Python Course Lecture 6b Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python Course Lecture 6b: Introduction to Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Our Goals\n",
    "  - To **explicitly label** our data.\n",
    "  - To **explicitly associate** our data\n",
    "  - To support **mixed-type** data\n",
    "  - To make data exploration more natural\n",
    "  \n",
    "Pandas provides a few new data collection types to help us toward this goal: the **Series**, **DataFrame**, and **GroupBy** classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas Series Associates Each Data Point with an Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series(data=[1, 2, 3, 4])#, index=[0, 1, 2, 3])\n",
    "print(type(a))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Indices Can be Of Any Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nick    1\n",
       "Bill    2\n",
       "John    3\n",
       "Bob     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.Series(data=[1, 2, 3, 4], index=['Nick', 'Bill', 'John', 'Bob'])\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Series Can Contain Mixed Type Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nick     1\n",
       "Bill     2\n",
       "John     3\n",
       "Bob     hi\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = pd.Series(data=[1, 2, 3, 'hi'], index=['Nick', 'Bill', 'John', 'Bob'])\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Series Can Contain a \"Name\", used to describe the data as a whole\n",
    "Since Python doesn't store variable names as data, this Name property helps make data labeling possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nick    1\n",
       "Bill    2\n",
       "John    3\n",
       "Bob     4\n",
       "Name: SubjNums, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.name = 'SubjNums'\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Series Data can be Indexed like a Dictionary or an Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b['Nick']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.Nick"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Data is Represented in NumPy Arrays\n",
    "As a result, everything that works on a NumPy array also works on a pandas object!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(b)\n",
    "b.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]    (4,)    int64\n"
     ]
    }
   ],
   "source": [
    "print(b.values, b.shape, b.dtype, sep='    ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Series can Be Combined into a 2D Object called a DataFrame\n",
    "Indices will automatically be lined up.  Non-matching values will be filled by a **NaN** (Not a Number) value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b\n",
       "1  1.0  NaN\n",
       "2  2.0  1.0\n",
       "3  3.0  2.0\n",
       "4  NaN  3.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.Series(data=[1, 2, 3], index=[1, 2, 3])\n",
    "b = pd.Series(data=[1, 2, 3], index=[2, 3, 4])\n",
    "pd.DataFrame({'a': a, 'b': b})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The DataFrame\n",
    "DataFrames allow us to **label** data and to **associate data of different dtypes** into a single variable, while still retaingin the performance benefits of NumPy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### DataFrame Dictionary-Like Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject</th>\n",
       "      <th>Acc</th>\n",
       "      <th>isRat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bob</td>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amy</td>\n",
       "      <td>0.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bill</td>\n",
       "      <td>0.9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject  Acc  isRat\n",
       "0     bob  0.8   True\n",
       "1     amy  0.7  False\n",
       "2    bill  0.9  False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['Subject'] = ['bob', 'amy', 'bill']\n",
    "df['Acc'] = [0.8, 0.7, 0.9]\n",
    "df['isRat'] = [True, False, False]\n",
    "print(type(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### DataFrame Creation from a Matrix"
   ]
  },
  {
   "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>Speed</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Speed  Accuracy\n",
       "0      0         1\n",
       "1      2         3\n",
       "2      4         5\n",
       "3      6         7\n",
       "4      8         9\n",
       "5     10        11"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.arange(12).reshape(-1, 2)\n",
    "pd.DataFrame(data, columns=['Speed', 'Accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### DataFrame Creation from File\n",
    "DataFrames can be made from lots of different file types! Pandas includes lots of reader functions, like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['read_clipboard', 'read_csv', 'read_excel', 'read_fwf', 'read_gbq', 'read_hdf', 'read_html', 'read_json', 'read_msgpack', 'read_pickle', 'read_sas', 'read_sql', 'read_sql_query', 'read_sql_table', 'read_stata', 'read_table']\n"
     ]
    }
   ],
   "source": [
    "print([fun for fun in dir(pd) if 'read' in fun])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Selecting Columns and Rows of DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Selecting Columns of DataFrames works like Dictionary or like an Object--Your choice!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      bob\n",
       "1      amy\n",
       "2    Harry\n",
       "Name: Subject, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subject']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      bob\n",
       "1      amy\n",
       "2    Harry\n",
       "Name: Subject, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject</th>\n",
       "      <th>Acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bob</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amy</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject  Acc\n",
       "0     bob  0.8\n",
       "1     amy  0.7\n",
       "2   Harry  0.9"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Subject', 'Acc']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Changing Index of a DataFrame can make Data Organization simpler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Acc</th>\n",
       "      <th>isRat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amy</th>\n",
       "      <td>0.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill</th>\n",
       "      <td>0.9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Acc  isRat\n",
       "Subject            \n",
       "bob      0.8   True\n",
       "amy      0.7  False\n",
       "bill     0.9  False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = df.set_index('Subject')\n",
