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October 2, 2016 11:46
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Small example" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Introduction" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "[Heirarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) is a method of cluster analysis which seek to build a heirarchy of clusters. \n", | |
| "\n", | |
| "In this example we'll used agglomerative clustering which is a bottom up approach where we take a series of observations and successively group (or cluster) them until we end up with one cluster. \n", | |
| "\n", | |
| "From the inforamtion developed during the clustering operation, we can then visualse the heirarchical relationship between the observations (e.g. in a dendogram as illustrated below on the left), and how they cluster together " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 75, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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8vDxz5coVrtv666+/GDk5Oeb7779v1TyRjvLy5UtGU1OTmTt3bruGhI0YMYK5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\nAAAAALb5G+UE7Dq9EYQDAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<IPython.core.display.Image object>" | |
| ] | |
| }, | |
| "execution_count": 75, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "from IPython.display import Image\n", | |
| "Image('http://cs.jhu.edu/~razvanm/fs-expedition/hclust-example.png')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The data we'll use for this example is from SAS and is a sample of 100 from ~1,005 survey responses received by a supplier of hydraulic and pneumatic products in the USA. \n", | |
| "\n", | |
| "The anonymous survey sought feedback from customes on the importance of various attributes:\n", | |
| "\n", | |
| "|Label | Description |\n", | |
| "|-|-|\n", | |
| "|Av_pay | Availability of different electronic payment options|\n", | |
| "|Av_br | Availability of a large number of brands to choose from|\n", | |
| "|Av_spec| Availability of well documented technical specifications|\n", | |
| "|Credit | Credit policy of the supplier|\n", | |
| "|Price | Price|\n", | |
| "|Reliab | Reliability of teh supplier to supply |\n", | |
| "|Return | Return policy of the supplier|\n", | |
| "|Talk_dir| Ability to talk directly to a representative about supply needs|\n", | |
| "|Time | Timeliness of deliveries|\n", | |
| "|Warranty| Warranty coverage provided by the supplier |\n", | |
| "\n", | |
| "Survey responses rated each of these attributes on a scale of 1 to 9 with 1 being not important and 9 being extremelly important to the customer in chosing a supplier of hydraulic or pneumatic products" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "To complete this analysis, we'll be using python, and the [Scipy heirarchical clustering library](http://scipy.github.io/devdocs/generated/scipy.cluster.hierarchy.linkage.html)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Import libraries" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 113, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import math\n", | |
| "import scipy.cluster.hierarchy as hac # heirarchical clustering\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Load data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We'll load the data into a pandas dataframe to facilitate further analysis. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 77, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.read_sas('SAS_data/smallexample.sas7bdat')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Quick look at what data was loaded" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 78, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>id</th>\n", | |
| " <th>reliab</th>\n", | |
| " <th>time</th>\n", | |
| " <th>av_br</th>\n", | |
| " <th>av_spec</th>\n", | |
| " <th>price</th>\n", | |
| " <th>credit</th>\n", | |
| " <th>av_pay</th>\n", | |
| " <th>return</th>\n", | |
| " <th>warranty</th>\n", | |
| " <th>talk_dir</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>b'1'</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>b'2'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>b'3'</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>b'4'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>b'5'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " id reliab time av_br av_spec price credit av_pay return \\\n", | |
| "0 b'1' 8.0 8.0 6.0 7.0 7.0 5.0 8.0 7.0 \n", | |
| "1 b'2' 9.0 9.0 6.0 7.0 6.0 4.0 1.0 5.0 \n", | |
| "2 b'3' 1.0 2.0 5.0 5.0 3.0 6.0 8.0 3.0 \n", | |
| "3 b'4' 9.0 9.0 9.0 9.0 9.0 7.0 1.0 9.0 \n", | |
| "4 b'5' 9.0 9.0 9.0 9.0 9.0 7.0 6.0 8.0 \n", | |
| "\n", | |
| " warranty talk_dir \n", | |
| "0 7.0 8.0 \n", | |
| "1 7.0 8.0 \n", | |
| "2 3.0 1.0 \n", | |
| "3 9.0 9.0 \n", | |
| "4 8.0 9.0 " | |
| ] | |
| }, | |
| "execution_count": 78, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Summary statistics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now the data is loaded, we can look at some summary statistics to get a feel for what it looks like.\n", | |
| "\n", | |
| "### General summary stats\n", | |
| "The builtin 'decribe' method for dataframes give us a resonable summary of the data, and we can easily add other statistics such as 'Median', 'Kurtosis', and 'Skewness' to get a better feel for the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 79, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>count</th>\n", | |
| " <th>mean</th>\n", | |
| " <th>std</th>\n", | |
| " <th>min</th>\n", | |
| " <th>25%</th>\n", | |
| " <th>50%</th>\n", | |
| " <th>75%</th>\n", | |
| " <th>max</th>\n", | |
| " <th>Median</th>\n", | |
| " <th>Kurtosis</th>\n", | |
| " <th>Skewness</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>reliab</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>8.51</td>\n", | |
| " <td>1.010001</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>8.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>30.55</td>\n", | |
| " <td>-4.59</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>time</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>8.49</td>\n", | |
| " <td>0.926545</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>8.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>23.82</td>\n", | |
| " <td>-3.94</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>av_br</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>6.66</td>\n", | |
| " <td>1.689062</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.75</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>0.10</td>\n", | |
| " <td>-0.42</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>av_spec</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>7.33</td>\n", | |
