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May 21, 2017 07:30
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Paul_G_DataScience_Blog
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Pandas.DataFrame.Sum() - Which axis is which??" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Pandas is a great tool for working with datasets in Python. There are a large number of ways to import data, explore data, engineer data, and cleanse data. Pandas also comes with a great set of wrappers for MatPlotLib, the core plotting library of the data science world (for now).\n", | |
| "\n", | |
| "For newbies, pandas can be confusing as hell. Unless you've already spent a lot of time dealing with data, and concerning yourself with indices, columns, and all of the functionality of Pandas that relies on basic understanding of what is meant by these words and the numbers that represent them, it can be difficult to grasp whats going on.\n", | |
| "\n", | |
| "I am no exception to this. I find myself repeating '0 is row, 1 is column, 0 is row, 1 is column', ad nauseum. And yet still, I find situations where I am newly confused on what they mean by 0 and 1." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Introducing pandas.crosstab() ...okay its new to me!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Before I get to where I've been confused, let me introduce my most recent pandas discovery: The pd.crosstab function. This function is great for doing quick cross tabulations of data for quick comparisons. It can also provide a useful new data structure on which you can create useful plots." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from pandas import DataFrame\n", | |
| "from pandas import crosstab" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Lets say we've got some data:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "data = DataFrame({'Survived':[1,1,1,0,0,1,0,1,1,0,1,1,1,0], 'Sex': ['M','F','M','M','F','M','F','F','F','M','F','M','F','M']})" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "1 9\n", | |
| "0 5\n", | |
| "Name: Survived, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "data.Survived.value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "F 7\n", | |
| "M 7\n", | |
| "Name: Sex, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "data.Sex.value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "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>Sex</th>\n", | |
| " <th>Survived</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>M</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>F</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>F</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>M</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>M</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Sex Survived\n", | |
| "0 M 1\n", | |
| "1 F 1\n", | |
| "2 M 1\n", | |
| "3 M 0\n", | |
| "4 F 0\n", | |
| "5 M 1\n", | |
| "6 F 0\n", | |
| "7 F 1\n", | |
| "8 F 1\n", | |
| "9 M 0\n", | |
| "10 F 1\n", | |
| "11 M 1\n", | |
| "12 F 1\n", | |
| "13 M 0" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Here we've got 14 rows of toy data inspired from the 'Titanic' data. There are two columns, 'Sex' and 'Survived'. Lets say we want to know the total number of males and females that survived. We can use crosstab to accomplish this.\n", | |
| "\n", | |
| "Our labels are:\n", | |
| "\n", | |
| "`0 = Perished` \n", | |
| "`1 = Survived` \n", | |
| "\n", | |
| "`F = Female \n", | |
| "M = Male `" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Survived</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sex</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>F</th>\n", | |
| " <td>2</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>M</th>\n", | |
| " <td>3</td>\n", | |
| " <td>4</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Survived 0 1\n", | |
| "Sex \n", | |
| "F 2 5\n", | |
| "M 3 4" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tabs = crosstab(data.Sex, data.Survived)\n", | |
| "tabs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Here we see that two Females and three males perished, while five females and four males survived. From here we can make some plots." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# to make plots in the notebook\n", | |
| "%matplotlib inline \n", | |
| "from matplotlib import style # to make our plots nice looking\n", | |
| "style.use('fivethirtyeight') # random choice- 538 looks cool\n", | |
| "\n", | |
| "### Note styles are only availabe in recent versions of matplotlib" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x1af740b8a20>" | |
| ] | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1af714b4f98>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "tabs.plot(kind='bar')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This looks cool, but lets try stacking." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1af7418bc88>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "tabs.plot(kind='bar', stacked=True); #add semicolon to remove message printout" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This is neat! \n", | |
| "\n", | |
| "You may have noticed at the beginning that we were dealing with the same number of males and females (7 and 7), so its easy to make direct comparison between the two. What if these two groups weren't the same? Say we have 56 males and 137 females. How could we compare them?\n", | |
