10-minutes-to-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://pandas.pydata.org/pandas-docs/stable/10min.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 Minutes to pandas\n",
    "\n",
    "This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the [Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customarily, we import as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Creation\n",
    "\n",
    "See the [Data Structure Intro section](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a Series by passing a list of values, letting pandas create a default integer index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dates = pd.date_range('20130101', periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>-0.118588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-1.330950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.571507  0.160021 -0.015071 -0.118588\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  0.187037\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722\n",
       "2013-01-06  1.638509 -0.601126 -1.043931 -1.330950"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DataFrame by passing a dict of objects that can be converted to series-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'A':1.,\n",
    "                   'B':pd.Timestamp('20130102'),\n",
    "                   'C':pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                   'D':np.array([3]*4,dtype='int32'),\n",
    "                   'E':pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                   'F':'foo'})"
   ]
  },
  {
   "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",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having specific [dtypes](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you’re using IPython, tab completion for column names (as well as public attributes) is automatically enabled. Here’s a subset of the attributes that will be completed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# df2.<TAB>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing Data\n",
    "\n",
    "See the [Basics section](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the top & bottom rows of the frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>-0.118588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.571507  0.160021 -0.015071 -0.118588\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  0.187037\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-1.330950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  0.187037\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722\n",
       "2013-01-06  1.638509 -0.601126 -1.043931 -1.330950"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the index, columns, and the underlying numpy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'A', u'B', u'C', u'D'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.57150668,  0.16002149, -0.01507058, -0.11858772],\n",
       "       [-1.03769719, -0.89119559,  0.49544709,  0.45309462],\n",
       "       [-1.68238391, -0.02600566, -0.15295699, -0.21261369],\n",
       "       [-0.10875693, -0.9582675 ,  0.40733062,  0.18703669],\n",
       "       [ 1.09238026,  2.84177746, -0.12571427, -0.76072186],\n",
       "       [ 1.63850927, -0.60112596, -1.04393119, -1.33095031]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe shows a quick statistic summary of your data"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.245593</td>\n",
       "      <td>0.087534</td>\n",
       "      <td>-0.072483</td>\n",
       "      <td>-0.297124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.407466</td>\n",
       "      <td>1.423367</td>\n",
       "      <td>0.549378</td>\n",
       "      <td>0.651149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-1.330950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.805462</td>\n",
       "      <td>-0.818678</td>\n",
       "      <td>-0.146146</td>\n",
       "      <td>-0.623695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.491812</td>\n",
       "      <td>-0.313566</td>\n",
       "      <td>-0.070392</td>\n",
       "      <td>-0.165601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.451725</td>\n",
       "      <td>0.113515</td>\n",
       "      <td>0.301730</td>\n",
       "      <td>0.110631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.245593  0.087534 -0.072483 -0.297124\n",
       "std    1.407466  1.423367  0.549378  0.651149\n",
       "min   -1.682384 -0.958267 -1.043931 -1.330950\n",
       "25%   -0.805462 -0.818678 -0.146146 -0.623695\n",
       "50%    0.491812 -0.313566 -0.070392 -0.165601\n",
       "75%    1.451725  0.113515  0.301730  0.110631\n",
       "max    1.638509  2.841777  0.495447  0.453095"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.108757</td>\n",
       "      <td>1.092380</td>\n",
       "      <td>1.638509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.601126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.015071</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-1.043931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.118588</td>\n",
       "      <td>0.453095</td>\n",
       "      <td>-0.212614</td>\n",
       "      <td>0.187037</td>\n",
       "      <td>-0.760722</td>\n",
       "      <td>-1.330950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A    1.571507   -1.037697   -1.682384   -0.108757    1.092380    1.638509\n",
       "B    0.160021   -0.891196   -0.026006   -0.958267    2.841777   -0.601126\n",
       "C   -0.015071    0.495447   -0.152957    0.407331   -0.125714   -1.043931\n",
       "D   -0.118588    0.453095   -0.212614    0.187037   -0.760722   -1.330950"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.118588</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>1.571507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.453095</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>-1.037697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.212614</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-1.682384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.187037</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>-0.108757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.760722</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>1.092380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-1.330950</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>1.638509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01 -0.118588 -0.015071  0.160021  1.571507\n",
       "2013-01-02  0.453095  0.495447 -0.891196 -1.037697\n",
       "2013-01-03 -0.212614 -0.152957 -0.026006 -1.682384\n",
       "2013-01-04  0.187037  0.407331 -0.958267 -0.108757\n",
       "2013-01-05 -0.760722 -0.125714  2.841777  1.092380\n",
       "2013-01-06 -1.330950 -1.043931 -0.601126  1.638509"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-1.330950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>-0.118588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  0.187037\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095\n",
       "2013-01-06  1.638509 -0.601126 -1.043931 -1.330950\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614\n",
       "2013-01-01  1.571507  0.160021 -0.015071 -0.118588\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc, .iloc and .ix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the indexing documentation [Indexing and Selecting Data](http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing) and [MultiIndex / Advanced Indexing](http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single column, which yields a Series, equivalent to df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.571507\n",
       "2013-01-02   -1.037697\n",
       "2013-01-03   -1.682384\n",
       "2013-01-04   -0.108757\n",
