Retrieving_Kiva_data_from_API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving data from Kiva API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using [KIVA](https://www.kiva.org/) data for the entirety of this course. Kiva makes it's data publically available through it's [API](http://build.kiva.org/). \n",
    "\n",
    "Not sure what an API is? It stands for Application Program Interface. Code Academy has a fantastic short course (link [here](https://www.codecademy.com/en/tracks/placekitten) that introduces you to APIs and allows you to pull images of kittens from a website by the end of the session.\n",
    "\n",
    "Below, we import the packages we need in order to retrieve data from the API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlopen, Request\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "import pandas as pd\n",
    "import requests as r\n",
    "import os\n",
    "import logging\n",
    "import time\n",
    "pd.set_option('display.max_columns', 80)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below enables Jupyter to show the output of multiple cells at once. There are a lot of cool jupyter specific commands to allow you to work faster. [Here](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/) is a great article that goes through some less well known shortcuts and tricks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below we set the maximum number of columns to 100 so we can see our entire data set. If we did not set this option, we would not be able to see some columns in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 100) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using % in the section below indicates a magic command. You can find out more about magic commands [here](https://ipython.org/ipython-doc/3/interactive/magics.html). To see all available magic commands run this line of code:\n",
    "\n",
    "`%lsmagic`\n",
    "\n",
    "Using the % in the block of code below allows us to run commands on the terminal from within jupyter. We make a folder in our user directory where we will store the data we retrieve from the api."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% mkdir ~/_intro_machine_learning_course\n",
    "% cd ~/_intro_machine_learning_course\n",
    "% pwd\n",
    "% ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the line below, we create a data cache. A cache is a great way to store data, when it is costly to retrive it from scratch every time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store = pd.HDFStore('kiva_cache.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is great documentation on Kiva's API [here](http://build.kiva.org/api). The documentation explains what parameters (conditions) we need to pass in our request to Kiva's database in order to get the data we want.\n",
    "\n",
    "We are trying to retrive all Kiva data from Kenya. So we will be using two main parameters where we set country_code=KE (KE is the two letter [ISO](https://en.wikipedia.org/wiki/ISO_3166-2) code for Kenya), and we increase the results per page to 500 (this is the maximum KIVAs API appears to allow). You can see the HTML results of the api call by pasting the url below into your browser, HTML is a format that is really easy to read.\n",
    "\n",
    "http://api.kivaws.org/v1/loans/search/?country_code=KE&per_page=500\n",
    "\n",
    "1) Go ahead and play with the url in order to retrieve different data. For example, how would you retrieve data from South Africa (ZA)?\n",
    "\n",
    "2) How would you only retrive 200 results?\n",
    "\n",
    "Answers:\n",
    "\n",
    "1) http://api.kivaws.org/v1/loans/search/?country_code=ZA&per_page=500\n",
    "\n",
    "2)  http://api.kivaws.org/v1/loans/search/?country_code=ZA&per_page=200\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We want to request this data from the api and store it in a format that is more intuitive to us - a dataframe. Let's get started. The code below retrieves the first 500 results and converts it into a pandas dataframe. You will get to know a lot more about dataframes over the next few classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = r.get('http://api.kivaws.org/v1/loans/search.json?country_code=KE&per_page=500')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that in the request above we specify json as the type of text we want returned. This is easier to handle and change into a python dataframe. You can past the link into your browser to understand the difference between [JSON](https://en.wikipedia.org/wiki/JSON) and [HTML](https://en.wikipedia.org/wiki/HTML)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By running d.headers below we can see all the data associated with