pandas_intro_example-names.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Names in the Wild\n",
    "\n",
    "This example is drawn from Wes McKinney's excellent book on the *Pandas* library, O'Reilly's [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do).\n",
    "\n",
    "We'll be taking a look at a freely available dataset: the database of names given to babies in the United States over the last century.\n",
    "\n",
    "First things first, we need to download the data, which can be found at http://www.ssa.gov/oact/babynames/limits.html.\n",
    "If you uncomment the following commands, it will do this automatically (note that these are linux shell commands; they will not work on Windows):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !curl -O http://www.ssa.gov/oact/babynames/names.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !mkdir -p data/names\n",
    "# !mv names.zip data/names/\n",
    "# !cd data/names/ && unzip names.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we should have a ``data/names`` directory which contains a number of text files, one for each year of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NationalReadMe.pdf yob1913.txt        yob1947.txt        yob1981.txt\r\n",
      "yob1880.txt        yob1914.txt        yob1948.txt        yob1982.txt\r\n",
      "yob1881.txt        yob1915.txt        yob1949.txt        yob1983.txt\r\n",
      "yob1882.txt        yob1916.txt        yob1950.txt        yob1984.txt\r\n",
      "yob1883.txt        yob1917.txt        yob1951.txt        yob1985.txt\r\n",
      "yob1884.txt        yob1918.txt        yob1952.txt        yob1986.txt\r\n",
      "yob1885.txt        yob1919.txt        yob1953.txt        yob1987.txt\r\n",
      "yob1886.txt        yob1920.txt        yob1954.txt        yob1988.txt\r\n",
      "yob1887.txt        yob1921.txt        yob1955.txt        yob1989.txt\r\n",
      "yob1888.txt        yob1922.txt        yob1956.txt        yob1990.txt\r\n",
      "yob1889.txt        yob1923.txt        yob1957.txt        yob1991.txt\r\n",
      "yob1890.txt        yob1924.txt        yob1958.txt        yob1992.txt\r\n",
      "yob1891.txt        yob1925.txt        yob1959.txt        yob1993.txt\r\n",
      "yob1892.txt        yob1926.txt        yob1960.txt        yob1994.txt\r\n",
      "yob1893.txt        yob1927.txt        yob1961.txt        yob1995.txt\r\n",
      "yob1894.txt        yob1928.txt        yob1962.txt        yob1996.txt\r\n",
      "yob1895.txt        yob1929.txt        yob1963.txt        yob1997.txt\r\n",
      "yob1896.txt        yob1930.txt        yob1964.txt        yob1998.txt\r\n",
      "yob1897.txt        yob1931.txt        yob1965.txt        yob1999.txt\r\n",
      "yob1898.txt        yob1932.txt        yob1966.txt        yob2000.txt\r\n",
      "yob1899.txt        yob1933.txt        yob1967.txt        yob2001.txt\r\n",
      "yob1900.txt        yob1934.txt        yob1968.txt        yob2002.txt\r\n",
      "yob1901.txt        yob1935.txt        yob1969.txt        yob2003.txt\r\n",
      "yob1902.txt        yob1936.txt        yob1970.txt        yob2004.txt\r\n",
      "yob1903.txt        yob1937.txt        yob1971.txt        yob2005.txt\r\n",
      "yob1904.txt        yob1938.txt        yob1972.txt        yob2006.txt\r\n",
      "yob1905.txt        yob1939.txt        yob1973.txt        yob2007.txt\r\n",
      "yob1906.txt        yob1940.txt        yob1974.txt        yob2008.txt\r\n",
      "yob1907.txt        yob1941.txt        yob1975.txt        yob2009.txt\r\n",
      "yob1908.txt        yob1942.txt        yob1976.txt        yob2010.txt\r\n",
      "yob1909.txt        yob1943.txt        yob1977.txt        yob2011.txt\r\n",
      "yob1910.txt        yob1944.txt        yob1978.txt        yob2012.txt\r\n",
      "yob1911.txt        yob1945.txt        yob1979.txt        yob2013.txt\r\n",
      "yob1912.txt        yob1946.txt        yob1980.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls data/names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look at one of these files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\r",
      "\r\n",
      "Anna,F,2604\r",
      "\r\n",
      "Emma,F,2003\r",
      "\r\n",
      "Elizabeth,F,1939\r",
      "\r\n",
      "Minnie,F,1746\r",
      "\r\n",
      "Margaret,F,1578\r",
      "\r\n",
      "Ida,F,1472\r",
      "\r\n",
      "Alice,F,1414\r",
      "\r\n",
      "Bertha,F,1320\r",
      "\r\n",
      "Sarah,F,1288\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head data/names/yob1880.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each file is just a comma-separated list of names, genders, and counts of babies with that name in each year.\n",
    "\n",
    "We can load these files using ``pd.read_csv``, which is specifically designed for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>Mary</th>\n",
       "      <th>F</th>\n",
       "      <th>7065</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>      Anna</td>\n",
       "      <td> F</td>\n",
       "      <td> 2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>      Emma</td>\n",
       "      <td> F</td>\n",
       "      <td> 2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> Elizabeth</td>\n",
       "      <td> F</td>\n",
       "      <td> 1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>    Minnie</td>\n",
       "      <td> F</td>\n",
       "      <td> 1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>  Margaret</td>\n",
       "      <td> F</td>\n",
       "      <td> 1578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Mary  F  7065\n",
       "0       Anna  F  2604\n",
       "1       Emma  F  2003\n",
       "2  Elizabeth  F  1939\n",
       "3     Minnie  F  1746\n",
       "4   Margaret  F  1578"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880 = pd.read_csv('data/names/yob1880.txt')\n",
    "names1880.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oops! Something went wrong. Our algorithm tried to be smart, and use the first line as index labels.\n",
    "Let's fix this by specifying the index names manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>      Mary</td>\n",
       "      <td> F</td>\n",
       "      <td> 7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>      Anna</td>\n",
       "      <td> F</td>\n",
