Day1_StatAndML_3_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pandas](http://pandas.pydata.org/), written by Wes McKinney and others, is a powerful module that you can think of as _Excel for Python_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this section, we'll be closely following the presentation in Wes McKinney's book [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do).  I can't recommend this book enough!  I've added context to give you a solid understanding of Pandas, as well as exercises to pace the lesson and give you an opportunity to experiment.\n",
    "\n",
    "**Warning:**  Learning to use Pandas well takes time and practice.  There are no shortcuts :-("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, like `numpy`, most people import `pandas` with the shorthand `pd`.  Its two key data structures, `Series` and `DataFrame`, are so common that people tend to import them directly as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `Series` is like a NumPy array, but with a set of element labels, called an _index_.  Think of it as a single column in a spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -5\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series([4,7,-5,3])\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the index is just the set of numbers $0, 1, \\ldots, N-1$, so you can access the rows in a series like an array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also grab the underlying values and index directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  7, -5,  3])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a series with a different index as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 = Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c'])\n",
    "obj2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can use that index label to get one or more rows in the series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2['d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[['a', 'c']]  # Get more than one row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    3\n",
       "a   -5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series have a defined ordering\n",
    "obj2[['c', 'a']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like a NumPy array, you can perform element-wise operations and filtering on Series, but unlike NumPy, these operations preserve the indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2[obj2 > 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    5\n",
       "b    8\n",
       "a   -4\n",
       "c    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj2 + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series can also be thought of as \"ordered dictionaries\".  In fact, you can create a series from a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        11259000\n",
       "France         67063000\n",
       "Germany        81276000\n",
       "Luxembourg       570000\n",
       "Netherlands    16933000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_pops = {\n",
    "    'Belgium':     11259000,\n",
    "    'Netherlands': 16933000,\n",
    "    'Luxembourg':    570000,\n",
    "    'France':      67063000,\n",
    "    'Germany':     81276000,\n",
    "}\n",
    "obj3 = Series(country_pops)\n",
    "obj3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, all the keys in the dictionary are included, in sorted order.  You can be more specific by specifying the index explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        11259000\n",
       "Netherlands    16933000\n",
       "Spain               NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4 = Series(country_pops, index=['Belgium', 'Netherlands', 'Spain'])\n",
    "obj4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that because there was no value for `'Spain'` in `country_pops`, the value in the series is `NaN` (Not-a-Number).  This is Pandas' standard way of representing **missing data**.  You can see if each element in a series was missing data with the `isnull` and `notnull` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        False\n",
       "Netherlands    False\n",
       "Spain           True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium         True\n",
       "Netherlands     True\n",
       "Spain          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.notnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vector operations between `Series` align along the index labels, not along the row numbers, which can be very useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        11259000\n",
       "France         67063000\n",
       "Germany        81276000\n",
       "Luxembourg       570000\n",
       "Netherlands    16933000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        11259000\n",
       "Netherlands    16933000\n",
       "Spain               NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        22518000\n",
       "France              NaN\n",
       "Germany             NaN\n",
       "Luxembourg          NaN\n",
       "Netherlands    33866000\n",
       "Spain               NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 + obj4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can assign names to both the `Series` itself and the `index`.  Think of these as column names in a spreasheet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Belgium        11259000\n",
       "Netherlands    16933000\n",
       "Spain               NaN\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4.name = 'population'\n",
    "obj4.index.name = 'country'\n",
    "obj4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.1 Starting from a dictionary, build a `Series` to represent the population of Belgium broken down by province.  Give both the series and the index a proper name.  From that `Series`, use indexing to sort the `Series` by number of customers.**  \n",
    "Note: We'll see how to sort `Series` easily later.  For now, use explicit indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DataFrame` is the 2D analog to the 1D `Series` object.  Think of it as a spreadsheet.  You have a set of columns (each of a single, fixed type).  Both the columns and the rows have an index as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common way to create a `DataFrame` is with two nested dictionaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>1913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>1913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  gdp_per_capita  year\n",
       "0      BE            4220  1913\n",
       "1      BE            5462  1950\n",
       "2      BE           21205  2003\n",
       "3      NL            4049  1913\n",
       "4      NL            5996  1950\n",
       "5      NL           21480  2003"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From https://en.wikipedia.org/wiki/List_of_regions_by_past_GDP_%28PPP%29_per_capita\n",
    "data = {\n",
    "    'country': ['BE', 'BE', 'BE', 'NL', 'NL', 'NL'],\n",
    "    'year': [1913, 1950, 2003, 1913, 1950, 2003],\n",
    "    'gdp_per_capita': [4220, 5462, 21205, 4049, 5996, 21480]\n",
    "}\n",
    "frame = DataFrame(data)\n",
    "frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that IPython has special support for rendering `DataFrame`s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reorder the columns as we wish and give the rows some other index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita\n",
       "one    1913      BE            4220\n",
       "two    1950      BE            5462\n",
       "three  2003      BE           21205\n",
       "four   1913      NL            4049\n",
       "five   1950      NL            5996\n",
       "six    2003      NL           21480"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame(data,\n",
    "          columns=['year', 'country', 'gdp_per_capita'],\n",
    "          index=['one', 'two', 'three', 'four', 'five', 'six'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with Series' any columns you specify not in the dictionary are filled with missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita beer_quality\n",
       "one    1913      BE            4220          NaN\n",
       "two    1950      BE            5462          NaN\n",
       "three  2003      BE           21205          NaN\n",
       "four   1913      NL            4049          NaN\n",
       "five   1950      NL            5996          NaN\n",
       "six    2003      NL           21480          NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2 = DataFrame(data,\n",
    "                   columns=['year', 'country', 'gdp_per_capita', 'beer_quality'],\n",
    "                   index=['one', 'two', 'three', 'four', 'five', 'six'])\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with Series and their indices, we can get at the underlying data in a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'year', u'country', u'gdp_per_capita', u'beer_quality'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'one', u'two', u'three', u'four', u'five', u'six'], dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1913, 'BE', 4220, nan],\n",
       "       [1950, 'BE', 5462, nan],\n",
       "       [2003, 'BE', 21205, nan],\n",
       "       [1913, 'NL', 4049, nan],\n",
       "       [1950, 'NL', 5996, nan],\n",
       "       [2003, 'NL', 21480, nan]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can select out one column from a DataFrame, either like a dictionary or (thanks to some Python magic) as if it was an attribute of a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      1913\n",
       "two      1950\n",
       "three    2003\n",
       "four     1913\n",
       "five     1950\n",
       "six      2003\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      BE\n",
       "two      BE\n",
       "three    BE\n",
       "four     NL\n",
       "five     NL\n",
       "six      NL\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how a column in a DataFrame is represented as a Series, with an appropriate index and name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.2 Build a DataFrame that captures how the population in each  province of Flanders changed in the years 2013, 2014 and 2015.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can index rows with the `.ix[]` notation as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year              1913\n",
       "country             BE\n",
       "gdp_per_capita    4220\n",
       "beer_quality       NaN\n",
       "Name: one, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.ix['one']    # Single row, returned as a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year country  gdp_per_capita beer_quality\n",
       "one  1913      BE            4220          NaN\n",
       "two  1950      BE            5462          NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.ix[['one', 'two']]  # Multiple rows, returned as a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use slicing notation, but unlike lists and NumPy arrays, DataFrame slices have an **inclusive** endpoint:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita beer_quality\n",
       "one    1913      BE            4220          NaN\n",
       "two    1950      BE            5462          NaN\n",
       "three  2003      BE           21205          NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['one':'three']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also select only some of the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year country  gdp_per_capita\n",
       "one  1913      BE            4220\n",
       "two  1950      BE            5462"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.ix[['one', 'two'], ['year', 'country', 'gdp_per_capita']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It might be easier to `drop` certain rows or columns instead of selecting the complement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year country  gdp_per_capita beer_quality\n",
       "two   1950      BE            5462          NaN\n",
       "four  1913      NL            4049          NaN\n",
       "five  1950      NL            5996          NaN\n",
       "six   2003      NL           21480          NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.drop(['one', 'three'])  # Returns a copy with rows removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  gdp_per_capita beer_quality\n",
       "one        BE            4220          NaN\n",
       "two        BE            5462          NaN\n",
       "three      BE           21205          NaN\n",
       "four       NL            4049          NaN\n",
       "five       NL            5996          NaN\n",
       "six        NL           21480          NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.drop('year', axis=1)  # Returns a copy with columns removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can modify columns in a frame: either set them to the same value, or specify a list of values, one for each row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita  beer_quality\n",
       "one    1913      BE            4220            10\n",
       "two    1950      BE            5462            10\n",
       "three  2003      BE           21205            10\n",
       "four   1913      NL            4049            10\n",
       "five   1950      NL            5996            10\n",
       "six    2003      NL           21480            10"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.beer_quality = 10.0\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita  beer_quality\n",
       "one    1913      BE            4220            10\n",
       "two    1950      BE            5462            10\n",
       "three  2003      BE           21205            10\n",
       "four   1913      NL            4049             4\n",
       "five   1950      NL            5996             3\n",
       "six    2003      NL           21480             2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Not quite right...\n",