       "2013-01-05    1.092380\n",
       "2013-01-06    1.638509\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting via [], which slices the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>-0.118588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.571507  0.160021 -0.015071 -0.118588\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  0.187037"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20130102':'20130104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by Label\n",
    "\n",
    "See more in [Selection by Label](Selection by Label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a cross section using a label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.571507\n",
       "B    0.160021\n",
       "C   -0.015071\n",
       "D   -0.118588\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selection by Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01  1.571507  0.160021\n",
       "2013-01-02 -1.037697 -0.891196\n",
       "2013-01-03 -1.682384 -0.026006\n",
       "2013-01-04 -0.108757 -0.958267\n",
       "2013-01-05  1.092380  2.841777\n",
       "2013-01-06  1.638509 -0.601126"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing label slicing, both endpoints are included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-02 -1.037697 -0.891196\n",
       "2013-01-03 -1.682384 -0.026006\n",
       "2013-01-04 -0.108757 -0.958267"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102':'20130104',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduction in the dimensions of the returned object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.037697\n",
       "B   -0.891196\n",
       "Name: 2013-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a scalar value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.571506676720408"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by Position\n",
    "\n",
    "See more in [Selection by Position](http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select via the position of the passed integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.108757\n",
       "B   -0.958267\n",
       "C    0.407331\n",
       "D    0.187037\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By integer slices, acting similar to numpy/python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-04 -0.108757 -0.958267\n",
       "2013-01-05  1.092380  2.841777"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By lists of integer position locations, similar to the numpy/python style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>0.495447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.152957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>-0.125714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2013-01-02 -1.037697  0.495447\n",
       "2013-01-03 -1.682384 -0.152957\n",
       "2013-01-05  1.092380 -0.125714"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing rows explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing columns explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2013-01-01  0.160021 -0.015071\n",
       "2013-01-02 -0.891196  0.495447\n",
       "2013-01-03 -0.026006 -0.152957\n",
       "2013-01-04 -0.958267  0.407331\n",
       "2013-01-05  2.841777 -0.125714\n",
       "2013-01-06 -0.601126 -1.043931"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a value explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.89119558600132898"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting fast access to a scalar (equiv to the prior method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.89119558600132898"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a single column’s values to select data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>-0.118588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-1.330950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.571507  0.160021 -0.015071 -0.118588\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722\n",
       "2013-01-06  1.638509 -0.601126 -1.043931 -1.330950"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A where operation for getting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.571507  0.160021       NaN       NaN\n",
       "2013-01-02       NaN       NaN  0.495447  0.453095\n",
       "2013-01-03       NaN       NaN       NaN       NaN\n",
       "2013-01-04       NaN       NaN  0.407331  0.187037\n",
       "2013-01-05  1.092380  2.841777       NaN       NaN\n",
       "2013-01-06  1.638509       NaN       NaN       NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the isin() method for filtering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2['E'] = ['one','one', 'two','three','four','three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.571507</td>\n",
       "      <td>0.160021</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>-0.118588</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.453095</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>0.187037</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-1.330950</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01  1.571507  0.160021 -0.015071 -0.118588    one\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  0.453095    one\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614    two\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  0.187037  three\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722   four\n",
       "2013-01-06  1.638509 -0.601126 -1.043931 -1.330950  three"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-0.212614</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-0.760722</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -0.212614   two\n",
       "2013-01-05  1.092380  2.841777 -0.125714 -0.760722  four"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two','four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting a new column automatically aligns the data by the indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102',periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-02    1\n",
       "2013-01-03    2\n",
       "2013-01-04    3\n",
       "2013-01-05    4\n",
       "2013-01-06    5\n",
       "2013-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['F'] = s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting values by label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.at[dates[0],'A'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Settomg values by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.iat[0,1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting by assigning with a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[:,'D'] = np.array([5] * len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of the prior setting operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.092380</td>\n",
       "      <td>2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -0.015071  5  NaN\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  5  1.0\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957  5  2.0\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  5  3.0\n",
       "2013-01-05  1.092380  2.841777 -0.125714  5  4.0\n",
       "2013-01-06  1.638509 -0.601126 -1.043931  5  5.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A where operation with setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2[df2 > 0] = -df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
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       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
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       "      <td>-5</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>-0.407331</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.092380</td>\n",
       "      <td>-2.841777</td>\n",
       "      <td>-0.125714</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-1.638509</td>\n",
