4.1 Data Story Project exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Story Exercise\n",
    "####  This exercise provides an opportunity for exploratory data analysis, as a basis for developing a data story and as a means to generate questions and hypotheses for further work.  In this exercise, I use the data set on Kiva microloans, on which I have proposed to focus my capstone project.  Below I have addressed the exercise's tasks, and have presented graphics with descriptive titles.  At the end of the analysis, I make some observations, attempt to draw some insights, and begin to develop a narrative that can accompany and illuminate my analysis of this data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "import cPickle as pk\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
       "    padding-left: 0.25em;\n",
       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
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       "    border-right: 1px dashed #888;\n",
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       "table.dataframe td {\n",
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       "    background-color: #f4f4ff;\n",
       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "</style>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "css = open('style-table.css').read() + open('style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "handle=open('../../capstone_project/data/Kenya.pickle','r')\n",
    "df2=pk.load(handle)\n",
    "handle.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                                      u'activity',\n",
       "                                        u'basket_amount',\n",
       "                             u'bonus_credit_eligibility',\n",
       "                                              u'country',\n",
       "                                         u'country_code',\n",
       "                        u'currency_exchange_loss_amount',\n",
       "                                           u'delinquent',\n",
       "                                          u'description',\n",
       "                                        u'funded_amount',\n",
       "                                          u'funded_date',\n",
       "                                            u'geo.level',\n",
       "                                            u'geo.pairs',\n",
       "                                             u'geo.type',\n",
       "                                                   u'id',\n",
       "                                            u'languages',\n",
       "                                         u'lender_count',\n",
       "                                          u'loan_amount',\n",
       "                                                 u'name',\n",
       "                                                  u'obs',\n",
       "                                          u'paid_amount',\n",
       "                                            u'paid_date',\n",
       "                                           u'partner_id',\n",
       "                                             u'payments',\n",
       "                              u'planned_expiration_date',\n",
       "                                          u'posted_date',\n",
       "                                               u'sector',\n",
       "                                               u'status',\n",
       "                                                 u'tags',\n",
       "                                             u'texts.ar',\n",
       "                                             u'texts.en',\n",
       "                                             u'texts.es',\n",
       "                                             u'texts.fr',\n",
       "                                             u'texts.id',\n",
       "                                             u'texts.mn',\n",
       "                                             u'texts.pt',\n",
       "                                             u'texts.ru',\n",
       "                                             u'texts.vi',\n",
       "                                               u'themes',\n",
       "                                                 u'town',\n",
       "                                           u'translator',\n",
       "                                                  u'use',\n",
       "                                                u'video',\n",
       "                                          u'prop_female',\n",
       "                                        u'num_borrowers',\n",
       "                                                u'idnew',\n",
       "                                          u'template_id',\n",
       "                                          u'bulkEntries',\n",
       "                                              u'entries',\n",
       "                                     u'disbursal_amount',\n",
       "                                   u'disbursal_currency',\n",
       "                                       u'disbursal_date',\n",
       "                                       u'local_payments',\n",
       "                     u'loss_liability.currency_exchange',\n",
       "       u'loss_liability.currency_exchange_coverage_rate',\n",
       "                            u'loss_liability.nonpayment',\n",
