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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Solution: Hands-on with Pandas\n",
      "\n",
      "This notebook will walk you through some exercises to get practice using Pandas for data manipulation.\n",
      "\n",
      "As you use this, feel free to make ample use of the [Pandas Documentation](http://pandas.pydata.org), the [Pandas StackOverflow Channel](http://stackoverflow.com/questions/tagged/pandas), and your favorite search engine. For example, if you search phrases like [\"Pandas sum all columns\"](https://www.google.com/search?q=pandas+sum+all+columns), you're very likely to find an answer to the question you have in mind.\n",
      "\n",
      "Also, if it comes down to it, note that solutions are available in the Git repository."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Start with our normal batch of imports and settings\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "\n",
      "# Following is optional: set plotting styles\n",
      "import seaborn; seaborn.set()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Diving Deeper into Baby Names\n",
      "\n",
      "In the lecture, we looked at the US Social Security Baby Names data. Here let's dive a little bit deeper into this.\n",
      "\n",
      "Try to do the following with the Baby Names data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 0. Load the baby names data\n",
      "\n",
      "(Here you can copy the code from the other notebook; make sure you understand what it's doing!)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_year(year):\n",
      "    data = pd.read_csv('../data/names/yob{0}.txt'.format(year),\n",
      "                       names=['name', 'gender', 'births'])\n",
      "    data['year'] = year\n",
      "    return data\n",
      "\n",
      "names = pd.concat([load_year(year) for year in range(1880, 2014)])\n",
      "names.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>name</th>\n",
        "      <th>gender</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "        name gender  births  year\n",
        "0       Mary      F    7065  1880\n",
        "1       Anna      F    2604  1880\n",
        "2       Emma      F    2003  1880\n",
        "3  Elizabeth      F    1939  1880\n",
        "4     Minnie      F    1746  1880"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1. Find your own name within the data. \n",
      "\n",
      "- How many babies per year are born with your name?\n",
      "- What *fraction* of births each year have your name?\n",
      "\n",
      "Note: there are multiple ways to do this, but the first part will use *masking* and *pivot tables*, while the second part might also throw-in a *groupby*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the prevalence of my name over time\n",
      "my_name = 'Jacob'\n",
      "subset = names[names.name == my_name]\n",
      "\n",
      "births = subset.pivot_table('births', index='year',\n",
      "                            columns='gender', aggfunc='sum')\n",
      "births.plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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rJ5FqH9WRyQ+dT3iTFmPMAuAHwNettd8zxvyvUS9XA93kwnh0j0THaB+rbfQ8\nJtTWFpvMZCWvqSmqvnCpL0aoL0aoL0Z42RdtvR2E/CEGujPEfd7/exTi52JF/dns6tzLL7b9movn\nrnnda+NtaIw7bG6MmQU8BXzSWvsdt/klY8zl7s/XAeuB54FLjTERY0wNsJzcyWwbgetHT2utjQFJ\nY8wSY4wPuNqdh4iIlJDuRC+1kWp8Pp/XpRSsoaHzF49sPqH3TbTnfTu5oe5PGWOGjn1/HLjHPSHt\nVeAR92zze4AN5DYIbrfWJowx3wS+a4zZACSA97jzuBV4CAgAT1prXzihqkVEpKCls2liqT5mVzZ7\nXUpBayivZ1H1ArZ376Iv2T/pwwsTHfP+OLmwPtYVY0x7P3D/MW1x4KYxpn0OuHBSFYqISNHpSeSG\np3Wy2sTOa17B3t79bGrbwsXz1kz8BnSTFhERyYOZ/CjQE7WqaQUALx6d/NC5wltERKacLhObvIby\nutcNnU+GwltERKacbtByYs5rXkHWyfJy2+Su+VZ4i4jIlOvWrVFPyOrmc/Hh41cHf43jOBNOr/AW\nEZEpNzxsroeSTEpdWS3nNp3F/r5D7O7ZO+H0Cm8REZlyQ8PmNZHCuaNZobti/sUAPHNg44TTKrxF\nRGTKdSd6iIaqCPonvJGnuE6rXcLcytm81PbK8MjF8Si