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hdemers/Thinkful.ipynb

Created December 16, 2015 23:00
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Thinkful.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import calendar\n",
"from datetime import datetime, date, timedelta\n",
"import time\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy import stats\n",
"from IPython.display import display\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import statsmodels.formula.api as sm\n",
"import requests\n",
"\n",
"sns.set_style('darkgrid', rc={'font.family': 'Ubuntu'})\n",
"palette = sns.color_palette()\n",
"\n",
"data_dir = \"/home/hdemers/Projets/data/thinkful/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Unit 1: The Data Science Toolset\n",
"\n",
"## Lesson 1: Programming in Python\n",
"\n",
"### Assignment 6: Managing Data with Pandas"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lecz_dir = data_dir + \"lecz-urban-rural-population-land-area-estimates-v2-csv/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading the codebook:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Variable</th>\n",
" <th>Description</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ISO3v10</td>\n",
" <td>Unique identifying 3 character country code fr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Continent</td>\n",
" <td>The Global Rural-Urban Mapping Project, Versio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Country</td>\n",
" <td>The Global Rural-Urban Mapping Project, Versio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GeoRegion</td>\n",
" <td>The geographical regions used by the United Na...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>GeoSubregion</td>\n",
" <td>Within macro geographical groupings, more deta...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ElevationZone</td>\n",
" <td>Elevation data used to generate the low elevat...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Income Group</td>\n",
" <td>From the World Bank: \"Economies are divided ac...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Lending Category</td>\n",
" <td>From the World Bank: \"IDA countries are those ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>UrbanRuralDesignation</td>\n",
" <td>The Global Rural-Urban Mapping Project, Versio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Population1990</td>\n",
" <td>Population counts in low elevation zones in th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Population2000</td>\n",
" <td>Population counts in low elevation zones in th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Population2010</td>\n",
" <td>Population counts in low elevation zones in th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Population2100</td>\n",
" <td>Extrapolated population counts in low elevatio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>LandArea</td>\n",
" <td>Land Areas in square kilometers</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>PercentofCountryPopulation1990</td>\n",
" <td>Percent of total country population year 1990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>PercentofCountryPopulation2000</td>\n",
" <td>Percent of total country population year 2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>PercentofCountryPopulation2010</td>\n",
" <td>Percent of total country population year 2010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>PercentofCountryPopulation2100</td>\n",
" <td>Percent of total country population year 2100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>PercentofCountryLandArea</td>\n",
" <td>Percent of total country land area</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Variable \\\n",
"0 ISO3v10 \n",
"1 Continent \n",
"2 Country \n",
"3 GeoRegion \n",
"4 GeoSubregion \n",
"5 ElevationZone \n",
"6 Income Group \n",
"7 Lending Category \n",
"8 UrbanRuralDesignation \n",
"9 Population1990 \n",
"10 Population2000 \n",
"11 Population2010 \n",
"12 Population2100 \n",
"13 LandArea \n",
"14 PercentofCountryPopulation1990 \n",
"15 PercentofCountryPopulation2000 \n",
"16 PercentofCountryPopulation2010 \n",
"17 PercentofCountryPopulation2100 \n",
"18 PercentofCountryLandArea \n",
"\n",
" Description \n",
"0 Unique identifying 3 character country code fr... \n",
"1 The Global Rural-Urban Mapping Project, Versio... \n",
"2 The Global Rural-Urban Mapping Project, Versio... \n",
"3 The geographical regions used by the United Na... \n",
"4 Within macro geographical groupings, more deta... \n",
"5 Elevation data used to generate the low elevat... \n",
"6 From the World Bank: \"Economies are divided ac... \n",
"7 From the World Bank: \"IDA countries are those ... \n",
"8 The Global Rural-Urban Mapping Project, Versio... \n",
"9 Population counts in low elevation zones in th... \n",
"10 Population counts in low elevation zones in th... \n",
"11 Population counts in low elevation zones in th... \n",
"12 Extrapolated population counts in low elevatio... \n",
"13 Land Areas in square kilometers \n",
"14 Percent of total country population year 1990 \n",
"15 Percent of total country population year 2000 \n",
"16 Percent of total country population year 2010 \n",
"17 Percent of total country population year 2100 \n",
"18 Percent of total country land area "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(lecz_dir +\n",
" \"lecz-urban-rural-population-land-area-estimates_codebook.csv\")\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"120\n"
]
}
],
"source": [
"df = pd.read_csv(\n",
" lecz_dir +\n",
" \"lecz-urban-rural-population-land-area-estimates_continent-90m.csv\"\n",
")\n",
"\n",
"print len(df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Continent</th>\n",
" <th>ElevationZone</th>\n",
" <th>UrbanRuralDesignation</th>\n",
" <th>Population1990</th>\n",
" <th>Population2000</th>\n",
" <th>Population2010</th>\n",
" <th>Population2100</th>\n",
" <th>LandArea</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Africa</td>\n",
" <td>Elevations &gt; 20m or Non-Contiguous</td>\n",
" <td>Rural</td>\n",
" <td>333302081</td>\n",
" <td>418265923</td>\n",
" <td>530184094</td>\n",
" <td>1249908321</td>\n",
" <td>23749644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Africa</td>\n",
" <td>Elevations &gt; 20m or Non-Contiguous</td>\n",
" <td>Urban</td>\n",
" <td>157846329</td>\n",
" <td>210381843</td>\n",
" <td>266776037</td>\n",
" <td>504662423</td>\n",
" <td>204719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Africa</td>\n",
" <td>Elevations Less Than or Equal To 10m</td>\n",
" <td>Rural</td>\n",
" <td>18167003</td>\n",
" <td>22569516</td>\n",
" <td>28154645</td>\n",
" <td>76368900</td>\n",
" <td>172460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Africa</td>\n",
" <td>Elevations Less Than or Equal To 10m</td>\n",
" <td>Urban</td>\n",
" <td>22412955</td>\n",
" <td>28679117</td>\n",
" <td>36746774</td>\n",
" <td>94042536</td>\n",
" <td>13838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Africa</td>\n",
" <td>Elevations Less Than or Equal To 12m</td>\n",
" <td>Rural</td>\n",
" <td>20569411</td>\n",
" <td>25576481</td>\n",
" <td>31957186</td>\n",
" <td>85839163</td>\n",
" <td>202456</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Continent ElevationZone UrbanRuralDesignation \\\n",
"0 Africa Elevations > 20m or Non-Contiguous Rural \n",
"1 Africa Elevations > 20m or Non-Contiguous Urban \n",
"2 Africa Elevations Less Than or Equal To 10m Rural \n",
"3 Africa Elevations Less Than or Equal To 10m Urban \n",
"4 Africa Elevations Less Than or Equal To 12m Rural \n",
"\n",
" Population1990 Population2000 Population2010 Population2100 LandArea \n",
"0 333302081 418265923 530184094 1249908321 23749644 \n",
"1 157846329 210381843 266776037 504662423 204719 \n",
"2 18167003 22569516 28154645 76368900 172460 \n",
"3 22412955 28679117 36746774 94042536 13838 \n",
"4 20569411 25576481 31957186 85839163 202456 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lesson 3: Working With Databases\n",
"\n",
"### Assignment 1: Getting Familiar with SQLite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm not using the command line in the following. I'm doing it all from python using sqlite3.\n",
"\n",
"\n",
"Let's create the first table: `cities`."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sqlite3 as lite\n",
"\n",
"con = lite.connect('getting_started.db')\n",
"\n",
"with con:\n",
" cur = con.cursor()\n",
" try:\n",
" cur.execute(\"drop table cities\")\n",
" except lite.OperationalError as e:\n",
" print e\n",
" \n",
" cur.execute(\"create table cities (name text, state text);\")\n",
" cur.execute(\"\"\"INSERT INTO cities (name, state) VALUES\n",
" ('New York City', 'NY'),\n",
" ('Boston', 'MA'),\n",
" ('Chicago', 'IL'),\n",
" ('Miami', 'FL'),\n",
" ('Dallas', 'TX'),\n",
" ('Seattle', 'WA'),\n",
" ('Portland', 'OR'),\n",
" ('San Francisco', 'CA'),\n",
" ('Los Angeles', 'CA');\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code creates a database, *getting_started_db*, if one doesn't already exist. It creates a table in that database called `cities` and insert data into it.\n",
"\n",
"The second table, `weather`, should contain the following columns:\n",
"- `city` text,\n",
"- `year` integer,\n",
"- `warm_month` text,\n",
"- `cold_month` text,\n",