    "dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bob', 'amy', 'bill'], dtype='object', name='Subject')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Selecting Rows of DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Specific Index Values: DataFrame.loc"
   ]
  },
  {
   "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",
       "      <th></th>\n",
       "      <th>Acc</th>\n",
       "      <th>isRat</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amy</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nate</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Acc  isRat new\n",
       "Subject                \n",
       "bob      0.8      1  hi\n",
       "amy      0.7      0  hi\n",
       "bill     0.9      0  hi\n",
       "nate     1.0      1  hi"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs.loc['nate'] = 1\n",
    "dfs['new'] = 'hi'\n",
    "dfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Indexing Like a NumPy Matrix: DataFrame.iloc\n",
    "If the indices match their row numbers, then the result will be the same as loc"
   ]
  },
  {
   "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>Acc</th>\n",
       "      <th>isRat</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amy</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Acc  isRat new\n",
       "Subject                \n",
       "bob      0.8      1  hi\n",
       "amy      0.7      0  hi"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs.iloc[1]\n",
    "dfs.iloc[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## While DataFrames are Mutable, Pandas will try to warn you about data copying and modification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      bob\n",
      "1      amy\n",
      "2    Harry\n",
      "Name: Subject, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nickdg/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "print(df['Subject'])\n",
    "df['Subject'][2] = 'Harry'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Solution: When Modifying Data, do so on the original dataframe using DataFrame.ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Subject</th>\n",
       "      <th>Acc</th>\n",
       "      <th>isRat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bob</td>\n",
       "      <td>0.8</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amy</td>\n",
       "      <td>0.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>0.9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject  Acc  isRat\n",
       "0     bob  0.8   True\n",
       "1     amy  0.7  False\n",
       "2   Harry  0.9  False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[2, 'Subject'] = 'Harry'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## DataFrames can be transposed like a NumPy Arrray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Subject</th>\n",
       "      <td>bob</td>\n",
       "      <td>amy</td>\n",
       "      <td>Harry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acc</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isRat</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1      2\n",
       "Subject   bob    amy  Harry\n",
       "Acc       0.8    0.7    0.9\n",
       "isRat    True  False  False"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Boolean Indexing on DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Subject</th>\n",
       "      <th>Acc</th>\n",
       "      <th>isRat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>amy</td>\n",
       "      <td>0.7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry</td>\n",
       "      <td>0.9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Subject  Acc  isRat\n",
       "1     amy  0.7  False\n",
       "2   Harry  0.9  False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['isRat'] == False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plotting with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"http://pandas.pydata.org/pandas-docs/stable/visualization.html\" width=900 height=400></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iFrame src=\"http://pandas.pydata.org/pandas-docs/stable/visualization.html\" width=900 height=400></iFrame>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## DataFrames come with their own Plotting Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f52a394d3c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Izfm+n8zbK3+YDSv3xK0jRHbX2k+An+RDImcAz/OHjkr9RMi1vXLxx712AedE\nxE0vWCjNKaxoswK4J27Jk/QKSQfVLDoSeDB/HJMvO7nubXMljZG0D/AGYGXd+huBv5H0onwfB0va\nHbgJeJ+k3fLlY/P2T5PNLGM2rNwTt07wMuDL+VRdzwO/JBtaORRYLOkpoFr3nrvzZfsA/5ifCJ3K\nH3rklwLTgDskCXgMOCkibpR0BPAzSZuBG4CPA5cDX5P0O+DY7cMtZjubvzvFLCfpaOBzEfHGsmsx\na5WHU8zYEeDfAL5Ydi1mg+GeuJlZwtwTNzNLmEPczCxhDnEzs4Q5xM3MEuYQNzNLmEPczCxh/x9V\nLSXUJXogmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52a394d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(x='Subject', y='Acc', title='Subject Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Plotting Functions can Take advantage of the Index for more Intelligent Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f52a1588898>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bmVl9dadTImKTpAuBhWShPy8iOiXNylbH3IjoknQH8DCwCZgbEY8OaOVmZlZ/OqXQzjyd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\nwhziZmYJc4ibmSXMIW5mljCHuJlZwv4/Tbv8ci2zmI0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52a15c7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs.plot.bar(y='Acc', title='Subject Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Pandas Plotting uses Matplotlib, so all Matplotlib functions work with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f52a0c8af60>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YyODNzCy7TG34Gd79+STwxnxDMzOzPLmnrZlZSTjhm5mVhBO+mVlJOOGbmZWE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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52a0d10a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "df.plot.bar(x='Subject', y='Acc', ax=axes[0])\n",
    "df.plot.hist(y='Acc', ax=axes[1])\n",
    "axes[1].set_title('Accuracy Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Saving DataFrame to File\n",
    "Pandas provides many filetypes to save dataframes to.  Many of these functions are found as DataFrame methods starting with the word \"to_\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['to_clipboard', 'to_csv', 'to_dense', 'to_dict', 'to_excel', 'to_gbq', 'to_hdf', 'to_html', 'to_json', 'to_latex', 'to_msgpack', 'to_panel', 'to_period', 'to_pickle', 'to_records', 'to_sparse', 'to_sql', 'to_stata', 'to_string', 'to_timestamp', 'to_wide', 'to_xarray']\n"
     ]
    }
   ],
   "source": [
    "print([fun for fun in dir(df) if 'to_' in fun[:3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Exploration\n",
    "Pandas' data organization features really come to life when we look at large mixed datasets.  In the following section, we'll explore some subject data taken from the MovieLens dataset, which recorded movie ratings from almost 1000 people.  http://grouplens.org/datasets/movielens/100k/"