       "      <td>-0.601126</td>\n",
       "      <td>-1.043931</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -0.015071 -5  NaN\n",
       "2013-01-02 -1.037697 -0.891196 -0.495447 -5 -1.0\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957 -5 -2.0\n",
       "2013-01-04 -0.108757 -0.958267 -0.407331 -5 -3.0\n",
       "2013-01-05 -1.092380 -2.841777 -0.125714 -5 -4.0\n",
       "2013-01-06 -1.638509 -0.601126 -1.043931 -5 -5.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data\n",
    "\n",
    "pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the Missing Data section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1.loc[dates[0]:dates[1],'E'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>2013-01-01</th>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
       "      <td>-0.152957</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-01  0.000000  0.000000 -0.015071  5  NaN  1.0\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  5  1.0  1.0\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957  5  2.0  NaN\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  5  3.0  NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To drop any rows that have missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
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       "      <td>-0.891196</td>\n",
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       "                   A         B         C  D    F    E\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  5  1.0  1.0"
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     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>2013-01-02</th>\n",
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       "      <th>2013-01-03</th>\n",
       "      <td>-1.682384</td>\n",
       "      <td>-0.026006</td>\n",
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       "      <td>5</td>\n",
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       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.108757</td>\n",
       "      <td>-0.958267</td>\n",
       "      <td>0.407331</td>\n",
       "      <td>5</td>\n",
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       "      <td>5.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-01  0.000000  0.000000 -0.015071  5  5.0  1.0\n",
       "2013-01-02 -1.037697 -0.891196  0.495447  5  1.0  1.0\n",
       "2013-01-03 -1.682384 -0.026006 -0.152957  5  2.0  5.0\n",
       "2013-01-04 -0.108757 -0.958267  0.407331  5  3.0  5.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the boolean mask where values are nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2013-01-01  False  False  False  False   True  False\n",
       "2013-01-02  False  False  False  False  False  False\n",
       "2013-01-03  False  False  False  False  False   True\n",
       "2013-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operations\n",
    "\n",
    "See the [Basic section on Binary Ops](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-binop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stats\n",
    "\n",
    "Operations in general exclude missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing a descriptive statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.016325\n",
       "B    0.060864\n",
       "C   -0.072483\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same operation on the other axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.246232\n",
       "2013-01-02    0.913311\n",
       "2013-01-03    1.027731\n",
       "2013-01-04    1.468061\n",
       "2013-01-05    2.561689\n",
       "2013-01-06    1.998690\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    NaN\n",
       "2013-01-02    NaN\n",
       "2013-01-03    1.0\n",
       "2013-01-04    3.0\n",
       "2013-01-05    5.0\n",
       "2013-01-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.682384</td>\n",
       "      <td>-1.026006</td>\n",
       "      <td>-1.152957</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-3.108757</td>\n",
       "      <td>-3.958267</td>\n",
       "      <td>-2.592669</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-3.907620</td>\n",
       "      <td>-2.158223</td>\n",
       "      <td>-5.125714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D    F\n",
       "2013-01-01       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-02       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-03 -2.682384 -1.026006 -1.152957  4.0  1.0\n",
       "2013-01-04 -3.108757 -3.958267 -2.592669  2.0  0.0\n",
       "2013-01-05 -3.907620 -2.158223 -5.125714  0.0 -1.0\n",
       "2013-01-06       NaN       NaN       NaN  NaN  NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying functions to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015071</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.037697</td>\n",
       "      <td>-0.891196</td>\n",
       "      <td>0.480377</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.720081</td>\n",
       "      <td>-0.917201</td>\n",
       "      <td>0.327420</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.828838</td>\n",
       "      <td>-1.875469</td>\n",
       "      <td>0.734750</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.736458</td>\n",
       "      <td>0.966309</td>\n",
       "      <td>0.609036</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.097949</td>\n",
       "      <td>0.365183</td>\n",
       "      <td>-0.434895</td>\n",
       "      <td>30</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D     F\n",
       "2013-01-01  0.000000  0.000000 -0.015071   5   NaN\n",
       "2013-01-02 -1.037697 -0.891196  0.480377  10   1.0\n",
       "2013-01-03 -2.720081 -0.917201  0.327420  15   3.0\n",
       "2013-01-04 -2.828838 -1.875469  0.734750  20   6.0\n",
       "2013-01-05 -1.736458  0.966309  0.609036  25  10.0\n",
       "2013-01-06 -0.097949  0.365183 -0.434895  30  15.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3.320893\n",
       "B    3.800045\n",
       "C    1.539378\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogramming\n",
    "\n",
    "See more at [Histogramming and Discretization](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-discretization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    4\n",
       "2    1\n",
       "3    5\n",
       "4    1\n",
       "5    2\n",
       "6    1\n",
       "7    6\n",
       "8    2\n",
       "9    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3\n",
       "4    2\n",
       "2    2\n",
       "6    1\n",
       "5    1\n",
       "0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String Methods\n",
    "\n",
    "Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses [regular expressions](https://docs.python.org/2/library/re.html) by default (and in some cases always uses them). See more at [Vectorized String Methods](http://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat\n",
    "\n",
    "pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.\n",
    "\n",
    "See the [Merging section](http://pandas.pydata.org/pandas-docs/stable/merging.html#merging)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenating pandas objects together with concat():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.478688</td>\n",
       "      <td>-1.065907</td>\n",
       "      <td>-0.813218</td>\n",
       "      <td>0.096970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.414418</td>\n",
       "      <td>-0.364717</td>\n",
       "      <td>-0.515997</td>\n",
       "      <td>1.658804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.596550</td>\n",
       "      <td>-0.336011</td>\n",
       "      <td>0.080922</td>\n",
       "      <td>0.524613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.896945</td>\n",