our request. It shows the time of our request, the fact that we are requesting json text 'Content-Type': 'application/json; charset=UTF-8', in addition to other details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Date': 'Wed, 03 May 2017 23:56:20 GMT', 'Server': 'Apache/2.4.7 (Ubuntu)', 'Access-Control-Allow-Origin': '*', 'Expires': 'Tue, 03 Jul 2001 06:00:00 GMT', 'Last-Modified': 'Wed, 03 May 2017 23:56:20 GMT', 'Cache-Control': 'private, no-store, no-cache, must-revalidate, max-age=0, post-check=0, pre-check=0, proxy-revalidate, no-transform', 'Pragma': 'no-cache', 'X-RateLimit-Overall-Limit': '60', 'X-RateLimit-Overall-Remaining': '60', 'Vary': 'Accept-Encoding', 'Content-Encoding': 'gzip', 'Content-Length': '26111', 'Content-Type': 'application/json; charset=UTF-8'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.headers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By running the command d.json() below we can get an idea of what our data looks like before we change it into a pandas data frame. In order to see the output, remove the `;` from the end of the codeblock. We have added the `;` to supress output for easier reading in our repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'code': 'org.kiva.RateLimitExceeded',\n",
       " 'message': 'You are making requests to the server too often and have been temporarily blocked. You can find out more details at http://build.kiva.org/docs/data/rate.'}"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`json.loads` turns the json object into a python object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = json.loads(d.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`json_normalize` normalizes semi-structured json data into a flat table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans=json_normalize(data['loans'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now extracted the first 500 rows of loans from the API. We can confirm how many rows we have in a dataset using the len() function below. Now, we need to extract this data more systematically for all Kenyan loan results. KIVA has a parameter called page but does not allow for range of pages, so we will have to create a python loop to go through each page of results and add to our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(loans.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the function defined below we are doing a few things. Firstly, we create an empty dataframe, then we loop through each page and call the api. We add the results of each page to our dataframe.\n",
    "\n",
    "The first part of the coad extracts all the loans in Kenya, the second part of the function extracts additional loan information (like the longer description about the loan) by making a second api call. This is inserted in a second dataframe called loans_details. There is a restriction on the second API call which only allows us to pass 100 loans in one call. To address this, we have a parameter in `pages=100` which means we only extract 100 loans in every call which allows us to satisfy the second restriction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_loans(pages,country_iso_code):\n",
    "    loans_full=pd.DataFrame()\n",
    "    loans_details=pd.DataFrame()\n",
    "    for n in range(1, pages+1):\n",
    "        s=str(n)\n",
    "        print(s)\n",
    "        d = r.get('http://api.kivaws.org/v1/loans/search.json?country_code='+country_iso_code+'&per_page=100&page='+s)\n",
    "        data = json.loads(d.text)\n",
    "        loans=json_normalize(data['loans'])\n",
    "        \n",
    "        #below we take the loan id column from our retrieved loan data, and use it to pull additional details\n",
    "        #about our loans. We store this in a seperate table loans_details\n",
    "        \n",
    "        loan_id= loans['id'].tolist()\n",
    "        loan_id_str = ','.join(str(e) for e in loan_id)\n",
    "        ld=r.get('https://api.kivaws.org/v1/loans/'+loan_id_str+'.json')\n",
    "        ld_data = json.loads(ld.text)\n",
    "        loans_ext=json_normalize(ld_data['loans'])\n",
    "        loans_details=loans_details.append(loans_ext, ignore_index=True)\n",
    "        \n",
    "        loans_full=loans_full.append(loans,ignore_index=True)\n",
    "        print(len(loans_full.index))\n",
    "        time.sleep(10)\n",
    "        \n",
    "    return loans_full, loans_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "loans_full,loans_details = extract_loans(1279,'KE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loans_full.to_csv('~/intro_course_data_science_for_good/data/loans_full_large.csv')\n",