       "                                   u'repayment_interval',\n",
       "                                       u'repayment_term',\n",
       "                                   u'scheduled_payments'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Count something interesting  \n",
    "Here are the twenty most-frequent descriptors of the Kiva loans in Kenya. Each entry has only one of these descriptors. There seems to be some overlap in the areas covered by the activities. For example, it is not clear that *Retail* should not also include *Clothing Sales*. Does *Agriculture* include *Poultry* or *Cereals*, and should they all not fall under *Farming*?  Below this one finds counts of the loans in each of several sectors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loans for Farming and Retail Activities Dominate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Farming                  18326\n",
       "Dairy                     6762\n",
       "Retail                    4622\n",
       "Clothing Sales            4528\n",
       "General Store             4436\n",
       "Fruits & Vegetables       4426\n",
       "Agriculture               4191\n",
       "Poultry                   3504\n",
       "Cereals                   2713\n",
       "Grocery Store             2649\n",
       "Tailoring                 2536\n",
       "Motorcycle Transport      2532\n",
       "Food Stall                2484\n",
       "Charcoal Sales            1917\n",
       "Beauty Salon              1830\n",
       "Fish Selling              1572\n",
       "Food Production/Sales     1566\n",
       "Used Clothing             1328\n",
       "Food                      1294\n",
       "Food Market               1138\n",
       "Name: activity, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['activity'].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agriculture, Food, and Retail Loans Dominate Economic Sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agriculture       35675\n",
       "Food              21470\n",
       "Retail            15618\n",
       "Services           8024\n",
       "Clothing           6650\n",
       "Transportation     3287\n",
       "Construction       1626\n",
       "Education          1361\n",
       "Health             1175\n",
       "Personal Use        977\n",
       "Manufacturing       816\n",
       "Housing             731\n",
       "Arts                457\n",
       "Entertainment       128\n",
       "Wholesale           124\n",
       "Name: sector, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['sector'].value_counts().head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Find trends and extremes  \n",
    "### Two of the largest loans have been in the area of renewable energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity</th>\n",
       "      <th>funded_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18281</th>\n",
       "      <td>Utilities</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18939</th>\n",
       "      <td>Renewable Energy Products</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22991</th>\n",
       "      <td>Renewable Energy Products</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33910</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        activity  funded_amount\n",
       "18281                  Utilities          50000\n",
       "18939  Renewable Energy Products          50000\n",
       "22991  Renewable Energy Products          50000\n",
       "33910                Agriculture          50000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=df2[df2.funded_amount==df2.funded_amount.max()].iloc[:,[0,8]]\n",
    "test\n",
    "#test.reset_index().iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The smallest loans have been for just 25 USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity</th>\n",
       "      <th>sector</th>\n",
       "      <th>funded_amount</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43440</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59197</th>\n",
       "      <td>Farming</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>in_repayment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67390</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67721</th>\n",
       "      <td>Bicycle Repair</td>\n",
       "      <td>Services</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71473</th>\n",
       "      <td>Food Production/Sales</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72923</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74082</th>\n",
       "      <td>Shoe Sales</td>\n",
       "      <td>Retail</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76591</th>\n",
       "      <td>Bakery</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>paid</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    activity       sector  funded_amount  loan_amount  \\\n",
       "43440            Agriculture  Agriculture             25           25   \n",
       "59197                Farming  Agriculture             25           25   \n",
       "67390            Agriculture  Agriculture             25           25   \n",
       "67721         Bicycle Repair     Services             25           25   \n",