       "      <td>0.584915</td>\n",
       "      <td>2.178190</td>\n",
       "      <td>-0.102500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.310291</td>\n",
       "      <td>0.680342</td>\n",
       "      <td>0.322909</td>\n",
       "      <td>0.319864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.924658</td>\n",
       "      <td>0.485691</td>\n",
       "      <td>-0.105201</td>\n",
       "      <td>-0.438313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.515372</td>\n",
       "      <td>-0.275894</td>\n",
       "      <td>0.200431</td>\n",
       "      <td>-0.342667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.259821</td>\n",
       "      <td>1.387931</td>\n",
       "      <td>-1.520080</td>\n",
       "      <td>0.236932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.625367</td>\n",
       "      <td>1.485963</td>\n",
       "      <td>-0.075421</td>\n",
       "      <td>-1.865674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.015742</td>\n",
       "      <td>-0.935150</td>\n",
       "      <td>-1.148272</td>\n",
       "      <td>-1.007878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.478688 -1.065907 -0.813218  0.096970\n",
       "1  0.414418 -0.364717 -0.515997  1.658804\n",
       "2  0.596550 -0.336011  0.080922  0.524613\n",
       "3  0.896945  0.584915  2.178190 -0.102500\n",
       "4 -1.310291  0.680342  0.322909  0.319864\n",
       "5  0.924658  0.485691 -0.105201 -0.438313\n",
       "6 -1.515372 -0.275894  0.200431 -0.342667\n",
       "7  1.259821  1.387931 -1.520080  0.236932\n",
       "8  0.625367  1.485963 -0.075421 -1.865674\n",
       "9 -1.015742 -0.935150 -1.148272 -1.007878"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# break it into pieces\n",
    "pieces = [df[:3], df[3:7], df[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.478688</td>\n",
       "      <td>-1.065907</td>\n",
       "      <td>-0.813218</td>\n",
       "      <td>0.096970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.414418</td>\n",
       "      <td>-0.364717</td>\n",
       "      <td>-0.515997</td>\n",
       "      <td>1.658804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.596550</td>\n",
       "      <td>-0.336011</td>\n",
       "      <td>0.080922</td>\n",
       "      <td>0.524613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.896945</td>\n",
       "      <td>0.584915</td>\n",
       "      <td>2.178190</td>\n",
       "      <td>-0.102500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.310291</td>\n",
       "      <td>0.680342</td>\n",
       "      <td>0.322909</td>\n",
       "      <td>0.319864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.924658</td>\n",
       "      <td>0.485691</td>\n",
       "      <td>-0.105201</td>\n",
       "      <td>-0.438313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.515372</td>\n",
       "      <td>-0.275894</td>\n",
       "      <td>0.200431</td>\n",
       "      <td>-0.342667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.259821</td>\n",
       "      <td>1.387931</td>\n",
       "      <td>-1.520080</td>\n",
       "      <td>0.236932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.625367</td>\n",
       "      <td>1.485963</td>\n",
       "      <td>-0.075421</td>\n",
       "      <td>-1.865674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.015742</td>\n",
       "      <td>-0.935150</td>\n",
       "      <td>-1.148272</td>\n",
       "      <td>-1.007878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.478688 -1.065907 -0.813218  0.096970\n",
       "1  0.414418 -0.364717 -0.515997  1.658804\n",
       "2  0.596550 -0.336011  0.080922  0.524613\n",
       "3  0.896945  0.584915  2.178190 -0.102500\n",
       "4 -1.310291  0.680342  0.322909  0.319864\n",
       "5  0.924658  0.485691 -0.105201 -0.438313\n",
       "6 -1.515372 -0.275894  0.200431 -0.342667\n",
       "7  1.259821  1.387931 -1.520080  0.236932\n",
       "8  0.625367  1.485963 -0.075421 -1.865674\n",
       "9 -1.015742 -0.935150 -1.148272 -1.007878"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join\n",
    "\n",
    "SQL style merges. See the [Database style joining](http://pandas.pydata.org/pandas-docs/stable/merging.html#merging-join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Append\n",
    "\n",
    "Append rows to a dataframe. See the [Appending](http://pandas.pydata.org/pandas-docs/stable/merging.html#merging-concatenation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.512297</td>\n",
       "      <td>0.206320</td>\n",
       "      <td>0.806578</td>\n",
       "      <td>-1.023023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.667535</td>\n",
       "      <td>0.552143</td>\n",
       "      <td>-0.956896</td>\n",
       "      <td>-0.447428</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.365881</td>\n",
       "      <td>0.308602</td>\n",
       "      <td>0.710462</td>\n",
       "      <td>-0.475598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.496544</td>\n",
       "      <td>-0.215146</td>\n",
       "      <td>0.678528</td>\n",
       "      <td>-0.222261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.646788</td>\n",
       "      <td>0.065329</td>\n",
       "      <td>-1.191621</td>\n",
       "      <td>-0.348686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.228013</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.555290</td>\n",
       "      <td>1.709495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.284006</td>\n",
       "      <td>-1.252317</td>\n",
       "      <td>0.994436</td>\n",
       "      <td>1.883158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.980527</td>\n",
       "      <td>1.249698</td>\n",
       "      <td>0.268433</td>\n",
       "      <td>-0.926866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.512297  0.206320  0.806578 -1.023023\n",
       "1  0.667535  0.552143 -0.956896 -0.447428\n",
       "2  1.365881  0.308602  0.710462 -0.475598\n",
       "3  0.496544 -0.215146  0.678528 -0.222261\n",
       "4 -0.646788  0.065329 -1.191621 -0.348686\n",
       "5 -1.228013  0.866045 -0.555290  1.709495\n",
       "6 -0.284006 -1.252317  0.994436  1.883158\n",
       "7 -0.980527  1.249698  0.268433 -0.926866"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s = df.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.512297</td>\n",
       "      <td>0.206320</td>\n",
       "      <td>0.806578</td>\n",
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       "      <td>-0.956896</td>\n",
       "      <td>-0.447428</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.365881</td>\n",
       "      <td>0.308602</td>\n",
       "      <td>0.710462</td>\n",
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       "    <tr>\n",
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       "      <td>0.866045</td>\n",
       "      <td>-0.555290</td>\n",
       "      <td>1.709495</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.284006</td>\n",
       "      <td>-1.252317</td>\n",
       "      <td>0.994436</td>\n",
       "      <td>1.883158</td>\n",
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       "      <th>7</th>\n",
       "      <td>-0.980527</td>\n",
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       "      <td>0.268433</td>\n",
       "      <td>-0.926866</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.496544</td>\n",
       "      <td>-0.215146</td>\n",
       "      <td>0.678528</td>\n",
       "      <td>-0.222261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.512297  0.206320  0.806578 -1.023023\n",
       "1  0.667535  0.552143 -0.956896 -0.447428\n",
       "2  1.365881  0.308602  0.710462 -0.475598\n",
       "3  0.496544 -0.215146  0.678528 -0.222261\n",
