Kiva loan   \n",
       "1   The name of the borrower or group receiving th...   \n",
       "2   The gender of the primary borrower OR majority...   \n",
       "3     The description of the loan profile in English.   \n",
       "4                          All possible loan statuses   \n",
       "5   ​The amount of the loan that has been purchase...   \n",
       "6   The amount of the unfunded loan that is sittin...   \n",
       "7                   The picture for this loan profile   \n",
       "8   The activity is a structured categorization of...   \n",
       "9   The sector is a more general classification of...   \n",
       "10  Text describing what the loan is to be used fo...   \n",
       "11  The physical location of the borrower and/or b...   \n",
       "12                                Kiva Id for partner   \n",
       "13         When the loan started fundraising on Kiva.   \n",
       "14  When the loan will expire if it is not fully f...   \n",
       "15       The amount of this loan, as shown to lenders   \n",
       "16  The number of borrowers participating in the loan   \n",
       "17  The number of borrowers participating in the loan   \n",
       "18  The loan is eligible for matching funds if a s...   \n",
       "19                        A list of tags on this loan   \n",
       "20                  Unique identifier for a Kiva loan   \n",
       "21  The name of the borrower or group receiving th...   \n",
       "22  The gender of the primary borrower OR majority...   \n",
       "23  The description of the loan profile in English...   \n",
       "24                         All possible loan statuses   \n",
       "25  ​The amount of the loan that has been purchase...   \n",
       "26  The amount of the unfunded loan that is sittin...   \n",
       "27  The picture for this loan profile, returns a d...   \n",
       "28  The activity is a structured categorization of...   \n",
       "29  The sector is a more general classification of...   \n",
       "..                                                ...   \n",
       "32                                Kiva Id for partner   \n",
       "33         When the loan started fundraising on Kiva.   \n",
       "34       The amount of this loan, as shown to lenders   \n",
       "35  The number of borrowers participating in the loan   \n",
       "36  The loan is eligible for matching funds if a s...   \n",
       "37                        A list of tags on this loan   \n",
       "38  Information about the borrowers associated wit...   \n",
       "39  Information about terms of the loand, returns ...   \n",
       "40               Unique identifier for a Kiva partner   \n",
       "41         The name of the microfinance field partner   \n",
       "42  The status of the partner’s relationship with ...   \n",
       "43                          Field Partner Risk Rating   \n",
       "44  Field Partner logo, returns dictionary with im...   \n",
       "45    When the partner started offering loans on kiva   \n",
       "46  The physical location of the borrower and/or b...   \n",
       "47  Kiva defines a partner's delinquency (arrears)...   \n",
       "48  the percentage of Kiva loans being paid back b...   \n",
       "49  The total of all loans made by the partner on ...   \n",
       "50    The number of loans made by the partner on Kiva   \n",
       "51  Kiva uses a calculation called \"portfolio yiel...   \n",
       "52   an indication of a Field Partner's profitability   \n",
       "53  Returns a list of Social performance types tha...   \n",
       "54  A Kiva-supplied note that is shown in lieu of ...   \n",
       "55  A Kiva-supplied note that is shown in lieu of ...   \n",
       "56  A Kiva-supplied note that is shown in lieu of ...   \n",
       "57  A true/false property for whether the partner ...   \n",
       "58  A ratio of the partner's average loan size to ...   \n",
       "59  A decimal representing the percentage of the p...   \n",
       "60  Kiva calculates the Currency Exchange Loss Rat...   \n",
       "61                      Partner's web address or link   \n",
       "\n",
       "                                     additional_notes  \n",
       "0                                                 Int  \n",
       "1                                              String  \n",
       "2                  possible values are male or female  \n",
       "3                                              String  \n",
       "4   The public dataset does not reveal defaulted l...  \n",
       "5                            ​The dollar amount in US  \n",
       "6                            ​The dollar amount in US  \n",
       "7                                               Image  \n",
       "8   A property of loan which is more descriptive t...  \n",
       "9   A sector is a broad category for a loan, e.g. ...  \n",
       "10                                             String  \n",
       "11  Kiva Geocode = city, state, postalCode, llatit...  \n",
       "12                                                int  \n",
       "13  Returns a UTC datetime in ISO8601 format. As a...  \n",
       "14  Returns a UTC datetime in ISO8601 format. As a...  \n",
       "15                           The dollar amount in USD  \n",
       "16                                                Int  \n",
       "17                                                Int  \n",
       "18                                            Boolean  \n",
       "19                                             String  \n",
       "20                                                Int  \n",
       "21                                             String  \n",
       "22                 possible values are male or female  \n",
       "23                       includes fields: languages:   \n",
       "24  The public dataset does not reveal defaulted l...  \n",
       "25                           ​The dollar amount in US  \n",
       "26                           ​The dollar amount in US  \n",
       "27                                              Image  \n",
       "28  A property of loan which is more descriptive t...  \n",
       "29  A sector is a broad category for a loan, e.g. ...  \n",
       "..                                                ...  \n",
       "32                                                int  \n",
       "33  Returns a UTC datetime in ISO8601 format. As a...  \n",
       "34                           The dollar amount in USD  \n",
       "35                                                Int  \n",
       "36                                            Boolean  \n",
       "37                                             String  \n",
       "38                                                NaN  \n",
       "39  \"local_payments\", \"scheduled_paymnets\" and \"pa...  \n",
       "40                                                Int  \n",
       "41                                             String  \n",
       "42                                             String  \n",
       "43                                  Float as a string  \n",
       "44                                              Image  \n",
       "45  Returns a UTC datetime in ISO8601 format. As a...  \n",
       "46  Kiva Geocode = city, state, postalCode, llatit...  \n",
       "47                                              Float  \n",
       "48                                              Float  \n",
       "49                                                Int  \n",
       "50                                                Int  \n",
       "51                                              Float  \n",
       "52                                              Float  \n",
       "53                                              Array  \n",
       "54  is an empty string to be shown user w/o appror...  \n",
       "55  is an empty string to be shown user w/o appror...  \n",
       "56  is an empty string to be shown user w/o appror...  \n",
       "57                                            Boolean  \n",
       "58                                              Float  \n",
       "59                                                NaN  \n",
       "60                                              Float  \n",
       "61                                             String  \n",
       "\n",
       "[62 rows x 5 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kiva_data_dictionary.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below we perform a very important piece of code. We merge our datasets together in order to have a single final dataset. You will often have to merge/join datasets because many databases are relational databases. This means data is stored in tables that can be accessed and/or reassembled in many different ways (you can learn more [here](http://searchsqlserver.techtarget.com/definition/relational-database)). This has important benefits:\n",
    "- allowing the flexible addition of new data without exsisting tables needing to be altered\n",
    "- limiting the size of the data we store\n",
    "\n",
    "Instead of having one single huge table in our database, we have many different tables that we can piece together as needed. This act of piecing together datasets is called a merge.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So when does it make sense to merge data?\n",
    "\n",
    "In order to merge data, you need to have a columns in both datasets that have shared values. The columns you use to perform the join are called \"join keys\".\n",
    "\n",
    "For example, the join key in both loans_full and loans_details is the id. This is the unique identifier for a loan and is shared between both datasets. In both datasets this is a unique key, which means we only expect a single row for every row (no duplicates).\n",
    "\n",
    "The join key between loans_full and loans_partner_details is the partner_id. This is unique in loans_partner_details (every row is a unique partner_id), but is not unique in loans_full since many rows (loans) belong to the same partner. We can show that is not unique below by doing a value_count of the number of loans that belong to each partner_id in loans_full. We show the top 10 loan partners by size, you can see that the top loan partner '133' has been involved in 28,560 loans. When we join loans_full to loans_partner_details we can associate a name with each number identifier, and it will be a lot more interesting.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133.0    28560\n",
       "164.0    21552\n",
       "138.0    18101\n",
       "156.0    18081\n",
       "202.0    10356\n",
       "142.0     5922\n",
       "388.0     3210\n",
       "203.0     2987\n",
       "386.0     2388\n",
       "6.0       1102\n",
       "Name: partner_id, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans_full['partner_id'].value_counts().nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to how to join! There are actually a few different types of merges, and the type of merge you choose depends upon what data you want to keep in your final data set.\n",
    "\n",
    "There are two fantastic blog posts which do a great job explaining the different types of merges [here](http://www.datacarpentry.org/python-ecology-lesson/04-merging-data/) and [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/). We will reference the language in both posts below to add intuition.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**inner join** \n",
    "\n",
    "We will be using an inner join to join loans_full and loans_details because we only want the rows that match in both the original dataframes.\n",
    "\n",
    "Inner joins yield a DataFrame that contains only rows where the value being joins exists in BOTH tables. The venn diagram below shows what is kept from both data frames.\n",
    "\n",
    "![Visual of an inner join](module_1_images/inner_join.png)\n",
    "\n",
    "Below we test it in python using our two datasets, loans_full and loan_difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loans_full and loan_difference contain many columns that are duplicates (exsist in both). There is no need to merge all the columns so we start out by figuring out what additional columns ARE in loans_details that ARE NOT in loans_full. We can see below that only borrower_count is additional, so we only merge these two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['borrower_count', 'id']\n"
     ]
    }
   ],
   "source": [
    "additional_columns = loans_full.columns.difference(loans_details.columns).tolist() + ['id']\n",
    "print(additional_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we are ready to merge. We do not need to specify inner join since it is the default option. However, we do need to specify what are the join keys in each data set. In this case the naming is the same in both datasets, so we specify 'id' as the join key for the loans_details data set and 'id' for the loans_full data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = loans_details.merge(loans_full[['borrower_count', 'id']], left_on='id', right_on='id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can take a look at the output below and confirm that borrow_count is now there. The new dataframe only contains loans that are in both loans_full and loans_details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>activity</th>\n",
       "      <th>basket_amount</th>\n",
       "      <th>bonus_credit_eligibility</th>\n",
       "      <th>borrowers</th>\n",
       "      <th>currency_exchange_loss_amount</th>\n",
       "      <th>description.languages</th>\n",
       "      <th>description.texts.en</th>\n",
       "      <th>description.texts.es</th>\n",
       "      <th>description.texts.fr</th>\n",
       "      <th>description.texts.ru</th>\n",
       "      <th>funded_amount</th>\n",
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       "      <th>image.id</th>\n",
       "      <th>image.template_id</th>\n",
       "      <th>journal_totals.bulkEntries</th>\n",
       "      <th>journal_totals.entries</th>\n",
       "      <th>lender_count</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>location.country</th>\n",
       "      <th>location.country_code</th>\n",
       "      <th>location.geo.level</th>\n",
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       "      <th>terms.loss_liability.nonpayment</th>\n",
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       "      <th>translator.byline</th>\n",
       "      <th>translator.image</th>\n",
       "      <th>use</th>\n",
       "      <th>video.id</th>\n",
       "      <th>video.thumbnailImageId</th>\n",
       "      <th>video.title</th>\n",
       "      <th>video.youtubeId</th>\n",
       "      <th>borrower_count</th>\n",
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       "  </thead>\n",
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       "      <th>39705</th>\n",
       "      <td>39685</td>\n",
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       "      <td>1923581</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>500</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>Kisauni</td>\n",
       "      <td>Joyce</td>\n",
       "      <td>164.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2015-08-12T18:00:02Z</td>\n",
       "      <td>2015-07-13T18:00:02Z</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[{'name': 'user_favorite'}, {'name': '#Vegan'}...</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2015-07-06T07:00:00Z</td>\n",
       "      <td>500</td>\n",
       "      <td>[]</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lender</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michelle Archer</td>\n",
       "      <td>162079.0</td>\n",
       "      <td>to purchase seeds and fertilizers to do farmin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <th>25802</th>\n",
       "      <td>25788</td>\n",
       "      <td>Primary/secondary school costs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'first_name': 'Eunes', 'last_name': '', 'gen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Eunes is a smallholder farmer who lives in the...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400</td>\n",
       "      <td>2016-03-08T11:51:11Z</td>\n",
       "      <td>1032704</td>\n",
       "      <td>2116800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>14</td>\n",
       "      <td>400</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>Nandi Hills</td>\n",
       "      <td>Eunes</td>\n",
       "      <td>156.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2016-04-05T22:30:07Z</td>\n",
       "      <td>2016-03-06T22:30:08Z</td>\n",
       "      <td>Education</td>\n",
       "      <td>funded</td>\n",
       "      <td>[{'name': 'volunteer_like'}, {'name': '#School...</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2016-02-18T08:00:00Z</td>\n",
       "      <td>400</td>\n",
       "      <td>[]</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>['Rural Exclusion']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To pay tuition fees.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>98690</td>\n",
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       "      <td>True</td>\n",
       "      <td>[{'first_name': 'Ali', 'last_name': '', 'gende...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2012-04-23T02:19:10Z</td>\n",
       "      <td>415841</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>Tiribe</td>\n",
       "      <td>Ali</td>\n",
       "      <td>164.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2012-05-22T01:20:05Z</td>\n",
       "      <td>2012-04-22T01:20:05Z</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[]</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2012-03-21T07:00:00Z</td>\n",
       "      <td>250</td>\n",
       "      <td>[]</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lender</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to purchase seedlings and fertilizers.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                        activity  basket_amount  \\\n",
       "39705       39685                         Farming            NaN   \n",
       "25802       25788  Primary/secondary school costs            NaN   \n",
       "98748       98690                         Farming            NaN   \n",
       "\n",
       "       bonus_credit_eligibility  \\\n",
       "39705                      True   \n",
       "25802                     False   \n",
       "98748                      True   \n",
       "\n",
       "                                               borrowers  \\\n",
       "39705  [{'first_name': 'Joyce', 'last_name': '', 'gen...   \n",
       "25802  [{'first_name': 'Eunes', 'last_name': '', 'gen...   \n",
       "98748  [{'first_name': 'Ali', 'last_name': '', 'gende...   \n",
       "\n",
       "       currency_exchange_loss_amount description.languages  \\\n",
       "39705                            NaN                ['en']   \n",
       "25802                            NaN                ['en']   \n",
       "98748                            NaN                ['en']   \n",
       "\n",
       "                                    description.texts.en description.texts.es  \\\n",
       "39705  Joyce is a married woman with three children, ...                  NaN   \n",
       "25802  Eunes is a smallholder farmer who lives in the...                  NaN   \n",
       "98748  Ali is a married man with 8 children, all enro...                  NaN   \n",
       "\n",
       "      description.texts.fr description.texts.ru  funded_amount  \\\n",
       "39705                  NaN                  NaN            500   \n",
       "25802                  NaN                  NaN            400   \n",
       "98748                  NaN                  NaN            250   \n",
       "\n",
       "                funded_date       id  image.id  image.template_id  \\\n",
       "39705  2015-08-10T19:25:40Z   913488   1923581                  1   \n",
       "25802  2016-03-08T11:51:11Z  1032704   2116800                  1   \n",
       "98748  2012-04-23T02:19:10Z   415841   1072813                  1   \n",
       "\n",
       "       journal_totals.bulkEntries  journal_totals.entries  lender_count  \\\n",
       "39705                           0                       0             8   \n",
       "25802                           0                       0            14   \n",
       "98748                           0                       0             1   \n",
       "\n",
       "       loan_amount location.country location.country_code location.geo.level  \\\n",
       "39705          500            Kenya                    KE               town   \n",
       "25802          400            Kenya                    KE               town   \n",
       "98748          250            Kenya                    KE               town   \n",
       "\n",
       "      location.geo.pairs location.geo.type location.town   name  partner_id  \\\n",
       "39705               1 38             point       Kisauni  Joyce       164.0   \n",
       "25802               1 38             point   Nandi Hills  Eunes       156.0   \n",
       "98748               1 38             point        Tiribe    Ali       164.0   \n",
       "\n",
       "      payments planned_expiration_date           posted_date       sector  \\\n",
       "39705       []    2015-08-12T18:00:02Z  2015-07-13T18:00:02Z  Agriculture   \n",
       "25802       []    2016-04-05T22:30:07Z  2016-03-06T22:30:08Z    Education   \n",
       "98748       []    2012-05-22T01:20:05Z  2012-04-22T01:20:05Z  Agriculture   \n",
       "\n",
       "       status                                               tags  \\\n",
       "39705  funded  [{'name': 'user_favorite'}, {'name': '#Vegan'}...   \n",
       "25802  funded  [{'name': 'volunteer_like'}, {'name': '#School...   \n",
       "98748  funded                                                 []   \n",
       "\n",
       "       terms.disbursal_amount terms.disbursal_currency  terms.disbursal_date  \\\n",
       "39705                 50000.0                      KES  2015-07-06T07:00:00Z   \n",
       "25802                 40000.0                      KES  2016-02-18T08:00:00Z   \n",
       "98748                 20000.0                      KES  2012-03-21T07:00:00Z   \n",
       "\n",
       "       terms.loan_amount terms.local_payments  \\\n",
       "39705                500                   []   \n",
       "25802                400                   []   \n",
       "98748                250                   []   \n",
       "\n",
       "      terms.loss_liability.currency_exchange  \\\n",
       "39705                                 shared   \n",
       "25802                                 shared   \n",
       "98748                                 shared   \n",
       "\n",
       "       terms.loss_liability.currency_exchange_coverage_rate  \\\n",
       "39705                                                0.1      \n",
       "25802                                                0.1      \n",
       "98748                                                0.1      \n",
       "\n",
       "      terms.loss_liability.nonpayment terms.repayment_interval  \\\n",
       "39705                          lender                      NaN   \n",
       "25802                          lender                      NaN   \n",
       "98748                          lender                      NaN   \n",
       "\n",
       "       terms.repayment_term terms.scheduled_payments               themes  \\\n",
       "39705                    13                       []                  NaN   \n",
       "25802                    14                       []  ['Rural Exclusion']   \n",
       "98748                    14                       []                  NaN   \n",
       "\n",
       "      translator.byline  translator.image  \\\n",
       "39705   Michelle Archer          162079.0   \n",
       "25802               NaN               NaN   \n",
       "98748               NaN               NaN   \n",
       "\n",
       "                                                     use  video.id  \\\n",
       "39705  to purchase seeds and fertilizers to do farmin...       NaN   \n",
       "25802                               To pay tuition fees.       NaN   \n",
       "98748             to purchase seedlings and fertilizers.       NaN   \n",
       "\n",
       "       video.thumbnailImageId video.title video.youtubeId  borrower_count  \n",
       "39705                     NaN         NaN             NaN               1  \n",
       "25802                     NaN         NaN             NaN               1  \n",
       "98748                     NaN         NaN             NaN               1  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The sample() function takes a random sample from the specified data frame. \n",
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**left join**\n",
    "\n",
    "Now we perform a different type of merge in order to incorporate the loan_partner_details dataset. This merge is called a left join. The description below is directly from [this](http://www.datacarpentry.org/python-ecology-lesson/04-merging-data/) blog post.\n",
    "\n",
    "Like an inner join, a **left join** uses join keys to combine two DataFrames. Unlike an inner join, a left join will return all of the rows from the left DataFrame, even those rows whose join key(s) do not have values in the right DataFrame. Rows in the left DataFrame that are missing values for the join key(s) in the right DataFrame will simply have null (i.e., NaN or None) values for those columns in the resulting joined DataFrame.\n",