    "loans_details.to_csv('~/intro_course_data_science_for_good/data/loans_details.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also pull data about Kiva's partners, who are the intermediaries between Kiva and end recipients for the majority of loans made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_path = '~/intro_course_data_science_for_good/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(data_path+'/loans_details.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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>386.0</td>\n",
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       "      <td>2017-06-08T00:40:03Z</td>\n",
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       "      <td>[{'name': '#Woman Owned Biz'}, {'name': '#Pare...</td>\n",
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       "      <td>500</td>\n",
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       "      <td>Julie Keaton</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Bungoma</td>\n",
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       "      <td>Manufacturing</td>\n",
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       "      <td>50000.0</td>\n",
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       "      <td>[{'due_date': '2017-07-01T07:00:00Z', 'amount'...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Hello Kiva Community! &lt;br /&gt;&lt;br /&gt;Meet Rose, w...</td>\n",
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       "      <td>Eldoret</td>\n",
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       "      <td>2017-06-08T00:30:03Z</td>\n",
       "      <td>2017-05-09T00:30:04Z</td>\n",
       "      <td>Personal Use</td>\n",
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       "      <td>[{'name': '#Eco-friendly'}, {'name': '#Technol...</td>\n",
       "      <td>6000.0</td>\n",
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       "      <td>2017-04-28T07:00:00Z</td>\n",
       "      <td>75</td>\n",
       "      <td>[{'due_date': '2017-05-14T07:00:00Z', 'amount'...</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>14</td>\n",
       "      <td>[{'due_date': '2017-07-01T07:00:00Z', 'amount'...</td>\n",
       "      <td>['Green', 'Earth Day Campaign']</td>\n",
       "      <td>Julie Keaton</td>\n",
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       "      <td>to buy a solar lantern.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>KE</td>\n",
       "      <td>town</td>\n",
       "      <td>0.566667 34.566667</td>\n",
       "      <td>point</td>\n",
       "      <td>Bungoma</td>\n",
       "      <td>Jane</td>\n",
       "      <td>386.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>2017-06-08T00:20:04Z</td>\n",
       "      <td>2017-05-09T00:20:04Z</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>fundraising</td>\n",
       "      <td>[{'name': '#Eco-friendly'}]</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2017-04-03T07:00:00Z</td>\n",
       "      <td>500</td>\n",
       "      <td>[{'due_date': '2017-05-08T07:00:00Z', 'amount'...</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lender</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>14</td>\n",
       "      <td>[{'due_date': '2017-07-01T07:00:00Z', 'amount'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Julie Keaton</td>\n",
       "      <td>892591.0</td>\n",
       "      <td>to buy more clothes to meet the needs and tast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
       "      <td>[{'first_name': 'Alice', 'last_name': '', 'gen...</td>\n",
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       "      <td>Nandi Hills</td>\n",
       "      <td>Alice</td>\n",
       "      <td>156.0</td>\n",
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       "      <td>2017-06-08T00:20:03Z</td>\n",
       "      <td>2017-05-09T00:20:03Z</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>fundraising</td>\n",
       "      <td>[{'name': '#Woman Owned Biz'}]</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>KES</td>\n",
       "      <td>2017-05-27T07:00:00Z</td>\n",
       "      <td>400</td>\n",
       "      <td>[{'due_date': '2017-05-27T07:00:00Z', 'amount'...</td>\n",
       "      <td>shared</td>\n",
       "      <td>0.1</td>\n",
       "      <td>lender</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>13</td>\n",
       "      <td>[{'due_date': '2017-07-01T07:00:00Z', 'amount'...</td>\n",
       "      <td>['Rural Exclusion']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to buy farming inputs (fertilizers, pesticides...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          activity  basket_amount  bonus_credit_eligibility  \\\n",
       "0           0           Farming            0.0                     False   \n",
