{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminary Analysis of Gender Participation in AAS Talks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initial analysis led by Brett Morris and Jim Davenport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['axes']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from urllib.request import urlopen\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.size'] = 15\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "stableURL = 'https://raw.githubusercontent.com/jradavenport/aas225-gender/master/data.csv'\n",
    "raw = urlopen(stableURL).read().splitlines()\n",
    "\n",
    "num = []\n",
    "time = []\n",
    "sid = []\n",
    "speaker = []\n",
    "qs = []\n",
    "comments = []\n",
    "\n",
    "for i in range(len(raw)):\n",
    "    splitline = str(raw[i]).split(';')[0].split(',')\n",
    "    splitline = [j for j in splitline if j != ' ' and j != '']\n",
    "    \n",
    "    # If all columns filled and there is only one entry (M, F) for speaker gender:\n",
    "    if len(splitline) >= 5 and len(splitline[3]) == 1 and ('M' in splitline[4].upper() or 'F' in splitline[4].upper()):\n",
    "        iscleansid = splitline[2].replace('.', '').isdigit()\n",
    "        if iscleansid:\n",
    "            # Fix sid if necessary\n",
    "            if len(splitline[2]) != 6:\n",
    "                splitsid = splitline[2].split('.')\n",
    "                splitline[2] = '.'.join([splitsid[0], splitsid[1].zfill(2)])\n",
    "            \n",
    "            # If this talk ID has not been added:\n",
    "            if splitline[2] not in sid:\n",
    "                num.append(splitline[0])\n",
    "                time.append(splitline[1])\n",
    "                sid.append(splitline[2])\n",
    "                speaker.append(splitline[3].upper())\n",
    "                qs.append(splitline[4].upper())\n",
    "                comments.append(','.join(splitline[5:]))\n",
    "            \n",
    "            # If this talk ID has been added already:\n",
    "            if splitline[2] in sid:\n",
    "                # Check if the number of questions in new entry is longer. \n",
    "                # If so, replace earlier entry with the longer one.\n",
    "                if len(qs[sid.index(splitline[2])]) < len(splitline[4].upper()):\n",
    "                    replaceindex = sid.index(splitline[2])\n",
    "                    num[replaceindex] = splitline[0]\n",
    "                    time[replaceindex] = splitline[1]\n",
    "                    sid[replaceindex] = splitline[2]\n",
    "                    speaker[replaceindex] = splitline[3].upper()\n",
    "                    qs[replaceindex] = splitline[4].upper()\n",
    "                    comments[replaceindex] = ','.join(splitline[5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overall demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f80fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male speakers/All speakers= 62.0%\n",
      "Female speakers/All speakers = 38.0%\n"
     ]
    }
   ],
   "source": [
    "def annotateformat(number):\n",
    "    if number < 1:\n",
    "        return '%.2f' % number\n",
    "    else: \n",
    "        return '%d' % number\n",
    "\n",
    "def nicehist(axis, female, male, title):\n",
    "    axis.bar([0, 1], [female, male], color='k', width=0.5)\n",
    "    axis.set_xticklabels(['Female', 'Male'])\n",
    "    axis.set_xticks([0.25, 1.25])\n",
    "    axis.set_xlim([-0.2, 1.7])\n",
    "    \n",
    "    axis.annotate(annotateformat(female), xy=(0.25, female), va='bottom', ha='center')\n",