| "\n", | |
| "Lets rework this and say we are doing cross tab on more columns. We'll use the same data as above, but we'll throw in a third category - unknown sex.\n", | |
| "\n", | |
| "`Unk = Unknown`\n", | |
| "\n", | |
| "We could have also changed the number of males or famales in the previous example." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "data2 = DataFrame({'Survived':[1,1,1,0,0,1,0,1,1,0,1,1,1,0], \n", | |
| " 'Sex': ['M','F','Unk','M','Unk','M','F','F','F','Unk','F','M','F','M'], })" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "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>Sex</th>\n", | |
| " <th>Survived</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>Unk</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>M</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>Unk</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>F</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>Unk</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>M</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>F</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>M</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Sex Survived\n", | |
| "0 M 1\n", | |
| "1 F 1\n", | |
| "2 Unk 1\n", | |
| "3 M 0\n", | |
| "4 Unk 0\n", | |
| "5 M 1\n", | |
| "6 F 0\n", | |
| "7 F 1\n", | |
| "8 F 1\n", | |
| "9 Unk 0\n", | |
| "10 F 1\n", | |
| "11 M 1\n", | |
| "12 F 1\n", | |
| "13 M 0" | |
| ] | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "data2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Survived</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sex</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>F</th>\n", | |
| " <td>1</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>M</th>\n", | |
| " <td>2</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Unk</th>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Survived 0 1\n", | |
| "Sex \n", | |
| "F 1 5\n", | |
| "M 2 3\n", | |
| "Unk 2 1" | |
| ] | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tab2 = crosstab(data2.Sex, data2.Survived)\n", | |
| "tab2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1af7414cc50>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "tab2.plot(kind='bar', stacked=True);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now we can plainly see that we are comparing three different groups with uneven numbrs of members, and here comes the point of this post.\n", | |
| "\n", | |
| "If I want to normalize all of my data, so that I can make it directly comparible, I can put them all on a scale from 0 to 1. This will basically allow me to then interpret the proportions of individuals who have survived or not as a percentage of their total group size.\n", | |
| "\n", | |
| "Lets see what this looks like." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#I redid the plots using this cell to make the plots wider\n", | |
| "import matplotlib as plt\n", | |
| "plt.rcParams['figure.figsize'] = (10, 4)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1af7411f630>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "normalized = tab2.div(tab2.sum(1).astype(float), axis=0);\n", | |
| "normalized.plot(kind='bar', stacked = True);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Great! So now we are looking a proportions of each group that survived or didn't survive. \n", | |
| "\n", | |
| "This is really useful information because we can see that a greater proportion of females were able to survive which indicates some connection between the sex of an individual and their survival aboard the Titanc. If you were trying to classify survival across passengers for example, you may want to include Sex as a feature for determining the survival label." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### But back to the main question - what in the HECK is going on in this code:\n", | |
| "\n", | |
| "`normalized = tab2.div(tab2.sum(1).astype(float), axis=0);`\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Lets break it down.\n", | |
| "\n", | |
| "First, `tab2.div()`. This is a handy method for dividing the values of a dataframe by the values of something else." | |
| ] | |
| }, | |
| { | |
| "cell_type": "raw", | |
| "metadata": {}, | |
| "source": [ | |
| "The doc string looks like this.\n", | |
| "\n", | |
| "tab2.div(other, axis='columns', level=None, fill_value=None)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "'Other' is the value that you are dividing by and it can a Series, DataFrame, or even a constant. In this case, we are dividing by a constant: `tab2.sum(1)`" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### `For the full docs:`\n", | |
| " \n", | |
| "`other : Series, DataFrame, or constant` \n", | |
| "`axis : {0, 1, ‘index’, ‘columns’}` \n", | |
| "`For Series input, axis to match Series index on` \n", | |
| "`fill_value : None or float value, default None` \n", | |
| "`Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing` \n", | |
| "`level : int or name` \n", | |
| "`Broadcast across a level, matching Index values on the passed MultiIndex level` " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### With the DataFrame.div() bit sorted, whats the meaning of tab2.sum(1)?\n", | |
| "\n", | |
| "DataFrames and Series have a handy sum function built in.\n", | |
| "\n", | |
