tutorial_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Lesson 1**  \n",
    "\n",
    "**Create Data** - We begin by creating our own data set for analysis. This prevents the end user reading this tutorial from having to download any files to replicate the results below. We will export this data set to a text file so that you can get some experience pulling data from a text file.  \n",
    "**Get Data** - We will learn how to read in the text file. The data consist of baby names and the number of baby names born in the year 1880.  \n",
    "**Prepare Data** - Here we will simply take a look at the data and make sure it is clean. By clean I mean we will take a look inside the contents of the text file and look for any anomalities. These can include missing data, inconsistencies in the data, or any other data that seems out of place. If any are found we will then have to make decisions on what to do with these records.  \n",
    "**Analyze Data** - We will simply find the most popular name in a specific year.  \n",
    "**Present Data** - Through tabular data and a graph, clearly show the end user what is the most popular name in a specific year.       \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ***pandas*** library is used for all the data analysis excluding a small piece of the data presentation section. The ***matplotlib*** library will only be needed for the data presentation section. Importing the libraries is the first step we will take in the lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Import all libraries needed for the tutorial\n",
    "\n",
    "# General syntax to import specific functions in a library: \n",
    "##from (library) import (specific library function)\n",
    "from pandas import DataFrame, read_csv\n",
    "\n",
    "# General syntax to import a library but no functions: \n",
    "##import (library) as (give the library a nickname/alias)\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd #this is how I usually import pandas\n",
    "import sys #only needed to determine Python version number\n",
    "import matplotlib #only needed to determine Matplotlib version number\n",
    "\n",
    "# Enable inline plotting\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version 2.7.9 (default, Mar  1 2015, 12:57:24) \n",
      "[GCC 4.9.2]\n",
      "Pandas version 0.19.2\n",
      "Matplotlib version 1.4.2\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print('Python version ' + sys.version)\n",
    "print('Pandas version ' + pd.__version__)\n",
    "print('Matplotlib version ' + matplotlib.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Data  \n",
    "\n",
    "The data set will consist of 5 baby names and the number of births recorded for that year (1880)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The inital set of baby names and bith rates\n",
    "names = ['Bob','Jessica','Mary','John','Mel']\n",
    "births = [968, 155, 77, 578, 973]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To merge these two lists together we will use the ***zip*** function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zip?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BabyDataSet = list(zip(names,births))\n",
    "BabyDataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are basically done creating the data set. We now will use the ***pandas*** library to export this data set into a csv file. \n",
    "\n",
    "***df*** will be a ***DataFrame*** object. You can think of this object holding the contents of the BabyDataSet in a format similar to a sql table or an excel spreadsheet. Lets take a look below at the contents inside ***df***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Names</th>\n",
       "      <th>Births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Births\n",
       "0      Bob     968\n",
       "1  Jessica     155\n",
       "2     Mary      77\n",
       "3     John     578\n",
       "4      Mel     973"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export the dataframe to a ***csv*** file. We can name the file ***births1880.csv***. The function ***to_csv*** will be used to export the file. The file will be saved in the same location of the notebook unless specified otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only parameters we will use is ***index*** and ***header***. Setting these parameters to True will prevent the index and header names from being exported. Change the values of these parameters to get a better understanding of their use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.to_csv('births1880.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To pull in the csv file, we will use the pandas function *read_csv*. Let us take a look at this function and what inputs it takes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though this functions has many parameters, we will simply pass it the location of the text file.  \n",
    "\n",
    "Location = C:\\Users\\ENTER_USER_NAME\\.xy\\startups\\births1880.csv  \n",
    "\n",
    "***Note:*** Depending on where you save your notebooks, you may need to modify the location above.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "File C:\\Users\\david\\notebooks\\update\\births1880.csv does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-a4fab12583dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mLocation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mr'C:\\Users\\david\\notebooks\\update\\births1880.csv'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLocation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    644\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    645\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    728\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m    921\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    922\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 923\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    924\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1388\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1390\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1392\u001b[0m         \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4184)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:8449)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: File C:\\Users\\david\\notebooks\\update\\births1880.csv does not exist"
     ]
    }
   ],
   "source": [
    "Location = r'C:\\Users\\david\\notebooks\\update\\births1880.csv'\n",
    "df = pd.read_csv(Location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the ***r*** before the string. Since the slashes are special characters, prefixing the string with a ***r*** will escape the whole string.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Names</th>\n",
       "      <th>Births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jessica</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mary</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Names  Births\n",
       "0      Bob     968\n",
       "1  Jessica     155\n",
       "2     Mary      77\n",
       "3     John     578\n",
       "4      Mel     973"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This brings us the our first problem of the exercise. The ***read_csv*** function treated the first record in the csv file as the header names. This is obviously not correct since the text file did not provide us with header names.  \n",
    "\n",