    "\n",
    "Note: a left join will still discard rows from the right DataFrame that do not have values for the join key(s) in the left DataFrame.\n",
    "\n",
    "The venn diagram below shows what is kept from both data frames.\n",
    "\n",
    "![Visual of an left join](module_1_images/left_join.png)\n",
    "\n",
    "What this means is that we will keep our entire df dataframe, and we will only pull in data from loan_partner_details of the partner_id for partner_id listed in df. We will discard everything else. The code below goes through how this is done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, we rename all the columns in loans_partner_details with the prefix partner in order to avoid confusion once we merge. Then we take list all columns that are in loans_partner_details but not in df. These are the new columns we want to merge. Finally, we do a left join.\n",
    "\n",
    "A left join is performed in pandas by calling the same merge function used for inner join, but using the how='left' argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_names = [(i,'partner_'+i) for i in loan_partner_details.iloc[:, 1:].columns.values]\n",
    "loan_partner_details.rename(columns = dict(new_names), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['partner_average_loan_size_percent_per_capita_income', 'partner_charges_fees_and_interest', 'partner_countries', 'partner_currency_exchange_loss_rate', 'partner_default_rate', 'partner_default_rate_note', 'partner_delinquency_rate', 'partner_delinquency_rate_note', 'partner_image.id', 'partner_image.template_id', 'partner_loans_at_risk_rate', 'partner_loans_posted', 'partner_name', 'partner_portfolio_yield', 'partner_portfolio_yield_note', 'partner_profitability', 'partner_rating', 'partner_social_performance_strengths', 'partner_start_date', 'partner_status', 'partner_total_amount_raised', 'partner_url', 'partner_id']\n"
     ]
    }
   ],
   "source": [
    "additional_columns = loan_partner_details.columns.difference(df.columns).tolist() + ['partner_id']\n",
    "print(additional_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.merge(loan_partner_details[['partner_average_loan_size_percent_per_capita_income', \n",
    "                                    'partner_charges_fees_and_interest', 'partner_countries', \n",
    "                                    'partner_currency_exchange_loss_rate', 'partner_default_rate', \n",
    "                                    'partner_default_rate_note', 'partner_delinquency_rate', 'partner_delinquency_rate_note', \n",
    "                                    'partner_image.id', 'partner_image.template_id', 'partner_loans_at_risk_rate', 'partner_loans_posted', \n",
    "                                    'partner_name', 'partner_portfolio_yield', 'partner_portfolio_yield_note', 'partner_profitability', \n",
    "                                    'partner_rating', 'partner_social_performance_strengths', 'partner_start_date', 'partner_status', \n",
    "                                    'partner_total_amount_raised', 'partner_url', 'partner_id']], how='left', left_on='partner_id', right_on='partner_id') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have successully merged our dataframe and we are ready to get started with our exploratory analysis. For completness, below is a short description of the other join types you are likely to come across as you work with data more.\n",
    "\n",
    "**full join**\n",
    "\n",
    "![Visual of an full join](module_1_images/full_join.png)\n",
    "\n",
    "A full join returns all rows from both datasets even if there is no match. All records will be kept so this join is used when we do not want to lose any data but want to see what rows match.\n",
    "Invoked by passing how='outer' as an argument. \n",
    "\n",
    "**right join**\n",
    "\n",
    "A right join is the same concept as the left join except it keeps all rows in the right data set rather than the left. Invoked by passing how='right' as an argument. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't want to have to join all our datsets everytime we get started so we save our work to our data folder using the command below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/anthonymiyoro/Documents/code/kivaMachineLearning'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('/Users/anthonymiyoro/Documents/code/kivaMachineLearning/df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2:  Data Cleaning & Creating New Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(data_path+'/df.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, we take a random sample of 3 rows to get a feel for the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
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       "      <th>journal_totals.bulkEntries</th>\n",
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       "      <th>lender_count</th>\n",
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       "      <th>terms.loss_liability.currency_exchange</th>\n",
       "      <th>terms.loss_liability.currency_exchange_coverage_rate</th>\n",
       "      <th>terms.loss_liability.nonpayment</th>\n",
       "      <th>terms.repayment_interval</th>\n",
       "      <th>terms.repayment_term</th>\n",
       "      <th>terms.scheduled_payments</th>\n",
       "      <th>themes</th>\n",
       "      <th>translator.byline</th>\n",
       "      <th>translator.image</th>\n",
       "      <th>use</th>\n",
       "      <th>video.id</th>\n",
       "      <th>video.thumbnailImageId</th>\n",
       "      <th>video.title</th>\n",
       "      <th>video.youtubeId</th>\n",
       "      <th>borrower_count</th>\n",
       "      <th>partner_average_loan_size_percent_per_capita_income</th>\n",
       "      <th>partner_charges_fees_and_interest</th>\n",
       "      <th>partner_countries</th>\n",
       "      <th>partner_currency_exchange_loss_rate</th>\n",
       "      <th>partner_default_rate</th>\n",
       "      <th>partner_default_rate_note</th>\n",
       "      <th>partner_delinquency_rate</th>\n",
       "      <th>partner_delinquency_rate_note</th>\n",
       "      <th>partner_image.id</th>\n",
       "      <th>partner_image.template_id</th>\n",
       "      <th>partner_loans_at_risk_rate</th>\n",
       "      <th>partner_loans_posted</th>\n",
       "      <th>partner_name</th>\n",
       "      <th>partner_portfolio_yield</th>\n",
       "      <th>partner_portfolio_yield_note</th>\n",
       "      <th>partner_profitability</th>\n",
       "      <th>partner_rating</th>\n",
       "      <th>partner_social_performance_strengths</th>\n",
       "      <th>partner_start_date</th>\n",
       "      <th>partner_status</th>\n",
       "      <th>partner_total_amount_raised</th>\n",
       "      <th>partner_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111193</th>\n",
       "      <td>111193</td>\n",
       "      <td>111135</td>\n",
       "      <td>Poultry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'first_name': 'Enes', 'last_name': '', 'gend...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Enes is married with 7 children all going to s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1125</td>\n",
       "      <td>2011-08-01T19:11:11Z</td>\n",
       "      <td>318769</td>\n",
       "      <td>817410</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>1125</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1.016667 35</td>\n",
       "      <td>point</td>\n",
       "      <td>Kitale</td>\n",
       "      <td>Enes</td>\n",
       "      <td>156.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-08-01T07:00:48Z</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[]</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2011-07-04T07:00:00Z</td>\n",
       "      <td>1125</td>\n",
       "      <td>[]</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.2</td>\n",
       "      <td>lender</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to purchase a water pump and buy poultry birds.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>49.6</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.431935</td>\n",
       "      <td>2.575299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.536684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1834079.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.200354</td>\n",
       "      <td>18150.0</td>\n",
       "      <td>Juhudi Kilimo</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...</td>\n",
       "      <td>2010-01-15T20:20:17Z</td>\n",
       "      <td>active</td>\n",
       "      <td>7705925.0</td>\n",
       "      <td>http://www.juhudikilimo.com/</td>\n",
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       "      <th>51700</th>\n",
       "      <td>51700</td>\n",
       "      <td>51674</td>\n",
       "      <td>Farming</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'first_name': 'Alice', 'last_name': '', 'gen...</td>\n",
       "      <td>3.56</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Alice is a married woman. She has three childr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "      <td>2015-01-13T01:32:07Z</td>\n",
       "      <td>825811</td>\n",
       "      <td>1789846</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>225</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>karatina</td>\n",
       "      <td>Alice</td>\n",
       "      <td>138.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2015-02-08T20:50:10Z</td>\n",
       "      <td>2015-01-09T20:50:10Z</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[{'name': 'user_favorite'}, {'name': '#Woman O...</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2015-01-05T08:00:00Z</td>\n",
       "      <td>225</td>\n",
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       "      <td>[]</td>\n",
       "      <td>['Rural Exclusion']</td>\n",
       "      <td>Julie Keaton</td>\n",
       "      <td>892591.0</td>\n",
       "      <td>to buy farm inputs.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>40.1</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.214680</td>\n",
       "      <td>1.483890</td>\n",
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       "      <td>10.519745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328197.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.498507</td>\n",
       "      <td>17262.0</td>\n",
       "      <td>SMEP Microfinance Bank</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>[{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...</td>\n",
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       "      <td>active</td>\n",
       "      <td>6764500.0</td>\n",
       "      <td>http://www.smep.co.ke/index.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35942</th>\n",
       "      <td>35942</td>\n",
       "      <td>35926</td>\n",
       "      <td>Poultry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'first_name': 'Sofia', 'last_name': '', 'gen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Sofia is a married woman and has been blessed ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "      <td>2015-09-09T18:37:28Z</td>\n",
       "      <td>941591</td>\n",
       "      <td>1965098</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>Likoni</td>\n",
       "      <td>Sofia</td>\n",
       "      <td>164.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2015-10-03T08:50:04Z</td>\n",
       "      <td>2015-09-03T08:50:04Z</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[{'name': '#Woman Owned Biz'}, {'name': '#Anim...</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2015-09-04T07:00:00Z</td>\n",
       "      <td>200</td>\n",
       "      <td>[]</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lender</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tim Gibson</td>\n",
       "      <td>1632475.0</td>\n",
       "      <td>to purchase chicks and poultry feeds.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>24.3</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.164711</td>\n",
       "      <td>0.085473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2081417.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21415.0</td>\n",
       "      <td>Yehu Microfinance Trust</td>\n",
       "      <td>33.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.23</td>\n",
       "      <td>3.5</td>\n",
       "      <td>[{'id': 3, 'name': 'Client Voice', 'descriptio...</td>\n",
       "      <td>2010-03-30T20:50:03Z</td>\n",
       "      <td>active</td>\n",
       "      <td>7646925.0</td>\n",
       "      <td>http://www.yehu.org</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Unnamed: 0.1 activity  basket_amount  \\\n",
       "111193      111193        111135  Poultry            NaN   \n",
       "51700        51700         51674  Farming            NaN   \n",
       "35942        35942         35926  Poultry            NaN   \n",
       "\n",
       "        bonus_credit_eligibility  \\\n",
       "111193                     False   \n",
       "51700                       True   \n",
       "35942                       True   \n",
       "\n",
       "                                                borrowers  \\\n",
       "111193  [{'first_name': 'Enes', 'last_name': '', 'gend...   \n",
       "51700   [{'first_name': 'Alice', 'last_name': '', 'gen...   \n",
       "35942   [{'first_name': 'Sofia', 'last_name': '', 'gen...   \n",
       "\n",
       "        currency_exchange_loss_amount description.languages  \\\n",
       "111193                            NaN                ['en']   \n",
       "51700                            3.56                ['en']   \n",
       "35942                             NaN                ['en']   \n",
       "\n",
       "                                     description.texts.en  \\\n",
       "111193  Enes is married with 7 children all going to s...   \n",
       "51700   Alice is a married woman. She has three childr...   \n",
       "35942   Sofia is a married woman and has been blessed ...   \n",
       "\n",
       "       description.texts.es description.texts.fr description.texts.ru  \\\n",
       "111193                  NaN                  NaN                  NaN   \n",
       "51700                   NaN                  NaN                  NaN   \n",
       "35942                   NaN                  NaN                  NaN   \n",
       "\n",
       "        funded_amount           funded_date      id  image.id  \\\n",
       "111193           1125  2011-08-01T19:11:11Z  318769    817410   \n",
       "51700             225  2015-01-13T01:32:07Z  825811   1789846   \n",
       "35942             200  2015-09-09T18:37:28Z  941591   1965098   \n",
       "\n",
       "        image.template_id  journal_totals.bulkEntries  journal_totals.entries  \\\n",
       "111193                  1                           0                       0   \n",
       "51700                   1                           0                       0   \n",
       "35942                   1                           0                       0   \n",
       "\n",
       "        lender_count  loan_amount location.country location.country_code  \\\n",
       "111193            37         1125            Kenya                    KE   \n",
       "51700              6          225            Kenya                    KE   \n",
       "35942              4          200            Kenya                    KE   \n",
       "\n",
       "       location.geo.level location.geo.pairs location.geo.type location.town  \\\n",
       "111193               town        1.016667 35             point        Kitale   \n",
       "51700                town               1 38             point      karatina   \n",
       "35942                town               1 38             point        Likoni   \n",
       "\n",
       "         name  partner_id payments planned_expiration_date  \\\n",
       "111193   Enes       156.0       []                     NaN   \n",
       "51700   Alice       138.0       []    2015-02-08T20:50:10Z   \n",
       "35942   Sofia       164.0       []    2015-10-03T08:50:04Z   \n",
       "\n",
       "                 posted_date       sector  status  \\\n",
       "111193  2011-08-01T07:00:48Z  Agriculture  funded   \n",
       "51700   2015-01-09T20:50:10Z  Agriculture  funded   \n",
       "35942   2015-09-03T08:50:04Z  Agriculture  funded   \n",
       "\n",
       "                                                     tags  \\\n",
       "111193                                                 []   \n",
       "51700   [{'name': 'user_favorite'}, {'name': '#Woman O...   \n",
       "35942   [{'name': '#Woman Owned Biz'}, {'name': '#Anim...   \n",
       "\n",
       "        terms.disbursal_amount terms.disbursal_currency  terms.disbursal_date  \\\n",
       "111193                100000.0                      KES  2011-07-04T07:00:00Z   \n",
       "51700                  20000.0                      KES  2015-01-05T08:00:00Z   \n",
       "35942                  20000.0                      KES  2015-09-04T07:00:00Z   \n",
       "\n",
       "        terms.loan_amount terms.local_payments  \\\n",
       "111193               1125                   []   \n",
       "51700                 225                   []   \n",
       "35942                 200                   []   \n",
       "\n",
       "       terms.loss_liability.currency_exchange  \\\n",
       "111193                                 shared   \n",
       "51700                                  shared   \n",
       "35942                                  shared   \n",
       "\n",
       "        terms.loss_liability.currency_exchange_coverage_rate  \\\n",
       "111193                                                0.2      \n",
       "51700                                                 0.1      \n",
       "35942                                                 0.1      \n",
       "\n",
       "       terms.loss_liability.nonpayment terms.repayment_interval  \\\n",
       "111193                          lender                      NaN   \n",
       "51700                           lender                      NaN   \n",
       "35942                           lender                      NaN   \n",
       "\n",
       "        terms.repayment_term terms.scheduled_payments               themes  \\\n",
       "111193                    14                       []                  NaN   \n",
       "51700                     13                       []  ['Rural Exclusion']   \n",
       "35942                     13                       []                  NaN   \n",
       "\n",
       "       translator.byline  translator.image  \\\n",
       "111193               NaN               NaN   \n",
       "51700       Julie Keaton          892591.0   \n",
       "35942         Tim Gibson         1632475.0   \n",
       "\n",
       "                                                    use  video.id  \\\n",
       "111193  to purchase a water pump and buy poultry birds.       NaN   \n",
       "51700                               to buy farm inputs.       NaN   \n",
       "35942             to purchase chicks and poultry feeds.       NaN   \n",
       "\n",
       "        video.thumbnailImageId video.title video.youtubeId  borrower_count  \\\n",
       "111193                     NaN         NaN             NaN               1   \n",
       "51700                      NaN         NaN             NaN               1   \n",
       "35942                      NaN         NaN             NaN               1   \n",
       "\n",
       "        partner_average_loan_size_percent_per_capita_income  \\\n",
       "111193                                               49.6     \n",
       "51700                                                40.1     \n",
       "35942                                                24.3     \n",
       "\n",
       "       partner_charges_fees_and_interest  \\\n",
       "111193                              True   \n",
       "51700                               True   \n",
       "35942                               True   \n",
       "\n",
       "                                        partner_countries  \\\n",
       "111193  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "51700   [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "35942   [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "\n",
       "        partner_currency_exchange_loss_rate  partner_default_rate  \\\n",
       "111193                             0.431935              2.575299   \n",
       "51700                              0.214680              1.483890   \n",
       "35942                              0.164711              0.085473   \n",
       "\n",
       "        partner_default_rate_note  partner_delinquency_rate  \\\n",
       "111193                        NaN                  2.536684   \n",
       "51700                         NaN                 10.519745   \n",
       "35942                         NaN                  0.000000   \n",
       "\n",
       "        partner_delinquency_rate_note  partner_image.id  \\\n",
       "111193                            NaN         1834079.0   \n",
       "51700                             NaN          328197.0   \n",
       "35942                             NaN         2081417.0   \n",
       "\n",
       "        partner_image.template_id  partner_loans_at_risk_rate  \\\n",
       "111193                        1.0                   24.200354   \n",
       "51700                         1.0                   18.498507   \n",
       "35942                         1.0                    0.000000   \n",
       "\n",
       "        partner_loans_posted             partner_name  \\\n",
       "111193               18150.0            Juhudi Kilimo   \n",
       "51700                17262.0   SMEP Microfinance Bank   \n",
       "35942                21415.0  Yehu Microfinance Trust   \n",
       "\n",
       "        partner_portfolio_yield  partner_portfolio_yield_note  \\\n",
       "111193                     33.0                           NaN   \n",
       "51700                      29.0                           NaN   \n",
       "35942                      33.1                           NaN   \n",
       "\n",
       "        partner_profitability partner_rating  \\\n",
       "111193                  -7.10            2.0   \n",
       "51700                    0.00            3.5   \n",
       "35942                    2.23            3.5   \n",
       "\n",
       "                     partner_social_performance_strengths  \\\n",
       "111193  [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...   \n",
       "51700   [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...   \n",
       "35942   [{'id': 3, 'name': 'Client Voice', 'descriptio...   \n",
       "\n",
       "          partner_start_date partner_status  partner_total_amount_raised  \\\n",
       "111193  2010-01-15T20:20:17Z         active                    7705925.0   \n",
       "51700   2009-07-19T04:50:04Z         active                    6764500.0   \n",
       "35942   2010-03-30T20:50:03Z         active                    7646925.0   \n",
       "\n",
       "                            partner_url  \n",
       "111193     http://www.juhudikilimo.com/  \n",
       "51700   http://www.smep.co.ke/index.php  \n",
       "35942               http://www.yehu.org  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
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       "      <th>video.thumbnailImageId</th>\n",
       "      <th>video.title</th>\n",
       "      <th>video.youtubeId</th>\n",
       "      <th>borrower_count</th>\n",
       "      <th>partner_average_loan_size_percent_per_capita_income</th>\n",
       "      <th>partner_charges_fees_and_interest</th>\n",
       "      <th>partner_countries</th>\n",
       "      <th>partner_currency_exchange_loss_rate</th>\n",
       "      <th>partner_default_rate</th>\n",
       "      <th>partner_default_rate_note</th>\n",
       "      <th>partner_delinquency_rate</th>\n",
       "      <th>partner_delinquency_rate_note</th>\n",
       "      <th>partner_image.id</th>\n",
       "      <th>partner_image.template_id</th>\n",
       "      <th>partner_loans_at_risk_rate</th>\n",
       "      <th>partner_loans_posted</th>\n",
       "      <th>partner_name</th>\n",
       "      <th>partner_portfolio_yield</th>\n",
       "      <th>partner_portfolio_yield_note</th>\n",
       "      <th>partner_profitability</th>\n",
       "      <th>partner_rating</th>\n",
       "      <th>partner_social_performance_strengths</th>\n",
       "      <th>partner_start_date</th>\n",
       "      <th>partner_status</th>\n",
       "      <th>partner_total_amount_raised</th>\n",
       "      <th>partner_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>542</td>\n",
       "      <td>542</td>\n",
       "      <td>Clothing Sales</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'first_name': 'Salome', 'last_name': '', 'ge...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Salome is 40 years old and married with 3 chil...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1285843</td>\n",
       "      <td>2508289</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>-1.516667 37.266667</td>\n",
       "      <td>point</td>\n",