    "frame2.beer_quality = [10.0, 10.0, 10.0, 4.0, 3.0, 2.0]\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also assign a Series to a column, in which case rows are matched by index and missing elements are set to `NaN`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one     10\n",
       "four     2\n",
       "five     1\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = Series([10.0, 2.0, 1.0], index=['one', 'four', 'five'])\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita  beer_quality\n",
       "one    1913      BE            4220            10\n",
       "two    1950      BE            5462           NaN\n",
       "three  2003      BE           21205           NaN\n",
       "four   1913      NL            4049             2\n",
       "five   1950      NL            5996             1\n",
       "six    2003      NL           21480           NaN"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.beer_quality = val\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create a new column by simply assigning something to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>beer_quality</th>\n",
       "      <th>livable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita  beer_quality livable\n",
       "one    1913      BE            4220            10    True\n",
       "two    1950      BE            5462           NaN   False\n",
       "three  2003      BE           21205           NaN   False\n",
       "four   1913      NL            4049             2   False\n",
       "five   1950      NL            5996             1   False\n",
       "six    2003      NL           21480           NaN   False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2['livable'] = (frame2.beer_quality >= 9.0)\n",
    "# Can't do this though: frame2.livable = ...\n",
    "\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also delete columns in place with `del`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>livable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1913</td>\n",
       "      <td>BE</td>\n",
       "      <td>4220</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1950</td>\n",
       "      <td>BE</td>\n",
       "      <td>5462</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2003</td>\n",
       "      <td>BE</td>\n",
       "      <td>21205</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>1913</td>\n",
       "      <td>NL</td>\n",
       "      <td>4049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>1950</td>\n",
       "      <td>NL</td>\n",
       "      <td>5996</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>2003</td>\n",
       "      <td>NL</td>\n",
       "      <td>21480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year country  gdp_per_capita livable\n",
       "one    1913      BE            4220    True\n",
       "two    1950      BE            5462   False\n",
       "three  2003      BE           21205   False\n",
       "four   1913      NL            4049   False\n",
       "five   1950      NL            5996   False\n",
       "six    2003      NL           21480   False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del frame2['beer_quality']\n",
    "frame2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.3 Quick: what's the difference between `frame2.drop('beer_quality')` and `del frame2['beer_quality']`?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also create a `DataFrame` using nested dictionaries.  The outer keys are the columns, the inner ones are the rows, and missing data is marked appropriately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Netherlands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>4220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>5462</td>\n",
       "      <td>5996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>21205</td>\n",
       "      <td>21480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Belgium  Netherlands\n",
       "1913     4220          NaN\n",
       "1950     5462         5996\n",
       "1973      NaN        13082\n",
       "2003    21205        21480"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    'Belgium': {1913: 4220.0, 1950: 5462.0, 2003: 21205.0},\n",
    "    'Netherlands': {1950: 5996.0, 1973: 13082.0, 2003: 21480.0}\n",
    "}\n",
    "frame3 = DataFrame(data)\n",
    "frame3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like with matrices, you can transpose a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1913</th>\n",
       "      <th>1950</th>\n",
       "      <th>1973</th>\n",
       "      <th>2003</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>4220</td>\n",
       "      <td>5462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5996</td>\n",
       "      <td>13082</td>\n",
       "      <td>21480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1913  1950   1973   2003\n",
       "Belgium      4220  5462    NaN  21205\n",
       "Netherlands   NaN  5996  13082  21480"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's good form to explicitly name the row and column indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>country</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Netherlands</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>1913</th>\n",
       "      <td>4220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>5462</td>\n",
       "      <td>5996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>21205</td>\n",
       "      <td>21480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country  Belgium  Netherlands\n",
       "year                         \n",
       "1913        4220          NaN\n",
       "1950        5462         5996\n",
       "1973         NaN        13082\n",
       "2003       21205        21480"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.index.name = 'year'\n",
    "frame3.columns.name = 'country'\n",
    "frame3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.5 Let's get some practice with slicing and dicing data.  Given the following DataFrame, extract:**\n",
    "1. **the first 2 rows**\n",
    "2. **the second and third column**\n",
    "3. **all rows where gdp_per_capita exceeds 5000**\n",
    "\n",
    "```\n",
    "data = {\n",
    "    'year': [1913, 1950, 2003, 1913, 1950, 2003],\n",
    "    'country': ['BE', 'BE', 'BE', 'NL', 'NL', 'NL'],\n",
    "    'gdp_per_capita': [4220, 5462, 21205, 4049, 5996, 21480]\n",
    "}\n",
    "frame = DataFrame(data, columns=['year', 'country', 'gdp_per_capita'])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From now on, we'll quickly survey a few DataFrame operations, then work through a long and interesting example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with Series, arithmetic between DataFrames is defined and aligns over row and column indices.  For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df1 = DataFrame(np.arange(9.).reshape((3,3)),\n",
    "                columns=['b', 'c', 'd'],\n",
    "                index=['Belgium', 'Netherlands', 'Luxembourg'])\n",
    "df2 = DataFrame(np.arange(12.).reshape((4,3)),\n",
    "                columns=['b', 'd', 'e'],\n",
    "                index=['France', 'Belgium', 'Netherlands', 'Germany'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             b  c  d\n",
       "Belgium      0  1  2\n",
       "Netherlands  3  4  5\n",
       "Luxembourg   6  7  8"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             b   d   e\n",
       "France       0   1   2\n",
       "Belgium      3   4   5\n",
       "Netherlands  6   7   8\n",
       "Germany      9  10  11"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              b   c   d   e\n",
       "Belgium       3 NaN   6 NaN\n",
       "France      NaN NaN NaN NaN\n",
       "Germany     NaN NaN NaN NaN\n",
       "Luxembourg  NaN NaN NaN NaN\n",
       "Netherlands   9 NaN  12 NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 + df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in places where either `df1` or `df2` was missing data, the result of `df1 + df2` will be missing as well.  We can instead tell `pandas` to assume some other value, like 0, if we use the `add` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             b  c  d\n",
       "Belgium      0  1  2\n",
       "Netherlands  3  4  5\n",
       "Luxembourg   6  7  8"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             b   d   e\n",
       "France       0   1   2\n",
       "Belgium      3   4   5\n",
       "Netherlands  6   7   8\n",
       "Germany      9  10  11"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             b   c   d   e\n",
       "Belgium      3   1   6   5\n",
       "France       0 NaN   1   2\n",
       "Germany      9 NaN  10  11\n",
       "Luxembourg   6   7   8 NaN\n",
       "Netherlands  9   4  12   8"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.add(df2, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that there are still some entries for which data wasn't available in either `df1` or `df2`, and those are still marked as missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also analogous methods for `-` (`sub`), `*` (`mul`) and `/` (`div`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also do arithmetic between DataFrames and Series.  `pandas` lines these up along the columns of the DataFrame, then uses the same Series for every row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BeerLovers</th>\n",
       "      <th>WineLovers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    BeerLovers  WineLovers\n",
       "BE          55          10\n",
       "FR          15          30"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame({\n",
    "        'BeerLovers': {'BE': 55, 'FR': 15},\n",
    "        'WineLovers': {'BE': 10, 'FR': 30}\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BeerLovers</th>\n",
       "      <th>WineLovers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BeerLovers  WineLovers\n",
       "BE             55          10\n",
       "FR             15          30\n",
       "Total          70          40"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix['Total'] = df.ix['BE'] + df.ix['FR']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BeerLovers</th>\n",
       "      <th>WineLovers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BeerLovers  WineLovers\n",
       "BE       0.785714        0.25\n",
       "FR       0.214286        0.75\n",
       "Total    1.000000        1.00"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype(float) / df.ix['Total']  # Need to avoid integer division!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps that's a bit more natural in the other orientation.  We can specify an axis along which to replicate the series when using the explicit `div` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BE</th>\n",
       "      <th>FR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeerLovers</th>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WineLovers</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BE  FR\n",
       "BeerLovers  55  15\n",
       "WineLovers  10  30"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame({\n",
    "        'BE': {'BeerLovers': 55, 'WineLovers': 10},\n",
    "        'FR': {'BeerLovers': 15, 'WineLovers': 30}\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BE</th>\n",
       "      <th>FR</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeerLovers</th>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WineLovers</th>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BE  FR  Total\n",
       "BeerLovers  55  15     70\n",
       "WineLovers  10  30     40"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total'] = df.BE + df.FR\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BE</th>\n",
       "      <th>FR</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BeerLovers</th>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WineLovers</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BE        FR  Total\n",
       "BeerLovers  0.785714  0.214286      1\n",
       "WineLovers  0.250000  0.750000      1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.astype(float).div(df.Total, axis=0)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BeerLovers    1\n",
       "WineLovers    1\n",
       "Name: Total, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Total']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or maybe you want the proportions within each country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            BE  FR  Total\n",
      "BeerLovers  55  15     70\n",
      "WineLovers  10  30     40\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BeerLovers</th>\n",
       "      <th>WineLovers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BeerLovers  WineLovers\n",
       "BE             55          10\n",
       "FR             15          30\n",
       "Total          70          40"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df2 = df.T\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BeerLovers</th>\n",
       "      <th>WineLovers</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BeerLovers  WineLovers  Total\n",
       "BE             55          10     65\n",
       "FR             15          30     45\n",
       "Total          70          40    110"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Total'] = df2.BeerLovers + df2.WineLovers\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BeerLovers</th>\n",
       "      <th>WineLovers</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BeerLovers  WineLovers  Total\n",
       "BE       0.846154    0.153846      1\n",
       "FR       0.333333    0.666667      1\n",
       "Total    0.636364    0.363636      1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.div(df2.Total, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can apply NumPy functions element by element to a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.260945</td>\n",
       "      <td>0.644361</td>\n",
       "      <td>0.554564</td>\n",
       "      <td>0.632985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.383714</td>\n",
       "      <td>0.674574</td>\n",
       "      <td>0.756515</td>\n",