       "71473  Food Production/Sales         Food             25           25   \n",
       "72923            Agriculture  Agriculture             25           25   \n",
       "74082             Shoe Sales       Retail             25           25   \n",
       "76591                 Bakery         Food             25           25   \n",
       "\n",
       "             status  \n",
       "43440          paid  \n",
       "59197  in_repayment  \n",
       "67390          paid  \n",
       "67721          paid  \n",
       "71473          paid  \n",
       "72923          paid  \n",
       "74082          paid  \n",
       "76591          paid  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2a = df2[df2.funded_amount>0]\n",
    "test2=df2a[(df2a.funded_amount==df2a.funded_amount.min())&((df2a.status== 'paid')|(df2a.status=='in_repayment'))]\n",
    "test2[['activity','sector','funded_amount','loan_amount','status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mktime(x):\n",
    "    try:\n",
    "        tym=dt.datetime.strptime(x,\"%Y-%m-%dT%H:%M:%SZ\")\n",
    "        return tym\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getyear(x):\n",
    "    try:\n",
    "        yr=np.int(x.year)\n",
    "        return yr\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fun_dat=df2a['funded_date'].apply(mktime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:284: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/Applications/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:461: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "df2a.loc[:,'funded_datetime']=fun_dat  # I checked to see that it is there, and it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                                      u'activity',\n",
       "                                        u'basket_amount',\n",
       "                             u'bonus_credit_eligibility',\n",
       "                                              u'country',\n",
       "                                         u'country_code',\n",
       "                        u'currency_exchange_loss_amount',\n",
       "                                           u'delinquent',\n",
       "                                          u'description',\n",
       "                                        u'funded_amount',\n",
       "                                          u'funded_date',\n",
       "                                            u'geo.level',\n",
       "                                            u'geo.pairs',\n",
       "                                             u'geo.type',\n",
       "                                                   u'id',\n",
       "                                            u'languages',\n",
       "                                         u'lender_count',\n",
       "                                          u'loan_amount',\n",
       "                                                 u'name',\n",
       "                                                  u'obs',\n",
       "                                          u'paid_amount',\n",
       "                                            u'paid_date',\n",
       "                                           u'partner_id',\n",
       "                                             u'payments',\n",
       "                              u'planned_expiration_date',\n",
       "                                          u'posted_date',\n",
       "                                               u'sector',\n",
       "                                               u'status',\n",
       "                                                 u'tags',\n",
       "                                             u'texts.ar',\n",
       "                                             u'texts.en',\n",
       "                                             u'texts.es',\n",
       "                                             u'texts.fr',\n",
       "                                             u'texts.id',\n",
       "                                             u'texts.mn',\n",
       "                                             u'texts.pt',\n",
       "                                             u'texts.ru',\n",
       "                                             u'texts.vi',\n",
       "                                               u'themes',\n",
       "                                                 u'town',\n",
       "                                           u'translator',\n",
       "                                                  u'use',\n",
       "                                                u'video',\n",
       "                                          u'prop_female',\n",
       "                                        u'num_borrowers',\n",
       "                                                u'idnew',\n",
       "                                          u'template_id',\n",
       "                                          u'bulkEntries',\n",
       "                                              u'entries',\n",
       "                                     u'disbursal_amount',\n",
       "                                   u'disbursal_currency',\n",
       "                                       u'disbursal_date',\n",
       "                                       u'local_payments',\n",
       "                     u'loss_liability.currency_exchange',\n",