       "      <td>Machakos</td>\n",
       "      <td>Salome</td>\n",
       "      <td>386.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2017-05-31T22:40:03Z</td>\n",
       "      <td>2017-05-01T22:40:03Z</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>fundraising</td>\n",
       "      <td>[{'name': '#Parent'}]</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2017-03-29T07:00:00Z</td>\n",
       "      <td>200</td>\n",
       "      <td>[{'due_date': '2017-05-09T07:00:00Z', 'amount'...</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lender</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>15</td>\n",
       "      <td>[{'due_date': '2017-07-01T07:00:00Z', 'amount'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ginny Maceda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to buy more clothes to grow her business and s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.120642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.017031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1592272.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.165398</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>Kenya ECLOF</td>\n",
       "      <td>40.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[{'id': 3, 'name': 'Client Voice', 'descriptio...</td>\n",
       "      <td>2014-05-29T13:30:02Z</td>\n",
       "      <td>active</td>\n",
       "      <td>863375.0</td>\n",
       "      <td>http://www.eclof-kenya.org/</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     Unnamed: 0  Unnamed: 0.1        activity  basket_amount  \\\n",
       "542         542           542  Clothing Sales            0.0   \n",
       "\n",
       "     bonus_credit_eligibility  \\\n",
       "542                     False   \n",
       "\n",
       "                                             borrowers  \\\n",
       "542  [{'first_name': 'Salome', 'last_name': '', 'ge...   \n",
       "\n",
       "     currency_exchange_loss_amount description.languages  \\\n",
       "542                            NaN                ['en']   \n",
       "\n",
       "                                  description.texts.en description.texts.es  \\\n",
       "542  Salome is 40 years old and married with 3 chil...                  NaN   \n",
       "\n",
       "    description.texts.fr description.texts.ru  funded_amount funded_date  \\\n",
       "542                  NaN                  NaN              0         NaN   \n",
       "\n",
       "          id  image.id  image.template_id  journal_totals.bulkEntries  \\\n",
       "542  1285843   2508289                  1                           0   \n",
       "\n",
       "     journal_totals.entries  lender_count  loan_amount location.country  \\\n",
       "542                       0             0          200            Kenya   \n",
       "\n",
       "    location.country_code location.geo.level   location.geo.pairs  \\\n",
       "542                    KE               town  -1.516667 37.266667   \n",
       "\n",
       "    location.geo.type location.town    name  partner_id payments  \\\n",
       "542             point      Machakos  Salome       386.0       []   \n",
       "\n",
       "    planned_expiration_date           posted_date    sector       status  \\\n",
       "542    2017-05-31T22:40:03Z  2017-05-01T22:40:03Z  Clothing  fundraising   \n",
       "\n",
       "                      tags  terms.disbursal_amount terms.disbursal_currency  \\\n",
       "542  [{'name': '#Parent'}]                 20000.0                      KES   \n",
       "\n",
       "     terms.disbursal_date  terms.loan_amount  \\\n",
       "542  2017-03-29T07:00:00Z                200   \n",
       "\n",
       "                                  terms.local_payments  \\\n",
       "542  [{'due_date': '2017-05-09T07:00:00Z', 'amount'...   \n",
       "\n",
       "    terms.loss_liability.currency_exchange  \\\n",
       "542                                 shared   \n",
       "\n",
       "     terms.loss_liability.currency_exchange_coverage_rate  \\\n",
       "542                                                0.1      \n",
       "\n",
       "    terms.loss_liability.nonpayment terms.repayment_interval  \\\n",
       "542                          lender                  Monthly   \n",
       "\n",
       "     terms.repayment_term                           terms.scheduled_payments  \\\n",
       "542                    15  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "\n",
       "    themes translator.byline  translator.image  \\\n",
       "542    NaN      Ginny Maceda               NaN   \n",
       "\n",
       "                                                   use  video.id  \\\n",
       "542  to buy more clothes to grow her business and s...       NaN   \n",
       "\n",
       "     video.thumbnailImageId video.title video.youtubeId  borrower_count  \\\n",
       "542                     NaN         NaN             NaN               1   \n",
       "\n",
       "     partner_average_loan_size_percent_per_capita_income  \\\n",
       "542                                                0.0     \n",
       "\n",
       "    partner_charges_fees_and_interest  \\\n",
       "542                              True   \n",
       "\n",
       "                                     partner_countries  \\\n",
       "542  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "\n",
       "     partner_currency_exchange_loss_rate  partner_default_rate  \\\n",
       "542                             0.120642                   0.0   \n",
       "\n",
       "     partner_default_rate_note  partner_delinquency_rate  \\\n",
       "542                        NaN                  7.017031   \n",
       "\n",
       "     partner_delinquency_rate_note  partner_image.id  \\\n",
       "542                            NaN         1592272.0   \n",
       "\n",
       "     partner_image.template_id  partner_loans_at_risk_rate  \\\n",
       "542                        1.0                   21.165398   \n",
       "\n",
       "     partner_loans_posted partner_name  partner_portfolio_yield  \\\n",
       "542                1948.0  Kenya ECLOF                     40.3   \n",
       "\n",
       "     partner_portfolio_yield_note  partner_profitability partner_rating  \\\n",
       "542                           NaN                   2.54            2.0   \n",
       "\n",
       "                  partner_social_performance_strengths    partner_start_date  \\\n",
       "542  [{'id': 3, 'name': 'Client Voice', 'descriptio...  2014-05-29T13:30:02Z   \n",
       "\n",
       "    partner_status  partner_total_amount_raised                  partner_url  \n",
       "542         active                     863375.0  http://www.eclof-kenya.org/  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['name']=='Salome'].head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A big part of exploratory analysis involves data validation. This means gaining an understanding of the distribution of data, any potential data issues that can affect the quality of your model. As we learnt during our theory lesson, \" Data preparation accounts for about 80% of the work of data scientists.” [source](https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#27df67b36f63).\n",
    "\n",
    "What does data cleaning look like:\n",
    "\n",
    "1) Are my variables the right type?\n",
    "\n",
    "Type is very important in Python programing, because it affects the types of functions you can apply to a series. There are a few different types of data you will see regularly (see [this](https://en.wikibooks.org/wiki/Python_Programming/Data_Types) link for more detail):\n",
    "* **int** - a number with no decimal places. example: loan_amount field\n",
    "* **float** - a number with decimal places. example: partner_id field\n",
    "* **str** - str is short for string. This type formally defined as a sequence of unicode characters. More simply, string means that the data is treated as a word, not a number. example: sector\n",
    "* **boolean** - can only be True or False. There is not currently an example in the data, but we will be creating a gender field shortly.\n",
    "* **datetime** - values meant to hold time data. Example: posted_date\n",
    "\n",
    "2) Is there missing data?\n",
    "\n",
    "If we have missing data, is the missing data at random or not at random? If data is missing at random, the data distribution is still representative of the population. You can probably ignore the missing values as an inconvienience. However, if the data is missing systematically, any modeling you do may be biased. You should carefully consider the best way to clean the data, it may involve dropping some data. See [here](https://en.wikipedia.org/wiki/Missing_data) for additional information.\n",
    "\n",
    "3) Sanity check the data, does it match what you expected to find:\n",
    "- is the range of values what you would expect. For example, are all loan_amounts above 0.\n",
    "- do you have the number of rows you would expect\n",
    "- is your data for the date range you would expect. For example, is there a strange year in the data like 1880.\n",
    "- are there unexpected spikes when you plot the data over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are my variables the right type?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the type of our variables using the examples we in the cell above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "type_example=df[['loan_amount','partner_id', 'sector','posted_date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>partner_id</th>\n",
       "      <th>sector</th>\n",
       "      <th>posted_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67072</th>\n",
       "      <td>125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>2014-04-04T03:00:46Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67041</th>\n",
       "      <td>250</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>2014-04-05T00:10:05Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3089</th>\n",
       "      <td>300</td>\n",
       "      <td>156.0</td>\n",
       "      <td>Food</td>\n",
       "      <td>2017-03-26T19:20:05Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amount  partner_id       sector           posted_date\n",
       "67072          125         NaN  Agriculture  2014-04-04T03:00:46Z\n",
       "67041          250       133.0  Agriculture  2014-04-05T00:10:05Z\n",
       "3089           300       156.0         Food  2017-03-26T19:20:05Z"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_example.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_example['loan_amount'].dtype\n",
    "type_example['partner_id'].dtype\n",
    "type_example['sector'].dtype\n",
    "type_example['posted_date'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Immediately, we notice we have an issue. Type 'O' means object which in the pandas world means a string. This makes sense for the sector field but not for the posted_date field. We want pandas to treat this field as a datetime field, we will have to tell pandas to convert it. This is annoying but important because we will be ploting time series charts. We need python to recognize posted_date as a date in order to apply some of the operations necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the panda function `to_datetime`, which converts our format from object to datetime. You can run the `.dtype` command above to verify we have converted successfully. We also want to create a YY-MM variable for each, which will allow us to more easily aggregate our data later in the module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['posted_datetime'] = pd.to_datetime(df['posted_date'])\n",
    "df['planned_expiration_datetime'] = pd.to_datetime(df['planned_expiration_date'])\n",
    "df['funded_datetime'] = pd.to_datetime(df['funded_date'])\n",
    "df['dispursal_datetime'] = pd.to_datetime(df['terms.disbursal_date'])\n",
    "df['number_of_loans']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['posted_date']=df['posted_datetime'].dt.date\n",
    "df['planned_expiration_date']=df['planned_expiration_datetime'].dt.date\n",
    "df['funded_date']=df['funded_datetime'].dt.date\n",
    "df['dispersal_date']=df['dispursal_datetime'].dt.date\n",
    "df['posted_year']=df['posted_datetime'].dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check if we were successful by checking the type again. We were successful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-05-09 00:40:03\n",
       "Name: posted_datetime, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['posted_datetime'].head(1)\n",
    "df['posted_datetime'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have successfully manipulated our data fields we can plot the time range of our data. This is an important piece of sanity checking because if we see an unexpected year, or big spikes in one year we should be suspicious about the quality of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_loans</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>511</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2143</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>384</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1730</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6050</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13182</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16054</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16228</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19222</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22594</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20630</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9230</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    number_of_loans  Date\n",
       "0               511  2006\n",
       "1              2143  2007\n",
       "2               384  2008\n",
       "3              1730  2009\n",
       "4              6050  2010\n",
       "5             13182  2011\n",
       "6             16054  2012\n",
       "7             16228  2013\n",
       "8             19222  2014\n",
       "9             22594  2015\n",
       "10            20630  2016\n",
       "11             9230  2017"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAIso4AAAAIBFFHAAAADAIgo4AAAAYBEFHAAAALCIAg4AAABYRAEHAAAALKKA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\nx40x8ng82rBhg5UBAQAAADc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5cq6Z5OTkaOfOnVqxYoXmzJmj/Px8/e1vf7M1tuuV52uF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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1341186a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036531736667)>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=df[['posted_datetime','number_of_loans']]\n",
    "ts = ts.set_index(['posted_datetime'])\n",
    "ts=ts.groupby(ts.index.year).sum()\n",
    "ts['Date'] = ts.index\n",
    "ts.reset_index(drop=True)\n",
    "\n",
    "ggplot(ts , aes(x = 'Date', y = 'number_of_loans')) + geom_point(color = \"blue\") + \\\n",
    "ggtitle(\"Number of Kenyan Loans on Kiva each year\") + xlab(\"Date\") + ylab(\"Number of Loans\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the chart above we can see that we have loan data from 2006 through to 2017. This seems reasonable and means KIVA has been lending in Kenya for 11 years. We also see that although the number of loans has grown each year there is a dip in 2017. However, this isn't that suspicious because remember that we are only in May, so the total volume will likely increase by December. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>274975</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1172000</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>364500</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>788150</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2985625</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5788500</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7163925</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8973675</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9196175</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9638775</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9978550</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4107050</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_amount  Date\n",
       "0        274975  2006\n",
       "1       1172000  2007\n",
       "2        364500  2008\n",
       "3        788150  2009\n",
       "4       2985625  2010\n",
       "5       5788500  2011\n",
       "6       7163925  2012\n",
       "7       8973675  2013\n",
       "8       9196175  2014\n",
       "9       9638775  2015\n",
       "10      9978550  2016\n",
       "11      4107050  2017"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAA\nAIBBFHAAAADAIAo4AAAAYJA31gM0le3bt2vx4sWaOHFixPp169apqKhIbrdbl1xyifr06ROjCQEA\nANASObKAL1++XGvWrFFcXFzE+mAwqEWLFumOO+5QXFyc/vjHPyo7O1utW7eO0aQAAKClmDnzLH34\n4Vn67nel++6TvI5sYTgZjow+LS1NY8eO1ezZsyPW7969W2lpafL5fJKkzMxMbd26VRdeeKEkqaKi\nQn6/P2KbmpoaJSUlGZnb++/vRK8DviM9Hs8xPwA1R07KRHJGLmRiT+RiP2RiL7NmJejhh5MlSR9+\nKO3Zk6xf/7oixlM1TnPPJJac8V15lB49emj//v3HrK+trVViYmJ4OT4+XjU1NeHlkpISFRUVRWwz\nYMAA5eTkNN2wx5Gammp0f4iOTOyHTOyJXOyHTOzhX/+KXP6//0tQRkZGbIZBzDmygDckISFBtbW1\n4eW6urqIQt6nTx9lZ2dHbFNTU6Pdu3cbmc/r9So1NVX79+9XIBAwss+mcvRz3Vw5KRPJGbmQiT2R\ni/2Qib307JkgKTm83LdvrXbvbt5nwE1n4qQfWFpUAc/IyNC+fftUVVWl+Ph4bd26Vf379w/fnpyc\nrOTk5IhtysrKVF9fb3TOQCBgfJ9nmtfrbfbHcCQnZCI5KxcysSdysR8ysYf8/HodOhTSRx/5dPHF\nCbrzzopmfTxS888kllpEAV+zZo3q6urUt29fDRkyRG+++aYsy9Ill1xyTOEGAABoCjffXKVbbqlX\nRkaGdu+W6K4tl2MLeGpqqm6//XZJ0kUXXRRen52dfcxlJgAAAIApfBAPAAAAYBAFHAAAADCIAg4A\nAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAABgW++959M112To6quTVFwcH+txgDPC\nsR/EAwAAmrdNmzz6r/9KUTDokiTddluaSkrKddZZVownAxqHM+AAAMCWduzwhsu3JFVUuLV3L9UF\nzR9fxQAAwJYuuqhOZ58dCC9ffHGdzj47GMOJgDODS1AAAIAtpaRYmjNnj/7yl1Zq1cqr8eMPyOOJ\n9VRA41HAAQCAbZ19dkgPPHBIPp9P1dVc+w1n4BIUAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQB\nBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcA\nwGG2bPHo+uvbqGdP6ZVXfLEeB8BRvLEeAAAAnFl33JGmr76KkyQ98khrZWXVauDA2hhPBeAwzoAD\nAOAwmzZ5T7gMILYo4AAAOExubk3474mJlq66irPfgJ3wIzEAAA7z0kv71bt3UH5/a33/+weUnR2I\n9UgAjkABBwDAYRISpLvvrlZGRmvt3h1QfX2sJwJwJC5BAQAAAAyigAMAAAAGUcABAAAAgyjgAABI\nCgalp55KUn5+Wz34oE81NdG3AYDTwYswo4iPj5fH4zGyL5fLpaqqKsXFxcnrbd7RuN1u+XzN/9PX\nnJSJ5IxcyMSenJDL888n6re//TaLkhLJ40nTk09Wx3iq0+eETI7E94r9OCWTWGjeyRtQV1dnbF9x\ncXFKSUlRZWWl6pv5S9Z9Pp+qq5vvf1yHOSkTyRm5kIk9OSGX//u/xIjlzz93NetsnJDJkfhesR/T\nmaSmphrbV1PjEhQAAKRjPqzm6qv58BoATYMz4AAASBo/vkper6WVKxPUt680frw/1iMBcCgKOAAA\n/zZuXLXGjav+96/WYz0NAKfiEhQAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAA\nAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAG\nUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHA\nAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEA\nAACDKOAAAACAQRRwAAAAwCBvrAc400KhkObPn6/y8nJ5PB7l5+crPT09fHtxcbG++OILuVwuXX31\n1brgggtiOC0AAABaGsedAV+7dq0CgYAmTZqk3NxcFRYWhm+rrq7WqlWrdNttt6mgoECLFi2K4aQA\nAABoiRx3Bry0tFRZWVmSpE6dOqmsrCx8W3x8vFJSUlRfX6+6ujq5XK6IbSsqKuT3+yPW1dTUKCkp\nqekHl+T1eiP+bM48Ho/i4uJiPUajOSkTyRm5kIk9kYv9kIk9OSkXp2QSC80//aPU1tYqMTExvOxy\nuRQMBuXxeCRJycnJ+t3vfqdQKKSrr746YtuSkhIVFRVFrBswYIBycnKafvAjpKamGt0foiMT+yET\neyIX+yETeyKXls1xBTwhIUG1tbXhZcuywuV7w4YN8vv9+q//+i9J0ptvvqlOnTrpnHPOkST16dNH\n2dnZEY9XU1Oj3bt3G5nd6/UqNTVV+/fvVyAQMLLPpnJ0Ds2VkzKRnJELmdgTudgPmdiTk3IxnUlG\nRoaxfTU1xxXwzMxMrVu3Tj179tS2bdvUvn378G0+n09er1der1cul0uJiYmqqakJ356cnKzk5OSI\nxysrK1N9fb2x+SUpEAgY3+eZ5vV6m/0xHMkJmUjOyoVM7Ilc7IdM7MkJuTgtE5McV8C7d++uTZs2\nafr06ZKkESNGqLi4WGlpaerevbu+/vprTZ8+XS6XS5mZmerSpUuMJwYAAEBL4rgC7na7lZeXF7Hu\nyF9Z5OTkGL+mGwAAADjMcW9DCAAAANgZBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAA\ngyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo\n4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAA\nAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAGi02lopFIr1FADQPFDAAQCN8otftFFmZlu1aSMt\nWBAf63EAwPYo4ACA01ZUlKDXX28ly3LJ75cmT05WMBjrqQDA3ijgAIDTduCAK2K5qsqluroYDQMA\nzQQFHABw2gYNqlVWVn14+Yc/rJbPF8OBAKAZ8MZ6AABA85WUZGnevD0qKmqlzMxk9e3rV3199O0A\noCWjgAMAGiU52dLo0bXKyJB27471NABgf1yCAgAAABhEAQcAAAAMooADAAAABrksy7JiPYSd7dmz\nRx6Px8i+XC6X4uPjVVdXp+Yei9vtVsgBH4vnpEwkZ+RCJvZELvZDJvbkpFxMZ5KammpsX02NF2FG\nUWfwDW3j4uKUkpKiyspK1TfztxHw+Xyqrq6O9RiN5qRMJGfkQib2RC72Qyb25KRcTGfipALOJSgA\nAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAA\ngEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBB\nFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRw\nAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEEUcAAA\nAMAgCjgAAABgEAUcAAAAMMgb6wHOtFAopPnz56u8vFwej0f5+flKT08P375hwwYtW7ZMktShQwcN\nHz5cLpcrRtMCAACgpXHcGfC1a9cqEAho0qRJys3NVWFhYfi22tpaFRYW6qabbtLtt9+ulJQUVVVV\nxXBaAAAAtDSOOwNeWlqqrKwsSVKnTp1UVlYWvm3btm1q3769CgsLtX//fvXu3VutWrUK315RUSG/\n3x/xeDU1NUpKSjIyu9frjfizOfN4PIqLi4v1GI3mpEwkZ+RCJvZELvZDJvbkpFyckkksNP/0j1Jb\nW6vExMTwssvlUjAYlMfjUVVVlTZv3qwf//jHio+P18yZM3XOOeeobdu2kqSSkhIVFRVFPN6AAQOU\nk5Nj9BhSU1ON7g/RkYn9kIk9kYv9kIk9kUvL5rgCnpCQoNra2vCyZVnyeDySJJ/Pp44dO4bPaJ97\n7rn65ptvwgW8T58+ys7Ojni8mpoa7d6928jsXq9Xqamp2r9/vwKBgJF9NpWjc2iunJSJ5IxcyMSe\nyMV+yMSenJSL6UwyMjKM7aupOa6AZ2Zmat26derZs2f4kpPDOnTooF27dqmyslKJiYnavn27evfu\nHb49OTlZycnJEY9XVlam+vp6Y/NLUiAQML7PM83r9Tb7YziSEzKRnJULmdgTudgPmdiTE3JxWiYm\nOa6Ad+/eXZs2bdL06dMlSSNGjFBxcbHS0tLUvXt3DR48WG+++aYk6cILL4wo6AAAAEBTc1wBd7vd\nysvLi1h35K8sevXqpV69epkeCwAAAJDkwLchBIDmJBSK9QQAANMo4AAQA0uXJqhnz/bq0CFFTzyR\nHH0DAIBjUMABwLBgUJo8OVX793tUX+/SK6+01vLl8bEeCwBgCAUcAAyrq3PJ74/853fvXv45BoCW\ngn/xAcAwn8/SmDFV4eVzzw1owIDm//7GAICT47h3QQGA5uCFFw5oyJAaVVcnKCenQikpVqxHAgAY\nQgEHgBhwuaRhw2rk87lUXU35BoCWhEtQAAAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCI\nAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIO\nAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEFRC3h5efkx6zZu3NgkwwAAAABO12ABP3Dg\ngA4cOKDbb79dBw8eDC/v2bNHkydPNjkjAAAA4Bjehm64//77tWLFCknS5Zdf/p8NvF7l5uY2/WQA\nAACAAzVYwGfMmCFJ+vnPf66nnnrK2EAAAACAkzVYwA976qmntGPHDh08eFCWZYXXX3jhhU06GAAA\nAOBEUQv4c889pzfeeEPp6enhdS6XS0uXLm3SwQAAAAAnilrAFyxYoMLCQrVv397EPAAAAICjRX0b\nwg4dOlC+AQAAgDMk6hnwfv366dlnn9XgwYOVmJgYXs814AAAAMCpi1rAZ8+eLUlatGhReB3XgAMA\nAACnx2Ud+dYmOMaePXvk8XiM7Mvlcik+Pl51dXVq7rG43W6FQqFYj9FoTspEckYuZGJP5GI/ZGJP\nTsrFdCapqanG9tXUop4Bnzlz5nHXT5w48YwPY0d1dXXG9hUXF6eUlBRVVlaqvr7e2H6bgs/nU3V1\ndazHaDQnZSI5IxcysSdysR8ysScn5WI6kxZVwNevXx/+e11dnUpKSiI+GRMAAADAyTupD+I50r59\n+/TTn/60yQYCAAAAnCzq2xAeLS0tTTt27GiKWQAAAADHO6VrwC3L0pdffhnxqZgAAAAATt4pXQMu\nffvBPFyCAgAAAJyek74GfMeOHQoEAjr33HObfCgAAADAqaIW8K1bt2ry5MnatWuXQqGQUlNT9eqr\nr6pLly4m5gMAAAAcJeqLMB977DFNmjRJq1evVklJie6880798pe/NDEbAAAA4DhRC/jevXs1atSo\n8PL111+v/fv3N+lQAAAAgFNFLeDBYFAHDhwIL+/bt69JBwIAAACcLOo14OPHj9fYsWM1dOhQSdLC\nhQs1YcKEJh8MAAAAcKKoBXzs2LHKzMzU8uXLFQqF9Mgjj6h///4mZgMAAAAcJ2oBl6SsrCxlZGSE\nlzdu3KisrKwmGwoAAABwqpN6H/A///nPSkpKkvTtp2G6XC6tXLmyyYcDAAAAnCZqAV+8eLE++eQT\npaammpgHAAAAcLSo74Jy3nnnKTk52cQsAAAAgONFPQNeUFCg8ePH6/LLL5fX+5+733333U06GAAA\nAOBEUQtUOWyAAAAc0ElEQVT4yy+/rPT0dB06dMjEPAAAAICjRS3g1dXVeu2110zMAgAAADhe1GvA\nu3btqrVr15qYBQAAAHC8qGfAd+3apTFjxqhjx46Kj48Pr583b16TDgYAAAA4UdQCPmXKFBNzAAAA\nAC1C1AJ+2WWX6cCBA6qurpZlWQoGgyotLTUxGwAAAOA4UQv4iy++qD/84Q+SJI/Ho/r6emVlZXEJ\nCgAAAHAaor4Ic86cOfroo480ZMgQFRYW6umnn1ZWVpaJ2QAAAADHiVrA09LS1K5dO3Xu3Flr167V\niBEjtHXrVhOzAQAAAI4TtYB7vV6Vlpaqc+fO+uyzzxQIBFRRUWFiNgAAAMBxohbwH/3oR5o6daoG\nDhyoxYsXa+DAgbriiitMzAYAAAA4TtQXYebk5CgnJ0eS9P7772vr1q3Kzs5u8sEAAAAAJ4pawI/k\n8/nUvXv3ppoFAAAAcLyol6AAAAAAOHMo4AAAAIBBJ3UJyo4dO3Tw4EFZlhVed+GFFzbZUAAAALAn\ny5LefdennTsTlZNTr549A7EeqdmJWsB/9atf6c0331R6enp4ncvl0tKlS5t0MAAAANjPk08m65VX\nWkuSnn8+UXPm7FGvXvUxnqp5iVrAFy5cqMLCQrVv397EPAAAALCxOXMSw3+vrXVp0aJECvgpinoN\neIcOHSjfAAAAkCR16hSMWD7nnGAD90RDop4B79evn5599lkNHjxYiYn/+YnHrteAh0IhzZ8/X+Xl\n5fJ4PMrPz4+4fObwff7yl78oOztbl156aYwmBQAAaH6ef/6A7r03Vdu2eTV8eJXGjq2K9UjNTtQC\nPnv2bEnSokWLwuvsfA342rVrFQgENGnSJG3btk2FhYW68cYbI+7z4Ycfqrq6OkYTAgAANF/nnhvU\nnDl75PP56FOnKWoB//DDD03MccaUlpYqKytLktSpUyeVlZVF3P7VV1/J5XKF73OkiooK+f3+iHU1\nNTVKSkpquoGP4PV6I/5szjwej+Li4mI9RqM5KRPJGbmQiT2Ri/2QiT05KRenZBILUdPft2+f5s6d\nq8rKSlmWpVAopK1bt+rXv/61iflOWW1tbcSlMi6XS8FgUB6PR+Xl5friiy/0gx/8QEVFRcdsW1JS\ncsz6AQMGKCcnp8nnPlJqaqrR/SE6MrEfMrEncrEfMrEncmnZohbw++67T4mJidq4caP69++v4uJi\n9enTx8RspyUhIUG1tbXhZcuy5PF4JEmff/65Dh06pD/96U86cOCAPB6PUlJS1LVrV0lSnz59lJ2d\nHfF4NTU12r17t5HZvV6vUlNTtX//fgUCzfs9NY/OoblyUiaSM3IhE3siF/shE3tyUi6mM8nIyDC2\nr6YWtYCXlZVpyZIlevTRRzVu3Djdc889uvfee03MdloyMzO1bt069ezZU9u2bYt4B5fvfe974b9/\n9NFHat26dbh8S1JycrKSk5MjHq+srEz19WbfWicQCBjf55nm9Xqb/TEcyQmZSM7KhUzsiVzsh0zs\nyQm5OC0Tk6K+DWHbtm0lSeedd57Wr1+v9u3b2/ontu7du8vr9Wr69On629/+piFDhqi4uFhr166N\n9WgAAABA9DPg6enpmj59ui6++GK9/PLLat269TEvVLQTt9utvLy8iHXH+5WF6eu6AQAAAOkkzoA/\n9thjio+PV9++fdWzZ0+99NJLeuCBB0zMBgAAADjOSZ0Bv/766/X3v/9dV155pe688061bt3axGwA\nAACA40Qt4GvWrNHkyZOVnp6uUCik8vJyTZs2Tb179zYxHwAAAOAoUQv4M888o+eee05XXHGFJGnl\nypV6+umnNWvWrCYfDgAAAHCaqNeA+/3+cPmWpH79+vGxowAAAMBpilrA3W63duzYEV7evn17+INt\nAAAAAJyaqJeg3HXXXRo7dqz69esny7JUXFysRx55xMRsAAAAgONELeC5ubnq3LmzVq1aJcuydOed\nd6pLly4mZgMAAAAcp8ECXlhYGLF8+BMxN23apE2bNkV8rDsAAACAk9NgAX/jjTca3MjlclHAAQAA\ngNNwWgUcAAAAwOmJ+i4oAAAAAM4cCjgAAABgUIMFfPHixZKkuro6Y8MAAAAATtdgAX/ppZckSWPH\njjU2DAAAAOB0Db4Is1WrVhoyZIjKy8uVl5d3zO3z5s1r0sEAAAAAJ2qwgE+fPl3/+te/9Itf/EJT\np041ORMAAADgWA0W8NatW+vSSy/Vq6++qnbt2umrr75SIBDQRRddpNatW5ucEQAAAHCMqB9Ff+jQ\nIRUUFKht27YKBoMqLy/XtGnT1Lt3bxPzAQAAAI4StYA/88wzeu6553TFFVdIklauXKmnn35as2bN\navLhAAAAAKeJ+j7gfr8/XL4lqV+/fqqurm7SoQAAAACnilrA3W63duzYEV7evn27PB5Pkw4FAAAA\nOFXUS1DuuusujR07Vv369ZMkrVixQo888kiTDwYAAAA4UdQCnpubq86dO2vVqlWyLEs//vGP1aVL\nFxOzAQAAAI4TtYBLUufOndW5c+emngUAAABwvKjXgAMAAAA4cyjgAAAAgEEUcAAAAMCgqNeAL1iw\nQC+++KIqKiokSZZlyeVyaeXKlU0+HAAAAOA0UQv4r371Kz388MPKzMw0MQ8AAADgaFELeMeOHTV4\n8GATswAAAACOF7WAjxw5Us8884yuueYaeb3/ufull17apIMBAAAAThS1gK9YsULFxcVavnx5xPp5\n8+Y12VAAAACAU0Ut4GvWrNHHH3+shIQEE/MAAAAAjhb1bQgzMjIUCARMzAIAJ1Rd7dKUKa3Vu7f0\n8MOtVF8f64kAADh1Uc+AZ2ZmasSIEerfv7/i4+PD6x9++OEmHQwAjvbkk0l6802fJOmf/zxLyckB\n/eQn/hhPBQDAqTmpAt6S34IwPj5eHo/HyL5cLpeqqqoUFxcX8YLX5sjtdsvn88V6jEZzUiZS889l\n48bIS+E2bUqUzxeM0TRnRnPP5DC+V+yHTOzJSbk4JZNYiJr83XffbWIO26qrqzO2r7i4OKWkpKiy\nslL1zfx36z6fT9XV1bEeo9GclInU/HO55hqPPvkkLrx89dWVzfp4pOafyWF8r9gPmdiTk3IxnUlq\naqqxfTW1qAU8Ly/vuOt5FxQApk2e7Fdamkvr1yfpkksqlJfX/P8zBgC0PFEL+NSpU8N/r6+v15Il\nS9SuXbsmHQoAGlJQUKOMjCTt3l3LizABAM1S1AJ+2WWXRSz3799f48aN05133tlkQwEAAABOFfVt\nCI+2f/9+7dq1qylmAQAAABzvlK8BLysr09ixY5tsIAAAAMDJTukacJfLpbS0NHXp0qVJhwIAAACc\n6oQF3LIs9e7dW16vV36/X8XFxcrIyDA1GwAAAOA4DV4DvnHjRg0ePFiffPKJampqdMMNN+iFF17Q\nrbfeqhUrVpicEQAAAHCMBgv4s88+q/vuu085OTmaP3++LMvSBx98oD//+c96+eWXTc4IAAAAOEaD\nBXznzp3Kz8+XJH366afKzc2V2+1Whw4d5Pf7jQ0IAAAAOEmDBdzt/s9N//znP3XppZeGl2tra5t2\nKgAAAMChGnwRZps2bbR27Vr5/X7t3r07XMD/8Y9/qH379sYGBAAAAJykwQI+ZcoU3XLLLfL7/Xrg\ngQd01llnacaMGZo2bZp+97vfmZwRAAAAcIwGC/jFF1+sjz/+WDU1NUpOTpYkXXLJJXrnnXd03nnn\nmZoPAAAAcJQTvg94fHy84uPjw8u9e/du8oEAAAAAJ2vwRZgAAAAAzjwKOAAAAGAQBRwAAAAwiAIO\nAAAAGEQBBwAAAAyigAMAAAAGUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAA\nABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAY\nRAEHAAAADPLGeoAzLRQKaf78+SovL5fH41F+fr7S09PDt69cuVJffvmlJKlr164aOHBgjCYFAABA\nS+S4M+Br165VIBDQpEmTlJubq8LCwvBt+/bt05o1a3Tbbbdp0qRJ2rRpk7755psYTgsAAICWxnFn\nwEtLS5WVlSVJ6tSpk8rKysK3tWnTRuPHj5fb/e3PHcFgUF7vf56CiooK+f3+iMerqalRUlKSgckV\nnuXImZorj8ejuLi4WI/RaE7KRHJGLmRiT+RiP2RiT07KxSmZxELzT/8otbW1SkxMDC+7XC4Fg0F5\nPB55PB61atVKlmWpsLBQHTp0UNu2bcP3LSkpUVFRUcTjDRgwQDk5Ocbml6TU1FSj+0N0ZGI/ZGJP\n5GI/ZGJP5NKyOa6AJyQkqLa2NrxsWZY8Hk94ub6+XnPmzFFCQoKGDx8esW2fPn2UnZ0dsa6mpka7\nd+9u2qH/zev1KjU1Vfv371cgEDCyz6ZydA7NlZMykZyRC5nYE7nYD5nYk5NyMZ1JRkaGsX01NccV\n8MzMTK1bt049e/bUtm3b1L59+/BtlmXprbfe0vnnn6+rrrrqmG2Tk5OVnJwcsa6srEz19fVNPveR\nAoGA8X2eaV6vt9kfw5GckInkrFzIxJ7IxX7IxJ6ckIvTMjHJcQW8e/fu2rRpk6ZPny5JGjFihIqL\ni5WWlibLsrRlyxYFAgFt2LBBkpSbm6tOnTrFcmQAAAC0II4r4G63W3l5eRHrjvyVxdSpU02PBAAA\nAIQ57m0IAQAAADujgAMAAAAGUcABAAAAgyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAA\nABhEAQcAAAAMooADAAAABlHAAQAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAY\nRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQB\nBwAAAAyigAMAAAAGUcBxRpWVuXX99enq1q2N7rsvRfX1sZ4IAADAXijgOKMeeihFq1YlaM8et955\n5yzNmNEq1iMBAADYCgUcZ9T27Z6I5R07PA3cEwAAoGWigOOMGjmyOvx3r9fS8OE1MZwGAADAfryx\nHgDOcvfdfp1/fkBbtvjUv79fl1zCReAAAABHooDjjBs+vEY+n0vV1ZRvAACAo3EJCgAAAGAQBRwA\nAAAwiAIOAAAAGOSyLMuK9RB2tmfPHnk8Zt5Kz+VyKT4+XnV1dWrusbjdboVCoViP0WhOykRyRi5k\nYk/kYj9kYk9OysV0Jqmpqcb21dR4EWYUdXV1xvYVFxenlJQUVVZWqr6Zf4Skz+dTdXV19DvanJMy\nkZyRC5nYE7nYD5nYk5NyMZ2Jkwo4l6AAAAAABlHAAQAAAIMo4AAAAIBBFHAAAADAIAo40AJUVrr0\n8ccJ+n//j295AABijf+NAYc7eNCl4cPb6sYb03XVVW302mutYj0SAAAtGgUccLj33/dpw4a48PKv\nf50Uw2kAAAAFHHC4xETrhMsAAMAsCjjgcKNGVeuaa2okfVu+n3rqYIwnAgCgZeOTMAGHi4+X/vKX\nfSorc6tduwTFxdXEeiQAAFo0zoADLYDLJXXsGFJycqwnAQAAFHAAAADAIAo4AAAAYBAFHAAAADCI\nAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEAAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRw4AcuSgsFY\nTwEAAJyEAg404N13fTr//Lby+aTnnz8r1uMAAACHoIADx3HwoEv335+iqiqX6uulp55qpa++8sZ6\nLAAA4AAUcOA4/H636utdEev27+fbBQAANB6NwibKytyaNStBy5fHehJI0tlnBzVkSHV4+aKL6tWn\nT10MJwIAAE7B79RtYPNmj/Ly2mr/fo8k6fHHfbr11voYT9WyuVzSa6/tV2FhnRIS2uiqqw4oPj7W\nUwEAACfgDLgNzJ59Vrh8S9Jrr/liOA0O83ik/Pw6jR8vtWoV62kAAIBTUMBtoE2b0AmXAQAA4BwU\ncBsoKKhUbm6NJOnss6Xf/MYf44kAAADQVLgG3AYSEqQ//WmfgsE4deqUod27A6rnEnAAAABH4gy4\njSQmxnoCAAAANDUKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAxy3LughEIhzZ8/X+Xl5fJ4PMrP\nz1d6enr49pKSEn322Wdyu9265pprlJ2dHcNpAQAA0NI47gz42rVrFQgENGnSJOXm5qqwsDB826FD\nh/Tpp5/qtttuU0FBgZYuXapAIBDDaQEAANDSOO4MeGlpqbKysiRJnTp1UllZWfi2HTt2qFOnTvJ6\nvfJ6vUpLS1N5ebk6duwoSaqoqJDfH/khODU1NUpKSjIyu9frjfizOfN4PIqLi4v1GI3mpEwkZ+RC\nJvZELvZDJvbkpFyckkksNP/0j1JbW6vEI95Q2+VyKRgMyuPxHHNbfHy8ampqwsslJSUqKiqKeLwB\nAwYoJyen6Qc/QmpqqtH9IToysR8ysSdysR8ysSdyadkcV8ATEhJUW1sbXrYsSx6P57i31dXVRRTy\nPn36HHNNeE1NjXbv3t3EU3/L6/UqNTVV+/fvb/aXxhz9XDdXTspEckYuZGJP5GI/ZGJPTsrFdCYZ\nGRnG9tXUHFfAMzMztW7dOvXs2VPbtm1T+/btw7d17NhRH374oerr6xUMBrV79261a9cufHtycrKS\nk5MjHq+srEz1hj8XPhAIGN/nmeb1epv9MRzJCZlIzsqFTOyJXOyHTOzJCbk4LROTHFfAu3fvrk2b\nNmn69OmSpBEjRqi4uFhpaWnq3r27Lr/8cs2cOVOWZWnw4MFcuwQAAACjHFfA3W638vLyItYd+SuL\nPn36qE+fPqbHAgAAACQ58G0IAQAAADujgAMAAAAGUcABAAAAgyjgAAAAgEEuy7KsWA+Bb1VUVKik\npER9+vQ55u0QERtkYj9kYk/kYj9kYk/kAokz4Lbi9/tVVFQkv98f61Hwb2RiP2RiT+RiP2RiT+QC\niQIOAAAAGEUBBwAAAAyigAMAAAAGeR599NFHYz0EvmVZluLj43XeeecpISEh1uNAZGJHZGJP5GI/\nZGJP5AKJd0ExIhgMas6cOTpw4IACgYCuueYaZWRk6P3335fL5VK7du00bNgwud1uLVu2TOvXr5fb\n7db3v/99nXPOOfL7/Zo3b56qq6tlWZZGjRqltLS0WB9Ws9bYTHbu3KkPPvhAbrdb6enpys/Pl9vN\nL5Qa61RykaS9e/fq7bff1uTJkyVJlZWVevfddxUIBJSUlKQRI0YoPj4+lofU7DU2kwMHDmjOnDkK\nhUKSpLy8PLVt2zZmx+MEjc3ksC1btmj27NmaMmVKLA7DcRqbS11dnT744AMdOHBAwWBQQ4cO1Tnn\nnBPLQ0IT8sZ6gJZgzZo18vl8Gj16tKqqqjRt2jR95zvf0aBBg3T++edr3rx5Wrdundq0aaMtW7bo\n9ttv18GDBzVr1izdcccdWrx4sXr16qWePXtq8+bN2rNnDwW8kRqbSVFRkQYMGKBu3brp3Xff1YYN\nG5SdnR3rw2r2TjaXCy64QJ9//rlWrVqlysrK8PZFRUXq1auXLrnkEn3yyScqKSlRv379YnhEzV9j\nM/noo4902WWX6YILLtDGjRu1ZMkSjRs3LoZH1Pw1NhNJOnjwoFauXKlgMBijo3CexuayYsUKtWvX\nTqNHj9Y333yj8vJyCriDccrOgB49emjQoEHhZbfbrZ07d+q8886TJHXt2lVff/21SktL1aVLF7lc\nLqWkpCgUCqmyslLbtm1TRUWF/vSnP2nNmjXh7XD6GpvJd77znfBvJGprazn7fYacbC6SlJiYqIkT\nJ0ZsX1paqqysrGPui9PX2Ey+973vqVu3bpKkUCgkr5fzPo3V2Ezq6+v1wQcfaPjw4cZmbgkam8um\nTZvk8Xj0xhtv6OOPP1aXLl2MzQ7zaA0GJCQkKCEhQbW1tZo1a5YGDRoky7LkcrkkSfHx8aqpqVFt\nba0SExPD2x1ef+DAAfl8Pk2YMEFt2rTR8uXLY3UojtHYTNLT07Vw4UL99re/VWVlJT8UnSEnm4sk\nZWdnH3N5yZF5HXlfnL7GZtKqVSt5PB7t2bNHhYWFGjhwoOlDcJzGZrJgwQL179+fD4E5wxqbS1VV\nlWpqalRQUKBu3bqpsLDQ+DHAHAq4IQcPHtTrr7+uiy66SBdddFH4G1L69rqvxMTE8Dfu0et9Pl/4\n8obs7GyVlZUZn9+JGpPJwoULdeutt+qee+7Rd7/7Xf6hPINOJpeGHJlXtPvi5DUmE0navHmz3nrr\nLY0aNYrrv8+Q082koqJCpaWlWrZsmWbOnKnq6mq98847psZ2vMZ8r/B/fctCATfA7/frjTfe0LXX\nXqvevXtLkjp06KDNmzdLkjZs2KBzzz1XmZmZ2rRpk0KhkA4cOCDLstSqVStlZmZqw4YNkqStW7eq\nXbt2MTsWp2hsJj6fL/zq9aSkJFVXV8fsWJzkZHNpyJHfK9Hui5PT2Ew2b96shQsXavz48erYsaOR\nmZ2uMZkkJyfrnnvu0cSJEzVx4kT5fD7dcMMNxmZ3sjP57xf/1zsfF+MZ8Mknn6i6ulpFRUUqKiqS\nJA0dOlQLFy7U0qVL1bZtW/Xo0UNut1uZmZmaMWOGLMvSsGHDJElDhgzR3LlztXr1aiUmJur666+P\n5eE4QmMzyc/P11//+le53W55PB7l5eXF8nAc42Rzacg111yj9957T//4xz901lln8b1yBjQ2k0WL\nFikYDOq9996TJLVt25bvl0ZqbCZoGo3N5eqrr9bcuXM1ffp0ud1ujRo1ytToiAHehhAAAAAwiEtQ\nAAAAAIMo4AAAAIBBFHAAAADAIAo4AAAAYBAFHAAAADCIAg4AAAAYRAEHAAAADKKAAwAAAAZRwAEA\nAACDKOAAAACAQRRwAAAAwCAKOAAAAGAQBRwAAAAwiAIOAAAAGEQBBwAAAAyigAMAAAAGUcABAAAA\ngyjgAAAAgEEUcAAAAMAgCjgAAABgEAUcAAAAMIgCDgAAABjkjfUAAID/2L59u6699lp169ZNkhQK\nhRQXF6cf/vCHGjly5Am3/e1vf6vu3bsrNzfXxKgAgNNEAQcAm0lMTNScOXPCyzt27NAtt9win8+n\nIUOGNLjdp59+qqysLBMjAgAagQIOADbXsWNH3XvvvZoxY4a6deumxx57TFVVVdq1a5e6d++uF154\nQX/961/15Zdf6tlnn5XH49GAAQP03HPPafXq1QoGg+rRo4cefvhhtW7dOtaHAwAtHteAA0Az0L17\nd61fv16zZs3SyJEj9fbbb6uwsFDbt2/XsmXLdPPNN6tnz5766U9/qmuvvVZ/+MMf5PF4NHv2bM2d\nO1ft2rXTc889F+vDAACIM+AA0Cy4XC4lJibqv//7v7VixQq99tpr2rJli3bt2qWqqqpj7r9s2TId\nOnRIxcXFkqT6+nqlp6ebHhsAcBwUcABoBr744gt169ZNU6ZMUTAY1NChQzVw4EDt3LlTlmUdc/9Q\nKKSHHnpIA/5/u3ZvbFAYhWH0RWBE6lCAzAxSidGC0ImIJAo4iVAHOtGACszowYy/495EKL3HDdaq\n4NvZM/vbw2GS5HK55Ha71f1sAD5wggLwz51Op+x2u8zn8xwOhxRFkclkkkajkePxmKqqkiStVivP\n5zNJMhgMst/vc7/f83q9stlsst1uvzkGAG824AD/zPV6zXQ6TZI0m8202+2sVquMRqMsl8sURZFu\nt5tOp5N+v5/z+ZwkGY/HKcsyj8cji8UiZVlmNpulqqr0er2s1+tvjgXAW+Pn098lAADwJ5ygAABA\njQQ4AADUSIADAECNBDgAANRIgAMAQI0EOAAA1EiAAwBAjX4BO8MQ6HeysOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130bdc898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036532299148)>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=df[['posted_datetime','loan_amount']]\n",
    "ts = ts.set_index(['posted_datetime'])\n",
    "ts=ts.groupby(ts.index.year).sum()\n",
    "ts['Date'] = ts.index\n",
    "ts.reset_index(drop=True)\n",
    "\n",
    "ggplot(ts , aes(x = 'Date', y = 'loan_amount')) + geom_point(color = \"blue\") + \\\n",
    "ggtitle(\"Volume of loan amount on Kiva each year\") + xlab(\"Date\") + ylab(\"Sum of loan amount\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a similar patten in the sum of dollars loaned. It mimics the distribution of the number of loans which is a good sign."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do I have missing values?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember when we used a left join to merge in the partner data. That means we could have null (missing) values in our partner_id field. We check this in the cell below and find that we have 9,642 loans that are missing. Let's investigate and try and understand whether the data is missing at random or systematically missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9642"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)-df['partner_id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "null_partner_id=df.loc[df['partner_id'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9642"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(null_partner_id.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>activity</th>\n",
       "      <th>basket_amount</th>\n",
       "      <th>bonus_credit_eligibility</th>\n",
       "      <th>borrowers</th>\n",
       "      <th>currency_exchange_loss_amount</th>\n",
       "      <th>description.languages</th>\n",
       "      <th>description.texts.en</th>\n",
       "      <th>description.texts.es</th>\n",
       "      <th>description.texts.fr</th>\n",
       "      <th>description.texts.ru</th>\n",
       "      <th>funded_amount</th>\n",
       "      <th>funded_date</th>\n",
       "      <th>id</th>\n",
       "      <th>image.id</th>\n",
       "      <th>image.template_id</th>\n",
       "      <th>journal_totals.bulkEntries</th>\n",
       "      <th>journal_totals.entries</th>\n",
       "      <th>lender_count</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>location.country</th>\n",
       "      <th>location.country_code</th>\n",
       "      <th>location.geo.level</th>\n",
       "      <th>location.geo.pairs</th>\n",
       "      <th>location.geo.type</th>\n",
       "      <th>location.town</th>\n",
       "      <th>name</th>\n",
       "      <th>partner_id</th>\n",
       "      <th>payments</th>\n",
       "      <th>planned_expiration_date</th>\n",
       "      <th>posted_date</th>\n",
       "      <th>sector</th>\n",
       "      <th>status</th>\n",
       "      <th>tags</th>\n",
       "      <th>terms.disbursal_amount</th>\n",
       "      <th>terms.disbursal_currency</th>\n",
       "      <th>terms.disbursal_date</th>\n",
       "      <th>terms.loan_amount</th>\n",
       "      <th>terms.local_payments</th>\n",
       "      <th>...</th>\n",
       "      <th>terms.repayment_term</th>\n",
       "      <th>terms.scheduled_payments</th>\n",
       "      <th>themes</th>\n",
       "      <th>translator.byline</th>\n",
       "      <th>translator.image</th>\n",
       "      <th>use</th>\n",
       "      <th>video.id</th>\n",
       "      <th>video.thumbnailImageId</th>\n",
       "      <th>video.title</th>\n",
       "      <th>video.youtubeId</th>\n",
       "      <th>borrower_count</th>\n",
       "      <th>partner_average_loan_size_percent_per_capita_income</th>\n",
       "      <th>partner_charges_fees_and_interest</th>\n",
       "      <th>partner_countries</th>\n",
       "      <th>partner_currency_exchange_loss_rate</th>\n",
       "      <th>partner_default_rate</th>\n",
       "      <th>partner_default_rate_note</th>\n",
       "      <th>partner_delinquency_rate</th>\n",
       "      <th>partner_delinquency_rate_note</th>\n",
       "      <th>partner_image.id</th>\n",
       "      <th>partner_image.template_id</th>\n",
       "      <th>partner_loans_at_risk_rate</th>\n",
       "      <th>partner_loans_posted</th>\n",
       "      <th>partner_name</th>\n",
       "      <th>partner_portfolio_yield</th>\n",
       "      <th>partner_portfolio_yield_note</th>\n",
       "      <th>partner_profitability</th>\n",
       "      <th>partner_rating</th>\n",
       "      <th>partner_social_performance_strengths</th>\n",
       "      <th>partner_start_date</th>\n",
       "      <th>partner_status</th>\n",
       "      <th>partner_total_amount_raised</th>\n",
       "      <th>partner_url</th>\n",
       "      <th>posted_datetime</th>\n",
       "      <th>planned_expiration_datetime</th>\n",
       "      <th>funded_datetime</th>\n",
       "      <th>dispursal_datetime</th>\n",
       "      <th>number_of_loans</th>\n",
       "      <th>dispersal_date</th>\n",
       "      <th>posted_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36064</th>\n",
       "      <td>36064</td>\n",
       "      <td>36048</td>\n",
       "      <td>Health</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'first_name': 'Doroffine', 'last_name': '', ...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>['en']</td>\n",
       "      <td>I grew up in Kitui county, was faced with many...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "      <td>2015-09-04</td>\n",
       "      <td>1090127</td>\n",
       "      <td>2208140</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>225</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>country</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doroffine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>2015-10-17</td>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>Health</td>\n",
       "      <td>funded</td>\n",
       "      <td>[]</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2015-09-14T11:07:00Z</td>\n",
       "      <td>225</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>buy medicines, stethoscope, glucometer and exa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-09-02 17:52:12</td>\n",
       "      <td>2015-10-17 10:52:11</td>\n",
       "      <td>2015-09-04 02:44:28</td>\n",
       "      <td>2015-09-14 11:07:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-14</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>36068</th>\n",
       "      <td>36068</td>\n",
       "      <td>36052</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'first_name': 'Brenda Bugutsa', 'last_name':...</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>I grew up in Vihiga county Western Kenya in a ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>1090116</td>\n",
       "      <td>2208117</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>225</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>country</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brenda Bugutsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>2015-10-17</td>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[]</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2015-09-10T16:23:00Z</td>\n",
       "      <td>225</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>purchase more cereals stocks  and charcoal sto...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-09-02 17:44:11</td>\n",
       "      <td>2015-10-17 10:44:09</td>\n",
       "      <td>2015-09-03 15:10:11</td>\n",
       "      <td>2015-09-10 16:23:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-09-10</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Unnamed: 0.1     activity  basket_amount  \\\n",
       "36064       36064         36048       Health            NaN   \n",
       "36068       36068         36052  Agriculture            NaN   \n",
       "\n",
       "       bonus_credit_eligibility  \\\n",
       "36064                     False   \n",
       "36068                     False   \n",
       "\n",
       "                                               borrowers  \\\n",
       "36064  [{'first_name': 'Doroffine', 'last_name': '', ...   \n",
       "36068  [{'first_name': 'Brenda Bugutsa', 'last_name':...   \n",
       "\n",
       "       currency_exchange_loss_amount description.languages  \\\n",
       "36064                           0.01                ['en']   \n",
       "36068                           0.01                ['en']   \n",
       "\n",
       "                                    description.texts.en description.texts.es  \\\n",
       "36064  I grew up in Kitui county, was faced with many...                  NaN   \n",
       "36068  I grew up in Vihiga county Western Kenya in a ...                  NaN   \n",
       "\n",
       "      description.texts.fr description.texts.ru  funded_amount funded_date  \\\n",
       "36064                  NaN                  NaN            225  2015-09-04   \n",
       "36068                  NaN                  NaN            225  2015-09-03   \n",
       "\n",
       "            id  image.id  image.template_id  journal_totals.bulkEntries  \\\n",
       "36064  1090127   2208140                  1                           0   \n",
       "36068  1090116   2208117                  1                           0   \n",
       "\n",
       "       journal_totals.entries  lender_count  loan_amount location.country  \\\n",
       "36064                       0             6          225            Kenya   \n",
       "36068                       0             6          225            Kenya   \n",
       "\n",
       "      location.country_code location.geo.level location.geo.pairs  \\\n",
       "36064                    KE            country               1 38   \n",
       "36068                    KE            country               1 38   \n",
       "\n",
       "      location.geo.type location.town            name  partner_id payments  \\\n",
       "36064             point           NaN       Doroffine         NaN       []   \n",
       "36068             point           NaN  Brenda Bugutsa         NaN       []   \n",
       "\n",
       "      planned_expiration_date posted_date       sector  status tags  \\\n",
       "36064              2015-10-17  2015-09-02       Health  funded   []   \n",
       "36068              2015-10-17  2015-09-02  Agriculture  funded   []   \n",
       "\n",
       "       terms.disbursal_amount terms.disbursal_currency  terms.disbursal_date  \\\n",
       "36064                 20000.0                      KES  2015-09-14T11:07:00Z   \n",
       "36068                 20000.0                      KES  2015-09-10T16:23:00Z   \n",
       "\n",
       "       terms.loan_amount terms.local_payments     ...      \\\n",
       "36064                225                   []     ...       \n",
       "36068                225                   []     ...       \n",
       "\n",
       "      terms.repayment_term  terms.scheduled_payments themes translator.byline  \\\n",
       "36064                    6                        []    NaN               NaN   \n",
       "36068                    6                        []    NaN               NaN   \n",
       "\n",
       "       translator.image                                                use  \\\n",
       "36064               NaN  buy medicines, stethoscope, glucometer and exa...   \n",
       "36068               NaN  purchase more cereals stocks  and charcoal sto...   \n",
       "\n",
       "      video.id video.thumbnailImageId  video.title video.youtubeId  \\\n",
       "36064      NaN                    NaN          NaN             NaN   \n",
       "36068      NaN                    NaN          NaN             NaN   \n",
       "\n",
       "       borrower_count  partner_average_loan_size_percent_per_capita_income  \\\n",
       "36064               1                                                NaN     \n",
       "36068               1                                                NaN     \n",
       "\n",
       "      partner_charges_fees_and_interest partner_countries  \\\n",
       "36064                               NaN               NaN   \n",
       "36068                               NaN               NaN   \n",
       "\n",
       "       partner_currency_exchange_loss_rate  partner_default_rate  \\\n",
       "36064                                  NaN                   NaN   \n",
       "36068                                  NaN                   NaN   \n",
       "\n",
       "      partner_default_rate_note partner_delinquency_rate  \\\n",
       "36064                       NaN                      NaN   \n",
       "36068                       NaN                      NaN   \n",
       "\n",
       "       partner_delinquency_rate_note  partner_image.id  \\\n",
       "36064                            NaN               NaN   \n",
       "36068                            NaN               NaN   \n",
       "\n",
       "       partner_image.template_id  partner_loans_at_risk_rate  \\\n",
       "36064                        NaN                         NaN   \n",
       "36068                        NaN                         NaN   \n",
       "\n",
       "       partner_loans_posted  partner_name  partner_portfolio_yield  \\\n",
       "36064                   NaN           NaN                      NaN   \n",
       "36068                   NaN           NaN                      NaN   \n",
       "\n",
       "       partner_portfolio_yield_note  partner_profitability partner_rating  \\\n",
       "36064                           NaN                    NaN            NaN   \n",
       "36068                           NaN                    NaN            NaN   \n",
       "\n",
       "       partner_social_performance_strengths  partner_start_date  \\\n",
       "36064                                   NaN                 NaN   \n",
       "36068                                   NaN                 NaN   \n",
       "\n",
       "       partner_status partner_total_amount_raised partner_url  \\\n",
       "36064             NaN                         NaN         NaN   \n",
       "36068             NaN                         NaN         NaN   \n",
       "\n",
       "          posted_datetime planned_expiration_datetime     funded_datetime  \\\n",
       "36064 2015-09-02 17:52:12         2015-10-17 10:52:11 2015-09-04 02:44:28   \n",
       "36068 2015-09-02 17:44:11         2015-10-17 10:44:09 2015-09-03 15:10:11   \n",
       "\n",
       "       dispursal_datetime number_of_loans dispersal_date posted_year  \n",
       "36064 2015-09-14 11:07:00               1     2015-09-14        2015  \n",
       "36068 2015-09-10 16:23:00               1     2015-09-10        2015  \n",
       "\n",
       "[2 rows x 84 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_partner_id.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_of_loans</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
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       "      <th>1</th>\n",
       "      <td>359</td>\n",
       "      <td>2012</td>\n",
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       "      <th>2</th>\n",
       "      <td>1077</td>\n",
       "      <td>2013</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4725</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3465</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_loans  Date\n",
       "0               16  2011\n",
       "1              359  2012\n",
       "2             1077  2013\n",
       "3             4725  2014\n",
       "4             3465  2015"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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w+++/Xy+88IIKCws1Y8YM7dq1S/fee6+N2QAAAICoE7KAf/nll3rggQe0fPly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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132d9b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (321755982)>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts=null_partner_id[['posted_datetime','number_of_loans']]\n",
    "ts = ts.set_index(['posted_datetime'])\n",
    "ts=ts.groupby(ts.index.year).sum()\n",
    "ts['Date'] = ts.index\n",
    "ts.reset_index(drop=True)\n",
    "\n",
    "ggplot(ts , aes(x = 'Date', y = 'number_of_loans')) + geom_point(color = \"blue\") + \\\n",
    "ggtitle(\"Number of Kenyan Loans on Kiva each year\") + xlab(\"Date\") + ylab(\"Number of Loans\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the number of loans peaked in 2014, and has decreased. All of the loans issued in the second half of 2015, 2016, 2017 have a partner. This tells us these values are not missing at random, they are more likely to occur in data before June 2015, and are most likely to occur in the first half of 2014. We should probably just drop data with the partner_id missing. We do so below using the notnull() function which checks if the field is populated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=df.loc[df['partner_id'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check that our filtering was correctly done we check the new number of rows. It makes sense!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118316"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Sanity Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Does the dataset have the number of rows you would expect?**\n",
    "\n",
    "In the command below we find out the number of loans and number of columns by using the function shape. You can also use len(df.index) to find the number of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 118316 observations and 84 features\n"
     ]
    }
   ],
   "source": [