    "To correct this we will pass the ***header*** parameter to the *read_csv* function and set it to ***None*** (means null in python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "File C:\\Users\\david\\notebooks\\update\\births1880.csv does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-e5adc149ff91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLocation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    644\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    645\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    728\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m    921\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    922\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 923\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    924\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1388\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1390\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1392\u001b[0m         \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4184)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:8449)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: File C:\\Users\\david\\notebooks\\update\\births1880.csv does not exist"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(Location, header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to give the columns specific names, we would have to pass another paramter called ***names***. We can also omit the *header* parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "File C:\\Users\\david\\notebooks\\update\\births1880.csv does not exist",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-47eb451a785f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLocation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Names'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Births'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    644\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    645\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    648\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    728\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m    921\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    922\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 923\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    924\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/users/Etu7/3407027/.local/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1388\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1389\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1390\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1392\u001b[0m         \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4184)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:8449)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: File C:\\Users\\david\\notebooks\\update\\births1880.csv does not exist"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(Location, names=['Names','Births'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can think of the numbers [0,1,2,3,4] as the row numbers in an Excel file. In pandas these are part of the ***index*** of the dataframe. You can think of the index as the primary key of a sql table with the exception that an index is allowed to have duplicates.  \n",
    "\n",
    "***[Names, Births]*** can be though of as column headers similar to the ones found in an Excel spreadsheet or sql database."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Delete the csv file now that we are done using it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\david\\\\notebooks\\\\update\\\\births1880.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-7c08b4d39d39>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLocation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\david\\\\notebooks\\\\update\\\\births1880.csv'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.remove(Location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data we have consists of baby names and the number of births in the year 1880. We already know that we have 5 records and none of the records are missing (non-null values).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ***Names*** column at this point is of no concern since it most likely is just composed of alpha numeric strings (baby names). There is a chance of bad data in this column but we will not worry about that at this point of the analysis. The ***Births*** column should just contain integers representing the number of babies born in a specific year with a specific name. We can check if the all the data is of the data type integer. It would not make sense to have this column have a data type of float. I would not worry about any possible outliers at this point of the analysis.  \n",
    "\n",
    "Realize that aside from the check we did on the \"Names\" column, briefly looking at the data inside the dataframe should be as far as we need to go at this stage of the game. As we continue in the data analysis life cycle we will have plenty of opportunities to find any issues with the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Names     object\n",
       "Births     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data type of the columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data type of Births column\n",
    "df.Births.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the *Births* column is of type ***int64***, thus no floats (decimal numbers) or alpha numeric characters will be present in this column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the most popular name or the baby name with the higest birth rate, we can do one of the following.  \n",
    "\n",
    "* Sort the dataframe and select the top row\n",
    "* Use the ***max()*** attribute to find the maximum value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Names</th>\n",
       "      <th>Births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Names  Births\n",
       "4   Mel     973"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method 1:\n",
    "Sorted = df.sort_values(['Births'], ascending=False)\n",
    "Sorted.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "973"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method 2:\n",
    "df['Births'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Present Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can plot the ***Births*** column and label the graph to show the end user the highest point on the graph. In conjunction with the table, the end user has a clear picture that **Mel** is the most popular baby name in the data set. \n",
    "\n",
    "***plot()*** is a convinient attribute where pandas lets you painlessly plot the data in your dataframe. We learned how to find the maximum value of the Births column in the previous section. Now to find the actual baby name of the 973 value looks a bit tricky, so lets go over it.  \n",
    "\n",
    "**Explain the pieces:**  \n",
    "*df['Names']* - This is the entire list of baby names, the entire Names column  \n",
    "*df['Births']* - This is the entire list of Births in the year 1880, the entire Births column  \n",
    "*df['Births'].max()* - This is the maximum value found in the Births column  \n",
    "\n",
    "[df['Births'] == df['Births'].max()] **IS EQUAL TO** [Find all of the records in the Births column where it is equal to 973]  \n",
    "df['Names'][df['Births'] == df['Births'].max()] **IS EQUAL TO** Select all of the records in the Names column **WHERE** [The Births column is equal to 973]  \n",
    "\n",
    "An alternative way could have been to use the ***Sorted*** dataframe:  \n",
    "Sorted['Names'].head(1).value  \n",
    "\n",
    "The ***str()*** function simply converts an object into a string.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most popular name\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mel</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Names  Births\n",
       "4   Mel     973"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HnUGDdCtT6k8VXjL6UVmkuncPHViGDYsdiTTUww/D669r3E6kseWU8MysrZm9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\nkbKghCciImVBCU9ERMrC/wd1xQp/dCJsGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2f512ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create graph\n",
    "df['Births'].plot()\n",
    "\n",
    "# Maximum value in the data set\n",
    "MaxValue = df['Births'].max()\n",
    "\n",
    "# Name associated with the maximum value\n",
    "MaxName = df['Names'][df['Births'] == df['Births'].max()].values\n",
    "\n",
    "# Text to display on graph\n",
    "Text = str(MaxValue) + \" - \" + MaxName\n",
    "\n",
    "# Add text to graph\n",
    "plt.annotate(Text, xy=(1, MaxValue), xytext=(8, 0), \n",
    "                 xycoords=('axes fraction', 'data'), textcoords='offset points')\n",
    "\n",
    "print(\"The most popular name\")\n",
    "df[df['Births'] == df['Births'].max()]\n",
    "#Sorted.head(1) can also be used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:** [David Rojas](http://www.hedaro.com/)  "
   ]
  }
 ],
 "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
}