    "    axis.annotate(annotateformat(male), xy=(1.25, male), va='bottom', ha='center')\n",
    "    axis.set_title(title)    \n",
    "    axis.spines['right'].set_color('none')\n",
    "    axis.spines['top'].set_color('none')\n",
    "    axis.xaxis.set_ticks_position('bottom')\n",
    "    axis.yaxis.set_ticks_position('left')\n",
    "\n",
    "Nfemalespeakers = len([i for i in speaker if i == 'F'])\n",
    "Nmalespeakers = len([i for i in speaker if i == 'M'])\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "nicehist(ax, Nfemalespeakers, Nmalespeakers, \n",
    "         'Gender of Speakers ({0} Total)'.format(Nfemalespeakers+Nmalespeakers))\n",
    "plt.show()\n",
    "\n",
    "print('Male speakers/All speakers= {0:.1f}%'\n",
    "      .format(100*float(Nmalespeakers)/(Nfemalespeakers + Nmalespeakers)))\n",
    "print('Female speakers/All speakers = {0:.1f}%'\n",
    "      .format(100*float(Nfemalespeakers)/(Nfemalespeakers + Nmalespeakers)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male qs/All qs = 81.1%\n",
      "Female qs/All qs = 18.9%\n"
     ]
    },
    {
     "data": {
      "image/png": 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qbw38s5R2sKS3JM2WNE7SWqXpw4CWYkJEvAi05mlmZtZAtTQZtSFpOLAXcGAh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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ba12860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allqs = ''.join([i for i in qs if 'M' in i or 'F' in i])\n",
    "Nfemaleqs = len([i for i in allqs if i == 'F'])\n",
    "Nmaleqs = len([i for i in allqs if i == 'M'])\n",
    "    \n",
    "fig, ax = plt.subplots(1)\n",
    "nicehist(ax, Nfemaleqs, Nmaleqs, 'Gender of Question-Askers (%d Total)' % (Nfemaleqs+Nmaleqs))\n",
    "\n",
    "print('Male qs/All qs = {0:.1f}%'.format((100*float(Nmaleqs)/(Nfemaleqs + Nmaleqs))))\n",
    "print('Female qs/All qs = {0:.1f}%'.format((100*float(Nfemaleqs)/(Nfemaleqs + Nmaleqs))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ei4iLI2Jco9VQSD8tIl4aERtEREdE/G6wymBmZgPjq7KamVmFg4OZmVU4OJiZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab517b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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w7K6VzZs3U6NGjZDJHaBGjRoht3vZsmW0a9eOpKQk6tSpw+jRozl48GCu+X/7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\nxhgTp/4/XqmPmeRqqpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b791b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "Nbins = 7\n",
    "\n",
    "ax.hist([len(i) for i in qs], Nbins, histtype='stepfilled', color='w', lw=2)\n",
    "ax.set_title('Number of Questions Per Talk:')\n",
    "ax.set_xlabel('Number of Questions')\n",
    "plt.show()\n",
    "\n",
    "malespeaker_Nqs = [len(qs[i]) for i in range(len(num)) if speaker[i] == 'M']\n",
    "femalespeaker_Nqs = [len(qs[i]) for i in range(len(num)) if speaker[i] == 'F']\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "ax.hist(malespeaker_Nqs, Nbins, histtype='step', color='b', lw=2, label='Male Speaker', range=[1,8])\n",
    "ax.hist(femalespeaker_Nqs, Nbins, histtype='step', color='r', lw=2, label='Female Speaker', range=[1,8])\n",
    "ax.set_title('Number of Questions Per Talk:')\n",
    "ax.set_xlabel('Number of Questions')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who asks questions first?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZMzNb9lXWo6mLezRm1XCPpn4ddq7qmAPutzebmVmtHGjMzKxWDjRmZlYrBxoz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\nwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10baad0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male first qs/All qs = 76.4%\n",