       "       u'loss_liability.currency_exchange_coverage_rate',\n",
       "                            u'loss_liability.nonpayment',\n",
       "                                   u'repayment_interval',\n",
       "                                       u'repayment_term',\n",
       "                                   u'scheduled_payments',\n",
       "                                      u'funded_datetime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2a.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fun_year=df2a['funded_datetime'].apply(getyear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(fun_year)          # turns out the effort to get an int here isn't going"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2a.loc[:,'funded_year']=fun_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2b=df2a[(df2a.funded_year.isnull()==False)&(df2a.funded_year!=2016.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funded loans are increasing again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I7mJm4V0p9Ipou57O36YztnFbO15uY0NBCZv3lGKrcwIwqE8Y45OiGBnbg2dX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\nNht2u53Q0NDm5cHBwc3LrVb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06QwZMoT58+eTmprK448/zrp16xg1ahTZ2dmsWbOG+vp6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1254ecd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2b.funded_year.value_counts().sort_index().plot(title='Funded Loans by Year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agriculture       29243\n",
       "Food              19199\n",
       "Retail            13785\n",
       "Services           7364\n",
       "Clothing           5987\n",
       "Transportation     2811\n",
       "Construction       1530\n",
       "Health             1076\n",
       "Education           994\n",
       "Personal Use        922\n",
       "Manufacturing       775\n",
       "Housing             669\n",
       "Arts                418\n",
       "Entertainment       126\n",
       "Wholesale           114\n",
       "Name: sector, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2b.sector.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2c=df2b.groupby(['sector','funded_year']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The increase is due to increasing support for agriculture "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x138e62690>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JSaxb9zWOjo7MmjXzrnakpaWxYcOXhIZuwsrKilWrltG0aXMCAuoybtwkrKys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GM4K+qTx0S8l4EjC7wgVDx3cK7TS/jZA3MdQU47BmduLrFpJNcZ9ey5g91jX7ahEH/SNcxIaeEa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\n02aqmqVcK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c844wxmZmbYu3cvAF/60peAaD7uLVu28MUvfhGAb3zjG/z2b/82ABdeeGHH\nDf5v//ZveJ53zOv6jd/4DT784Q9z8cUXY1kWp512GrOzszz66KMA/PM//zPXXHMNs7OzjI+P8653\nvYuLLrqIhx56qK9ykZCQcGwSyzshYZWRy+V4+9vfzhve8AbWr1/f52ZeqFe4aZrccccd7N69G9M0\nOfvsszvbbr/9dj7wgQ/wt3/7t9i2zV133QXADTfcwLXXXsu9997LOeec05kf/Wjs3LkTIURnTmXb\ntvmrv/orbr75ZjzPI5fL8ed//ucUCgWuuOIKLrvsMvL5POeddx6tVotWq7XUrElI+KkhmRI0ISHh\npLB3716uv/567rvvvuW+lISE//YklndCQsJx8eCDD/I3f/M3fdZ9u03913/91/nkJz/J3XffvYxX\nmJDw00NieSckJCQkJKwykg5rCQkJCQkJq4xEvBMSEhISElYZiXgnJCQkJCSsMhLxTkhISEhIWGUk\n4p2QkJCQkLDKSMQ7ISEhISFhlfH/Aff+LPPNpoVWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d321390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2c.unstack('sector').fillna(0).plot(title='Number of Loans per Sector')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sector</th>\n",
       "      <th>Agriculture</th>\n",
       "      <th>Arts</th>\n",
       "      <th>Clothing</th>\n",
       "      <th>Construction</th>\n",
       "      <th>Education</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Food</th>\n",
       "      <th>Health</th>\n",
       "      <th>Housing</th>\n",
       "      <th>Manufacturing</th>\n",
       "      <th>Personal Use</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Services</th>\n",
       "      <th>Transportation</th>\n",
       "      <th>Wholesale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>183.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>578.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>130.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>1887.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>4367.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>896.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2123.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>3589.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4072.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>4198.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3855.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>1242.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>4868.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3242.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>9404.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3213.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2222.0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sector       Agriculture  Arts  Clothing  Construction  Education  \\\n",
       "funded_year                                                         \n",
       "2005.0              17.0   0.0       1.0           2.0        0.0   \n",