       "      <td>0.595260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.416512</td>\n",
       "      <td>0.526751</td>\n",
       "      <td>0.380712</td>\n",
       "      <td>0.967233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0  0.260945  0.644361  0.554564  0.632985\n",
       "1  0.383714  0.674574  0.756515  0.595260\n",
       "2  0.416512  0.526751  0.380712  0.967233"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(np.random.rand(3,4), columns=['a','b','c','d'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.298156</td>\n",
       "      <td>1.904770</td>\n",
       "      <td>1.741181</td>\n",
       "      <td>1.883224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.467726</td>\n",
       "      <td>1.963197</td>\n",
       "      <td>2.130836</td>\n",
       "      <td>1.813502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.516663</td>\n",
       "      <td>1.693421</td>\n",
       "      <td>1.463326</td>\n",
       "      <td>2.630655</td>\n",
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       "          a         b         c         d\n",
       "0  1.298156  1.904770  1.741181  1.883224\n",
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       "2  1.516663  1.693421  1.463326  2.630655"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can apply a function to each row or column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def dynamic_range(xs):\n",
    "    return xs.max() - xs.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.155568\n",
       "b    0.147823\n",
       "c    0.375802\n",
       "d    0.371973\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(dynamic_range)  # Apply it to a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.383417\n",
       "1    0.372800\n",
       "2    0.586521\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(dynamic_range, axis=1) # Apply it to each row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of collapsing the DataFrame to a Series, the applied function can itself return a Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dynamic_range_2(xs):\n",
    "    return Series([xs.max(), xs.min()], index=['min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "          a         b         c         d\n",
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       "2  0.416512  0.526751  0.380712  0.967233"
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     "execution_count": 66,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.416512</td>\n",
       "      <td>0.674574</td>\n",
       "      <td>0.756515</td>\n",
       "      <td>0.967233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.260945</td>\n",
       "      <td>0.526751</td>\n",
       "      <td>0.380712</td>\n",
       "      <td>0.595260</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a         b         c         d\n",
       "min  0.416512  0.674574  0.756515  0.967233\n",
       "max  0.260945  0.526751  0.380712  0.595260"
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     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.apply(dynamic_range_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.644361</td>\n",
       "      <td>0.260945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.756515</td>\n",
       "      <td>0.383714</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.967233</td>\n",
       "      <td>0.380712</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        min       max\n",
       "0  0.644361  0.260945\n",
       "1  0.756515  0.383714\n",
       "2  0.967233  0.380712"
      ]
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     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(dynamic_range_2, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of predefined functions that are useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.554564</td>\n",
       "      <td>0.632985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.383714</td>\n",
       "      <td>0.674574</td>\n",
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       "      <td>0.595260</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.416512</td>\n",
       "      <td>0.526751</td>\n",
       "      <td>0.380712</td>\n",
       "      <td>0.967233</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0  0.260945  0.644361  0.554564  0.632985\n",
       "1  0.383714  0.674574  0.756515  0.595260\n",
       "2  0.416512  0.526751  0.380712  0.967233"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.061171\n",
       "b    1.845687\n",
       "c    1.691791\n",
       "d    2.195478\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.092856\n",
       "1    2.410063\n",
       "2    2.291208\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>0.416512</td>\n",
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       "      <td>0.380712</td>\n",
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       "          a         b         c         d\n",
       "0  0.260945  0.644361  0.554564  0.632985\n",
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       "2  0.416512  0.526751  0.380712  0.967233"
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     "execution_count": 72,
     "metadata": {},
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.353724\n",
       "b    0.615229\n",
       "c    0.563930\n",
       "d    0.731826\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>a</th>\n",
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       "      <th>c</th>\n",
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       "      <td>0.632985</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.644659</td>\n",
       "      <td>1.318936</td>\n",
       "      <td>1.311078</td>\n",
       "      <td>1.228245</td>\n",
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       "          a         b         c         d\n",
       "0  0.260945  0.644361  0.554564  0.632985\n",
       "1  0.644659  1.318936  1.311078  1.228245\n",
       "2  1.061171  1.845687  1.691791  2.195478"
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     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A particularly useful method for exploring data is `describe`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.353724</td>\n",
       "      <td>0.615229</td>\n",
       "      <td>0.563930</td>\n",
       "      <td>0.731826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.082005</td>\n",
       "      <td>0.078099</td>\n",
       "      <td>0.188076</td>\n",
       "      <td>0.204739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.260945</td>\n",
       "      <td>0.526751</td>\n",
       "      <td>0.380712</td>\n",
       "      <td>0.595260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.322329</td>\n",
       "      <td>0.585556</td>\n",
       "      <td>0.467638</td>\n",
       "      <td>0.614123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.383714</td>\n",
       "      <td>0.644361</td>\n",
       "      <td>0.554564</td>\n",
       "      <td>0.632985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.400113</td>\n",
       "      <td>0.659468</td>\n",
       "      <td>0.655539</td>\n",
       "      <td>0.800109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.416512</td>\n",
       "      <td>0.674574</td>\n",
       "      <td>0.756515</td>\n",
       "      <td>0.967233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              a         b         c         d\n",
       "count  3.000000  3.000000  3.000000  3.000000\n",
       "mean   0.353724  0.615229  0.563930  0.731826\n",
       "std    0.082005  0.078099  0.188076  0.204739\n",
       "min    0.260945  0.526751  0.380712  0.595260\n",
       "25%    0.322329  0.585556  0.467638  0.614123\n",
       "50%    0.383714  0.644361  0.554564  0.632985\n",
       "75%    0.400113  0.659468  0.655539  0.800109\n",
       "max    0.416512  0.674574  0.756515  0.967233"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On non-numeric data, `describe` yields different results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'a', 'b', 'a', 'b']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['a', 'b'] * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     16\n",
       "unique     3\n",
       "top        a\n",
       "freq       8\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = Series(['a', 'a', 'b', 'c'] * 4)\n",
    "obj.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One final topic we'll touch on is how to read and write CSV files.  CSV files (or also TSV files) are the most common way of getting large amounts of data into and out of Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a CSV file in the current directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We haven't discussed Python's functions for reading and writing\n",
    "# text in files, but I hope these are self-explanatory\n",
    "f = open('mysample.csv', 'w')\n",
    "f.write(\n",
    "\"\"\"Year,Belgium,Netherlands\n",
    "1913,4220,4049\n",
    "1950,5462,5996\n",
    "2003,21205,21480\n",
    "\"\"\")\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the file in place, here's how we can read it into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Netherlands</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1913</td>\n",
       "      <td>4220</td>\n",
       "      <td>4049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950</td>\n",
       "      <td>5462</td>\n",
       "      <td>5996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>21205</td>\n",
       "      <td>21480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Belgium  Netherlands\n",
       "0  1913     4220         4049\n",
       "1  1950     5462         5996\n",
       "2  2003    21205        21480"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('mysample.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you show the help for `read_csv`, you'll see it has a *ton* of options.  With the right options, you can read almost any CSV file out there into the exact DataFrame of your dreams.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's save the data to a csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('mysample2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",Year,Belgium,Netherlands\n",
      "0,1913,4220,4049\n",
      "1,1950,5462,5996\n",
      "2,2003,21205,21480\n"
     ]
    }
   ],
   "source": [
    "# Read out file and print it\n",
    "for line in open('mysample2.csv', 'r'):\n",
    "    print line.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extended demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo closely follows the extended example in Wes McKinney's book that traces US baby names through the years.  We'll introduce lots of functionality \"by osmosis\".  The goal is that you can get an idea of what kinds of higher-order manipulations there exist in Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the US, the equivalent to an ID number in the National Registry is called a _Social Security Number_.  Everyone is assigned one of these at birth (or immigration).  As a result, the Social Security Administration has a detailed record of the names of everyone born in the US over the last century:\n",
    "\n",
    "http://www.ssa.gov/oact/babynames/limits.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.6 Go to the website above and download the \"National Data\" set.  Extract the files into a folder \"names\" next to your IPython Notebook**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The names data set is provided as one CSV file per year.  Let's look at one of these files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emma,F,20799\n",
      "Olivia,F,19674\n",
      "Sophia,F,18490\n",
      "Isabella,F,16950\n",
      "Ava,F,15586\n",
      "Mia,F,13442\n",
      "Emily,F,12562\n",
      "Abigail,F,11985\n",
      "Madison,F,10247\n",
      "Charlotte,F,10048\n"
     ]
    }
   ],
   "source": [
    "for line in open('names/yob2014.txt', 'r').readlines()[:10]:\n",
    "    print line.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so the format is \"name\", \"sex\" and \"count\".  Let's read one of the files into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('names/yob1880.txt',\n",
    "                        names=['name', 'sex', 'births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>Philo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>Phineas</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>Presley</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>Ransom</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>Reece</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>Rene</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>Roswell</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>Rowland</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>Sampson</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>Samual</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>Santos</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>Schuyler</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>Sheppard</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>Spurgeon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>Starling</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>Sylvanus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>Theadore</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>Theophile</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>Tilmon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>Tommy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>Vann</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>Wes</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>Winston</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>Wood</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  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\n",
       "5      Margaret   F    1578\n",
       "6           Ida   F    1472\n",
       "7         Alice   F    1414\n",
       "8        Bertha   F    1320\n",
       "9         Sarah   F    1288\n",
       "10        Annie   F    1258\n",
       "11        Clara   F    1226\n",
       "12         Ella   F    1156\n",