      "Female first qs/All qs = 23.6%\n"
     ]
    }
   ],
   "source": [
    "Nmalesfirst = len([i for i in qs if i[0] == 'M'])\n",
    "Nfemalesfirst = len([i for i in qs if i[0] == 'F'])\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "nicehist(ax, Nfemalesfirst, Nmalesfirst, \n",
    "         'Gender of First Question-Askers ({0} Total)'.format(Nfemalesfirst+Nmalesfirst))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "print('Male first qs/All qs = {0:.1f}%'.format((100*float(Nmalesfirst)/(Nfemalesfirst + Nmalesfirst))))\n",
    "print('Female first qs/All qs = {0:.1f}%'.format((100*float(Nfemalesfirst)/(Nfemalesfirst + Nmalesfirst))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When males/females ask first Q, are subsequent Qs affected?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TR/wL68FOEzNz34g4piWuPYC9I+LAzGzvd92XzLwnIv4KbBARS2WXESAj4jnAk3jiyV2v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\neppPjM3MzMzMzKyn/X9YOKzSoxqVFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10baab4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "malesfirst = [i for i in qs if i[0] == 'M']\n",
    "malesfirst_percentagefemaleafter = [float(i.count('F'))/len(i) for i in malesfirst[1:] \\\n",
    "                                    if len(i) >= 2]\n",
    "\n",
    "femalesfirst = [i for i in qs if i[0] == 'F']\n",
    "femalesfirst_percentagefemaleafter = [float(i.count('F'))/len(i) for i in femalesfirst[1:] \n",
    "                                      if len(i) >= 2]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16,8))\n",
    "Nbins = 5\n",
    "\n",
    "ax[0].hist(malesfirst_percentagefemaleafter, Nbins, color='k', \n",
    "           range=[0,1], histtype='step', lw=2, \n",
    "           weights=len(malesfirst_percentagefemaleafter)*[1./len(malesfirst_percentagefemaleafter)])\n",
    "ax[0].set_title('Male Asks First Question')\n",
    "\n",
    "ax[1].hist(femalesfirst_percentagefemaleafter, Nbins, color='k', \n",
    "           range=[0,1], histtype='step', lw=2,\n",
    "           weights=len(femalesfirst_percentagefemaleafter)*[1./len(femalesfirst_percentagefemaleafter)])\n",
    "\n",
    "ax[1].set_title('Female Asks First Question')\n",
    "\n",
    "for axes in ax:\n",
    "    axes.set_xlim([0, 1])\n",
    "    axes.set_xlabel(\"Fraction subsequent Q's asked by females\")\n",
    "    axes.set_ylabel(\"Fraction of all talks\")\n",
    "    axes.set_ylim([0, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yi5uZjx49Osd6cjr+H3/84Wbmw4cPj7R98sknbmbepUsXP3DgQKT922+/9fLl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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ba6b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10,8))\n",
    "Nbins = 5\n",
    "ax.hist(malesfirst_percentagefemaleafter, Nbins, color='b', \n",
    "           range=[0,1], histtype='step', lw=3, \n",
    "           weights=len(malesfirst_percentagefemaleafter)*[1./len(malesfirst_percentagefemaleafter)],\n",
    "           label='Male Asks First Question')\n",
    "\n",
    "ax.hist(femalesfirst_percentagefemaleafter, Nbins, color='r', \n",
    "           range=[0,1], histtype='step', lw=3,\n",
    "           weights=len(femalesfirst_percentagefemaleafter)*[1./len(femalesfirst_percentagefemaleafter)],\n",
    "           label='Female Asks First Question')\n",
    "\n",
    "ax.set_xlim([0, 1])\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_xlabel(\"Fraction subsequent Q's asked by females\")\n",