| " <td>1.511271</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>7.00</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>9.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>0.05</td>\n", | |
| " <td>-0.76</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>price</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>7.67</td>\n", | |
| " <td>1.363707</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>7.00</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>9.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>0.51</td>\n", | |
| " <td>-0.94</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>credit</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>6.26</td>\n", | |
| " <td>1.883581</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.00</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.25</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>-0.11</td>\n", | |
| " <td>-0.38</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>av_pay</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>3.49</td>\n", | |
| " <td>2.320288</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.00</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>5.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>-0.57</td>\n", | |
| " <td>0.57</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>return</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>7.17</td>\n", | |
| " <td>1.442886</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.00</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.25</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>-0.45</td>\n", | |
| " <td>-0.39</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>warranty</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>7.74</td>\n", | |
| " <td>1.446836</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>7.00</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>9.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>1.23</td>\n", | |
| " <td>-1.25</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>talk_dir</th>\n", | |
| " <td>100.0</td>\n", | |
| " <td>8.24</td>\n", | |
| " <td>1.422191</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>8.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.00</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.84</td>\n", | |
| " <td>-2.84</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " count mean std min 25% 50% 75% max Median Kurtosis \\\n", | |
| "reliab 100.0 8.51 1.010001 1.0 8.00 9.0 9.00 9.0 9.0 30.55 \n", | |
| "time 100.0 8.49 0.926545 2.0 8.00 9.0 9.00 9.0 9.0 23.82 \n", | |
| "av_br 100.0 6.66 1.689062 1.0 5.75 7.0 8.00 9.0 7.0 0.10 \n", | |
| "av_spec 100.0 7.33 1.511271 3.0 7.00 7.0 9.00 9.0 7.0 0.05 \n", | |
| "price 100.0 7.67 1.363707 3.0 7.00 8.0 9.00 9.0 8.0 0.51 \n", | |
| "credit 100.0 6.26 1.883581 1.0 5.00 6.0 7.25 9.0 6.0 -0.11 \n", | |
| "av_pay 100.0 3.49 2.320288 1.0 1.00 3.0 5.00 9.0 3.0 -0.57 \n", | |
| "return 100.0 7.17 1.442886 3.0 6.00 7.0 8.25 9.0 7.0 -0.45 \n", | |
| "warranty 100.0 7.74 1.446836 3.0 7.00 8.0 9.00 9.0 8.0 1.23 \n", | |
| "talk_dir 100.0 8.24 1.422191 1.0 8.00 9.0 9.00 9.0 9.0 9.84 \n", | |
| "\n", | |
| " Skewness \n", | |
| "reliab -4.59 \n", | |
| "time -3.94 \n", | |
| "av_br -0.42 \n", | |
| "av_spec -0.76 \n", | |
| "price -0.94 \n", | |
| "credit -0.38 \n", | |
| "av_pay 0.57 \n", | |
| "return -0.39 \n", | |
| "warranty -1.25 \n", | |
| "talk_dir -2.84 " | |
| ] | |
| }, | |
| "execution_count": 79, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dfSummary = df.describe()\n", | |
| "dfSummary.loc['Median', :] = df.apply(lambda x: round(x.median(),2), axis=0)\n", | |
| "dfSummary.loc['Kurtosis', :] = df.apply(lambda x: round(x.kurt(),2), axis=0)\n", | |
| "dfSummary.loc['Skewness', :] = df.apply(lambda x: round(x.skew(), 2), axis=0)\n", | |
| "dfSummary.T" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can see from the table above that;\n", | |
| "* there are 100 date points for each survey attribute.\n", | |
| "* the mean, on a 1-9 scale, and the median is 5. We can see from the summary statistics that there are some large deviations from this, especially for 'reliab', 'time', and 'talk_dir'\n", | |
| "* there are some large standard deviations, 'credit', and 'av_pay' in particular\n", | |
| "* there are some very high kurtosis ans skewness statistics.\n", | |
| "\n", | |
| "As a refresher, the image below illustrates what skewness adn kurtosis measures mean. So, we can see that 'reliab', 'time' and 'talk_dir' are very peaky or 'laptokurtic', and the other attributes are more broadly distributed or 'platykurtic'\n", | |
| "\n", | |
| "Similarly, 'reliab', 'time' and 'talk_dir' are heavily left skewed." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 87, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<IPython.core.display.Image object>" | |
| ] | |
| }, | |
| "execution_count": 87, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "Image('/home/quizzicol/Pictures/Selection_036.png')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Histograms\n", | |
| "To understand these distributions better, we can look at histograms for each attribute." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 111, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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BxM505lxlDbMo+WIp83nOmpVZVW4qmVYfqaxzzmxWZp01snFmZmZmZmbWa2Z1nrM5BbgP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\nkno3ynVrTXPOqupbzdd/TPJh4GTg08ufNzMz400RJ6TdMUd/b9rXrc0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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9adbbfc9b0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df.hist(figsize=(15,10), bins=[1,2,3,4,5,6,7,8,9]);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "From the histograms, we can see that most of the attribute scores are highly negatively skewed, with the exeption of 'av_pay'.\n", | |
| "\n", | |
| "So, availability of electronic payments options don't appear to be interesting to potential customers, but reliability, timeliness and the ability to talk directly to a supplier representative are very important." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Correlation\n", | |
| "\n", | |
| "Next we can have a quick look at correlations. We can do this both in a table and using a heatmap." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 107, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>reliab</th>\n", | |
| " <th>time</th>\n", | |
| " <th>av_br</th>\n", | |
| " <th>av_spec</th>\n", | |
| " <th>price</th>\n", | |
| " <th>credit</th>\n", | |
| " <th>av_pay</th>\n", | |
| " <th>return</th>\n", | |
| " <th>warranty</th>\n", | |
| " <th>talk_dir</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>reliab</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.788060</td>\n", | |