       "13     Florence   F    1063\n",
       "14         Cora   F    1045\n",
       "15       Martha   F    1040\n",
       "16        Laura   F    1012\n",
       "17       Nellie   F     995\n",
       "18        Grace   F     982\n",
       "19       Carrie   F     949\n",
       "20        Maude   F     858\n",
       "21        Mabel   F     808\n",
       "22       Bessie   F     796\n",
       "23       Jennie   F     793\n",
       "24     Gertrude   F     787\n",
       "25        Julia   F     783\n",
       "26       Hattie   F     769\n",
       "27        Edith   F     768\n",
       "28       Mattie   F     704\n",
       "29         Rose   F     700\n",
       "...         ...  ..     ...\n",
       "1970      Philo   M       5\n",
       "1971    Phineas   M       5\n",
       "1972    Presley   M       5\n",
       "1973     Ransom   M       5\n",
       "1974      Reece   M       5\n",
       "1975       Rene   M       5\n",
       "1976    Roswell   M       5\n",
       "1977    Rowland   M       5\n",
       "1978    Sampson   M       5\n",
       "1979     Samual   M       5\n",
       "1980     Santos   M       5\n",
       "1981   Schuyler   M       5\n",
       "1982   Sheppard   M       5\n",
       "1983   Spurgeon   M       5\n",
       "1984   Starling   M       5\n",
       "1985   Sylvanus   M       5\n",
       "1986   Theadore   M       5\n",
       "1987  Theophile   M       5\n",
       "1988     Tilmon   M       5\n",
       "1989      Tommy   M       5\n",
       "1990    Unknown   M       5\n",
       "1991       Vann   M       5\n",
       "1992        Wes   M       5\n",
       "1993    Winston   M       5\n",
       "1994       Wood   M       5\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that IPython only shows the beginning and end of a long DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Straight away, let's count how many men and women were born in 1880.  We'll use the `groupby` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110491\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .sum() adds up the numbers in the 'births' column\n",
    "# separately for each group\n",
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.7 How many different male and female names were used in 1880?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to paste together all the names from all the years.  I'll do two years, then you'll do all the years as an exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trick is that we can paste two DataFrames one after another using `pd.concat`.  If we just paste the frames directly, we'll lose the information about years because that was implicit in the filenames.  But we can add it to each data frame easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names1880 = pd.read_csv('names/yob1880.txt', names=['name', 'sex', 'births'])\n",
    "names1880['year'] = 1880\n",
    "\n",
    "names1881 = pd.read_csv('names/yob1881.txt', names=['name', 'sex', 'births'])\n",
    "names1881['year'] = 1881\n",
    "\n",
    "# We use ignore_index to create a new index column, instead of \n",
    "# pasting the indices from each smaller DataFrame\n",
    "names = pd.concat([names1880, names1881], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3905</th>\n",
       "      <td>Mercer</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3906</th>\n",
       "      <td>Monte</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3907</th>\n",
       "      <td>Montgomery</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3908</th>\n",
       "      <td>Nolan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3909</th>\n",
       "      <td>Okey</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3910</th>\n",
       "      <td>Orley</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>Page</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3912</th>\n",
       "      <td>Philo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3913</th>\n",
       "      <td>Primus</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3914</th>\n",
       "      <td>Prosper</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3915</th>\n",
       "      <td>Pryor</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3916</th>\n",
       "      <td>Rene</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3917</th>\n",
       "      <td>Robin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3918</th>\n",
       "      <td>Roll</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3919</th>\n",
       "      <td>Rolland</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3920</th>\n",
       "      <td>Seward</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3921</th>\n",
       "      <td>Shannon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>Talmage</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>Urban</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>Vaughn</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>Verner</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>Waverly</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>Webster</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3928</th>\n",
       "      <td>Weldon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3929</th>\n",
       "      <td>Wells</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>Wiliam</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>Wilton</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3932</th>\n",
       "      <td>Wing</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>Wood</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3935 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name sex  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\n",
       "5       Margaret   F    1578  1880\n",
       "6            Ida   F    1472  1880\n",
       "7          Alice   F    1414  1880\n",
       "8         Bertha   F    1320  1880\n",
       "9          Sarah   F    1288  1880\n",
       "10         Annie   F    1258  1880\n",
       "11         Clara   F    1226  1880\n",
       "12          Ella   F    1156  1880\n",
       "13      Florence   F    1063  1880\n",
       "14          Cora   F    1045  1880\n",
       "15        Martha   F    1040  1880\n",
       "16         Laura   F    1012  1880\n",
       "17        Nellie   F     995  1880\n",
       "18         Grace   F     982  1880\n",
       "19        Carrie   F     949  1880\n",
       "20         Maude   F     858  1880\n",
       "21         Mabel   F     808  1880\n",
       "22        Bessie   F     796  1880\n",
       "23        Jennie   F     793  1880\n",
       "24      Gertrude   F     787  1880\n",
       "25         Julia   F     783  1880\n",
       "26        Hattie   F     769  1880\n",
       "27         Edith   F     768  1880\n",
       "28        Mattie   F     704  1880\n",
       "29          Rose   F     700  1880\n",
       "...          ...  ..     ...   ...\n",
       "3905      Mercer   M       5  1881\n",
       "3906       Monte   M       5  1881\n",
       "3907  Montgomery   M       5  1881\n",
       "3908       Nolan   M       5  1881\n",
       "3909        Okey   M       5  1881\n",
       "3910       Orley   M       5  1881\n",
       "3911        Page   M       5  1881\n",
       "3912       Philo   M       5  1881\n",
       "3913      Primus   M       5  1881\n",
       "3914     Prosper   M       5  1881\n",
       "3915       Pryor   M       5  1881\n",
       "3916        Rene   M       5  1881\n",
       "3917       Robin   M       5  1881\n",
       "3918        Roll   M       5  1881\n",
       "3919     Rolland   M       5  1881\n",
       "3920      Seward   M       5  1881\n",
       "3921     Shannon   M       5  1881\n",
       "3922     Talmage   M       5  1881\n",
       "3923       Urban   M       5  1881\n",
       "3924      Vaughn   M       5  1881\n",
       "3925      Verner   M       5  1881\n",
       "3926     Waverly   M       5  1881\n",
       "3927     Webster   M       5  1881\n",
       "3928      Weldon   M       5  1881\n",
       "3929       Wells   M       5  1881\n",
       "3930      Wiliam   M       5  1881\n",
       "3931      Wilton   M       5  1881\n",
       "3932        Wing   M       5  1881\n",
       "3933        Wood   M       5  1881\n",
       "3934      Wright   M       5  1881\n",
       "\n",
       "[3935 rows x 4 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.8 Write a function to read all names data from 1880 to 2014 into a single `DataFrame`.  Store the result in `names`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# HERE'S THE SOLUTION TO THE EXERCISE!\n",
    "#\n",
    "# We need it to read in the `names` DataFrame, so we can\n",
    "# use it throughout the rest of this notebook\n",
    "\n",
    "# First, we'll write a function to read in one year\n",
    "def read_one_year(year):\n",
    "    df = pd.read_csv('names/yob{0}.txt'.format(year),\n",
    "                     names=['name', 'sex', 'births'])\n",
    "    df['year'] = int(year)\n",
    "    return df\n",
    "\n",
    "# Then use list comprehension to read all years in and concat them\n",
    "names = pd.concat([read_one_year(y) for y in range(1880, 2014+1)],\n",
    "                 ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33014</th>\n",
       "      <td>Zedrick</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33015</th>\n",
       "      <td>Zeid</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33016</th>\n",
       "      <td>Zeidan</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33017</th>\n",
       "      <td>Zekeriah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33018</th>\n",
       "      <td>Zenas</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33019</th>\n",
       "      <td>Zephen</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33020</th>\n",
       "      <td>Zerick</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33021</th>\n",
       "      <td>Zhaiden</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33022</th>\n",
       "      <td>Zhalen</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33023</th>\n",
       "      <td>Zhayden</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33024</th>\n",
       "      <td>Ziar</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33025</th>\n",
       "      <td>Zichen</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33026</th>\n",
       "      <td>Ziden</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33027</th>\n",
       "      <td>Zierre</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33028</th>\n",
       "      <td>Ziion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33029</th>\n",
       "      <td>Zijun</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33030</th>\n",
       "      <td>Zilas</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33031</th>\n",
       "      <td>Zirui</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33032</th>\n",
       "      <td>Zivon</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33033</th>\n",
       "      <td>Ziyah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33034</th>\n",
       "      <td>Ziyang</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33035</th>\n",
       "      <td>Zmarion</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33036</th>\n",
       "      <td>Zo</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33037</th>\n",
       "      <td>Zyeir</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33038</th>\n",
       "      <td>Zyel</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33039</th>\n",
       "      <td>Zykeem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33040</th>\n",
       "      <td>Zymeer</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33041</th>\n",
       "      <td>Zymiere</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33042</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33043</th>\n",
       "      <td>Zyrin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1825433 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name sex  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\n",
       "5       Margaret   F    1578  1880\n",
       "6            Ida   F    1472  1880\n",
       "7          Alice   F    1414  1880\n",
       "8         Bertha   F    1320  1880\n",
       "9          Sarah   F    1288  1880\n",
       "10         Annie   F    1258  1880\n",
       "11         Clara   F    1226  1880\n",
       "12          Ella   F    1156  1880\n",
       "13      Florence   F    1063  1880\n",
       "14          Cora   F    1045  1880\n",
       "15        Martha   F    1040  1880\n",
       "16         Laura   F    1012  1880\n",
       "17        Nellie   F     995  1880\n",
       "18         Grace   F     982  1880\n",
       "19        Carrie   F     949  1880\n",
       "20         Maude   F     858  1880\n",
       "21         Mabel   F     808  1880\n",
       "22        Bessie   F     796  1880\n",
       "23        Jennie   F     793  1880\n",
       "24      Gertrude   F     787  1880\n",
       "25         Julia   F     783  1880\n",
       "26        Hattie   F     769  1880\n",
       "27         Edith   F     768  1880\n",
       "28        Mattie   F     704  1880\n",
       "29          Rose   F     700  1880\n",
       "...          ...  ..     ...   ...\n",
       "33014    Zedrick   M       5  2014\n",
       "33015       Zeid   M       5  2014\n",
       "33016     Zeidan   M       5  2014\n",
       "33017   Zekeriah   M       5  2014\n",
       "33018      Zenas   M       5  2014\n",
       "33019     Zephen   M       5  2014\n",
       "33020     Zerick   M       5  2014\n",
       "33021    Zhaiden   M       5  2014\n",
       "33022     Zhalen   M       5  2014\n",
       "33023    Zhayden   M       5  2014\n",
       "33024       Ziar   M       5  2014\n",
       "33025     Zichen   M       5  2014\n",
       "33026      Ziden   M       5  2014\n",
       "33027     Zierre   M       5  2014\n",
       "33028      Ziion   M       5  2014\n",
       "33029      Zijun   M       5  2014\n",
       "33030      Zilas   M       5  2014\n",
       "33031      Zirui   M       5  2014\n",
