diff --git a/examples/gender_bias_example.ipynb b/examples/gender_bias_example.ipynb new file mode 100644 index 000000000..9001a794b --- /dev/null +++ b/examples/gender_bias_example.ipynb @@ -0,0 +1,741 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data visualization example\n", + "\n", + "In a [recent blog post](https://www.allendowney.com/blog/2019/01/30/data-visualization-for-academics/), I showed figures from [a recent paper](https://osf.io/preprints/socarxiv/j2tw9/) and invited readers to redesign them to communicate their message more effectively.\n", + "\n", + "This notebook shows one way we might redesign the figures. At the same time, it demonstrates a simple use of a Pandas MultiIndex." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The study reports the distribution of student evaluation scores for instructors under eight conditions. At the top level, they report scores from evaluations with a 10-point of 6-points scale." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "scale = ['10-point', '6-point']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the next level, they distinguish fields of study as \"least\" or \"most\" male-dominated." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "area = ['LeastMaleDominated', 'MostMaleDominated']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And they distinguish between male and female instructors." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "instructor = ['Male', 'Female']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can assemble those levels into a MultiIndex like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MultiIndex(levels=[['10-point', '6-point'], ['LeastMaleDominated', 'MostMaleDominated'], ['Female', 'Male']],\n", + " labels=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1], [1, 0, 1, 0, 1, 0, 1, 0]],\n", + " names=['Scale', 'Area', 'Instructor'])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index = pd.MultiIndex.from_product([scale, area, instructor], \n", + " names=['Scale', 'Area', 'Instructor'])\n", + "index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each of these eight conditions, the original paper reports the entire distribution of student evaluation scores. To make a simpler and clearer visualization of the results, I am going to present a summary of these distributions.\n", + "\n", + "I could take the mean of each distribution, and that would show the effect. But to make it even clearer, I will use the fraction of \"top\" scores, meaning a 9 or 10 on the 10-point scale and a 6 on the 6-point scale. \n", + "\n", + "Now, to get the data, I used the figures from the paper and estimated numbers by eye. **So these numbers are only approximate!**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " TopScore%\n", + "Area Instructor \n", + "LeastMaleDominated Male 60\n", + " Female 60\n", + "MostMaleDominated Male 54\n", + " Female 38" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc['10-point']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To extract subsets at other levels, we can use `xs`. This example takes a cross-section of the second level." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " TopScore%\n", + "Scale Instructor \n", + "10-point Male 54\n", + " Female 38\n", + "6-point Male 41\n", + " Female 41" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.xs('MostMaleDominated', level='Area')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example takes a cross-section of the third level." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " TopScore%\n", + "Scale Area \n", + "10-point LeastMaleDominated 60\n", + " MostMaleDominated 54\n", + "6-point LeastMaleDominated 43\n", + " MostMaleDominated 41" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.xs('Male', level='Instructor')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, now to think about presenting the data. At the top level, the 10-point scale and the 6-point scale are different enough that I want to put them on different axes. So I'll start by splitting the data at the top level." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " TopScore%\n", + "Area Instructor \n", + "LeastMaleDominated Male 60\n", + " Female 60\n", + "MostMaleDominated Male 54\n", + " Female 38" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ten = df.loc['10-point']\n", + "ten" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the primary thing I want the reader to see is a discrepancy in percentages. For comparison of two or more values, a bar plot is often a good choice.\n", + "\n", + "As a starting place, I'll try the Pandas default for showing a bar plot of this data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ten.unstack().plot(kind='bar');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As defaults go, that's not bad. From this figure it is immediately clear that there is a substantial difference in scores between male and female instructors in male-dominated areas, and no difference in other areas.\n", + "\n", + "The following function cleans up some of the details in the presentation." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def make_bar_plot(df):\n", + " # make the plot (and set the rotation of the x-axis)\n", + " df.unstack().plot(kind='bar', rot=0);\n", + " \n", + " # clean up the legend\n", + " plt.gca().legend(['Female', 'Male'])\n", + " \n", + " # label the y axis\n", + " plt.ylabel('Fraction of instructors getting top scores')\n", + " \n", + " # set limits on the 7-axis (in part to make room for the legend)\n", + " plt.ylim([0, 75])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the results for the 10-point scale." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_bar_plot(ten)\n", + "plt.title('10-point scale');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here are the results for the six-point scale, which show clearly that the effect disappears when a 6-point scale is used (at least in this experiment)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "six = df.loc['6-point']\n", + "make_bar_plot(six)\n", + "plt.title('6-point scale');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Presenting two figures might be the best option, but in my challenge I asked for a single figure.\n", + "\n", + "Here's a version that uses Pandas defaults with minimal customization." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.unstack().plot(kind='barh', xlim=[0, 68]);\n", + "plt.gca().legend(['Female', 'Male'])\n", + "plt.xlabel('Fraction of instructors getting top scores');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a little tuning, this could be a good choice. It clearly shows that there is only a substantial difference in one of the four conditions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}