       "      <td> 2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>      Emma</td>\n",
       "      <td> F</td>\n",
       "      <td> 2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> Elizabeth</td>\n",
       "      <td> F</td>\n",
       "      <td> 1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>    Minnie</td>\n",
       "      <td> F</td>\n",
       "      <td> 1746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name gender  births\n",
       "0       Mary      F    7065\n",
       "1       Anna      F    2604\n",
       "2       Emma      F    2003\n",
       "3  Elizabeth      F    1939\n",
       "4     Minnie      F    1746"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880 = pd.read_csv('data/names/yob1880.txt',\n",
    "                        names=['name', 'gender', 'births'])\n",
    "names1880.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks better. Now we can start playing with the data a bit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy: aggregates on values\n",
    "\n",
    "First let's think about how we might count the total number of females and males born in the US in 1880.\n",
    "\n",
    "If you're used to NumPy, you might be tempted to use masking like this:\n",
    "\n",
    "First, we can get a mask over all females & males, and then use it to select a subset of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "males = names1880[names1880.gender == 'M']\n",
    "females = names1880[names1880.gender == 'F']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take the sum of the births for each of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110491, 90993)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "males.births.sum(), females.births.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But there's an easier way to do this, using one of Pandas' very powerful features: ``groupby``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10eefb890>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = names1880.groupby('gender')\n",
    "grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This grouped object is now an abstract representation of the data, where the data is split on the given column.\n",
    "In order to actually do something with this data, we need to specify an **aggregation** operation to do across the data.\n",
    "In this case, what we want is the sum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>  90993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td> 110491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        births\n",
       "gender        \n",
       "F        90993\n",
       "M       110491"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do other aggregations as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "F          942\n",
       "M         1058\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>  96.595541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td> 104.433837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            births\n",
       "gender            \n",
       "F        96.595541\n",
       "M       104.433837"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, if we wish, we can get a description of the grouping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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></th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">F</th>\n",
       "      <th>count</th>\n",
       "      <td>  942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>   96.595541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  328.152904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>    5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>    7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>   13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>   43.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 7065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">M</th>\n",
       "      <th>count</th>\n",
       "      <td> 1058.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  104.433837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  561.232488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>    5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>    7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>   12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>   41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 9655.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   births\n",
       "gender                   \n",
       "F      count   942.000000\n",
       "       mean     96.595541\n",
       "       std     328.152904\n",
       "       min       5.000000\n",
       "       25%       7.000000\n",
       "       50%      13.000000\n",
       "       75%      43.750000\n",
       "       max    7065.000000\n",
       "M      count  1058.000000\n",
       "       mean    104.433837\n",
       "       std     561.232488\n",
       "       min       5.000000\n",
       "       25%       7.000000\n",
       "       50%      12.000000\n",
       "       75%      41.000000\n",
       "       max    9655.000000"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating multiple data sources\n",
    "\n",
    "But here we've just been looking at a single year. Let's try to put together all the data in all the years.\n",
    "To do this, we'll have to use pandas ``concat`` function to concatenate all the data together.\n",
    "First we'll create a function which loads the data as we did the above data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load all the data into a list, and call ``pd.concat`` on that list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>"
      ],
      "text/plain": [
       "        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"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = pd.concat([load_year(year) for year in range(1880, 2014)])\n",
    "names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we've done it!\n",
    "\n",
    "Let's start with something easy: we'll use ``groupby`` again to see the total number of births per year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880    201484\n",
       "1881    192700\n",
       "1882    221537\n",
       "1883    216952\n",
       "1884    243468\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births = names.groupby('year').births.sum()\n",