       "2006.0             183.0  18.0      38.0          10.0        0.0   \n",
       "2007.0             578.0  58.0     198.0          32.0        1.0   \n",
       "2008.0              22.0  10.0      66.0           8.0        0.0   \n",
       "2009.0             130.0  10.0     245.0          16.0       21.0   \n",
       "2010.0            1887.0  43.0     490.0         131.0       58.0   \n",
       "2011.0            4367.0  69.0     896.0         348.0      105.0   \n",
       "2012.0            3589.0  57.0    1300.0         304.0      242.0   \n",
       "2013.0            4198.0  53.0    1021.0         234.0      248.0   \n",
       "2014.0            4868.0  43.0     885.0         201.0      180.0   \n",
       "2015.0            9404.0  57.0     847.0         244.0      139.0   \n",
       "\n",
       "sector       Entertainment    Food  Health  Housing  Manufacturing  \\\n",
       "funded_year                                                          \n",
       "2005.0                 0.0     4.0     0.0      5.0            1.0   \n",
       "2006.0                 0.0    88.0     4.0      9.0            3.0   \n",
       "2007.0                 2.0   553.0    46.0     12.0           28.0   \n",
       "2008.0                 2.0   138.0    17.0      7.0            4.0   \n",
       "2009.0                 8.0   420.0    26.0     13.0           51.0   \n",
       "2010.0                14.0  1103.0    47.0     43.0          132.0   \n",
       "2011.0                16.0  2511.0    98.0    106.0          171.0   \n",
       "2012.0                47.0  4072.0   116.0    170.0          133.0   \n",
       "2013.0                16.0  3855.0   286.0    148.0           96.0   \n",
       "2014.0                14.0  3242.0   285.0     88.0           95.0   \n",
       "2015.0                 7.0  3213.0   151.0     68.0           61.0   \n",
       "\n",
       "sector       Personal Use  Retail  Services  Transportation  Wholesale  \n",
       "funded_year                                                             \n",
       "2005.0                0.0     4.0       6.0             0.0        0.0  \n",
       "2006.0                0.0    70.0      48.0             8.0        0.0  \n",
       "2007.0                0.0   316.0     213.0            23.0        2.0  \n",
       "2008.0                0.0    64.0      47.0             5.0        1.0  \n",
       "2009.0                0.0   382.0     268.0            71.0        9.0  \n",
       "2010.0                9.0  1055.0     662.0           234.0       26.0  \n",
       "2011.0               26.0  2123.0    1248.0           438.0       21.0  \n",
       "2012.0              127.0  3107.0    1580.0           673.0       23.0  \n",
       "2013.0              500.0  2303.0    1242.0           486.0       21.0  \n",
       "2014.0              157.0  2139.0    1088.0           413.0        6.0  \n",
       "2015.0              103.0  2222.0     962.0           460.0        5.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2c.unstack('sector').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>prop_female</th>\n",
       "      <th>funded_year</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>165</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>500</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>76</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>765</td>\n",
       "      <td>1122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>1621</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1677</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>1777</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>2681</td>\n",
       "      <td>1532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>5293</td>\n",
       "      <td>2227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "prop_female  funded_year  Female  Male\n",
       "0                 2005.0      16     1\n",
       "1                 2006.0     165    18\n",
       "2                 2007.0     500    78\n",
       "3                 2008.0      10    10\n",
       "4                 2009.0      76    54\n",
       "5                 2010.0     765  1122\n",
       "6                 2011.0    1621  1982\n",
       "7                 2012.0    1677  1572\n",
       "8                 2013.0    1777  1744\n",
       "9                 2014.0    2681  1532\n",
       "10                2015.0    5293  2227"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2c1= df2b[(df2b['sector']=='Agriculture')&((df2b.prop_female==1)|(df2b.prop_female==0))].groupby(['prop_female','funded_year'],as_index=False).size()\n",
    "df2c1=df2c1.unstack('prop_female').reset_index()\n",
    "df2c1.loc[:,'Female']=df2c1.iloc[:,2]\n",
    "df2c1.loc[:,'Male']=df2c1.iloc[:,1]\n",
    "cols=[1,2]\n",
    "df2c1=df2c1.drop(df2c1.columns[cols],axis=1)\n",
    "df2c1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Growth in Agriculture Sector Loans: More Loans to Women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MmT9hYGCA4OCeVdYaGRnh9Olo/PlnNFJTU/DPP2koKMiHIAiQyyuqPX96+l0AwNq1EViz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\nVdGKTxM2dzol9amAhZjgMqbP4x++kodjvxewYj7O5nhraRSMBIOzUJI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QyWQdq+8JCQmI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ee49610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(12,8),facecolor='white')          #open a figure\n",