    "print('There are %d observations and %d features' % (df.shape[0],df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us there are 127,958 observations and 84 features. We learnt in the theory lesson that each row is an observation and each column is a potential feature. 127,000 matches with what we expect based upon our conversations with Kiva. This is a very healthy sample size for applying machine learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the theory part of the lesson, we learnt about mean, frequency and percentiles as a powerful way to understand teh distribution of the data. If you are unfamiliar with these terms or need a refresher [this](https://www.mathsisfun.com/data/frequency-grouped-mean-median-mode.html) overview should be helpful.The describe command below provides key summary statistics for each numeric column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Unnamed: 0   Unnamed: 0.1  basket_amount  \\\n",
      "count  118316.000000  118316.000000     944.000000   \n",
      "mean    64433.371843   64398.230239       0.185381   \n",
      "std     38178.638548   38157.158427       2.145937   \n",
      "min         0.000000       0.000000       0.000000   \n",
      "25%     29578.750000   29564.750000       0.000000   \n",
      "50%     66559.500000   66517.500000       0.000000   \n",
      "75%     98296.250000   98238.250000       0.000000   \n",
      "max    127957.000000  127899.000000      25.000000   \n",
      "\n",
      "       currency_exchange_loss_amount  funded_amount            id  \\\n",
      "count                   15690.000000  118316.000000  1.183160e+05   \n",
      "mean                        7.017447     473.439771  7.096035e+05   \n",
      "std                        15.655207     676.663803  3.412929e+05   \n",
      "min                         0.010000       0.000000  2.510000e+02   \n",
      "25%                         1.220000     250.000000  4.202852e+05   \n",
      "50%                         3.490000     350.000000  6.975760e+05   \n",
      "75%                         8.280000     600.000000  1.003961e+06   \n",
      "max                      1285.510000   50000.000000  1.292273e+06   \n",
      "\n",
      "           image.id  image.template_id  journal_totals.bulkEntries  \\\n",
      "count  1.183160e+05           118316.0                    118316.0   \n",
      "mean   1.540915e+06                1.0                         0.0   \n",
      "std    6.043467e+05                0.0                         0.0   \n",
      "min    4.090000e+02                1.0                         0.0   \n",
      "25%    1.013473e+06                1.0                         0.0   \n",
      "50%    1.575774e+06                1.0                         0.0   \n",
      "75%    2.052755e+06                1.0                         0.0   \n",
      "max    2.516905e+06                1.0                         0.0   \n",
      "\n",
      "       journal_totals.entries   lender_count    loan_amount     partner_id  \\\n",
      "count                118316.0  118316.000000  118316.000000  118316.000000   \n",
      "mean                      0.0      14.608227     495.159784     165.037552   \n",
      "std                       0.0      20.216517     704.079168      66.001909   \n",
      "min                       0.0       0.000000      25.000000       6.000000   \n",
      "25%                       0.0       7.000000     250.000000     133.000000   \n",
      "50%                       0.0      11.000000     375.000000     156.000000   \n",
      "75%                       0.0      18.000000     600.000000     164.000000   \n",
      "max                       0.0    1589.000000   50000.000000     526.000000   \n",
      "\n",
      "       terms.disbursal_amount  terms.loan_amount  \\\n",
      "count            1.183160e+05      118316.000000   \n",
      "mean             4.178579e+04         495.159784   \n",
      "std              4.474124e+04         704.079168   \n",
      "min              2.500000e+01          25.000000   \n",
      "25%              2.000000e+04         250.000000   \n",
      "50%              3.000000e+04         375.000000   \n",
      "75%              5.000000e+04         600.000000   \n",
      "max              1.596948e+06       50000.000000   \n",
      "\n",
      "       terms.loss_liability.currency_exchange_coverage_rate  \\\n",
      "count                                      114447.000000      \n",
      "mean                                            0.122154      \n",
      "std                                             0.041529      \n",
      "min                                             0.100000      \n",
      "25%                                             0.100000      \n",
      "50%                                             0.100000      \n",
      "75%                                             0.100000      \n",
      "max                                             0.200000      \n",
      "\n",
      "       terms.repayment_term  translator.image     video.id  \\\n",
      "count         118316.000000      6.301700e+04    76.000000   \n",
      "mean              13.461079      1.165835e+06  1291.697368   \n",
      "std                8.147379      7.036716e+05  1027.999559   \n",
      "min                2.000000      2.392400e+04   150.000000   \n",
      "25%               11.000000      5.186710e+05   487.750000   \n",
      "50%               14.000000      1.186147e+06   665.500000   \n",
      "75%               14.000000      1.668411e+06  2154.250000   \n",
      "max              122.000000      2.499150e+06  3008.000000   \n",
      "\n",
      "       video.thumbnailImageId  borrower_count  \\\n",
      "count            7.600000e+01   118316.000000   \n",
      "mean             6.155305e+05        1.930686   \n",
      "std              4.622450e+05        3.031490   \n",
      "min              2.975740e+05        1.000000   \n",
      "25%              3.244948e+05        1.000000   \n",
      "50%              3.361080e+05        1.000000   \n",
      "75%              6.247900e+05        1.000000   \n",
      "max              1.754457e+06       46.000000   \n",
      "\n",
      "       partner_average_loan_size_percent_per_capita_income  \\\n",
      "count                                      118316.000000     \n",
      "mean                                           30.026747     \n",
      "std                                            17.099929     \n",
      "min                                             0.000000     \n",
      "25%                                            24.300000     \n",
      "50%                                            34.900000     \n",
      "75%                                            40.100000     \n",
      "max                                            54.800000     \n",
      "\n",
      "       partner_currency_exchange_loss_rate  partner_default_rate  \\\n",
      "count                        118316.000000         118316.000000   \n",
      "mean                              0.212524              3.821426   \n",
      "std                               0.262458             10.477770   \n",
      "min                               0.000000              0.000000   \n",
      "25%                               0.089354              0.085473   \n",
      "50%                               0.164711              1.483890   \n",
      "75%                               0.364948              3.652283   \n",
      "max                               7.513861             94.939083   \n",
      "\n",
      "       partner_default_rate_note  partner_delinquency_rate  \\\n",
      "count                        0.0             118316.000000   \n",
      "mean                         NaN                  4.358150   \n",
      "std                          NaN                  5.472373   \n",
      "min                          NaN                  0.000000   \n",
      "25%                          NaN                  0.000000   \n",
      "50%                          NaN                  2.536684   \n",
      "75%                          NaN                  8.017062   \n",
      "max                          NaN                100.000000   \n",
      "\n",
      "       partner_delinquency_rate_note  partner_image.id  \\\n",
      "count                            0.0      1.183160e+05   \n",
      "mean                             NaN      1.496709e+06   \n",
      "std                              NaN      6.022014e+05   \n",
      "min                              NaN      3.560000e+02   \n",
      "25%                              NaN      1.495190e+06   \n",
      "50%                              NaN      1.592689e+06   \n",
      "75%                              NaN      2.081410e+06   \n",
      "max                              NaN      2.520600e+06   \n",
      "\n",
      "       partner_image.template_id  partner_loans_at_risk_rate  \\\n",
      "count                   118316.0               118316.000000   \n",
      "mean                         1.0                   11.528738   \n",
      "std                          0.0                   10.893620   \n",
      "min                          1.0                    0.000000   \n",
      "25%                          1.0                    0.000000   \n",
      "50%                          1.0                   16.058249   \n",
      "75%                          1.0                   18.498507   \n",
      "max                          1.0                  100.000000   \n",
      "\n",
      "       partner_loans_posted  partner_portfolio_yield  \\\n",
      "count         118316.000000            111917.000000   \n",
      "mean           18174.752434                31.054133   \n",
      "std             9423.900289                10.444965   \n",
      "min                0.000000                 0.000000   \n",
      "25%             9546.000000                29.000000   \n",
      "50%            18150.000000                33.100000   \n",
      "75%            21415.000000                36.000000   \n",
      "max            30794.000000                41.000000   \n",
      "\n",
      "       partner_portfolio_yield_note  partner_profitability  \\\n",
      "count                           0.0          106890.000000   \n",
      "mean                            NaN               2.362943   \n",
      "std                             NaN              11.299052   \n",
      "min                             NaN            -117.790000   \n",
      "25%                             NaN              -1.700000   \n",
      "50%                             NaN               0.000000   \n",
      "75%                             NaN               2.230000   \n",
      "max                             NaN              30.300000   \n",
      "\n",
      "       partner_total_amount_raised  number_of_loans    posted_year  \n",
      "count                 1.183160e+05         118316.0  118316.000000  \n",
      "mean                  7.309958e+06              1.0    2013.558783  \n",
      "std                   3.289116e+06              0.0       2.343340  \n",
      "min                   0.000000e+00              1.0    2006.000000  \n",
      "25%                   6.764500e+06              1.0    2012.000000  \n",
      "50%                   7.646925e+06              1.0    2014.000000  \n",
      "75%                   8.133425e+06              1.0    2016.000000  \n",
      "max                   1.136698e+07              1.0    2017.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get the same summary statistics for categorical columns (string) we need to do a little data wrangling. The first line of code filters for all columns that are a data type object. As we know from before this means they are considered to be a string. The final row of code provides summary statistics for these character fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity</th>\n",
       "      <th>borrowers</th>\n",
       "      <th>description.languages</th>\n",
       "      <th>description.texts.en</th>\n",
       "      <th>description.texts.es</th>\n",
       "      <th>description.texts.fr</th>\n",
       "      <th>description.texts.ru</th>\n",
       "      <th>funded_date</th>\n",
       "      <th>location.country</th>\n",
       "      <th>location.country_code</th>\n",
       "      <th>location.geo.level</th>\n",
       "      <th>location.geo.pairs</th>\n",
       "      <th>location.geo.type</th>\n",
       "      <th>location.town</th>\n",
       "      <th>name</th>\n",
       "      <th>payments</th>\n",
       "      <th>planned_expiration_date</th>\n",
       "      <th>posted_date</th>\n",
       "      <th>sector</th>\n",
       "      <th>status</th>\n",
       "      <th>tags</th>\n",
       "      <th>terms.disbursal_currency</th>\n",
       "      <th>terms.disbursal_date</th>\n",
       "      <th>terms.local_payments</th>\n",
       "      <th>terms.loss_liability.currency_exchange</th>\n",
       "      <th>terms.loss_liability.nonpayment</th>\n",
       "      <th>terms.repayment_interval</th>\n",
       "      <th>terms.scheduled_payments</th>\n",
       "      <th>themes</th>\n",
       "      <th>translator.byline</th>\n",
       "      <th>use</th>\n",
       "      <th>video.title</th>\n",
       "      <th>video.youtubeId</th>\n",
       "      <th>partner_charges_fees_and_interest</th>\n",
       "      <th>partner_countries</th>\n",
       "      <th>partner_name</th>\n",
       "      <th>partner_rating</th>\n",
       "      <th>partner_social_performance_strengths</th>\n",
       "      <th>partner_start_date</th>\n",
       "      <th>partner_status</th>\n",
       "      <th>partner_url</th>\n",
       "      <th>dispersal_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Farming</td>\n",
       "      <td>[{'first_name': 'Evaline', 'last_name': '', 'g...</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Evaline is a married lady aged 44 years old an...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>-0.583333 35.183333</td>\n",
       "      <td>point</td>\n",
       "      <td>litein</td>\n",
       "      <td>Evaline</td>\n",
       "      <td>[]</td>\n",
       "      <td>2017-06-08</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>fundraising</td>\n",
       "      <td>[{'name': '#Woman Owned Biz'}, {'name': '#Pare...</td>\n",
       "      <td>KES</td>\n",
       "      <td>2017-04-03T07:00:00Z</td>\n",
       "      <td>[{'due_date': '2017-05-10T07:00:00Z', 'amount'...</td>\n",
       "      <td>shared</td>\n",
       "      <td>lender</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>[{'due_date': '2017-07-01T07:00:00Z', 'amount'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Julie Keaton</td>\n",
       "      <td>to purchase more tea leaves to sell to the tea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>Kenya ECLOF</td>\n",
       "      <td>2.0</td>\n",
       "      <td>[{'id': 3, 'name': 'Client Voice', 'descriptio...</td>\n",
       "      <td>2014-05-29T13:30:02Z</td>\n",
       "      <td>active</td>\n",
       "      <td>http://www.eclof-kenya.org/</td>\n",
       "      <td>2017-04-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity                                          borrowers  \\\n",
       "0  Farming  [{'first_name': 'Evaline', 'last_name': '', 'g...   \n",
       "\n",
       "  description.languages                               description.texts.en  \\\n",
       "0                ['en']  Evaline is a married lady aged 44 years old an...   \n",
       "\n",
       "  description.texts.es description.texts.fr description.texts.ru funded_date  \\\n",
       "0                  NaN                  NaN                  NaN         NaN   \n",
       "\n",
       "  location.country location.country_code location.geo.level  \\\n",
       "0            Kenya                    KE               town   \n",
       "\n",
       "    location.geo.pairs location.geo.type location.town     name payments  \\\n",
       "0  -0.583333 35.183333             point        litein  Evaline       []   \n",
       "\n",
       "  planned_expiration_date posted_date       sector       status  \\\n",
       "0              2017-06-08  2017-05-09  Agriculture  fundraising   \n",
       "\n",
       "                                                tags terms.disbursal_currency  \\\n",
       "0  [{'name': '#Woman Owned Biz'}, {'name': '#Pare...                      KES   \n",
       "\n",
       "   terms.disbursal_date                               terms.local_payments  \\\n",
       "0  2017-04-03T07:00:00Z  [{'due_date': '2017-05-10T07:00:00Z', 'amount'...   \n",
       "\n",
       "  terms.loss_liability.currency_exchange terms.loss_liability.nonpayment  \\\n",
       "0                                 shared                          lender   \n",
       "\n",
       "  terms.repayment_interval                           terms.scheduled_payments  \\\n",
       "0                  Monthly  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "\n",
       "  themes translator.byline                                                use  \\\n",
       "0    NaN      Julie Keaton  to purchase more tea leaves to sell to the tea...   \n",
       "\n",
       "  video.title video.youtubeId partner_charges_fees_and_interest  \\\n",
       "0         NaN             NaN                              True   \n",
       "\n",
       "                                   partner_countries partner_name  \\\n",
       "0  [{'iso_code': 'KE', 'region': 'Africa', 'name'...  Kenya ECLOF   \n",
       "\n",
       "  partner_rating               partner_social_performance_strengths  \\\n",
       "0            2.0  [{'id': 3, 'name': 'Client Voice', 'descriptio...   \n",
       "\n",
       "     partner_start_date partner_status                  partner_url  \\\n",
       "0  2014-05-29T13:30:02Z         active  http://www.eclof-kenya.org/   \n",
       "\n",
       "  dispersal_date  \n",
       "0     2017-04-03  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity</th>\n",
       "      <th>borrowers</th>\n",
       "      <th>description.languages</th>\n",
       "      <th>description.texts.en</th>\n",
       "      <th>description.texts.es</th>\n",
       "      <th>description.texts.fr</th>\n",
       "      <th>description.texts.ru</th>\n",
       "      <th>funded_date</th>\n",
       "      <th>location.country</th>\n",
       "      <th>location.country_code</th>\n",
       "      <th>location.geo.level</th>\n",
       "      <th>location.geo.pairs</th>\n",
       "      <th>location.geo.type</th>\n",
       "      <th>location.town</th>\n",
       "      <th>name</th>\n",
       "      <th>payments</th>\n",
       "      <th>planned_expiration_date</th>\n",
       "      <th>posted_date</th>\n",
       "      <th>sector</th>\n",
       "      <th>status</th>\n",
       "      <th>tags</th>\n",
       "      <th>terms.disbursal_currency</th>\n",
       "      <th>terms.disbursal_date</th>\n",
       "      <th>terms.local_payments</th>\n",
       "      <th>terms.loss_liability.currency_exchange</th>\n",
       "      <th>terms.loss_liability.nonpayment</th>\n",
       "      <th>terms.repayment_interval</th>\n",
       "      <th>terms.scheduled_payments</th>\n",
       "      <th>themes</th>\n",
       "      <th>translator.byline</th>\n",
       "      <th>use</th>\n",
       "      <th>video.title</th>\n",
       "      <th>video.youtubeId</th>\n",
       "      <th>partner_charges_fees_and_interest</th>\n",
       "      <th>partner_countries</th>\n",
       "      <th>partner_name</th>\n",
       "      <th>partner_rating</th>\n",
       "      <th>partner_social_performance_strengths</th>\n",
       "      <th>partner_start_date</th>\n",
       "      <th>partner_status</th>\n",
       "      <th>partner_url</th>\n",
       "      <th>dispersal_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>113988</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>112704</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>110407</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>93403</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>944</td>\n",
       "      <td>118316</td>\n",
       "      <td>29014</td>\n",
       "      <td>82362</td>\n",
       "      <td>113989</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>113754</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>114249</td>\n",
       "      <td>118316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>149</td>\n",
       "      <td>29987</td>\n",
       "      <td>4</td>\n",
       "      <td>113859</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3452</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1249</td>\n",
       "      <td>16047</td>\n",
       "      <td>1</td>\n",
       "      <td>1860</td>\n",
       "      <td>3174</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5426</td>\n",
       "      <td>2</td>\n",
       "      <td>5413</td>\n",
       "      <td>736</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>277</td>\n",
       "      <td>27</td>\n",
       "      <td>425</td>\n",
       "      <td>58864</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>3081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Farming</td>\n",
       "      <td>[{'first_name': 'Anonymous', 'last_name': '', ...</td>\n",
       "      <td>['en']</td>\n",
       "      <td>Hello Kiva Community! &lt;br /&gt;&lt;br /&gt;Meet Jane, w...</td>\n",
       "      <td>Richard is 47 years old, married to Roseline a...</td>\n",
       "      <td>Mwanajuma is a married woman with two children...</td>\n",
       "      <td>Gerald is a married man. He has 2 children. He...</td>\n",
       "      <td>2016-03-08</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>1 38</td>\n",
       "      <td>point</td>\n",
       "      <td>Likoni</td>\n",
       "      <td>Anonymous</td>\n",
       "      <td>[]</td>\n",
       "      <td>2014-03-26</td>\n",
       "      <td>2014-02-24</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>funded</td>\n",
       "      <td>[]</td>\n",
       "      <td>KES</td>\n",
       "      <td>2017-02-01T08:00:00Z</td>\n",
       "      <td>[]</td>\n",
       "      <td>shared</td>\n",
       "      <td>lender</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>[]</td>\n",
       "      <td>['Rural Exclusion']</td>\n",
       "      <td>Tim Gibson</td>\n",
       "      <td>to purchase a solar light and gain access to c...</td>\n",
       "      <td>Kiva Borrower PETRE from Kenya</td>\n",
       "      <td>thgCQSzTxdw</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>VisionFund Kenya</td>\n",
       "      <td>3.5</td>\n",
       "      <td>[{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...</td>\n",
       "      <td>2009-05-29T11:35:11Z</td>\n",
       "      <td>active</td>\n",
       "      <td>http://www.visionfundkenya.co.ke/</td>\n",
       "      <td>2017-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>26243</td>\n",
       "      <td>2420</td>\n",
       "      <td>118306</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>413</td>\n",
       "      <td>118316</td>\n",
       "      <td>118316</td>\n",
       "      <td>110407</td>\n",
       "      <td>77254</td>\n",
       "      <td>118316</td>\n",
       "      <td>5035</td>\n",
       "      <td>3680</td>\n",
       "      <td>118316</td>\n",
       "      <td>621</td>\n",
       "      <td>621</td>\n",
       "      <td>45638</td>\n",
       "      <td>112704</td>\n",
       "      <td>65318</td>\n",
       "      <td>115076</td>\n",
       "      <td>2498</td>\n",
       "      <td>117372</td>\n",
       "      <td>113783</td>\n",
       "      <td>115735</td>\n",
       "      <td>622</td>\n",
       "      <td>117372</td>\n",
       "      <td>15663</td>\n",
       "      <td>6917</td>\n",
       "      <td>1405</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>117376</td>\n",
       "      <td>106639</td>\n",
       "      <td>28570</td>\n",
       "      <td>39681</td>\n",
       "      <td>46681</td>\n",
       "      <td>28570</td>\n",
       "      <td>104271</td>\n",
       "      <td>28570</td>\n",
       "      <td>2498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       activity                                          borrowers  \\\n",
       "count    118316                                             118316   \n",
       "unique      149                                              29987   \n",
       "top     Farming  [{'first_name': 'Anonymous', 'last_name': '', ...   \n",
       "freq      26243                                               2420   \n",
       "\n",
       "       description.languages  \\\n",
       "count                 118316   \n",
       "unique                     4   \n",
       "top                   ['en']   \n",
       "freq                  118306   \n",
       "\n",
       "                                     description.texts.en  \\\n",
       "count                                              113988   \n",
       "unique                                             113859   \n",
       "top     Hello Kiva Community! <br /><br />Meet Jane, w...   \n",
       "freq                                                    5   \n",
       "\n",
       "                                     description.texts.es  \\\n",
       "count                                                   3   \n",
       "unique                                                  3   \n",
       "top     Richard is 47 years old, married to Roseline a...   \n",
       "freq                                                    1   \n",
       "\n",
       "                                     description.texts.fr  \\\n",
       "count                                                   3   \n",
       "unique                                                  3   \n",