| " <td>0.197407</td>\n", | |
| " <td>0.279064</td>\n", | |
| " <td>0.394772</td>\n", | |
| " <td>0.099501</td>\n", | |
| " <td>-0.051680</td>\n", | |
| " <td>0.418162</td>\n", | |
| " <td>0.485660</td>\n", | |
| " <td>0.666362</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>time</th>\n", | |
| " <td>0.788060</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.268889</td>\n", | |
| " <td>0.207970</td>\n", | |
| " <td>0.417060</td>\n", | |
| " <td>0.209866</td>\n", | |
| " <td>-0.079921</td>\n", | |
| " <td>0.511285</td>\n", | |
| " <td>0.502881</td>\n", | |
| " <td>0.546090</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>av_br</th>\n", | |
| " <td>0.197407</td>\n", | |
| " <td>0.268889</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.531122</td>\n", | |
| " <td>0.336702</td>\n", | |
| " <td>0.501132</td>\n", | |
| " <td>0.403771</td>\n", | |
| " <td>0.488156</td>\n", | |
| " <td>0.368528</td>\n", | |
| " <td>0.362299</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>av_spec</th>\n", | |
| " <td>0.279064</td>\n", | |
| " <td>0.207970</td>\n", | |
| " <td>0.531122</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.161200</td>\n", | |
| " <td>0.299560</td>\n", | |
| " <td>0.117614</td>\n", | |
| " <td>0.474295</td>\n", | |
| " <td>0.478497</td>\n", | |
| " <td>0.446842</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>price</th>\n", | |
| " <td>0.394772</td>\n", | |
| " <td>0.417060</td>\n", | |
| " <td>0.336702</td>\n", | |
| " <td>0.161200</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.556751</td>\n", | |
| " <td>0.198464</td>\n", | |
| " <td>0.485678</td>\n", | |
| " <td>0.350274</td>\n", | |
| " <td>0.358948</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>credit</th>\n", | |
| " <td>0.099501</td>\n", | |
| " <td>0.209866</td>\n", | |
| " <td>0.501132</td>\n", | |
| " <td>0.299560</td>\n", | |
| " <td>0.556751</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.370394</td>\n", | |
| " <td>0.466733</td>\n", | |
| " <td>0.340106</td>\n", | |
| " <td>0.146153</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>av_pay</th>\n", | |
| " <td>-0.051680</td>\n", | |
| " <td>-0.079921</td>\n", | |
| " <td>0.403771</td>\n", | |
| " <td>0.117614</td>\n", | |
| " <td>0.198464</td>\n", | |
| " <td>0.370394</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.222270</td>\n", | |
| " <td>0.104528</td>\n", | |
| " <td>0.095626</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>return</th>\n", | |
| " <td>0.418162</td>\n", | |
| " <td>0.511285</td>\n", | |
| " <td>0.488156</td>\n", | |
| " <td>0.474295</td>\n", | |
| " <td>0.485678</td>\n", | |
| " <td>0.466733</td>\n", | |
| " <td>0.222270</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.650396</td>\n", | |
| " <td>0.486922</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>warranty</th>\n", | |
| " <td>0.485660</td>\n", | |
| " <td>0.502881</td>\n", | |
| " <td>0.368528</td>\n", | |
| " <td>0.478497</td>\n", | |
| " <td>0.350274</td>\n", | |
| " <td>0.340106</td>\n", | |
| " <td>0.104528</td>\n", | |
| " <td>0.650396</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.604978</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>talk_dir</th>\n", | |
| " <td>0.666362</td>\n", | |
| " <td>0.546090</td>\n", | |
| " <td>0.362299</td>\n", | |
| " <td>0.446842</td>\n", | |
| " <td>0.358948</td>\n", | |
| " <td>0.146153</td>\n", | |
| " <td>0.095626</td>\n", | |
| " <td>0.486922</td>\n", | |
| " <td>0.604978</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " reliab time av_br av_spec price credit \\\n", | |
| "reliab 1.000000 0.788060 0.197407 0.279064 0.394772 0.099501 \n", | |
| "time 0.788060 1.000000 0.268889 0.207970 0.417060 0.209866 \n", | |
| "av_br 0.197407 0.268889 1.000000 0.531122 0.336702 0.501132 \n", | |
| "av_spec 0.279064 0.207970 0.531122 1.000000 0.161200 0.299560 \n", | |
| "price 0.394772 0.417060 0.336702 0.161200 1.000000 0.556751 \n", | |
| "credit 0.099501 0.209866 0.501132 0.299560 0.556751 1.000000 \n", | |
| "av_pay -0.051680 -0.079921 0.403771 0.117614 0.198464 0.370394 \n", | |
| "return 0.418162 0.511285 0.488156 0.474295 0.485678 0.466733 \n", | |
| "warranty 0.485660 0.502881 0.368528 0.478497 0.350274 0.340106 \n", | |
| "talk_dir 0.666362 0.546090 0.362299 0.446842 0.358948 0.146153 \n", | |
| "\n", | |
| " av_pay return warranty talk_dir \n", | |
| "reliab -0.051680 0.418162 0.485660 0.666362 \n", | |
| "time -0.079921 0.511285 0.502881 0.546090 \n", | |
| "av_br 0.403771 0.488156 0.368528 0.362299 \n", | |
| "av_spec 0.117614 0.474295 0.478497 0.446842 \n", | |
| "price 0.198464 0.485678 0.350274 0.358948 \n", | |
| "credit 0.370394 0.466733 0.340106 0.146153 \n", | |
| "av_pay 1.000000 0.222270 0.104528 0.095626 \n", | |
| "return 0.222270 1.000000 0.650396 0.486922 \n", | |
| "warranty 0.104528 0.650396 1.000000 0.604978 \n", | |
| "talk_dir 0.095626 0.486922 0.604978 1.000000 " | |
| ] | |
| }, | |
| "execution_count": 107, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.corr()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 110, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.colorbar.Colorbar at 0x7f9af006c588>" | |
| ] | |
| }, | |
| "execution_count": 110, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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bnDcDmJHZvhC4sMF5H63bnl63PQkzs05Q8S4wbR/gZmZmncVTYpiZ5a1D2gpa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\n5eZtYm+mABARd0t6XUGxejO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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9adbba9438>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, ax = plt.subplots(figsize=[6,6])\n", | |
| "im = ax.imshow(df.corr(), interpolation='None')\n", | |
| "\n", | |
| "ax.set_xticks([x for x in range(len(df.columns[1:]))])\n", | |
| "ax.set_xticklabels(df.columns[1:], rotation=90)\n", | |
| "\n", | |
| "ax.set_yticks([x for x in range(len(df.columns[1:]))])\n", | |
| "ax.set_yticklabels(df.columns[1:])\n", | |
| "\n", | |
| "fig.colorbar(im)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can see that there are some reasonable correlations between:\n", | |
| "* 'time' and 'reliability'\n", | |
| "* 'talk_dir' and 'reliability'\n", | |
| "* 'return' and 'warranty'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Clustering\n", | |
| "\n", | |
| "Now, we'll look at clusting the responses to see it there are discrete groups of customers based on their attribute ratings. For this we will use the Scipy hierarchical aglomerative clustering library; we'll refer to this as '**hac**' and we'll be using the linkage and dendogram methods.\n", | |
| "\n", | |