    "ax=fig.add_subplot(1,1,1)#, axisbg='white')\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.set_xlabel('Year',fontsize=20)\n",
    "ax.set_ylabel('Number of Agricultural Sector Loans',fontsize=20)\n",
    "\n",
    "ax.plot(df2c1.iloc[:,0], df2c1.iloc[:,1],linewidth=4)\n",
    "ax.plot(df2c1.iloc[:,0], df2c1.iloc[:,2],linewidth=4)\n",
    "\n",
    "ax.legend(loc='upper center', prop={'size':18})         #display a legend\n",
    "for tick in ax.xaxis.get_major_ticks():                 #set the size of the tick labels on the x-axis\n",
    "                tick.label.set_fontsize(14) \n",
    "for tick in ax.yaxis.get_major_ticks():                 #set the size of the tick labels on the y-axis\n",
    "                tick.label.set_fontsize(14)\n",
    "\n"
   ]
  },
  {
   "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": [
    "## 2.3 Make bar plots or histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2d=df2a[(df2a.funded_year.isnull()==False)&(df2a.funded_year!=2016.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "arr =np.array(df2d.funded_amount[df2d.funded_amount<=2500])\n",
    "loans=pd.cut(arr,bins=[-0.1,250,500,750,1000,1500,2000,2500],labels=['<250','<500','<750','<1000','<1500','<2000','<2500'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most Loans Are Less Than 500 US Dollars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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djRo1Sk888YTi4+O1ZcsWXXfddYqPj9eCBQtUWVmpiooKlZSUqFWrVurYsaMK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\nrEfeVrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ee5d510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loans.value_counts().plot(kind='bar',title='Most Loans Are Less Than 500 USD').set_ylabel('Number Loans');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defaults On Loans Are Rare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17f2a9890>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XFoPYsdPS4jcdIa60tPjV1NRqOgbOQEaGg/cOMcU2dfpO9mElagnn5eWppqZGhYWFqq+v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\nAwBgyP8H+xdc0OxnjgIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17f2a90d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series1=df2d.status.value_counts()/df2d.status.value_counts().sum()\n",
    "#series1\n",
    "pd.DataFrame({'proportion':series1}).plot(kind='bar')\n",
    "#pd.DataFrame(series1,columns='proportion',index=series1.index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2e=df2b[(df2b.num_borrowers==1)&((df2b.prop_female==1)|(df2b.prop_female==0))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sex_status=df2e.groupby(['status','prop_female']).size().unstack('status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sex_status['total']=sex_status.paid+sex_status.in_repayment+sex_status.defaulted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>status</th>\n",
       "      <th>defaulted</th>\n",
       "      <th>in_repayment</th>\n",
       "      <th>paid</th>\n",
       "      <th>total</th>\n",
       "      <th>prop_defaulted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prop_female</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1242</td>\n",
       "      <td>3780</td>\n",
       "      <td>23026</td>\n",
       "      <td>28048</td>\n",
       "      <td>0.044281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2369</td>\n",
       "      <td>8571</td>\n",
       "      <td>39993</td>\n",
       "      <td>50933</td>\n",
       "      <td>0.046512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status       defaulted  in_repayment   paid  total  prop_defaulted\n",
       "prop_female                                                       \n",
       "0.0               1242          3780  23026  28048        0.044281\n",
       "1.0               2369          8571  39993  50933        0.046512"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_status['prop_defaulted']= sex_status.defaulted/sex_status.total\n",
    "sex_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default Rates For Men And Women Are Similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O6L//+7+rNLgxRjNnztSuXbsUFBSk5ORktWjRwtuekZGhtLQ02e12DRkyRLGx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\nEOwAAFgIwQ4AgIX8P6wZUKyYqMDAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1839ff950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph1=sex_status.iloc[:,4].plot(kind='bar',title='Default Rate by Gender of Recipient').set_ylabel('Proportion Defaulted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_borrowers</th>\n",
       "      <th>funded_amount</th>\n",
       "      <th>lender_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>438.915056</td>\n",
       "      <td>13.851129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>380.360825</td>\n",
       "      <td>12.010309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>813.666667</td>\n",
       "      <td>26.344444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1181.807780</td>\n",
       "      <td>37.782609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1515.092593</td>\n",
       "      <td>49.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1431.783537</td>\n",
