AllenDowney/gender_bias_example.ipynb

## gender_bias_example.ipynb
{
 "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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scale</th>\n",
       "      <th>Area</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10-point</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">6-point</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        TopScore%\n",
       "Scale    Area               Instructor           \n",
       "10-point LeastMaleDominated Male               60\n",
       "                            Female             60\n",
       "         MostMaleDominated  Male               54\n",
       "                            Female             38\n",
       "6-point  LeastMaleDominated Male               43\n",
       "                            Female             42\n",
       "         MostMaleDominated  Male               41\n",
       "                            Female             41"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [60, 60, 54, 38, 43, 42, 41, 41]\n",
    "df = pd.DataFrame(data, columns=['TopScore%'], index=index)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract the subset of the data on a 10-point scale, we can use `loc` in the usual way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scale</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10-point</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6-point</th>\n",
       "      <th>Male</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scale</th>\n",
       "      <th>Area</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10-point</th>\n",
       "      <th>LeastMaleDominated</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MostMaleDominated</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6-point</th>\n",
       "      <th>LeastMaleDominated</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MostMaleDominated</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TopScore%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area</th>\n",
       "      <th>Instructor</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LeastMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MostMaleDominated</th>\n",
       "      <th>Male</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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, alpha=0.7);\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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f2Ac1NlAzM6uMPL2Pdq1GIGZm1jJy3T4qaRjZMwEAtRHR8mOrmZlZs8hz++jFZPf/P5dep6cyMzNrA/JcERwODEoD1CBpHPAU7hrCzKxNyNvFRPHtoptWIhAzM2sZea4I/hd4StJksjGL9wPOr2hUZmZWNXnuGhovqRYYQpYIzk0Pi5mZWRuQp7F4UkTMj4gJEXFnRLwhaVI1gjMzs8qr94pAUhdgQ2DzNHiM0qJNgE9XITYzM6uCclVD/w2cQfZP/wk+SQSLgd9XOC4zM6uSehNBRFwGXCbpOxExuooxmZlZFeVpLHYSMLPquP7Ylo4gnzY2cFKlxyw2M7NWzonAzKyda7BqSNKuJYoXAa9ExIrmD8nMzKopz5PFfyAbYnIG2Z1DO6XpHpK+EREPVDA+MzOrsDxVQ3OBwWkg+d2AwcAzwMHALyoYm5mZVUGeRLB9RDxbmImI58gSw5zKhWVmZtWSp2polqTLgRvS/LHAC5LWB5ZXLDIzM6uKPFcEJwEvkj1lfCbZIPQnkSWBAysVmJmZVUeeB8o+AH6VXnW9V25bSXOBJcBKYEVE1EjqDtwI9CVrfzgmIt5pVNRmZtZs8vQ+uo+kiZJekDSn8GrEMQ6MiEERUZPmzwMmRUR/YBIe6czMrEXlaSMYS1Yl9ATZmf3aOgI4IE2PA2qBc5thv2Zm1gR5EsGiiLi3ifsP4AFJAVwZEWOALSJiPkBEzJf0qVIbSjoFOAWgT58+TTy8mZk1JE8imCzpl8BtwIeFwoh4Mse2+0TE6+mf/URJz+cNLCWNMQA1NTWRdzszM2ucPIlgj/SzpqgsgKENbRgRr6efb0m6HdgdeFNSr3Q10At4q5Exm5lZM8pz11CTbhGVtBHQISKWpOlDgIuACcBw4OL0886m7N/MzJpHuaEqT4yIayWdVWp5RFzawL63AG6XVDjO9RFxn6QpwE2SRgCvAkc3LXQzM2sO5a4INko/u5ZY1mCdfeqCYpcS5QuBg3JFZ2ZmFVduqMor0+TfIuKR4mWS9qloVGZmVjV5upgoNVSlh680M2sjyrUR7AXsDfSs006wCdCx0oGZmVl1lGsjWA/YOK1T3E6wGDiqkkGZmVn1lGsjeAh4SNIHEbHaADSSjgZmVzo4MzOrvDxtBMeVKPt+cwdiZmYto1wbwWHA4cCWkn5btGgTwIPWm5m1EeXaCF4HpgLDyHoeLVhC1hupmZm1AeXaCKYD0yVdn9brExGzqhaZmZlVRZ42gs8B04D7ACQNkjSholGZmVnV5EkEo8h6DX0XICKmkQ0zaWZmbUCeRLAiIhZVPBIzM2sRecYjeEbSV4COkvoDpwGPVjYsMzOrljxXBN8BdiQbnWw82ZPFZ1QyKDMzq548A9O8D/wgvczMrI1pMBFIuos1xx9YRPaMwZURsawSgZmZWXXkqRqaA7wHXJVei4E3ge3SvJmZrcPyNBYPjoj9iubvkvRwROwn6dlKBWZmZtWR54qgp6Q+hZk0vXma/agiUZmZWdXkuSL4LvB3SS8BAvoB35K0ETCuksGZmVnl5blr6K/p+YHtyRLB80UNxL9paHtJHckall+LiM9L6gfcAHQHngS+GhG+sjAzayF5qoaIiA8jYnpETGvCXUKnAzOL5n8O/Doi+gPvACMauT8zM2tGuRJBU0nqDfwX8Mc0L2AocEtaZRzwxUrGYGZm5VU0EZBVHX0P+DjN9wDejYjCwDbzgC1LbSjpFElTJU1dsGBBhcM0M2u/GkwEkvZJDcNIOlHSpZK2zrHd54G3IqJ4UBuVWLXuw2pZYcSYiKiJiJqePXs2dDgzM2uiPFcElwPvS9qF7Oz+FeAvObbbBxgmaS5Z4/BQsiuEbpIKjdS9yUZCMzOzFpK3G+oAjgAui4jLgK4NbRQR34+I3hHRFzgOeDAiTgAmA0el1YYDdzYpcjMzaxZ5EsESSd8HTgTuSbeDdl6LY54LnCXpRbI2g7FrsS8zM1tLeR4oOxb4CjAiIt5ITxb/sjEHiYhaoDZNzyEb8czMzFqBsokgnf1fGxEHF8oi4lXytRGYWSsx4uopLR1CLmPXa+kI2qeyVUMRsZKsoXjTKsVjZmZVlqdqaBnwtKSJwNJCYUScVrGozMysavIkgnvSy8zM2qA8nc