| "The 'linkage' method will perform the hierchichial agglomerative clustering of, in our case, 2D array (our dataframe) of $n$ obsevations. Returned from this method is an $(n-1) x 4$ array. If you aren't use to this, the returned array can be a little confusing so here is an example.\n", | |
| "\n", | |
| "The array looks like this:\n", | |
| "\n", | |
| "\n", | |
| "Taking the first row as an example, this indicates that observation or cluster '5' (the first column, column[0]) was merged with observation or cluster '81' (the second column, column[1]) to form a new cluster $n + 1$. \n", | |
| "\n", | |
| "Since we started with 100 observations, [0...99], the first cluster is called cluster 100. So, for this example, all original observations have numbers less than 100, and all clusters will have a number from 100 up. \n", | |
| "\n", | |
| "The third column (column[2]) shows the distance between the two cluster being merged, in this case 0. The last column (column[3]) show the number of original observations in the newly formed cluster.\n", | |
| "\n", | |
| "Each subsequent row indicates what has been merged with what etc.\n", | |
| "\n", | |
| "Scipy supports many different clustinerg methods (single, complete, average etc) and different [distance metrics](http://scipy.github.io/devdocs/generated/scipy.spatial.distance.pdist.html#scipy.spatial.distance.pdist) (euclidean, cityblock, minkowski etc). For this example we'll use average linking with a euclidean distance metric.\n", | |
| "\n", | |
| "Since our data frame containing the observartions includes an 'id' in the first row, which is meaningless for the clustering operation, we ignore it.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 134, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "observations = df.iloc[:, 1:]\n", | |
| "cluster = hac.linkage(observations, method='average', metric='euclidean')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Looking at the first 10 clustering operations" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 135, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 5. , 81. , 0. , 2. ],\n", | |
| " [ 46. , 61. , 0. , 2. ],\n", | |
| " [ 32. , 51. , 1. , 2. ],\n", | |
| " [ 14. , 79. , 1.41421356, 2. ],\n", | |
| " [ 53. , 100. , 1.41421356, 3. ],\n", | |
| " [ 72. , 76. , 1.41421356, 2. ],\n", | |
| " [ 84. , 104. , 1.41421356, 4. ],\n", | |
| " [ 4. , 102. , 1.57313218, 3. ],\n", | |
| " [ 12. , 69. , 1.73205081, 2. ],\n", | |
| " [ 19. , 99. , 1.73205081, 2. ]])" | |
| ] | |
| }, | |
| "execution_count": 135, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cluster[:10]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can now use this clustering operation information to produce a dendogram. We specify the labels to be the 'id' values which we ignored for the clusteirng operations, so whe know whcic observations are clustered together" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 139, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9adb1dd2e8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(25, 10))\n", | |
| "plt.title('Hierarchical Clustering Dendrogram')\n", | |
| "plt.xlabel('Observation')\n", | |
| "plt.ylabel('Distance')\n", | |
| "hac.dendrogram(\n", | |
| " cluster,\n", | |
| " leaf_rotation=90., # rotates the x axis labels\n", | |
| " leaf_font_size=12., # font size for the x axis labels\n", | |
| " labels=df.id.values\n", | |
| ");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can see from the dendogram that there are soe outliers which are clustered very late. For example observation '23' as a cluster distance of around 10, observation '3' has a distance of over 16.\n", | |
| "\n", | |
| "We can plot how the distance at which each cluster operation occurs (see chart below), and we can see a very large increase in distance after observation 93. This is telling us that there isn't a lot of similarity between observations at this point.\n", | |
| "\n", | |
| "Looking back at the start of the agglomeration, we can see that the distances get pretty flat fron operation #3 on.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 177, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x7f9ada730470>" | |
| ] | |
| }, | |
| "execution_count": 177, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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REREpA5SUiYiIiJQBSspEREREygAlZSIiIiJlgJIyERERkTJASZmIiIhIGaCkTERERKQM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\nIqIBkpRFRERENECSsoiIiIgG+A+RxgSPnKtDeAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ada760550>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df_ = pd.DataFrame(cluster)\n", | |
| "fig, ax = plt.subplots(figsize=(10,5))\n", | |
| "ax.plot(df_.index, df_.iloc[:, 2])\n", | |
| "ax.set_title('Cluster distance at each cluster aglomeration operation')\n", | |
| "ax.set_xlabel('Cluster operation')\n", | |
| "ax.set_ylabel('Distance')\n", | |
| "obs_high = 93\n", | |
| "obs_low = 3\n", | |
| "ax.text(obs_high, cluster[obs_high, 2], 'large increase at observation ' + \n", | |
| " str(obs_high) + ' -->', horizontalalignment='right')\n", | |
| "ax.text(obs_low, cluster[obs_low, 2], '<-- large increase at observation ' + \n", | |
| " str(obs_low), horizontalalignment='left', verticalalignment='top')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "To learn more about our cluster, and the sub-clusters along the way, Scipy has some other options.\n", | |
| "\n", | |
| "Firstly, we can set different colors for each cluster below a specified threshold distance.\n", | |
| "\n", | |
| "The dendogram below illustrates this wis a cluster distance of '7'. This clearly sholws that the is a very large central 'red' cluster, a reasonable sized 'light blue' cluster on the right, a small 'green' cluster on the left and then the outlier, large distance cluster in 'blue'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 190, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ada9fa208>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(25, 10))\n", | |
| "plt.title('Hierarchical Clustering Dendrogram')\n", | |
| "plt.xlabel('Observation')\n", | |
| "plt.ylabel('Distance')\n", | |
| "hac.dendrogram(\n", | |
| " cluster,\n", | |
| " leaf_rotation=90., # rotates the x axis labels\n", | |
| " leaf_font_size=12., # font size for the x axis labels\n", | |
| " labels=df.id.values,\n", | |
| " color_threshold=7\n", | |
| ");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "If we change the threshold to 5, we get a lot more clusters." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 191, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ada1bf7b8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(25, 10))\n", | |
| "plt.title('Hierarchical Clustering Dendrogram')\n", | |
| "plt.xlabel('Observation')\n", | |
| "plt.ylabel('Distance')\n", | |
| "hac.dendrogram(\n", | |
| " cluster,\n", | |