| "`Signature: tab2.sum(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)`\n", | |
| "\n", | |
| "`Docstring:`\n", | |
| "`Return the sum of the values for the requested axis`\n", | |
| "\n", | |
| "If you have NaN values, you can set `tab2.sum(skipna=True)`, specify only numeric values, and more.\n", | |
| "In this case, we are only setting the axis.\n", | |
| "\n", | |
| "What does summing each axis look like?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Survived</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sex</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>F</th>\n", | |
| " <td>1</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>M</th>\n", | |
| " <td>2</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Unk</th>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Survived 0 1\n", | |
| "Sex \n", | |
| "F 1 5\n", | |
| "M 2 3\n", | |
| "Unk 2 1" | |
| ] | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tab2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Survived\n", | |
| "0 5\n", | |
| "1 9\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tab2.sum(0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Sex\n", | |
| "F 6\n", | |
| "M 5\n", | |
| "Unk 3\n", | |
| "dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tab2.sum(1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### For me this is where things got confusing. If I sum by row, I should get row sums... right?\n", | |
| "\n", | |
| "`Rows = 0, Columns = 1`\n", | |
| "\n", | |
| "But here, my intution tells me that if I do `tab2.sum('rows')`, I should get the result I see from `tab2.sum('columns')`, and vice versa. In other words for exampmle, if I wanted to know how many females there were, I would want the sum of that row.\n", | |
| "\n", | |
| "### In Pandas, the reverse will actually give you want you want! \n", | |
| "\n", | |
| "To remind ourselves, we are trying to understand:\n", | |
| "\n", | |
| "`normalized = tab2.div(tab2.sum(1).astype(float), axis=0);`\n", | |
| "\n", | |
| "So we are dividing `tab2` elements columnwise along the row axis (axis=0)..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Survived</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sex</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>F</th>\n", | |
| " <td>1</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>M</th>\n", | |
| " <td>2</td>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Unk</th>\n", | |
| " <td>2</td>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Survived 0 1\n", | |
| "Sex \n", | |
| "F 1 5\n", | |
| "M 2 3\n", | |
| "Unk 2 1" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tab2" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "... by the sum of each row. \n", | |
| "\n", | |
| "In longer words: tab2.sum(1) says sum up each row and return a Series (column?) coresponding to the row sums. How this translates to summing along the column axis, I'm not sure. Then `tab2.div(tab2.sum(1).astype(float), axis=0)` says divide each row (axis=0... that one kind of makes sense) by the corresponding row in the tab2.sum(1) object. Since they are row sums, you get proportion/total, which all together always sums to one.\n", | |
| "\n", | |
| "The final output is the normalized table,which looks like this:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>Survived</th>\n", | |
| " <th>0</th>\n", | |
| " <th>1</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Sex</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>F</th>\n", | |
| " <td>0.166667</td>\n", | |
| " <td>0.833333</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>M</th>\n", | |
| " <td>0.400000</td>\n", | |
| " <td>0.600000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Unk</th>\n", | |
| " <td>0.666667</td>\n", | |
| " <td>0.333333</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| "Survived 0 1\n", | |
| "Sex \n", | |
| "F 0.166667 0.833333\n", | |
| "M 0.400000 0.600000\n", | |
| "Unk 0.666667 0.333333" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "normalized" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Sex\n", | |
| "F 1.0\n", | |
| "M 1.0\n", | |
| "Unk 1.0\n", | |
| "dtype: float64" | |
| ] | |
| }, | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "normalized.sum(1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "And we can seethat each row in the normalized data frame crosstabulated data frame sums to one. If we plot these using the stacked barplots we were using before we can clearly see the proportion of each group that survived or perished." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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/rjVr1kzNmzev2dg6rgUAAKBBcv9PqvSfVOP7ufhAuPF9OAounQMAAMAIgiYAAACMIGgC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\nIn8AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1af74540be0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "normalized.plot(kind='bar', stacked=True);" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "So there you have it. \n", | |
| "\n", | |
| "Maybe this is a bug, or maybe yet to grasp a deeper Pandas... but it is currently beyond me why Pandas.DataFrame.sum() works the way it does. \n", | |
| "\n", | |
| "But at least it works, because its really handy!" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.11" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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