   ]
  },
  {
   "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>Num</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>ZipCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>executive</td>\n",
       "      <td>98101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>91344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>05201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>01002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>53</td>\n",
       "      <td>M</td>\n",
       "      <td>lawyer</td>\n",
       "      <td>90703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>30329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>06405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>29206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>scientist</td>\n",
       "      <td>55106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>educator</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>10309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>M</td>\n",
       "      <td>programmer</td>\n",
       "      <td>06355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>37212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>librarian</td>\n",
       "      <td>02138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>homemaker</td>\n",
       "      <td>95660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>30068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>40206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>artist</td>\n",
       "      <td>48197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>artist</td>\n",
       "      <td>94533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>55107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>21044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>40</td>\n",
       "      <td>F</td>\n",
       "      <td>librarian</td>\n",
       "      <td>30030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>55369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>M</td>\n",
       "      <td>programmer</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>914</td>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>08105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>915</td>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>60614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>916</td>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>N2L5N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>917</td>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>20006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>918</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>scientist</td>\n",
       "      <td>70116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>919</td>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>other</td>\n",
       "      <td>14216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>920</td>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>artist</td>\n",
       "      <td>90008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>921</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>98801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>922</td>\n",
       "      <td>29</td>\n",
       "      <td>F</td>\n",
       "      <td>administrator</td>\n",
       "      <td>21114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>923</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>E2E3R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>924</td>\n",
       "      <td>29</td>\n",
       "      <td>M</td>\n",
       "      <td>other</td>\n",
       "      <td>11753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>925</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>salesman</td>\n",
       "      <td>49036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>926</td>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>01701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>927</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>programmer</td>\n",
       "      <td>55428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>928</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>929</td>\n",
       "      <td>44</td>\n",
       "      <td>M</td>\n",
       "      <td>scientist</td>\n",
       "      <td>53711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>930</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>scientist</td>\n",
       "      <td>07310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>931</td>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>33556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>932</td>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>06437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>933</td>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>48105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>934</td>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>22902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>935</td>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>doctor</td>\n",
       "      <td>66221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>936</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>other</td>\n",
       "      <td>32789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>937</td>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>98072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>938</td>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>technician</td>\n",
       "      <td>55038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>939</td>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>33319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>940</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>941</td>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>942</td>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>librarian</td>\n",
       "      <td>78209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>943</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>77841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>943 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Num  Age Sex            Job ZipCode\n",
       "0      1   24   M     technician   85711\n",
       "1      2   53   F          other   94043\n",
       "2      3   23   M         writer   32067\n",
       "3      4   24   M     technician   43537\n",
       "4      5   33   F          other   15213\n",
       "5      6   42   M      executive   98101\n",
       "6      7   57   M  administrator   91344\n",
       "7      8   36   M  administrator   05201\n",
       "8      9   29   M        student   01002\n",
       "9     10   53   M         lawyer   90703\n",
       "10    11   39   F          other   30329\n",
       "11    12   28   F          other   06405\n",