       "4 -0.646788  0.065329 -1.191621 -0.348686\n",
       "5 -1.228013  0.866045 -0.555290  1.709495\n",
       "6 -0.284006 -1.252317  0.994436  1.883158\n",
       "7 -0.980527  1.249698  0.268433 -0.926866\n",
       "8  0.496544 -0.215146  0.678528 -0.222261"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping\n",
    "\n",
    "By “group by” we are referring to a process involving one or more of the following steps\n",
    "\n",
    "* **Splitting** the data into groups based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure\n",
    "\n",
    "See the [Grouping section](http://pandas.pydata.org/pandas-docs/stable/groupby.html#groupby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n",
    "                                    'B' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],\n",
    "                                    'C' : np.random.randn(8),\n",
    "                                     'D' : np.random.randn(8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>one</td>\n",
       "      <td>1.065510</td>\n",
       "      <td>0.648988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>1.363103</td>\n",
       "      <td>0.565078</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.663853</td>\n",
       "      <td>0.672468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.500158</td>\n",
       "      <td>-1.056229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>0.289049</td>\n",
       "      <td>-0.736059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.738343</td>\n",
       "      <td>0.417824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>0.373007</td>\n",
       "      <td>-0.744985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -0.511975  0.127436\n",
       "1  bar    one  1.065510  0.648988\n",
       "2  foo    two  1.363103  0.565078\n",
       "3  bar  three  1.663853  0.672468\n",
       "4  foo    two -1.500158 -1.056229\n",
       "5  bar    two  0.289049 -0.736059\n",
       "6  foo    one  0.738343  0.417824\n",
       "7  foo  three  0.373007 -0.744985"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping and then applying a function sum to the resulting groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
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       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>3.018412</td>\n",
       "      <td>0.585397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>0.462319</td>\n",
       "      <td>-0.690875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  3.018412  0.585397\n",
       "foo  0.462319 -0.690875"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1.065510</td>\n",
       "      <td>0.648988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1.663853</td>\n",
       "      <td>0.672468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.289049</td>\n",
       "      <td>-0.736059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.226368</td>\n",
       "      <td>0.545260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.373007</td>\n",
       "      <td>-0.744985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.137056</td>\n",
       "      <td>-0.491150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.065510  0.648988\n",
       "    three  1.663853  0.672468\n",
       "    two    0.289049 -0.736059\n",
       "foo one    0.226368  0.545260\n",
       "    three  0.373007 -0.744985\n",
       "    two   -0.137056 -0.491150"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping\n",
    "\n",
    "See the sections on [Hierarchical Indexing](http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-hierarchical) and [Reshaping](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-stacking)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],\n",
    "                                ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.509132</td>\n",
       "      <td>-1.047747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.738164</td>\n",
       "      <td>-0.161886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.522663</td>\n",
       "      <td>-0.572879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.650861</td>\n",
       "      <td>-0.248318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.509132 -1.047747\n",
       "      two    -0.738164 -0.161886\n",
       "baz   one    -1.522663 -0.572879\n",
       "      two     2.650861 -0.248318"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stack() method “compresses” a level in the DataFrame’s columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stacked = df2.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.509132\n",
       "               B   -1.047747\n",
       "       two     A   -0.738164\n",
       "               B   -0.161886\n",
       "baz    one     A   -1.522663\n",
       "               B   -0.572879\n",
       "       two     A    2.650861\n",
       "               B   -0.248318\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack() is unstack(), which by default unstacks the **last level**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.509132</td>\n",
       "      <td>-1.047747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.738164</td>\n",
       "      <td>-0.161886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.522663</td>\n",
       "      <td>-0.572879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.650861</td>\n",
       "      <td>-0.248318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.509132 -1.047747\n",
       "      two    -0.738164 -0.161886\n",
       "baz   one    -1.522663 -0.572879\n",
       "      two     2.650861 -0.248318"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.509132</td>\n",
       "      <td>-0.738164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.047747</td>\n",
       "      <td>-0.161886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-1.522663</td>\n",
       "      <td>2.650861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.572879</td>\n",
       "      <td>-0.248318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.509132 -0.738164\n",
       "      B -1.047747 -0.161886\n",
       "baz   A -1.522663  2.650861\n",
       "      B -0.572879 -0.248318"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.509132</td>\n",
       "      <td>-1.522663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.047747</td>\n",
       "      <td>-0.572879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.738164</td>\n",
       "      <td>2.650861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.161886</td>\n",
       "      <td>-0.248318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.509132 -1.522663\n",
       "       B -1.047747 -0.572879\n",
       "two    A -0.738164  2.650861\n",
       "       B -0.161886 -0.248318"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Tables\n",
    "\n",
    "See the section on [Pivot Tables](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-pivot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n",
    "                                    'B' : ['A', 'B', 'C'] * 4,\n",
    "                                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "                                    'D' : np.random.randn(12),\n",
    "                                    'E' : np.random.randn(12)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.609099</td>\n",
       "      <td>0.860723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.565647</td>\n",
       "      <td>-0.529125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.405552</td>\n",
       "      <td>-0.469248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.486600</td>\n",
       "      <td>-0.434941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.357269</td>\n",
       "      <td>1.063793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.015696</td>\n",