    "births.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the ``plot()`` method to see a quick plot of these (note that because we used the ``%matplotlib inline`` magic at the start of the notebook, the resulting plot will be shown inline within the notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c558710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "births.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The so-called \"baby boom\" generation after the second world war is abundantly clear!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use other aggregates: let's see *how many* names are used each year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4b0650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names.groupby('year').births.count().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently there's been a huge increase of the diversity of names with time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``groupby`` can also be used to add columns to the data: think of it as a *view* of the data that you're modifying. Let's add a column giving the frequency of each name within each year & gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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_freq</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>"
      ],
      "text/plain": [
       "        name gender  births  year  birth_freq\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"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_frequency(group):\n",
    "    group['birth_freq'] = group.births / group.births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'gender']).apply(add_frequency)\n",
    "names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the ``apply()`` function iterates over each group, and calls a function which modifies the group.\n",
    "This result is then re-constructed into a container which looks ike the original dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables\n",
    "\n",
    "Next we'll discuss Pivot Tables, which are an even more powerful way of (re)organizing your data.\n",
    "\n",
    "Let's say that we want to plot the men and women separately.\n",
    "We could do this by using masking, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "men = names[names.gender == 'M']\n",
    "women = names[names.gender == 'W']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we could proceed as above, using ``groupby`` to group on the year.\n",
    "But we would end up with two different views of the data. A better way to do this is to use a ``pivot_table``, which is essentially a groupby in multiple dimensions at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>  90993</td>\n",
       "      <td> 110491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>  91954</td>\n",
       "      <td> 100746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td> 107850</td>\n",
       "      <td> 113687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td> 112322</td>\n",
       "      <td> 104630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td> 129022</td>\n",
       "      <td> 114446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender       F       M\n",
       "year                  \n",
       "1880     90993  110491\n",
       "1881     91954  100746\n",
       "1882    107850  113687\n",
       "1883    112322  104630\n",
       "1884    129022  114446"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births = names.pivot_table('births',\n",
    "                           index='year', columns='gender',\n",
    "                           aggfunc=sum)\n",
    "births.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this has grouped the index by the value of ``year``, and grouped the columns by the value of ``gender``.\n",
    "Let's plot the results now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b36a9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "births.plot(title='Total Births');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name Evolution Over Time\n",
    "\n",
    "Some names have shifted from being girls names to being boys names. Let's take a look at some of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x101935690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names_to_check = ['Allison', 'Alison']\n",
    "\n",
    "# filter on just the names we're interested in\n",
    "births = names[names.name.isin(names_to_check)]\n",
    "\n",
    "# pivot table to get year vs. gender\n",
    "births = births.pivot_table('births', index='year', columns='gender')\n",
    "\n",
    "# fill all NaNs with zeros\n",
    "births = births.fillna(0)\n",
    "\n",
    "# normalize along columns\n",
    "births = births.div(births.sum(1), axis=0)\n",
    "\n",
    "births.plot(title='Fraction of babies named Allison');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that prior to about 1905, all babies named Allison were male. Over the 20th century, this reversed, until the end of the century nearly all Allisons were female!\n",
    "\n",
    "There's some noise in this data: we can smooth it out a bit by using a 5-year rolling mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116dd8610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.rolling_mean(births, 5).plot(title=\"Allisons: 5-year moving average\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives a smoother picture of the transition, and is an example of the bias/variance tradeoff that we'll often see in modeling: a smoother model has less variance (variation due to sampling or other noise) but at the expense of more bias (the model systematically mis-represents the data slightly).\n",
    "\n",
    "We'll discuss this type of tradeoff more in coming sessions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where to Find More\n",
    "\n",
    "We've just scratched the surface of what can be done with Pandas, but we'll get a chance to play with this more in the breakout session coming up.\n",
    "\n",
    "For more information on using Pandas, check out the [pandas documentation](http://pandas.pydata.org/) or the book [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Pandas creator Wes McKinney."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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