       "1           1  Furniture Making            0.0                     False   \n",
       "2           2       Home Energy            0.0                     False   \n",
       "3           3     Used Clothing            0.0                     False   \n",
       "4           4           Farming            0.0                     False   \n",
       "\n",
       "                                           borrowers  \\\n",
       "0  [{'first_name': 'Evaline', 'last_name': '', 'g...   \n",
       "1  [{'first_name': 'Julias', 'last_name': '', 'ge...   \n",
       "2  [{'first_name': 'Rose', 'last_name': '', 'gend...   \n",
       "3  [{'first_name': 'Jane', 'last_name': '', 'gend...   \n",
       "4  [{'first_name': 'Alice', 'last_name': '', 'gen...   \n",
       "\n",
       "   currency_exchange_loss_amount description.languages  \\\n",
       "0                            NaN                ['en']   \n",
       "1                            NaN                ['en']   \n",
       "2                            NaN                ['en']   \n",
       "3                            NaN                ['en']   \n",
       "4                            NaN                ['en']   \n",
       "\n",
       "                                description.texts.en description.texts.es  \\\n",
       "0  Evaline is a married lady aged 44 years old an...                  NaN   \n",
       "1  Aged 42 years is a man by the name of Julias. ...                  NaN   \n",
       "2  Hello Kiva Community! <br /><br />Meet Rose, w...                  NaN   \n",
       "3  Jane was born in the 1980, and she is happily ...                  NaN   \n",
       "4  Alice (the woman pictured above in her small s...                  NaN   \n",
       "\n",
       "  description.texts.fr description.texts.ru  funded_amount funded_date  \\\n",
       "0                  NaN                  NaN              0         NaN   \n",
       "1                  NaN                  NaN              0         NaN   \n",
       "2                  NaN                  NaN             50         NaN   \n",
       "3                  NaN                  NaN              0         NaN   \n",
       "4                  NaN                  NaN              0         NaN   \n",
       "\n",
       "        id  image.id  image.template_id  journal_totals.bulkEntries  \\\n",
       "0  1291548   2516002                  1                           0   \n",
       "1  1291532   2515992                  1                           0   \n",
       "2  1291530   2515991                  1                           0   \n",
       "3  1291525   2515986                  1                           0   \n",
       "4  1291518   2515975                  1                           0   \n",
       "\n",
       "   journal_totals.entries  lender_count  loan_amount location.country  \\\n",
       "0                       0             0          500            Kenya   \n",
       "1                       0             0          500            Kenya   \n",
       "2                       0             2           75            Kenya   \n",
       "3                       0             0          500            Kenya   \n",
       "4                       0             0          400            Kenya   \n",
       "\n",
       "  location.country_code location.geo.level   location.geo.pairs  \\\n",
       "0                    KE               town  -0.583333 35.183333   \n",
       "1                    KE               town   0.566667 34.566667   \n",
       "2                    KE               town   0.516667 35.283333   \n",
       "3                    KE               town   0.566667 34.566667   \n",
       "4                    KE               town                 1 38   \n",
       "\n",
       "  location.geo.type location.town     name  partner_id payments  \\\n",
       "0             point        litein  Evaline       386.0       []   \n",
       "1             point       Bungoma   Julias       386.0       []   \n",
       "2             point       Eldoret     Rose       156.0       []   \n",
       "3             point       Bungoma     Jane       386.0       []   \n",
       "4             point   Nandi Hills    Alice       156.0       []   \n",
       "\n",
       "  planned_expiration_date           posted_date         sector       status  \\\n",
       "0    2017-06-08T00:40:03Z  2017-05-09T00:40:03Z    Agriculture  fundraising   \n",
       "1    2017-06-08T00:30:05Z  2017-05-09T00:30:05Z  Manufacturing  fundraising   \n",
       "2    2017-06-08T00:30:03Z  2017-05-09T00:30:04Z   Personal Use  fundraising   \n",
       "3    2017-06-08T00:20:04Z  2017-05-09T00:20:04Z       Clothing  fundraising   \n",