8RURkSjmOM3x3NZk8n8/HFQsuJutk2XDw\n1+NOq/AWEZEpFU8PksqmdJnYSTh/1ioqguX8SuEtIiLTaeQab52sdqLCgTAXz11DX2r8670V3iIi\nMqVGrvHWnvfJuHTehfgY/2EuCm8REZlSI9d4a8/7ZDSU1/H7p10/7jQKbxERmVIH+18DoKm80eNK\nitfahZeP+7rCW0REplRrzz78Pj+Lqud7XUrJUniLiMiUSWVS7I8dZH7VHMKBsNfllCyFt4iITJn9\nfQfJOBkW1yzyupSSpvAWEZEpM3Rf7iXVCu98UniLiMiUae3ZB6A97zxTeIuIyJRwHIfWnr1Uh6PU\nl9V5XU5JU3iLiMiU6Ep005PsZXHNIny+8W8yIqdG4S0iIlOi1T3evbh6oceVlD6Ft4iITAkd754+\nCm8REZkSu3v24vf5WRjVzVnyTeEtIiKnLJlJsb/vIAuq5hEOhLwup+QpvEVE5JTtix0g62RZXKPj\n3dNB4S0iIqds+GQ1He+eFgpvERE5Za297slqurPatFB4i4jIKck6WXZ07aIuUkt9Wa3X5cwICm8R\nETkle3t2qDZ+AAAf9ElEQVT3M5COc2bDMt2cZZoovEVE5JRs7bAAnNlwhseVzBzByUxkjFkDfNFa\ne6UxZhXwX8AO9+VvWGsfNsZ8CLgFSAN3WmsfN8aUAw8CTUAMuNla226MuQC42532KWvt56Z2tURE\nZLq82mnx+/yYuqVelzJjTLjnbYz5JHAfEHGbVgNftdZe6f552BgzG/gocBFwDXCXMSYM3AZsstZe\nBjwA3OHO417g3dbaS4A1xpiVU7pWIiIyLfqS/ezrPcCSmkWUB8u9LmfGmMyw+U7gHcDQgYzVwO8Z\nY54xxtxvjKkC3gxstNamrLW97ntWABcD69z3rQPWGmOiQNha2+q2PwmsnZrVERGR6bStczsODmfW\nG69LmVEmDG9r7Q/IDW8PeQ74hLX2cmA38GkgCvSMmiYG1ADVQO84baPbRUSkyGzt1PFuL0zqmPcx\nHrXWDgX1o8DXgPXkAnxIFOgmF9LRcdogF+bdk1lwU1N04olmCPXFCPXFCPXFCPXFiHz1RdbJYrt2\nUFtWzarFxXGmeal8Lk4mvNcZYz5mrX2B3HD3b4Dngc8bYyJAGbAc2AJsBK4HXgCuA9Zba2PGmKQx\nZgnQClwNfGYyC25ri51EuaWnqSmqvnCpL0aoL0aoL0bksy/2xQ7Qk4ixZvZq2tv78rKMqVRsn4vx\nNjROJLwd9+9bga8bY1LAa8At1to+Y8w9wAZyQ/G3W2sTxphvAt81xmwAEsB7Rs3jISAAPOluCIiI\nSBF5tWM7AGc26Hj3dJtUeFtr95A7kxxr7SbgkjGmuR+4/5i2OHDTGNM+B1x44uWKiEiheLVjGz58\nnFF/utelzDi6SYuIiJyw3mSM1t59tFQvoCpU6XU5M47CW0RETtgj239E1sny5tmrvS5lRlJ4i4jI\nCdnUtpXfHt3E4upFXDJvjdflzEgKbxERmbSBVJx/t48S9AV47/I/xO9TjHhBvS4iIpP2w12P05Ps\n5dqWtcypnOV1OTOWwltERCZlR9cuNh56nnlVc3jrosu9LmdGU3iLiMikbDj4awDetewPCPpP5h5f\nMlUU3iIiMqFUNs3Wjm00lNWzpGaR1+XMeApvERGZkO3cwWAmwblNZxXFPcxLncJbREQmtKltKwAr\nm87xuBIBhbeIiEwg62TZ3L6VaLiKxTULvS5HUHiLiMgEdnXvoS/Vz7mNZ+m67gKhfwURERnXprYt\nAJzbdLbHlcgQhbeIiByX4zi83LaF8mAZy+qWel2OuBTeIiJyXPv7DtKV6ObshuW6truAKLxFROS4\nNh3VkHkhUniLiMiYsk6W3x7dRMgf5MwG43U5MorCW0RExrSpbStt8Q7eNGsVkUDY63JkFIW3iIi8\ngeM4PLX3F/jw8daFeghJoVF4i4jIG2zr3MG+2EFWNp3NrMpmr8uRYyi8RUTkDZ7c+wsArm650uNK\nZCwKbxEReZ3Wnr3s6N7N8vplLIzO97ocGYPCW0REXufJvU8DcM0i7XUXKoW3iIgM+9XBX/NK+6ss\nrl7EabVLvC5HjkO3yxEREdLZNA9vf4xfHXqOylAFf7Ts7XpudwFTeIuIzHCxZB/3vfIAu3r2MK9q\nDrecczON5fVelyXjUHiLiMxwD29/jF09eziveQV/svwm3ZClCCi8RURmsLaBDl48upl5VXP407Pe\nq6HyIqET1kREZrCf71+Pg8PVi65UcBcRhbeIyAzVm4zx36+9QENZPauazvG6HDkBkxo2N8asAb5o\nrb3SGHMa8B0gC2wBPmytdYwxHwJuAdLAndbax40x5cCDQBMQA2621rYbYy4A7nanfcpa+7mpXjER\nERnfL/dvJJ1Ns3bh5QT8Aa/LkRMw4Z63MeaTwH1AxG36KnC7tfYywAfcaIyZDXwUuAi4BrjLGBMG\nbgM2udM+ANzhzuNe4N3W2kuANcaYlVO4TiIiMoF4epD1B58lGqrigjlv8rocOUGTGTbfCbyDXFAD\nnGetXe/+/ASwFjgf2GitTVlre933rAAuBta5064D1hpjokDYWtvqtj/pzkNERKbJxkPPEU8PcsWC\nSwgHQl6XIydowvC21v6A3PD2kNFnNMSAGqAa6DlOe+84baPbRURkGsTTcX627xkigTCXzbvA63Lk\nJJzMpWLZUT9XA93kwjg6qj06RvtYbaPnMaGmpujEE80Q6osR6osR6osR6osRx/bFt3/7E2LJPm46\n+wYWzZ3lUVXeKJXPxcmE90vGmMuttc8A1wE/B54HPm+MiQBlwHJyJ7NtBK4HXnCnXW+tjRljksaY\nJUArcDXwmcksuK0tdhLllp6mpqj6wqW+GKG+GKG+GHFsX+zt3c+TO59hVkUTFzVeOKP6qdg+F+Nt\naJxIeDvu338N3OeekPYq8Ih7tvk9wAZyQ/G3W2sTxphvAt81xmwAEsB73HncCjwEBIAnrbUvnMgK\niYjIictkM3xv23/i4PDH5h2E/LpPV7HyOY4z8VSFwSmmLaZ8Kratx3xSX4xQX4xQX4wY3RdP7/8V\nj+z4EWtmr+b9Z77L48qmX7F9Lpqaose9a45u0iIiMgN0J3r4r93rqAxW8Aen/Z7X5cgpUniLiMwA\nP2n9KYlMkhuXXkc0XOV1OXKKFN4iIiXuSP9R/vu13zC7olk3ZCkRCm8RkRL3X61PkXWy3LD0Wt0G\ntUQovEVEStiuzr28dHQzLdULObfxLK/LkSmi8BYRKWH/tvmHANy49Do98rOEKLxFRErUts4dvHJk\nG8vrl7GsbqnX5cgUUniLiJSon7T+DIC3L73W40pkqim8RURK0N7e/ezqaWXl7DNZGJ3vdTkyxRTe\nIiIl6Of7ck9ufpvRE5dLkcJbRKTEdMS7eKntFeZVzeGcWWd4XY7kgcJbRKTE/PLAr8g6Wa5acJnO\nMC9RCm8RkRIST8d59tDz1ISrWT3rXK/LkTxReIuIlJCNh55nMJPgivkXE9QjP0uWwltEpERknSy/\n3L+RcCDMJfPWeF2O5JHCW0SkROzp3UdXopvVzedSEarwuhzJI4W3iEiJeLltCwArm872uBLJN4W3\niEgJcByHTW1biQTCmLrTvC5H8kzhLSJSAl7rP0J7vIMzG84gFAh5XY7kmcJbRKQEbBoaMtdjP2cE\nhbeISAnY1LaFgC/AWY26o9pMoPAWESlyHfEu9vcdYlndUsqD5V6XI9NA4S0iUuQ2t28F4FydZT5j\nKLxFRIrcprYt+PCxovFMr0uRaaLwFhEpYrFkHzu7W2mpXkhNpNrrcmSaKLxFRIrUQGqAf936bzg4\nrGzWkPlMorvWi4gUoSMDbdy76V85Gm/nnMYzuWzehV6XJNNI4S0iUmR2drdy7+bvEE/HeevCK3j7\n0mvx+zSQOpMovEVEiszD2x8jkUnw/uXvYs2c1V6XIx7QppqISBE50n+UA32HOLPeKLhnMIW3iEgR\n+e3RTQCsnnWux5WIl0562NwY8yLQ4/66G7gL+A6QBbYAH7bWOsaYDwG3AGngTmvt48aYcuBBoAmI\nATdba9tPei1ERGYAx3H47ZFNhPxBztE13TPaSe15G2PKAKy1V7p//gz4KnC7tfYywAfcaIyZDXwU\nuAi4BrjLGBMGbgM2udM+ANx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TWTLVpYwpmckde6sIlb/upCLHcehx946i4VywBv1B6hoq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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b07e2d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Whoah... there are some female \"Jacob\"s? Let's look at this:\n",
      "births['F'].fillna(0).plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uH2VZdcmEpBidsAWpjw8DVJQVUeTzTNj3eDqxWdOeyRWxm/5UEYuISMqOn+tz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\nfm7xeNAkLZm3omw3QHJLz0C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hrR3PcnNEJEWqiEUKgDHm94G9wN8qCYvkF1XEIiIiWaSK\nWEREJIuUiEVERLJIiVhERCSLlIhFRESySIlYREQki/4fWvh+kax6RVwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bae46d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now we'll normalize the births against the total for each year\n",
      "def add_birth_frac(group):\n",
      "    group['birth_frac'] = group.births / group.births.sum()\n",
      "    return group\n",
      "\n",
      "names = names.groupby(['year', 'gender']).apply(add_birth_frac)\n",
      "names.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>name</th>\n",
        "      <th>gender</th>\n",
        "      <th>births</th>\n",
        "      <th>year</th>\n",
        "      <th>birth_frac</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>      Mary</td>\n",
        "      <td> F</td>\n",
        "      <td> 7065</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.077643</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      Anna</td>\n",
        "      <td> F</td>\n",
        "      <td> 2604</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.028618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>      Emma</td>\n",
        "      <td> F</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.022013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Elizabeth</td>\n",
        "      <td> F</td>\n",
        "      <td> 1939</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.021309</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    Minnie</td>\n",
        "      <td> F</td>\n",
        "      <td> 1746</td>\n",
        "      <td> 1880</td>\n",
        "      <td> 0.019188</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "        name gender  births  year  birth_frac\n",
        "0       Mary      F    7065  1880    0.077643\n",
        "1       Anna      F    2604  1880    0.028618\n",
        "2       Emma      F    2003  1880    0.022013\n",
        "3  Elizabeth      F    1939  1880    0.021309\n",
        "4     Minnie      F    1746  1880    0.019188"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names[names.name == my_name].pivot_table('birth_frac', index='year',\n",
      "                                         columns='gender', aggfunc='sum').plot();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QF+ib1n2fqfECOwfoGvE8FOsmH1zWOWJZN5ALYFnWb4h2k4/0BvB5y7IuBQ4BX5ts0SIi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\nMYg6ItE7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114d92f90>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "My name (Jacob) seems to have dipped in popularity in the 1960s, only to make a profound rise around the time I was born (1981). I'll choose to presume that I was the trend-setter."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 2. Find names which have switched genders.\n",
      "\n",
      "This is a bit tricky: you might be tempted to use a ``groupby`` and ``apply`` over the multiple indices ``['year', 'gender', 'name']``, but if you try this you'll find that it's **very** computationally intensive.\n",