    "ax.set_ylabel(\"Fraction of all talks\")\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When M/F asks first Q, who asks following questions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NkqReVrNu7st7TCVJkiRJ6pKBqSRJkiSpVAamkiRJkqRSGZhKkiRJkkplYCpJ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\nKhNTSZIkSVKr/h+tAtg0YeJEOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ba4bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "malesfirst_lenafter = float(len(''.join(malesfirst)))\n",
    "malesfirst_Nfemaleafter = sum([i.count('F') for i in malesfirst[1:]\n",
    "                                    if len(i) >= 2])/malesfirst_lenafter\n",
    "femalesfirst_lenafter = float(len(''.join(femalesfirst)))\n",
    "femalesfirst_Nfemaleafter = sum([i.count('F') for i in femalesfirst[1:]\n",
    "                                      if len(i) >= 2])/femalesfirst_lenafter\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16,8))\n",
    "\n",
    "nicehist(ax[0], malesfirst_Nfemaleafter, 1-malesfirst_Nfemaleafter,\n",
    "         'Male Asks First Q ({0} Total)'.format(len(''.join(malesfirst))))\n",
    "\n",
    "nicehist(ax[1], femalesfirst_Nfemaleafter, 1-femalesfirst_Nfemaleafter,\n",
    "         'Female Asks First Q (%d Total)'.format(len(''.join(femalesfirst))))\n",
    "\n",
    "for axes in ax:\n",
    "    axes.set_ylim([0,1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5t2vjLUnmjzif5yU5Lcmt3fSlS5O8e4I6105y\nfpKLkqzbpa2QZN8kVyW5N8nlSXYfUfbAJL/tfpZHApNdM0lSD4zNgLFZekz8xlSzRpIVBz9X1YND\nWfYGzgJ255FpRc8ETgEOBR4CXg+cluSlY6O8STYHzgUuBfYEbgO2ADamBb69gYOBNwC/B8ZGe4tH\nj9aeAmwIfIA2xWlv4AdJXlBVvx7ItxstQL8L2Ag4rKt73OAFbAssqKq7h3dU1YNJTgXePpD2r0PZ\nvtpdl3cAd3bX5k8nOB7AJsDRwFXAE2jX9odJnltV13Z5TgUuAXYF7qdNr3riqMqSPBk4A7gH2KGq\n7ux2Hd6V/xjwv7SpYUclubmqTu/Kfhj4F9rI9I+At9B+LpKknhibjc0YmzVdqsrNbZneaM+QPDS0\nPQi8qtu/Ypd2/iT1pMs7H/j8QPoJwNXAE8Ypt0t3vA2G0t/Zpa/cfX5D93nrgTxr0KYUfXYg7Vrg\nciADaZ8Frpmk/fcBh0yw/8Pd8dceZ/89wI5L8HMYu35XAh/t0tbvrv1mE5Q7B/ga8FTgZ7Q/UNYY\n2L9pV8euQ+W+Ciwc+BnfBHx6KM+CUT8bNzc3N7eluxmbH85jbDY2u03T5lRezRaLgJfQRku3ALYE\nLhjK893hQkk27KbvXEdbjOB+4JW0X7hjXgkcV1X3L2EbtwRurKrzxxKqajHwHeBlQ3nPrKrBEd3L\ngKcnCUtuvAUHfgIcmuRtSTacSkVJNk9yUpKbaEHmftpo7tj1+x1wHXBEkrckeco4VW0AnA1cD+zU\nXZcxr6YF9pO7qV8rdiPwZwJjU5OeQQueJw/V+62pnIckaakwNk+dsVmahB1TzRYPVNUlVXXxwLZ4\nKM9vBz8kWYEWeLYA9gG26/5/PrDqQNZ1gRunoY1PH27DQLvWHUpbNPT5Ptr9+IQJ6r+BNn1nPJsA\n91bVHePsfxNtWs+ngWuS/DjJduNVluSJwPeBp9GmMb2Mdv0upbt+VfUQ8BpaEDwSuDHJ2UmeP1Td\n82gB85h69MIX6wEr01ZVvH9g+yKwcpKn0kZ/C7h5qOzwZ0nSzDE2G5uNzZo2PmOquWR4NHIz4M+A\n7avqrLHEJKsN5buVFriW1I20kcNh63fHWFJnAzslWa2q7hnc0QX61wHnjVe4qm4A9uhGfreiLW3/\n7SQbjRMwt6Fdl22r6uqBY60zVO/lwJuTrER71uYTtOd5BgP16cAvgCOT3FJVpw3suxX4v+54o9wC\nrEWbrjR8fUddb0nSssPYbGyWpsRvTDWXjQW5h99lluSZwNZD+RYAuyYZb0R0rPyq4+wfcwGwQZKH\n60+yBi0onTPVRk/gc7TR3b1H7HsPbUrNkZNVUs0FtIUK1qQtJDHKqOv3ctoCEqPqfaD7I+PTwIbD\nKxlW1YHAZ4BvJtl2YNeZtFHZtYZG3ce2B4Hf0EZ+dxk67BsnO19J0jLF2DyCsVnyG1PNbZfSptgc\nlmQesA6wP22Bg0HzaIHrh0kOo40Cvhi4qaqOoVsMAXhfkv8GFlfVpcMHq6rvJvkf4IQk+9CmBO1F\nu88+taQnU1UXdudxQJKNgeN55CXe7wO+VVVfH1W2W/r9FOAY4ApgddqCDNcDvxznkOfRFmX4cpJP\n0kZZ9+vKjNX7ItrLxI+nLVLxZNo5X1RVdw1XWFV7JVkLODXJq6vqwqq6LMmXgG8kORT4MS3wbg48\no6reW1UPJPkEcHCS24CFwFuZfOVCSdKyxdjcMTZLQ/pefcnNbbKNFpxunmD/irSH//ccsW9sIYbF\ntCD2t7Ql1s8byvd82gINd9CC1kLgFQP7/5n2LrL7gCu6tD9Y+a9LW6+r/5bumPOBFwwd6xrg40Np\nj6prgvP9C9rqebd3ZR6iBdcVJiizKnAEbcrOXbRna04CnjPJsV4L/By4m/YMzGuAHwJf6/avzyNL\n1t9NC4xHM7ASH21E+qsDn0MLwr8DNh9I/2B3rHu69p0J7DZU7kDasyu3A0d1P09X/nNzc3Ob4c3Y\n/KhzMjYbm92WcEvVeIuESVrWdSOc59MC2nY19HyLJEmaWcZm6fHxGVNpFqv2EuxdgGcBx/bcHEmS\nlnvGZunx8RtTSZIkSVKv/MZUkiRJktQrO6aSJEmSpF7ZMZUkSZIk9cqOqSRJkiSpV3ZMJUmSJEm9\nsmMqSZIkSerV/wOgfHAEybDvcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107ed50f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "malespeaker_qs = [qs[i] for i in range(len(num)) if speaker[i] == 'M']\n",