| " leaf_rotation=90., # rotates the x axis labels\n", | |
| " leaf_font_size=12., # font size for the x axis labels\n", | |
| " labels=df.id.values,\n", | |
| " color_threshold=5\n", | |
| ");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can also simplify the dendogram and only show the last $n$ cluster joins. \n", | |
| "\n", | |
| "We can set the [show_contracted](http://scipy.github.io/devdocs/generated/scipy.cluster.hierarchy.dendrogram.html#scipy.cluster.hierarchy.dendrogram) attribute of the dendogram to 'True' which, with a truncated dendogram, plots the heights of the non-singleton nodes.\n", | |
| "\n", | |
| "And, if we remove the observations, we get the cluster sizes in brackets if a cluster was truncated.\n", | |
| "\n", | |
| "Trying this for the last 10 cluster joins we get the following dendogram." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 192, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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Yv+0uhD6tqtZPs7ySLOhQDwAAAAAAs8guQ+jW2tx9VQgAAAAAALPP7npCAwAA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\nvKILL7xQ27dv1+rVq9Xe3q5UKrXXiuhoNCpjjIL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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ada279588>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(25, 10))\n", | |
| "plt.title('Hierarchical Clustering Dendrogram')\n", | |
| "plt.xlabel('Observation')\n", | |
| "plt.ylabel('Distance')\n", | |
| "hac.dendrogram(\n", | |
| " cluster,\n", | |
| " truncate_mode='lastp', # truncate based on the last 'p' clusters\n", | |
| " p=10,\n", | |
| " leaf_rotation=90., # rotates the x axis labels\n", | |
| " leaf_font_size=12., # font size for the x axis labels\n", | |
| " show_contracted = True\n", | |
| ");" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "It's aparent from this dendogram, that there is a really dense cluster in the middle, similar to the red cluster we saw above when we set the distance threshold to '7'." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The dendogram is the final fully agglomerated cluster where all points are clustered and we therefore have **one** cluster.\n", | |
| "\n", | |
| "We've already seen that the distance between clusters jumps sharply at one point, and this really indicates that the relationship between things being clustered is pretty meaningless. \n", | |
| "\n", | |
| "However, what this heirarchical cluster allows us to do is effectively draw a horizonal line across the dendogram and produce 3, 4, or however many clusters we think make sense to differentiate observation groups. This is simllar to what has been shown above with coloring etc\n", | |
| "\n", | |
| "We'll get to some better ways of doing this below, but a simple was is to specify a distance cutoff at which we want to stop clustering. If we draw a horizontal line across the dendogram at a certain distance, the number of cluster branches the line cuts is the number of clusters we would get at that distance.\n", | |
| "\n", | |
| "Drawing a red line at a distance of 8 in on the dendogram below would give us four clusters." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 265, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.collections.PathCollection at 0x7f9ad81395f8>" | |
| ] | |
| }, | |
| "execution_count": 265, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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Mx/UCAMxatdZ2P6BqXZLfyqB1RyXZluRPW2v/bY93WnVEko8l+eUkdyb5aJKP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\n1/r167V+/Xq98cYbSiaT0hSppaVF48aN04QJEzR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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ad8342cc0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(25, 10))\n", | |
| "plt.title('Hierarchical Clustering Dendrogram')\n", | |
| "plt.xlabel('Observation')\n", | |
| "plt.ylabel('Distance')\n", | |
| "hac.dendrogram(\n", | |
| " cluster,\n", | |
| " truncate_mode='lastp', # truncate based on the last 'p' clusters\n", | |
| " p=10,\n", | |
| " leaf_rotation=90., # rotates the x axis labels\n", | |
| " leaf_font_size=12., # font size for the x axis labels\n", | |
| " show_contracted = True\n", | |
| ");\n", | |
| "x_ticks = cluster[:,0] \n", | |
| "plt.plot(x_ticks, [8 for x in range(len(x_ticks))], linewidth=1, color='r')\n", | |
| "plt.scatter([5, 15, 30, 67.5], [8, 8, 8, 8],s=200, color='r', alpha=0.50)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "And we can see the same on the whole dendogram" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 255, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.collections.PathCollection at 0x7f9ad8845b00>" | |
| ] | |
| }, | |
| "execution_count": 255, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ad8910dd8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(25, 10))\n", | |
| "plt.title('Hierarchical Clustering Dendrogram')\n", | |
| "plt.xlabel('Observaation')\n", | |
| "plt.ylabel('Distance')\n", | |
| "hac.dendrogram(\n", | |
| " cluster,\n", | |
| " leaf_rotation=90., # rotates the x axis labels\n", | |
| " leaf_font_size=12., # font size for the x axis labels\n", | |
| " labels=df.id.values,\n", | |
| " color_threshold=8\n", | |
| ");\n", | |
| "plt.plot([0,cluster[0:].max()*10], [8, 8], linewidth=2, color='r')\n", | |
| "plt.scatter([5, 15, 32, 637], [8, 8, 8, 8],s=200, color='r', alpha=0.50)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "There is a [wikipedia article](https://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set) about how to determine the number of clusters to use, and there are many different ways. All 'automated' methods give you an indication of the number to use but you should still use your own judgetment.\n", | |
| "\n", | |
| "A common method is the 'elbow' method or scree plot. We can quite easily calucalte this from out cluster information.\n", | |
| "\n", | |
| "First we plot the distance between clusters and the number of clusters in reverse. To make this clearer, looking at our dendogram, we ended up with 'one' cluster at a distance of just over 16. The second last cluster was at a distance of just over 10.\n", | |
| "\n", | |
| "We can sort our array backwards, and look at the last 10 clustering operations, using the following notation." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 279, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 2. , 197. , 16.27662276, 100. ],\n", | |
| " [ 22. , 196. , 10.08077354, 99. ],\n", | |
| " [ 193. , 195. , 9.2936884 , 98. ],\n", | |
| " [ 192. , 194. , 7.47018091, 95. ],\n", | |
| " [ 181. , 191. , 6.58061471, 11. ],\n", | |
| " [ 34. , 185. , 6.50266174, 3. ],\n", | |
| " [ 189. , 190. , 6.3315016 , 84. ],\n", | |
| " [ 170. , 178. , 5.9135046 , 6. ],\n", | |
| " [ 186. , 188. , 5.8551797 , 45. ],\n", | |