      "\n",
      "I'd suggest doing the following:\n",
      "\n",
      "- Use a pivot table, and find the total number of births for each name before some early date (say, 1920) and after some later date (say, 1980).\n",
      "- Compute the percentage of males for each name within those groups.\n",
      "- Use masking to find which names have transitioned from a low percentage to a high percentage, and vice versa.\n",
      "\n",
      "Is a name more likely to transition from female to male, or from male to female?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "births = names.pivot_table('births', index=['year', 'name'], columns='gender')\n",
      "births = births.fillna(0)\n",
      "births.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">1880</th>\n",
        "      <th>Aaron</th>\n",
        "      <td>  0</td>\n",
        "      <td> 102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ab</th>\n",
        "      <td>  0</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abbie</th>\n",
        "      <td> 71</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abbott</th>\n",
        "      <td>  0</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abby</th>\n",
        "      <td>  6</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "gender        F    M\n",
        "year name           \n",
        "1880 Aaron    0  102\n",
        "     Ab       0    5\n",
        "     Abbie   71    0\n",
        "     Abbott   0    5\n",
        "     Abby     6    0"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "early_period = births[:1920].fillna(0).sum(axis=0, level=1)\n",
      "late_period = births[1980:].fillna(0).sum(axis=0, level=1)\n",
      "\n",
      "early_period.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>gender</th>\n",
        "      <th>F</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Aaron</th>\n",
        "      <td>   0</td>\n",
        "      <td> 196</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ab</th>\n",
        "      <td>   0</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abbie</th>\n",
        "      <td> 152</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abbott</th>\n",
        "      <td>   0</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Abby</th>\n",
        "      <td>  13</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "gender    F    M\n",
        "name            \n",
        "Aaron     0  196\n",
        "Ab        0    5\n",
        "Abbie   152    0\n",
        "Abbott    0    5\n",
        "Abby     13    0"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "early_pct_M = 100 * early_period['M'] / early_period.sum(1)\n",
      "late_pct_M = 100 * late_period['M'] / late_period.sum(1)\n",
      "fractions = pd.DataFrame({'early_pct_M': early_pct_M,\n",
      "                         'late_pct_M': late_pct_M})\n",
      "fractions.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>early_pct_M</th>\n",
        "      <th>late_pct_M</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Aaban</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aabha</th>\n",
        "      <td>NaN</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aabid</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aabriella</th>\n",
        "      <td>NaN</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aadam</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "           early_pct_M  late_pct_M\n",
        "Aaban              NaN         100\n",
        "Aabha              NaN           0\n",
        "Aabid              NaN         100\n",
        "Aabriella          NaN           0\n",
        "Aadam              NaN         100"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Names which switched from female to male:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fractions[(fractions.early_pct_M < 40) & (fractions.late_pct_M > 60)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>early_pct_M</th>\n",