       "4    2017-06-08T00:20:03Z  2017-05-09T00:20:03Z    Agriculture  fundraising   \n",
       "\n",
       "                                                tags  terms.disbursal_amount  \\\n",
       "0  [{'name': '#Woman Owned Biz'}, {'name': '#Pare...                 50000.0   \n",
       "1                                                 []                 50000.0   \n",
       "2  [{'name': '#Eco-friendly'}, {'name': '#Technol...                  6000.0   \n",
       "3                        [{'name': '#Eco-friendly'}]                 50000.0   \n",
       "4                     [{'name': '#Woman Owned Biz'}]                 40000.0   \n",
       "\n",
       "  terms.disbursal_currency  terms.disbursal_date  terms.loan_amount  \\\n",
       "0                      KES  2017-04-03T07:00:00Z                500   \n",
       "1                      KES  2017-04-03T07:00:00Z                500   \n",
       "2                      KES  2017-04-28T07:00:00Z                 75   \n",
       "3                      KES  2017-04-03T07:00:00Z                500   \n",
       "4                      KES  2017-05-27T07:00:00Z                400   \n",
       "\n",
       "                                terms.local_payments  \\\n",
       "0  [{'due_date': '2017-05-10T07:00:00Z', 'amount'...   \n",
       "1  [{'due_date': '2017-05-09T07:00:00Z', 'amount'...   \n",
       "2  [{'due_date': '2017-05-14T07:00:00Z', 'amount'...   \n",
       "3  [{'due_date': '2017-05-08T07:00:00Z', 'amount'...   \n",
       "4  [{'due_date': '2017-05-27T07:00:00Z', 'amount'...   \n",
       "\n",
       "  terms.loss_liability.currency_exchange  \\\n",
       "0                                 shared   \n",
       "1                                 shared   \n",
       "2                                 shared   \n",
       "3                                 shared   \n",
       "4                                 shared   \n",
       "\n",
       "   terms.loss_liability.currency_exchange_coverage_rate  \\\n",
       "0                                                0.1      \n",
       "1                                                0.1      \n",
       "2                                                0.1      \n",
       "3                                                0.1      \n",
       "4                                                0.1      \n",
       "\n",
       "  terms.loss_liability.nonpayment terms.repayment_interval  \\\n",
       "0                          lender                  Monthly   \n",
       "1                          lender                  Monthly   \n",
       "2                          lender                  Monthly   \n",
       "3                          lender                  Monthly   \n",
       "4                          lender                  Monthly   \n",
       "\n",
       "   terms.repayment_term                           terms.scheduled_payments  \\\n",
       "0                    14  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "1                    14  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "2                    14  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "3                    14  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "4                    13  [{'due_date': '2017-07-01T07:00:00Z', 'amount'...   \n",
       "\n",
       "                            themes translator.byline  translator.image  \\\n",
       "0                              NaN      Julie Keaton          892591.0   \n",
       "1                              NaN      Morena Calvo         1832928.0   \n",
       "2  ['Green', 'Earth Day Campaign']      Julie Keaton          892591.0   \n",
       "3                              NaN      Julie Keaton          892591.0   \n",
       "4              ['Rural Exclusion']               NaN               NaN   \n",
       "\n",
       "                                                 use  video.id  \\\n",
       "0  to purchase more tea leaves to sell to the tea...       NaN   \n",
       "1  to buy timber to make more furniture for his e...       NaN   \n",
       "2                            to buy a solar lantern.       NaN   \n",
       "3  to buy more clothes to meet the needs and tast...       NaN   \n",
       "4  to buy farming inputs (fertilizers, pesticides...       NaN   \n",
       "\n",
       "   video.thumbnailImageId video.title video.youtubeId  \n",
       "0                     NaN         NaN             NaN  \n",
       "1                     NaN         NaN             NaN  \n",
       "2                     NaN         NaN             NaN  \n",
       "3                     NaN         NaN             NaN  \n",