| " [ 184. , 187. , 5.71845925, 39. ]])" | |
| ] | |
| }, | |
| "execution_count": 279, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "cluster[::-1][:10]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The second thing we want to look at is how fast the distance changes bewtwen clustering operations. This gives us additional information on where we are getting the most or least improvement from continuing or stopping clustering" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 285, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x7f9ad3f701d0>" | |
| ] | |
| }, | |
| "execution_count": 285, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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ZcMSyiyIiIiLA8VvOyoC8ml4Cwqy1wY1VsSPqUaeWs3c3vsuq1FU8PPHhJqhV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\ndv9aIkMimXza5OM2NReWFpKWm0aXFl1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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ad3edaba8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, ax = plt.subplots(figsize=(10,5))\n", | |
| "x_ticks = range(len(cluster))\n", | |
| "ax.plot(x_ticks, cluster[::-1, 2]) # plot the distances and clusters backwards\n", | |
| "ax.plot(x_ticks[2:], np.diff(cluster[::-1, 2], 2)) # change in distance\n", | |
| "cluster_num = 5\n", | |
| "ax.text(cluster_num, cluster[len(cluster)-cluster_num,2], '<-- possible knee point at ' + str(cluster_num) + ' clusters')\n", | |
| "ax.set_title('Scree plot')\n", | |
| "ax.set_xlabel('Clusters')\n", | |
| "ax.set_ylabel('Distance')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Based on this, it looks like five clusters is a good place to stop.\n", | |
| "\n", | |
| "Now we need to figure out which observations are in which clusters. We can do this using the [Scipy flcuster](http://scipy.github.io/devdocs/generated/scipy.cluster.hierarchy.fcluster.html#scipy.cluster.hierarchy.fcluster) method.\n", | |
| "\n", | |
| "This method takes our cluster array as input, as well as the number of clusters, and who we want the clusters formed. In our case we use 'maxclust' because we have specified the maximum number of cluster we want." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 293, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "array([2, 2, 5, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4,\n", | |
| " 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,\n", | |
| " 2, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2,\n", | |
| " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2,\n", | |
| " 3, 2, 2, 2, 2, 2, 3, 2], dtype=int32)" | |
| ] | |
| }, | |
| "execution_count": 293, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "from scipy.cluster.hierarchy import fcluster\n", | |
| "cluster_num = 5\n", | |
| "cluster_id = fcluster(cluster, cluster_num, criterion='maxclust')\n", | |
| "cluster_id" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now, we can add this data back to our original data frame so we can identify each observation with its cluster." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 294, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>id</th>\n", | |
| " <th>reliab</th>\n", | |
| " <th>time</th>\n", | |
| " <th>av_br</th>\n", | |
| " <th>av_spec</th>\n", | |
| " <th>price</th>\n", | |
| " <th>credit</th>\n", | |
| " <th>av_pay</th>\n", | |
| " <th>return</th>\n", | |
| " <th>warranty</th>\n", | |
| " <th>talk_dir</th>\n", | |
| " <th>cluster</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>b'1'</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>b'2'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>b'3'</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>b'4'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>b'5'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " id reliab time av_br av_spec price credit av_pay return \\\n", | |
| "0 b'1' 8.0 8.0 6.0 7.0 7.0 5.0 8.0 7.0 \n", | |
| "1 b'2' 9.0 9.0 6.0 7.0 6.0 4.0 1.0 5.0 \n", | |
| "2 b'3' 1.0 2.0 5.0 5.0 3.0 6.0 8.0 3.0 \n", | |
| "3 b'4' 9.0 9.0 9.0 9.0 9.0 7.0 1.0 9.0 \n", | |
| "4 b'5' 9.0 9.0 9.0 9.0 9.0 7.0 6.0 8.0 \n", | |
| "\n", | |
| " warranty talk_dir cluster \n", | |
| "0 7.0 8.0 2 \n", | |
| "1 7.0 8.0 2 \n", | |
| "2 3.0 1.0 5 \n", | |
| "3 9.0 9.0 2 \n", | |
| "4 8.0 9.0 2 " | |
| ] | |
| }, | |
| "execution_count": 294, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df['cluster'] = cluster_id\n", | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "So what observations are in what cluster, and how many are in each etc?\n", | |
| "\n", | |
| "Lets have a look" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 319, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "2 84\n", | |
| "3 11\n", | |
| "1 3\n", | |
| "5 1\n", | |
| "4 1\n", | |
| "Name: cluster, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 319, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.cluster.value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 324, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.text.Text at 0x7f9ad3b1eac8>" | |
| ] | |
| }, | |
| "execution_count": 324, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ad3b935f8>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "fig, ax = plt.subplots(figsize=(5,5))\n", | |
| "cluster_counts = df.cluster.value_counts()\n", | |
| "ax.bar(cluster_counts.index-0.4, cluster_counts.values)\n", | |
| "ax.set_title('Observations per cluster')\n", | |
| "ax.set_xlabel('Cluster number')\n", | |
| "ax.set_ylabel('Number of observations')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "So our clusters aren't very evenly distributed but that's not unusual.\n", | |
| "\n", | |
| "So what are the simlarities in these groups. For that, let looks go back to descriptive statistics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 335, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>reliab</th>\n", | |
| " <th>1.0</th>\n", | |
| " <th>6.0</th>\n", | |
| " <th>7.0</th>\n", | |
| " <th>8.0</th>\n", | |
| " <th>9.0</th>\n", | |
| " <th>All</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>cluster</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>-</td>\n", | |
| " <td>6</td>\n", | |
| " <td>-</td>\n", | |
| " <td>8</td>\n", | |
| " <td>-</td>\n", | |
| " <td>7.333333</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>9</td>\n", | |
| " <td>8.654762</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>9</td>\n", | |
| " <td>8.363636</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>9</td>\n", | |
| " <td>9.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>-</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>All</th>\n", | |
| " <td>1</td>\n", | |
| " <td>6</td>\n", | |
| " <td>7</td>\n", | |
| " <td>8</td>\n", | |
| " <td>9</td>\n", | |