        "      <th>late_pct_M</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Byrd</th>\n",
        "      <td> 0</td>\n",
        "      <td> 74.163180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Donnie</th>\n",
        "      <td> 0</td>\n",
        "      <td> 89.382491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Texas</th>\n",
        "      <td> 0</td>\n",
        "      <td> 61.595547</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "        early_pct_M  late_pct_M\n",
        "Byrd              0   74.163180\n",
        "Donnie            0   89.382491\n",
        "Texas             0   61.595547"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Names which switched from male to female:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fractions[(fractions.late_pct_M < 40) & (fractions.early_pct_M > 60)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>early_pct_M</th>\n",
        "      <th>late_pct_M</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Addison</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 11.834706</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Allison</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  1.339702</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ashley</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  1.829521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Aubrey</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 27.222843</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Avery</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 35.322797</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bailey</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 19.046721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Beverly</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  1.213195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Billie</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 23.801506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dana</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 21.765366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dee</th>\n",
        "      <td>  76.923077</td>\n",
        "      <td> 31.270896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Edie</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  1.747844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hollie</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  7.608124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Holly</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  0.772741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ivey</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 38.790821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jodie</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 11.670106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Kelly</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 14.757141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Leslie</th>\n",
        "      <td>  90.804598</td>\n",
        "      <td> 29.935140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lindsay</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  3.829148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lindsey</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  4.732257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lonie</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 38.973799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lynn</th>\n",
        "      <td>  85.714286</td>\n",
        "      <td> 22.320630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Madison</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  2.156303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Marion</th>\n",