"- `average_high` integer"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with con:\n",
" cur = con.cursor()\n",
" try:\n",
" cur.execute(\"drop table weather\")\n",
" except lite.OperationalError as e:\n",
" print e\n",
" cur = con.execute(\n",
" \"\"\"create table weather\n",
" (city text, year integer, warm_month text, cold_month text,\n",
" average_high integer);\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And the following data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = [\n",
" (\"New York City\", 2013, \"July\", \"January\", 62),\n",
" (\"Boston\", 2013, \"July\", \"January\", 59),\n",
" (\"Chicago\", 2013, \"July\", \"January\", 59),\n",
" (\"Miami\", 2013, \"August\", \"January\", 84),\n",
" (\"Dallas\", 2013, \"July\", \"January\", 77),\n",
" (\"Seattle\", 2013, \"July\", \"January\", 61),\n",
" (\"Portland\", 2013, \"July\", \"December\", 63),\n",
" (\"San Francisco\", 2013, \"September\", \"December\", 64),\n",
" (\"Los Angeles\", 2013, \"September\", \"December\", 75),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with con:\n",
" cur = con.cursor()\n",
" cur.executemany('insert into weather values (?, ?, ?, ?, ?)', data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point we have a database with two tables and some data inserted into them.\n",
"\n",
"Let's make sure we have the data we think we have in there:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'New York City', u'NY'),\n",
" (u'Boston', u'MA'),\n",
" (u'Chicago', u'IL'),\n",
" (u'Miami', u'FL'),\n",
" (u'Dallas', u'TX'),\n",
" (u'Seattle', u'WA'),\n",
" (u'Portland', u'OR'),\n",
" (u'San Francisco', u'CA'),\n",
" (u'Los Angeles', u'CA')]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with con:\n",
" cur = con.cursor()\n",
" cur.execute(\"select * from cities\")\n",
" rows = cur.fetchall()\n",
"\n",
"rows"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'New York City', 2013, u'July', u'January', 62),\n",
" (u'Boston', 2013, u'July', u'January', 59),\n",
" (u'Chicago', 2013, u'July', u'January', 59),\n",
" (u'Miami', 2013, u'August', u'January', 84),\n",
" (u'Dallas', 2013, u'July', u'January', 77),\n",
" (u'Seattle', 2013, u'July', u'January', 61),\n",
" (u'Portland', 2013, u'July', u'December', 63),\n",
" (u'San Francisco', 2013, u'September', u'December', 64),\n",
" (u'Los Angeles', 2013, u'September', u'December', 75)]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with con:\n",
" cur = con.cursor()\n",
" cur.execute(\"select * from weather\")\n",
" rows = cur.fetchall()\n",
"rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All is good.\n",
"\n",
"Now the above rows can be put into a pandas DataFrame, like so:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New York City</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Boston</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chicago</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Miami</td>\n",
" <td>2013</td>\n",
" <td>August</td>\n",
" <td>January</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dallas</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Seattle</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Portland</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>December</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>San Francisco</td>\n",
" <td>2013</td>\n",
" <td>September</td>\n",
" <td>December</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Los Angeles</td>\n",
" <td>2013</td>\n",
" <td>September</td>\n",
" <td>December</td>\n",
" <td>75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"0 New York City 2013 July January 62\n",
"1 Boston 2013 July January 59\n",
"2 Chicago 2013 July January 59\n",
"3 Miami 2013 August January 84\n",
"4 Dallas 2013 July January 77\n",
"5 Seattle 2013 July January 61\n",
"6 Portland 2013 July December 63\n",
"7 San Francisco 2013 September December 64\n",
"8 Los Angeles 2013 September December 75"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(rows)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or better yet... let's use the full power of pandas."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>city</th>\n",
" <th>year</th>\n",
" <th>warm_month</th>\n",
" <th>cold_month</th>\n",
" <th>average_high</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New York City</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Boston</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chicago</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Miami</td>\n",
" <td>2013</td>\n",
" <td>August</td>\n",
" <td>January</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Dallas</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Seattle</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>January</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Portland</td>\n",
" <td>2013</td>\n",
" <td>July</td>\n",
" <td>December</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>San Francisco</td>\n",
" <td>2013</td>\n",
" <td>September</td>\n",
" <td>December</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Los Angeles</td>\n",
" <td>2013</td>\n",
" <td>September</td>\n",
" <td>December</td>\n",
" <td>75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city year warm_month cold_month average_high\n",
"0 New York City 2013 July January 62\n",
"1 Boston 2013 July January 59\n",
"2 Chicago 2013 July January 59\n",
"3 Miami 2013 August January 84\n",
"4 Dallas 2013 July January 77\n",
"5 Seattle 2013 July January 61\n",
"6 Portland 2013 July December 63\n",
"7 San Francisco 2013 September December 64\n",
"8 Los Angeles 2013 September December 75"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"con = lite.connect('getting_started.db')\n",
"df = pd.read_sql_query('select * from weather', con)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice we didn't need to use any cursor. We just opened up a connection to a database and that's it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment 3: Joining and Filtering Data in SQLite"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"We're asked here to write a SQL statement which finds the mean of the average high temperatures for all of the cities within a state.\n",
"\n",
"We need to know which cities is in which state. We need to combine information from two tables. That's a `join`. We need to take an average on the cities within a given state, that's a `group by`."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The warmest state is WA\n"
]
}
],
"source": [
"import sqlite3 as lite\n",
"con = lite.connect('getting_started.db')\n",
"\n",
"query = \"\"\"\n",
"SELECT state, AVG(average_high) FROM weather\n",
"JOIN cities ON city = name\n",
"GROUP BY state\"\"\"\n",
"\n",
"df = pd.read_sql_query(query, con)\n",
"s = df.max()\n",
"s[0]\n",
"\n",
"print \"The warmest state is {}\".format(s[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Unit 2: Analyzing Data\n",
"\n",
"## Lesson 1: Probability and Statistics\n",
"\n",
"### Assignment 1: Overview of Statistics\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_str = '''Region, Alcohol, Tobacco\n",
"North, 6.47, 4.03\n",
"Yorkshire, 6.13, 3.76\n",
"Northeast, 6.19, 3.77\n",
"East Midlands, 4.89, 3.34\n",
"West Midlands, 5.63, 3.47\n",
"East Anglia, 4.52, 2.92\n",
"Southeast, 5.89, 3.20\n",
"Southwest, 4.79, 2.71\n",
"Wales, 5.27, 3.53\n",
"Scotland, 6.08, 4.51\n",
"Northern Ireland, 4.02, 4.56'''\n",
"\n",
"# First, split the string on the (hidden characters that indicate) newlines\n",
"data = data_str.splitlines() # we could also do data.split('\\n')\n",
"\n",
"# Then, split each item in this list on the commas the bracketed expression\n",
"# is a list comprehension\n",
"data = [i.split(', ') for i in data] \n",
"\n",
"# Now, create a pandas dataframe\n",
"column_names = data[0] # this is the first row\n",
"data_rows = data[1::] # these are all the following rows of data\n",
"df = pd.DataFrame(data_rows, columns=column_names)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Region</th>\n",
" <th>Alcohol</th>\n",
" <th>Tobacco</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>North</td>\n",
" <td>6.47</td>\n",
" <td>4.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Yorkshire</td>\n",
" <td>6.13</td>\n",
" <td>3.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Northeast</td>\n",
" <td>6.19</td>\n",
" <td>3.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>East Midlands</td>\n",
" <td>4.89</td>\n",
" <td>3.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>West Midlands</td>\n",
" <td>5.63</td>\n",
" <td>3.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>East Anglia</td>\n",
" <td>4.52</td>\n",
" <td>2.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Southeast</td>\n",
" <td>5.89</td>\n",
" <td>3.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Southwest</td>\n",
" <td>4.79</td>\n",
" <td>2.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Wales</td>\n",
" <td>5.27</td>\n",
" <td>3.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Scotland</td>\n",
" <td>6.08</td>\n",
" <td>4.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Northern Ireland</td>\n",
" <td>4.02</td>\n",
" <td>4.56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Region Alcohol Tobacco\n",
"0 North 6.47 4.03\n",
"1 Yorkshire 6.13 3.76\n",
"2 Northeast 6.19 3.77\n",