6Nk7Qe2UA0ALMiYnllwzIzs2rJM1TlAWR9As0lezJ4K0nDI+LhyoZmZmbVkKdq6FfAIRExC0DSdsB4YLdKBmZmZtWR54GyzoUkABARL7B2D5SZmVkrkueKYKqkscA1af4E4Iky65uZ2TokTyL4JnAqcBpZG8HDwO8rGZSZmVVPnkTwjYi4FLi0UCDpdOCyikVlZmZVk6eNYHiJspOaOQ4zM2sh9V4RSDqerLO5fpImFC3qCiysdGBmZlYd5aqGHgXmA5uT3UJasASYUcmgzMyseupNBBHxCvCKpBOA1yNiGYCkDchGFptblQjNzKyi8rQR3MQng88DrARurkw4ZmZWbXkSQaeI+Kgwk6bda7iZWRuRJxEskDSsMCPpCODtyoVkZmbVlOs5AuA6Sb8je6DsX8D/rWhUZmZWNXm6oX4J2FPSxoAiYknlwzIzs2rJ0w31hXXmAYiIixrYrgtZdxTrp+PcEhE/ktQPuAHoDjwJfLW4DcLMzKorTxvB0qLXSuAwoG+O7T4EhkbELsAg4HOS9gR+Dvw6IvoD7wAjmhC3mZk1kzxVQ8UPkyHpEmBCPasXbxfAe2m2c3oFMJTsiWXIBrwZBVyeO2IzM2tWea4I6toQ2CbPipI6SpoGvAVMBF4C3o2IFWmVecCW9Wx7iqSpkqYuWLCgCWGamVkeedoIniY7kwfoCPQEyrYPFETESmCQpG7A7cAOpVarZ9sxwBiAmpqakuuYmdnay3P76OeLplcAbxad0ecSEe9KqgX2BLpJ6pT20Rt4vTH7MjOz5lW2akhSB+CeiHglvV7LmwQk9UxXAoX+iQ4GZgKTgaPSasOBO5scvZmZrbWyiSAiPgamS+rThH33AiZLmgFMASZGxN3AucBZkl4EegBjm7BvMzNrJnmqhnoBz0p6nOwWUgAiYlj9m0BEzAAGlyifA+zeyDjNzKxC8iSC/6l4FGZm1mLyJILDI+Lc4gJJPwceqkxIZmZWTXmeI/hsibLDmjsQMzNrGeXGLP4m8C1g29TgW9AVeKTSgZmZWXWUqxq6HrgX+F/gvKLyJRHx74pGZWZmVVNv1VBELIqIucAPgTfSGMb9gBMLzweYmdm6L08bwa3ASkn/QXbPfz+yqwUzM2sD8iSCj9PTxF8GfhMRZ5I9W2BmZm1AnkSwXNLxZMNT3p3KOlcuJDMzq6Y8ieBrwF7ATyPi5TTC2LWVDcvMzKolz8A0zwGnFc2/DFxcyaDMzKx68oxHsA/ZKGJbp/VFNgBZrsFpzMysdcvTxcRY4EzgCbIxi83MrA3JkwgWRcS9FY/EzMxaRJ5EMFnSL4HbgA8LhRHxZMWiMjOzqsmTCPZIP2uKygIY2vzhmJlZteW5a+jAagRiZmYto1zvoydGxLWSziq1PCIurVxYZmZWLeWuCDZKP7tWIxAzM2sZ9SaCiLgy/fRQlWZmbVieLiaaRNJWkiZLminpWUmnp/LukiZKmp1+blapGMzMrGEVSwTACuC7EbEDsCdwqqTPkA1yMyki+gOTWH3QGzMzq7J6E0HRGfw+TdlxRMwvPGsQEUuAmcCWwBHAuLTaOOCLTdm/mZk1j3JXBF9LP0ev7UEk9QUGA/8EtoiI+ZAlC+BT9WxziqSpkqYuWLBgbUMwM7N6lLtraKakuUDPOoPXFzqdG5jnAJI2Jhvl7IyIWCwpV2ARMQYYA1BTUxO5NjIzs0Yrd9fQ8ZL+D3A/MKwpO5fUmSwJXBcRt6XiNyX1ioj5knoBbzVl32Zm1jzKNhZHxBsRsQswn+x5gq7A62kg+7KUnfqPBWbWefhsAjA8TQ8H7mxK4GZm1jzyjEewP/AXYC5ZtdBWkoZHxMMNbLoP8FXgaUnTUtn5ZIPa3CRpBPAqcHQTYzczs2aQp9O5S4FDImIWgKTtgPHAbuU2ioi/kyWOUg5qTJBmZlY5eZ4j6FxIAgAR8QIevN7MrM3Ic0UwVdJY4Jo0fwLZaGVmZtYG5EkE3wROJRvAXsDDwB8qGZSZmVVPnvEIPiRrJ3C302ZmbVAl+xoyM7N1gBOBmVk750RgZtbO5XmgbDvgHGDr4vUjwoPXm5m1AXnuGroZuAK4ClhZ2XDMzKza8iSCFRFxecUjMTOzFpGnjeAuSd+S1CsNM9ldUveKR2ZmZlWR54qg0FPoOUVlAWzT/OGYmVm15XmgrF81AjEzs5aR566hzmTdTOyXimqBKyNieQXjMjOzKslTNXQ5WW+jhf6FvprKRlYqKDMzq548iWBIGqWs4EFJ0ysVkJmZVVeeu4ZWStq2MCNpG/w8gZlZm5HniuAcYLKkOWTdUG8NfK2iUZmZWdXkuWtokqT+wACyRPB86prazMzagHoTgaShEfGgpC/XWbStJCLitgrHZmZmVVDuimB/4EHgCyWWBVA2EUj6E/B54K2I2CmVdQduBPoCc4FjIuKdRkdtZmbNpt5EEBE/SpMXRcTLxcsk5XnI7Grgd8BfisrOAyZFxMWSzkvz5zYqYjMza1Z57hq6tUTZLQ1tFBEPA/+uU3wEMC5NjwO+mOP4ZmZWQeXaCLYHdgQ2rdNOsAnQpYnH2yIi5gNExHxJnypz/FOAUwD69OnTxMOZmVlDyrURDCCr4+/G6u0ES4CvVzIogIgYA4wBqKmpiUofz8ysvSrXRnAncKekvSLisWY63puSeqWrgV7AW820XzMza6I8bQTfkNStMCNps3RHUFNM4JNurYcDdzZxP2Zm1kzyJIKBEfFuYSbd7jm4oY0kjQceAwZImidpBHAx8FlJs4HPpnkzM2tBebqY6CBps8L9/ulZgDxPJB9fz6KDGhGfmZlVWJ5E8CvgUUmFW0aPBn5auZDMzKya8pzZ/0XSE8CBZH0NfTkinqt4ZGZmVhV5rgiIiGclLSA9PyCpT0S8WtHIzMysKhpsLJY0LDXuvgw8RNZH0L0VjsvMzKokz11DPwb2BF5IA9kfBDxS0ajMzKxq8iSC5RGxkOzuoQ4RMRkYVOG4zMysSvK0EbwraWPgYeA6SW8BKyoblpmZVUueK4IjgPeBM4H7gJcoPUaBmZmtg8peEUjqCNwZEQcDH/NJF9JmZtZGlL0iiIiVwPuSNq1SPGZmVmV52giWAU9LmggsLRRGxGkVi8rMzKomTyK4J73MzKwNKjdCWZ+IeDUi3C5gZtaGlWsjuKMwIanUuMVmZtYGlEsEKpreptKBmJlZyyiXCKKeaTMza0PKNRbvImkx2ZXBBmmaNB8RsUnFozMzs4orN3h9x2oGYmZmLSNPFxNmZtaGORGYmbVzLZIIJH1O0ixJL0o6ryViMDOzTNUTQerI7vfAYcBngOMlfabacZiZWaYlrgh2B16MiDkR8RFwA1lX12Zm1gIUUd1HBCQdBXwuIkam+a8Ce0TEt+usdwpwSpodAMyqaqBt2+bA2y0dhFkJ/m42r60jomdDK+XpdK65qUTZGtkoIsYAYyofTvsjaWpE1LR0HGZ1+bvZMlqiamgesFXRfG/g9RaIw8zMaJlEMAXoL6mfpPWA44AJLRCHmZnRAlVDEbFC0reB+4GOwJ8i4tlqx9HOucrNWit/N1tA1RuLzcysdfGTxWZm7ZwTgZlZO+dEkIOk9yq0326SvlU031dSSPpxUdnmkpZL+l0D+zop5zoLJD0labak+yXtvfbvZNX+H12LbU+S9OlGbtNX0jNNPWZbkr431xTNd0q/67ubsK++kr5SNH9A2v+IorLBqezsBvY1Kuc6r0malr6XtzVXbwOSPi3plrXY/gxJGzZymwOa8rm3JCeCltUN+FadsjnA54vmjwaaszH9xogYHBH9gYuB2yTt0Bw7joi1SSonAY1KBLaapcBOkjZI858FXmvivvoCX6lT9jRwbNH8ccD0Ju6/lF9HxKD0vbwReFBSgw9CNSQiXo+Io9ZiF2cAjUoE6yIngiaS1FPSrZKmpNc+qXx3SY+ms+5HJQ1I5TtKejyd9cyQVPhHvG0q+2Xa9QfATEmFh2qOBW5GfbWBAAAH3UlEQVQqOu4XJP0z7f9vkrbIG1tdETGZ7C6NU9J2gyT9I8V3u6TNUnmtpF9LeljSTElD0lnbbEk/KTrue+nnAWmbWyQ9L+k6SUrLLkwxPSNpjDJHATXAdemz2EDSbpIekvREunLplbbfTdJ0SY8Bpzbpl9d23Qv8V5o+HhhfWCCpu6Q70u/2H5IGpvL902c+LX2nupJ9L/8zlZ2ZdvEq0EXSFul3+bl0vML+v55+r9PTd2+Nf56StpV0X/qd/j9J25d6ExFxI/AAKRlJOijF9rSkP0laP5XPlfQzSY9Jmipp1/RdeUnSN9I6q64alV113pZimC3pF0WxXZ728ayk/0llp5GdnEyWNDmVHZKO96SkmyVtnMo/l77rfwe+nP9X1kpEhF8NvID3SpRdD+ybpvsAM9P0JkCnNH0wcGuaHg2ckKbXAzYgO/N6pmiffYFngGHAJWQP200iO1v+XVpnMz6522sk8Ks0XbxOfbGtWqfomF8E7k3TM4D90/RFwG/SdC3w8zR9OtkDgL2A9ckeEOxR/DkBBwCLUvwdgMeK4uledOxrgC8UHaMmTXcGHgV6pvljyW4zrhvjL4s/v/b8At4DBgK3AF2Aaen3cHfR9+9HaXooMC1N3