       "33032      Zivon   M       5  2014\n",
       "33033      Ziyah   M       5  2014\n",
       "33034     Ziyang   M       5  2014\n",
       "33035    Zmarion   M       5  2014\n",
       "33036         Zo   M       5  2014\n",
       "33037      Zyeir   M       5  2014\n",
       "33038       Zyel   M       5  2014\n",
       "33039     Zykeem   M       5  2014\n",
       "33040     Zymeer   M       5  2014\n",
       "33041    Zymiere   M       5  2014\n",
       "33042      Zyran   M       5  2014\n",
       "33043      Zyrin   M       5  2014\n",
       "\n",
       "[1825433 rows x 4 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, we have all our data in memory.  Since `pandas` is basically `Excel for Python`, let's summarize the data with a Pivot Table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</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>2010</th>\n",
       "      <td>1772738</td>\n",
       "      <td>1913851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1753500</td>\n",
       "      <td>1893230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1753922</td>\n",
       "      <td>1889414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1745339</td>\n",
       "      <td>1881463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1768775</td>\n",
       "      <td>1901376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2010  1772738  1913851\n",
       "2011  1753500  1893230\n",
       "2012  1753922  1889414\n",
       "2013  1745339  1881463\n",
       "2014  1768775  1901376"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = names.pivot_table('births',\n",
    "                                 index='year',\n",
    "                                 columns='sex',\n",
    "                                 aggfunc=sum)\n",
    "total_births.tail()  # Show last rows only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot this new DataFrame easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1079dd090>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VdS8PGM7VULGn0zjg/4A/bLJPbCPl/vdtCZsJ/BoY51VTGympE5XUk0qqUbHzkomSGgbc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\npThhmJlZKU4YZmZWyr/8Ea8kt3cJ1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1079c8950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need '%matplotlib inline' magic here to integrate plots into IPython\n",
    "%matplotlib inline\n",
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert raw numbers of births to proportions.  What we'll do is to group by 'year' and 'sex', and then for each group, divide the number of births for each name by the total number of births in that group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</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 sex  births  year      prop\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": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_prop(group):\n",
    "    births = group.births.astype(float)\n",
    "    group['prop'] = births / births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)\n",
    "names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, for example, 7.8% of all girls born in 1880 were called 'Mary'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.9 What percentage of _all_ babies were called 'Mary' in 1880?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that the values of `prop` within each group add up to 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1\n",
       "      M      1\n",
       "1881  F      1\n",
       "      M      1\n",
       "1882  F      1\n",
       "      M      1\n",
       "1883  F      1\n",
       "      M      1\n",
       "1884  F      1\n",
       "      M      1\n",
       "1885  F      1\n",
       "      M      1\n",
       "1886  F      1\n",
       "      M      1\n",
       "1887  F      1\n",
       "      M      1\n",
       "1888  F      1\n",
       "      M      1\n",
       "1889  F      1\n",
       "      M      1\n",
       "1890  F      1\n",
       "      M      1\n",
       "1891  F      1\n",
       "      M      1\n",
       "1892  F      1\n",
       "      M      1\n",
       "1893  F      1\n",
       "      M      1\n",
       "1894  F      1\n",
       "      M      1\n",
       "            ..\n",
       "2000  F      1\n",
       "      M      1\n",
       "2001  F      1\n",
       "      M      1\n",
       "2002  F      1\n",
       "      M      1\n",
       "2003  F      1\n",
       "      M      1\n",
       "2004  F      1\n",
       "      M      1\n",
       "2005  F      1\n",
       "      M      1\n",
       "2006  F      1\n",
       "      M      1\n",
       "2007  F      1\n",
       "      M      1\n",
       "2008  F      1\n",
       "      M      1\n",
       "2009  F      1\n",
       "      M      1\n",
       "2010  F      1\n",
       "      M      1\n",
       "2011  F      1\n",
       "      M      1\n",
       "2012  F      1\n",
       "      M      1\n",
       "2013  F      1\n",
       "      M      1\n",
       "2014  F      1\n",
       "      M      1\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's reduce the data set by focusing on the top 1000 names for each sex/year combination:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">F</th>\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>796</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2014</th>\n",
       "      <th rowspan=\"30\" valign=\"top\">M</th>\n",
       "      <th>20037</th>\n",
       "      <td>Dario</td>\n",
       "      <td>M</td>\n",
       "      <td>214</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20038</th>\n",
       "      <td>Kale</td>\n",
       "      <td>M</td>\n",
       "      <td>214</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20039</th>\n",
       "      <td>Broderick</td>\n",
       "      <td>M</td>\n",
       "      <td>213</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20040</th>\n",
       "      <td>Jayvion</td>\n",
       "      <td>M</td>\n",
       "      <td>213</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20041</th>\n",
       "      <td>Kaison</td>\n",
       "      <td>M</td>\n",
       "      <td>213</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20042</th>\n",
       "      <td>Koen</td>\n",
       "      <td>M</td>\n",
       "      <td>213</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20043</th>\n",
       "      <td>Magnus</td>\n",
       "      <td>M</td>\n",
       "      <td>213</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20046</th>\n",
       "      <td>Valentin</td>\n",
       "      <td>M</td>\n",
       "      <td>212</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20045</th>\n",
       "      <td>Teagan</td>\n",
       "      <td>M</td>\n",
       "      <td>212</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20044</th>\n",
       "      <td>Darien</td>\n",
       "      <td>M</td>\n",
       "      <td>212</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20047</th>\n",
       "      <td>Bodie</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20048</th>\n",
       "      <td>Brayson</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20049</th>\n",
       "      <td>Chace</td>\n",
       "      <td>M</td>\n",
       "      <td>211</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20050</th>\n",
       "      <td>Kylen</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20051</th>\n",
       "      <td>Yehuda</td>\n",
       "      <td>M</td>\n",
       "      <td>209</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20052</th>\n",
       "      <td>Bridger</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20053</th>\n",
       "      <td>Howard</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20054</th>\n",
       "      <td>Maddux</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20055</th>\n",
       "      <td>Osvaldo</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20056</th>\n",
       "      <td>Rocky</td>\n",
       "      <td>M</td>\n",
       "      <td>208</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20060</th>\n",
       "      <td>Jair</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20062</th>\n",
       "      <td>Tyree</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20061</th>\n",
       "      <td>Reyansh</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20059</th>\n",
       "      <td>Foster</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20058</th>\n",
       "      <td>Boden</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20057</th>\n",
       "      <td>Ayan</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20063</th>\n",
       "      <td>Ean</td>\n",
       "      <td>M</td>\n",
       "      <td>206</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20064</th>\n",
       "      <td>Leif</td>\n",
       "      <td>M</td>\n",
       "      <td>205</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20065</th>\n",
       "      <td>Reagan</td>\n",
       "      <td>M</td>\n",
       "      <td>205</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20066</th>\n",
       "      <td>Rylen</td>\n",
       "      <td>M</td>\n",
       "      <td>205</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>269877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name sex  births  year      prop\n",
       "year sex                                             \n",
       "1880 F   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\n",
       "         5       Margaret   F    1578  1880  0.017342\n",
       "         6            Ida   F    1472  1880  0.016177\n",
       "         7          Alice   F    1414  1880  0.015540\n",
       "         8         Bertha   F    1320  1880  0.014507\n",
       "         9          Sarah   F    1288  1880  0.014155\n",
       "         10         Annie   F    1258  1880  0.013825\n",
       "         11         Clara   F    1226  1880  0.013474\n",
       "         12          Ella   F    1156  1880  0.012704\n",
       "         13      Florence   F    1063  1880  0.011682\n",
       "         14          Cora   F    1045  1880  0.011484\n",
       "         15        Martha   F    1040  1880  0.011429\n",
       "         16         Laura   F    1012  1880  0.011122\n",
       "         17        Nellie   F     995  1880  0.010935\n",
       "         18         Grace   F     982  1880  0.010792\n",
       "         19        Carrie   F     949  1880  0.010429\n",
       "         20         Maude   F     858  1880  0.009429\n",
       "         21         Mabel   F     808  1880  0.008880\n",
       "         22        Bessie   F     796  1880  0.008748\n",
       "         23        Jennie   F     793  1880  0.008715\n",
       "         24      Gertrude   F     787  1880  0.008649\n",
       "         25         Julia   F     783  1880  0.008605\n",
       "         26        Hattie   F     769  1880  0.008451\n",
       "         27         Edith   F     768  1880  0.008440\n",
       "         28        Mattie   F     704  1880  0.007737\n",
       "         29          Rose   F     700  1880  0.007693\n",
       "...                   ...  ..     ...   ...       ...\n",
       "2014 M   20037      Dario   M     214  2014  0.000113\n",
       "         20038       Kale   M     214  2014  0.000113\n",
       "         20039  Broderick   M     213  2014  0.000112\n",
       "         20040    Jayvion   M     213  2014  0.000112\n",
       "         20041     Kaison   M     213  2014  0.000112\n",
       "         20042       Koen   M     213  2014  0.000112\n",
       "         20043     Magnus   M     213  2014  0.000112\n",
       "         20046   Valentin   M     212  2014  0.000111\n",
       "         20045     Teagan   M     212  2014  0.000111\n",
       "         20044     Darien   M     212  2014  0.000111\n",
       "         20047      Bodie   M     211  2014  0.000111\n",
       "         20048    Brayson   M     211  2014  0.000111\n",
       "         20049      Chace   M     211  2014  0.000111\n",
       "         20050      Kylen   M     209  2014  0.000110\n",
       "         20051     Yehuda   M     209  2014  0.000110\n",
       "         20052    Bridger   M     208  2014  0.000109\n",
       "         20053     Howard   M     208  2014  0.000109\n",
       "         20054     Maddux   M     208  2014  0.000109\n",
       "         20055    Osvaldo   M     208  2014  0.000109\n",
       "         20056      Rocky   M     208  2014  0.000109\n",
       "         20060       Jair   M     207  2014  0.000109\n",
       "         20062      Tyree   M     207  2014  0.000109\n",
       "         20061    Reyansh   M     207  2014  0.000109\n",
       "         20059     Foster   M     207  2014  0.000109\n",
       "         20058      Boden   M     207  2014  0.000109\n",
       "         20057       Ayan   M     207  2014  0.000109\n",
       "         20063        Ean   M     206  2014  0.000108\n",
       "         20064       Leif   M     205  2014  0.000108\n",
       "         20065     Reagan   M     205  2014  0.000108\n",
       "         20066      Rylen   M     205  2014  0.000108\n",
       "\n",
       "[269877 rows x 5 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_index(by='births', ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "\n",
    "top1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It might be hard to see what's going on.  Let's do this in a more pedestrian way.  It's possible to iterate through each of the groups generated by `groupby` with a for loop.  Let's look at a bit of the first three groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(1880, 'F')\n",
      "        name sex  births  year      prop\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\n",
      "...\n",
      "\n",
      "(1880, 'M')\n",
      "        name sex  births  year      prop\n",
      "942     John   M    9655  1880  0.087383\n",
      "943  William   M    9532  1880  0.086269\n",
      "944    James   M    5927  1880  0.053642\n",
      "945  Charles   M    5348  1880  0.048402\n",
      "946   George   M    5126  1880  0.046393\n",
      "...\n",
      "\n",
      "(1881, 'F')\n",