"3 East Midlands 4.89 3.34\n",
"4 West Midlands 5.63 3.47\n",
"5 East Anglia 4.52 2.92\n",
"6 Southeast 5.89 3.20\n",
"7 Southwest 4.79 2.71\n",
"8 Wales 5.27 3.53\n",
"9 Scotland 6.08 4.51\n",
"10 Northern Ireland 4.02 4.56"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What kind of data types are we dealing with here?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Region object\n",
"Alcohol object\n",
"Tobacco object\n",
"dtype: object"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to convert the `Alcohol` and `Tobacco` columns to float"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Convert Alcohol and Tobacco columns to float\n",
"df['Alcohol'] = df['Alcohol'].astype(float)\n",
"df['Tobacco'] = df['Tobacco'].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Region object\n",
"Alcohol float64\n",
"Tobacco float64\n",
"dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can compute some statistics. \n",
"\n",
"What are the mean, median and mode of Alcohol and Tobacco in the dataset provided? "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Alcohol:\n",
" Mean: 5.44363636364\n",
" Median: 5.63\n",
" scipy Mode: (array([ 4.02]), array([ 1.]))\n",
" pandas Mode: Series([], dtype: float64)\n",
"\n",
"Tobacco:\n",
" Mean: 3.61818181818\n",
" Median: 3.53\n",
" scipy Mode: (array([ 2.71]), array([ 1.]))\n",
" pandas Mode: Series([], dtype: float64)\n"
]
}
],
"source": [
"# There's no built-in mode method in Python, so we need scipy.stats\n",
"from scipy import stats\n",
"\n",
"print \"Alcohol:\"\n",
"print \" Mean: {}\".format(df['Alcohol'].mean())\n",
"print \" Median: {}\".format(df['Alcohol'].median())\n",
"\n",
"# If all numbers occur equally often (which is the case here), stats.mode()\n",
"# will return the smallest number\n",
"print \" scipy Mode: {}\".format(stats.mode(df['Alcohol']))\n",
"print \" pandas Mode: {}\".format(df['Alcohol'].mode())\n",
"\n",
"print\n",
"print \"Tobacco:\"\n",
"print \" Mean: {}\".format(df['Tobacco'].mean())\n",
"print \" Median: {}\".format(df['Tobacco'].median())\n",
"print \" scipy Mode: {}\".format(stats.mode(df['Tobacco']))\n",
"print \" pandas Mode: {}\".format(df['Tobacco'].mode())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas's `mode` return an empty series if nothing occurs at least 2 times\n",
"\n",
"#### Other Statistical Measures of Importance\n",
"\n",
"What are the range, variance, and standard deviation of Alcohol and Tobacco? \n",
"\n",
"Let's use the `describe` method of a DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Alcohol</th>\n",
" <th>Tobacco</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>11.000000</td>\n",
" <td>11.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.443636</td>\n",
" <td>3.618182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.797763</td>\n",
" <td>0.590708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.020000</td>\n",
" <td>2.710000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.840000</td>\n",
" <td>3.270000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.630000</td>\n",
" <td>3.530000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.105000</td>\n",
" <td>3.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>6.470000</td>\n",
" <td>4.560000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Alcohol Tobacco\n",
"count 11.000000 11.000000\n",
"mean 5.443636 3.618182\n",
"std 0.797763 0.590708\n",
"min 4.020000 2.710000\n",
"25% 4.840000 3.270000\n",
"50% 5.630000 3.530000\n",
"75% 6.105000 3.900000\n",
"max 6.470000 4.560000"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The range is `max` - `min`:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Alcohol 2.45\n",
"Tobacco 1.85\n",
"dtype: float64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().loc['max'] - df.describe().loc['min']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Ranking Data in a Distribution\n",
"\n",
"Write a script called \"stats.py\" that prints the mean, median, mode, range, variance, and standard deviation for the Alcohol and Tobacco dataset with full text (ex. \"The range for the Alcohol and Tobacco dataset is ...\").\n",
"\n",
"Let's write a function instead of a script. That function will take as argument a dataframe and a list of columns. The function should output statistics about each column."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def describe(df, *args):\n",
" for column in args:\n",
" print\n",
" print \"== {} ===================\".format(column)\n",
" print \"mean: {}\".format(df[column].mean())\n",
" print \"median: {}\".format(df[column].median())\n",
" print \"mode: {}\".format(df[column].mode().values)\n",
" print \"range: {}\".format(max(df[column]) - min(df[column]))\n",
" print \"variance: {}\".format(df[column].var())\n",
" print \"standard deviation: {}\".format(df[column].std())"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"== Tobacco ===================\n",
"mean: 3.61818181818\n",
"median: 3.53\n",
"mode: []\n",
"range: 1.85\n",
"variance: 0.348936363636\n",
"standard deviation: 0.590708357514\n",
"\n",
"== Alcohol ===================\n",
"mean: 5.44363636364\n",
"median: 5.63\n",
"mode: []\n",
"range: 2.45\n",
"variance: 0.636425454545\n",
"standard deviation: 0.797762780873\n"
]
}
],
"source": [
"describe(df, 'Tobacco', 'Alcohol')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment 2: Overview of Probability\n",
"\n",
"#### Plotting Probability Distributions in Python"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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I8lcgldo/tCFUDEsVBJ8mkoIX09YZOtR0BBASJJa6EARfJpKClzKbLdTWGzrU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\nQRAEweP/AbijVywEEbKwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x62dcf10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_samples = 200\n",
"x = (np.ndarray((n_samples, 1), buffer=np.linspace(-6, 10, n_samples))\n",
" .repeat(3, axis=1))\n",
"\n",
"means = [-2, 0, 2]\n",
"stds = [0.5, 1, 2]\n",
"columns = [r'$\\mu$ = {}, $\\sigma$ = {}'.format(m, s)\n",
" for m, s in zip(means, stds)]\n",
"\n",
"df = pd.DataFrame(stats.norm.pdf(x, loc=means, scale=stds),\n",
" index=x[:, 0], columns=columns)\n",
"\n",
"ax = df.plot()\n",
"ax.set_title(\"Probability densities of various normal distributions\")\n",
"ax.set_xlabel('x')\n",
"_ = ax.set_ylabel('p(x)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment 3: Hypothesis Testing\n",
"\n",
"#### Khan Academy video on Pearson's $\\chi^2$ test (goodness of fit)\n",
"\n",
"Our hypothesis are:\n",
"\n",
"$H_0$: the restaurant's owner frequency distribution is correct.\n",
"\n",
"$H_1$: my observed distribution is correct.\n",
"\n",
"\n",
"The p-value is the probability of the null hypothesis being correct.\n",
"\n",
"Remember that you cannot accept the null hypothesis, but only find evidence against it. In an hypothesis test you either _reject_ the null hypothesis or _fail to reject_ it. Note the wording: fail to reject... that doesn't mean to accept."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 20\n",
"1 20\n",
"2 30\n",
"3 40\n",
"4 60\n",
"5 30\n",
"dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0 30\n",
"1 14\n",
"2 34\n",
"3 45\n",
"4 57\n",
"5 20\n",
"dtype: int64"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expected_percent = pd.Series([10, 10, 15, 20, 30, 15])\n",
"observed = pd.Series([30, 14, 34, 45, 57, 20])\n",
"expected = expected_percent / 100. * observed.sum()\n",
"display(expected)\n",
"display(observed)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(11.441666666666666, 0.043293130315804972)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.chisquare(observed, expected)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The p-value is 4% and we can safely reject the null hypothesis (at the significance level of 5%) that the owner's distribution is correct."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lessons 2: Univariate Analysis\n",
"\n",
"### Assignment 1: Probability Distributions and Densities"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({9: 3, 1: 1, 4: 1, 5: 1, 6: 1})\n",
"\n",
"The frequency of number 1 is 0.143\n",
"The frequency of number 4 is 0.143\n",
"The frequency of number 5 is 0.143\n",
"The frequency of number 6 is 0.143\n",
"The frequency of number 9 is 0.429\n"
]
}
],
"source": [
"import collections\n",
"\n",
"testlist = [1, 4, 5, 6, 9, 9, 9]\n",
"\n",
"c = collections.Counter(testlist)\n",
"\n",
"print c\n",
"print\n",
"\n",
"# calculate the number of instances in the list\n",
"count_sum = sum(c.values())\n",
"for k,v in c.iteritems():\n",
" print \"The frequency of number {} is {:.3}\".format(k, float(v) / count_sum)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generating a basic box plot"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x62cb250>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD9CAYAAACC7q1lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAGSCDBAEgEGSCLAAEkEGCDJvwHwcmS0W9wE8QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x62cb150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4,\n",
" 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 9]\n",
"\n",