        "      <td>  62.171053</td>\n",
        "      <td> 27.595220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Merida</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Morgan</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 16.627275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nova</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  7.898499</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Orla</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 29.629630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ossie</th>\n",
        "      <td>  64.285714</td>\n",
        "      <td> 36.361646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pat</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 39.964689</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Payton</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 33.960323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Presley</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 20.217981</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Reese</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 38.903715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sandy</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 13.159606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Shelby</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 10.215817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Stacy</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 11.978544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sydney</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  5.343434</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Taylor</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 26.055568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tracy</th>\n",
        "      <td> 100.000000</td>\n",
        "      <td> 19.573779</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "         early_pct_M  late_pct_M\n",
        "Addison   100.000000   11.834706\n",
        "Allison   100.000000    1.339702\n",
        "Ashley    100.000000    1.829521\n",
        "Aubrey    100.000000   27.222843\n",
        "Avery     100.000000   35.322797\n",
        "Bailey    100.000000   19.046721\n",
        "Beverly   100.000000    1.213195\n",
        "Billie    100.000000   23.801506\n",
        "Dana      100.000000   21.765366\n",
        "Dee        76.923077   31.270896\n",
        "Edie      100.000000    1.747844\n",
        "Hollie    100.000000    7.608124\n",
        "Holly     100.000000    0.772741\n",
        "Ivey      100.000000   38.790821\n",
        "Jodie     100.000000   11.670106\n",
        "Kelly     100.000000   14.757141\n",
        "Leslie     90.804598   29.935140\n",
        "Lindsay   100.000000    3.829148\n",
        "Lindsey   100.000000    4.732257\n",
        "Lonie     100.000000   38.973799\n",
        "Lynn       85.714286   22.320630\n",
        "Madison   100.000000    2.156303\n",
        "Marion     62.171053   27.595220\n",
        "Merida    100.000000    0.000000\n",
        "Morgan    100.000000   16.627275\n",
        "Nova      100.000000    7.898499\n",
        "Orla      100.000000   29.629630\n",
        "Ossie      64.285714   36.361646\n",
        "Pat       100.000000   39.964689\n",
        "Payton    100.000000   33.960323\n",
        "Presley   100.000000   20.217981\n",
        "Reese     100.000000   38.903715\n",
        "Sandy     100.000000   13.159606\n",
        "Shelby    100.000000   10.215817\n",
        "Stacy     100.000000   11.978544\n",
        "Sydney    100.000000    5.343434\n",
        "Taylor    100.000000   26.055568\n",
        "Tracy     100.000000   19.573779"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Apparently it's much more common for a name to transition from male to female than the other way around!"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}