       "      <td>2.033839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.143828</td>\n",
       "      <td>0.250495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.290815</td>\n",
       "      <td>1.228481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>2.717677</td>\n",
       "      <td>0.147774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.894453</td>\n",
       "      <td>0.370378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.891609</td>\n",
       "      <td>2.113584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.212073</td>\n",
       "      <td>-0.598682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo  0.609099  0.860723\n",
       "1     one  B  foo -0.565647 -0.529125\n",
       "2     two  C  foo  0.405552 -0.469248\n",
       "3   three  A  bar  0.486600 -0.434941\n",
       "4     one  B  bar  0.357269  1.063793\n",
       "5     one  C  bar  0.015696  2.033839\n",
       "6     two  A  foo  0.143828  0.250495\n",
       "7   three  B  foo -0.290815  1.228481\n",
       "8     one  C  foo  2.717677  0.147774\n",
       "9     one  A  bar -0.894453  0.370378\n",
       "10    two  B  bar -0.891609  2.113584\n",
       "11  three  C  bar  0.212073 -0.598682"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can produce pivot tables from this data very easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.894453</td>\n",
       "      <td>0.609099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.357269</td>\n",
       "      <td>-0.565647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.015696</td>\n",
       "      <td>2.717677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>0.486600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.290815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.212073</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.143828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.891609</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.405552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A -0.894453  0.609099\n",
       "      B  0.357269 -0.565647\n",
       "      C  0.015696  2.717677\n",
       "three A  0.486600       NaN\n",
       "      B       NaN -0.290815\n",
       "      C  0.212073       NaN\n",
       "two   A       NaN  0.143828\n",
       "      B -0.891609       NaN\n",
       "      C       NaN  0.405552"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series\n",
    "\n",
    "pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the [Time Series section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#timeseries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=100, freq='S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    25031\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5Min').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time zone representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06    1.306330\n",
       "2012-03-07    0.217167\n",
       "2012-03-08   -0.781594\n",
       "2012-03-09   -1.642100\n",
       "2012-03-10    0.332460\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts_utc = ts.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    1.306330\n",
       "2012-03-07 00:00:00+00:00    0.217167\n",
       "2012-03-08 00:00:00+00:00   -0.781594\n",
       "2012-03-09 00:00:00+00:00   -1.642100\n",
       "2012-03-10 00:00:00+00:00    0.332460\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Convert to another time zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00    1.306330\n",
       "2012-03-06 19:00:00-05:00    0.217167\n",
       "2012-03-07 19:00:00-05:00   -0.781594\n",
       "2012-03-08 19:00:00-05:00   -1.642100\n",
       "2012-03-09 19:00:00-05:00    0.332460\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Converting between time span representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=5, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31   -0.096878\n",
       "2012-02-29   -1.470980\n",
       "2012-03-31   -0.199913\n",
       "2012-04-30   -0.145233\n",
       "2012-05-31    1.051845\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ps = ts.to_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01   -0.096878\n",
       "2012-02   -1.470980\n",
       "2012-03   -0.199913\n",
       "2012-04   -0.145233\n",
       "2012-05    1.051845\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01   -0.096878\n",
       "2012-02-01   -1.470980\n",
       "2012-03-01   -0.199913\n",
       "2012-04-01   -0.145233\n",
       "2012-05-01    1.051845\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(prng)), prng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-03-01 09:00   -0.290171\n",
       "1990-06-01 09:00    0.445195\n",
       "1990-09-01 09:00    1.493887\n",
       "1990-12-01 09:00    0.804911\n",
       "1991-03-01 09:00   -0.101456\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categoricals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see the [categorical introduction](http://pandas.pydata.org/pandas-docs/stable/categorical.html#categorical) and the [API documentation](http://pandas.pydata.org/pandas-docs/stable/api.html#api-categorical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'a', 'e']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the raw grades to a categorical data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reorder the categories and simultaneously add the missing categories (methods under Series .cat return a new Series per default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad, bad, medium, good, very good]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting is per order in the categories, not lexical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "5   6         e   very bad\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "0   1         a  very good\n",
       "3   4         a  very good\n",
       "4   5         a  very good"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"grade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by a categorical column shows also empty categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "[Plotting](http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization) docs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114a60ed0>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KDjF7NnDcccCXX3Z8LzQsa84cG7qmeENhYfhIEyWYvn25YJdkpGYCZExqXNRE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a457d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On DataFrame, plot() is a convenience to plot all of the columns with labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "                  columns=['A', 'B', 'C', 'D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x116ecc890>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149fbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wyY+O5ojDQZ02OdW53QGGXQhBms3GrPh4qofJpHs8EhHDLoQYBZwPPG04fDHwnHb/OeCS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\nJjhZp7YWMjJUhqlWZwTAww8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9GXbd49Y/yzXXwHXX+X852uMvVFfTrn0v+w3r377qsJsM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\n4/8BcMRKeUH3kAAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ecc6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(); df.plot(); plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Data In/Out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV\n",
    "[Writing to a csv file](http://pandas.pydata.org/pandas-docs/stable/io.html#io-store-in-csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Reading from a csv file](http://pandas.pydata.org/pandas-docs/stable/io.html#io-read-csv-table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-0.067377</td>\n",
       "      <td>-0.035638</td>\n",
       "      <td>-0.021100</td>\n",