"sns.boxplot(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generate a histrogram of the data"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x62dc8d0>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAECCAYAAAA4vfbqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQFkDgAUoawCwAGUNABb4PyKSbNmcCneSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x62dc290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(x, bins=10, kde=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### QQ-Plots"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_data = np.random.normal(size=1000)\n",
"test_data2 = np.random.uniform(size=1000) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"QQ plot"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kJK5cuUJMTAwXL17EZDLRsWNHunXrVuFzZmZmVzlPdQkM9HH6nK6QERybc2zQ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\npCSEEEKU6/8BgK7Uo9FHGqwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6d75550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from statsmodels.graphics.gofplots import ProbPlot\n",
"pp = ProbPlot(test_data2, dist=\"norm\")\n",
"_ = pp.qqplot(line='r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Probability plot"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rrwDwzz//MHTo0HJ1HBUVRa9evXjhhRf466+/Cq03GAw899xzTJ48uVz9CFEu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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x70ec4d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = pp.probplot(line='r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PP plot"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IcDsYxo3LsludQpRHVoXDmjVriI2NxdXVFYBq1apx8uRJq94gKiqKnj170qdP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\nhIMQQog8JByEEELk8f/KSzzThrB3TgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7102ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = pp.ppplot(line='r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment 2: Visualizing Lending Club Data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"loansData = pd.read_csv(\n",
" 'https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Amount.Requested</th>\n",
" <th>Amount.Funded.By.Investors</th>\n",
" <th>Interest.Rate</th>\n",
" <th>Loan.Length</th>\n",
" <th>Loan.Purpose</th>\n",
" <th>Debt.To.Income.Ratio</th>\n",
" <th>State</th>\n",
" <th>Home.Ownership</th>\n",
" <th>Monthly.Income</th>\n",
" <th>FICO.Range</th>\n",
" <th>Open.CREDIT.Lines</th>\n",
" <th>Revolving.CREDIT.Balance</th>\n",
" <th>Inquiries.in.the.Last.6.Months</th>\n",
" <th>Employment.Length</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>81174</th>\n",
" <td>20000</td>\n",
" <td>20000</td>\n",
" <td>8.90%</td>\n",
" <td>36 months</td>\n",
" <td>debt_consolidation</td>\n",
" <td>14.90%</td>\n",
" <td>SC</td>\n",
" <td>MORTGAGE</td>\n",
" <td>6541.67</td>\n",
" <td>735-739</td>\n",
" <td>14</td>\n",
" <td>14272</td>\n",
" <td>2</td>\n",
" <td>&lt; 1 year</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99592</th>\n",
" <td>19200</td>\n",
" <td>19200</td>\n",
" <td>12.12%</td>\n",
" <td>36 months</td>\n",
" <td>debt_consolidation</td>\n",
" <td>28.36%</td>\n",
" <td>TX</td>\n",
" <td>MORTGAGE</td>\n",
" <td>4583.33</td>\n",
" <td>715-719</td>\n",
" <td>12</td>\n",
" <td>11140</td>\n",
" <td>1</td>\n",
" <td>2 years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80059</th>\n",
" <td>35000</td>\n",
" <td>35000</td>\n",
" <td>21.98%</td>\n",
" <td>60 months</td>\n",
" <td>debt_consolidation</td>\n",
" <td>23.81%</td>\n",
" <td>CA</td>\n",
" <td>MORTGAGE</td>\n",
" <td>11500.00</td>\n",
" <td>690-694</td>\n",
" <td>14</td>\n",
" <td>21977</td>\n",
" <td>1</td>\n",
" <td>2 years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15825</th>\n",
" <td>10000</td>\n",
" <td>9975</td>\n",
" <td>9.99%</td>\n",
" <td>36 months</td>\n",
" <td>debt_consolidation</td>\n",
" <td>14.30%</td>\n",
" <td>KS</td>\n",
" <td>MORTGAGE</td>\n",
" <td>3833.33</td>\n",
" <td>695-699</td>\n",
" <td>10</td>\n",
" <td>9346</td>\n",
" <td>0</td>\n",
" <td>5 years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33182</th>\n",
" <td>12000</td>\n",
" <td>12000</td>\n",
" <td>11.71%</td>\n",
" <td>36 months</td>\n",
" <td>credit_card</td>\n",
" <td>18.78%</td>\n",
" <td>NJ</td>\n",
" <td>RENT</td>\n",
" <td>3195.00</td>\n",
" <td>695-699</td>\n",
" <td>11</td>\n",
" <td>14469</td>\n",
" <td>0</td>\n",
" <td>9 years</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Amount.Requested Amount.Funded.By.Investors Interest.Rate Loan.Length \\\n",
"81174 20000 20000 8.90% 36 months \n",
"99592 19200 19200 12.12% 36 months \n",
"80059 35000 35000 21.98% 60 months \n",
"15825 10000 9975 9.99% 36 months \n",
"33182 12000 12000 11.71% 36 months \n",
"\n",
" Loan.Purpose Debt.To.Income.Ratio State Home.Ownership \\\n",
"81174 debt_consolidation 14.90% SC MORTGAGE \n",
"99592 debt_consolidation 28.36% TX MORTGAGE \n",
"80059 debt_consolidation 23.81% CA MORTGAGE \n",
"15825 debt_consolidation 14.30% KS MORTGAGE \n",
"33182 credit_card 18.78% NJ RENT \n",
"\n",
" Monthly.Income FICO.Range Open.CREDIT.Lines Revolving.CREDIT.Balance \\\n",
"81174 6541.67 735-739 14 14272 \n",
"99592 4583.33 715-719 12 11140 \n",
"80059 11500.00 690-694 14 21977 \n",
"15825 3833.33 695-699 10 9346 \n",
"33182 3195.00 695-699 11 14469 \n",
"\n",
" Inquiries.in.the.Last.6.Months Employment.Length \n",
"81174 2 < 1 year \n",
"99592 1 2 years \n",
"80059 1 2 years \n",
"15825 0 5 years \n",
"33182 0 9 years "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData.head()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2500 entries, 81174 to 3116\n",
"Data columns (total 14 columns):\n",
"Amount.Requested 2500 non-null int64\n",
"Amount.Funded.By.Investors 2500 non-null float64\n",
"Interest.Rate 2500 non-null object\n",
"Loan.Length 2500 non-null object\n",
"Loan.Purpose 2500 non-null object\n",
"Debt.To.Income.Ratio 2500 non-null object\n",
"State 2500 non-null object\n",
"Home.Ownership 2500 non-null object\n",
"Monthly.Income 2499 non-null float64\n",
"FICO.Range 2500 non-null object\n",
"Open.CREDIT.Lines 2498 non-null float64\n",
"Revolving.CREDIT.Balance 2498 non-null float64\n",
"Inquiries.in.the.Last.6.Months 2498 non-null float64\n",
"Employment.Length 2500 non-null object\n",
"dtypes: float64(5), int64(1), object(8)\n",
"memory usage: 293.0+ KB\n"
]
}
],
"source": [
"loansData.info()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Amount.Requested</th>\n",
" <th>Amount.Funded.By.Investors</th>\n",
" <th>Monthly.Income</th>\n",
" <th>Open.CREDIT.Lines</th>\n",
" <th>Revolving.CREDIT.Balance</th>\n",
" <th>Inquiries.in.the.Last.6.Months</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2500.000000</td>\n",
" <td>2500.000000</td>\n",
" <td>2499.000000</td>\n",
" <td>2498.000000</td>\n",
" <td>2498.000000</td>\n",
" <td>2498.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>12406.500000</td>\n",
" <td>12001.573236</td>\n",
" <td>5688.931321</td>\n",
" <td>10.075661</td>\n",
" <td>15244.559648</td>\n",
" <td>0.906325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7801.544872</td>\n",
" <td>7745.320754</td>\n",
" <td>3963.118185</td>\n",
" <td>4.508644</td>\n",
" <td>18308.549795</td>\n",
" <td>1.231036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1000.000000</td>\n",
" <td>-0.010000</td>\n",
" <td>588.500000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6000.000000</td>\n",
" <td>6000.000000</td>\n",
" <td>3500.000000</td>\n",
" <td>7.000000</td>\n",
" <td>5585.750000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>5000.000000</td>\n",
" <td>9.000000</td>\n",
" <td>10962.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17000.000000</td>\n",
" <td>16000.000000</td>\n",
" <td>6800.000000</td>\n",
" <td>13.000000</td>\n",
" <td>18888.750000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>35000.000000</td>\n",
" <td>35000.000000</td>\n",
" <td>102750.000000</td>\n",
" <td>38.000000</td>\n",
" <td>270800.000000</td>\n",
" <td>9.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Amount.Requested Amount.Funded.By.Investors Monthly.Income \\\n",
"count 2500.000000 2500.000000 2499.000000 \n",
"mean 12406.500000 12001.573236 5688.931321 \n",
"std 7801.544872 7745.320754 3963.118185 \n",
"min 1000.000000 -0.010000 588.500000 \n",
"25% 6000.000000 6000.000000 3500.000000 \n",
"50% 10000.000000 10000.000000 5000.000000 \n",
"75% 17000.000000 16000.000000 6800.000000 \n",
"max 35000.000000 35000.000000 102750.000000 \n",
"\n",
" Open.CREDIT.Lines Revolving.CREDIT.Balance \\\n",
"count 2498.000000 2498.000000 \n",
"mean 10.075661 15244.559648 \n",
"std 4.508644 18308.549795 \n",
"min 2.000000 0.000000 \n",
"25% 7.000000 5585.750000 \n",
"50% 9.000000 10962.000000 \n",
"75% 13.000000 18888.750000 \n",
"max 38.000000 270800.000000 \n",
"\n",
" Inquiries.in.the.Last.6.Months \n",
"count 2498.000000 \n",
"mean 0.906325 \n",
"std 1.231036 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 1.000000 \n",