    "femalespeaker_qs = [qs[i] for i in range(len(num)) if speaker[i] == 'F']\n",
    "\n",
    "malespeaker_Nfemaleqs = ''.join(malespeaker_qs).count('F')\n",
    "malespeaker_Nquestions = len(''.join(malespeaker_qs))\n",
    "malespeaker_percentagefemaleqs = malespeaker_Nfemaleqs/float(malespeaker_Nquestions)\n",
    "\n",
    "femalespeaker_Nfemaleqs = ''.join(femalespeaker_qs).count('F')\n",
    "femalespeaker_Nquestions = len(''.join(femalespeaker_qs))\n",
    "femalespeaker_percentagefemaleqs = femalespeaker_Nfemaleqs/float(femalespeaker_Nquestions)\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(16,8))\n",
    "\n",
    "nicehist(ax[0], malespeaker_percentagefemaleqs, 1-malespeaker_percentagefemaleqs, \n",
    "         'Male Speaker ({0} Total)'.format(malespeaker_Nquestions))\n",
    "\n",
    "nicehist(ax[1], femalespeaker_percentagefemaleqs, 1-femalespeaker_percentagefemaleqs,\n",
    "          'Female Speaker ({0} Total)'.format(femalespeaker_Nquestions))\n",
    "for axes in ax:\n",
    "    axes.set_xlabel(\"Fraction Q's asked\")\n",
    "    axes.set_ylim([0,1])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When is first question asked by female?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rft8qCl/dUxy8/PLL6tevn9577z21bdu2sKtT4uX0vxRfLD/rZAwWAACAZ3QR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\neDGuWyCtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107fa5978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def getfirstindex(searchstring, letter):\n",
    "    if letter in searchstring:\n",
    "        return searchstring.index(letter) + 1\n",
    "    return None\n",
    "    \n",
    "malespeaker_orderfemale = [getfirstindex(i, \"F\") for i in malespeaker_qs if 'F' in i]\n",
    "femalespeaker_orderfemale = [getfirstindex(i, \"F\") for i in femalespeaker_qs if 'F' in i]\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(10,8))\n",
    "Nbins = 3\n",
    "ax.hist(malespeaker_orderfemale, Nbins, color='b', \n",
    "           range=[1,3], histtype='step', lw=3, \n",
    "           label='Male Speaker')\n",
    "\n",
    "ax.hist(femalespeaker_orderfemale, Nbins, color='r', \n",
    "           range=[1,3], histtype='step', lw=3,\n",
    "           label='Female Speaker')\n",
    "\n",
    "ax.set_xlim([1, 3])\n",
    "ax.set_xticks([1,2,3])\n",
    "#ax.set_ylim([0, 1])\n",
    "ax.set_xlabel(\"First Female Question Position in Sequence\")\n",
    "#ax.set_ylabel(\"Fraction of all talks\")\n",
    "ax.set_title('In talks when a female asked a question:')\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## # Qs asked versus % of Female Qs asked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bmmorris/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n",
      "/Users/bmmorris/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:70: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/Users/bmmorris/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n",
      "/Users/bmmorris/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:94: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