       "      <td>-0.752226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-0.341526</td>\n",
       "      <td>0.867899</td>\n",
       "      <td>0.147792</td>\n",
       "      <td>-2.190171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-0.118822</td>\n",
       "      <td>1.501776</td>\n",
       "      <td>-0.472641</td>\n",
       "      <td>-1.448117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>-0.727838</td>\n",
       "      <td>0.889072</td>\n",
       "      <td>-2.704155</td>\n",
       "      <td>-2.934499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>0.531075</td>\n",
       "      <td>1.073829</td>\n",
       "      <td>-2.847804</td>\n",
       "      <td>-2.517378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>0.091479</td>\n",
       "      <td>1.478376</td>\n",
       "      <td>-3.029743</td>\n",
       "      <td>-0.906359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>1.492265</td>\n",
       "      <td>2.113159</td>\n",
       "      <td>-3.549888</td>\n",
       "      <td>-2.117010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000-01-08</td>\n",
       "      <td>1.851265</td>\n",
       "      <td>2.703705</td>\n",
       "      <td>-3.141079</td>\n",
       "      <td>0.079449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-09</td>\n",
       "      <td>2.213547</td>\n",
       "      <td>3.685811</td>\n",
       "      <td>-3.029953</td>\n",
       "      <td>1.620792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>2.028055</td>\n",
       "      <td>2.034899</td>\n",
       "      <td>-2.115068</td>\n",
       "      <td>3.566303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>2.562459</td>\n",
       "      <td>1.562458</td>\n",
       "      <td>-1.266220</td>\n",
       "      <td>3.125902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000-01-12</td>\n",
       "      <td>3.563649</td>\n",
       "      <td>2.535037</td>\n",
       "      <td>-1.609729</td>\n",
       "      <td>1.636481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000-01-13</td>\n",
       "      <td>3.723012</td>\n",
       "      <td>3.932004</td>\n",
       "      <td>-1.801761</td>\n",
       "      <td>3.817881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000-01-14</td>\n",
       "      <td>5.097816</td>\n",
       "      <td>4.143041</td>\n",
       "      <td>-2.183664</td>\n",
       "      <td>2.784085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-15</td>\n",
       "      <td>5.415682</td>\n",
       "      <td>2.042796</td>\n",
       "      <td>-1.186153</td>\n",
       "      <td>3.828601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000-01-16</td>\n",
       "      <td>5.875182</td>\n",
       "      <td>0.968725</td>\n",
       "      <td>-1.122289</td>\n",
       "      <td>3.771920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2000-01-17</td>\n",
       "      <td>5.352968</td>\n",
       "      <td>-0.637527</td>\n",
       "      <td>-2.741543</td>\n",
       "      <td>2.230048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>5.403275</td>\n",
       "      <td>0.561341</td>\n",
       "      <td>-1.943048</td>\n",
       "      <td>1.291451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-19</td>\n",
       "      <td>5.898989</td>\n",
       "      <td>0.260370</td>\n",
       "      <td>0.010189</td>\n",
       "      <td>2.686019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-01-20</td>\n",
       "      <td>6.446354</td>\n",
       "      <td>0.214986</td>\n",
       "      <td>-0.621627</td>\n",
       "      <td>2.265080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000-01-21</td>\n",
       "      <td>6.093474</td>\n",
       "      <td>0.696028</td>\n",
       "      <td>-0.604788</td>\n",
       "      <td>2.150748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000-01-22</td>\n",
       "      <td>4.816564</td>\n",
       "      <td>2.795033</td>\n",
       "      <td>-1.992706</td>\n",
       "      <td>2.617393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000-01-23</td>\n",
       "      <td>4.484142</td>\n",
       "      <td>2.215204</td>\n",
       "      <td>-2.216621</td>\n",
       "      <td>4.585891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2000-01-24</td>\n",
       "      <td>6.218927</td>\n",
       "      <td>2.262602</td>\n",
       "      <td>-2.310701</td>\n",
       "      <td>3.788079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-01-25</td>\n",
       "      <td>7.490060</td>\n",
       "      <td>1.679666</td>\n",
       "      <td>-2.538864</td>\n",
       "      <td>2.202103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>6.878468</td>\n",
       "      <td>0.292143</td>\n",
       "      <td>-2.203920</td>\n",
       "      <td>3.033513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2000-01-27</td>\n",
       "      <td>8.265415</td>\n",
       "      <td>-1.160655</td>\n",
       "      <td>-2.457205</td>\n",
       "      <td>1.806101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>8.800961</td>\n",
       "      <td>0.506590</td>\n",
       "      <td>-1.682888</td>\n",
       "      <td>2.444211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>9.069957</td>\n",
       "      <td>0.362689</td>\n",
       "      <td>-1.158481</td>\n",
       "      <td>5.313443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2000-01-30</td>\n",
       "      <td>9.342882</td>\n",
       "      <td>0.817050</td>\n",
       "      <td>-1.591316</td>\n",
       "      <td>3.924335</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>970</th>\n",
       "      <td>2002-08-28</td>\n",
       "      <td>28.436666</td>\n",
       "      <td>12.185562</td>\n",
       "      <td>0.713590</td>\n",
       "      <td>33.470501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2002-08-29</td>\n",
       "      <td>27.263426</td>\n",
       "      <td>12.515911</td>\n",
       "      <td>-0.883309</td>\n",
       "      <td>33.574743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2002-08-30</td>\n",
       "      <td>26.365767</td>\n",
       "      <td>12.070123</td>\n",
       "      <td>-1.070257</td>\n",
       "      <td>33.533270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2002-08-31</td>\n",
       "      <td>24.797556</td>\n",
       "      <td>12.432728</td>\n",
       "      <td>-2.800671</td>\n",
       "      <td>31.792798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>24.063454</td>\n",
       "      <td>12.938684</td>\n",
       "      <td>-2.392343</td>\n",
       "      <td>31.930125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2002-09-02</td>\n",
       "      <td>24.246746</td>\n",
       "      <td>13.716965</td>\n",
       "      <td>-1.796432</td>\n",
       "      <td>32.215315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2002-09-03</td>\n",
       "      <td>25.025802</td>\n",
       "      <td>15.046681</td>\n",
       "      <td>0.176345</td>\n",
       "      <td>31.351909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2002-09-04</td>\n",
       "      <td>25.722266</td>\n",
       "      <td>15.208363</td>\n",
       "      <td>-2.028918</td>\n",
       "      <td>30.888133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2002-09-05</td>\n",
       "      <td>27.017412</td>\n",
       "      <td>16.800099</td>\n",
       "      <td>-3.470389</td>\n",
       "      <td>30.260138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>2002-09-06</td>\n",
       "      <td>28.739792</td>\n",
       "      <td>17.267548</td>\n",
       "      <td>-4.587964</td>\n",
       "      <td>30.492176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2002-09-07</td>\n",
       "      <td>28.691180</td>\n",
       "      <td>17.406774</td>\n",
       "      <td>-3.824464</td>\n",