"max 9.000000 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData.describe()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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xxRdpamoiISGB733vewQCAeLj409r+1ToHTp0iO985zssXLiQhoYGfvrTn1Jd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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x73b6890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = loansData.boxplot(column='Amount.Funded.By.Investors', return_type='axes')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YMkT2Axbio6Ts+9HUqTN44YXnibVUS9n3ISvaRufxdVjhJgKBTBYuvImZM+fI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\n3wcRAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x73b3d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = sns.violinplot(loansData['Amount.Funded.By.Investors'].values)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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G7y/m7Y6xTkvIuQ6F0f53Hi6SU2QMWFRsNht1dXW9jxVFwWazAWC32/F4PL3b\nPB4Pdrsdm83W+/z15xISEvB6vX3aZmZmoqpqv/EPHz7M6dOn+ad/+idaWlr467/+a7Zu3TrozjQ3\newdtM5ySkixhzelwVSMA4yxGvMrAd40bDVoC/Sz7a4k33fLa27W9k7j30tYSb8Lb3kVLixcI5YNa\nxdveNWhcnU5D/syJfFRpp/zkZXa9f4aCOZNDitnfcboxbui5hle4/6bCQXIKTbTmFG4Dnv6aN28e\nH374IYFAgKNHj5KVlYVe31OHHA4HJ06cQFEULl26hKIopKWlMX/+fPbt2wfA/v37WbBgAUajkYyM\nDCorK/H7/Rw8eJC8vLx+4xsMBt566y22b9/O9u3bSUxM5Gc/+1nYd3yk6fT5qT7vJn2ihdiYAb8L\niBtoNRp+8I0HMJv0vP7/VXP2YlukUxJiVBvw0yk9PZ3CwkKWLl2K0WikuLiYrVu34nA4yMvLY/Xq\n1axcuRKA4uJiNBoNS5YsoaKigqKiIpKTk9m8eTPQc6F+/fr1+Hw+CgsLSU1NBbglvujf6Xo3gaDK\nrMxxkU5lxBlnNfHct2fyyr9V8mppFRueeZhxVpkzTYihoFFDvWAxAkRj1zJcOW3/jzN8cOQia74z\nhyb3tUFP/Yy0018d17p4dFYKoZ7+OlDVGNLprxtjvv/xeV7/Yw3pEy2s+c6DxPS5ebRvzMFOf4We\na3hF6ykUyWlw0ZpTuMnNjyOAqqpU1V4lNkZP1mTr4C8Q/Xri4SksnJVC/WUv//Tvx/EPdqVfCHHH\npKiMAE2ua7S0dZI91Y5OK7+yu6XRaFj5tfuYmTmOqs+u8trbJwkGR01HXYioIJ9QI0DVZz0zCszK\nHB/hTEY+vU7LXz41i+lTEig/dYX/+95pmXhSiDCSojICHP98apaZGXKRPhxiDDr+a1Eu6RMs/Kmy\nkZJ3T0dJj0Ud9KfnEuiNP0JEFxmbGuV83QHONLiYnGj+fMSSfJCEQ5xJzwvLZ/M//+0oHx5rJBAI\nMiPNFum0OHTiMv4BCpzF3Iq3vQu9VsP8nInDmJkQoZGeSpSrvuDG5w8yU4YSh118rIH/d/lssiZZ\nOXSiiT9VXop4j8X/+Wi52/4EIRBQByw8QkSSFJUo13vqS66nDIk4k4HVy2YzfUoC5y55+VPlJQLy\ngS3EXZOiEuWqPruK0aBlxpSEwRuLuxIbo+ev/tzBxPFxNDQp/OenFwnIcGMh7ooUlSjW0naNxqsd\n3J9mx6ARBRNJAAAgAElEQVSXVR6Hksmo5yuPTCFlfBwXmtvZ+1Gd3McixF2QohLFjp/rOfUlQ4mH\nh16n5csPTmZykpmGJi8fHLkohUWIOyRFJYp9cT1FLtIPF51Oy2NzJpExyUrj1Q72VUhhEeJOSFGJ\nUv5AkJN1rSTbYplgj4t0OmOKTqvlybyppE2I53JrB386GvlRYUKMFFJUolTtxTY6fQHppUSITqvh\n0dyU3mssB6saQ14sToixTIpKlLp+PUWGEkeOTqvlsTmTSbKZONfo5ePTV6SwCDEIKSpRquqzq+h1\nGu6Pgru8xzKDXsuXH5qCLd7I6Xo3p+pckU5JiKgmRSUq9J3PqU3ppKFJYfoUGyaj7pbtYnjFGHR8\n+aEpxMbo+eRMM+caPYO/SIgxKqS5v0pKSnjrrbcwGAy8/PLLTJ8+vXfb3r17ee2119BoNPzkJz9h\n7ty5+Hw+NmzYwNmzZ3tXf4yPj6e6upr169fj9/tZvHgx3/ve924b/ze/+Q1vv/02fr+fJ598kuee\ne25IDkC0uHHOp7MXepa8NcfqOfD5uvTXxejle0AkxMcaePyhyfyhrIEDRxvJe2ACM1LtkU5LiKgz\n6CdUfX09paWl7Nq1i7Vr17Jp06bebYqisGXLFrZv386rr77Khg0bUFWVPXv2YDKZ2L17N7Nnz2bb\ntm1Az5LC69atY+fOnZSWlnL+/Pnbxnc6nezatYudO3fyu9/9jtbW1iE6BNHhxjmfzl9RAEgZb75l\n7ieZ8ylyxllNPDZnMkFV5R9/X0Xj1fZIpyRE1Bm0qJSVlbFw4UK0Wi25ubnU1tbi9/sBqKysJDs7\nG7PZTEpKCmazmYaGBsrKyigoKACgoKCAQ4cO0d3dTV1dHQ6HA51OR35+PocPH6a8vJz8/Pxb4sfH\nxwPQ2tqKXq8nNjZ2CA9D9AiqKo0tHcTF6LHFGyOdjrjJpEQz+bMm0t7p55WdlbS1+yKdkhBRZdCi\n4na7sVq/WMLWYrHgdrsBcLlcJCR8MSeV1WrF5XLhcrl6X2OxWHC5XLjdbiwWS79tb4xxY/x//ud/\n5hvf+AaLFy8eM0Wlta2Tru4Ak5LMaDTDvwa6GNz0VBtLFk6lpa2TX+6qpMsXiHRKQkSNQa+p2Gw2\n6urqeh8rioLN1jMiyW634/F8cdHS4/Fgt9ux2Wy9z19/LiEhAa/X26dtZmYmqqreNv5zzz3HM888\nw49+9CP279/PY489NmCuSUmWAbdHQig5qaqKxdxKIAinG3oKatYUG5Z40y1tjfqeU2XB4MAFZ6B2\nN8cNNeZQtrWYY0hMtIRUSG88XgPRabmnmP0d/+txv/3YdDp8Qd4vb+DX757mb74/F53u3q53hbpf\nlnjTHe3bcBip//aGWzTmFG6DFpV58+axY8cOXnjhBaqqqsjKykKv73mZw+Fg48aNKIqCx+NBURTS\n0tKYP38++/btY9GiRezfv58FCxZgNBrJyMigsrKSnJwcDh48yIoVKwgGg/3GDwaDaLVaYmNjSUpK\n6u29DKS52Ttom+GUlGQJMScVb3sXgYDKuUttaDRgNxvwKp23tDQatAQ+v/4ykNu1s8Sbbokbasyh\namuJN+Ft76KlxQuE8iH5xfEaiE6nueuY/R2nG+NevaqwdFEmjc0Kn5xq4u9/V8F3n7zvHj/kB9+v\n63nd2b4NrdD/zoeP5BSaoShygxaV9PR0CgsLWbp0KUajkeLiYrZu3YrD4SAvL4/Vq1ezcuVKAIqL\ni9FoNCxZsoSKigqKiop6R39Bz4X69evX4/P5KCwsJDU1FeCW+ADr16+ntrYWVVWZPn063/zmN8O+\n89Gmyxegxd1Joi0Wo0FmJY52ep2W5789k1/89gj7j14iPs5A4ZeyIp2WEBGlUUfRLcLR+C0g1J7K\ngapGai+08afKRmZPT8SR1f+d9KO1p9JxrYtHZ6UQaq/iQFVjSD2Vu405WE/lxrhupYuf//YIV1zX\n+PbCDL61MCOE9xs8h/7c2FMJfd+GVrR+A5ecBjcUPRW56SGKXGzpGaI6OdEc4UzEnbDFx/Di03NI\nTDDxxofneOujOpnORYxZUlSihKqqXGxux2TUMc4aE+l0xB0aZzXx4tNzGG+N4d//9Bm/fb+aQFCm\nzBdjjxSVKHG1rZNOX4BJiTKUeKRKtMWy9i8eYkqSmX1HLvKPv6+i0+ePdFpCDCspKlHi/JWeU19T\nkuTUV3jdPG/aQD/3bpzVxNq/eIicjHEcq73KS//n495pd4QYC0Ka+0sMvQtXFDSanju2RXjotBoO\nnWgKaWqbcM6pFhuj578WOSj9z894r7yBn+2o4Mm5aXxzwVTiTPJPToxu8hceBdrafbS0dTJhnAwl\nDjd/iCPV/NrwXljX67T8+ZenMXt6ItveOcUfyhv4U+UlvvpIKk88PIU4kyGs7ydEtJCiEgWqaq8C\nMCUpPsKZiHCbkWpj07Nz+eORC/yhrIE3PjzH24fqmT09kfk5E5iZMQ6DXr5IiNFDikoUOPZ5UZks\n11NGpRijjq/npfPlByfzwacX+fBYI5+cvsInp69gNGjJmTqO3GmJOLJk6Wgx8klRiTB/IMiJulbi\nYw0kmGVW4tHMZNTzZ/PS+drcNBqaFMpONnH0bAuf1vT8ACQmmEifaGHa5ARijNKDESOPFJUIq7nQ\nxrWuAA+kR8/kgGJoaTQa0idaSJ9o4c+/PI2m1g4qz7ZQWdvC6QY3LW2dHK1pIWOSFUfmeOLj5PqL\nGDmkqETYsdqeb6hTkuV6ylg1YVwcX52bxlfnpvJ+xQWqG9ycaXBz9kIb5y55mJU1npyp9nueBVmI\n4SB/pRF2rPYqRoOWiePjIp2KiAImo46cjHE89aUM8mdNxKDXcrSmhbc+qqdN6Yp0ekIMSopKBF1x\nX6PxagfZ6Xb08i1U3ECj0ZA1OYFvP5rBfWk2PO0+9h5uoO6SZ/AXCxFB8kkWQcfO9pz6ut2MxGI0\nuJM7+m+9V8Zo0DEvewILHSkEgyrvfHSOU3Wu4UpeiDsm11QiqPLzocSOrPGcqJcPitHq0InL93xX\nf+YkKwlmIx98epGPT19B2zv1vRDRRXoqEdLp83OmwUVqcjzjrP0vWytGh+t39Q/2M1jhGZ9g4tuL\nsjAZdZSdaOKDTy8O0x4IETopKhFyqs6FP6DKqS9xR+wWE1+dm4rJqGP7e9V8fPpKpFMSoo+QikpJ\nSQlOp5Ply5dTU1PTZ9vevXspLCzE6XRSXl4OgM/nY82aNRQVFbFq1SoURQGgurqaZcuW4XQ6KSkp\nGTD+m2++ydKlSykqKuKv/uqv8PtH1xTi10995WYlRjgTMdLY4mN4cl4qMUYdv377JHWX5eK9iB6D\nFpX6+npKS0vZtWsXa9euZdOmTb3bFEVhy5YtbN++nVdffZUNGzagqip79uzBZDKxe/duZs+ezbZt\n24CeNerXrVvHzp07KS0t5fz587eN73A4eP3119m9ezctLS0cOHBgiA7B8FNVlarPrhIfayBzkjXS\n6USR4Z2mfiQbZzXxo29l0+0P8g+7j+HyynBjER0GvVBfVlbGwoUL0Wq15ObmUltbi9/vR6/XU1lZ\nSXZ2NmazufenoaGBsrIyFi9eDEBBQQEbNmzg+eefp66uDofDAUB+fj6HDx/u/f+b40+dOhXo+QBu\nb28nMXH0fKOvu+zF5e1ifs4EtFoN8iEZuWnqR7LZ0xJZWjCNnR+c5dXSY/xkxYMyy7WIuEH/dbrd\nbqzWL75NWywW3G43AC6Xi4SEhN5tVqsVl8uFy+XqfY3FYsHlcuF2u7FYLP22vTHGjfEBfv7zn5Od\nnc2sWbPuYTejy6c1zQA8OCMpwplEl3Bd0B5LnpybSv6siZxr9LJt7ylUVY6NiKxBeyo2m426urre\nx4qiYLPZALDb7Xg8X5zP9Xg82O12bDZb7/PXn0tISMDr9fZpm5mZiaqqt43/y1/+kqamJl555ZWQ\ndiYpyTJ4o2HWX06Vta0Y9VoeeyQdU4weVVWxmFsJhLCkuVHf8+EbDA48T9hA7SzxppDb3u3732lb\ni9kY9rj3muvNx+k6nRYSE0Obq20ofreWeFOfHF74i4dp/dVHlJ+6wvT0cSz/yn2Dv1mYjZR/e5EW\njTmF26BFZd68eezYsYMXXniBqqoqsrKy0Ot7XuZwONi4cSOKouDxeFAUhbS0NObPn8++fftYtGgR\n+/fvZ8GCBRiNRjIyMqisrCQnJ4eDBw+yYsUKgsFgv/HLysooLy/nN7/5TcgTLTY3ewdvNIySkiy3\n5HS5tYPzTV5mT0vE67lGz1YVb3tXSItJGQ1aAiEsPHW7dpZ4E16l865iDlVbS7wJb7sv7HHvJdf+\njtN1Op2GlhYvEMrfZXh/t9fzujmHHy3O5uXffMJv/3CaBJOeh+9PDiG38Ojv7zzSJKfQDEWRG7So\npKenU1hYyNKlSzEajRQXF7N161YcDgd5eXmsXr2alStXAlBcXIxGo2HJkiVUVFRQVFREcnIymzdv\nBnou1K9fvx6fz0dhYSGpqakAt8QHeP/992lqauqNXVBQwA9/+MOwH4DhdqRaTn2J8LOajfy4yMHf\n7qjgtbdPMj7BREaKDAIRw0+jjqKTsNH4LeDmnIr/7yfUNXr5+x8vJD72+pTmKgeqGsd0T6Wra2T1\nVHruZg+tpxLO3+2NPZX+cjha08I//v4YCfFG1j/zCHZLTAg5fpHrndNE7TdwyWlwEempiPBxebv4\n7JKH+9NsNxQUIcJn9vQvRoT9w+5jvLhiDrExof8zD3VKGb1Ww/ycifeSqhilZGzmMLo+6muOnPoS\nQ+jJual8KXcS9U1efrn7GF3dgZBfKyPwxL2SojKMKs58fj1luhQVMXQ0Gg3ffXIGD9+XRPV5N/+r\ntIpufwjDz4QIAykqw6St3cfpBhdZk6yMT5AJJMXQ0mm1/D/fysGRNZ7j51r5x9JjXOsaXVMdiegk\nRWWYfHL6CqoKcx+YEOlURFhE/5Qyep2WVd+eyazM8Rz/rJX/sb2CK66OML5Dz/71jPWJ3uMghpdc\nqB8mH59qQgPDev+AGBojaUoZo0HHj4tmsXNfLe9/cp6Xf/MJK74yg7zsCSHf/9WfG4+BxdyKt/32\nc4/JRf2xRYrKMGj1dFJzoY3pqbY7HOIpopU/xKHKfm3kv6HrtFqefmI6k5PM7PiPav7lrZN8cOQi\nf/7laWRNst51cem9qB8kpGMhxgYpKsPgk9NXUIG5D0gvRUTOl3InkZ1u598+OEvFmWb+dnsFKePj\nmJc9gZyp45iSZI50imIUkKIyDMpPX0GjgYfuk6IiwulOewcaEm2x/OVTszhd72LfpxepPNvCGwfO\n8caBc2gAi9lAfKwRS5yB+NieH0ucAVt8zOczagsxMCkqQ6bnAmaLu4PPLnnInmonwWyg/w8COXUg\n7sydXNfp75rG/el27k+3c63LT+XZFs41ejl/xcu5Ri+e9vZbY+g0TBgXx+REM5mTrBgNMsZH9E+K\nyhDp6PLzp08v8J8V5wFIspkoP9XUb9uJ9tjhTE2MEqFe1xlIbIyevJyJ5OVM5PqUMte6/Cgd3SjX\nulE6umlr99HkusbF5nYuNrdzpLqZGak2sjPsxMXIzBCiLykqQ0RVoaPTz+kGNzqthpREMx1d/d/Z\n7AtlXnQhholRr2OcVcc4a9/7qdo7uzl3ycPpBjcn61ycaXDjyBrPvFkpEcpURCMpKkOo8Wo73o7u\nntMFelmRT4xsZpOBmZnjyZ46jvPNCuUnm/i0poX6JoUFMyfcUoRGvzvrJY6iuXsHJEVlCJ2ucwGQ\nNVmmIBejh1arYfqUBCYnmfnk1BWqz7fx7uEG8nImkDU5YfAAo8idTMC5eNHoX6AL5I76IdPVHeDs\nBTdxJj0Tx8VFOh0hwi7GoCMvZyJfXzAVrVbDwarLlJ1sIjiGJpuUCThvJUVliBw7e5Vuf5CsyQn3\ndOeyENEuY1IC35ifji3eyJkGN/s/vYhfrhOOWVJUhsjhk5cByJokp77E6Gc1G/laXhop4+O40NzO\nf5Sfp9MnE1iORSEXlZKSEpxOJ8uXL6empqbPtr1791JYWIjT6aS8vBwAn8/HmjVrKCoqYtWqVSiK\nAkB1dTXLli3D6XRSUlIyaPxLly6xZMkS9u7dey/7Oawar7ZTfb6NSYlmrGZjpNMRYlgY9Tq+/NAU\nMidZaWnr5N3DDXg7fEP4jrefvPL2k1yKoRbShfr6+npKS0t54403qKqqYtOmTezYsQMARVHYsmUL\ne/bswePx8P3vf593332XPXv2YDKZ2L17N1u3bmXbtm38+Mc/5qWXXmLdunXk5OTw1FNP8fjjjxMM\nBvuN//HHH/Pyyy8TEzOy5svaV3ERgFnTEiOciRDDS6fVkD9rIrExek6ca+Xdww18ZW7qkL3f7S6U\n3zzJpUxqOXxC6qmUlZWxcOFCtFotubm51NbW4vf3dG0rKyvJzs7GbDaTkpKC2WymoaGBsrIyCgoK\nACgoKODQoUN0d3dTV1eHw+FAp9ORn5/P4cOHKS8vJz8//5b4s2fP5o033mDatGlDdwTC7FqXnw+P\nN2KLN5IxaWyNhBECehYJe+i+JOY+kEynL8C7hxo4fq51SN7rthfKP5/kcixeKI+0kIqK2+3Gav3i\n2oDFYsHtdgPgcrlISPjiw9NqteJyuXC5XL2vsVgsuFwu3G43Foul37Y3xrge32AwoNWOrMs+H1Y1\n0uULsNCRgk7mShJj2P3pdhbNnoSqqvxy1zEOVjVGOiUxDEI6/WWz2airq+t9rCgKNpsNALvdjsfj\n6d3m8Xiw2+3YbLbe568/l5CQgNfr7dM2MzMTVVVvG/9OJCVFdhx4MKjyn0cvYdBr+cajmRyvbcUS\nP/gNYRaLCaUrSCgDZoz6nm9nweDABWugdjfnFGrMoWxrMRvDHvdec73d7y7Sx8sSb7qjuDotJCZa\nBh2FqKo9a6Pczd/h7Y7VzGkmEu2x/OFwA79+5xRdAZWlj88Iy+SUg+V7Y06hHoNwvv+NdJ9/N470\nZ9RwCKmozJs3jx07dvDCCy9QVVVFVlYWen3PSx0OBxs3bkRRFDweD4qikJaWxvz589m3bx+LFi1i\n//79LFiwAKPRSEZGBpWVleTk5HDw4EFWrFhBMBi8bfw70dzsHbzREKr67CqXWtrJnzkR/+dLt3qV\nzkFf543V4m3vCmkeJ6NBSyCEOZ9u184Sb7olp1BjDlVbS7wJb7sv7HHvJdf+jlM44t5r2+t53Ulc\nnU5DS4sXGOwDVb2rv8OBjhWANc7Amu/M4ZWdlez4w2mqapr5wTeziY+913nDbp/vzTmFfgzC8/43\n0+l63jfSn1E3G4oiF9Ind3p6OoWFhSxduhSj0UhxcTFbt27F4XCQl5fH6tWrWblyJQDFxcVoNBqW\nLFlCRUUFRUVFJCcns3nzZgBeeukl1q9fj8/no7CwkNTUnot4N8cfifYeqgfgiYeH7sKkECPR5EQz\nG555hK1vnaCy9iqb/s/H/JdvPsB9afZIpybCTKOOoglpIvkt4HS9i82vf8qszPH81Z/n0t7ZzekL\nnpB6KlOS4mhsvTameypdXdJTGcqeyqOzUgilp3KgqjHsPZUb3z8YVHnrozre/PAcKpA/cyJLC6bd\n5dD72+fbX08ltGMQnve/mU6n4amCGbS0KGF8/3sXsZ6KGNybB88B8K2FUyObiBBRTKvVsGRhBjMz\nx7H9vTMcPH6ZIzXNPDZnMk88lCrLbY8CUlTC4EyDi9MNbmZmjiNLhhELMaisSQmsf+ZhPjhykbcP\n1fPu4Qb+o/w8s6clMnt6Io6s8Vji5MbhkUiKShi8ebAOgCX5GZFNRIgRRKfV8sTDqSyaPYlDJ5p4\n/+PzVFQ3U1HdjAZIsseSmhxPyngztngj1jgjCfFGrOae/zcZR9btBmOFFJV7dPzcVU7Vu8jJGDfm\npv0WIhwMeh1fyp3Eo44ULrd2cLSmhePnWmlo8lJxphlo7vd1Rr0Wo0GLyagnPs6AJdaALT4GuzUG\ns1lOo0WKFJV70O0P8tv3a9BoYOljWZFOR4gBhDIeJ7JjdjQaDSnj40gZn8af5aWhqioubxdNrddo\n6/DhaffR1v7Ff70dPprdnVz1dNLS1neggEGvJdkWy4TxcUwaH0eibawtIBY5UlTuwX983EBTaweP\nPzSFtAmj/6YmMTLptBoOnWgadKqSGH10nE663XxesSY9sSY9E8f3rE8Uo9fS5Q/S3R2ko8uP0tGN\ny9tFq6eTq94uLra0c7GlnSOAOVbP+csKs6cncV+aDb0uOvZ1NJKicpeutnXy1kd1WOMMPPWoXEsR\n0c0fwvBjvzY67i4IJVf4Il+tVkN8rIH4WENvwbHEm2hqUbjc2s6F5nYuNbfzxyMX+eORi8TG6JiZ\nMb53QIDZdK83YX5BVVW6ugN0+4MEgiqoEBujJ9Y0dpYTl6JyF1RV5bfvV+PrDvLdr95HXBj/KIUQ\n4RFn0pM5KYHMSQloNJCcEMunZ1s4WtPCx6ev8PHpK2g1GmakJjB7ehK5WeNJtseGNJVLMKjS7O6g\nrtHD1bYu3EoXbe0+2q914++nIOp1Gg5UXmbaZCsPpI/jgXQ7hijpGYabFJW78MeKCxw928L9aTbm\nz5TptIWIdlqthgem2nlg6jiefnw6F1vaOVrTwtGzLZxucHO6wc2//rGG2Bg9qUlmksfFER9rwGzq\n+Yjs9vecYrva1snVtk4ut3bg8/ed9Mug12KJMxIfa8Bo0PZOKNvRFaCjs5v6y15qL7bxXvl54mMN\n5M+ayKLZk0fdcuNSVO7QuUYP/7bvLJY4Az9cnINWlgoWIgShnlob+lNwGo2GKUnxTEmK55sLptKm\ndFFZe5Xj51q5cEWh5mIb1Rfabvt6o15Lsj2O1GQz3YEgCWYjdksMsTH62/ZydDoNX1+YxeHKi1TV\nXuWj45d5r/w875Wf55H7k1myMINJieah2uVhJUXlDnR0dvOrN44TDKr8cHG23P0rRAhCHSgAkRks\nkBAfw5dyJ/Gl3EkAdHUHcHu7UK51097ZjVajwaDXEmPUMc5qwhJr+Lx4hD5NC0CMQUfO1HHkTB2H\nc1EWR6qb+UN5Ax+fvsInp68wf+ZEnIuyRvznihSVEHX5Avz97mO0tHXyzQXpzMwYH+mUhBgx7vTi\neyTFGHRMGBfHhCF8D4Ney7zsCcx9IJmjZ1v49z+d46Pjl/nkzBW+Pi+dJ+elEWMYmRf3paiEwNcd\n4B9+f4yzF9qY+0Ay316YGemUhBB37E4L1tCf2tZoNMyZnkRuViIHqxr5/Z8+440Pz/GflZcoeiyL\nedkTRtwpdikqg7jW5edXbxznVL2LB2ck8V++mR2WBYaEEMPnTk7BRWI9e61Ww6O5k3j4/mT2Hq7n\nvfLz/MtbJ/ljxQWefnz6iJqtQ4rKAC40K/yv0iqaXNdwZI3nR9/KkZumhBihQj0FF0mxMXqci7JY\nlDuJXftr+fj0Ff7H9grmZU/AuSiTxITYSKc4KCkq/QgEg/zp6CX+7YOz+LqD/Nm8NAoXZaLTSkER\nYmyI7LQ2ibZYnv/2TB4/3zPUuexkE5+cvsKCmRP5xvx0ku3ROwxZisoNVFWl6rOr7Pqglost7cTG\n6PjLp2bx0H1JkU5NCDFMomlamxmpNv7mmYcpO9nEWwfrOHCskQ+rGsnNSuSxOZOYmTE+6k7HD1pU\nSkpKeOuttzAYDLz88stMnz69d9vevXt57bXX0Gg0/OQnP2Hu3Ln4fD42bNjA2bNne5cRjo+Pp7q6\nmvXr1+P3+1m8eDHf+973bhvf6/Xy4osv0tzczPTp0/npT3+KwTB0d627vF0cPnmZA5WNXG7tQKOB\nL+Wm8O1HM7HFj+zhfUKIOxdN09poNT3XeOY9MIFPzlzh3bIGjp7tuXHTFm9kzowkHpyRxIwptqi4\nS3/AolJfX09paSlvvPEGVVVVbNq0iR07dgCgKApbtmxhz549eDwevv/97/Puu++yZ88eTCYTu3fv\nZuvWrWzbto0f//jHvPTSS6xbt46cnByeeuopHn/8cYLBYL/xf/3rX/Pggw/ywx/+kI0bN/Lmm2/i\ndDrDssPXuvxcbu3gYnM79Ze9nGpwcamlvedg6HqG+X0jL50pyfFheT8hhAgHrVbD3AcmMPeBCdRd\n9rD/00tUnLnCB0cu8sGRi+h1GqamWJk2KYFJiWYmJZpJtJmINxmGtTczYFEpKytj4cKFaLVacnNz\nqa2txe/3o9frqaysJDs7G7PZ3PvT0NBAWVkZixcvBqCgoIANGzbw/PPPU1dXh8PhACA/P5/Dhw/3\n/v+N8bu7uykrK+Pll1/ujfH2228PWlR+/eZxvN4uAqpKINAzmZs/EKTTF+idwdStdNHpC/R5ndGg\nZWbGOHKnJTIvewLxsZGZx0sf4i9dr9WENNDxdu102p67e+8m5lC11WmHJu69xOzvOIUj7r22vZ5X\nNB2vgY7VvcS9l7Y35xQNxyucpk608r0/s/LdJ2dQ3eDm07Mt1Jxvo/ZiG2dvmg1AowFLrAGL2Yjl\n84k3DXodMQYtL3z3kbDmBYMUFbfbjdVq7X1ssVhwu90kJibicrlISPhimJvVasXlcuFyuXpfY7FY\ncLlcuN1uLBbLLW2BPjGux78xRnx8fG/bgfzgWzND2d9hkwRMTR0X6TSEEFEkKSn8S2RMnJDAlx5J\nD3vcuzXgCTibzYbH4+l9rCgKNpsNALvd3mebx+PBbrf3ec315xISEvB6vQO2vTH+jc97vV7sdnsY\ndlUIIcRQG7CozJs3jw8//JBAIMDRo0fJyspCr+/p3DgcDk6cOIGiKFy6dAlFUUhLS2P+/Pns27cP\ngP3797NgwQKMRiMZGRlUVlbi9/s5ePAgeXl5/cY3GAx9YnzwwQcsWLBgiA+DEEKIcNCoqjrgEIaS\nkhLefPNNjEYjxcXF7Nu3D4fDQV5eXu/oL4C1a9fyyCOP0N3dzYYNG6ipqekz+qumpob169fj8/n4\n1qY5/g8AAAYcSURBVLe+1Wf0143xp02bhqIovPjii1y5coXp06fz8ssv9xYzIYQQ0WvQoiKEEEKE\nKvKDmoUQQowaUlSEEEKEjRQVIYQQYTMiisqFCxeYO3cuK1asYMWKFdTU1ABQXl6O0+mksLCQvXv3\n9rbfvHkzRUVFrFy5kqamJgCamppYuXIlTqeTzZs3D3nOJSUlOJ1Oli9f3pvvUPrud7/LsmXLWLFi\nBa+99hqKovD8889TVFTE2rVr6e7uBu7smN2tQCDAL37xC374wx8CPcPC7zWXe/393ZxTpP+mLl68\nyLPPPsvTTz+N0+nk5MmTET9O/eUU6eN07tw5li9fzvLly3n22Wdxu90RP063yyvSx+q6pqYmvvKV\nr7B169awfA7ccU7qCFBbW6uuXr26z3OBQED96le/qjY2Nqper1d94oknVK/Xq3700UfqD37wA1VV\nVfWdd95RX3zxRVVVVfW///f/rr7zzjuqqqrqD37wA/XQoUNDlm9dXZ26ePFiNRAIqEePHlW/853v\nDNl7Xbds2TLV5/P1Pn7llVfUrVu3qqqqqhs2/P/tnV8oe30cx99tGnKUfu1sLsyfy5WSC7WfC0sp\nciX5k/InckG4UVbSnlxpI1mSaJIiJdJc+lO7JJRSs6KkSZoytqEp830u9mwPe+Z5Hpz5bvV51S52\nLnbe5/397Hz2Ped83/uDra2tfdqzrxAMBll9fT3r6elhnZ2dkmn5zvjF0sS7pp6fn5nL5WKMMba+\nvs76+vqYxWLh6lMsTefn51x9CgaD7OnpiTHGmMlkYnNzc9zr6SNdvL1ijLFAIMDa29tZf38/m52d\n5eJVUsxUPB4P/H4/jo+PEQyGYlZcLhcEQUB2djYEQYBWq8Xx8TH29vZQXl4OANDr9djd3QUQipwJ\nby8vL49sjwcfxdvEE5/Ph8PDQ3g8noiG6OO9vLz8lGdfQSaTYWlpCa2trWB/PVgohZbvjF8sTbxr\nSqFQQKPRAAC8Xi+USuW7/fDwKVqTKIq4vb3l6pNMJkN6ejpeX19xc3MDURS511O0LrfbDZVKxd0r\nABgfH0dbWxsKCgo+/Lx4e5UUTUWtVkOn02Fubg41NTXw+Xz/KyYmIyMjspLf7/cjPT30Bzfh+Jh4\n8VG8TTxpa2uD3W5HXV0ddnZ2YkbdfNazr6JQKCInbwCSaPnu+EVrSpSaOjo6wsrKCrq7uxPCp2hN\nieCT0+lEZWUlTk9PodfrE8Ynp9OJqqoqnJ2doaysjLtXDocDXq8Xer0+Uus8vEq4FYU2mw0LCwvv\ntlmtVnR0dAAILbLc3NxESUlJzJiYt/Exj4+PkcwxQRAQCASQlpYGn8+HX7/il8uVlZWFi4uLyPu3\n8TbxorGxEQBQUVGBiYmJiA8qlSoSdRMdi/NfnklFeL/f0SL1+Gk0Gu41dXJygsHBQczMzEAUxYTw\n6a0mpVIJANx90mq12N7exvLyMoxGY0L4FNa1tbUV0TU5OcnVK7vdjvPzc7S0tODq6goymQxXV1c/\n7lXCzVRqampgs9nevcLZX4yxyMHm5eXB7/fj+voaDw8PcDqdKCoqgk6ng91uB/B3TAyAf8TH/P79\nO27H8G/xNvEgPNUGQr8qMjMzY0bdfNYzqZBCi9TjF75hyaumXl5eYDAYYLFYkJ+fnxA+xdIUvmzL\ny6e3s8vc3Fzc3d2htLSUez1F67q/v+fuVW9vL1ZXV7G4uIja2lo0NDSgq6vrx71KihX1ZrMZ+/v7\nYIyhuLgYRqMRQOgJBrPZDMYYOjs7UV1dDQAYGxvD3t4eBEHA6Ogo1Go13G43DAYDHh4eoNPpMDAw\nEFfNseJn4sXBwQFGRkagUCiQlpaG4eFhiKIYM+rmM559h/39fVitVlit1g9jd356/N5qMplMODg4\n4FZTDocDzc3N0Gq1AICUlBRMT09z9Slak1wuR2FhIdfv3tbWFubn5yGXyyGTyTA0NIScnBzu9RRL\n18bGRsKcp6amppCamoqmpqYf9yopmgpBEASRHCTc5S+CIAgieaGmQhAEQUgGNRWCIAhCMqipEARB\nEJJBTYUgCIKQDGoqBEEQhGRQUyEIgiAkg5oKQRAEIRl/AujkMSlnplWUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6d84250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_ = sns.distplot(loansData['Amount.Funded.By.Investors'].values)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LJX/TVgmWRko6FyGEQ1hPh1VHYTjreYunacUZSoL7WMdWJFQaNbs6l3379lFSUgLA1q1b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\niYyMJDs7G4Ds7GwiIyMJCws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LiIiIcOvHiQvHknARTUbz5s0pKSkBrKenli5dyq233lpp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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6cafed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pp = ProbPlot(loansData['Amount.Funded.By.Investors'].values, dist=\"norm\")\n",
"_ = pp.qqplot(line='r')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x652a3d0>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EzJOgEof6Z9JPzUiJcklGP9M4A4/ePxuvL8AvX/+IgEyKFOKGhBVUSktLsdvt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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6c8dd10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(loansData['Amount.Requested'].values)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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iiy/w+/2MGjWKBQsWUFpayujRoxk1ahSpqance++9LFmyhPvuu49Ro0Zx1113\n8atf/YqZM2fGf1p44oknyMvL44orruC+++7juuuu4+mnn+aDDz7gnnvuIS0tjZtvvpkHH3yQxx57\njKqqKt544w16enq44YYbhu3fTC4+Ku8gcp5UVlZyyy23UFxcfKGHIqIPfEVE7EgrfxERG9LKX0TE\nhhT+IiI2pPAXEbEhhb+IiA0p/EVEbEjhLyJiQ/8XGOjVAKU64iUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6f2d4d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = loansData.boxplot(column='Amount.Requested', return_type='axes')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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UGy6ff/45q1ev5tZbb7Uta968ObNmzaJ///58//339OvXzyGF+Pj4kJ2dTaNG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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x78cd3d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pp = ProbPlot(loansData['Amount.Requested'].values, dist=\"norm\")\n",
"_ = pp.qqplot(line='r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment 3: Testing Loan Data"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8 262\n",
"9 237\n",
"6 232\n",
"7 216\n",
"11 187\n",
"10 185\n",
"13 158\n",
"5 153\n",
"12 153\n",
"14 138\n",
"4 106\n",
"15 96\n",
"16 66\n",
"3 60\n",
"17 58\n",
"18 51\n",
"19 30\n",
"21 26\n",
"2 24\n",
"20 23\n",
"23 11\n",
"22 8\n",
"24 7\n",
"25 4\n",
"26 3\n",
"36 1\n",
"31 1\n",
"34 1\n",
"38 1\n",
"dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['Open.CREDIT.Lines'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x6f2d2d0>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1755YqBFUZ6TFUp090e1LSWw2W2hk9tBDD/HJJ59QUFDAlStXmDlzJjabjfLycgoLC1m0\naBFFRUUkJSX1rHoxiNXhn5ZpP324LdJfuj2Sz87OJjs7G2iZh9+wYUOH+2zfvj1y1UlMcthtVNac\n6/BKpaFuLz8elTIAVYncmG7QL0NJX2vuZDonoEtURfqVvvkjImIwhbyIiMEU8iIiBlPIi4gYTCEv\nImIwhbyIiMEU8iIiBlPIi4gYTCEvImIwhbyIiMEU8iIiBlPIi4gYTCEvImIwhbyIiMEU8iIiBlPI\ni4gYTCEvImIwhbyIiMEU8iIiBtM9XmUAdHWfV1u/VCFyI1DIS79y2KGy5hzNHdzQO85uY8rY4QNQ\nlYi5FPLS75qDFoFAV6N5EYkEzcmLiBhMIS8iYjBDp2u6mgrQVIGI3BgMDXmorPm6ww/3AAbF6Q1M\nbOrOL2ddmSNyNWNDPtyHe812jeRjVWe/vHVljkjHjA15MZOuzBHpGc1biIgYrNcj+YULF9LU1ITD\n4eCuu+5i7ty5PPbYY9TV1ZGRkUFZWRnx8fGRrFVERHqo1yP5K1eusGXLFt566y2WLFnCpk2bmDBh\nAtu2bcPpdLJr165I1inSDVYXf0RuPL0eyft8Pj788EPGjBlDamoqhw4dory8HIC8vDx2795Nfn5+\nxAoVCcdht2m5BJEO9DrkH3zwQSoqKli+fDlPPPEEXq+X5ORkAFwuF16vt8s2PB53b58+LMuycCdd\nIBDseLszruUDvGCw/eV2HW1zuxK6PK437XZnG7Ss95KW5sZma7/96nNtrbO77fZVvV1tdyU5O9wW\n7jwh/Osa7jm7arcjffWzGWmqM7Jipc6e6HXIP/DAAwDcfffdrF69mpSUFHw+H8OGDcPv95OSktJl\nG3V1/t4+fRcs/A2NnV6F4Yy3E+jkKo1rt7ldCfjrv+/yuJ62291tAA6HjfPn/XR8DXjLuQ5OHBSq\ns7vt9lW94bb/39AE6huaOtwW/jwh3Osa7jm7brctj8fdhz+bkaM6IyuW6uyJXs3JBwKB0N/9fj9u\nt5spU6Zw4MABACoqKsjJyelN0yIiEkG9Gsl//PHHPPvsszidThISEnj66afxeDwUFxdTUFBARkYG\nM2fOjHStIiLSQ70K+UmTJrFz5852j2/YsOG6CxIRkcjRl6FERAymkBcRMZhCXkTEYAp5ERGDKeRF\nRAymkBcRMZhCXkTEYAp5ERGDKeRFRAym2//JDUI3AZcbk0JejBdurXnQevNiNoW83BB0A3C5USnk\nRcLSNI/ENoW8SBcqa77WbQUlZsVsyF9pDvCt7/sOt9lsEAwE0QhLuu9/IW5ZV9/429JUj8S0mA35\npuYg/++rix1uczhsBIIWdrtCXrp27Qez7qQL+BsaARgUp6uMJbbFbMiLRNLVo/VAkNDfm+0awUts\n0zBFRMRgCnkREYMp5EVEDKaQFxExmEJeRMRgCnkREYPpEkqRPqMlEWTgKeRF+pCWRJCBppAXuS7h\nRutaEkEGnkJepJe6Wqf++pZE0C8GiQyFvMh1CDdSv94lETTVI5GgkBeJUuGneqxrVsu8lj7QlRYR\nDflXX32Vt99+m/j4eMrLy8nIyIhk8yLC/6aJEhO9odUyW7WM8m/qRiv6JXCjiFjInzx5kh07dvDP\nf/6T//znPzzzzDNs2bIlUs2LyFWag1ab1TJb6X62cq2IhfyhQ4eYNm0adrudn/zkJ3zxxRc0NzcT\nFzcwM0JxDhu2TtaTj7PbOh3HXLvNYW9Zn76r43rabne3tW7v/G25RZzd1qbOnrTbV/V2tt1ut/33\nfDo+rqurVcIdG6l6u/uaX2//Xu+5dvaaBzoJ+Kvb7k/hp5WiR//U2f/voCKWwBcvXiQ5OTn0b7fb\nzcWLF0lLS+v0GI/H3evn8wCj0lN7fbxJZv50yECX0G90rrHJ40nueqcoECt19kTEljUYOnQoPp8v\n9O/6+nqGDh0aqeZFRKQXIhbyd9xxB++//z6BQIBPPvmEW2+9dcCmakREpEXEUnjkyJHMnj2bOXPm\n4HQ6+f3vfx+ppkVEpJdsVsunDSIiYiAtNSwiYjCFvIiIwRTyIiIGG5DLX2Jl+YOFCxfS1NSEw+Eg\nLy+PpUuXDnRJIYFAgBdeeIHPP/+cl19+Gb/fT3FxMXV1dWRkZFBWVkZ8fPxAl9muztOnTzN79mxG\njx4NwNNPP80Pf/jDAavvzJkzlJaWcvnyZZqamigvLyc9PT3q+rKjOpOTk6OqLwFOnDhBSUkJAIMH\nD+bFF1/E4XBEXX92VGd9fX3U9Werc+fOsWDBAubMmcP8+fN57LHHut2f/f7B68mTJ3n44YdDyx88\n//zzUbv8wdy5c3njjTcG/AfyWsFgkLlz5zJs2DAaGxt5+eWXWbNmDUlJSSxdupQVK1aQlZVFfn5+\n1NX55Zdfsn79elatWjWgtbVqamri3LlzpKens3PnTioqKrj11lsZPHhwVPVlR3UWFRWxbt26qOlL\naHnNGxsbSUxMZOXKlaSlpeH3+6PyZ/PaOu+6666o60+AxsZGli1bRkpKCmPGjOG7777rUX/2+3RN\nZ8sfRCOfz8eHH37IhQsXBrqUNux2O1u2bGHRokW0/o4+dOgQeXl5AOTl5VFZWTmQJQId13nhwgX8\nfj/V1dUEAoEBrhCcTifp6ekAXLp0ibS0ND744IOo68tr6/R4PHz77bdR1ZfQ8ponJiYSDAb55ptv\n8Hg8Ufuz2VrnuXPnGDZsWFT2J8CqVat48MEH+cEPfgD0/P96v4d8Z8sfRKMHH3yQiooKCgoKeOed\ndwa6nDacTidXvwnzer2hfnW5XHi93oEqrY1r67zpppuYPHkymzZt4v7772/zLemBdOTIEbZu3cqy\nZcuiti+hbZ3R2pe1tbXcc889fPbZZ0yfPj1q+7O2tpYZM2Zw/PhxcnNzo7I/a2pquHTpEtOnTw/9\nP+ppf/Z7yMfS8gcPPPAATzzxBM899xyvvPLKQJcT1tX96vf7SUlJGeCKOpaens7ixYv505/+xLhx\n49i7d+9Al8TRo0cpKSnhL3/5Cx6PJ2r78uo609LSorIvATIzM9m3bx/z5s2jtLQ0avszMzOTf/3r\nX6E6o7E/Kyoq+PLLL1m4cCE7d+7k73//OydPnuxRf/Z7yMfK8gdXv13z+/243b1fTK0/TJkyhQMH\nDgAtPxg5OTkDXFHHrly5ArSs+NfQ0DDg/drc3ExxcTFr1qxh1KhRQHT2ZUd1tk5zRktfttbSasSI\nEXi9XnJycqKuP6+t8+LFi1HZn4WFhfzjH//gjTfeYPbs2fz617/mN7/5TY/6s9/TNVaWP/j44495\n9tlncTqdJCQk8PTTTw90Se3YbDZstpalSx966CGKi4spKCggIyODmTNnDnB1/3N1natWreLw4cNY\nlsX48eOZMWPGgNZ27Ngxzpw5Q1lZGQBxcXFs2LAh6vry2jodDgfjxo2jqqoqavoSYN++fWzevBmH\nw4Hdbmf58uXccsstUdefHdW5atWqqOvPa9lsth7/X9eyBiIiBtOXoUREDKaQFxExmEJeRMRgCnkR\nEYMp5EVEDKaQFxExmEJeZADt37+fP/zhDwNdhhhM18lLn2lsbGT16tXs3r2by5cvk5GRwaOPPsrE\niRP77DkvXbrEypUrQ98IHDFiBGvXruXTTz+lpKSEhIQEEhMTmThxIo8//jipqans2LGDp556CpfL\nFWpn7dq13HHHHWRnZ2Oz2bAsixEjRjB37lwKCgoAOH36NLNmzeLw4cP88pe/pK6uju+//x7LskhM\nTCQhIYF///vfoTZvu+02PvzwwzbPI9LXom89ATHG448/zpUrV9i1axcpKSm88847PPzww7z66quM\nGTOmT56zqKiI9PR09u/fj8vloqamBo/Hw3fffcfUqVNZt24dTU1NvPjii6xYsYI///nPAPz0pz9l\n3bp17dprXYnU5XJx9OhRli9fzrlz5/jtb3/bZr+3334bgHXr1uH3+0NrlYsMNE3XSJ84ceIE77//\nPs899xypqanYbDbuvvtu5s2bx+bNm9m4cSPTp09n6tSpzJo1i4MHD4aO3bt3LzNmzGDatGk88cQT\nBINBAGbOnElubi4TJ06ksLCQhoaGNs9ZWVnJqVOnWLFiRWi0PHbs2NDaSK1vWp1OJ/fffz+ffvpp\n6NjuvKH90Y9+xOrVq9m0aRONjY2d7teTN8c7duwI/cLYtm1bqE9yc3PZv39/aL+O+qSpqYmioiLu\nuOMOpkyZwkcffdTt55Ubh0Je+kRtbS233XZbu6mJSZMmUVNTw8mTJ3nooYc4ePAgpaWlPPLII3zz\nzTecPXuWtWvX8uabb1JRUUFdXR27d+8G4LPPPmPPnj188MEHNDQ0sG3btjZt19TUcPvtt2O3h/+x\nvnz5Mrt27WrzbuK9995j2rRpTJs2jTlz5nR67KhRoxgyZAiff/55T7ukS2fPnmXGjBkcPHiQP/7x\njzz55JNYltVpn3z00UccO3aMgwcP8t577/HjH/844jVJ7NN0jfSJ1gXJwmkd8U6YMIEJEyZw6NAh\nLMvizJkz/OpXvwJaVq3Myspqc1xcXBxTpkzhiy++aNdeuOd97733mDp1KpcuXWLcuHGhqRqAO++8\nk/Xr13f7/Prqo6zWdidPnsx3333Ht99+y+HDhzvsk2nTphEIBHjuuedYsGBBaHVKkasp5KVPZGZm\ncuzYMerr69uM5quqqhg3bly7/e12O5ZlEQwGGT9+PK+++mrY9gcNGhRatrjVmDFj2LFjR6dhn5ub\ny7p163jnnXcoLy8nMTGxx+d14sQJLl68yOjRozl//nyPj++JQYMG0dTUFLZP3n77bXbv3s2SJUso\nKiriF7/4RZ/WJLFH0zXSJ0aNGsXPf/5zfve733HhwgUCgQD79+9n69atoTnor7/+Gsuy+OKLLzh8\n+DC33347EydO5NNPP+XQoUNAy11wWufkr9bRSDo3Nxe3283KlStD8/UnT57k+++/b3PMz372M6ZO\nndrte3m2/vKpqanhkUceYcmSJSQkJHS6b1dt9VRnfdLQ0EAgEGD27Nnk5+fz7rvv9rhtMZ9G8tJn\nnnrqKVavXs3MmTNDl1CuXbuW9PR0LMviwIED7Nq1i4SEBJYvX87NN98MwPPPP095eTl1dXUMGTKE\nv/3tb6EPb1td/fcnn3yS8ePHk5+fz0svvcSzzz5LXl4edrsdj8fDmjVr2qxpD/Doo49y7733smjR\nImw2G++++y6TJ08ObV+wYAGFhYUMHTqUu+++m/j4eG6++WaWLl3Kfffd12Edrf9ufaypqYmFCxdS\nVlYWmv/Pzc0N7TtnzhwyMzND+19bY+vfb7nllg775Pjx4zz66KMEAgE8Hk/U3ptBBpauk5cBUVJS\nQmZmJosWLRroUkSMpukaERGDaSQvImIwjeRFRAymkBcRMZhCXkTEYAp5ERGDKeRFRAymkBcRMdj/\nB8dRzJ7IiT7pAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x692d390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(loansData['Open.CREDIT.Lines'].dropna(), kde=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The question posited here is: is the data evenly distributed? And the suggestion is to perform a $\\chi^2$ test.\n",
"\n",
"If the data was evenly distributed, we would expect to have the same frequency for each number of open credit line. Let's assume that this common frequency is the mean of the observed frequencies."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"86.137931034482762"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts = loansData['Open.CREDIT.Lines'].value_counts()\n",
"counts.mean()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8 86.137931\n",
"9 86.137931\n",
"6 86.137931\n",
"7 86.137931\n",
"11 86.137931\n",
"10 86.137931\n",
"13 86.137931\n",
"5 86.137931\n",
"12 86.137931\n",
"14 86.137931\n",
"4 86.137931\n",
"15 86.137931\n",
"16 86.137931\n",
"3 86.137931\n",
"17 86.137931\n",
"18 86.137931\n",
"19 86.137931\n",
"21 86.137931\n",
"2 86.137931\n",
"20 86.137931\n",
"23 86.137931\n",
"22 86.137931\n",
"24 86.137931\n",
"25 86.137931\n",
"26 86.137931\n",
"36 86.137931\n",
"31 86.137931\n",
"34 86.137931\n",
"38 86.137931\n",
"dtype: float64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uniform = counts.copy()\n",
"uniform.loc[:] = counts.mean()\n",
"uniform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And compare that expected distribution with the observed one, by performing a $\\chi^2$ test at the usual significance level of 0.5, with the null hypothesis:\n",
"\n",
"$H_0$: the observed distribution is uniform."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2408.433146517214, 0.0)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.chisquare(counts, uniform)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The p-value is 0 which is obviously smaller than 0.5 and we can safely reject the null hypothesis that the observed distribution is uniform."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Going back to the curriculum, the suggested code is:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2408.433146517214, 0.0)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.chisquare(counts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which gives the same answer... because when no expected frequencies are given, by default, it is assumed that the expected frequencies are uniform and given by the mean of the observed frequencies. Cf. [`stats.chisquare`](http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.chisquare.html) documentation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lessons 3: Linear Regression and Correlation\n",
"\n",
"Our task is to determine the linear equation that fits the trend between FICO scores and interest rates\n",
"\n",
"### Assignment 2: Clean and Plot Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removing the '%' symbols from the `Interest.Rate` column."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81174 8.90%\n",
"99592 12.12%\n",
"80059 21.98%\n",
"15825 9.99%\n",
"33182 11.71%\n",
"Name: Interest.Rate, dtype: object"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['Interest.Rate'].head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81174 8.90\n",
"99592 12.12\n",
"80059 21.98\n",
"15825 9.99\n",
"33182 11.71\n",
"Name: Interest.Rate, dtype: float64"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['Interest.Rate'] = (\n",
" loansData['Interest.Rate']\n",
" .str\n",
" .replace('%', '')\n",
" .astype(float)\n",
")\n",
"\n",
"loansData['Interest.Rate'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removing the word 'months' from the `Loan.Length` column."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81174 36 months\n",
"99592 36 months\n",
"80059 60 months\n",
"15825 36 months\n",
"33182 36 months\n",
"Name: Loan.Length, dtype: object"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['Loan.Length'].head()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81174 36\n",
"99592 36\n",
"80059 60\n",
"15825 36\n",
"33182 36\n",
"Name: Loan.Length, dtype: int64"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['Loan.Length'] = (\n",
" loansData['Loan.Length']\n",
" .str\n",
" .replace(' months', '')\n",
" .astype(int)\n",
")\n",
"\n",