       "4                     NaN         NaN             NaN  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=df[df['partner_id'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['partner_id']=df['partner_id'].astype(int).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "partner_id=df['partner_id'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['386',\n",
       " '156',\n",
       " '133',\n",
       " '218',\n",
       " '388',\n",
       " '164',\n",
       " '138',\n",
       " '202',\n",
       " '258',\n",
       " '262',\n",
       " '526',\n",
       " '520',\n",
       " '466',\n",
       " '477',\n",
       " '397',\n",
       " '512',\n",
       " '505',\n",
       " '500',\n",
       " '502',\n",
       " '436',\n",
       " '491',\n",
       " '469',\n",
       " '276',\n",
       " '322',\n",
       " '473',\n",
       " '405',\n",
       " '340',\n",
       " '350',\n",
       " '341',\n",
       " '412',\n",
       " '286',\n",
       " '447',\n",
       " '277',\n",
       " '324',\n",
       " '282',\n",
       " '213',\n",
       " '385',\n",
       " '259',\n",
       " '257',\n",
       " '212',\n",
       " '203',\n",
       " '336',\n",
       " '345',\n",
       " '272',\n",
       " '142',\n",
       " '24',\n",
       " '36',\n",
       " '92',\n",
       " '27',\n",
       " '25',\n",
       " '6',\n",
       " '32']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partner_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = r.get('https://api.kivaws.org/v1/partners/322.json?app_id=org.deltanalytics')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Date': 'Tue, 16 May 2017 22:02:10 GMT', 'Server': 'Apache/2.4.7 (Ubuntu)', 'Access-Control-Allow-Origin': '*', 'Expires': 'Tue, 03 Jul 2001 06:00:00 GMT', 'Last-Modified': 'Tue, 16 May 2017 22:02:10 GMT', 'Cache-Control': 'private, no-store, no-cache, must-revalidate, max-age=0, post-check=0, pre-check=0, proxy-revalidate, no-transform', 'Pragma': 'no-cache', 'X-RateLimit-Overall-Limit': '500', 'X-RateLimit-Overall-Remaining': '500', 'Vary': 'Accept-Encoding', 'Content-Encoding': 'gzip', 'Content-Length': '447', 'Content-Type': 'application/json; charset=UTF-8'}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'partners': [{'average_loan_size_percent_per_capita_income': 0,\n",
       "   'charges_fees_and_interest': False,\n",
       "   'countries': [{'iso_code': 'KE',\n",
       "     'location': {'geo': {'level': 'country',\n",
       "       'pairs': '1 38',\n",
       "       'type': 'point'}},\n",
       "     'name': 'Kenya',\n",
       "     'region': 'Africa'}],\n",
       "   'currency_exchange_loss_rate': 0.29602128530495,\n",
       "   'default_rate': 16.580365296804,\n",
       "   'default_rate_note': '',\n",
       "   'delinquency_rate': 75.834468155791,\n",
       "   'delinquency_rate_note': '',\n",
       "   'id': 322,\n",
       "   'image': {'id': 2081491, 'template_id': 1},\n",
       "   'loans_at_risk_rate': 100,\n",
       "   'loans_posted': 84,\n",
       "   'name': 'iSmart Kenya',\n",
       "   'portfolio_yield_note': '',\n",
       "   'rating': 'Not Rated',\n",
       "   'start_date': '2013-12-17T17:50:04Z',\n",
       "   'status': 'active',\n",
       "   'total_amount_raised': 122150,\n",
       "   'url': 'http://www.facebook.com/livelyhoods'}]}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.json();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_loan_partners(partner_ids):\n",
    "    loan_partner_details=pd.DataFrame()\n",
    "    for n in partner_ids:\n",
    "        print (n)\n",
    "        d = r.get('https://api.kivaws.org/v1/partners/'+n+'.json?app_id=org.deltanalytics')\n",
    "        data = json.loads(d.text)\n",
    "        partners=json_normalize(data['partners'])\n",
    "        loan_partner_details=loan_partner_details.append(partners, ignore_index=True)\n",
    "        print(len(loan_partner_details.index))\n",
    "    \n",
    "    return loan_partner_details  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "386\n",
      "1\n",
      "156\n",
      "2\n",
      "133\n",
      "3\n",
      "218\n",
      "4\n",
      "388\n",
      "5\n",
      "164\n",
      "6\n",
      "138\n",
      "7\n",
      "202\n",
      "8\n",
      "258\n",
      "9\n",
      "262\n",
      "10\n",
      "526\n",
      "11\n",
      "520\n",
      "12\n",
      "466\n",
      "13\n",
      "477\n",
      "14\n",
      "397\n",
      "15\n",
      "512\n",
      "16\n",
      "505\n",
      "17\n",
      "500\n",
      "18\n",
      "502\n",
      "19\n",
      "436\n",
      "20\n",
      "491\n",
      "21\n",
      "469\n",
      "22\n",
      "276\n",
      "23\n",
      "322\n",
      "24\n",
      "473\n",
      "25\n",
      "405\n",
      "26\n",
      "340\n",