       "      <td>46.597561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1417.291667</td>\n",
       "      <td>46.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1445.182292</td>\n",
       "      <td>45.770833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1440.409836</td>\n",
       "      <td>44.629508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>956.516768</td>\n",
       "      <td>29.538110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1086.720430</td>\n",
       "      <td>34.032258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1089.931507</td>\n",
       "      <td>33.386301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1171.863118</td>\n",
       "      <td>34.634981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1270.063025</td>\n",
       "      <td>37.373950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1363.647959</td>\n",
       "      <td>40.729592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1405.575540</td>\n",
       "      <td>39.928058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1085.294118</td>\n",
       "      <td>33.607843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>807.812500</td>\n",
       "      <td>25.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>851.408451</td>\n",
       "      <td>26.915493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>862.686567</td>\n",
       "      <td>26.716418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>919.117647</td>\n",
       "      <td>28.382353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>907.352941</td>\n",
       "      <td>27.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1013.333333</td>\n",
       "      <td>32.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1045.000000</td>\n",
       "      <td>33.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>2346.875000</td>\n",
       "      <td>77.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>937.500000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>33.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1225.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>1225.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1283.333333</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>45.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>2150.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>37</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>39</td>\n",
       "      <td>1525.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>40</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>41</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>42</td>\n",
       "      <td>1625.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>46</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num_borrowers  funded_amount  lender_count\n",
       "0               1     438.915056     13.851129\n",
       "1               2     380.360825     12.010309\n",
       "2               3     813.666667     26.344444\n",
       "3               4    1181.807780     37.782609\n",
       "4               5    1515.092593     49.233333\n",
       "5               6    1431.783537     46.597561\n",
       "6               7    1417.291667     46.283333\n",
       "7               8    1445.182292     45.770833\n",
       "8               9    1440.409836     44.629508\n",
       "9              10     956.516768     29.538110\n",
       "10             11    1086.720430     34.032258\n",
       "11             12    1089.931507     33.386301\n",
       "12             13    1171.863118     34.634981\n",
       "13             14    1270.063025     37.373950\n",
       "14             15    1363.647959     40.729592\n",
       "15             16    1405.575540     39.928058\n",
       "16             17    1085.294118     33.607843\n",
       "17             18     807.812500     25.656250\n",
       "18             19     851.408451     26.915493\n",
       "19             20     862.686567     26.716418\n",
       "20             21     919.117647     28.382353\n",
       "21             22     907.352941     27.058824\n",
       "22             23    1013.333333     32.066667\n",
       "23             24    1045.000000     33.400000\n",
       "24             25    1025.000000     29.800000\n",
       "25             26    2346.875000     77.375000\n",
       "26             27     937.500000     22.000000\n",
       "27             28    1150.000000     33.666667\n",
       "28             29    1225.000000     44.000000\n",
       "29             30    1225.000000     39.000000\n",
       "30             31    1450.000000     50.000000\n",
       "31             32    1283.333333     37.000000\n",
       "32             33    1275.000000     45.500000\n",
       "33             34    2150.000000     78.000000\n",
       "34             37    1550.000000     45.000000\n",
       "35             39    1525.000000     50.000000\n",
       "36             40    5000.000000    133.000000\n",
       "37             41    2600.000000     99.000000\n",
       "38             42    1625.000000     45.000000\n",
       "39             45    4000.000000    116.000000\n",
       "40             46    1775.000000     60.000000"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2f=df2b[['num_borrowers','funded_amount','lender_count']].groupby('num_borrowers',as_index=False).mean()\n",