| " <td>8.510000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "reliab 1.0 6.0 7.0 8.0 9.0 All\n", | |
| "cluster \n", | |
| "1 - 6 - 8 - 7.333333\n", | |
| "2 - - 7 8 9 8.654762\n", | |
| "3 - - 7 8 9 8.363636\n", | |
| "4 - - - - 9 9.000000\n", | |
| "5 1 - - - - 1.000000\n", | |
| "All 1 6 7 8 9 8.510000" | |
| ] | |
| }, | |
| "execution_count": 335, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pd.crosstab(df.cluster, df.reliab, df.reliab, aggfunc='mean', margins=True).fillna('-')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 338, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>av_br</th>\n", | |
| " <th>av_pay</th>\n", | |
| " <th>av_spec</th>\n", | |
| " <th>credit</th>\n", | |
| " <th>price</th>\n", | |
| " <th>reliab</th>\n", | |
| " <th>return</th>\n", | |
| " <th>talk_dir</th>\n", | |
| " <th>time</th>\n", | |
| " <th>warranty</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>cluster</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>5.333333</td>\n", | |
| " <td>6.666667</td>\n", | |
| " <td>7.333333</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>3.666667</td>\n", | |
| " <td>7.333333</td>\n", | |
| " <td>4.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>6.964286</td>\n", | |
| " <td>3.761905</td>\n", | |
| " <td>7.571429</td>\n", | |
| " <td>6.690476</td>\n", | |
| " <td>7.928571</td>\n", | |
| " <td>8.654762</td>\n", | |
| " <td>7.511905</td>\n", | |
| " <td>8.523810</td>\n", | |
| " <td>8.666667</td>\n", | |
| " <td>8.071429</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>5.454545</td>\n", | |
| " <td>1.363636</td>\n", | |
| " <td>6.181818</td>\n", | |
| " <td>3.727273</td>\n", | |
| " <td>6.636364</td>\n", | |
| " <td>8.363636</td>\n", | |
| " <td>5.727273</td>\n", | |
| " <td>7.909091</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>6.545455</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " <td>9.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>6.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " av_br av_pay av_spec credit price reliab return \\\n", | |
| "cluster \n", | |
| "1 5.000000 3.000000 5.000000 5.333333 6.666667 7.333333 5.000000 \n", | |
| "2 6.964286 3.761905 7.571429 6.690476 7.928571 8.654762 7.511905 \n", | |
| "3 5.454545 1.363636 6.181818 3.727273 6.636364 8.363636 5.727273 \n", | |
| "4 1.000000 1.000000 9.000000 1.000000 5.000000 9.000000 5.000000 \n", | |
| "5 5.000000 8.000000 5.000000 6.000000 3.000000 1.000000 3.000000 \n", | |
| "\n", | |
| " talk_dir time warranty \n", | |
| "cluster \n", | |
| "1 3.666667 7.333333 4.000000 \n", | |
| "2 8.523810 8.666667 8.071429 \n", | |
| "3 7.909091 8.000000 6.545455 \n", | |
| "4 9.000000 9.000000 9.000000 \n", | |
| "5 1.000000 2.000000 3.000000 " | |
| ] | |
| }, | |
| "execution_count": 338, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "pd.pivot_table(df,index=['cluster'], aggfunc='mean')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 362, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x7f9ad2be1da0>" | |
| ] | |
| }, | |
| "execution_count": 362, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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oPXkpJYGN6JqOWHhO/sRCnaSU7DxxwqfM/f4V1tZS4XCQEdcUbezgxMKz8hf5NkOHkmex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\nvTmw+51bgE84TuBwKY0BtxFOUlwS3TN1zLKDEKm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2EE5B8fKJv8tV9QT0Huml3e+nbzgh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\n1xobu3w+LdCrvo9Gu52gylQ9vgHv/HlDBDpZXq5ruIPJbYKTzfn4RIS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5zMMJlke1U9funSyVuPj2CtnrXIBmYy+e3AauCslPJ6YCWQcREvEsguFseCfHd3WNUeZZC/arpx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\nILYcxzRMlN0IQjVIXmI58Qt5a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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9ad2ce0ef0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "dfPivot = pd.pivot_table(df,index=['cluster'], aggfunc='mean')\n", | |
| "fig, ax = plt.subplots(figsize=(6,6))\n", | |
| "x_ticks = [x for x in range(len(dfPivot.columns))]\n", | |
| "for i, row in dfPivot.iterrows():\n", | |
| " ax.plot(x_ticks, row.values, linewidth=5, label='Cluster ' + str(i), alpha=0.7)\n", | |
| " \n", | |
| "ax.set_xticks(x_ticks)\n", | |
| "ax.set_xticklabels(dfPivot.columns, rotation=90);\n", | |
| "\n", | |
| "ax.set_title('Average observation score by attribute for each cluster')\n", | |
| "ax.set_ylabel('Average score')\n", | |
| "\n", | |
| "ax.legend(loc=(1.1, .5))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "What can we tell from thios?\n", | |
| "* Cluster 1 appears to be largely middle of the road for most survey attributes\n", | |
| "* Cluster 2 is more positive across the board\n", | |
| "* Cluster 3 appears to value reliability, return, talk_dir, time and warranty far more than other attributes\n", | |
| "* Cluster 4 is a story of extremes with av_spec, reliability, talk_dir, time and warranty all scoring 9's and av_br, av_pay and credit scoreing 1.\n", | |
| "* Cluster 5 is the opposite of most of the others on average" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Because each observation contains 10 attributes, we have 10 dimensions data, so its a little difficult to visualise.\n", | |
| "\n", | |
| "But, we can use the [TSNE algorithm from Scikit Learn](http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE.fit_transform)\n", | |
| "\n", | |
| "[t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding), or t-distributed stochastic neighbor embedding, is an algorithm to reduce multiple dimensional data to fewer dimensions. As outlined on Wikipedia:\n", | |
| "> It is a nonlinear dimensionality reduction technique that is particularly well-suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 385, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.manifold import TSNE" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can reduce it to 2D first. To help us visualise, we can get some categorical colors from [here](http://vrl.cs.brown.edu/color)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 470, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>id</th>\n", | |
| " <th>reliab</th>\n", | |
| " <th>time</th>\n", | |
| " <th>av_br</th>\n", | |
| " <th>av_spec</th>\n", | |
| " <th>price</th>\n", | |
| " <th>credit</th>\n", | |
| " <th>av_pay</th>\n", | |
| " <th>return</th>\n", | |
| " <th>warranty</th>\n", | |
| " <th>talk_dir</th>\n", | |
| " <th>cluster</th>\n", | |
| " <th>tsne_1</th>\n", | |
| " <th>tsne_2</th>\n", | |
| " <th>tsne_3</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>b'1'</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>80.847884</td>\n", | |
| " <td>30.921712</td>\n", | |
| " <td>-10.430252</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>b'2'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>-28.817635</td>\n", | |