      "/Users/bmmorris/anaconda/lib/python3.5/site-packages/numpy/core/_methods.py:116: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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4v9KTpNUyl6/6EKq+fp73sm8Z2N2p/QH2ACz6cN0/+1a9HemLwJW8\n+qGYxzOkD9TqOPsatmdTQFaS213Sa4GdgW+T2lG2qXPKPqTqvf/t21Dri0xEXANck7W7fIC0QNv3\nSO0jfU7K7jlZ0rYR8XDFvtmk6r1PsOSa7X099/L+Di0Hl0SGmOw/zenAx0jf0Cs9RvrPVbl9lzaE\nsU/V632BOyOZT1ri9S0RcVeNx5NN3O/PwC6SVu7bIGlL0gqMNxW81u2kD6dFg/uUeo2NrTouz3t5\nC2k8ysg6P+u8yoMjYkGk7sdnAOtWJKVFpYR6ImIhqZpn/6pdB5LaN25pdH6D674UEVeRvpw0GkS6\nIimJVRpPnS8BETE3Ii4CLqtx3VeAD5Ha5aZVvcdTgTcBM2u8n//Ijsn7O7QcXBIZms4Bjid9a+yp\n2H4t6Rv0zySdTqpW+AStH4C1m6STSPXU+5Hqsfeq2P9FYIqkINXhzwXeSPrGfnxETC94vzNIy35e\nL+kUUoP2yaQeYr8tcqGIuFfSFcDZWVvAk6QeTvOqDu33vYyIf0uaBHxP0gakNd6XAd4CjImIfSW9\nDfgWqdroIVK107HA3Vm1EqTeWe/Pel89C/Rm7RjVTgCulfRTUi+ttwNfA86NiOoSXl1ZT7qPkdq2\nHiWViI6koqdVDZOBoyV9m9SzaxtSEqm87hGkqqhrSSXOjUhJ7+fVF4uIlyTtSerJNVXS+yLiGVIv\nsSOBG5W6nT9EqjZ8N6mTx3cL/A4tj0637PvR3gf1ewkdR/oGOrVq+y6kBs7nSQnmLdlxlb2zHiLr\n6VKx7WfAbVXbDsnOXSl7vX32eiypqup50ofQkTXi2zI7Zg4pifyd9GG6Sq1r53gf3kH6wHmeVOXx\nS2CtqmOW+LnqXGtVUtfiuaSqsq+QRrs/VHVcv+9ldtzBpG/H80hJ4Bbgs9m+tYBfkDopzCd9uJ4P\nrFdx/ihSQ/Nz2fU/0uD3tD8peb6YvfdfA5ap9zur9d6QPtwvBh4BXsiu8wNgtX7et2Oyc+YC1wGj\ns3sdle1/DynBPJb9rDOAbwDLVVxj0fHZ69WAu0jVkn1/G6uQqtceqfg5LwW27ud3eGr179CP/h9e\nY92sBSSdBuwXERt2OhazgeQ2ETMza5qTiJmZNc3VWWZm1jSXRMzMrGlOImZm1jQnETMza5qTiJmZ\nNc1JxMzMmuYkYmZmTfv/03hBZv/Eh10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be96438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_qs = np.array([len(i) for i in qs])\n",
    "percentagefemaleqs = np.array([i.count('F')/float(len(i)) for i in qs])\n",
    "order = np.argsort(N_qs)\n",
    "\n",
    "#plt.plot(N_qs, percentagefemaleqs, '.')\n",
    "\n",
    "for i in range(1, max(N_qs)):\n",
    "    considertalks = i == N_qs\n",
    "    medianpercentagefemaleqs = np.median(percentagefemaleqs[considertalks])\n",
    "    stdpercentagefemaleqs = np.std(percentagefemaleqs[considertalks])\n",
    "    plt.errorbar(i, medianpercentagefemaleqs, yerr=stdpercentagefemaleqs, fmt='o', color='k')\n",
    "plt.ylim([0, 1.0])\n",
    "plt.xlabel('Number of questions asked')\n",
    "plt.ylabel('Fraction of Qs asked by females')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations by genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "infile = 'aas225-all-talks.txt'\n",
    "raw = open(infile).read().splitlines()\n",
    "\n",
    "rawchairgender = []\n",
    "catagory = []\n",
    "sessionID = []\n",
    "\n",
    "for i,line in enumerate(raw):\n",
    "    if i % 5 == 3:\n",
    "        a, b, = line.split(',')\n",
    "        catagory.append(a)\n",
    "        rawchairgender.append(b.strip())\n",
    "    if i % 5 == 0:\n",
    "        sessionID.append(line[0:3])\n",
    "\n",
    "rawchairgender = np.array(rawchairgender) \n",
    "sessionID = np.array(sessionID)\n",
    "chair = []\n",
    "for talknumber in sid:\n",
    "    session = talknumber.split('.')[0]\n",
    "    chair.append(rawchairgender[sessionID == session])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Questions to ask the data: \n",
    "    \n",
    "Show how reliable responses are by looking for simultaneous responses from individual talks?\n",
    "\n",
    "Does the order of questions asked matter?\n",
    "\n",
    "Are females more likely to ask follow-up questions rather than leading quetions?\n",
    "\n",
    "Do women ask N questions proportionate to their size in the population?\n",
    "\n",
    "If a woman asks a question, are others more likely to follow? Clustering?\n",
    "\n",
    "Are the speaker genders similar in M/F to the overall population?\n",
    "\n",
    "What are the biases in the talks that get survey responses?\n",
    "\n",
    "Do people submit responses more often for talks given by women compared to the general pop?\n",
    "\n",
    "How late in Q session is the first female Q?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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