      "27\n",
      "350\n",
      "28\n",
      "341\n",
      "29\n",
      "412\n",
      "30\n",
      "286\n",
      "31\n",
      "447\n",
      "32\n",
      "277\n",
      "33\n",
      "324\n",
      "34\n",
      "282\n",
      "35\n",
      "213\n",
      "36\n",
      "385\n",
      "37\n",
      "259\n",
      "38\n",
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      "39\n",
      "212\n",
      "40\n",
      "203\n",
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      "336\n",
      "42\n",
      "345\n",
      "43\n",
      "272\n",
      "44\n",
      "142\n",
      "45\n",
      "24\n",
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      "36\n",
      "47\n",
      "92\n",
      "48\n",
      "27\n",
      "49\n",
      "25\n",
      "50\n",
      "6\n",
      "51\n",
      "32\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "loan_partner_details=extract_loan_partners(partner_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_loan_size_percent_per_capita_income</th>\n",
       "      <th>charges_fees_and_interest</th>\n",
       "      <th>countries</th>\n",
       "      <th>currency_exchange_loss_rate</th>\n",
       "      <th>default_rate</th>\n",
       "      <th>default_rate_note</th>\n",
       "      <th>delinquency_rate</th>\n",
       "      <th>delinquency_rate_note</th>\n",
       "      <th>id</th>\n",
       "      <th>image.id</th>\n",
       "      <th>image.template_id</th>\n",
       "      <th>loans_at_risk_rate</th>\n",
       "      <th>loans_posted</th>\n",
       "      <th>name</th>\n",
       "      <th>portfolio_yield</th>\n",
       "      <th>portfolio_yield_note</th>\n",
       "      <th>profitability</th>\n",
       "      <th>rating</th>\n",
       "      <th>social_performance_strengths</th>\n",
       "      <th>start_date</th>\n",
       "      <th>status</th>\n",
       "      <th>total_amount_raised</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.000000</td>\n",
       "      <td></td>\n",
       "      <td>7.017031</td>\n",
       "      <td></td>\n",
       "      <td>386</td>\n",
       "      <td>1592272</td>\n",
       "      <td>1</td>\n",
       "      <td>21.165398</td>\n",
       "      <td>1948</td>\n",
       "      <td>Kenya ECLOF</td>\n",
       "      <td>40.3</td>\n",
       "      <td></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</td>\n",
       "      <td>http://www.eclof-kenya.org/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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></td>\n",
       "      <td>2.536684</td>\n",
       "      <td></td>\n",
       "      <td>156</td>\n",
       "      <td>1834079</td>\n",
       "      <td>1</td>\n",
       "      <td>24.200354</td>\n",
       "      <td>18150</td>\n",
       "      <td>Juhudi Kilimo</td>\n",
       "      <td>33.0</td>\n",
       "      <td></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</td>\n",
       "      <td>http://www.juhudikilimo.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.9</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.089354</td>\n",
       "      <td>3.652283</td>\n",
       "      <td></td>\n",
       "      <td>8.017062</td>\n",
       "      <td></td>\n",
       "      <td>133</td>\n",
       "      <td>1495190</td>\n",
       "      <td>1</td>\n",
       "      <td>16.058249</td>\n",
       "      <td>30794</td>\n",
       "      <td>VisionFund Kenya</td>\n",
       "      <td>36.0</td>\n",
       "      <td></td>\n",
       "      <td>-1.70</td>\n",
       "      <td>2.5</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>11366975</td>\n",
       "      <td>http://www.visionfundkenya.co.ke/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.307016</td>\n",
       "      <td>1.978274</td>\n",
       "      <td></td>\n",
       "      <td>0.000000</td>\n",
       "      <td></td>\n",
       "      <td>218</td>\n",
       "      <td>976374</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77</td>\n",
       "      <td>Strathmore University</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.0</td>\n",
       "      <td>[{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...</td>\n",
       "      <td>2012-02-03T18:10:02Z</td>\n",
       "      <td>active</td>\n",
       "      <td>638400</td>\n",
       "      <td>http://www.strathmore.edu/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'iso_code': 'KE', 'region': 'Africa', 'name'...</td>\n",
       "      <td>0.217001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td></td>\n",
       "      <td>0.644281</td>\n",
       "      <td></td>\n",
       "      <td>388</td>\n",
       "      <td>1582473</td>\n",
       "      <td>1</td>\n",
       "      <td>3.858614</td>\n",