wXsk6Y3JrulfNV2Rb/Pu4HTgG8D+wB/BkYBZ6d1ehSt/xPgO2m6eJ1JQP80vQfwYN11ivZxBnB5ei//ArZL5X8BzkjTc4Fvpulfp+9GV6An8Fbx31TR938OsGna7yvAVsXfS7Jb2muBgUXH2DxNbw48DGyU5s8FLiyKsT9Zzwk3FX9+68KrJbqYaCsOBj6TTnQBNklnU5sC49IZf5D9U4Psn+EPJPUGbouI2UXb1nUf8GPgTbLL5GK9gRvTGfJ6wMuNiK2Uwpn6pkC3iHgolY8Dbi5ar/DQ39PAsxExP203h+xJ8YV19vt4RMxL60wj+4P8O3CgpO+RXW53J6v2uqvOtgOAnYCJ6T10BOaXiPEasl5sDYiIGZL6kl0N/LXO4n2BI9N6D0rqkT7PR4BLJV1H9r2cV+Z7eRPZ93F7squN4qrAndLVYTeyhHJ/8YbpzHlv4Oai/a9f5u0UVhoAvBwRL6T5cWRXgr9J88Xfy40jYgmwRNIySd1K7HdSRCxKMT0HbE32T/wYZf2bdSI7yfkMWWIptmcqfyS9h/XI/q63TzHOTvu9lk/6SVsnOBE0XQdgr4j4oLhQ0mhgckR8Kf1R1gJExPWS/kl26X6/pJFkZydriIiPJD0BfBfYEfhC0eLRwKURMUHSAWRnU3ljK3W4wcDMcm80+TD9/LhoujBf6ntUvM5KoJOkLsAfyM78/yVpFNnZVF0iSzZ71Ym/GyX6pbLVTCC7mjwA6FFUXrKPr4i4WNI9wOHAPyQdXN+OI+INScvJ2h9OZ/VEcDXwxYiYLumkdPxiHYB3I2JQzvcxGJhaT9zFmuN72Q84GxgSEe9Iupr6v5cTI+L41QqlQazj30u3ETTdA2SXycCqLwNkVwSFRrqTipZvA8yJiN+S/bEOBJaQXcqW8ivg3Iioe6ZdvP/hjYxtNZL2JztzuSqdJb0j6T/T4q8CD5Xabi0U/rjeTmeIxY14xZ/FLKCnpL1SnJ0l7RgR7wKLJO2b1juhmeNrC/4EXBQRT9cpf5j0eaUTiLcjYrGkbSPi6Yj4Odk/3u0p/728kOx7ubJOeVeyq7bOlPi9RMRi4GVJR6cYJGmXUgeQdCRwCNlVx/NAX0n/kRZX4nu5CVlj+yJlbW7FV5nFn8U/gH0KsUjaUNJ2KcZ+krZN662WKNYFviLIZ0NJ84rmLyWrL/29pBlkn+PDwDeAX5BVDZ0FPFi0zbHAiemM6g2yP9Z/S3okNWbdSzZgDwCRdbtR6m6hUWSX16+RfTH7lVinvtgAjk3/SDckq1Y6MiIKVwTDgStSQ98c4GsNfzT5RcS7kq4iu4yfS9bvVMHV6dgfAHuRJYnfpuqLTmRVAc+mmP4k6X3qVD8YpOq4y0osGgX8OX0n3ueTk4gzJB1Idnb8HNn38GNghaTpZL+Xp4r2X98twhcA/ySrd3+a0onkBOByST8kqzK9gU/uPDpT0onARmTtZEMjYgGApK+Rfec7kX1nrij/KTROuop5iuz7NYesuqxgDHCvpPkRcWC62hmv1GAN/DAiXkjVSvdIepusCnSn5oyx0tzFhJlZO+eqITOzds6JwMysnXMiMDNr55wIzMzaOScCM7N2zonArB6SvqSsh82SfeKYtRVOBGb1O57snvDj6i6Q1LH64ZhVhhOBWQnpyed9gBGkRKCsV9XJkq4ne2gKSSfqk15lrywkCJXozdKstXIiMCvti8B9qbOzf0vaNZXvDvwgIj6jbByHY8l67xxE9nRuoXuFH0REDVlXIvsrdfts1ho5EZiVdjxZFwikn4X+Yx6PiEKPrwcBuwFTUg+rBwHbpGXHSHqSrHuGHcl6rTRrldzXkFkdknqQ9dm/k6Qg6wY7yLp2Xlq8KjAuIr5fZ/u8vVmatQq+IjBb01HAXyJi64joGxFbkXXQt2+d9SYBR0n6FKwaBWxryvdmadbqOBGYrel44PY6ZbdSZxzfiHgO+CHwQOrVcyLQKyKmk1UJPUvWLfQjmLVi7n3UzKyd8xWBmVk750RgZtbOORGYmbVzTgRmZu2cE4GZWTvnRGBm1s45EZiZtXP/H4aDDz6AlWkMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.unstack().plot(kind='barh', xlim=[0, 65], alpha=0.7);\n",
    "plt.gca().legend(['Female', 'Male'])\n",
    "plt.gca().invert_yaxis()\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
}