       "top     Mwanajuma is a married woman with two children...   \n",
       "freq                                                    1   \n",
       "\n",
       "                                     description.texts.ru funded_date  \\\n",
       "count                                                   4      112704   \n",
       "unique                                                  4        3452   \n",
       "top     Gerald is a married man. He has 2 children. He...  2016-03-08   \n",
       "freq                                                    1         413   \n",
       "\n",
       "       location.country location.country_code location.geo.level  \\\n",
       "count            118316                118316             118316   \n",
       "unique                1                     1                  2   \n",
       "top               Kenya                    KE               town   \n",
       "freq             118316                118316             110407   \n",
       "\n",
       "       location.geo.pairs location.geo.type location.town       name payments  \\\n",
       "count              118316            118316        110407     118316   118316   \n",
       "unique                 47                 1          1249      16047        1   \n",
       "top                  1 38             point        Likoni  Anonymous       []   \n",
       "freq                77254            118316          5035       3680   118316   \n",
       "\n",
       "       planned_expiration_date posted_date       sector  status    tags  \\\n",
       "count                    93403      118316       118316  118316  118316   \n",
       "unique                    1860        3174           15       3    5426   \n",
       "top                 2014-03-26  2014-02-24  Agriculture  funded      []   \n",
       "freq                       621         621        45638  112704   65318   \n",
       "\n",
       "       terms.disbursal_currency  terms.disbursal_date terms.local_payments  \\\n",
       "count                    118316                118316               118316   \n",
       "unique                        2                  5413                  736   \n",
       "top                         KES  2017-02-01T08:00:00Z                   []   \n",
       "freq                     115076                  2498               117372   \n",
       "\n",
       "       terms.loss_liability.currency_exchange terms.loss_liability.nonpayment  \\\n",
       "count                                  118316                          118316   \n",
       "unique                                      3                               2   \n",
       "top                                    shared                          lender   \n",
       "freq                                   113783                          115735   \n",
       "\n",
       "       terms.repayment_interval terms.scheduled_payments               themes  \\\n",
       "count                       944                   118316                29014   \n",
       "unique                        3                      277                   27   \n",
       "top                     Monthly                       []  ['Rural Exclusion']   \n",
       "freq                        622                   117372                15663   \n",
       "\n",
       "       translator.byline                                                use  \\\n",
       "count              82362                                             113989   \n",
       "unique               425                                              58864   \n",
       "top           Tim Gibson  to purchase a solar light and gain access to c...   \n",
       "freq                6917                                               1405   \n",
       "\n",
       "                           video.title video.youtubeId  \\\n",
       "count                               76              76   \n",
       "unique                              72              74   \n",
       "top     Kiva Borrower PETRE from Kenya     thgCQSzTxdw   \n",
       "freq                                 2               2   \n",
       "\n",
       "       partner_charges_fees_and_interest  \\\n",
       "count                             118316   \n",
       "unique                                 2   \n",
       "top                                 True   \n",
       "freq                              117376   \n",
       "\n",
       "                                        partner_countries      partner_name  \\\n",
       "count                                              118316            118316   \n",
       "unique                                                 11                52   \n",
       "top     [{'iso_code': 'KE', 'region': 'Africa', 'name'...  VisionFund Kenya   \n",
       "freq                                               106639             28570   \n",
       "\n",
       "       partner_rating               partner_social_performance_strengths  \\\n",
       "count          118316                                             113754   \n",
       "unique              9                                                 16   \n",
       "top               3.5  [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...   \n",
       "freq            39681                                              46681   \n",
       "\n",
       "          partner_start_date partner_status  \\\n",
       "count                 118316         118316   \n",
       "unique                    52              3   \n",
       "top     2009-05-29T11:35:11Z         active   \n",
       "freq                   28570         104271   \n",
       "\n",
       "                              partner_url dispersal_date  \n",
       "count                              114249         118316  \n",
       "unique                                 34           3081  \n",
       "top     http://www.visionfundkenya.co.ke/     2017-02-01  \n",
       "freq                                28570           2498  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = df.dtypes[df.dtypes == \"object\"].index\n",
    "df[categorical].head(1)\n",
    "df[categorical].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 a: What sectors get the most funding?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pie chart below shows that agriculuture and food have the most loans in Kenya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154f79f98>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TSWkUKsoqvwutfl3c+CVy6EBeuqXbblkQz/MIwjjyBwBfqCkvUYwORIgfkZQiVFFW+U0A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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e8aedd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['sector'].value_counts().plot(kind='pie',title='Outliers by Sector')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the average loan amount for each sector? We see that Agriculture has the largest average loan value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector\n",
       "Agriculture        563.734169\n",
       "Arts               406.397638\n",
       "Clothing           429.577926\n",
       "Construction       439.261651\n",
       "Education          876.895799\n",
       "Entertainment      476.286765\n",
       "Food               379.565182\n",
       "Health            1031.538797\n",
       "Housing            512.875000\n",
       "Manufacturing      546.231721\n",
       "Personal Use       242.357294\n",
       "Retail             490.601514\n",
       "Services           452.595146\n",
       "Transportation     514.767473\n",
       "Wholesale          590.485075\n",
       "Name: loan_amount, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('sector')['loan_amount'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize this as a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1571dd5c0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15601a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('sector')['loan_amount'].mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we change out metric of interest to the total sum of dollars invested. Agriculture still comes out on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154208f98>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgGMz8/aRbXsBf5+Zz59ey9SiiHgj3SpYdwL/OTMvmXKTtEKzOlCqbfYaTegA\nmXljP/e5RL8Kz/F002pAd9fj03cy3Esz8+l9jfqn6aaI3eUVPsZVmdRn324Rcc/Rrpb+Sn0mB7nn\nG1Ip2IAdT1dYcDLd/CRvGSPWv/QXDKvozr25x3NTEOyqSh7jqkzqs+9U4DMRsYmu1vtBdBUlJ021\nVcs3mFKwAbuuXw3rnpn5o4h48hix7mDhhVO2MlLJtAsqeYyrMqnPuMz8bEQcRreG6APoltV6x4Cu\nmuZKwX467YY07BP9vQuXRsT36I75TsnMQ8s1qynFjnFtDpSqqr4U7Lfolumy+6WgiHj2ApvnjvEX\nJt0ezQav1FVVZj5p2m1o2KV05a2b6LpNRheeNqkXNKTl/QYx2KbhiYi/GXl82sjjDVNpUJseCJwB\n3Bu4JTPf3H+VWCRD2xvMRIImddWy78jjZ4w8fvikG9KwvYHfpnsfD2bWzoHa2k8BPvPsflEtq/ty\nuNXAmpHHg3hjDMQ/Ad8DNgNHRsQRbOtTj6m2rD2DWd7PpK5aRsvhRh8Pol9yIB417QbsQr467QYs\nl9UvkrQDEfGazDx/2u1YCf8UlqQdO3baDVgpu18kacceGhGvW+gH9qlL0vDcAdzMgFZ+MqlL0o79\nJDM/OO1GrIR96pK0Yx+adgNWyuoXSWqIV+qS1BCTuiQ1xKQuLSIiXhwRj592O6TlMqlLi3sxYFLX\nYDhQqiZExCOB84F70F2s/GVmfjQi1gBn0s0UuRtw7miJWkQ8B1jf/2wNcGJmfqv/2YV066veAFwP\nnJWZ/zDy+94LzK0X+7bM/NxI3OuA09i2WPHRmflP1Q6A1LNOXa34U+C/ZubZ87afQDdr4TMjYg/g\nioj4Umb+MCIeAbwPOCIzr5sfMDOP7+d/vzQzL5n34w8Dp2fmp/o4V0TEUzLz+pF9Hg88PTO9ctLE\n2P2iVnwUOC4izomIQyNi7g7Ao+impf08sJHuSv5x/c+eD1yyUEJfTETcG3h4Zn4KoH/9lWw/bzzA\naSZ0TZpX6mpCZn45Ip4KHAT8GXAMsA64HThlrttkAWt28lfOv218Fd0yctJUeaWuJvRrSN7eL7h8\nFvD0/kefAN7cX10zcgUP8Gng2IjYbyTO/GR9G/CA/merATLzJuDaiHhRv/1RwMHAV4r/w6QVMqmr\nFS+PiK9ExJfokvqfAGTmRcDHgc/3P/viXILPzGuB44D3RcSVEfFF4Jnz4v4d8Ma+++acke2vAF4b\nEV8GLgKpWDleAAAANklEQVSOy8wb6v3zpOWx+kWSGuKVuiQ1xKQuSQ0xqUtSQ0zqktQQk7okNcSk\nLkkNMalLUkP+P37ZOI3CRQwQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x156002cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('sector')['loan_amount'].sum().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the distribution of loans by sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 149 different activities\n",
      "\n",
      " Just looking at the top 15 activites\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x157073d30>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pEQsNHCnXWHwduKrvVQ5AZp5fMM/uVN0irtfS49mCql8z+yP5pwPrRs6M+xjg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\nA1hAS5IkSQP8f7ZvPYNr/q8QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129c62e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"There are %d different activities\" % df.activity.value_counts().size)\n",
    "print(\"\\n Just looking at the top 15 activites\")\n",
    "df.activity.value_counts()[0:16].plot(kind='bar',figsize=(12,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we know agriculture loans tend to be larger on average, it would be interesting to see if they tend to have more borrowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "borrower_count\n",
      "1     105297\n",
      "2        847\n",
      "3        656\n",
      "4        587\n",
      "5        762\n",
      "6        622\n",
      "7        724\n",
      "8       1044\n",
      "9       1004\n",
      "10      1432\n",
      "11      1124\n",
      "12      1096\n",
      "13       855\n",
      "14       754\n",
      "15       615\n",
      "16       475\n",
      "17        51\n",
      "18        32\n",
      "19        71\n",
      "20       135\n",
      "21        34\n",
      "22        17\n",
      "23        15\n",
      "24        20\n",
      "25        15\n",
      "26         8\n",
      "27         2\n",
      "28         3\n",
      "29         1\n",
      "30         2\n",
      "31         3\n",
      "32         3\n",
      "33         2\n",
      "34         1\n",
      "37         1\n",
      "39         1\n",
      "40         1\n",
      "41         1\n",
      "42         1\n",
      "45         1\n",
      "46         1\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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FK/NHRkbyww8/lNt26tQpDhw4QPfu3TGZTFgsFm6++WYyMjIAaN++PQDNmjWj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130ba32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036542684791)>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.groupby(\"borrower_count\").size());\n",
    "ggplot(df[df['borrower_count'] < 20], aes(x='borrower_count', fill='sector')) +\\\n",
    "    geom_histogram(binwidth=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the bulk of all loans only have a single borrower, but agriculture loans tends to be outliers and much more likely to have multiple borrowers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 b: What amount do people lend on average?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart below shows a strong relationship between loan amount and number of lenders. This suggests to us that as the loan amount goes up the amount donated by any lender does not. Instead larger loans are simply fulfiled by more individual lenders. This makes sense given our understanding of KIVAs platform, since the majority of lenders loan the minimum $25.00. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FF5g2bRq9e/emoKCAFi1aMGrUKADefvttJk2axMMPP4zT6aRXr1488sgjpKenX/c8L7nS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\nERERyRNK/kVERERE8sT/Bwmz9+JeyapGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135b59ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (283685468)>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df , aes(x = 'loan_amount', y = 'lender_count', color='sector')) + geom_point() + \\\n",
    "ggtitle(\"Relationship between loan amount and number of lenders\") + xlab(\"Loan Amount\") + ylab(\"Number of lenders\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That might suggest that larger loans are more difficult to fulfill, since they tend on average to require more lenders. Let's take a look. We start by plotting all loans. We can already see below that the chart is skewed by outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q3RIbrbRarbLb7dEuo1nC7YcZ0I/YQj9iDz2JLfQDaFyb/82yWq2aN2+e1q1bp0mTJmnL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\n9mrvKSd/8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135d91630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (322914354)>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(loans_full, aes(x='loan_amount', y = 'borrower_count', color='status')) + \\\n",
    "    geom_point() \n",
    "\n",
    "##Hugely skewed by a few big loans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We limit our data set to all loans below $5000.00."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SjjvkfpN6juXc7pE392tLRiYPYSRDQh1DREQkpD7//PNQR2gTh2ze4+PjiY+P\n56WXXsLlctHQ0MDekTbV1dUkJSUFPaSIiIiIiBzGF1ZfeuklHnroIdxut795NwyDtWvXBi2ciIiI\niIjsc1jN+6uvvsrgwYODmUekzdW76/nfdX+j0evi2t6TOD55UJPtDxa+zKKqlcRaonnr+PtaOUr4\nWlOzmQ93LqZXdHfOzxgT6jgiIiISRAHP856WlqbGXSLSBSunU+aqoMpbxz0bn2dZVYF/213rnmZh\n1Qp8mNT6Ghj/dfOLNoWzZVUFTF3/OF/sXsZLxR9y5/qnQx1JREREgijg5v3EE0/klVdeobS0lMrK\nSv//IuHs28rmw7qe2LpvytNldRvaM06be6u06ZSWq2oKQ5REREQkNDweD5MnT+bCCy+kqqrqiI5x\n4oknBrzvpEmT2L59+xE9TlsIeNjM008/jcvl4u67982LrTHvEu56OZrPaZ7q2PclawsGvgi+hmqS\nLaHJss2whiiJiIh0drsbq5ix/CmW7FyDy9d2c9s7LHZGpA5ixrDfkhLV/OKgZWVl1NXV8c4777TZ\nY4azgJv3lStXBjOHSFCkx6bQIyqNHxrLAbBhZeaAq/3bnxp8C79Z/YB/uVd0ertnPBp/zD6fNbWb\n2emuxIKFa7MnhTqSiIh0UjOWP8WishVtflyXz82ishXMWP4Uf//pLc22T58+nc2bN3PXXXeRl5fH\nRRddRFFRETNmzGDWrFmcddZZHH/88RQUFGAYBv/85z+JjY1l2rRpFBYW0rNnT1yuPVcgLykpYdq0\naTQ2NhIVFcU999xDRkYGjzzyCAsXLqR79+5UVFS0+XM8HAE375988kmL68eNG9dmYUSC4ckhe97o\n9e56Yu2xTbZ1j05lbv5fqHRWkxSdiN1uD0XEI+awOHh+6B14fB5sloDfziIiIm1uZUVwh6K2dvzp\n06dzww03tHr9obq6Os444wymTZvGjTfeyIIFC7BarTQ2NvLGG29QXFzMxx9/DMADDzzA5MmTGT16\nNF999RUPPfQQv/71r1myZAlvvfUW9fX1Ie99A/7bftasWf7bbrebgoICjj/++JA/AZFAHdi47y8p\nOrEdk7Q9Ne4iIhJqQ5P7BeXM+/7HP1KDBu2ZaS4jI4PGxkbKysoYOnQoAJmZmWRkZACwfv16nnrq\nKZ599llM08Rms7F582aGDBmCxWIhPj6e/v37H/2TOQpH1LwDbN26lYcffrjNA4mIiIhI5Jkx7LdB\nH/N+MFHaECZ+AAAgAElEQVRRUZSX7xkmu3r16ibbDMNospybm8t//vMfLrvsMkpLSyktLQWgT58+\nXHHFFQwfPpyioiKWLFlCbm4us2fPxufz4XQ6KSwM7eQQR3y6rlevXmzcuLEts4iIiIhIhEqJ6tLi\nmPT2cvrpp3PdddexZMmSQ05vPnbsWBYtWsT5559PZmYmycnJAEydOpUZM2bQ2NiI0+nkjjvuIC8v\nj1GjRjFx4kTS09Pp2rVrezydVhnm3sulHsL+Y95N0+T777/nyy+/ZM6cOQe5V+QrLi4O2rHtdjtp\naWmUl5fjdrfdJ9RQiYmJoaGhIaQZ3iv9kue2zwVMxnQ9jj8e8AXOs5be7L99TeZ5jM84ocl21ST8\nqCbhRzUJP6pJ+GmvmmRmZgbt2BKejmjYjGEYpKSkcP/99wcllMiR2O2s4tnt7/mXP9u1hKHxfTk1\nNR9o2rgDPF78TrPmXURERCScHXbz7vF4ME0z4mblkI5vcdWqZus+3/Wtv3kXERERiXQBX2F1165d\n/OY3v2HYsGEMHTqUX/3qV/7B/SLhYGSXIc3Wje46PARJRERERIIj4Ob97rvv5thjj2Xx4sUsXryY\n4447jhkzZgQxmsjhSY1O4tKM8VgwMICTk47ltNQR/u1z8//SZP9L0n/WzglFREREjk7Aw2Y2b97M\no48+6l++9tprOeOMM4ISSuRIXZA5lgsyx7a6/cAGXkRERCSSBHzm3ePx0NjY6F9uaGhoNmemiIiI\niIgET8DN+4QJE/j1r3/Nm2++yZtvvskVV1zBz3/+82BmExERERE5KI/Hw+TJk7nwwgupqqo6omOc\neOKJAe/79NNPs3Llyha3rV27lscee+yIMgQq4GEz11xzDRkZGSxYsACfz8d5553HxIkTg5lNRERE\nRCJETYPJy/MbWV/ixeNtu+ParNA/w8qlo6NIiGk+6qOsrIy6ujreeeedtnvQg7jqqqta3ZaXl0de\nXl5QHz/g5r22tpby8nL+9re/8cMPP/DCCy/Q0NBAbGxsMPOJHNJn5Ut4dOsb/uWzUk/isp4T+MvG\n2SyrLsBleprsH87j3v9c+AJfV+25pHN2TAb/GHRDiBOJiIgE5uX5jazZ3oZd+488Xliz3cvL8xv5\n3fjoZtunT5/O5s2bueuuu8jLy+Oiiy6iqKiIGTNmMGvWLM466yyOP/54CgoKMAyDf/7zn8TGxjJt\n2jQKCwvp2bMnLpcLgFtvvZXKykoqKyt54okneOihh9ixYwdlZWWMGTOG66+/nltvvZUJEybQs2dP\nbrvtNmw2Gz6fj4cffpitW7fy2muv8cgjjzBu3DiGDx/Opk2b6Nq1K//4xz9wu93ccsstlJWVkZGR\nwZIlS/jyyy8P6+cR8LCZ2267jcrKSgASExMxDINp06Yd1oOJBMP+jTvA3J1f8s6OL/imanWzxh2a\nX6wpXGxpKPE37gCbG0p4r3RhCBOJiIgEblNZ2zfugRx/+vTp5ObmkpaW1uL2uro6zjjjDF5++WXS\n09NZsGABn376KY2NjbzxxhvceOONTa7oO3LkSF577TXq6uoYNmwY//rXv3jrrbd47bXXmhx38eLF\nDB06lOeff57//d//paampsn2bdu28cc//pHXX3+d3bt3s2rVKl5//XWysrJ47bXX+MMf/sCuXbsO\n++cQcPO+efNmpk6dCkBCQgK33347GzZsOOwHFGkPFe7qUEc4bNsbyput29agaymIiEhkyEm3hu3x\nBw0aBEBGRgaNjY1s3ryZoUOHApCZmUlGRsa+x8nJASApKYlVq1Zx4403MnPmTP/Z+b0mTpxIYmIi\nU6ZMYfbs2VitTfMlJyf7j7v3cYuKihg+fM81aPr27UtKSsphP5fDmm2mtrbWv1xXV4dpmof9gCJt\nLd7S9J/QbFg5petwbEbLb/I+jsz2iHXYRnYZjPWAt+TE7mNClEZEROTwXDo6ikFZVmxt3MPbrDAo\na8+Y94OJioqivHzPibDVq1c32XbgDIm5ubksX74cgNLS0iYXHt277zvvvENCQgIPP/wwV1xxBU6n\ns0nvO2/ePPLz83nxxRcZP348zz777EEfE6B///4sW7YMgK1bt1JRUXHQ59SSgMe8/+IXv+D8889n\n/PjxGIbBp59+ynnnnXfYDyjS1l79yT1cveoBil07SbYl8OKxdwHw14HXsqqmiKqa3bxRtWc8Wbo1\niUePuT6UcVtltVp58Zhp/Hnji7hMN3/sPYnu0Yf/iVxERCQUEmKMFsekt5fTTz+d6667jiVLljB4\n8OCD7jt27FgWLVrE+eefT2ZmJsnJyc32OeGEE7jxxhtZvnw5DoeD3r17U1ZW5t8+ZMgQpk6dyhNP\nPIHP5+O2225rcqK7JRMnTuTWW2/lkksuITMzk6iog38gaYlhHsbp83nz5vHVV19hs9k44YQTGD16\nNAD19fUd9ourxcXFQTu23W4nLS2N8vJy3G530B6nvcTExDQZMxaJVJPwo5qEH9Uk/Kgm4ae9apKZ\nGZ7/miwt++6776ivr+ekk05i8+bNTJkyhc8+++ywjhHwmXfY8yll7NjmV6+85JJLmDNnzmE9sIiI\niIhIZ9KzZ09uuOEGHnvsMTweD3fddddhH+OwmvfWaOy7iIiIiMjBpaWlMWvWrKM6Rps07y0NyA8H\nXq+X9957j8rKSjweD6NGjSItLY13330XwzBIT09nwoQJWCwBf29XwsDkpdOppJ4kYpmV/6dm2/dO\nBXkMGczMD+950j/f+S0+fIxJOU6vQ5F25jG9rK/bSldfMt0sSaGOIyISkDZp3sPVypUriYmJ4bzz\nzqO+vp4nn3yS7t27M2bMGHJycpg7dy4FBQVBvxKWtJ3952ivpJ6zlt7c5KJL+29fRQm3L/1r2Dbw\nV33/ACWNOwF4pfhTnht6R4gTiXQebp+HOzc8zZraTQBcmvlzLsg4LcSpREQOrUM374MGDfLP6wlg\nsVgoKSkhOzsbgH79+lFUVORv3qurq5t9S9jpdJKQkBCUfDabrcmfkc5qtWK329v9cfc+5vivr2u2\nbRUlh5WpvWrybeVaf+MOUO6uZFHVKk5JHd6mjxOqmrQlvU/CT0eoyTe71vgbd4BXij9hUtbPcFgi\n8zl1hJrsT+8TkdZ16FfU3ul39l5Ba8yYMXzyySf+YT4OhwOn0+nff+nSpcyfP7/JMUaPHs2pp54a\n1JwtTU8kgWvtimqBbm9JsGuSZjS/IFNactcjytpZ6H0SfiK5JsnepsNkLIaF9LQ07BHavO8VyTXp\nqFQTaWsB/5ZavXp1q3NmhvM0kVVVVbz22muMGDGCoUOH8umnn/q3uVwuoqP3zUean5/PgAEDmtzf\n6XT6J/xvazabjeTkZCoqKvB4PEF5jPYUFRVFY2Njuz/u3vp8NPJvzc6+fzTyb4dVv/aqSW/SGBjf\nm3W1WwDoE5vJYGvvNn+thaombUnvk/DTEWoy0NKTE5KH8FXF91iw8Lvs86jcdfgXSwkXHaEm+9P7\nJHA66dP5BDzP++mnn86HH34Y7Dxtqra2lhdeeIEJEybQp08fAF555RVOOOEE/5j3nJwchgwZ0uox\nNM974NprXt4HN8xiYfVKTk4cyi39Jjfbvnfc+/5j4QPV3jUpqN2K1/QxKCE7KMfXXMnhRzUJH6Zp\nUtK4i5S4LkR7I3uIRkepyV56nwRO87x3PgGfeR8wYABz584lPz+/yZn2pKTw/Yb+woULaWhoYP78\n+f7hMHs/hMybN4/U1NQmY+IlMtzSbzK3HGT7kTTtoTIgvleoI4h0WoZhkBmdSowj8htFEek8Am7e\n582bx0cffdRknWEYrF27ts1DtZXTTz+d008/vdn6yy+/PARpRERERESOTsDN+6pVq4KZQ0RERERE\nDiHg5t3n8/H888+zYcMG7rzzTmbPns2UKVOwWq3BzCcCwAOFL/Fl1Z4PkL9IHcWVvc9qMqc7hOdw\nmWtXP0KJaycDY3txz4DfhjqOSKdQVP8DiypW0tXehfFpI7EaugCaiHQcAf9Ge/DBBykoKGDFihWY\npsnChQu57777gplNBIB/lyzwN+4A7+5cwOSldzbb75dLDzYSvv1dtuJuNjmLcfpcLK8t5Ia1j4Y6\nkkiHt6VhB7ese5w3d3zOk9vm8PiWt0IdSUSkTQXcvH/11Vfcf//9REVFkZCQwHPPPceiRYuCmU0E\ngFklHzdbV0nzKcRcBDRxUrvZ7alpslxUH7yZi0Rkj6VV63CZ+2b2WFz5fQjTiIi0vYCbd5vNhsWy\nb3eHw6Grhkm76BebFeoIR8R6wNsr2hLZU9GJRILuUV2bLGccsCwiEukCbt779+/P7Nmz8Xq9bNy4\nkbvuuouBAwcGM5sIADMH/g4Dw79sxdLi+PZwG/N+T7+r/LmtWHg876YQJxLp+P4n+RguyvgZ3Rwp\nDI7P4eacS0IdSUSkTQV8kaba2lpmzpzJF198gdfr5eSTT+aOO+7o8Jf91UWaAqeLaoQf1ST8qCbh\nRzUJP6pJ4HSRps4n4HEv3333HXfffbeGyoiIiIiIhEjAw2ZeeOEFRo0axdSpU5k3bx4ulyuYuURE\nRERE5AABN+/PPfccn3zyCaNGjeKDDz5g3Lhx3HjjjcHMJiIiIiIi+zmsMTC7du2isrKShoYGXC4X\nlZWVwcolndg5S2/B9+O0j/GWaF79yT0sq17P7OKP2V1XTTn7XnfXZU1ibLcRoYrqt7pmI7evfwof\nPuyGjZ72NGIdMVyZdRa5cZE5W46IiIiEn4Cb9zFjxuB2uznzzDO5+OKLeeSRR4iKigpmNumE/rDq\nr/7GHaDW5+Tjkq95Zse/afQ1/8LP37a/ERbN+97GHcBtetjoKgEXzCh8lueOuQOHpokUERGRNhBw\n837hhReycOFC/vvf/1JfX09DQwMjR44kISEhmPmkk9nuKm22bu6uRS027uFkb+N+oCpPHVWeOtIc\nSe2cSERERDqigMe8X3XVVcyaNYu3336bn/70pzzyyCOMHDkymNmkExqdNKzZuut7TSLV3iUEaQIX\nbXG0uD47JoMUe2I7pxEREZGOKuAz79u2bWPBggUsXLiQVatWMWLECK655ppgZpNO6Pq+F7F+5Ta2\nu8sB+FnyCPom9uT+Ab/n32ULsRgW3i1d4N8/XC7MNOuYafx2zV+o9tTRMyqdwQl9iLLYObf7KViN\ngD8ji4iIiBxUwM37pEmTGDNmDBdccAF///vfcThaPtMocrSeGHpLs3XdolL4Tc9zALgy66z2jnRI\n0bZoXhw6LdQxREREpIML+JTgokWL+P3vf09lZSXvvfceW7duDWYuERERERE5QMDN++LFi/nlL3/J\nZ599xrx58/y3RURERESkfQTcvP/tb3/j5Zdf5vHHH+fJJ5/klVde4bHHHgtmNulkvqlcxfWrH2ly\n/YAFZcu5bNmf+L5sewiT7fP81vdZsntNqGOEhOF0gqsx1DFEjprb56HSXYtpmofeWToM0zSpcNfg\nMb2hjiJyVAIe8+52u8nNzfUv9+vXD69XbwBpG+cuvRUPe15Pk4v+TH5cf76rW++f8f22bY/SZXsc\nLw+fEZJ8uxuquGzNvQC8Uz4f2yYrc/LvD0mWUIhZ9F9i/m8xpmHQcNIYnMdppimJTKtrN3Fv4fPU\nehsYHN+HGblXEkNMqGNJkNV6GrhrwzNsqN9Gki2e6f2uJDdWF9CTyBTwmffo6GhWrVrlX161ahUx\nMfqFJ21jb+O+19L9Gve9qsy69gt0gCvX3Ndk+cC8HZl1Zxkx/7cYAMM0iVk4D6O2OsSpRI7Mk1vf\nodbbAMDq2o18UL44xImkPbxbOp8N9dsAqPTU8uy2f4c4kciRC/jM+80338zVV19N7969Adi0aROP\nPvpo0IKJhBNvCxdhKm/YTVpMSgjStDN30wtkGYDh9jT7cCUSCZxeV9Nln6uVPaUjObDODV7VXSJX\nwM17TEwM//nPf1ixYgWmaXLssceSnJwczGzSiUVjx0n4XFX1yh5n8uwPc5us6xSNO+DtloG7dx/s\nWzYC0Ng/D19y53ju0vFMzBjD41vexsQkxZ7IaakjQh1J2sH4tJF8vmspNd56LFiY2P2UUEcSOWIB\nN+833XQTH374IaNHjw5mHumk5ub/hfOX3o4TN70c6Tx+zM0AnLP0FnyYxODgjfw/hyzfOd1HkWCN\n4fGt7xBnjeGlYXeFLEu7s1io+cUF2LduwjQseHplhzqRyBH7eepP6R/bk1LXbvLic+hiiwt1JGkH\nWdHpPD74RgrqtpIZlUavmG6hjiRyxAJu3gcMGMDcuXPJz88nNjbWvz4pKSkowaTzeTN/ZrN17+U/\nGIIkLRuTNoIxaZ30LJ3Fgju7b6hTiLSJnNhMcmIzQx1D2lmyPZGRSUNCHUPkqAXcvM+bN4+PPvqo\nyTrDMFi7dm2bhxIRERERkeYCbt5fffVVhgzRJ1YRERERkVA5rNlmPvzww2BmkQ7OcDZg31SIGR3D\n+N1PAZBoxHJS12NJsMXy9o4v8OAlxZrAFb3OwmHYOD5pMFbDgsvn5pvK1VgMC0ZVJcsq1zI4qR8L\n3JvxmSYen5e19Zvobe9OdG0fPD4LjfYyalxdwFZGhaUIrwFdXXZ2OtzE+qDeAAxIarRQGeUjxguN\ndgMfJlbA6wNMSPT2J8qbgtNSRqpzJC5LJVZrEY0kY5gb2BGzC0ywmeCxQJwb6ux77uuwgssHSRYH\njV43Fgx+4k5ijaWKASSyxFqJaZik+3rhcfXAsO3A60vCwEZ2VDlFvh/oa02h1F2FEw/HWPLY2JBA\nmrWeJGcRuywuiO/JNlcqFmsVw6x1lPqqyYs7lu+qDJKiYZPlWyrNBvLsGaxzlxCLgztSzmBHTTFJ\ncem8XTafLtZoahMTKWz8gVHxg7GU/kC0xcGWrrGsrd/MTxMGEV1Tg81iJSEliyW1BRwXP4B15d/j\nM00m5pxJibeS3tHdKHdV4ja9HJ80iGiLA0wT+8YNGG4XL1o28FHNd+TY0zknajB1rloSk7rz5a5l\n9I7JJDNjABXuGvrG9qCo/ge62hM5ruugoLwePaaXryu/xzRhZNJg7JaAfx1KBFlZU8hOVxXDEvuR\nYk8MdZygWVuzma+da8kmna7Wjvs829uK6g3sclfzk8R+JHfg149EFsMM8BJz1113HWPHju10Y96L\ni4uDdmy73U5aWhrl5eW43eEzs8qRiomJoaGhocVtRkM9ia88j7W6khNPqgz4CgP/k3QMN/e5hDvW\nP8Wa2k17Vprsma9w75/7GVB5NV0bh/+4mw8DCyY+ChNfoDzm6xbvc7B1PerG07v2vB9XmRgH7Ogx\n6liV8gANth0t5wpgXaKrH4MqrsOCvclj1No2syrlQUw8YECMJ4Njdk/FZu55/xUlvEylYw1Dd9+G\n3UwAYHP8W5RHf8PQ3bcT5dszG9S2uPfZFv/vQz/3I8zf2s+wX2xP7h/wO5I/+ZCotau4P7eWuRme\ngH7+FvZ8iAK4uMc4bhx+WZu+T7ymjxkbnmV5zQYAhsT34d7+V2E1rG1y/IM52PskUkTK767XSj5l\ndvEnACTZ4vlr3h9JczT/OyvSa/Lxzm/8M+jEWqN4YMA1ZMdkhDrWUQmHmrxS/AmvlnwKQIo9kYcH\n/i+pLbx+WtNe75PMTH1/o7MJ+CJN8+bN4+abb2bMmDGMHDmSkSNHcsIJJwQzm3Qg9o2FWKsr9ywc\n2LwdxOLKVXxXVbCvcd///gccx+FN8jfuezZb/H92rz+19cc+yLqM+rH7rWq+o82MI805svVcAazr\n1nAyFuzNHiPek02Cu69/v/SGE/yN+55sY0hzHu9v3Peu69qY72/cmzyHQz33I8zf2rE31G+joGI9\nUWv3XNztg+4tNO6t3Hdv4w7w3o4FLdzp6Gx3lvkbd4DvazeyqaGkzR9HQmtu2SL/7UpPLQt3Lw9h\nmuB5v2wR5o/vmXpvI/N2fhviRB3D3LIv/bd3u6v5smJlCNOI7BPwvxPvf3VVkcNlRkcf0f1shpVk\ne8KhdwS8RiM+PFhaeFl7LEd2dVaPUYeDLofc52h4jPpWt3n32+axNN3PY6lv9rw8lrom92mLfEcj\nzpGAabVheD3YfOAN+HTBPglBmMov1hrd5Oy+gUG8VVeM7mjirTFUe/a9/hNssQfZO3Id+NqN76DP\ns73FW2P8V+OFjvv6kcgT8F+lPp+PZ555hsmTJ3PRRRfx2GOP4fF4gplNOhB3n344j/kJJmAN8GXj\nMOxc2/t8+sX15PIeZ2DFghUD248nZY0DBnx5LQ0UJb6EFxcmXpyWnQA0WEvZFP/anuEZe+9jHvB/\nS+tMKEx8EZelChMfHhowf/xvb8Nd4VhFScx/D//Y+63bFvc+NbYfL4BkqfDn3xr3HnX2bf79SmLm\nUeH4HgCnZScbE2ZTGv0luxzLAHBZKilKeInyqG8oj/oGEx9uo5rCxOebPb7lxww23+FlbXZZ1R/X\nOX78VZJiT8Rh2LBg4ZLMcfRJ6EXt+LMx7XaeWB7X5HhW754cUd599Yw27BgYdHPsuQhUF1scN/W9\nmLaW5kjiqp7nYDOs2AwrV2adRfeorm3+OBJa12VfQJItHgODk5KPZUzX/FBHCoqre53rf/3+pEt/\nzkk/OcSJOobrci70v35GJQ/jlJThh76TSDsIeMz7X/7yF9atW8dFF12Ez+fj9ddfp0+fPtxxxx3B\nzhhSGvMeuIDGKHq9YLGAYfBBySImZJxIvceJw2LDZrExv+w7RqcPx2N6sWBgMfZ9vvSaXsDAalio\ndVYTH5344yWuvcRYYyiq3k7fxCwaXA3Ue110jenCd2XrGJ4+kJ21FVR4dtEvKZelW78iv9cJVFVU\nsMpVyEndRrBi81ccm30CjU4npY4q+kZl8dLaN7ko9xcAfLj9K07POoH3Ni/ghOiBpHdPZ82O1Qzq\nPpj5GxfQO74f2ekZLC1ZQn7GCBZv+YruMb3pk57JguL/Y1Tm8ZTXlGHDQnJCKttKC+nZLZfymjJc\neOmRkMF3pasZ3m0w5bUV7PZWMqBLDiWV28lIyqLeWUO9q47UxO5s37mFrNTeeJ1O6uorSUzpzsry\nDQxN64fX7d7zJdC4ZEqrd5LdrScNDQ0UVWymb3I2W2t+oJu9K1HR0bhdTuyOaKrrKoh2xOCwR1NS\nv5OM2FSczlpsNgc2m4Nt9aX0jO2Gy+MELDhsDna7qkhxdKHB7dzz87fH4fK5cVjs+EwfPkxs+48f\nN03w+cBqpaR6BxmJ3TF9PjxeN3Z7FPXOWmKj4zFNE4/pxW6x4fZ5sFtsQX2feM09n17aY6z7XuEw\nlvdoRdrvrr2vpdZ0lJp0SUmiandlRNTkUMKpJod6/bRGY94lWAJu3s8++2zefvtt7PY9Y3NdLhdn\nn312s7nfOxo174ELp1+2R0o1CT+qSfhRTcKPahJ+1LxLsAQ8bMY0TX/jDuBwOJosi4iIiIhIcAXc\nvA8cOJCZM2eydetWtm7dysyZM+nfv38ws4mIiIiIyH4CHsQ1ffp07r33Xs4//3wARo0axbRp04IW\nTCJPnaeB57fPpcJdw5iUfIZ3GUClq5Z7Nz7Pblc15e7KJvun2ZOardsr2uIAn4kLz75pA3/8cmRG\n/RjiPX2osW1kR+znTecc32/O8LSGE0hyDabeWkxx3MeYhpeuDSNIcQ3FaS1je9yHmIYHfDT/GHuQ\ndRZfNHmVfyDKl0KNbSOm4cNraWB77Pu4rTWk1o+gZ/05mIaHDQnPU+fYQrJzKNl152PioyjhZWqi\nNhDtSaNH3XjAQqNlFzHe7jitZdh9iViwURzzGfX27RgmmAdMz2jzxZFVeyY2Xxxl0QupjtrQNOt+\n0y9afXt+LA7TgtPq2/Nj2vuFVRN8BlgxmFacxSllBndl72J+Uh2mCV0sUTgNH9mWJJ75Lh6fAW9m\nNLDOVk2CJZYlcbUADDe7UeutIzEqkaXRNXjwke9Oob6+ggR7PMti63GZHi7MGMtpqcc3iWovXIdj\n/Tp8iV1o+OlJYLfzbdU6vtj9HYnWOGxWK/XbXJwQP5j8+AEtvl6ORLFzJ2/t+BwT+GX3U8iKTm+z\nY4u0hQpXNfcUPU+Vp44xXY/jksxxQXmc/5QtYk3tZvrFZXF2+slNvmsUqFpPPa+WfEa1p5bTuo7g\n2MR+QUh6+Ha5qni95DMafW4yorqyzVlGVnQ6E7ufqguzScQK+JW7a9cuNm7cSE1NDYZhUFpaSkND\nQ4e/SJME7p51z/Ft5VoAvty9ggcH/oGZRS+yy13V4v6tNe4ATp+r+UoDMuvHkV03EYC0xuOxGFaK\n4z5tNvd4asPx9Ku53H9XO3FUOQoYUPMb/zqHmURR4qw9TfqBc40fZN2xFXcQ4+0GQLQr1b850Z3L\nhoQX6Fc7xT9f+zGVU1mRfDcDq3/vn3d+SOUNLE29jcEVNxLlS2n1Z5DceCzLu07Hba1uti2v8g97\n5oAHUhvzWdH1nj0Xitprvw8xe6dndJo+/6a92zw//unD5K7MbVxfF8UXSY17thtQRSOYUOAt53fZ\nuxm1y84Tac4fH2TflJQ/sIU9U9XvhMY96/7DLohuuu7RLW+SGZXOoIRsAGxbNxM/923/j9pSXcl3\no/O5p/B5fPiaPOdP+ZqZ/a9mcEKfVn9mgWrwNnL7+if9r82lVet4YsjNxGm6SAkj1659hErPng/I\nr5V8Soo9gdPT2vb6Kv8u+5Jntr0HwIKK5bh8HiZljD3EvZqbWfQSq2qLAPiyYiV/HXgtObGhHYvt\nNX3cueFptjvLmm2r8tRyda9zQ5BK5OgF/PF62rRpTJw4kRUrVrB8+XJ+9rOfceeddwYzm0SYldWF\n/ttefKyt3czuVhr3I5Xo6n/QZf96d/P9Wr3vYV64Kcqb2sJGiPP0pGvj8CYXWrJgI835P/7Gfc9h\nrKQ0/OSgjTuA3Ywj1pPVPIZp9Tfuex7DTrw7J+D8rTLgtR6Nre63Me7/2Tvv+LiuKvF/X5s+GmnU\nixT5VAEAACAASURBVC3Jltwdtzi9JyQhpAEppLBsYEPohIXs/mBhCWUJZbMbFtilpJBASA8hDZKQ\nXpzmJO7dktX7SNPLK78/Rhpp1GVLtmzfrz/+aO59t5w3570357137jkG7+ceeHjYt/o2Zz5rzQ1Z\n02lNDWwN140w3CF9g7FlaLKuA6A10ZV1UxnQQzTHO6dlbIFguhgw3Ad4s3fLtM+xObQnq7xpWHky\nWJbFlvDeTFm3DLZH9h2wbAdKbyo0quEOsDm0d9R6geBwYNJP3oPBIFdccUWm/MlPfpKHH354RoSa\nTdhsNhRlZsLISZJENBpF0zRU9fB/fVfrmcOWYPqCKCGxJG8e3jZ3VpKUAyWiNeBPHpNVHrWdml0f\n1vaNqBteniwpOZiVwXSAmNJBr20zFdELMga8hUm34z3KY+dl1QXsW0lFw2iWZ8x5DBLE1JFZPy3J\nIKo04zLK+8cziKpN+7Uv2QPDBe027qhOjmrAl8ZlFoYV3vQfmAF/XMFSnM70E265Ym62CKXlLMmd\nB2PszuK8eZm+B0KlVoZXdRHS028PPIqTKl85Tm3mn7zLsjwt+3AoOdKuXbNVJ8OTBK3KWzimnPur\nk0W+Ktb1bs4q7893UeOpYGe4EQAZiUW51Qf0nU6HTkrsNgrteXQmAiO2LciZO+M6P9LOE8HsYdKh\nIr/yla/wmc98hhUrVgCwfft2fvWrX/GLX/xiRgU81IhQkZMnpqT4390P0ZMKcU7+sZyZv4bmeCff\n230HQT1CxIhntXcrjhF1AyjIWFiD/u4AVvqp85zIRXhT8wlpe2hwPw6SmeUmMtC2PHp+2uddbWaf\n51FMKUlp5Cz8yZXElA72eR7GkOMj+/b3H6vOpuexpO9GbKaPiNyCJScx5Dj17kdIqJ2URs6hInoB\nFga7c+6m176Z4uipzI1cioVJned+ul3rcafmMid8MZIlk1QCOIwi4koXmulBQqXZ+VeC9p0jZZDB\nZvipCn0c1fTQ5nyJHsf72e2GfReQTohkytl1A/skAZ9v8XNVk8Znl3SzzZM20DUkDEmiCCf3vuNG\nlhRunxtlqyOKExtb3HFAYomeQ8xM4FJdbHXGMCyTxSkvyUQYl+JkmytOyjK4pOg0PlF2Tpao9g3r\nse3ahpnjI3raOVgOJy90v8vz3evxKC5UWSEqJTjFdwxn501fkp3dkSbubXkGC7iq7EMsdM+dsM90\nIELgzT5mq04aY+18f/edhI0YJ+Qu46tVV4zZdn91Ylgm97U+x9ZQHQvcc7im7Lz98gXvTvZxZ9OT\n9Ophzi84nlP9K6c8xlCmSydN8Q7ubn6auJGk2OanJdFFhbOI68o/glOxH/D44yFCRQpmigmN94su\nugiASCRCe3s7CxcuRJZltm/fzvz58/nLX/5yUAQ9VAjjffLM1h/AqSB0MvsQOpl9CJ3MPoROZh/C\neBfMFBPeXouIMgKBQCAQCAQCwexgQuP9uOOOm6iJQCAQCAQCgUAgOAhMPZirQCAQCAQCgUAgOCSI\n5c+CCdkQ3M0v9z1E1Ezw8eIz+FjJGZltpmXym8bHeKXnA3JtXgzDIGzE8PdeQFHsJAzi2CwfMipJ\nKYRkyRhyDFNKoZk+TBLYrFwsLPa5H6HV/fdxZbHp+awIfAvV8mChI/UfwikpjIxMWG1gp+93mOis\n6vkuNtOPSRJdjiJbKs3uv9Hiem7S++7US1kQ/Cdshp+40oFDLySlhNmdcxdhrY5FgS+Tl1qKhUVS\n7kG1XMSVTmy6H1NOstf7J3rtmyeeaCwGFp6OtTLFkpgXvor8+FoSSje7cm4fjPc+kNAJBYdiQ5NU\nAnpo5HjjzKEgYUgWNkkjaaWQkbi0+DSuq7iQm3fewfrQdgCWe+bxo4Wfn/RubQ3X8fP6BwnpUYrt\nftoS3RTZ/Hxj3tXMmQXJkhJmitvqH+D94E4KNB8RI07KSnFZyZlcWnz6oRZPcAC0xDv5Wd2faEt0\nc3LeMXx+7seytj/e/ioPtj2PXbbxxbkfZ7Vv+hKDCaaPl7rf467mpwD4TMVFnHaAC2QFgsMJ8eRd\nMC6GZXDL3rtpS/YQ1CPc1fwUOyKDIRZf6F7P053rCBsxmmIdtCa70SILKY+ei2Z5cFgFyGiAhM3K\nQcODwyzEZZShWW7slh8JGRmFqsjlYMmZBEGj/V/S9yU0y4uEhIyG1P/PZnlRLTe5qcVUhS9nYd/n\nsJv5SEgo2LGbeWiWl6rw5Xj0qnHnGPq/Nvhp3PocNMuNV69Gw4PLKGFh3+coip2CP7U8I7/DLES1\n3Hj0Kmzk4DALWBj8LDK2Sc834v8AY2wvSpxESewMNMuNR59LTfC6EX110jdUGcN9+HjjzGFIaYs+\naaUXW5lYPNr+Mn/veidjuANsCu/ljcCmiQ+ofm7Zcw8tiS5CRpTd0SbCRoy9sWZ+Xv/ApMeYSR5t\ne4nXAhuIGDH2xdvoSvXSp0e4o+lJdkUaD7V4ggPgtvoHM8fcM11v8WzXW5lte6LN/K7pcfr0CB3J\nAD/e+weS5uG/+PNIoyvZy231D9CTCtKTCvLf9ffTnZzenCICwWxGGO+CcYkZyRHhHLuSg5lRR8ue\nahslBvpkkJCwmb5x26jm2HHRB7AbedjHGcdmTF6+sfbFZvpw6qUT9lcsB6rpmvR8U8VmZGc4tpkH\nJ+PxtlD9iLq90eZJ9U2ZOn1jxP7vmiU/wGNlBZ5om2D2M1x/Q42+4QZgzEyMGc5WcOjoTYUxhiRy\n0y2DvmEJrQSCIxlhvAvGxaM6WetbnCkXaLks9w5m9zwhdxkO2ZbVJ2DbiC6NNM6sUfwyhtYlpT6S\namB0F5H+ui7HO5k+Q/sO/dzpfIt252ujzpeQewjaRomdPsZ8nY43R52jy/E27a5XsDDG3ade2zaS\ncm/2HNawvxPIMF5dj+N9DGnQuOhyvDV239GY7LxDcMg2rqk4NyuTrARcMMm07ZqscnLeMaNuO8O/\nelJjzDSn5q1EGeXyWGTLY6ln3iGQSDBdnDnkGLNJGiflLc+Ul3iqKbIN3rCv9NaSp3kPqnyCial0\nljDPORgescZVwRxH8SGUSCA4uEw6SdPRiojznn5S+kLPemJGnNP8q/BrOVnbG2PtvNO3jTJPIbFE\nnEAqxIP71uNLHENS7uVSx2UkLDdvpf5CWLdIyWEMM47DKgKlB198LaYUZ0/OH9BJocgMmsQGactw\niB1VGjmb3MQSglo9+YljAIlmxzM48BNWGuizbwOgMHoC+cm1RJR9JNQeFMtBl/Y2KS2YnYRJZ8Tq\nDyUFhpZu4o8fi930E5b34bHmkpLDdKpvIikWnsQcyuIXYUhRerQNOKwiwtI+3FYFppSkw/4GlqyD\nObgPOaZMRDbxmiq9cjoZkmz2J1AaIpciSxhYqCiAiYmFFxtBM4lmgSGlh12o1yLHa/HIEdps6whK\nOm7NRQthFBQ+X3opYTmOX8vh5Z738dty2Nu9h04jxFx7EbuMDjRJxWuo9JgRapQCwok+7CgsK1lJ\nnd7FUk81uyNNKJLMDXM+Sq7NQ2usix/X/QETi29UX0Wlc+I3ERm1WgYvdr9HyIhS7Sxjb7SZInse\np+StGNH2UJ0nOyONbArtptxeSK8eJm4mON2/irxhx/9UEfGrDz2vBzbSlujmWN9iKp0lWTrpTYV5\nqec9HLKNs/OP3a+ERYeCw10nw5noPIkacZ7vfhcJibPy1+BSHAdRuskh4rwLZgphvE+AMN4njzBK\nZh9CJ7MPoZPZh9DJ7EPoZPII4/3oQ7jNCAQCgUAgEAgEhwnCeBcIBAKBQCAQCA4TDg9nPsGs5IWu\n9bQkuihQcvh969Ms8MwlFrXRFU/yoeJKHg+8SrmtkApnEdsidVxWcAb3tT5DqZZPNAadSYW1jhzW\n60Fskk7KUtBS5Ths9TTQDCq40IiSohQfrf1RIuwaJHSwq6DEygEJSzEpiK6lx7URzXSjWk589m3s\nMcPkKhpWpAYNLzh2IsWWErU34LDcSGYupbZ26pKg2YJYSpCgnuIEjqEttIKkrZGA8xUipsnxShXb\nEhYuNYmmhmnSQ5ypLmBdajcuNBa5FrEjEuEUWyHrIjqSmqDQ1cWWRAtneZfwVmw3ChILPfN4J7yd\ntZ5F7Is2Y2JxrL2K50KbWOucT1uqi5ie5BNlH+HRvtc4OW857/XtoEcP8e2qT7Mr2UCNq5x7d/2Z\nFr2Ha0s+wl+6XmaFZz5mxEZjLMTHixfywd6/M99XScDnZXtoDx+Zez5vxHcxx1FEheWmrreOtaVr\n8YcTWDY73R6V9kSAcjWfe+v+jE/zclXNpSP03hrvYmNoDyu8NZQ48g/yUSeY7ZiWyd5YC3bZNiti\n9o9Gd7KPrlQflc6SEQvuJyKoR2iJd1HhKMSjzlwkqanSkwzSFujDZzhQp/hcLqxHaYp3UuYoIEd1\nz5CEAoFguhA+7xMgfN5H58tbbqU+3pZVVxQ7mfnBa5FQCKv1bM67FVNODDboX4yZFz+GBX2fRcGG\nQQIFe/9mCwkJC4NNeT8lbKsbXMA5dIFp/+d5waspiZ0xrK+ViYKSkAOsz/8mS3q/Qm5qyYh2QP9n\nEwkZE51dOXcQUdpYFfj3TLuY3M6Ggu+zNPDP5KRqsDCp8zxAm/vFjCy+xGIW9X4BBXtmPICgupvN\n+T8dVf5J143GwFk7pE9R9BTmh65FQiak1rHFfysmyRHtho6tmnDnex56bBb/b3mcuGRktXNaCg8e\n++PMtC/3vM9/1v0pM+S/VF/LKf6Ri0ynk8P5PBmNI9mX17BMfrTnbt7u2wrAlSVnc235+YdKzFF5\nI7CJn9Xdi24ZlNsL+cnCL1CSUzgpneyKNPLvu35H2IjhU938x4LPUeksOQhSj8/6vu3csvceEmaK\nEns+P17wefJt44fdHWBfrI1/2/lr+vQIHsXJ92uvp9Y9Z4Ylnpgj+TyZboTP+9GHcJsRTJmkkRxh\nuANUhi5DQgHAo1dRGD8+u0G/QTg38lEU0k+7Bgz39Gap/6/CvNA1WX2GJxNy6IUZwz2772BDu5lH\nWfRcfKnFo7Yb/Jw+DWRUKsMfZ0nfF7PaOc1i8uOryUnVZNpXhS8DSxqyTxdn9kUaclrl6DU49OKR\nyZBG2acx60ZjaIKl/r9V4csyc3v1agpix43abuhnXYb/qonx26p+w31Yu5hk8F7HB5nynU1PZj5b\nwB1DygLBptDujOEO8EDb84T06CGUaCT3NP8V3Uof682JTv7W9eYEPQZ5oPXvhI20QdmnR3i47YUZ\nkXGq/LHlGRL9yaTaEt080fH6pPs+3PZCJu9C2IjxQOv4Wa4FAsGhRxjvgmnEGlYyJ9VudMbqO/kR\nACxp/HFGIE1u5PST+6Fx5mcDw6SY5L7IljTBfcLYl4nx+gmOPoYfSUNvkmcrU5FPkkbu32xEmpJY\nw/Zpap0FAsEhQBjvgiljU2zUuCpG1Nd7H8Tsj9Ae1PbQ5Xw7u0G/LbnP/QgGaXcag3j/JjPjymJi\nsMf7x6w+wxMaJdROWpzPDakambgpLnfR6nyWgLZpxHar/1/6s9E/b4p6z0Nszft5Vruo0ky34z36\ntO2Z9vXeBwZdUYAG95/RpVjWeAC92nbiavvkkzRNlLhp6LZhfeo8Dwz5/nfT5Xh71HZDP2smfGOX\ng8/VOXENLIEZ0s5tqqwqGkyodMOcS7LeStwwd6RPvODoZbl3PifnDh4v15Sdh0d1HkKJRvLpigux\nSRoAlY4SPjzJ5GIAV5eem/EJ92s5XFF69ozIOFX+sfwjOOX0m79yRyEXF50y6b5Xlp6dyd3hU91c\nXXrujMgoEAimD+HzPgHC531s3u3dRmuiixJbAb9seJgVvgVEotAYDXL5nDX8se0papzllDsK2RTe\ny9WFZ3JP89NUOorpi+jUp2Kc55rH32N1eGWZhGmRSPlZkivzWmI9KpCHh07CLFbK2RZrBgvy7C4C\nVpQ8yUUs6gZJwqEZOCOrCTo3I+NGMR2sdCZ4KbmLCs1DOF6IpHspcnTSGS9Bd7TgNL2kTBdrNInX\nk1347EkUOU47Ic4zT2BjuAxJaybi3EBXKs7F6kpejXfis0l4lBQ7Uu18zH48f4u/R77NyWLnMt4L\ntvOJ8lX8qWE3mpyixquzLr6LS3zH81JwEzYUluYt4sXAes71H8f20F5My+RE1wL+3LeOczzLaY60\nEyTOp2ov508tz3JW3rG8E9xKRzLAd+Z+ih2pJqpcpTy256/sS7TzjxUX8ufOV1jprcGKyewJ9fLx\nsmVs2Pk8NQXzCHhc7Azu5dyqc3k7tptyewGluGjo3ceK4lV4onHQbPS5NLqSvZQ5Cnl4zxPk2rx8\npPJDI/Telexle3gfiz1Vk/arPRAO9/NkOEe6L69lWTTFO7DJGsV2/yGScHz6UmF6UkEqHEVosjol\nnUSNOO2JHkrs+TgV+8QdDhIxkpgeGWdMQTam9vR8YJ+K7f5Zk+zoSD9PphPh8370IYz3CRDG++QR\nF9vZh9DJ7EPoZPYhdDL7EDqZPMJ4P/oQbjMCgUAgEAgEAsFhgjDeBQKBQCAQCASCwwSRpEkAwGPt\nL/NW71aWeqrHjcu8M9LI+r5tbGtv4H1zBwCuVAX++AoSag+djnXpBY8Dt4UWFMZPxG746bF/QFRp\nBqA6dBVOo5SgshOvUYMhRZFNOy6jlIC2kaTWi4mONzkfj15JUN2FhgdLMunS3sVpFZEgQFk87ZMd\nsG3Co1cSUvfhNIuQLZVO29s4rALicif+5EoUy0mD8zEi9npkU6Mm/ClU00Oj/a+EnDvAlKkJfwqb\nmUuL7Tl6XZuz9t2RLKQgdRwpOUyH6zUsy8CTqGZO7BIMKcpu3z2YcnoBbiZYzsB3YTIYttFM10km\nWDKDi0oH2qWjbaLpkJLBZsGnqi4hZiR4u+EN9shBHKZEibeMiBGjxFbArlgDuYqHu5pr0MIxHlzq\n4RF9K37NS1lUptsIscRXyxY6yVW9rMippTvZy1rfEmrc6cXHrwU20BBrp8ZVTn2sDbts47zC46ec\nxGamaIl38nzHO/hUD+cWHIciKRP2ebN3M3ujLSz3zme5dz4A7/ZtZ2ekgcWeKlblLNgvWVKmzrNd\nbxM2opzuX03JLPXtFghmgpSp80zXW0SMGGf4Vx/UtQ3v9G1jV6SRpZ5qVuTUHrR5BYLZhPB5n4Cj\nwef97uanebjtxUz5tLyV3DTvmhHttof38c2d/5eJkQxpw315z//LxG1vcf2deu+DmUQ/VcErKYul\nIzIYJNnkv4Xq0NX4UumL7tCkSgNMtm5/+liYvJ//XZYG/hm7mZdptzHvRywI/hNOozhTtzX3Nvrs\n2wCwGX5WdH8bzfIA0GV/l0bPE6zs/m4mlGJKCvFO0ddHT640XsKlsbbvxzh2A76628lPF8Qm1VeV\nFH688AtsDO3mnua/jhhvqaeaHy34HLJ06F7SaZqG7oZPvPQtwkY6ZvipeSv4l3nXjtvvyY7X+U3j\nY0A6Ms635/8jISPKbfUPZNrcVH0Np/lXTlmm/9jze97s3QJAjurmfxZ/bdKLd4Uv7+xD6GRqfH/3\nnbzTl7425qoefr7ka5mINdPFaDp5tuttfrHvISB9Tv/rvGs5Oe+Y0brPCoTPu2CmEG4zAl7p+SCr\nvD64fdR263o3ZRnuAP7EiozhDlAQX5v+0G8kFiTWZrYp2MhLrMCbqs7UjWaQT7Zuf/pIyBRFTsVm\n5ma1K46dhsMoGlZ3aqacm1ycMdwB8hOrKY6emhUDXbO82PTc0Y3riYI/TLbPBOMkZHioPDHpvrpl\n8GbvZl4ddgwMsCVcRyAVGn/Sg8C6zk0Zwx3g9cBGTGv8GP6vBjZkPltYvB7YyKs9G7LavBbYMLzb\nhKRMPWO4AwT1CBtCu6c8jkBwOBI3khnDHaBXD7MptOegzD30OmVh8Vpg40GZVyCYbQjjXTDiiclA\nHOPhFNlGvhpNKN3Dyl3ZZXn49m4MKb4/Yk4LFhZRWwPDg6jH1Jas+OzpusEsssP3IykHiGiNI8ZO\nysHpFXg/mBOb2mldZMsbVbcALtmBexbE6S5zFWaVC2y5E74NKLLlZZfteRTZ88ZtMxk0WR1xzuzP\nOALB4Yhd1shVPVl1B+v4H+6eU2TLHaOlQHBko9x88803H2ohZjOh0Mw9dVQUBbfbTTQaxTSnmAl0\nGjk+dwmv9mwgZibwqW5+uOCGUQ34+a5y+vQIPakgMTOdZCmqNqGYdmymn5jSwq6cu9DldKptJAja\nduLRKwGJDsertLieI6TtIj+xGgkFgxgS8pAkTRImKVJSHykpjIIDkLAwMomVYnI7lmSiE+3fnk6w\nJCFh9id/Aou43Ikl6STkAIqVbtdte49Gz+Mk5C5yk0uRgF5tC3U59xNVWshLHIOETFDdxW7fXZl9\nTyhdmKRwGsUklC525txOr30LzlQZTqMYC4M6z31E7PUHnm514An5kHE8kh2X5CBupUa2G8Ln97n4\n2l4XT5TpRJX0MaX1H1oOFHTJQpMUSu35aLLKmfmrubL0HJbnzKcu2kLK0qlyliJJ6Zu6r1VdSYWj\naOREBxFFUVhYWE0iFqcp1kGpPZ9vzLtmwtf0S73z2BdrI24kOD53KddVXMgx3vk0xjqIGnFW5yzk\n+jkXo8lTX/qz1FPNzkgjqqRwZek5nJ6/atJ9NU1D1/UpzzmbmC3XrulC6GTySJLEEk81OyMNaJLC\nVWXncqp/xbTPM5pOlniqaIynz981vkVcX3EJqjzx2pdDxcHSidfrnbGxBbMT4fM+AUeDz/t0IfxG\nZx9CJ7MPoZPZh9DJ7EPoZPIIn/ejD+E2IxAIBAKBQCAQHCYI410gEAgEAoFAIDhMEMa7QCAQCAQC\ngUBwmCCSNB3F7Ig0cE/zXzEsg75UhK5UL0W2PG5Z+AU8soN/2/UbdkebiJvJEX3tegHVoStRLTdt\njpfocr2NO1nJor4voJhOeuzvsdv3ezTTS3XoE9gNPyYmbqMCkxRhtRHNctBj/4Bm9zPIpp3q8JW4\n9DIMKYFHr8TCJKzWo1ou4kpnJryjZviwm7mkpAhq/4LWkFqPajmIaA3Uex7CtFIs7f06Hr2SpNLL\nJt9/oqtByiPn40+uIKZ0UOd5AEOOjhtPfaj8nY63aXO9OGq7CeumigVzoheRm1xKTGmhLecvGEqc\nC4pO4h/LL2BTy/v8rOlBEpicmyjma81+dvryudN1PLrupLasj6sWVxyQCHEzyR2NT1AXa+EYbw3X\nlJ2HaZn8vvkpdkQaWOSu5B8rPoI6iWRJw2lNdPG7xscJ6zEuLDp5v2KtQzpk5F/aX8WtOPjMnIsO\neHHt7mgTdzc9TcoyuLL07P1O4jTdbAju5v7W51AkmX8ov4AF7jmHWqQxeadvG4+0vYhd1riu4kKq\nnKWHWiSBQCA4ohDG+1FKxIhx867bCRvZC4Ia4u38285fU2EvYnN475j9F/d+CZeRXiTjTc0jrrWz\ntPfrmaguRYmTiESbyEsuIze5ZFhvJ/7U0nRffT4JJUBucglF8ZNGzJOXWtbfbt6IbQNzpdstybQz\nMbCZOfj0henZjBKW991Ek/spKiMf65d5PoplY0fub8aNiV4b/Cdyk4vTfcLzSajdBOwbR7Qbre+B\nUBI7kzmRi/plnYcF7PHdwyNtL7LEXcUtzX9C77eZH3O2sszo5VkuQY1UoQH1dRW86NnJBQuq9luG\nu5qe5G9dbwLpGz2P4iRkRHm847VMnUOxc23ZeVMe+3u77qQ50Zkep24fpfZ8aqdokNbHWvnp3nsx\n+9PZNu7q4HfL/h+StH8KiJtJbt51O316OlrSD3c38n/LbjrkYSC7k338YM+dJMz0grebd93O7cu/\niUtxTNDz4NMS7+RHe+7O5IOo33U7dyz/1n7d4AkEAoFgdITbzFFKZ7J3hOE+QEciQH28dcy+kqVk\nDHdIJz5y6RXIlj2rXU6yFndq4qe/bn0OLv3AnhJnj1cxYjybkTuibjJzDpd/MvszHbj08ux5h8j6\nfu8O9KFnrgQf+HRkK7tPfe+BRTeoj7VlletirdRFs4+L+ujUozGlTD1juAOYWOwbNtdkaIi1Zwx3\ngPZkTyaE6f4QSAUzhjtA0krREu8cp8fBoTXRnTHcAUJGlO7Uoc8nMBpN8c6sRG49qSB9qfAhlEgg\nEAiOPITxfpRSai+geIzEPLXuCo7NWTRmX0sy6NN2ZMoGSYLablLSoEFhYdHteJde+7asuhFjYdJn\n20afbduIbftLr20bAfvmrLqo2jxijkx5tGCp/XW99q3DZN0xarvpZrisvUPKZxSswW0MOXUtOKvT\nhiQNymaS4piiA3syu9Jbk13OqWVlzvC6qbuVaLLKEs9gll27rLHEUzXlcRa45+KUB28Ya11zDuhp\ndIGWS7l9MBmUT3VT7Tz0IdgqnSVZSXFK7fkUz9KkUPNd5XiUwaReVc4ScjURg1ogEAimE5GkaQKO\n1CRNqqRwYt5ydMugxl1Bqd1PytI5xlvDd+Zfx5rcRYT1KCEjlpWWfoAeezpNdUxpo877IFGtiQ7n\nOjzJaiwpRZPzaTpcrxGwbcLCIK500qfsQsVJXOmkw76OuNpBo/NJeh2b6dO2Y0hxEkovveoWVMtD\nQg7Q7niduNpBl/0t4monIa2OhBxAQiak7sWUUuhyKNOu3fE6ra7n6LNvRTFcqJabkLaHrb6fE1c7\niMiNGHKSbse7NLofBywwSbu6DDXE++uGyt/g+jNB+870diu7XVbd0HEmWWczwJRANdN/Y2orhtpJ\nUooSdL2HlPcuHtXJDXMvZY1vEad5l7GzcytOU+YzyjGcrlazyhdjkzeFaQtwQm0vJ5UVHVDymaWe\naryqmzzNy6XFp3F2/rEs8lSRp3nJUd18pOgkLig8cb/GPjF3GWAxx1nEZ+dcSrVrbCN5rPPEozoz\nPunLvfP5UuVl2GVtv+QBUCSZE3OXYWAyz1nGFysvG5GRdTqYqk7sssbxuUsxLINF7kq+XHkZYn3e\n8AAAIABJREFU3jGyIB8sxtKJS3FwrG8RpmWy1DOPL1VejkuxjzPS7EAkaZp9CJ1MHpGk6ehDJGma\nAJGkafKIpBqzD6GT2YfQyexD6GT2IXQyeUSSpqMP4TYjEAgEAoFAIBAcJgjjXSAQCAQCgUAgOEw4\nKnzem5qaeOSRR1i1ahXd3d3cd999fPDBBzQ3N1NTUzNuaLnDwec9ZqQjbCjSgd2LtSd6cMl2YkaC\niB7Hodi4aP1NhFpDRNQwz7a9yRr/Em7Zfj9lciEvtr3D7/c+z4fKVnP1Oz9AiaR4o+0DftpwLx8v\nO4vr3vkPEj19vN70Ht9rvYery87l6jf+lw+aNxJujfGtpj9yZcWpXLnul9zX/Cxao8qP6l/hY5XH\ncN27/85bLe9R2Gzny013c0XFqXz33d+zrm0zWnOULzX/lk+Un8kX372Vx1te4aKyU/jo+ptY3lnM\nT5ru54GWv3NJ2Wl89p0fszJexp17H+Ou5r9xUfmpXLr+JnytOfyx9S/8ovUhriw7l2vWfxtHL2yL\n7OH7G3/HxeWn86l3voMeCJPvyuNvra+xJLeG727+X+LJOJru4I76hzipcBW/3X4/4VQIn5LDcy3v\nsjB3Li93vU8kEcVr2Pnb3ldYWDCPlt4GIvEoDtXFrlAHBQ4PjdF2AqkQuZqXumgLObIL2TTBMEBR\nCOsxNElFtwxSpo4qD4bc0y2DhJlCk8eO+Hq4+Y0mzSS9qTAuxUHUiCNLEqqiHnW+vHEjiYV1wOf0\nTDHRtWtAfhmJiBHHdgBrEYYT1mOokrJfIUEHjil52Pc6XCez/fsfDeHzPjkO5PiZKsLnXTBTHPE+\n76+99hobN25E0zSuv/56/vSnP3HiiSdSXV3NE088QU1NDYsXLx6z/2z3ef91w595qvMNNEnlq1VX\ncLp/1ZTH2Bdr55+33UbSGudCaYGMjcWBL+NLLcQggYwNCQmDBAp2UlKY7b5fEbLVsbDvevITa9Cl\nGDt8v6ZP3saJPb9GQs5EnZGQsLAyf4fXmehIyP19BleGmqRQsJGU+tAsDyDR4H6cZs/TFEVPYV7o\namRUElIAu5VHSgqjWHZkNFqdL1CXc//gYtGBhapDF6zuR50rVcbi3q9gN/0E1T1sy/sfNNPLksBX\ncZiFhNUGtubehmxpLOm9EZdRSlRpYWvebSSV3qyv+t+3uzmxR+FrJ0hsl/vwKi5iRgIdgwsLT+aG\nuZeyLrCJW+vvI2GmODv/WL5aecWoP0aHk9/ogy3P84fWvwFgk1SSlo5bcfDtBZ/m3JqTjxpf3jua\nnuCx9ldQJYUvzv045xSsPYjSTY7xrl13ND7BYx2voCDjVpwEjQg1rgq+V/tP5BzAQtuoEed7u+9k\na7gOv5bDd2s+zTxX+cQdAdMyubXuPl4JfIBTtnPTvGtY6xu87g/Vye2Nj/OXjldRJYUvVV7G2fnH\n7rfMBxPh8z4+USPOzbvuYFukHr+Ww801nxl3ofx0IHzeBTPFEZ+kye/3c+WVV/Loo48C0NraSlVV\nFQC1tbXs2bMnY7wHg0HC4eyYxPF4fMbualVVzfo7VT7o28lTnW8AkLJ0fl7/IKcVrsY2zpPY0bh1\n673jG+4AEpRGzsKXSic+UhiMIDHwWbM8zA99kmb338hPrAFAtZzUBD+FQQKp30tLGpLFaODzaHXy\nkMNTGuLhpWADwGb5MnWVkUvps21lXugaZNJPp+1WXkauAUpjZ9HteJ/g0JCP0rC/+1FXFb4Su5kO\nvZmjz6csei4uvRyHmQ496NHnUhH5CKrlwmWkM066jDLmhi9ht+9uhvLj2ghXNNvZLqffqISGRPt5\nsvN1TslfwW37HsjE/n6++11OLVjJCXnLGI6iKGja9D31nEn+2PpM5vPA8Rgx4vz33vs4t+bk/T5P\nZhvj6WRrqI7H2l8B0m9WftXwCKcXrZ51CZnGunZtDdXxWEdafgOToJGOm7872sTD7S9yQ9VH93vO\nJ9ufZ2u4DkjHj/9t0+PcuvQrk+r7Svf7vBLoj5BlJvh5/QM8cOx/ZLYP6GRLaC9/6XgVSH//v9z3\nMKcXrsZ5GETMOdDfk9nGdF+7Hm/7O9si9UD6+Pld0+P8bOmXp2380TjSdCKYPRzxR9SSJUsIBAKZ\nsmVZmSeUNpuNeDye2bZ+/XpefvnlrP6nn346Z5555ozKmJe3f+HoFL0+q5yydHLzc3GrztE7jEFS\nmtyrScWceFzFcqJYzhF1B2N5hWp6Mob7eCjm9BtC6rDvRjUdqMPmUS0H6qjfTTaGBBF17Bdiilsj\nbiSz61zpJzyHM6PlAQAyiZf29zw5nNCs7Dd9umXgzcvBb/eN0ePQMlwnqtk8ZlvTJh3QMWp1ZF9D\nkpI+6fHkaLYRGDOTFBQUjHhbNVz+9PfvI89++LglHA3nyf5gtWfreirHz4EidCKYbo544304Qy/W\nyWQSh2PQwFqzZg0LFy7Mah+Px+nsnJksi6qqkpeXRyAQ2C/fvlqplHmuMvb2Z7m8oOgkooEwUaaW\n0fDy4rO4re6BsRv0u4Z0OF+nOHYqmuXJuLakNw9+bnE/Q7fjXcoiH8JhFqTrXM8SVPayNHjjiD6j\njTP5OjPzRL5X20avfQud9rcoTBzf385AQslqF1Yb6LNvG3SBGbJ/o+3zmHVDXWgkaHE9R23w00go\n6FKUdter2PVCvH01yKgYUpw218sopoPcxFJkNAyStLpeGDHHWZ0ql7TaebZYJ6KYWZ46813l1Mrl\nXFp6Oo+2vgTAHEcRS5S5ox6ndrudRGL/s44eTJZ757MptGdE/eVlZwPs93ky2xhPJ5VWIQvdc9kR\naQDgnIK1GMEknRz6TK9DGevaVUURC9xz2dkv/8C565TtnJ2z+oCupad7V/CY8hJhI4qMxMWFp0x6\nvBW2akrtBbQmugD4WMkZdHV1ZbYP6KSKQmrdc9gVaQTgQ4XHoQfjdBIfddzZxIH+nsw2pvvadbp3\nJY8rLxM2Yv3Hz6kz9ts+wMHSyeH+4EYwdY54n3dI/+g//PDDo/q8V1dXs2zZSHeDAWa7z3vcSPJe\ncAcuxb5f2S4H2BTaw7rAJpZ7atgXbyNuJnik/aUR7TyGH3uqmqTShSdZg03PpdP5Fk6zmITSQ1hL\nv9ZWTTe++CKSUh8hbTdI4A0tpCZ1LUmpj3bHK3iMSvrMPVTql6JLUVJSEK8+jzbtNaKOekwphaQr\n+MyF9EhbKNZPAcmg0fU0bquciNKIUy8CS6ZX3YylGpCCXHMpsqURsVrwSHOISW1o5KBYDnrZgulI\ngsHgy4A44CBtIQ+9nTXSdV7VToQkLmyEjQQooCBhmBYOWSJupE+hBfJyehMOyhwxNrMNRZKpTM2l\nN5nLHHuEGG0oyKwqOI+tgTBLCjy8GHoBE5MLCk9ifd82jvMt4dJoMRgGzWV+dsRbqHAU0quHiRkJ\nVucsxKGk3YY2hnYT0qOsylkwplvF4eTzDvBk+2u0JwOc7l9FW7Ibv5bDirwFR5Uvb8JM8V7fduyy\njVU5Cw7KwrqpMt61a6j8fi2Hxng7C9xzKbaPntF5KnQle9kW3keZo4D5k/R3HyCsx9gQ2oVP9bDM\nOy9r21CdZORXbKzyzs7vfzSEz/vEDBw/5Y6CSa+XOBCEz7tgpjjqjPeuri6eeOIJDMOgoKCAiy++\nGFke26Vjthvvs4nDzVAcDaGT2YfQyexD6GT2IXQy+xDGu2CmOCrcZvLy8rj++usBKCgo4LrrrjvE\nEgkEAoFAIBAIBFPn8AliKxAIBAKBQCAQHOUcFU/eBdm827ed9X3bWZ2zAIt0SLffNzxJS6obr+VE\nSc5HtjSiajNuvZKY0kpJ5Cw0y0uX/V0K48cRsu3FZviwGXkEtM2UJE4lIm+mw/M6Nt1H3OqkUD+e\nmNyJN1WFy6ygg3UUcjxJuRfFtOMyymmzv47bLCMlh+izb8GbqiGgbmdO9MOYUoI6+/245AoiVgMu\nqRQLk5jWgduYQ0TdR1zqAMBpFeJKVhHVmrHr/rTPu60ZT7KSmNaKpuegWg4M5y56rRB2WcaXWopl\n2HG72qg3m3ChoSgqUSPJMnc1WyP15Kgu8m0+WhPdrLGtobmrAlWN8QVvkvbeRuZWH8tet45TsRPt\nbmV7cA+nlpzAwuKlAOyLtbEv1sYC9xxK7PkANMU72BttYb6rnHLH9C402hzaSyAVYr6rnD3RZko9\nhdTYZuaVqmEZrO/bgYnJsb7FqNLEkX4OFSlT593gdhRk1vgWHVbJd2aaQCrI5tBeiux+FrrnHmpx\nBEcokUgPG+vW4XPlsWTeSWO2e6NnEz+ruxdVVrhn0bdxOicfPc2wTNb3bcewDI71LUaTVSzL4r3g\nDhJmikpnCXuizZQ7ClnYmURKxNELitA62zF8uRglwv1EcHhwVPi8HwhHms/7o20vc1fzk2Nur+37\nDIXxgWgtZiap0mSivwxlorqpR5ZJR44ZKpdJiq15/4NJiqWBr6Fgz4osM1q0mYjayCb/T5gbupiy\n2LkARJU2NvlvwZDH9q+06fms7v5BJvZ8SK1jU/4tyBaYo0Sg+VbhJci+PG7Zcw8GJg7Zxg8X3EDc\nSPC93XeSsnRsksp3az/DMd6aMeedCn9qeZb7Wp8DQEbC7I9Rc23ZeVxZes60zDGAaZn8YM/vebdv\nGwArvbXcXPtPM2IUH+h5YlgG3975WzaH9wJwQu5SvjXvU4dsIeJs8uVtT/Twje2/oFdPR6i6Yc6l\nXFh08oT9hH/17GM26yQY6uSmTf9Fiz0dceUavYZPHH/DiHbv9Gzl+3V3ZdU9seZnk5rDsixu2XsP\n63o3A7DUM48fLvgsv6h/iBd61gOD10XZgn/b4eL8DhuWJCFZ6UC10bPOJ7FizQHsaTbC510wU4jH\nT0cZj3W8POY2m5GbMdxhMDHSZJMqDWWiuvHGGb1OGVKXlktGozR6JiWxMzKJooYmcxroM7TOrc/B\nl1hKaezsTJ3LKMGfWDFC3qFURC7IShrl0avAlAcN9/REmb/3tz3P4x2vYpBOiR03k/y1cx1PdLxG\nqj8BUdLSebLj9XHnnQqPDokONGC4A/y5fWyd7y+N8Y6M4Q7wQWgXddGZu9E9EHZFGjOGO8CbvVto\nTXQfQolmDy90r88Y7jAzx4pA8PaeFzOGO8BD0u5R2/24/g8j6u5remaUliNpS3ZnDHeALeG9vN+3\nI2O4w+B10ZTg/op0GErJGsjuDY71b01qLoHgUCOM96MMu2wbc5shJTExDqI0B44hxTGkqT0xM6Qo\nhpSd5EifYAxdHiV2vmyO2d4payPCN7oUx6h104VbGf31slOe/qRUTsU+4gbNNUuzUDqHfccyEk5l\n7PPgaMI9g8ejQDCAS3Vlld3m6KaHY5Tfp0rX5J4qO2Q78rBrUo7qGdOdzzNK2HXLLq4LgsMDYbwf\nZdxUfXXmYiYPu9QZcpS9OX/EJH1VS8jpzLQGyUz2y6F/R6sb6GuQHLfdVMdLyL1YmFgYJKU+AKJK\nCw3ux2hyP0lYTSeFSUpBTHQszIz8KSmESfqVZavzeYL2HezKuRNDSide6XC8TsC+YTAT0ig0uP9M\nTO7IyNXsfAqAgkT6FFKRkfr7Ow2Zbyz6NNeVX0hpv5/7fFc5V5aczSfLP8wcRxEAcx3FfLLs/LEn\nnSJfrboCV7+hnq+lM3J6FCc3Vl0xbXMMUGTL49MVFyL3H0X/UP5hyqbZf3+6qHSWcE3ZuUhIKMh8\nds4l5Gk5h1qsWcH5hSewuj8/hE9188W5Hz/EEgmORE5Y8mHOiqWT9rl0ia/7R7/u3bvye1llCYmT\n/MsnNUee5uWzcy5FQUZC4urSD7HQM5cvVV6W+c0buC4WSC5urE9nzTU86b+my03k7AumvnMCwSFA\n+LxPwJHm8z5AZzJAoS2PlKljYmGXNe5qeJLL/WewIVZPa7yHy+acxj9v/D++UXApLyS3sLGnhZ+u\n/geuX/drPll4JpuCW3g7tY+7j/8an1z3c67xnkJTvJm/Wm/zyNof8om3fsC58gpcpptHzE08dOKN\n/OOb/8NqqYwC089Txj7uPeU6bnj7FyxVi7nQt5Zbup7gd8d/hX9/649UuP18xLWYn3U/wm1rv8Et\nH9xFrs3J55d8gk+98xPuXvuv/GTT7cSMJDev/AK/2fYoV5WezQPtL7Av3M4P13yO72z4HV8uvpg9\ntLI11MBnai/mzl0Pc37RGRiqyfZQEx8qWc0jDc9xqn8NqkNjt9nMye7lPNv6Jsd45mOXVXZEm1iT\nu4i3OvdS7fZTYPcQDwVw5RYSN5KosoJsQk+0mwJvUeZ7tiyLmJkY8UQzYsTGfFJ+IBiWScJM4lIc\nRI04uW4fyfjMZVhNmToWFjZZm7jxfjJd50nCTCEjocmHdp3+bPSvjhpxHLINeZJrFmazf/X+MBt1\nMlUOB53EYiFsNieKMv45+EbPJiq95ZRrU0/sNfQ3bWjdwNqjqBHHKdvT7jKGDpoNkon032leByN8\n3gUzhTDeJ+BINd5nAvEDOPsQOpl9CJ3MPoROZh9CJ5NHGO9HH8JtRiAQCAQCgUAgOEwQxrtAIBAI\nBAKBQHCYIJI0HYHsi7XxRMdraJLK5aVn4ddy2N7Ty5M7YkiSxRvy3SSVngnHOabrO7iMUgwSyKhI\nKCTkHjTLi04MzXIhoZIkjA0PJimiUgBNdtKrbkWWbKTMFGXJk5FQiModhG27McwkxclTkFFJSD30\n2bdhSHGaPH9Fl8M49VJKI2djSSmShHBaRUSVVhxGAZaUXojqMkoIaXtpd74CQGHsRHyphYS1Btqc\nL4AEBdG15KaWEpc7US03imWnzfESUVsjiuGkPHYBqumiz7mOiFqHCxXTZiOJzpn+1dww51K6kn38\ny/ZfEjZiFGg+ulN92GWNk3JXELcSnJC7lBNyl2V9b+sCm7mn+Wk0WeVrVZ+g2lVGS7yTP7e/giLJ\nfKzkDIpseTOi+wE2BHfzUs97FLn8fDT/NBxDoqsEk2G+vuOX9OlhVuXU8s35n5pRWYbyRmATb/dt\npdxeyEdLTp/RxE6mZfJEx+vsjTUz11FMW6IHRZL5eMmZFNpyZ2zemcSyLJ7uXMfOaANL3NWcV3j8\nxJ0OgISZ4qG2F+hK9nKafyWrcxaSMnUeanqRQHOYNa4FrPEsnFEZjhaiRpyH2l6gNxXmnPxjWeqd\nN+m+dZEWvrL9v4H0Is/H1/x0v2ToSvbycNuL6JbBJcWnZRbXT4aoEefB1hcI6mHO7pc/biZ5uPUF\nulNBTvevZGX/4ugBdkYa+FvnWzgVOy7ZTkcqwHG+JZycd8zok1gW9g3rUdtbMfL8yKEgAPFjT8T0\nZZ/Tv9z3MBtCuymz5VPmKCBlGcwJmjQGGyi05RIqLiRqJpnrKKI+3oZTsvN670YSZpIV3lpcqgOv\n6mJ7qJ4+I8qH1VqubdAwHS4sRUYJh0nWLiI1rxZME8e7b6IEuvlDWYQH9C2oksJ3Fl7HWYWzcyG/\n4PBG+LxPwOHm8x5Ihfjilv8kZEQBqHAU8b3qL/DrV9xoZnqlfVzu4oOCmzGHhUscyvLub+HVqyY1\n5/4kaRqtLqzuY1vu/7Cy+3tolmdSc9d578eQEtQEBw3QBvdjxNR2FvaNTAKiSzE+yP8etX2fxpdK\n/5AYJNmQ/wPiantW20+Wnc+Drc+TsMbWjYTEzbWfYXVO2oCpi7bwlW3/ndmuSgp3LPsmN277OQE9\nBECxzc+vln4ja0HVdLI70sQ3tv8iE2P+eN8Svl1zXWb7J97/DhEznimf7T+WG6uvnBFZhvJO3za+\nv/vOTPmCwhP5/NyPTdhvf8+Te1ue4f7Wv4+oL+n//mdyke14HIgv76NtL3FX81OZ8ufmXMpHJpFU\naX/58Z4/8HrvRiAdneonC7/As11v8Vz3O0D6+P9B7fWsyKmdMRkOBrPBv/o7O3/LB6FdAGiSyn8v\n/iqVzpJJ9b1o/U1ZZRmJv0zRgE+ZOl/ceiutiS4AclUPv1r6DXJU96T6jyb/vS3PZGKvK8j8dNEX\nWdCfxbcl3sVXtv0XCXPkOf2d+ddxeumaETpxvP0GrtdfHNHe8ObQ9w83gC39kOKX+x7mma5pjtlu\nwb/tcHJBh31oFaHLrsW2ezuOD97ltbwk/7osmsn5IQFvXXAPPT09wuddMK0It5kjjLpoS8ZwB2iK\nd7C+qyNjuAM4zAIc+vhPA1x6+aTn3J8kTaPVefRKvMnaSRvuAL7EYnyJxdl1ycX4kotHba9aTrzJ\neRnDHUDBRk5q/oi2r/Z8MK7hDumbj43BwYQjb/ZuydquWwZv9m3NGO4A7cke2mYwSdDm8J6M4Q5k\nflAHGGq4A7wX3DFjsgxlQ3DXsPLoiVqmb77Rx29L9tCemPjN02xkwzBdDtftTM5nYrIpvCdrTguL\nDaGZ1ePRgGVZbAztyZRTls6WIYnFpoo5XtzbMehIBjKGO0CvHmZfrG1SfdPyDx4HKUtna7gu6xw0\nMNk0ZB+3R/aNarjD2Me11lg/ar0SCqIEBq+pw68104IELxSmhlehNdajNdQB8HRJkqE/cxawITCz\n56jg6EQY70cY5Y5CbNKgN1Se6mVxrj8T0xwgJUVITOA2k5KDMybjWMTlLiLavkxM9skQ0RqJak0j\n69TGUdub6ES1JqLK4BsVC5OI2jyi7RJPNcokTpGqIUlElg171S0hcYxnflbyEa/imlG3jWpn9lOY\necPKmpTtLTfZp3sHSvWwZCvDy9M/X+mo9V7FRb7NN+q22c5Eup3p+aqdZaPWCQ4MSZKocg4erxLZ\n5YOBX8vJespulzXK7AWT6juW/MPPwaHHSqWzBHmM6+tYx7VeOLobj2m3Z7nNzJ2ha9oxwZFufnph\nMXpBMQDH92gj8oUszpu8+5NAMFmUm2+++eZDLcRsJhQKTdxoP1EUBbfbTTQaxTTHztY5FTyqk1rX\nHDqTvcxxFKZ9rj2FWM4W6oJhdKWH7Tl3kFC7xh2n1f48JfGzkNGwhjzFNUkhIWGiZ56aWxiAhIVJ\nigiWZBJS95FUekjQi83y9fdNEtbqiNOD3crLjBfS9hBVm9nlu5OE2klYrcdm+okr7YS0OgwpTp+2\nE12OEJc7CWl7MeUE3Y53aXQ/TlDbjYSKZMn02DfQ4H2UkLYXy7KQLJWQupeEHCCp9FLveZCQbQ8B\n+2YcRj4pOUSD+8+E7FtRLZD7fbAXe6r55vx/YJl3Pq/1bMDCwi7ZMDFRJIWF7koKbD4uKjqFDxee\nmPl+iu1+ZGT2RJtxyjZurLqSZTnzWeypoiMRoMxewFerrqSkP3nTTFBizydf8xHSIyzJmceX516W\nFWd+jXchLwfex7RMyu2F3Lr4KzMmy1DmucrQZJWEmeKYnBo+N+ejk3Jd2d/zZLl3PmEjhiLJLPfO\nwyO7KHMUcOMMf/8ToWkauj5KesdJsNQzj5iZjtt/mn8lV5edO+nY7PvDat8CupJ9uBUHl5eexen+\nVazKWUCvESbH7ubi4lM5N/+4GZv/YHEgOpku1vgW0pkM4FVcXFN+HsfnLp10X8s02Rypy5SfWPOz\nKc+vySorvDW0J3oosOXyxbmXTekGe03Oooz815afx3G5S1jtW5g5fq4oPZvT/Csz7f1aDhWOIgJ6\niDmOYqqdZbgVJx8uPJGLik8ZVSd6+VykZAIkiVT5XCynC9OXR/RDF2LmDZ7TJ+cuZ1NoDyEjSr7m\nY76rnAJbLitibpyJFIvNXPJzy/HbfCz3zMMh2yi25dHb/4a01F5AhaOQOc5iEkYSCYnTpLl8qasE\no6AIPb8Ay+EgvuYEkstWoFdWI0ciLEg42Jur0SiHUSSFz1V+jONKl03rb/xoeL3eGRtbMDsRPu8T\ncLj5vB9KZoPf6IEidDL7EDqZfQidzD6ETmYfIs67YKYQbjMCgUAgEAgEAsFhgjDeBQKBQCAQCASC\nwwRhvAsEAoFAIBAIBIcJIknTEUIgFeTWuvvYF2vDIdvoSAawSIdyG4vawPUUJI8dc7tBDFNOoUsx\nHEYBEjJxuQsFOyYp7KYf+hevyihYGEh4AYmE1MN7hd/EoRdRG/w0djOfuNyB3SjAkOKYcgqb6aPH\n/j57PfeBZDFKJMkRK/czjNZ2GA7JhkOxoaDQrfcB6UgjqqSgSDJdqXSdW3ZgkzWK7Hn8S80nKWTy\nSTUeb3+VR9tfxi5rOGU73ak+Su35dCZ7scs2Pjf30qzEJI2xdr667TZSlo6ChD8poVgS1+WcwknL\nLuS94A5+0/AYKUvn6tJzOadgLT/efQ+v920CwC5puBQHZY4Cvl59NQWaj9ubnuDVng8osufxz1VX\nUeYo4L63fs0zZh0Fpo0bq6+momz00Jmj8UzXW9zf8nfsssbn5350RmJ4K23NuJ99CjkRJ75yLfG1\nJ07c6TCkKd7Bf9XdT1eylzPyV/PpigsPtUiCIQyNj66h8OiaH2fKfakwt9bfR120hZU5tXy58vID\nyg1gWAa/2vco7/RtpcJRxNerr6JgSNSpvdFmfl7/IL16mPMLTuCqsg/xdu9Wflr3R5KmzgLTx+3v\nezDtDixZQolG+VOVwcM5nXhMhRsrP05h4YU81f469zU9i8NSuGkTrOm0+Ov/Z++84yMpzoT9dPfk\nJGkkjXJebdYum2BZcs4YTLLxEQzGZ2x8Ph/m7DMO2ObA4ZzuHL8ztrHvjM8YJ2ywAQMGliXtsjlp\ng6RV1mikyTMdvz9GO9JIo7AJJNPP/vTb6epKXVVd/Xb1W+9bofKjmjiSYVBgWOmzqpTrLoIeCwYC\nfpuXvnSIgK2IsBpDM3RKbIX0pgdpcFVyd/17cWrwz2/cT6dDQ9BBHzG84hBtPLri32nr3MK32x9l\nWFQoVx30WFJIiAzYVAygWLXw+w1enipVuH9hDJ3MfOYU7LgkG+5YgkFRpgIPvS4Bu2TDKdgYVMJU\nak6C6UGshsg/DVSwrlfn9pYh9tozZpGbXdV8Y9HHsB5oxfX807RLKW5a2pNtW3HkOSJmKTl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S8mJiajmDrv0zAbdd4/sO2BHHvfkxFInsa8yC1AxiLM7sLvMmTfBoCk2Tk5+G2EEena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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b621cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036542510974)>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df.query(\"loan_amount < 5000\"), aes(x='loan_amount', y = 'borrower_count', color='status')) + \\\n",
    "    geom_point() \n",
    "    \n",
    "## Expired loans tend to be bigger loans with fewer borrowers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chart it becomes clear that a loan is more likely to expire when it is a higher loan amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have suggested some topics below for exploratory analysis. Try and attempt at least one topic before next class. We will share our own research at the next class, but feel free to share visualizations or interesting observations in slack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 b: Who are the biggest lending partners?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First we list all the available partners and the amount that they lend respctively.\n"
     ]
    }
   ],
   "source": [
    "print ('First we list all the available partners and the amount that they lend respctively.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "partner_name\n",
       "Action Now: Kenya (ANK)                                                       620.975610\n",
       "Asante Kenya Foundation                                                       327.631579\n",
       "Barefoot Power                                                              50000.000000\n",
       "BrazAfric                                                                    1358.333333\n",
       "Burn Manufacturing                                                           4161.363636\n",