| " <td>-20.004174</td>\n", | |
| " <td>-10.362130</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>b'3'</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5</td>\n", | |
| " <td>-52.054020</td>\n", | |
| " <td>-54.912598</td>\n", | |
| " <td>-76.110795</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>b'4'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>-35.160051</td>\n", | |
| " <td>64.856625</td>\n", | |
| " <td>3.847015</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>b'5'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>12.898108</td>\n", | |
| " <td>27.428492</td>\n", | |
| " <td>20.453213</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " id reliab time av_br av_spec price credit av_pay return \\\n", | |
| "0 b'1' 8.0 8.0 6.0 7.0 7.0 5.0 8.0 7.0 \n", | |
| "1 b'2' 9.0 9.0 6.0 7.0 6.0 4.0 1.0 5.0 \n", | |
| "2 b'3' 1.0 2.0 5.0 5.0 3.0 6.0 8.0 3.0 \n", | |
| "3 b'4' 9.0 9.0 9.0 9.0 9.0 7.0 1.0 9.0 \n", | |
| "4 b'5' 9.0 9.0 9.0 9.0 9.0 7.0 6.0 8.0 \n", | |
| "\n", | |
| " warranty talk_dir cluster tsne_1 tsne_2 tsne_3 \n", | |
| "0 7.0 8.0 2 80.847884 30.921712 -10.430252 \n", | |
| "1 7.0 8.0 2 -28.817635 -20.004174 -10.362130 \n", | |
| "2 3.0 1.0 5 -52.054020 -54.912598 -76.110795 \n", | |
| "3 9.0 9.0 2 -35.160051 64.856625 3.847015 \n", | |
| "4 8.0 9.0 2 12.898108 27.428492 20.453213 " | |
| ] | |
| }, | |
| "execution_count": 470, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "model = TSNE(n_components=2)\n", | |
| "\n", | |
| "tsne = model.fit_transform(df.iloc[:, 1:11])\n", | |
| "df['tsne_1'] = tsne[:, 0]\n", | |
| "df['tsne_2'] = tsne[:, 1]\n", | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 471, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.collections.PathCollection at 0x7f9acda3f8d0>" | |
| ] | |
| }, | |
| "execution_count": 471, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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XMlz1Fe1Zr7nmmubrjRs3snHjxkUajiAcHdQLJYx5BWYa5VCbLABWEF645R623XA79WIZ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\n9nfSsrJnvs19S9J50Rp2fvEzPPfX36D3tT2jomtKGSFDyyLb2cYlP/e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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9acd9a6ef0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from matplotlib.pyplot import gca\n", | |
| "cat_colors = [\"#75eab6\", \"#a23a50\", \"#8cb3ab\", \"#0e503e\", \"#d4a07f\"]\n", | |
| "cluster_colors = [cat_colors[x-1] for x in df.cluster.values]\n", | |
| "\n", | |
| "fig, ax = plt.subplots(figsize=(6,6))\n", | |
| "ax.scatter(df.tsne_1, df.tsne_2, c=cluster_colors, s=200, edgecolors=cluster_colors, alpha=0.8, \n", | |
| " label=labels)\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Not the best cluster, but its not too bad. Lets try a 3D view" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 472, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>id</th>\n", | |
| " <th>reliab</th>\n", | |
| " <th>time</th>\n", | |
| " <th>av_br</th>\n", | |
| " <th>av_spec</th>\n", | |
| " <th>price</th>\n", | |
| " <th>credit</th>\n", | |
| " <th>av_pay</th>\n", | |
| " <th>return</th>\n", | |
| " <th>warranty</th>\n", | |
| " <th>talk_dir</th>\n", | |
| " <th>cluster</th>\n", | |
| " <th>tsne_1</th>\n", | |
| " <th>tsne_2</th>\n", | |
| " <th>tsne_3</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>b'1'</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>-7.195483</td>\n", | |
| " <td>-15.876501</td>\n", | |
| " <td>19.884048</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>b'2'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>21.337158</td>\n", | |
| " <td>1.075529</td>\n", | |
| " <td>-38.924603</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>b'3'</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>5</td>\n", | |
| " <td>26.833158</td>\n", | |
| " <td>64.263651</td>\n", | |
| " <td>64.244463</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>b'4'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>-13.745373</td>\n", | |
| " <td>31.417404</td>\n", | |
| " <td>-1.388042</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>b'5'</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>7.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>2</td>\n", | |
| " <td>-22.983281</td>\n", | |
| " <td>38.457049</td>\n", | |
| " <td>17.151066</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " id reliab time av_br av_spec price credit av_pay return \\\n", | |
| "0 b'1' 8.0 8.0 6.0 7.0 7.0 5.0 8.0 7.0 \n", | |
| "1 b'2' 9.0 9.0 6.0 7.0 6.0 4.0 1.0 5.0 \n", | |
| "2 b'3' 1.0 2.0 5.0 5.0 3.0 6.0 8.0 3.0 \n", | |
| "3 b'4' 9.0 9.0 9.0 9.0 9.0 7.0 1.0 9.0 \n", | |
| "4 b'5' 9.0 9.0 9.0 9.0 9.0 7.0 6.0 8.0 \n", | |
| "\n", | |
| " warranty talk_dir cluster tsne_1 tsne_2 tsne_3 \n", | |
| "0 7.0 8.0 2 -7.195483 -15.876501 19.884048 \n", | |
| "1 7.0 8.0 2 21.337158 1.075529 -38.924603 \n", | |
| "2 3.0 1.0 5 26.833158 64.263651 64.244463 \n", | |
| "3 9.0 9.0 2 -13.745373 31.417404 -1.388042 \n", | |
| "4 8.0 9.0 2 -22.983281 38.457049 17.151066 " | |
| ] | |
| }, | |
| "execution_count": 472, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "model = TSNE(n_components=3)\n", | |
| "\n", | |
| "tsne = model.fit_transform(df.iloc[:, 1:11])\n", | |
| "df['tsne_1'] = tsne[:, 0]\n", | |
| "df['tsne_2'] = tsne[:, 1]\n", | |
| "df['tsne_3'] = tsne[:, 2]\n", | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 477, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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Y5CZpPETRAzQXgS+TL8tjVJUqrTxJmv/k+/5EE0TqAVQdZr2u41KhWXjBwgKIVV/y36cI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lhPBSMYtJ8W3emcTsnQ4d28blZWhdNFc2INi37qpcRZ3rtVCc4SRa9ATKJSoyZq9CJNV+3uJ4rH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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9acb0cc5c0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from mpl_toolkits.mplot3d import Axes3D\n", | |
| "\n", | |
| "fig = plt.figure(figsize=(10,10))\n", | |
| "ax = fig.add_subplot(111, projection='3d',)\n", | |
| "cluster_colors = [cat_colors[x-1] for x in df.cluster.values]\n", | |
| "ax.scatter(df.tsne_1.values, df.tsne_2.values, df.tsne_3.values, \n", | |
| " c=cluster_colors, marker='o', s=100, linewidth=0, alpha=0.3)\n", | |
| "ax.view_init(30, 30)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| " Not really much better but you get the idea" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "anaconda-cloud": {}, | |
| "kernelspec": { | |
| "display_name": "Python [Root]", | |
| "language": "python", | |
| "name": "Python [Root]" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.5.2" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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