       "      <td>3132</td>\n",
       "      <td>Hand in Hand Eastern Africa</td>\n",
       "      <td>41.0</td>\n",
       "      <td></td>\n",
       "      <td>27.90</td>\n",
       "      <td>1.5</td>\n",
       "      <td>[{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...</td>\n",
       "      <td>2014-06-25T15:10:06Z</td>\n",
       "      <td>active</td>\n",
       "      <td>990175</td>\n",
       "      <td>http://handinhand-ea.org/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_loan_size_percent_per_capita_income  charges_fees_and_interest  \\\n",
       "0                                          0.0                       True   \n",
       "1                                         49.6                       True   \n",
       "2                                         34.9                       True   \n",
       "3                                          0.0                       True   \n",
       "4                                          0.0                       True   \n",
       "\n",
       "                                           countries  \\\n",
       "0  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "1  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "2  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "3  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "4  [{'iso_code': 'KE', 'region': 'Africa', 'name'...   \n",
       "\n",
       "   currency_exchange_loss_rate  default_rate default_rate_note  \\\n",
       "0                     0.120642      0.000000                     \n",
       "1                     0.431935      2.575299                     \n",
       "2                     0.089354      3.652283                     \n",
       "3                     0.307016      1.978274                     \n",
       "4                     0.217001      0.000000                     \n",
       "\n",
       "   delinquency_rate delinquency_rate_note   id  image.id  image.template_id  \\\n",
       "0          7.017031                        386   1592272                  1   \n",
       "1          2.536684                        156   1834079                  1   \n",
       "2          8.017062                        133   1495190                  1   \n",
       "3          0.000000                        218    976374                  1   \n",
       "4          0.644281                        388   1582473                  1   \n",
       "\n",
       "   loans_at_risk_rate  loans_posted                         name  \\\n",
       "0           21.165398          1948                  Kenya ECLOF   \n",
       "1           24.200354         18150                Juhudi Kilimo   \n",
       "2           16.058249         30794             VisionFund Kenya   \n",
       "3            0.000000            77        Strathmore University   \n",
       "4            3.858614          3132  Hand in Hand Eastern Africa   \n",
       "\n",
       "   portfolio_yield portfolio_yield_note  profitability rating  \\\n",
       "0             40.3                                2.54    2.0   \n",
       "1             33.0                               -7.10    2.0   \n",
       "2             36.0                               -1.70    2.5   \n",
       "3              NaN                                0.50    3.0   \n",
       "4             41.0                               27.90    1.5   \n",
       "\n",
       "                        social_performance_strengths            start_date  \\\n",
       "0  [{'id': 3, 'name': 'Client Voice', 'descriptio...  2014-05-29T13:30:02Z   \n",
       "1  [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...  2010-01-15T20:20:17Z   \n",
       "2  [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...  2009-05-29T11:35:11Z   \n",
       "3  [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...  2012-02-03T18:10:02Z   \n",
       "4  [{'id': 1, 'name': 'Anti-Poverty Focus', 'desc...  2014-06-25T15:10:06Z   \n",
       "\n",
       "   status  total_amount_raised                                url  \n",
       "0  active               863375        http://www.eclof-kenya.org/  \n",
       "1  active              7705925       http://www.juhudikilimo.com/  \n",
       "2  active             11366975  http://www.visionfundkenya.co.ke/  \n",
       "3  active               638400         http://www.strathmore.edu/  \n",
       "4  active               990175          http://handinhand-ea.org/  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_partner_details.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "loan_partner_details.to_csv('~/intro_course_data_science_for_good/data/loans_partner_details.csv')"
   ]
  }
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