      "        name sex  births  year      prop\n",
      "0       Mary   F    6919  1881  0.075244\n",
      "1       Anna   F    2698  1881  0.029341\n",
      "2       Emma   F    2034  1881  0.022120\n",
      "3  Elizabeth   F    1852  1881  0.020141\n",
      "4   Margaret   F    1658  1881  0.018031\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "for key, group in list(names.groupby(['year', 'sex']))[:3]:\n",
    "    print(\"\")  # Empty line\n",
    "    print(key)\n",
    "    print(group.head())\n",
    "    print(\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aha!  We get a DataFrame with only the rows corresponding to each group, along with the group key.  So we can iterate through all the groups as follows:\n",
    "```\n",
    "for (year, sex), group in names.groupby(['year', 'sex']):\n",
    "   ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.10 Using a `for` loop and `pd.concat`, build up a the `top1000` DataFrame, i.e., a DataFrame with year, sex and births columns containing only the top 1000 names for each year/sex combination.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build a pivot table that counts births for each year/name combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Aaden</th>\n",
       "      <th>Aaliyah</th>\n",
       "      <th>Aanya</th>\n",
       "      <th>Aarav</th>\n",
       "      <th>Aaron</th>\n",
       "      <th>Aarush</th>\n",
       "      <th>Ab</th>\n",
       "      <th>Abagail</th>\n",
       "      <th>Abb</th>\n",
       "      <th>Abbey</th>\n",
       "      <th>...</th>\n",
       "      <th>Zoa</th>\n",
       "      <th>Zoe</th>\n",
       "      <th>Zoey</th>\n",
       "      <th>Zoie</th>\n",
       "      <th>Zola</th>\n",
       "      <th>Zollie</th>\n",
       "      <th>Zona</th>\n",
       "      <th>Zora</th>\n",
       "      <th>Zula</th>\n",
       "      <th>Zuri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 7031 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name  Aaden  Aaliyah  Aanya  Aarav  Aaron  Aarush  Ab  Abagail  Abb  Abbey  \\\n",
       "year                                                                         \n",
       "1880    NaN      NaN    NaN    NaN    102     NaN NaN      NaN  NaN    NaN   \n",
       "1881    NaN      NaN    NaN    NaN     94     NaN NaN      NaN  NaN    NaN   \n",
       "1882    NaN      NaN    NaN    NaN     85     NaN NaN      NaN  NaN    NaN   \n",
       "1883    NaN      NaN    NaN    NaN    105     NaN NaN      NaN  NaN    NaN   \n",
       "1884    NaN      NaN    NaN    NaN     97     NaN NaN      NaN  NaN    NaN   \n",
       "\n",
       "name  ...   Zoa  Zoe  Zoey  Zoie  Zola  Zollie  Zona  Zora  Zula  Zuri  \n",
       "year  ...                                                               \n",
       "1880  ...     8   23   NaN   NaN     7     NaN     8    28    27   NaN  \n",
       "1881  ...   NaN   22   NaN   NaN    10     NaN     9    21    27   NaN  \n",
       "1882  ...     8   25   NaN   NaN     9     NaN    17    32    21   NaN  \n",
       "1883  ...   NaN   23   NaN   NaN    10     NaN    11    35    25   NaN  \n",
       "1884  ...    13   31   NaN   NaN    14       6     8    58    27   NaN  \n",
       "\n",
       "[5 rows x 7031 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births',\n",
    "                                   index='year',\n",
    "                                   columns='name',\n",
    "                                  aggfunc=sum)\n",
    "total_births.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot a couple of the columns in this pivot table to see how naming trends have changed with time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880     201194\n",
       "1881     192699\n",
       "1882     220903\n",
       "1883     216530\n",
       "1884     241959\n",
       "1885     239339\n",
       "1886     253252\n",
       "1887     245350\n",
       "1888     295750\n",
       "1889     285552\n",
       "1890     297278\n",
       "1891     282737\n",
       "1892     328717\n",
       "1893     320035\n",
       "1894     332649\n",
       "1895     344016\n",
       "1896     350269\n",
       "1897     340269\n",
       "1898     372861\n",
       "1899     332399\n",
       "1900     437621\n",
       "1901     338083\n",
       "1902     377190\n",
       "1903     371547\n",
       "1904     392482\n",
       "1905     411909\n",
       "1906     416445\n",
       "1907     450850\n",
       "1908     473146\n",
       "1909     493927\n",
       "         ...   \n",
       "1985    3230187\n",
       "1986    3208443\n",
       "1987    3241422\n",
       "1988    3303985\n",
       "1989    3416768\n",
       "1990    3496960\n",
       "1991    3429951\n",
       "1992    3364877\n",
       "1993    3287621\n",
       "1994    3229203\n",
       "1995    3174774\n",
       "1996    3145853\n",
       "1997    3109261\n",
       "1998    3134981\n",
       "1999    3130657\n",
       "2000    3176781\n",
       "2001    3124323\n",
       "2002    3107495\n",
       "2003    3144498\n",
       "2004    3132384\n",
       "2005    3135412\n",
       "2006    3195432\n",
       "2007    3203550\n",
       "2008    3129057\n",
       "2009    3024194\n",
       "2010    2918723\n",
       "2011    2888231\n",
       "2012    2880561\n",
       "2013    2878181\n",
       "2014    2918178\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1085e2ad0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x108bef050>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x10a7bd510>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x10ac80690>], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EvqHArXn/24HrJV1IGoLRD5iSe6XfkrRXPn4IcEnZMUOBScBxrGRSi5mZrdQhwCPNkTQD\n5GRwUnOcq4lxLAFulnQbcB5pAuLQiLhnVc4jqSOpF31z4OCIeDs/dTdwd/7gcTDQkfT3rbS9GRHP\n1RPXk5JeJ/VQ3bc612ZmHyeJFTUlqlNjY5yfJE3kewSYmpt/T/rq7hBJs0i9z+fn/Z8ijYV7Cvgb\ncHrZR5vTgT8As4Fn83g4gD8Cm0maTRoWMrx5Ls3MbI1xFCs6MNqdiFgSET8mDY8YI+nnSnWpl5PU\nQdJWkjqXVXMq9cbfRkqKDy9LmsvP/15E3BERN0fEuIh4KCIeqy9pLjMGTxI0azFXXHEF/fr1Y7PN\nNuPII49k3rx5tZ6fMGEC22+/PZtuuilnnHHG8vbRo0ez33778YMf/IAuXbqw7bbbMm7cuLqnX22N\n1nGOiAsi4hMRsUtEDM0VM16PiM9FxPYRcWhEvFm2/y8iom9E7BgRd5e1P5rP0Tcizixr/zAijo+I\nfhGxd67GYWZmFcjDGb5ISg7btYi4lzTUYgDwd0l/ypMMXwTeB54kDQ1cLOlVSc+QOnIWUOE47VVw\nPXCEpI2a8ZxmBtx3332MHDmSP//5z8ybN4/evXszeHDtKpB33nknjzzyCFOnTmXs2LHcfffylJMp\nU6aw4447snDhQn74wx82OvRjVTSaOJuZWVXbF3gx12hu9yLiFdIY5QuBO0jjnz8LbBgRW0bEJsB6\nwCdI46OPBU7Jwz6aO46JpCGGZu1CaahEU7emiAiuv/56hg0bxm677UbHjh355S9/yT//+U9efHHF\nr7nhw4ez8cYb07NnTw488ECeeOKJ5c/17t2bYcOGIYmTTjqJefPm8corrzQprpKKit6bmVnVatfD\nNOqTx2D/qYHnFwOv5K0ljSENMbyqhV/HrFVUy8TBl19+mQEDVlSj3GCDDdhss82YO3cuvXr1AqBb\ntxXl3tdff33efXfFPOa6zwG88847bLll0xc4dY+zmVkblcfyrnGJcxW5E+gvqXlqYpkZAN27d6em\npmb543fffZeFCxfSo0ePlR/UShpNnCXtIOnxsm2RpDMldVFarnVWXhGqc9kxIyTNljRT0qFl7QMk\nTcvPXVzW3knSjbl9kqTezX+pZmbtzq7AMmB60YGsiSLiI9JY55OKjsWsvZDEiSeeyFVXXcWTTz7J\nhx9+yMiRI9l7772X9zbXFRGt1lteyeTAZyJi94goTch4D/gLqfrFhIjYnlRCbjiApP6k4vL9gUHA\nZWUznC8HhkVEP9LKS4Ny+zBgYW6/iFTmzszMGnYUcGtVF2Zt/8YAQ3PtazNrIkkcfPDB/OxnP+PY\nY4+le/fuPP/88/zpT3+qtU/dY0pt9Y2zbs4Sdw0ugPKxnVPv8U8iYn9JM4HPRsQCSd1IK/ntKGkE\nsCwiRuVjxpHqb74A3Fe2CMpgYGBEfKu0UEpETF7ZIiheAMXMrDZJTwDfiYiHio5lTZU7hiYDv4mI\nq4uOx6wh1b4AyoABAzj33HM54ogjWuX1VmcBlFX9hDwYuCHf71q2gtMCoGu+3x2YU3bMHNJiKHXb\n5+Z28u1LsLzg/aK85KqZmdVD0jak36v/KDqWNVnOQk4nLSW+edHxmLVVM2bM4Omnn2b33XcvOpQG\nVVxVI6+89CXgnLrP5ZUBW/wjjKTzyh5OjIiJLf2aZmZV6kjgjohYWnQga7qIeETS9aQSeR7vbLaK\nzjnnHK677jouuOACevbs2eqvL2kgaSXQxvettMte0pHAaRExKD+eSRpqMV/SVsD9eajGcICIOD/v\nNw44lzRU4/6yoRonAgdExGml4RwRMclDNcxsTSTpQOA3wEOkIXGvNrL/ROBXEXFHK4RnjZC0IWmS\n5qkRMaHoeMzqU+1DNVpbSw/VOJEVwzQAbgeG5vtDWVEO6XZgsKSO+avEfsCUiJgPvCVprzwmbAgr\nVroqP9dxpMmGZmbtXq4q9D/AtaROhg+BpySdnb/pq++YzUkr6N3TepFaQyLiHdKQjd/mZb7NrB2q\nqMdZ0gakHuNtIuLt3NYFGAv0AmqA40tLb0saCZwCLAHOKi29LWkAMJq0qtNdpaW3JXUCriH9IVgI\nDK679LZ7nM2svZG0M3Ad8BzwjVIvs6SdgF8B2wHfB8ZHxIdlx30N+FJEHNvqQVuDJN1AWsnxY8Ma\nzYrmHufaVqfHeZWqahTJibOZtSeSTgV+SZo3cmV9f80kfQH4Bam85xukSdQv5se/iIhrWi9iq4Sk\nrsA04NCIeKKx/c1akxPn2pw4m5m1AZJ6kJKrvSNiVgX7dwC6AT3z1o2UbL/b4IFWCEmnAKcB++ZF\nUsyqQmsUcmhrnDibmVU5SReRChKdXXQs1vzyPJ4/A5sBx0XEwoJDMrNV0Jx1nM3MrAkkbUmaDP2/\nRcdiLSN/F34CMAWYnMesm1k7UFHiLKmzpJskPS3pqVwZo4ukCZJmSRovqXPZ/iMkzZY0M682WGof\nIGlafu7isvZOkm7M7ZMk9W7eyzQzqxrfBcZGxMtFB2ItJyKW5gmCPwMekDSo6JjMrOkq7XG+mFQF\nYyfgk8BMYDgwISK2J5WPGw4gqT/pk3Z/YBBwmVYsEn45MCwi+gH9yn6RDAMW5vaLgFFNvjIzsyqT\nOxi+iX/HrTEiYgxwDHCVpP/MVarMrI1qNHGWtAmwf0RcCWlJ7IhYBBwBjMm7jQGOyvePBG6IiMW5\npNyzwF55kZSNImJK3u/qsmPKz3UzcHCTrsrMrDqdAfw1Ip4vOhBrPRHxMLAP6W/eq5Kel/RXSaMk\nfcV1n83ajkp6nLch/Ue/StJjkq7In5i7RsSCvM8CoGu+3x2YU3b8HKBHPe1zczv59iVIiTmwKNeJ\nNjNrF/LKcmeSStDZGiYiaiLis8DGwOeBPwJvkxYXe1bSt1e24I2ZVY9KEue1gU8Bl0XEp4B3ycMy\nSvJEiLZRnsPMGpTnL3QoOo526JvAAxExs+hArDj5W9tZEfGXiPjviPgi8EXgcOAZSUP9/8+seq1d\nwT5zgDkR8a/8+CZgBDBfUreImJ+HYbySn59LqjNasnU+x9x8v2576ZhewMuS1gY2iYjX6wYi6byy\nhxMjYmIF8ZutcbSKVe7z/7svAN8ADiL9X/wVMCYi3m+hMNcYktYFvkdKjsxqiYjHgMMk7U9a8OZH\nkp4EFgFv5tt5pP+Pi4uL1Kx9kjQQGFjRvhUuuf0g8PWImJWT19J4rIURMUrScKBzRAzPkwOvBz5N\nGoJxD9A3IkLSZNJXlVOAO4FLImKcpNOBXSLiNEmDgaMiYnCdGFzH2awBeQjVF0gTkb4IvA78HXg4\n386IiKV5su56wCbAFsCxpAm6LwG/B8YCuwM/BPYCLgUudS3a1SfpNODw3LtotlL5/+fepA6ozqT/\np52Bw4CrI+KiAsMzWyM0eQEUSbsCfwA6Av8GTgY6kP7A9gJqgOMj4s28/0jgFGAJcFZE3J3bBwCj\nSX+074qIM3N7J+Aa0h/rhcDgPLGwooswW5NJOhz4OqmneBJwC3Ab6Q/ufsC+eesGfET6I7yEFb1Z\nE4ArImJqPefuT+opPQZ4ALgvbzO8bmvjchI0FPgf4IsRMbngkKyNyrWgHwQ+ERGvNLa/ma0+rxxo\n1sbkiWR3AZOBH9W3bG8eB/lz4Hjgp8Ad9Q1xKtu/C+kD76JVXQZY0hakajcH5W0jUiI9F/iQlJB/\nCLwP3BoRz63K+dsjSd2B35F6DodGxJMFh2RtXF5xcsOIOLXoWMzaMyfOZm1IHm98G/AaqXd4a+DE\niJhVts8mpCFR65G+7XmtlWPsDXwW2BzolLeOwKakRP5a4L8j4tXWjKsa5F7m/wAuBH4L/HxVP6iY\n1SfXAZ9JGvbzaNHxmLVXTpzN2oicdP0W6A18iTSk4jRSj/IPSPXO+5ES63uB/6y2yUJ5SemfkMps\n/Rq4KCLeLTaq1pHr8Y4mLQA11MmNNTdJXycNl9zPw6XMWoYTZ7M2Ik+0PQE4ICLeLmvfBbgBeJ40\n8fbHEXFFMVFWRtJ2wH+TeqZ/A1wZEfOLjarlSNoUuIM052NYRHxYbETWHuUhWpNJH0ivKzoes/ao\noZyzoiW3JdVImirpcUlTclsXSRMkzZI0Pn+FVNp/hKTZkmZKOrSsfYCkafm5i8vaO0m6MbdPyl8D\nm61RJJ1I6l0+vDxpBoiIacCewBPAsdWeNANExL8j4kRSpY/ewNOSbpL0OUm1fvdI6ihp89zj3uZI\n6gE8REpoTnLSbC0lIpaSqlONynMhzKwVVVpV43lgQPnEI0kXAK9FxAWSzgE2rVOObk9WlKPrl8vR\nTQHOiIgpku6idjm6nSPidEknAEe7HJ2tSSQdCNwIHJyT5HZH0sbAV4BvARuSxnBvnrf1SRMMryAN\nP2kbX4UBknYExpFK9v1P0fHYmkHStcCLETGy6FjM2pvmKEf3PLBHeR1XSTOBz0bEAkndSAuS7Chp\nBLAsIkbl/cYB5wEvAPdFxE65fTAwMCK+lfc5NyIm54lR8yJii0ovwqwtyr2rBwFnA3uQJgDeV2xU\nLS9f926kCYWv5W0RaSLkeFLd6bOrPXnO45k/Q5oIeU5EjCk4JFuD5G85pgKfiogXio7HrD1pKOes\nZOVASMtp3yNpKfC7/DVx14hYkJ9fAHTN97uTasmWzCH1PC9mxUqBkMpY9cj3e5AWXyAilkhaJKlL\nQ6W1zNoqSR2BwaSEeR1S9YVjI+KDQgNrJTkhfryep97IQ7vGAxdKqprkOXcOnAx8Etgmb5uQxpwP\ni4g7CwzP1kARMVfS74FzgNOLjsdsTVFp4rxvRMzLtVwn5N7m5fIwjKr4A2dWrXJP6xHAxcBsYDhw\nd7Ukh9UgIsqT519J+l6RPx9JewJnkZbKHkta8fT5vM2PiGVFxWZG+tD9jKSfR8TcooMxWxNUlDhH\nxLx8+6qkv5Bm9S+Q1C0i5kvaCiitZDSXVPC/ZGtST/PcfL9ue+mYXsDLeajGJvX1Nist910yMSIm\nVhK/WdEkbQNcQiold8qaMCRjddVJni+UdDlpDPQG+bYT8EDdCZTNJfcuf540FrsbqSLIdyLijZZ4\nPbPVlf8mXwX8kPQBz8xWg6SBwMCK9m2sMyeP4+sQEW9L2oD0x+ynwOeAhRExKpfQ6lxncuCnWTE5\nsG/ulZ5Mmg08hdRzUz45cJeIOC2PfT7KkwOtPVBaTv4HwHeBXwG/8mIYlcnl3cYCfYB3gffybQdg\ne9LP9U9N6ZGWtA6wMbA36Xfa50gf6u8n1cz+a65iYFaVcsfVDKB/ey73aNaamjQ5MPeU/SU/XBu4\nLiJ+qbR871hST3ENafWyN/MxI4FTSIs3nBURd+f2AaTFAdYD7oqIM3N7J+AaYHdgITA4ImoqvQiz\naiXpb6T/B9+p+2/aVp+kzwCXAm+SfrbT69mnA7AdsHPZ1h/owooebAHvAI+RPuTfAzwWEUta4TLM\nmoWkS4CPIuL7Rcdi1h54ARSzAkjaGbgb6BNVtrpfe5AT42+SqvbcQKrcsx2wbd56A/OAacD0vM0A\nXiX1Xr/n3n9rDyRtTaqwsUOsgcvcmzU3J85mBZD0W+DliPh/RcfSnuVJy+eQvhF7rmyriYj3iozN\nrLXk3zevu66zWdM5cTZrZXl87nPATh53aGYtTVIf4FHSgmMu5WrWBE1ectvMVtnJpHH8TprNrMXl\nORS34uoaZi3KPc5mzSyPvZ0FfCUiJjW2v5lZc5DUl7QA2W4RMaex/c2sfk3ucZbUQdLjku7Ij7tI\nmiBplqTxkjqX7TtC0mxJM3Mt1lL7AEnT8nMXl7V3knRjbp8kqffqX6pZVfgC8DowuehAzGzNERHP\nAr8AHvDfUrOWUelQjbOAp0hLb0Na8WxCRGwP3Jsfk2s4n0Aq+TQIuCyvlgZwOWlp2n5AP0mDcvsw\nUj3ofsBFwKimXZJZ4c4AfuMVAc2stUXEhaTVSR+QtG3R8Zi1N40mzrnMzWHAH0g1TyEtGzwm3x8D\nHJXvHwncEBGL83irZ4G9coH2jSJiSt7v6rJjys91M3Dwal+NWcEk7UCqR35j0bGY2ZopIi4Bzgcm\nStq+6HjM2pNKlty+iLRC18ZlbV0jYkG+vwDomu93J42vKplDWj1wMSuW14a0xHaPfL8H8BJARCyR\ntEhSF8/bktqrAAAgAElEQVQKtjbq28AfIuKDogMxszVXRPxW0kfA/ZIOiYinio7JrD1oMHGW9EXg\nlYh4PK/j/TF5Ke1W+Upa0nllDydGxMTWeF2zSkjaCPgqsGvRsZiZRcSVOXm+L68ueG1EvFh0XGbV\nJue4AyvZt7Ee588AR0g6DFgX2FjSNcACSd0iYn4ehvFK3n8u0LPs+K1JPc1z8/267aVjegEvS1ob\n2GRlvc0RcV4lF2VWkKHAfRHxUtGBmJkBRMS1kmaS5hM9JmkacA1wU0S8VWx0ZtUhd8ROLD2WdO7K\n9m1wjHNEjIyInhGxDTCYlBQMAW4nJQnk21vz/duBwZI6StoG6AdMybVs35K0V54sOAS4reyY0rmO\nI002NGtTcgm6s4BLio7FzKxcRDwSEaeRhkb+H/Al4AVJIyV1LDY6s7ZlVRdAKQ3JOB84RNIs4KD8\nmDyGaiypAsffgNPLKgucTppgOBt4NiLG5fY/AptJmg18l1yhw6yNOQ54FXio6EDMzOoTER9GxC0R\ncTRpEvO+wKOSPlNwaGZthhdAMWui/C3K48CPI+KvRcdjZlaJ/Lvry6QiALcDIyLizWY691rALkAH\n4L28vQu868nTVu0ayjkrqaphZg0bRPr25s6iAzEzq1T+RnispPHAL4GnJd0C3A3cHxFvr8r5ciK+\nK3AiaXjnR8DbwPrABvl2Q0nvs2L+01zgZeAtViTY7+XHD0bEO029TrPm5B5nsyaS9BBweURcX3Qs\nZmarKy9idjjweWAv4DHgfmAp0BnYJN9uBHxISorfBt4hDeU8nJQc35C3aXUXgsrJ9aakIgE98m13\nYMN87Hr5dkvgU6Se8KtJifzSlrlys9oayjmdOJs1gaT9SAv47BARS4qOx8ysOUjaADggb0uBN4FF\n+fZtoBMpgd6IlPR2Ik3un9Rcq6ZK2pLUez2UlEjfADxKWlxtdkQsao7XMatrtRNnSesCD5D+Q3QE\nbouIEZK6kFZG6w3UAMeXxkVJGgGcQvqPdmZEjM/tA4DRpLJ2d0XEWbm9E+nT5KeAhcAJEfHCqlyE\nWVEk3QncHhG/KzoWM7P2StLOwPHATkDfvH0AzCJNyp4A/N3jp605NKnHWdL6EfFerrH8MPB90jLZ\nr0XEBZLOATaNiOH5a57rgT1JX8HcA/TLi6RMAc6IiCmS7gIuiYhxkk4Hdo6I0yWdABwdEYNX5SLM\niiBpV1L1mG39y9rMrPXkIR9bAv2BA4FDgJ2BfwLjSR19s4uL0NqyhnLORsvRRcR7+W5H0uzYN0iJ\n85jcPgY4Kt8/ErghIhZHRA3p65S98iIpG0XElLzf1WXHlJ/rZuDgCq/LrGjDgYucNJuZta5IFkTE\n/RHxXxGxD2kBtstJvdEPSpoq6TxJu+RE26zJGq2qkUvKPAZsR5oANUNS14hYkHdZAHTN97sDk8oO\nn0PqeV7MipUCIc2i7ZHv9wBeAoiIJZIWSeqystUDzaqBpL6kHo5vFB2LmZlBHjL6F+Av+dvsfYBj\nSBMMl0l6lLTORGmbHREfrux8OdneMG8LImJZC1+CtQGNJs75H8pukjYB7pZ0YJ3nQ1KrzDCUdF7Z\nw4l5iUSzVpV/mZ5L+iC5SuWazMys5eXc5e/A3yV9H/gE8EnS0I7BpLHS20n6iFxfmlQG733ShMcu\npOofH+X2TpKmAk/k7XHgCVf6aB8kDQQGVrJvxXWcI2JRngg1AFggqVtEzM/DMF7Ju80lfVVSsjUr\najVuXU976ZhewMt5HPUmK+ttjojzKo3XrAWdRZrMekbRgZiZWcNylY/peVtOUgdW1JgubeuSqoa8\nAbxR6pHORRF2BXYD9gfOJq16fAdwK3BvRLzfKhdkzS53xE4sPZZ07sr2bXCMs6TNJXXO99cjfTX9\nOOlrj6F5t6GkfzTk9sGSOkraBugHTImI+cBbkvbKvXVDgNvKjimd6zhSORuzqiTpKOAHwGEuhWRm\n1nZFxNKIeDsi5kfEvyNiakRMiYinc9uHZfu+nsdTXxQRQyOiP/AZYAapaMJ8STdLOlpSx6KuyVpe\nY+XodiFN3Fsrb9dExP/kT15jST3FNdQuRzeSVI5uCXBWRNyd20vl6NYjlaM7M7d3Aq4BdieVoxuc\nJxbWjcVVNaxQkvYE7iIlzf8qOh4zM6sOkjYjFUgYCuwIXAdcFRHTCg3MVosXQDFrIkl9SOPlTo+I\n2xre28zM1lR58vjXSEn0AlLFsNuBp5prcRhrWU6czZogD1f6O/D7iLi46HjMzKz65THUA0k90UcA\ny0gJ9F+ByZ5cXr2cOJs1gaQ/Aa9GxHeKjsXMzNqePL9rF1ICfRhpouFLpCXEHwUeAR4pWzvDCuTE\n2Ww1SdoCmA30dO+AmZk1B0nrkEriDcjbHqTEegbwD3IpvYh4ubAg12BNWjlQUk9J90uaIWm6pNKk\nvi6SJkiaJWl8qfpGfm6EpNmSZko6tKx9gKRp+bmLy9o7Sboxt0+S1Ltpl2zWbAYDf3XSbGZmzSWv\nsDw1Iq6KiDMiYm9gc+B7wHxS9bFpkp6RdJmk4/IERCtYoz3OkroB3SLiCUkbkr5SOAo4GXgtIi6Q\ndA6waUQMl9QfuB7Yk7Qq4D1Av7xQyhTgjIiYIuku4JKIGJdX+Nk5Ik6XdAJwdEQMrhOHe5yt1Un6\nF/CjiBhfdCxmZrbmyCs3fxI4GDgI2A94njSs43HSQixT3bHT/Jp1qIakW4Hf5O2zEbEgJ9cTI2JH\nSSOAZRExKu8/DjgPeAG4LyJ2yu2DgYER8a28z7kRMTkvgjIvIrao9CLMWkL+EDgB6OXVoczMrEh5\neMcAUvne3UmLsXyCtJDc8tUMSSsazisqzvagoZyz4pUD84n6kN6syUDXiFiQn1oAdM33uwOTyg6b\nQ+p5XsyK1QIhvdE98v0epEHyRMQSSYskdVnZCoJmrWQIcJ2TZjMzK1pELCblV8tzrNzZuAMpid6N\nNNRjd0nLSKs6vwG8Wee2/P6rwKMR8UHrXUnbVnHinIdp3Exa1OTtNEE0ycMwWnyWoaTzyh5OzEsk\nmjW7XEboq8AXio7FzMysPhGxhDShcAZp0ZVSBY9uwGbApnnrXHbbm5RkdyZ1dm4v6X7gTtICdXNb\n+TIKJ2kgqXRgoypKnPPXAzeTVg4sLa+9QFK3iJgvaSvSJxtIPck9yw7fmtTTPDffr9teOqYX8HL+\n9LRJfb3NEXFeJfGaNYOBpBJ004sOxMzMrFJ5kZV5eWuUpM2BzwNfBEZJqiGtDn1DRLzQUnFWk9wR\nO7H0WNK5K9u3kqoaAv5IWvHm12VP3U5aFYd8e2tZ+2BJHSVtA/QDpkTEfOAtSXvlcw4BbqvnXMcB\n9zYWl1kLOwm4uuggzMzMWlJEvBYR10XEicCWwNlAH+BRSQ9LOl3SloUGWUUqqaqxH/AgMBUo7TwC\nmEL6RNILqAGOj4g38zEjgVOAJaShHXfn9gHAaGA90tcBpdJ2nYBrSOOnFwKDI6KmThyeHGitIg9L\nmgPsUDaO38zMbI0hqSNwKHAiaeGWTsBbwNv59g3gKdKkxMeB6e1lrLQXQDFbBZKGACdExBeLjsXM\nzKwa5E7OjYGN8u1mpEVbSlU++pEWDJtBSqifAp4Gno2Ij4qIeXU5cTZbBZImAFdExNiiYzEzM2sL\nJK1LKo+3E9C/bOsFPMeKZLqUUD9TrT3UTpzNKiRpa9KwpB4R8X7R8ZiZmbVlOaHentrJ9E7AdqRS\nxKVkejpwfzUsM+7E2axCeXx+n4j4RtGxmJmZtVe5YltfViTTuwEHAi8C44C/Af/I9atbO7aV5pyV\nVNW4UtICSdPK2rpImiBplqTxkjqXPTdC0mxJMyUdWtY+QNK0/NzFZe2dJN2Y2ydJ6r36l2q2eiTt\nI+k24EzgsqLjMTMza88iYnFEPB0RN0fEzyLiWFJVj2+Tikv8CnhV0i2SviGpV6EBZ40mzsBVwKA6\nbcOBCRGxPal03HBYvkTxCaRPDoOAy7RipZTLgWER0Q/oJ6l0zmHAwtx+ETCqCddjVjElX5D0AKlw\n/Dhgm4h4ouDQzMzM1jgRsSQi/h4RP46IPUhDPP4CfBZ4TNIMSRdJ+qakQyRtl3uuW01FQzXyUtt3\nRMQu+fFM4LMRsUBSN9IqfjtKGgEsi4hReb9xwHnAC8B9EbFTbh8MDIyIb+V9zo2IyXnxk3kRsUU9\nMXiohjWL/O/sONIHvrWA84GxeQUmMzMzqzJ5Rd8BpOEcfYFt89adtNjLc3l7vux2LjB/VYd7NJRz\nVrzkdh1dy+rbLgC65vvdKVtDnVQLtwewmBWrBEK6kB75fg/S4HAiYomkRZK61LdyoFlT5AkKXwN+\nQPo3OBL4W7SVgf5mZmZrqIhYSlpDZEp5e+5x7smKRHob4Kh82wPYQtJC4GVSzipS/rtO3joAS0nD\nQxbn25Va3cS5/EJCUqskHpI+R1qF8K067esDnyTVEfyIVObk6Yh4o85+HUjJfS/gHWB2RLzXDHFt\nCCyp1rIqzUlSV9JwmqnAryNiWcEhActXuNyAND5qS2BToHPZ7RbAYOAR4KSI+HtBoZqZmVkzyb3J\npd7mj8nfMm9JSqK7AstICXIpSV5CSp7XYUVC/fmVvd7qJs4LJHWLiPmStgJeye1zSVl/ydaknua5\n+X7d9tIxvYCX88Vt0kBv8x+BrSS9AfwLeB34FOkTxtOklWvWAb4J7CTpndzeAegNbAW8RpqxuRGw\nnaRXgGfytpiUaJW2zqRPKJOAfwKTI2JR2dcFh5J+uLsDHSS9SCr8PT3fvkZ6g5aWbTV5+fGPyQPf\njwOOBt4HHsrb5GoojSbpaNJY9auBY4BBkr7WmqVjJG0E7Ez6oFTaepL+U4j0afJV0gqUb5JWNnoT\nmA8cGhHT6jmtmZmZtUN5GObLeauXpIHAwErOt7pjnC8gTegbJWk40DkihufJgdcDnyZl9vcAfXOv\n9GRSxYIpwJ3AJRExTtLpwC4RcVoe+3xURAyuJ4Z8Gq0D7ArsTUouHwNm1F2VJvdAbk2qFbiENM76\npfL9cgLcG9ghbx1IiVZpezM/vzewDylZfomUpM0HxgN3k5YkX0paNWdnUgHwnUmJ91r5vGuRPqj0\nzXE/krfH82t/OR9/G3AT6QPA/nnbBXgix7QBsGHZ1gnoyIqvHNYh9bq/U2ebQ1rRp7Q9m48vXfv2\n+fVfAO4Cxpctob4xcHGO5aSI+Ef+kPMj4DTgGxFxe52f/Y55/46kDzgL8+3r+efSu2zrBazLig8X\npQ8b6+YYN2LFSkUbk+o9Ts3bNNKS768A73rYhZmZmTVFk+o4S7qBNJtxc1Jv3n+RkruxpISnBji+\nLMkaCZxCSlbPioi7c/sAYDSwHnBXRJyZ2zsB15B6bRcCgyOiZlUuorXkZHFn4LWImNPY/is5h4A+\nwB6kRHwAKVn9M2kC5ccGsEvaANiLlDS/A7xbdvsBKVFeXLZ1pHZyvRGpV7Zf2dY3n+MZYFa+fTY/\ndxhwAClZvx84ifQh4XsR8U6d2D4DXJufn0H6t3IA8B7pA8XbpGU5u5Rtb5P+3bxQtr3Pig8YHfL2\nQd73nXz7NrDAk/jMzMyspXgBFFtlktYjJcGHkBL6OxvYdxPgl6SE/QHgwYh4oVUCNTMzM2tGTpzN\nzMzMzCrQpJUDzczMzMzMibOZmZmZWUWqJnGWNEjSTEmzJZ1TdDxmZmZmZuWqInHOZeF+AwwC+gMn\nStqp2KisIbnmoVUJvx/Vxe9HdfH7UV38flQXvx+rpioSZ1Ld52cjoiaXY/sTcGTBMVnDBhYdgNUy\nsOgArJaBRQdgtQwsOgCrZWDRAVgtA4sOoC2plsS5B2lhkZI5uc2qV5+iA7Ba+hQdgNXSp+gArJY+\nRQdgtfQpOgCrpU/RAbQl1ZI4t42aeFauT9EBWC19ig7AaulTdABWS5+iA7Ba+hQdgNXSp+gA2pK1\niw4gm0ta2a6kJ6nXuRZJTrCriN+P6uL3o7r4/agufj+qi9+P6uL3o3JVsQBKXsr6GeBg4GVgCnBi\nRDxdaGBmZmZmZllV9DhHxBJJZwB3Ax2APzppNjMzM7NqUhU9zmZmZmZm1a5aJgeamZmZmVU1J85m\nZmZmZhVw4mxmZmZmVgEnzmZmZmZmFXDibGZmZmZWASfOZmZmZmYVcOJsZmZmZlYBJ85mZmZmZhVw\n4mxmZmZmVgEnzmZmZmZmFXDibGZmZmZWASfOZmZmZmYVcOJsZmZmZlaBRhNnSVdKWiBpWllbF0kT\nJM2SNF5S57LnRkiaLWmmpEPL2gdImpafu7isvZOkG3P7JEm9m/MCzczMzMyaQyU9zlcBg+q0DQcm\nRMT2wL35MZL6AycA/fMxl0lSPuZyYFhE9AP6SSqdcxiwMLdfBIxqwvWYmZmZmbWIRhPniHgIeKNO\n8xHAmHx/DHBUvn8kcENELI6IGuBZYC9JWwEbRcSUvN/VZceUn+tm4ODVuA4zMzMzsxa1umOcu0bE\ngnx/AdA13+8OzCnbbw7Qo572ubmdfPsSQEQsARZJ6rKacZmZmZmZtYgmTw6MiACiGWIxMzMzM6ta\na6/mcQskdYuI+XkYxiu5fS7Qs2y/rUk9zXPz/brtpWN6AS9LWhvYJCJer/uCkpycm5mZmVmLiwjV\n1766ifPtwFDSRL6hwK1l7ddLupA0BKMfMCUiQtJbkvYCpgBDgEvqnGsScBxpsuEqXYS1PkkTI2Jg\n0XG0V7m6zL+B8RFxWAX7+/2oIn4/qovfj+ri96O6+P34uIY6ayspR3cD8A9gB0kvSToZOB84RNIs\n4KD8mIh4ChgLPAX8DTg9D+UAOB34AzAbeDYixuX2PwKbSZoNfJdcocOqXk3RAbRzZwC/BXaWtHcF\n+9eU7kjaVdJkSce0VHDWqJqiA7BaaooOwGqpKToAq6Wm6ADakkZ7nCPixJU89bmV7P8L4Bf1tD8K\n7FJP+4fA8Y3FYVWnpugA2itJGwInA3sA04Hz+HhJyLpqJK0FnA2cA1wKXC5pckTMbcFwrX41RQdg\ntdQUHYDVUlN0AFZLTdEBtCWrO1TDbGLRAbRjQ4GJEVEj6UpghKR9IuKfDRwzA7gHWAfYMx+7DLhK\n0qCIWNYKcdsKE4sOwGqZWHQAVsvEogOwWiYWHUBbohUjKaqbpPAYZ2vvcq/xTOCUiHg4t30TOCYi\nPr+SY44GfgdcDJwfEUtz+9rAQ8CfIuLi+o41MzOz2hrKOZ04m1URSYcDPyX1Gkdu6wjMAv4jIv5R\nZ/8zSEMzji1bYKj8+b7AP4GBETGjpeM3M7NiuQrZqqkvt3TibNZGSJoAjImIa+u0nwp8OSIOzY8F\nnAv8B3BoXqlzZef8OvBtYO88p6CUjB9FqnDzIvBn4KFSb7WZmbVNOV8qOow2QZITZ7O2StIuwDhg\nm4j4qM5zHYFngK+SepAvAT4DfKFsFc+VnVekkpEzSZVtTiWNo54BXEWqvf5lYCvgFuAm4H7/5jUz\na3ucOFfOibNZGybpD8DzEfHzlTz/dVIP83zSMvZHRsSiCs+9BTAV6ACMAa6IiFl19ulLqqV+MimB\n/65/+5qZtS1OnCvnxNmsjcqJ7Sxg+4h4dSX7rEPqdZ4KDI6ID1bxNbYEFpWGazSwX2fgQeDGlSXx\nZmZWnZw4V251EmeXozOrDqcDN60saQaIiMWS9iAlv6s8FjkiXqlwvzclfR54WNKrEfH7VX0tMzOz\n9qjRlQPNrOUo+U/gm8AFje0fEa+3xgS+iJgHfB44T9KxLf16ZmbW/vXp04d77723Vtvo0aPZf//9\nC4po1bnH2awgecLfpcBewD4R8ULBIdUSEc/m8nh3S3ojIu4rOiYzM2u7JJHmqzfd0qVL6dChQ622\nZcuWsdZaLdsn7B5nswJI2hwYD2wJ7FttSXNJRDwOHA/cKGnfouMxM7P26/zzz6dv375svPHGfOIT\nn+DWW29d/tzo0aPZd999Ofvss9l8880577zzOPnkkznttNM47LDD2HDDDbnwwgvp1q0by5atWCz3\nlltuYbfddmu2GJ04m7UgSRtK2kZSP0k7StpZ0kBgMjAJODoi3i42yoZFxERSGby/5FUKzczMVktD\nExf79u3Lww8/zFtvvcW5557LV7/6VRYsWFFxdcqUKWy33Xa88sor/OhHPyIiuOGGG/jJT37CO++8\nw3e+8x0222wzxo8fv/yYa665hqFDhzZb/K6qYdaCJE0E+gIfAEuApcBi4MKIuLrA0FaZpAHA7cAv\nIuLSouMxM7OPq6SqRnMNl1jVHLJPnz4sXLiQtddeMVL4o48+YsCAATz44IMf23/33Xfnpz/9KUcc\ncQSjR4/m3HPP5YUXVnxBe/LJJxMRjB49ennbqFGjmDZtGtdeey2vv/46PXv25LnnnqNr164fO//q\nVNVoUo+zpBGSZkiaJul6SZ0kdZE0QdIsSeNzaavy/WdLminp0LL2AfkcsyVd3JSYzKqFpPWAPYAd\nIqJvROwYEZ+IiN3aWtIMEBGPAvsBZ0r6pZrrN6+ZmbWqiGiWbVVJ4rbbbuONN95Yvl122WXLz3X1\n1Vez++67s+mmm7Lpppsyffp0Fi5cuPz4nj17fuycddu+8pWvcMcdd/Dee+8xduxYDjjggHqT5tW1\n2omzpD6kFcg+FRG7kBZWGAwMByZExPbAvfkxkvoDJwD9gUHAZWV/eC8HhkVEP6CfpEGrG5dZFfk0\nMD0i3i06kOYSEc8D+wIDgaslbVBsRGZm1paVkuYXX3yRU089lUsvvZTXX3+dN954g5133rlWgl5J\nf83WW2/N3nvvzS233MK1117LkCFDmjXepvQ4v0X6ynl9SWsD6wMvA0eQViYj3x6V7x8J3BARiyOi\nBngW2EvSVsBGETEl73d12TFmbdkBpIVE2pWIeA04OD98UdKFedVBMzOz1fLuu++y1lprsfnmm7Ns\n2TKuuuoqpk+f3uAxK+v1Pumkkxg1ahTTp0/nmGOOadY4VztxjojXgV8BL5IS5jcjYgLQNSJKI7kX\nAKX+8e7AnLJTzAF61NM+N7ebtXXtMnEGiIj3ImIIMAD4CPinpL9JOlySJx2bmVlFSiXqdtppJ773\nve+xzz770K1bN6ZPn85+++33sf3qO7auY445hhdffJGjjz6addddt3njXd3JgZK2A+4A9gcWAX8G\nbgb+LyI2Ldvv9YjoIun/gEkRcV1u/wPwN6AGOD8iDsnt+wM/jIgv1Xk9Tw60NiMvj/060Csi3ig6\nnpaWx3OfAJwNvAAMzR+uzcysFXnJ7aRfv3787ne/46CDDlrpPq295PYewD8iYmF+kVuAfYD5+v/s\n3XmYXVWV/vHvCyHMo0oYQxgCgkozKHE2gDK0CLQioAJRURqRBrRRgf4p2Nq2ODA4QKsghDSzyKCM\nQYggAkGRZhIZNECCCcgokxB4f3/sXeSmqFRVajq3Ku/nec5T9557zr3r1k2qVu2z9trSarZn1zKM\njmV+ZwGtFdxrUUaaZ9XbrftndfWCko5quTuttsmKaEdbAPctCkkzgO3ngFMlnQEcDfxe0u62b2o4\ntIiIWMT8/Oc/R1K3SXOr2iZ2Ym+O7U/ifBfw5TrS9DzwXmA68AwwifLLcxLQ0b36IuAMScdQSjHG\nA9NtW9JTkibU8/cGvtfVC9o+qh/xRgylEVum0R3bLwCfk/Qb4BJJXwV+mOGPiIgYChMnTuSuu+5i\nypQpvT6nDsRO67gv6cgFHduvPs6SvkhJjl8GbgY+BSwPnAOMpZRh7G77iXr8EcAnKf1sD7Z9ed2/\nJXAqsDRwie2DunitlGrEsCHpF8Bk2z9rOpam1AmD5wJ3A58cSd1FIiLaVUo1eq8vpRpZACVigEla\nHPgb8PqWibKLpHpF6ifA6sBOtaQjIiIGSRLn3hvyBVAioktvAuYs6kkzvFL7PInSYec8SUs2HFJE\nRESfJXGOGHiLZH3zgth+iZI8Pw+cWTuOREREDDsp1YgYYJJ+Blxg+3+bjqWdSBoNnE9ZPGmvmlBH\nRMQAkjQ8Ers2kRrniAbVZeTnAG+2/UDT8bQbSUsBvwQeBPa1/XLDIUVERMwnNc4RQ2cj4NkkzV2z\n/TywCzAOuEjSKs1GFBER0XtJnCMGVuqbe1Db0m1HaVP3+9qOMiIiou0lcY4YWEmce8H2i7Y/D3wR\nuEzSfrXMJSIiom2lxjligNTE7wFgW9t3Nx3PcCFpI+A8yiJK+9VyjoiIiEakxjliaKwDLAHc03Qg\nw4ntPwETgFWBLzQcTkRExAIlcY4YOO8GrsmSTQuv1j1/BjhE0hpNxxMREdGVJM4RA+c9pL65z2z/\nhbI89381HUtERERXkjhHDABJSwM7A5c0Hcsw9w1gB0lbNB3IcCdplKT/lLRe07FERIwUSZwjBsZu\nwO9t/7npQIYz208BRwLHpstG30laDDgJ+ChwgaTlGg4pImJE6FfiLGklST+T9EdJd0qaIGkVSVMl\n3S3pCkkrtRx/uKR7JN0labuW/VtKuq0+dnx/YopoyL8CP2o6iBHiZGBl4F+aDmQ4qn9wHAdsAPwT\n8DvglPwhEhHRf/0dcT4euMT2xsCmwF3AYcBU2xsCv6r3kbQJsAewCbADcELLD/ITKcvvjgfGS9qh\nn3FFDBlJbwTWoywlHf1k+yXg88C3JS3ZdDzD0H8C7wDeXyddHgCMpf4sjoiIvutz4ixpReBdtn8K\nYHuu7ScpdZ6T62GTgV3r7V2AM+vCBzOAe4EJklYHlrc9vR53Wss5EcPBvwIn2X6x6UBGCttXAncC\n/9Z0LMOJpC9QyoZ2qD+PO5Y5/yBwoKR/bjK+iIjhrj8jzusCj0g6RdLNkn4iaVlgjO059Zg5wJh6\new1gZsv5M4E1u9g/q+6PaHuSlqHUkZ7UdCwj0KHAlySt03Qgw4Gk/Skt/d5r+5HWx2zPAnYHTpW0\nYRPxRUSMBKP6ee4WwIG2b5J0HJ0uBdq2pAHraSvpqJa702xPG6jnjuijPYDrbT/QdCAjje0/1f/z\n0z5l/cAAACAASURBVCXtZ/vCpmNqR5IWp7Tw2x14X02SX8X2dZL+H2Wy4DttPzaUcUZEtCtJE4GJ\nvTm2P4nzTGCm7Zvq/Z8BhwOzJa1me3Ytw3i4Pj4LWLvl/LXqc8yqt1v3L+gH/1H9iDdiMOwPfL3p\nIEYq2z+UdDNwpqRtgS/Y/kfTcbULSSsApwPLAVvZ/lt3x9v+saT1gd9I2tH2/UMRZ0REO6sDsdM6\n7ks6ckHH9rlUw/Zs4MGWy37vBe4AfgFMqvsmARfU2xcBe0oaLWldYDwwvT7PU7Ujh4C9W86JaFuS\nNgNWJ72bB5Xt64HNKSVc10sa33BIbaEmwNdTBiC26ylp7mD7S5SFZq6r/4YjIqKX+jPiDGXizumS\nRgP3AZ8AFgfOkbQvMINy+RDbd0o6hzLhZy5wQMvSxAcApwJLU7p0XNbPuCKGQsekwJeaDmSks/24\npN0oNby/lfRB29c2HVd/1IGC9YA3t2xvogw+HLWg0eB63naUidRftX3Cwr627WMlzQSukPQx21P7\n+DYiIhYpmpe7tjdJtp0+pNEWJC0P3A+8aUE1pTE4arvKnwITbD/YdDx9UbsS/QZYidJnuWP7E7Av\nZTDhNOAbHRP9JL0O+BjwSWAZ4F9t/6qfcbyLUmb3Bdun9ee5IiJGiu5yziTOEX0gaT9gR9tZpKMB\nkr5IuZr1LtvPNR3Pwqir+l0I3G/7wAUcMwb4f8zr2LI+pRzuIsoCMdfafnmA4tmY0oN8NiVZP8f2\n4wPx3BERw1ES54gBJGkUcCtwcC5xN6OWK5wOvATs4+Hyg4xXugNtC2xr+4Uejl0POJgyf+Tsjt7M\ngxDTEsD2wD6UMpArgCmUjjG9qp2OiBgpkjhHDCBJn6aMBG4znBK2kab20L4OOM32sU3H0xuSdgZ+\nCLylToxuO5JWpozmfwTYjDIn5a66/RE4Ja3sImIkS+IcMUDqIj93A7u2tGKMhtTFUW4A9q6rDbYt\nSa8HrgE+YPvGpuPpjTqyvyrw+rq9nTJa/qlM4o6IkSqJc8QAkfRlYBPbH2k6lihq4/qzKfW5sykr\nls5u2R4bqHrgvqr9lm8EvmP75CZj6S9J21AmZ14GHGr76YZDiogYUEmcIwZAnbB1J+Uy+5+bjifm\nkfQeYAKwWss2pn5dHniEkkT/FbgJuJLSR/7FIYhtWeBcymTAzwz26w2F2hXkOODdwCTbv2k4pIiI\nAZPEOWIASPoh8ILtzzUdS/SepCUp5QarURZReTulQ8X6lNKJX1ES6TsGumZd0lhKB43/A/braTLg\ncFNrtn8EfN32D5uOJyJiICRxjugnSRtRJqJtZPvRpuOJ/pP0WmBrShL9XmBZ5iXRVwFPAEsAo+u2\nODCjtwveSHo7pUfyd4FjRupE0roS7FRKqczXRur7jIhFRxLniH6SdB7l0v7RTccSg6MmgNtSkuiJ\nlJVMXwReqNtilNVWzwLOAH6/oCRR0iTg28DHbY/4JdklrUapef418Lmma8ojIvojiXNEP9SRw7Mo\no83DarGNGFi1M8ZHKO0ITZmU+DCwFCXRXgoYB2wF7Gz7zmYiHXqSVqIsFz4D+ORQ1I9HRAyGJM4R\n/SDpF8AFw70bQgyc2qbtzcBulBKP54Hn6vYM8L+LYklP7a19LvAysK/thxsOKSJioSVxjuijujzy\n34CNbc9pOp6IdldXIfw2ZRXCM4HvpgtNRAwn3eWciw11MBHDzHjgySTNEb1j+0XbhwCbAE8C0yWd\nJWmLhkOLiOi3fifOkhaX9Id6ORtJq0iaKuluSVfUureOYw+XdI+kuyRt17J/S0m31ceO729MEQPo\nrZSV6SJiIdiebfsIYF1gOnCRpEslvbXh0CIi+mwgRpwPpiwK0VHzcRgw1faGlNZOhwFI2gTYgzIK\nsQNwQq0TBDiRUg83HhgvaYcBiCtiICRxjugH23+3fQylb/aFwNmSLq+TbiMihpV+Jc6S1gL+GTgJ\n6EiCdwYm19uTgV3r7V2AM+tlvBnAvcAESasDy9ueXo87reWciKYlcY4YALb/Yft/KOVP5wFn1KuT\nW7cMokREtLX+jjgfC3yBMoO6w5iWetA5lGVvAdYAZrYcN5Oyilfn/bPq/ohG1aWSNwRuaTqWiJHC\n9gu2f0z5v3UmcAKlDvrDkhZvNrqIiO6N6uuJknYCHrb9B0kTuzrGtiUNWNsOSUe13J1me9pAPXdE\nF94M3Gr7H00HEjHS1OXHfyrpVGAn4IvANyV9FzjX9iNNxhcRi46ax07szbF9TpyBtwM7S/pnStP/\nFSRNAeZIWs327FqG0dHHcxawdsv5a1FGmmfV2637Z3X1graP6ke8EQsrZRoRg6yuMngRZfLg24FD\ngG9Iuhe4vG7XZ0GViBgsdSB2Wsd9SUcu6Ng+l2rYPsL22rbXBfYErrK9N+UH4KR62CTggnr7ImBP\nSaPr0rbjKUsYzwaekjSh1rnt3XJORJOSOEcMIdu/tb078Drg85S5M8cCD0s6vv7uiIhozED2ce4o\nyfgm8D5JdwPb1PvUpWfPoXTguBQ4wPNWXzmAMsHwHuBe25cNYFwRC63+EZfEOaIBdRL5NXWAZkvg\nTZRVGW+SdLakrRoOMSIWUVk5MKILktahJM1reLj8J4kY4SQtD3wS+BzwAHCI7ZubjSoiRpqsHBix\n8N4K3JCkOaJ91J7QxwMbAKcAl0n6Sl3mOyJi0CVxjuhayjQi2pTtubZPAbYA3gH8VtLGDYcVEYuA\nJM4RXUviHNHmbM+krER7MnCtpM+nF3REDKbUOEd0ImlJ4DFgVdvPNB1PRPRM0vqUBHolSu3ztGYj\niojhKjXOEQtnM+DuJM0Rw4ft+4Ctgf8CTpV0nqT1Gg4rIkaYJM4Rr5YyjYhhyMW5wMbAzZSlvP9b\n0nINhxYRI0QS54hXS+IcMYzZfs72fwGbUlaj/aOkD9f+7BERfZYa54hOJP0F2MH2n5qOJSL6T9K7\ngR8CfwX+Lf+3I6I7qXEeZJI+U2dzL990LNE/klYDVqSsYhkRI4Dtayit6y4FrqvlG6/PCHRELKwk\nzv0kaUvgKGAr4C+SvlGTrxieJgA32n656UAiYuDUZbyPpZRvrAxcDvy1LuF9gKRNkkhHRE+SOPdD\nXa3qZODfbe9JSZ5XAO6U9GNJGzYaYPRF6psjRjDbD9ne3/Y6lP/vlwBvqV/vl/Q/kj4gadlGA42I\ntpQa536Q9B+UVave37o0s6TXAZ8FDgB+A3zLdq+TMUlvBv6eOryhVT+33wEfs/2bpuOJiKFTR5tf\nD/wz8H5KMv1b4DrgJuAm239rLsKIGCrd5ZxJnPuoLu96DbCl7QcWcMyywCeAfwceBL4FXLKgMgBJ\no4GvAXsDi1NGPr8D/MbD5YMapurVg6nAb20f0XQ8EdEsSSsC21BGpd8MbAk8DtwIfNP2LQ2GFxGD\naFAmB0paW9LVku6QdLukg+r+VSRNlXS3pCskrdRyzuGS7pF0l6TtWvZvKem2+tjxfY2pFzEvJulD\nkm6UdL2kIyVN6GqJVklL15q3V/X/rMefDBy5oKQZwPYztn8AjAdOAP4TuE3Sx2uS3PqcG1BGNt4A\n/BMwjjKR5WTghtpKaem+vveBJOm1kkY1HccAOw54Gvhy04FERPNsP2n7fNtfsr0tsAplee/rgCsk\nfSvlHBGLnj6PONcJcKvZvqUml78HdqWMsP7N9rckfQlY2fZhkjYBzqBc/loTuBIYb9uSpgMH2p4u\n6RLge7Yv6/R63Y44S9qGkpguRkk4LwVutv1yTXT3AP4DeBb4OiVJ2pHyg3A1ymjjc8D6wAbAa4CZ\nlOVbTwZ+YPvB+loHAbsBExdmElm9FLgt8EVgE+BY4CfALsAxNf4fdCr7WBz4APA5Sg31LODOlu0v\nNc6HbP+jt7H0laS1gOnAz2wfNNivNxQk7Uf5/r7V9pNNxxMR7U3SqpSf328DDuj8+yoihrchKdWQ\ndAHwg7q9x/acmlxPs/16SYcDL9s+uh5/GaUbxf3AVbY3rvv3pCSk+3d+E8DngXNsz2rZvwXw35SE\n98vA3ygJ8Y6UEYIrKcn6w5QyiCs6lz1IWhvYjlIecR9wLzDT9kuS1gUOAiZRZmGfRUmk32777n58\nvzanJNA7Uco49rR9aw/nLFHf5yYt2zjKHyKrA09QkuhZnb7OBG6w/XQ3z70SpS772tq6qatjlgOu\nBS4GPg7sZXtab95vu5L0TuDnwDv783lGxKJH0vbAiZTyjZOB62w/12xUEdFfg544SxoH/Bp4I/CA\n7ZXrfgGP2V5Z0vcpydvp9bGTKKPCMyj1Yu+r+98FfNH2Bzq/CeAUyqj27cC5wNuB91BGkE+y/UKn\nc9YF3kfpyTutP3XCtd5tX+BA4Ie2v9vX5+r0vKsBT/b3h62kxYBVKatkrdnp6zjKErQnAt+3/UjL\neUsCnwGOoPyRsS2lS8j/dnr+xSkJ5t+AT1Em0Hwf2LS7hLydSRpLqSP/ZEaMIqIvJC0DHEIZBNmU\nckXuKuBXwO9sv9hgeBHRB90lzv2uU62jkOcBB9v+e2sbzFqGMZCT2h6grP60AWXW87XApxeUuNn+\nC/DjgXjhegn/mLoNGNuzB+h5XgZm1+13nR+XNB44FPiTpDMo72Mr4BuUko9tbN9eS2ourn90fL3l\nj43/prTa+3Ddd7GkDwHfpiTejat/PLwR2JpSYvMC8GLL19cA6wBjW7YvJ2mOiL6y/Szl5+g3JK0A\nvIsyAHEisK6ka5mXSN+WHvER7UfSRGBir47tz4hzLR34JXCp7ePqvrsopRazJa0OXF1LNQ4DsP3N\netxlwJGUUo2rW0o1PkIp9XhVqUY7ddUYruoI98GUZPc+4FDbV3dxzC+B/wP2B/YBvkSpAX6s5biV\ngFuBfW1PHZp3MD9JawLbA++l/LJ6ivJLag4wGlii5eujlD++Xtls/72BsCNiEVBbXE6k/GzalrLw\nyvWU0o4bgemZVxHRfgalVKOWYUwGHrX9uZb936r7jq7J8kqdJgduxbzJgRvUUekbKXXE0yn1sws9\nOTAWTv2jZ+6CylfqlYQzgdcC6wHv6qoGuNb4/Rh4k+2nWvavSpn0+AylF+r9Xb1WLQFZhzKh9KnO\njy8gtlGUGvb9KOU6l1P+Pf3K9v29eY6IiKFWJ1e/lbJC6QTKMuAPUAYqzqWUdqT1aETDBitxfiel\nj/GtQMeTHE5Jfs+hXAafAexu+4l6zhHAJ4G5lNKOy+v+LYFTgaUpfY5f1a0hifPQqwnqV4ArFzRh\nsB73I8rEyi8C/wLsSZmQeRmlHOgdlH8j11Nqipdk3uTGDSl106sAf6Yk2b+txz5ejx1dvy5DScb3\npUyo/BFwru1nBvBtR0QMiTqAsRll7s6HKVfGfgb8AliOsiDLRvXrOMpcosmUq7Qp+YgYJEPSVWOw\nJXFuX5KWB26jXIacCpwNXFxr/zquTqxDGR1+K6UlYEc7vbtsP137Wv9TPebtlDZPy1Lqk//Rsv0a\n+ElPHUgiIoaT+nNyU0qr0x2Bx4C7WraH6v59KPM1pgCT0w0oYuAlcY5BJ2kM8GxqhiMiBpekTSkt\nUj9G6TL1PcpgxUuNBhYxQiRxjoiIGGHqlboPUyZ8v4bSIvSUTDiM6J8kzhERESNULfOYQEmgd6CU\ntF1OWfDrviZjixiOkjhHREQsAmoLvPdR2nRuR+lsdHndrk45XUTPkjhHREQsYupI9JsoSfT2lFHp\nmylJ9K8oHY3mUhaImgs839u2oBEjWRLniIiIRZykZYF3U5LoiZTVYEdR2uCNorT8fAS4rmW7PZMO\nY1GTxDkiIiK6JWkxSm/9d7Rsa1Ba4z3bsj1T9z3Ssj0KvASobosBLwN/pazpMDu9p2O4SOIcERER\nC03SSsCKlNHoZSj99ZejLFr1upbtNZRk2ZSE2fX+GpTFW1aiLFz1APAE8DTw95avfwH+BNyTcpFo\nWhLniIiIaIykpSkrCo+lJOLL1W15SlK9LmW0ezzwFHAv8CTwPPBcy9e/1/1P1a/PAqu2PPfYev9O\n5q1C+wfb/+gUzyhKqcpTtud2Ee8SwPrAGyiJ/y3AdbafH4BvR7S5JM4RERHR9mq5yBrABpSkeqm6\nLV23Feq2Yv26HDCHMpL9AGVU+2/AGykr0L6dkpDfASxOGSlfhTJy/nT9+iTwcN2eBNarrz+rnvcA\nsCVlouWNlImV11AS+SWA0fVrV5uA+4A70197+EjiHBEREYskSctTljN/nlKb/ThlpPllSR3J9KqU\nkpOVKTXZf7L9bKfnWYEyufK9lIR8FKUjSXebKEn4JvW17wD+SClXeY4yYv4cpW78YWB23R73cEnQ\nRqAkzhERERENqSPp4yilHxtRRtOXoYyiL0MZOX8dsFrdlqEk0h2J/hMtW+f7f6/nr0wpe1m5Pv8/\nKAn50wv4+gwlaR/NvBH9pev9fzB/Yv88pWZ91AK2Jbp5TJQJpH+t21MdfxTU78sKNeZlgQfbYWQ+\niXNERETEMCFpKWAM85Lhlbq5vTwlGe5Iqh+nJNNLMm8y57Kdbnd8XRp4gXk15M9RkuYlmZfUdyTU\nLzGv5/fCbFD+KFidUoazGCWRXo5ScvNMjflZSo36s8A9wN2USaMv13MWr5uYN0rf8YfA0/U55wAP\n2356Yb/nrYZF4ixpB+A4yjflJNtHd3o8iXMbkTTR9rSm44gin0d7yefRXvJ5tJd8Hu1lqD+PWjrz\nWkpy/0Tr5My6aM/qlEmi4ymj9KIk7S/Xr6Yk8q1/ACxPSc5XpfzBYUqLxJeZ1yKxtVVi532vGkFf\nUM45aiC+Cf1Va4x+QKkbmgXcJOki239sNrLoxkRgWsMxxDwTyefRTiaSz6OdTCSfRzuZSD6PdjKR\nIfw86rLvXS79Xks4Hqrbr/vy/DX5XpaSnIuSRHdsL3e637G1jqS/RBmB71JbJM7AVsC9tmcASDoL\n2IVSQB/taVzTAcR8xjUdQMxnXNMBxHzGNR1AzGdc0wHEfMY1HcBAqsl3R/lGn5Tcu2uL9fVJB9ia\nlBYyHWbWfdG+xjUdQMxnXNMBxHzGNR1AzGdc0wHEfMY1HUDMZ1zTAQwn7TLi3KtCa0ntUZAdQD6P\ndpPPo73k82gv+TzaSz6P9pLPo/faJXGeBazdcn9tyqjzKzIxMCIiIiKa1C6lGr8DxksaJ2k0sAdw\nUcMxRURERES8oi1GnG3PlXQgcDmlHd3J6agREREREe2kbfo4R0RERES0s3Yp1YiIiIiIaGtJnCMi\nIiIieiGJc0RERERELyRxjoiIiIjohSTOERERERG9kMQ5IiIiIqIXkjhHRERERPRCEueIiIiIiF5I\n4hwRERER0QtJnCMiIiIieiGJc0RERERELyRxjoiIiIjohSTOERERERG90GPiLOlwSXdIuk3SGZKW\nlLSKpKmS7pZ0haSVOh1/j6S7JG3Xsn/L+hz3SDq+Zf+Sks6u+2+QtM7Av82IiIiIiP7pNnGWNA74\nNLCF7TcBiwN7AocBU21vCPyq3kfSJsAewCbADsAJklSf7kRgX9vjgfGSdqj79wUerfuPBY4esHcX\nERERETFAehpxfgp4EVhG0ihgGeAhYGdgcj1mMrBrvb0LcKbtF23PAO4FJkhaHVje9vR63Gkt57Q+\n13nAtv16RxERERERg6DbxNn2Y8B3gQcoCfMTtqcCY2zPqYfNAcbU22sAM1ueYiawZhf7Z9X91K8P\n1tebCzwpaZW+vqGIiIiIiMEwqrsHJa0PHAKMA54EzpW0V+sxti3JgxbhvFgG/TUiIiIiImyrq/3d\nJs7Am4Hf2n4UQNLPgbcBsyWtZnt2LcN4uB4/C1i75fy1KCPNs+rtzvs7zhkLPFTLQVasI929fhMx\n9CRNsz2x6TiiyOfRXvJ5tJd8Hu0ln0d7yefxat0N1vZU43wX8FZJS9dJfu8F7gR+AUyqx0wCLqi3\nLwL2lDRa0rrAeGC67dnAU5Im1OfZG7iw5ZyO59qNMtkw2t+MpgOI+cxoOoCYz4ymA4j5zGg6gJjP\njKYDiPnMaDqA4aTbEWfb/yfpNOB3wMvAzcCPgeWBcyTtS/mG716Pv1PSOZTkei5wgO2OrP0A4FRg\naeAS25fV/ScDUyTdAzxK6doR7W9G0wHEfGY0HUDMZ0bTAcR8ZjQdQMxnRtMBxHxmNB3AcKJ5eW17\nk+SUarQPSRNtT2s6jijyebSXfB7tJZ9He8nn0V7yebxadzlnEueIiIiIiKq7nLOnyYERERERMUyl\nK1n3FnZQNolzxAgk6T3ATNv3NR1LREQ0a7hUFwy1eYtb915PXTUiYhiRtLiko4H/BaZLOkXSBgtx\n/kRJF0v61OBFGRERMTz1mDhL2kjSH1q2JyUdJGkVSVMl3S3pCkkrtZxzuKR7JN0labuW/VtKuq0+\ndnzL/iUlnV333yBpnYF/qxEjm6QVKG0etwI2BzagrPp5g6RTu0ugJb1F0hXAScDFwOcl/VjSkkMQ\nekRExLDQY+Js+0+2N7e9ObAl8CxwPnAYMNX2hpTey4cBSNoE2APYBNgBOEHzxsJPBPa1PR4YL2mH\nun9f4NG6/1jg6IF6gxGLgpoU30BJlLez/Tfbj9s+kpJA/4WSQN8p6SJJx0j6jKSd68JG5wM/Aza2\nfQIwAXgNcI2ktbp+1Wh3kjaoC0tFRMQAWNhSjfcC99p+ENgZmFz3TwZ2rbd3Ac60/aLtGcC9wIS6\nwuDytqfX405rOaf1uc4Dtl3YNxKxqJK0NXAd8H3bB9h+sfVx20/Y/iplxc7dgVOAvwKbAQfWc8fb\n/nHHubb/TlmQ6HxKycd7huwNxYCQ9C7gduCX9WpERET008KOROwJnFlvj7E9p96eA4ypt9egjHx1\nmAmsCbzIvGW2oSy1vWa9vSbwIIDtubUcZJUFLb0dEYWkccA5wB62r+ruWNvPUxKp23vz3HXxom9K\nupmy4NG3ge96AbNMJL0JmAL8j+3/6fWbiAEnaUPKFYQPAjsB10nayfb9zUYWETG89XrEWdJo4APA\nuZ0fq79IM2UzYghJWpySqB7dU9LcH7avoNRNfxi4QNLKXcSyN3AV5UrSEXVV0WiApNcBlwD/YfsS\n4LOUFVqvlzSh0eAiIqpx48ax5JJL8uijj863f/PNN2exxRbjgQceaCiy7i3MiPOOwO9tP1Lvz5G0\nmu3ZtQzj4bp/FrB2y3lrUUaaZ9Xbnfd3nDMWeKjW463Y1WizpKNa7k7LSjexiPsS5UrOMYP9Qrbv\nr5f+vw38XtLutn9XJw8eRymv2sb2bZJ+AVwt6QXbUwY7tphH0tKUCaLn2D4JXhnYOE7SfZSyjQNs\nv2oAJCJiKElivfXW48wzz+TAAw8E4LbbbuO5557rU5u4uXPnMmpU36Z0SJoITOzNsQtT4/wR5pVp\nAFwETKq3JwEXtOzfU9JoSesC44HptmcDT0maUCcL7k35Ad/5uXajTDZ8FdtHtWzTFiL2iBFF0puB\nQ4BJtl8eite0/YLtg4EvApdKOgy4FlgVeIvt2+px9wDbAd+StMdQxBYgaTHKXJH7gf/X+XHbvwDe\nB3xX0s8lvWWI4xujvvw2jIgRa6+99uK000575f7kyZPZZ599Xuk7ffHFF7P55puz4oorMnbsWL76\n1a++cuyMGTNYbLHF+OlPf8o666zDtttuy0477cQPfvCD+V5j00035cILL6Q7tqe15pg9HdzjBiwL\n/I0yua9j3yrAlcDdwBXASi2PHUGZFHgXsH3L/i2B2+pj32vZvySlTvMeSn30uC5icG9izZZtpGzA\nG4HVu9i/LPAnSl1zU7FtAPwaOBTQAo7ZFJgN/EvT38tFYQO+DvwGWKqH45YFDqbMK5kKbLOgz3AA\nY9sOeB74XNPfp2zZFrWt5k9tZ9y4cb7yyiu90UYb+Y9//KPnzp3rtdZay/fff78l+f777/e0adN8\n++2327ZvvfVWjxkzxhdccIFt+y9/+YsledKkSX722Wf93HPP+ZxzzvGECRNeeY1bbrnFr3nNa/zi\niy92GcOCcsvuck7VA9ped+uGR4w0kj4LHAksAVwGfB+43rYlnQgsa3ufJmPsDUlbUOL/jO3zmo5n\npJK0BmXS5+ttP9zT8fWc0cDHKK1En6D8G/uZyyTSgYztvcAZlCskxwEfsH3jQL5GRCxYzZ+6e3xA\nXmdh88l1112Xk046iRtuuIFnnnmGd7/73Rx77LFccsklLLHEEsyYMYOxY8fOd84hhxzCYostxjHH\nHMOMGTNYb731+POf/8y4ceMAeP7551ljjTW46aabWH/99Tn00EN5/vnnXzUK3UESXeWW3eWcWTkw\noo3Ulf+Op7SJeyuwLuUqzGRKbfHRlP7o/9ZclL1n+2Zge+D7kvZrOp52JmmNWkKxdR9OPxD4394m\nzfBK6c0plJ7736SUz82UdGztx99VjKNqGd4SdRtVS0S6JGkbSonfh2yfAewHnC1plYV4bxExiBY0\nsrqwW19IYu+99+b0009/VZkGwI033sjWW2/NqquuykorrcSPfvSjV00mXHvtedPqllpqKXbffXem\nTJmCbc466yz23nvvvn1jFiCJc0SbkLQ8pe7/DcDbbP/ZpQfz8cBGlBKo9YGP2X6ywVAXiu0/AO8G\nDpP05dS5vpqktwM3USZZny3p3Qtx7rLAp4Hjezq2K7Zfsn2+7e2Bt1AWubqyruJ6maSbJP1Z0pPA\nP4Bn6jHP1fsvSvqVpH1qLB1xTQTOAnazfW19rQsovcFPyb+DiAAYO3Ys6623Hpdeeikf/OAHX9lv\nm49+9KPsuuuuzJw5kyeeeIL999+fl1+ef1pP5x8lkyZN4vTTT+fKK69kmWWWYcKEgW0mlMQ5og1I\nGkupT50F7Gj7idbHbb9s+zLbu9n+bSNB9oPte4F3AB+ijD7P97NHxWsXxWRK0r9SJlfvZ3t/Sr/8\nn0l6Ry+f4uPAtbbv628stv9i+z+AdYCvUJLxz1ImFa4DLGG7Yxtle3FKzfT/UFaMnaWyvPt+HZ+T\ndwAAIABJREFUlNalu9v+daeX+RKwGqV0IyKCk08+mauuuoqll156vv1PP/00K6+8MqNHj2b69Omc\nccYZPZaWvO1tb0MShx56KPvsM/AVjUmcIxpWL83fQOmBvL87rfw3Utj+K/Ae4E3AWXXJ7xMkXQs8\nRpmsduyikjxLWlLSj4GDgHfYvhjApSf3XsD5kt7Ww3MsTklAvzuQsbms/HqF7UttT7d9X7368aoO\nLraft32u7fcDrwdupST/e7qL7ke2X6Ak2Yelr3REAKy33npsscUWr9yXhCROOOEEvvKVr7DCCivw\nta99jT32mL9R04J+Xeyzzz7cdttt7LXXXgMeayYHRjSkJohfAD4P7G17asMhDQlJS1H6QS9N6bLT\nsc0FrgYutH1kcxEOvtqvfirwKPAJlyXOOx+zI6W2fSfb0xfwPLsChwNv7Xb2TxuStAtlRPuTlL78\nQ9JWMWJR09PkwJFoypQp/OQnP+Gaa67p9ri+TA7sVadoSSsBJ1FqLw18gtI67mzK5bsZlEtyT9Tj\nD6f8MHwJOMhl5TEkbQmcCiwFXOLSE5a6iMJpwBaUXyR7OEvDxggmaQXgFMpiQVvZbs8lkgZB7drQ\n5eRGSdsB10j6u+3vDG1kQ+o/KIvX7L6ghNH2pSorMP5C0q62r+/isM/TzTLo7cz2hZJWBI4FlpP0\nU+BU27MaDi0ihrFnn32WH/7wh68sqjLQeluqcTwl0d2Y0pv1LkoLo6m2N6QsWHIYQJ2NvQdlpvYO\nwAktl15PBPa1PR4YL2mHun9f4NG6/1jg6H6/s4g2JekNzJsI9q5FKWnuSe0K8V7gs7X2d8SR9Fbg\nAODjPY2yuixaMgm4qI4utz7PWygDFz8frFgHm+3TgM0ovzPWBm6TdImkIyTtLGn97rp2RES0uvzy\ny1l11VVZffXV+ehHPzoor9FjqUYdEfiD7fU67b8LeI/tOZJWo1xqe30dbX7Z9tH1uMuAoyirWV1V\nk28k7QlMtL1/PeZI2zfWS5h/tf26Tq+XUo0Y9iRtS2nP9QXbk5uOp11JWp+ywMqXbJ/edDwDpXZO\n+QPwRdu9TnhVVoq8CPi67RPqvjOBm2wP+pLrQ0XSMsDOlMWy3lC311J6VO9t++4Gw4sYlhbFUo3e\nGqxSjXWBRySdAvwT8HvKZJQxtufUY+YAY+rtNSgTnTrMBNakXJac2bJ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xUknaWNJJwH3A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\nASfOZmZmZmYVcOJsZmZmZlaB/w9GSyqyA9AR+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085e2690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot(subplots=True, figsize=(12,10), grid=False,\n",
    "           title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.11 Plot the proportion in each year of babies named John, Harry, Mary and Marilyn, all in one figure.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are these names dropping in popularity over time, or is the diversity of names going up with time?  Let's see what the data says...\n",
    "\n",
    "One measure of diversity is the fraction of names accounted for by the top 1000 names every year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = top1000.pivot_table('prop',\n",
    "                            index='year',columns='sex', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1065e33d0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FRKpYs4dnS8uMS8OzPW1mU3PPpYiI1KvZw7Ol8TQfAU5y99fMbIC7L65nXeo7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\nxEVEqpiCuIhIFfv/8004B6w8g94AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107b728d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(title='Sum of table1000.prop by year and sex',\n",
    "          yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like diversity has been going up over time: as the years go by, the top 1000 names account for a smaller and smaller proportion of baby names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at another measure of diversity: how many names account for the top 50% of all baby names?  This is tricky to do but perfectly possible with Pandas.  We'll build it up slowly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just take the top 1000 girl names in 1880:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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 rowspan=\"5\" valign=\"top\">F</th>\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 sex  births  year      prop\n",
       "year sex                                         \n",
       "1880 F   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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000_girls_1880 = top1000[(top1000.year == 1880) & (top1000.sex == 'F')]\n",
    "top1000_girls_1880.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we need to find is this: if we sort the names starting with the most common, at what point will the cumulative sum of `prop` reach or exceed 50%?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "      <th>cumprop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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 rowspan=\"5\" valign=\"top\">F</th>\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",
       "      <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",
       "      <td>0.106261</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",
       "      <td>0.128274</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",
       "      <td>0.149583</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",
       "      <td>0.168771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name sex  births  year      prop   cumprop\n",
       "year sex                                                   \n",
       "1880 F   0       Mary   F    7065  1880  0.077643  0.077643\n",
       "         1       Anna   F    2604  1880  0.028618  0.106261\n",
       "         2       Emma   F    2003  1880  0.022013  0.128274\n",
       "         3  Elizabeth   F    1939  1880  0.021309  0.149583\n",
       "         4     Minnie   F    1746  1880  0.019188  0.168771"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_names = top1000_girls_1880.sort_index(by='prop', ascending=False)\n",
    "sorted_names['cumprop'] = top1000_girls_1880.prop.cumsum()\n",
    "sorted_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could either code up a method to scan the `cumprop` column or find a built-in one that will do the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ex 1.4.12 Write a method that takes `cumprop` as an array of numbers and finds the index of the first element where cumprop > 0.50**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As luck would have it, NumPy already has a method to do what we want: given a sorted list, `searchsorted(x)` returns the index of the first element greater than x:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.searchsorted([0.05,0.10,0.15,0.20,0.25], 0.12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, if the vector above were a vector of cumulative fractions, we'd need to include the first 3 items (not the first 2) to cover 12% of the cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put this all together into a function that we can apply to a DataFrame in a `groupby` operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q = 0.5):\n",
    "    group = group.sort_index(by='prop', ascending=False)\n",
    "    \n",
    "    # float() to convert a 1-element\n",
    "    return np.searchsorted(group.prop.cumsum().values, q) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      38\n",
       "      M      14\n",
       "1881  F      38\n",
       "      M      14\n",
       "1882  F      38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity = top1000.groupby(['year', 'sex']).agg(get_quantile_count)\n",
    "diversity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above uses something called hierarchical indexing, where both `year` and `sex` together act as a label.  It's more natural to turn this into a 2D table.  We can do that with `unstack`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</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>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity = diversity.unstack()\n",
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10aea58d0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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MoEePHvTo0YOjjjqq9BfnnKs3FbnRCTU2jG1ahWJJ3Hnnney///7pBOCcq0UVqR+HGiuR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\n+i0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a27ce90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One final cute example: we can see how some names have changed switched genders over time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WvOnxsHO2CcB/ArfXaJNE3unp8Ql9xDtaJxtinxtlm2SJ94MUJ97JF9GThHRM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\nTZx+YSV7mlBH5WMqa3qskQLsqJfyK+B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a282ed0>"
      ]
     },
     "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='sex')\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": [
    "**(!) Ex 1.4.13  As you can see above, some names have alternate spellings.  It's a tough problem to automatically group all \"similar\" names together.  A first step might be to find groups of names that start with the same 4 letters.  Find all such groups, listing the group with most variants first.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}