       "12    13   47   M       educator   29206\n",
       "13    14   45   M      scientist   55106\n",
       "14    15   49   F       educator   97301\n",
       "15    16   21   M  entertainment   10309\n",
       "16    17   30   M     programmer   06355\n",
       "17    18   35   F          other   37212\n",
       "18    19   40   M      librarian   02138\n",
       "19    20   42   F      homemaker   95660\n",
       "20    21   26   M         writer   30068\n",
       "21    22   25   M         writer   40206\n",
       "22    23   30   F         artist   48197\n",
       "23    24   21   F         artist   94533\n",
       "24    25   39   M       engineer   55107\n",
       "25    26   49   M       engineer   21044\n",
       "26    27   40   F      librarian   30030\n",
       "27    28   32   M         writer   55369\n",
       "28    29   41   M     programmer   94043\n",
       "29    30    7   M        student   55436\n",
       "..   ...  ...  ..            ...     ...\n",
       "913  914   44   F          other   08105\n",
       "914  915   50   M  entertainment   60614\n",
       "915  916   27   M       engineer   N2L5N\n",
       "916  917   22   F        student   20006\n",
       "917  918   40   M      scientist   70116\n",
       "918  919   25   M          other   14216\n",
       "919  920   30   F         artist   90008\n",
       "920  921   20   F        student   98801\n",
       "921  922   29   F  administrator   21114\n",
       "922  923   21   M        student   E2E3R\n",
       "923  924   29   M          other   11753\n",
       "924  925   18   F       salesman   49036\n",
       "925  926   49   M  entertainment   01701\n",
       "926  927   23   M     programmer   55428\n",
       "927  928   21   M        student   55408\n",
       "928  929   44   M      scientist   53711\n",
       "929  930   28   F      scientist   07310\n",
       "930  931   60   M       educator   33556\n",
       "931  932   58   M       educator   06437\n",
       "932  933   28   M        student   48105\n",
       "933  934   61   M       engineer   22902\n",
       "934  935   42   M         doctor   66221\n",
       "935  936   24   M          other   32789\n",
       "936  937   48   M       educator   98072\n",
       "937  938   38   F     technician   55038\n",
       "938  939   26   F        student   33319\n",
       "939  940   32   M  administrator   02215\n",
       "940  941   20   M        student   97229\n",
       "941  942   48   F      librarian   78209\n",
       "942  943   22   M        student   77841\n",
       "\n",
       "[943 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = pd.read_csv('users.csv', delimiter='|')\n",
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## head()\n",
    "The **head()** method just returns the first N rows of the dataframe (5 by default).  This is convenient for a first pass of the data!"
   ]
  },
  {
   "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",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Job</th>\n",
       "      <th>ZipCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Num  Age Sex         Job ZipCode\n",
       "0    1   24   M  technician   85711\n",
       "1    2   53   F       other   94043\n",
       "2    3   23   M      writer   32067"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## describe()\n",
    "describe() gives us some quick statistics on any given column as a series or dataframe. If called on the whole dataframe, it will only give the statistics on the numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st = users['Job'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     943\n",
       "unique      2\n",
       "top         M\n",
       "freq      670\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.Sex.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## groupby()\n",
    "Need to seperate your data?  Instead of putting it into seperate variables, you can use the groupby() feature to make a GroupBy iterator, which you can use in a for loop.  In addition, Pandas functions will often treat the GrouBy object in a nice way!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex       \n",
       "F    count    273.000000\n",
       "     mean      33.813187\n",
       "     std       11.799095\n",
       "     min       13.000000\n",
       "     50%       32.000000\n",
       "     max       70.000000\n",
       "M    count    670.000000\n",
       "     mean      34.149254\n",
       "     std       12.356868\n",
       "     min        7.000000\n",
       "     50%       31.000000\n",
       "     max       73.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('Sex').Age.describe(percentiles=[.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F\n",
      "   Num  Age Sex    Job ZipCode\n",
      "1    2   53   F  other   94043\n",
      "4    5   33   F  other   15213\n",
      "\n",
      "M\n",
      "   Num  Age Sex         Job ZipCode\n",
      "0    1   24   M  technician   85711\n",
      "2    3   23   M      writer   32067\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for sex, data in users.groupby('Sex'):\n",
    "    print(sex)\n",
    "    print(data.head(2))\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "F    Axes(0.125,0.125;0.775x0.775)\n",
       "M    Axes(0.125,0.125;0.775x0.775)\n",
       "Name: Age, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HM+fY3jH6VvclzTt1vvpD9RyRsoqyDUXJmUKtGxGRilOhLxClbxelp6yceUXIGWUbipIz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52a0f29940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "users.groupby('Sex').Age.hist(alpha=.4, bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f52a0f2a160>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QGxRLw9Cex8p+YrSojSdGCzw2x62NY3O20LZPW5IaMm9oJzkkyTVJ/iHJl5O8tp++PsnW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\naoihLUkNMbQlqSH/H+e1hoB9raErAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52a0e549b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages = users.groupby('Sex').Age\n",
    "ages.mean().plot.bar(yerr=ages.std(), rot=0, title='Mean Subject Age by Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f52a0dc5c88>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j7ku6+7Lp2CHAXe6+AHAPcGgb8wiCIAiaoK2C3apcY2PgovT9ImCTNuYRBEEQNEFbBbsD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\n0PWGzzaQxYFaDtXbGqZ9Vft0R6EOnTxiz41+foh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52a0dd9828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "users.groupby('Job').count().Num.plot.bar(rot=70, title='Total Users by Job')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Questions?\n",
    "\n",
    "Note: We'll go into more advanced features of Pandas (time-series analysis, advanced grouping, hierarchical indexing) in future lectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}