       "Digital Divide Data (DDD)                                                     500.000000\n",
       "Ebony Foundation (Eb-F)                                                       837.905162\n",
       "EcoZoom                                                                      4177.500000\n",
       "Evidence Action                                                              1541.329480\n",
       "Faulu Kenya                                                                   506.344985\n",
       "Fledge                                                                      11000.000000\n",
       "Grameen Foundation AppLab                                                     250.000000\n",
       "Hand in Hand Eastern Africa                                                   329.973537\n",
       "Honey Care Africa                                                             282.177948\n",
       "Housing Finance Foundation                                                   1399.166667\n",
       "Juhudi Kilimo                                                                 455.895118\n",
       "Junior Achievement Kenya                                                      583.333333\n",
       "K-Met Savings and Credit Cooperative Ltd                                      401.387347\n",
       "KOMAZA                                                                       2401.731602\n",
       "Kenya ECLOF                                                                   486.955611\n",
       "Living Goods                                                                  219.444444\n",
       "MicroEnergy Credits                                                         50000.000000\n",
       "Milango Financial Services                                                    481.678368\n",
       "Moringa School                                                               2064.583333\n",
       "Motorbank Kenya                                                              1159.677419\n",
       "N/A, direct to Eco Fuels Kenya                                              50000.000000\n",
       "N/A, direct to EcoZoom                                                      50000.000000\n",
       "N/A, direct to ICT4D                                                        25000.000000\n",
       "N/A, direct to Moko Furniture (Watervale Investments)                       50000.000000\n",
       "N/A, direct to Pawame                                                       50000.000000\n",
       "N/A, direct to SCODE                                                        35000.000000\n",
       "N/A, direct to Vava Coffee                                                  50000.000000\n",
       "Nuru International                                                            383.967391\n",
       "One Acre Fund                                                                 946.145491\n",
       "One Degree Solar                                                             8565.909091\n",
       "Opportunity International- Wedco Ltd.                                        2774.553571\n",
       "Paradigm Project                                                             2937.500000\n",
       "People Microcredit Investment Bureau (PEMCI)                                  326.169591\n",
       "PowerGen Renewable Energy                                                    9578.571429\n",
       "SMEP Microfinance Bank                                                        427.704158\n",
       "SPIRE Education                                                               370.652174\n",
       "Sanergy                                                                       590.384615\n",
       "Strathmore University                                                        8571.202532\n",
       "Supporting Enterprises for Economic Development (SEED Development Group)      284.541985\n",
       "Takamoto Biogas                                                               757.594937\n",
       "The Alliance for Artisan Enterprise at The Aspen Institute                   8000.000000\n",
       "Unreasonable Institute                                                      20000.000000\n",
       "VisionFund Kenya                                                              418.651558\n",
       "Visionary Empowerment Programme                                              1937.500000\n",
       "Women`s Economic Empowerment Consort (WEEC)                                   420.440109\n",
       "Yehu Microfinance Trust                                                       360.425359\n",
       "iSmart Kenya                                                                 1661.111111\n",
       "Name: loan_amount, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('partner_name')['loan_amount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We can then display the top 10 loan givers\n",
      "We use the .nlargest() function to display the largest loan givers\n"
     ]
    }
   ],
   "source": [
    "print ('We can then display the top 10 loan givers')\n",
    "print ('We use the .nlargest() function to display the largest loan givers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x157657390>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M4G8oRFUdDXwIOCHJhiRv7hYMabqdAhxbVWcD6wALtGCymxNs3c3pzIZZRsECPS4rgL2Z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\nuLxd6niibhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135a89518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('partner_name')['loan_amount'].mean().nlargest().plot(kind='bar',figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 c: How many loans do not get funded? What are some qualities of loans that expire?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First we need to see all the loans that dindn't get funded.\n"
     ]
    }
   ],
   "source": [
    "print (\"First we need to see all the loans that dindn't get funded.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below we can see the statuses for all loans given.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "251             funded\n",
       "252             funded\n",
       "253             funded\n",
       "254             funded\n",
       "255             funded\n",
       "285             funded\n",
       "286             funded\n",
       "287             funded\n",
       "288             funded\n",
       "289             funded\n",
       "290             funded\n",
       "291             funded\n",
       "292             funded\n",
       "293             funded\n",
       "295             funded\n",
       "296             funded\n",
       "297             funded\n",
       "298             funded\n",
       "300             funded\n",
       "302             funded\n",
       "303             funded\n",
       "304             funded\n",
       "305             funded\n",
       "307             funded\n",
       "337             funded\n",
       "342             funded\n",
       "343             funded\n",
       "344             funded\n",
       "345             funded\n",
       "399             funded\n",
       "              ...     \n",
       "1291328    fundraising\n",
       "1291347    fundraising\n",
       "1291355    fundraising\n",
       "1291369         funded\n",
       "1291387    fundraising\n",
       "1291389    fundraising\n",
       "1291399    fundraising\n",
       "1291404         funded\n",
       "1291407    fundraising\n",
       "1291416    fundraising\n",
       "1291421    fundraising\n",
       "1291423    fundraising\n",
       "1291444    fundraising\n",
       "1291449         funded\n",
       "1291450    fundraising\n",
       "1291463    fundraising\n",
       "1291468    fundraising\n",
       "1291478    fundraising\n",
       "1291483    fundraising\n",
       "1291486    fundraising\n",
       "1291490    fundraising\n",
       "1291494    fundraising\n",
       "1291513    fundraising\n",
       "1291516    fundraising\n",
       "1291518    fundraising\n",
       "1291525    fundraising\n",
       "1291530    fundraising\n",
       "1291532    fundraising\n",
       "1291548    fundraising\n",
       "1292273         funded\n",
       "Name: status, Length: 118235, dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Below we can see the statuses for all loans given.\")\n",
    "df.groupby('id')['status'].first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loans that are not funded are marked as expired.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150b36fd0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b6xAi0qUg8rM6HVhkkDRnoA+8IBxGfI1zz3UWEdnkusjPfsl1CJFKoJGBPoj8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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150b29710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('The loans that are not funded are marked as expired.')\n",
    "explode = (0.5, 0.5, 0, )  # explode 1st and 2nd slice\n",
    "df['status'].value_counts().plot(kind='pie', title='STATUS', autopct='%1.1f%%',  explode=explode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 d: How does the loan amount vary by location?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First we view all the loans given by location.\n"
     ]
    }
   ],
   "source": [
    "print('First we view all the loans given by location.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "251                           Gathiga\n",
       "252                     Ongata Rongai\n",
       "253                     Ongata Rongai\n",
       "254                   Nkoroi Kiserian\n",
       "255                     Ongata Rongai\n",
       "285                            Isinya\n",
       "286                            Isinya\n",
       "287        Olturoto, Kajiado District\n",
       "288        Olturoto, Kajiado District\n",
       "289        Olturoto, Kajiado District\n",
       "290                            Isinya\n",
       "291        Olturoto, Kajiado District\n",
       "292        Olturoto, Kajiado District\n",
       "293               Ole Roimen, Kajiado\n",
       "295               Ole Roimen, Kajiado\n",
       "296               Ole Roimen, Kajiado\n",
       "297                   Nkoroi Kiserian\n",
       "298                   Nkoroi Kiserian\n",
       "300                   Nkoroi Kiserian\n",
       "302        Olturoto, Kajiado District\n",
       "303        Olturoto, Kajiado District\n",
       "304        Olturoto, Kajiado District\n",
       "305        Olturoto, Kajiado District\n",
       "307                   Nkoroi Kiserian\n",
       "337                     Ongata Rongai\n",
       "342                   Nkoroi Kiserian\n",
       "343                          Kiserian\n",
       "344                          Kiserian\n",
       "345                          Kiserian\n",
       "399                           Muranga\n",
       "                      ...            \n",
       "1291328                       Muranga\n",
       "1291347                        Kitale\n",
       "1291355                        litein\n",
       "1291369                        litein\n",
       "1291387                         Chuka\n",
       "1291389                        Othaya\n",
       "1291399                        Nakuru\n",
       "1291404                         nyeri\n",
       "1291407                         Bomet\n",
       "1291416                 ELDAMA RAVINE\n",
       "1291421               Nyandarua South\n",
       "1291423                         nyeri\n",
       "1291444                         Nkubu\n",
       "1291449                         Bomet\n",
       "1291450                      Kenyenya\n",
       "1291463                     Nyahururu\n",
       "1291468                       Muranga\n",
       "1291478                      Kerugoya\n",
       "1291483                        Kitale\n",
       "1291486                         Bomet\n",
       "1291490                        Limuru\n",
       "1291494                        Nakuru\n",
       "1291513                         Busia\n",
       "1291516                      Kerugoya\n",
       "1291518                   Nandi Hills\n",
       "1291525                       Bungoma\n",
       "1291530                       Eldoret\n",
       "1291532                       Bungoma\n",
       "1291548                        litein\n",
       "1292273                         Chuka\n",
       "Name: location.town, Length: 118235, dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('id')['location.town'].first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now that we have the basics done (Listing all the loans), we can start by counting the number of loans per location.\n"
     ]
    }
   ],
   "source": [
    "print(\"Now that we have the basics done (Listing all the loans), we can start by counting the number of loans per location.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We'll start with a bar chart\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150b06390>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oz3JkU/uHTj+HCsveiYqFV4Y+WmP7T2O/fUKW60Ku82O/u+Kcr4t9z0B28XjI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\nyJownqSgT0DcYNzpSeyxme2FPEdvAld5Wwagigwn7W1zcp/dKtz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H1zQmBpNNUIz1JV5a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LsOb5eZvYLCx1Is8PKF4VZahNDRQ4s85yaWkzju7kLJjfcDR4eQlZl0LktPIQp0hknAH9F8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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x154a69e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We'll start with a bar chart\")\n",
    "df.groupby('location.town')['id'].nlargest().plot(kind='bar',figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As you can see there is too much going on in the picture above\n",
      "Lets collect the top 10 and the bottom 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1324cc048>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157130390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('As you can see there is too much going on in the picture above')\n",
    "\n",
    "print('Lets collect the top 10 and the bottom 10')\n",
    "\n",
    "\n",
    "df.groupby('location.town')['id'].nlargest().value_counts()[0:10].plot(kind='bar',figsize=(12,4), title='Top 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Step 3 f: Are women asking for lower loan amounts than men?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('/Users/anthonymiyoro/Documents/intro_to_machine_learning/data_copy/df_module_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 f: Are women asking for lower loan amounts than men?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['gender']=np.where(df['borrowers'].apply(lambda x: x.split(\"gender': '\")[1][0]=='M'),\"Male\",(np.where(df['borrowers'].apply(lambda x: x.split(\"gender': '\")[1][0]=='F'),\"Female\",\"\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we can do some exploratory analysis to understand the effect of gender on loan amount.\n"
     ]
    }
   ],
   "source": [
    "print ('Now we can do some exploratory analysis to understand the effect of gender on loan amount.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b6659b0>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13376be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['borrower_count'] == 1].groupby('gender')['loan_amount'].mean().plot(kind=\"bar\", title=\"Average Loan Amount By Gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "Female    412.053819\n",
       "Male      490.876162\n",
       "Name: loan_amount, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['borrower_count'] == 1].groupby('gender')['loan_amount'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A more nuanced way to look at this is using a boxplot. Instead of only plotting the mean average it also plots the 25th, 50th and 75th percentile.\n"
     ]
    }
   ],
   "source": [
    "print('A more nuanced way to look at this is using a boxplot. Instead of only plotting the mean average it also plots the 25th, 50th and 75th percentile.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110613240>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2wHt1tn379tlTTjlltacBC2bZCcDK2nzZ9dl29smrPQ1YkJ07d+aMM86Ymm+bX7IDAACd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110623080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['borrower_count'] == 1)].boxplot(column='loan_amount', by='gender', showfliers=False, figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The chart above shows us some compelling results, suggesting that the distribution of loan amounts requested for mean and women is very different. It shows us at the 25th, 50th and 75th percentile the loan amount requested by women is lower.\n"
     ]
    }
   ],
   "source": [
    "print ('The chart above shows us some compelling results, suggesting that the distribution of loan amounts requested for mean and women is very different. It shows us at the 25th, 50th and 75th percentile the loan amount requested by women is lower.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The chart below shows that women tend to request more loans than men under 500 dollars whereas men request more loans than women at every value above that amount.\n"
     ]
    }
   ],
   "source": [
    "print ('The chart below shows that women tend to request more loans than men under 500 dollars whereas men request more loans than women at every value above that amount.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RzmIYzqLJJ8OZP4bZG43inzq78OW1ragusOHx7p6U76VUVEIa8QCqmsYKiYiIiMyBYThb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\nwrCIiIiIpKz/H1FpTFX8bj1kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109d1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (324686698)>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df[(df['borrower_count'] == 1)], aes(x='loan_amount',colour='gender')) + geom_density() + xlim(0,2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110a028d0>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAKikPAMAQCXlGQAAKinPAABQSXkGAIBKyjMAAFRSngEAoJLyDAAAlZRnAACo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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109ef6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['borrower_count'] == 1)].groupby(['sector','gender'])['loan_amount'].count().unstack().reset_index().plot(kind='bar', x='sector', title=\"Average Loan Amount in Dollars by Industry & Gender\",figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110a42128>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AzcDNEbGsaD8NOAE4BLg+IpaWGV+SJEkaina1MLGO6szEdKon5L+hOjOxL3ApcA7wKHBu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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a43e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sector_by_gender = df[(df['borrower_count'] == 1)].groupby(['sector','gender']).size().unstack().reset_index()\n",
    "sector_by_gender['total_count'] = (sector_by_gender['Male'] + sector_by_gender['Female'])\n",
    "sector_by_gender['pct_female'] = sector_by_gender['Female'] / sector_by_gender['total_count'] \n",
    "sector_by_gender['pct_male'] = sector_by_gender['Male'] / sector_by_gender['total_count']\n",
    "\n",
    "sector_by_gender[['sector','pct_male','pct_female']].plot(kind=\"barh\", x=\"sector\",title=\"% Gender of Total Loans by Sector\",figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the chart above we look at raw number of loans given to females and males, but it may be more useful to look at this in terms of %. In the chart below we can see that there are a higher % of male loans in wholesale, transportation, manufacturing, entertainment and construction. In all other categories the % of female loans is higher.\n"
     ]
    }
   ],
   "source": [
    "print ('In the chart above we look at raw number of loans given to females and males, but it may be more useful to look at this in terms of %. In the chart below we can see that there are a higher % of male loans in wholesale, transportation, manufacturing, entertainment and construction. In all other categories the % of female loans is higher.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let us look at the loan amount requested in each sector by each gender. In the chart below, we see that our hypothesis that the difference was driven by natural price differences between industry is not correct. Men are asking for larger loan amounts in all industries, and the difference is highest in industries where they are not a majority.\n"
     ]
    }
   ],
   "source": [
    "print ('Now, let us look at the loan amount requested in each sector by each gender. In the chart below, we see that our hypothesis that the difference was driven by natural price differences between industry is not correct. Men are asking for larger loan amounts in all industries, and the difference is highest in industries where they are not a majority.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110a86b38>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAKBCUAYAgApBGQAAKgRlAACoEJQBAKBCUAYAgApBGQAAKgRlAACoEJQBAKBC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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a48e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['borrower_count'] == 1)].groupby(['sector','gender'])['loan_amount'].mean().unstack().reset_index().plot(kind='bar', x='sector', title=\"Average loan amount in dollars by Industry & Gender\",figsize=(12, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finally, let us take a look at the relationship between partner and gender.\n"
     ]
    }
   ],
   "source": [
    "print ('Finally, let us take a look at the relationship between partner and gender.') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>partner_name</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action Now: Kenya (ANK)</td>\n",
       "      <td>75625.0</td>\n",
       "      <td>51675.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>595.472441</td>\n",
       "      <td>662.500000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asante Kenya Foundation</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>20800.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>322.222222</td>\n",
       "      <td>335.483871</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   partner_name      sum           count              mean  \\\n",
       "gender                            Female     Male Female  Male      Female   \n",
       "0       Action Now: Kenya (ANK)  75625.0  51675.0  127.0  78.0  595.472441   \n",
       "1       Asante Kenya Foundation  29000.0  20800.0   90.0  62.0  322.222222   \n",
       "\n",
       "                      min         \n",
       "gender        Male Female   Male  \n",
       "0       662.500000   50.0   75.0  \n",
       "1       335.483871  300.0  300.0  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>partner_name</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">min</th>\n",
       "      <th>total_count</th>\n",
       "      <th>pct_female</th>\n",
       "      <th>pct_male</th>\n",
       "      <th>partner_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Moringa School</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>43250.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2059.52381</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>49550.0</td>\n",
       "      <td>0.127144</td>\n",
       "      <td>0.872856</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          partner_name     sum           count          mean              \\\n",
       "gender                  Female     Male Female  Male  Female        Male   \n",
       "22      Moringa School  6300.0  43250.0    3.0  21.0  2100.0  2059.52381   \n",
       "\n",
       "           min         total_count pct_female  pct_male partner_female  \n",
       "gender  Female    Male                                                  \n",
       "22      1900.0  1250.0     49550.0   0.127144  0.872856             no  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partner_by_gender = df[(df['borrower_count'] == 1)].groupby(['partner_name','gender'])['loan_amount'].agg(['sum','count','mean','min']).unstack().reset_index()\n",
    "partner_by_gender.head(2)\n",
    "\n",
    "\n",
    "partner_by_gender['total_count'] = (partner_by_gender['sum']['Male'] + partner_by_gender['sum']['Female'])\n",
    "partner_by_gender['pct_female'] = partner_by_gender['sum']['Female'] / partner_by_gender['total_count'] \n",
    "partner_by_gender['pct_male'] = partner_by_gender['sum']['Male'] / partner_by_gender['total_count']\n",
    "partner_by_gender['partner_female'] = np.where(partner_by_gender['pct_female']>=.6, 'yes', 'no')\n",
    "partner_by_gender[(partner_by_gender['pct_female']<=.2)].head(20) #moringa school appears!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surprisingly, the chart below suggests that although women ask for less, they have a higher funded rate.Is this because they ask for less or because they are women?\n"
     ]
    }
   ],
   "source": [
    "print ('Surprisingly, the chart below suggests that although women ask for less, they have a higher funded rate.Is this because they ask for less or because they are women?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110ac0cc0>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a9e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df['borrower_count'] == 1)].groupby(['gender', 'status'])['loan_amount'].sum().unstack().plot(kind=\"bar\", stacked=True, )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}