"loansData['Loan.Length'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert `FICO.Range` into a numerical value, and save it in a new column called `FICO.Score`. Since the ranges are small, we're going to go ahead and pick the first number to represent the range."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81174 735-739\n",
"99592 715-719\n",
"80059 690-694\n",
"15825 695-699\n",
"33182 695-699\n",
"Name: FICO.Range, dtype: object"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['FICO.Range'].head()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"81174 735\n",
"99592 715\n",
"80059 690\n",
"15825 695\n",
"33182 695\n",
"Name: FICO.Score, dtype: int64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData['FICO.Score'] = (\n",
" loansData['FICO.Range']\n",
" .str.split('-')\n",
" .str.get(0)\n",
" .astype(int))\n",
"\n",
"loansData['FICO.Score'].head()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Amount.Requested</th>\n",
" <th>Amount.Funded.By.Investors</th>\n",
" <th>Interest.Rate</th>\n",
" <th>Loan.Length</th>\n",
" <th>Monthly.Income</th>\n",
" <th>Open.CREDIT.Lines</th>\n",
" <th>Revolving.CREDIT.Balance</th>\n",
" <th>Inquiries.in.the.Last.6.Months</th>\n",
" <th>FICO.Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2500.000000</td>\n",
" <td>2500.000000</td>\n",
" <td>2500.000000</td>\n",
" <td>2500.000000</td>\n",
" <td>2499.000000</td>\n",
" <td>2498.000000</td>\n",
" <td>2498.000000</td>\n",
" <td>2498.000000</td>\n",
" <td>2500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>12406.500000</td>\n",
" <td>12001.573236</td>\n",
" <td>13.066996</td>\n",
" <td>41.260800</td>\n",
" <td>5688.931321</td>\n",
" <td>10.075661</td>\n",
" <td>15244.559648</td>\n",
" <td>0.906325</td>\n",
" <td>705.888000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7801.544872</td>\n",
" <td>7745.320754</td>\n",
" <td>4.178230</td>\n",
" <td>9.930893</td>\n",
" <td>3963.118185</td>\n",
" <td>4.508644</td>\n",
" <td>18308.549795</td>\n",
" <td>1.231036</td>\n",
" <td>35.033161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1000.000000</td>\n",
" <td>-0.010000</td>\n",
" <td>5.420000</td>\n",
" <td>36.000000</td>\n",
" <td>588.500000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>640.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6000.000000</td>\n",
" <td>6000.000000</td>\n",
" <td>10.160000</td>\n",
" <td>36.000000</td>\n",
" <td>3500.000000</td>\n",
" <td>7.000000</td>\n",
" <td>5585.750000</td>\n",
" <td>0.000000</td>\n",
" <td>680.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>13.110000</td>\n",
" <td>36.000000</td>\n",
" <td>5000.000000</td>\n",
" <td>9.000000</td>\n",
" <td>10962.000000</td>\n",
" <td>0.000000</td>\n",
" <td>700.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17000.000000</td>\n",
" <td>16000.000000</td>\n",
" <td>15.800000</td>\n",
" <td>36.000000</td>\n",
" <td>6800.000000</td>\n",
" <td>13.000000</td>\n",
" <td>18888.750000</td>\n",
" <td>1.000000</td>\n",
" <td>725.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>35000.000000</td>\n",
" <td>35000.000000</td>\n",
" <td>24.890000</td>\n",
" <td>60.000000</td>\n",
" <td>102750.000000</td>\n",
" <td>38.000000</td>\n",
" <td>270800.000000</td>\n",
" <td>9.000000</td>\n",
" <td>830.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Amount.Requested Amount.Funded.By.Investors Interest.Rate \\\n",
"count 2500.000000 2500.000000 2500.000000 \n",
"mean 12406.500000 12001.573236 13.066996 \n",
"std 7801.544872 7745.320754 4.178230 \n",
"min 1000.000000 -0.010000 5.420000 \n",
"25% 6000.000000 6000.000000 10.160000 \n",
"50% 10000.000000 10000.000000 13.110000 \n",
"75% 17000.000000 16000.000000 15.800000 \n",
"max 35000.000000 35000.000000 24.890000 \n",
"\n",
" Loan.Length Monthly.Income Open.CREDIT.Lines \\\n",
"count 2500.000000 2499.000000 2498.000000 \n",
"mean 41.260800 5688.931321 10.075661 \n",
"std 9.930893 3963.118185 4.508644 \n",
"min 36.000000 588.500000 2.000000 \n",
"25% 36.000000 3500.000000 7.000000 \n",
"50% 36.000000 5000.000000 9.000000 \n",
"75% 36.000000 6800.000000 13.000000 \n",
"max 60.000000 102750.000000 38.000000 \n",
"\n",
" Revolving.CREDIT.Balance Inquiries.in.the.Last.6.Months FICO.Score \n",
"count 2498.000000 2498.000000 2500.000000 \n",
"mean 15244.559648 0.906325 705.888000 \n",
"std 18308.549795 1.231036 35.033161 \n",
"min 0.000000 0.000000 640.000000 \n",
"25% 5585.750000 0.000000 680.000000 \n",
"50% 10962.000000 0.000000 700.000000 \n",
"75% 18888.750000 1.000000 725.000000 \n",
"max 270800.000000 9.000000 830.000000 "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData.describe()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Amount.Requested</th>\n",
" <th>Amount.Funded.By.Investors</th>\n",
" <th>Interest.Rate</th>\n",
" <th>Loan.Length</th>\n",
" <th>Loan.Purpose</th>\n",
" <th>Debt.To.Income.Ratio</th>\n",
" <th>State</th>\n",
" <th>Home.Ownership</th>\n",
" <th>Monthly.Income</th>\n",
" <th>FICO.Range</th>\n",
" <th>Open.CREDIT.Lines</th>\n",
" <th>Revolving.CREDIT.Balance</th>\n",
" <th>Inquiries.in.the.Last.6.Months</th>\n",
" <th>Employment.Length</th>\n",
" <th>FICO.Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>102441</th>\n",
" <td>7500</td>\n",
" <td>-0.01</td>\n",
" <td>12.29</td>\n",
" <td>36</td>\n",
" <td>educational</td>\n",
" <td>21.34%</td>\n",
" <td>CA</td>\n",
" <td>MORTGAGE</td>\n",
" <td>8750.00</td>\n",
" <td>685-689</td>\n",
" <td>14</td>\n",
" <td>20947</td>\n",
" <td>7</td>\n",
" <td>5 years</td>\n",
" <td>685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102444</th>\n",
" <td>2200</td>\n",
" <td>-0.01</td>\n",
" <td>13.87</td>\n",
" <td>36</td>\n",
" <td>credit_card</td>\n",
" <td>10.35%</td>\n",
" <td>NJ</td>\n",
" <td>RENT</td>\n",
" <td>3333.33</td>\n",
" <td>640-644</td>\n",
" <td>10</td>\n",
" <td>11606</td>\n",
" <td>1</td>\n",
" <td>3 years</td>\n",
" <td>640</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Amount.Requested Amount.Funded.By.Investors Interest.Rate \\\n",
"102441 7500 -0.01 12.29 \n",
"102444 2200 -0.01 13.87 \n",
"\n",
" Loan.Length Loan.Purpose Debt.To.Income.Ratio State Home.Ownership \\\n",
"102441 36 educational 21.34% CA MORTGAGE \n",
"102444 36 credit_card 10.35% NJ RENT \n",
"\n",
" Monthly.Income FICO.Range Open.CREDIT.Lines \\\n",
"102441 8750.00 685-689 14 \n",
"102444 3333.33 640-644 10 \n",
"\n",
" Revolving.CREDIT.Balance Inquiries.in.the.Last.6.Months \\\n",
"102441 20947 7 \n",
"102444 11606 1 \n",
"\n",
" Employment.Length FICO.Score \n",
"102441 5 years 685 \n",
"102444 3 years 640 "
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData[loansData['Amount.Funded.By.Investors'] < 0]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"loansData.loc[\n",
" loansData['Amount.Funded.By.Investors'] < 0, \n",
" 'Amount.Funded.By.Investors'] = 0.0"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Amount.Requested</th>\n",
" <th>Amount.Funded.By.Investors</th>\n",
" <th>Interest.Rate</th>\n",
" <th>Loan.Length</th>\n",
" <th>Loan.Purpose</th>\n",
" <th>Debt.To.Income.Ratio</th>\n",
" <th>State</th>\n",
" <th>Home.Ownership</th>\n",
" <th>Monthly.Income</th>\n",
" <th>FICO.Range</th>\n",
" <th>Open.CREDIT.Lines</th>\n",
" <th>Revolving.CREDIT.Balance</th>\n",
" <th>Inquiries.in.the.Last.6.Months</th>\n",
" <th>Employment.Length</th>\n",
" <th>FICO.Score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>102441</th>\n",
" <td>7500</td>\n",
" <td>0</td>\n",
" <td>12.29</td>\n",
" <td>36</td>\n",
" <td>educational</td>\n",
" <td>21.34%</td>\n",
" <td>CA</td>\n",
" <td>MORTGAGE</td>\n",
" <td>8750.00</td>\n",
" <td>685-689</td>\n",
" <td>14</td>\n",
" <td>20947</td>\n",
" <td>7</td>\n",
" <td>5 years</td>\n",
" <td>685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102444</th>\n",
" <td>2200</td>\n",
" <td>0</td>\n",
" <td>13.87</td>\n",
" <td>36</td>\n",
" <td>credit_card</td>\n",
" <td>10.35%</td>\n",
" <td>NJ</td>\n",
" <td>RENT</td>\n",
" <td>3333.33</td>\n",
" <td>640-644</td>\n",
" <td>10</td>\n",
" <td>11606</td>\n",
" <td>1</td>\n",
" <td>3 years</td>\n",
" <td>640</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Amount.Requested Amount.Funded.By.Investors Interest.Rate \\\n",
"102441 7500 0 12.29 \n",
"102444 2200 0 13.87 \n",
"\n",
" Loan.Length Loan.Purpose Debt.To.Income.Ratio State Home.Ownership \\\n",
"102441 36 educational 21.34% CA MORTGAGE \n",
"102444 36 credit_card 10.35% NJ RENT \n",
"\n",
" Monthly.Income FICO.Range Open.CREDIT.Lines \\\n",
"102441 8750.00 685-689 14 \n",
"102444 3333.33 640-644 10 \n",
"\n",
" Revolving.CREDIT.Balance Inquiries.in.the.Last.6.Months \\\n",
"102441 20947 7 \n",
"102444 11606 1 \n",
"\n",
" Employment.Length FICO.Score \n",
"102441 5 years 685 \n",
"102444 3 years 640 "
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loansData.loc[[102441, 102444]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot Histograms of FICO Score"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7dff950>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rgagow6CGs55dXkPIPwLrWxnJ/PGT47zxt1Lmzk4k7Ap3drvYz2Eoyns1fPn/py/XDXy/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7dffa10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(loansData['FICO.Score'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Generate a Scatter-plot of the Data"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x693d590>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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