    "df2f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Larger Loans Involve More Lenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KIzOdHkMQBPxe8IdOj0HUFOn9lds5OTlQKBRo1qwZVqxYgZs3b2LBggWoqKhAVVUVJBKJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1839505d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color=df2f['lender_count']#/df2f['lender_count'].max()   #define the colors on raw values\n",
    "\n",
    "fig=plt.figure(figsize=(12,8)) #,facecolor='white')          #open a figure\n",
    "ax=fig.add_subplot(1,1,1) #,axisbg='white')                #open a plot in the figure \n",
    "ax.set_xlabel('Number of Borrowers',fontsize=20)\n",
    "ax.set_ylabel('Funded Amount',fontsize=20)\n",
    "ax.spines['bottom'].set_color('red')                   #This does not work here\n",
    "for child in ax.get_children():\n",
    "    if isinstance(child, mpl.spines.Spine):\n",
    "        child.set_color('black')\n",
    "ax.grid(True,color='white')\n",
    "for tick in ax.xaxis.get_major_ticks():                 #set the size of the tick labels on the x-axis\n",
    "                tick.label.set_fontsize(14) \n",
    "for tick in ax.yaxis.get_major_ticks():                 #set the size of the tick labels on the y-axis\n",
    "                tick.label.set_fontsize(14)\n",
    "                #tick.label.set_rotation('vertical')    # you can rotate the tick labels\n",
    "\n",
    "cmap=plt.cm.Blues                                        #define a colormap, see http://scipy.github.io/old-wiki/pages/Cookbook/Matplotlib/Show_colormaps\n",
    "                                                         \n",
    "bounds=np.linspace(1,df2f['lender_count'].max(),10,      #determine the breaks for the intervals in the colorbar\n",
    "                   endpoint=True).round()\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)           #adjust the color intervals to the breaks\n",
    "scat = plt.scatter(df2f.num_borrowers,df2f.funded_amount,c=color,s=(df2f.lender_count * 5),norm=norm,cmap=cmap)\n",
    "cb=fig.colorbar(scat,ax=ax)                           #plot the colorbar\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "cb.set_label(label='Mean Number Lenders',size=16,weight='bold')\n",
    "#cb.set_label(label='a label',weight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Correlations, Insights, Questions and Hypotheses\n",
    "#### Most Kiva loans in Kenya are in the agricultural, food, and retail sectors. The loans vary greatly in size, ranging from 25 to 50,000 USD.  The majority of loans have been for less than 500 USD. The number of lenders that contribute to a loan increases with loan size, which certainly is not surprising.  The number of loans made per year in Kenya decreased for two years until 2014, but then rebounded substantially in 2015.  This rebound was associated with an increase in the number of agricultural loans made to women.  The rate of default on loans is about 5%, with the default rate on single-borrower loans being roughly equal.\n",
    "**Questions and Hypotheses**: \n",
    ">What activities are subsumed by agricultural loans to women? I predict that they are primarily used for materials to increase productivity, such as newer seed varieties and fertilizer.\n",
    "\n",
    ">Do agricultural loans to women differ somehow from similar loans to men? It may be that women are commercializing household gardens, which in the data base may be described differently from loans to men\n",
    "\n",
    ">Some loans expire, having never been funded by Kiva donors.  Do these loans or the applicants somehow differ from the loans that recieve Kiva funding? It may be that there is *something about the loan that raises doubts* about the use to which the funds would go.\n",
    "\n",
    ">Is there anything that characterizes the loans or borrowers who end up in default? Are they *randomly distributed among loans of all sizes*?  Are they *geographically clustered*?  I predict that defaults are not randomly distributed among all loans, but I do not yet see a basis for making a distinct hypothesis as to how they might be characterized.\n",
    "\n",
    ">How do loans with multiple borrowers differ from loans with only a single borrower?  It may be that these loans are for more-complicated undertakings, ones that necessitate a larger social group or have a complicated administrative structure.\n",
    "\n",
    ">How are the funded loans distributed around Kenya? The increase in agriculture-sector loans to womend suggests that there may be increasing focus on rural areas.\n",
    "\n",
    "**Narrative**:\n",
    ">The Kiva loan program represents thousands of loans within Kenya.  These loans are generally small (<500 USD) and impact a range of sectors in Kenyan society.  The default rate on loans is about 5%, and men and women borrowers have similar risk of defaults.  Recently, more loans have been given to women in the agricultural sector. These loans appear to be driving the overall increase in Kiva loans over the last two years.  It would be interesting to know how these agricultural loans to women are being used, and if they indicate an increasingly rural emphasis in Kiva microloan activity.\n",
    "\n",
    "\n"
   ]
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