       "      <td>30.611210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>2002-09-08</td>\n",
       "      <td>28.051428</td>\n",
       "      <td>16.879897</td>\n",
       "      <td>-3.696306</td>\n",
       "      <td>30.545629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>26.684984</td>\n",
       "      <td>16.739758</td>\n",
       "      <td>-2.998523</td>\n",
       "      <td>31.096066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2002-09-10</td>\n",
       "      <td>26.921334</td>\n",
       "      <td>16.768293</td>\n",
       "      <td>-2.056697</td>\n",
       "      <td>30.499331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2002-09-11</td>\n",
       "      <td>27.003199</td>\n",
       "      <td>14.581637</td>\n",
       "      <td>-2.688241</td>\n",
       "      <td>30.131872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2002-09-12</td>\n",
       "      <td>25.007810</td>\n",
       "      <td>13.499850</td>\n",
       "      <td>-3.161634</td>\n",
       "      <td>32.054496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2002-09-13</td>\n",
       "      <td>24.787813</td>\n",
       "      <td>11.329320</td>\n",
       "      <td>-2.826720</td>\n",
       "      <td>33.077085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>2002-09-14</td>\n",
       "      <td>26.903225</td>\n",
       "      <td>12.068355</td>\n",
       "      <td>-2.648789</td>\n",
       "      <td>34.210246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>2002-09-15</td>\n",
       "      <td>25.777238</td>\n",
       "      <td>11.535393</td>\n",
       "      <td>-1.952559</td>\n",
       "      <td>35.667764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>2002-09-16</td>\n",
       "      <td>24.381155</td>\n",
       "      <td>10.885129</td>\n",
       "      <td>-4.349392</td>\n",
       "      <td>33.810491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2002-09-17</td>\n",
       "      <td>24.914723</td>\n",
       "      <td>10.461896</td>\n",
       "      <td>-4.523927</td>\n",
       "      <td>33.943282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>2002-09-18</td>\n",
       "      <td>25.144380</td>\n",
       "      <td>12.115287</td>\n",
       "      <td>-5.210419</td>\n",
       "      <td>34.402361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2002-09-19</td>\n",
       "      <td>26.253174</td>\n",
       "      <td>11.910041</td>\n",
       "      <td>-4.889495</td>\n",
       "      <td>36.015240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2002-09-20</td>\n",
       "      <td>25.736717</td>\n",
       "      <td>13.275022</td>\n",
       "      <td>-2.518543</td>\n",
       "      <td>35.721396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>24.350343</td>\n",
       "      <td>14.154565</td>\n",
       "      <td>-2.495837</td>\n",
       "      <td>36.593416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>25.003458</td>\n",
       "      <td>16.235255</td>\n",
       "      <td>-2.376958</td>\n",
       "      <td>34.964767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>23.977122</td>\n",
       "      <td>16.555848</td>\n",
       "      <td>-1.746319</td>\n",
       "      <td>35.222469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>24.738345</td>\n",
       "      <td>14.692118</td>\n",
       "      <td>-3.112445</td>\n",
       "      <td>36.535400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>23.977210</td>\n",
       "      <td>14.979042</td>\n",
       "      <td>-3.557790</td>\n",
       "      <td>36.189400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>23.442975</td>\n",
       "      <td>14.431692</td>\n",
       "      <td>-4.008666</td>\n",
       "      <td>35.876610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0          A          B         C          D\n",
       "0    2000-01-01  -0.067377  -0.035638 -0.021100  -0.752226\n",
       "1    2000-01-02  -0.341526   0.867899  0.147792  -2.190171\n",
       "2    2000-01-03  -0.118822   1.501776 -0.472641  -1.448117\n",
       "3    2000-01-04  -0.727838   0.889072 -2.704155  -2.934499\n",
       "4    2000-01-05   0.531075   1.073829 -2.847804  -2.517378\n",
       "5    2000-01-06   0.091479   1.478376 -3.029743  -0.906359\n",
       "6    2000-01-07   1.492265   2.113159 -3.549888  -2.117010\n",
       "7    2000-01-08   1.851265   2.703705 -3.141079   0.079449\n",
       "8    2000-01-09   2.213547   3.685811 -3.029953   1.620792\n",
       "9    2000-01-10   2.028055   2.034899 -2.115068   3.566303\n",
       "10   2000-01-11   2.562459   1.562458 -1.266220   3.125902\n",
       "11   2000-01-12   3.563649   2.535037 -1.609729   1.636481\n",
       "12   2000-01-13   3.723012   3.932004 -1.801761   3.817881\n",
       "13   2000-01-14   5.097816   4.143041 -2.183664   2.784085\n",
       "14   2000-01-15   5.415682   2.042796 -1.186153   3.828601\n",
       "15   2000-01-16   5.875182   0.968725 -1.122289   3.771920\n",
       "16   2000-01-17   5.352968  -0.637527 -2.741543   2.230048\n",
       "17   2000-01-18   5.403275   0.561341 -1.943048   1.291451\n",
       "18   2000-01-19   5.898989   0.260370  0.010189   2.686019\n",
       "19   2000-01-20   6.446354   0.214986 -0.621627   2.265080\n",
       "20   2000-01-21   6.093474   0.696028 -0.604788   2.150748\n",
       "21   2000-01-22   4.816564   2.795033 -1.992706   2.617393\n",
       "22   2000-01-23   4.484142   2.215204 -2.216621   4.585891\n",
       "23   2000-01-24   6.218927   2.262602 -2.310701   3.788079\n",
       "24   2000-01-25   7.490060   1.679666 -2.538864   2.202103\n",
       "25   2000-01-26   6.878468   0.292143 -2.203920   3.033513\n",
       "26   2000-01-27   8.265415  -1.160655 -2.457205   1.806101\n",
       "27   2000-01-28   8.800961   0.506590 -1.682888   2.444211\n",
       "28   2000-01-29   9.069957   0.362689 -1.158481   5.313443\n",
       "29   2000-01-30   9.342882   0.817050 -1.591316   3.924335\n",
       "..          ...        ...        ...       ...        ...\n",
       "970  2002-08-28  28.436666  12.185562  0.713590  33.470501\n",
       "971  2002-08-29  27.263426  12.515911 -0.883309  33.574743\n",
       "972  2002-08-30  26.365767  12.070123 -1.070257  33.533270\n",
       "973  2002-08-31  24.797556  12.432728 -2.800671  31.792798\n",
       "974  2002-09-01  24.063454  12.938684 -2.392343  31.930125\n",
       "975  2002-09-02  24.246746  13.716965 -1.796432  32.215315\n",
       "976  2002-09-03  25.025802  15.046681  0.176345  31.351909\n",
       "977  2002-09-04  25.722266  15.208363 -2.028918  30.888133\n",
       "978  2002-09-05  27.017412  16.800099 -3.470389  30.260138\n",
       "979  2002-09-06  28.739792  17.267548 -4.587964  30.492176\n",
       "980  2002-09-07  28.691180  17.406774 -3.824464  30.611210\n",
       "981  2002-09-08  28.051428  16.879897 -3.696306  30.545629\n",
       "982  2002-09-09  26.684984  16.739758 -2.998523  31.096066\n",
       "983  2002-09-10  26.921334  16.768293 -2.056697  30.499331\n",
       "984  2002-09-11  27.003199  14.581637 -2.688241  30.131872\n",
       "985  2002-09-12  25.007810  13.499850 -3.161634  32.054496\n",
       "986  2002-09-13  24.787813  11.329320 -2.826720  33.077085\n",
       "987  2002-09-14  26.903225  12.068355 -2.648789  34.210246\n",
       "988  2002-09-15  25.777238  11.535393 -1.952559  35.667764\n",
       "989  2002-09-16  24.381155  10.885129 -4.349392  33.810491\n",
       "990  2002-09-17  24.914723  10.461896 -4.523927  33.943282\n",
       "991  2002-09-18  25.144380  12.115287 -5.210419  34.402361\n",
       "992  2002-09-19  26.253174  11.910041 -4.889495  36.015240\n",
       "993  2002-09-20  25.736717  13.275022 -2.518543  35.721396\n",
       "994  2002-09-21  24.350343  14.154565 -2.495837  36.593416\n",
       "995  2002-09-22  25.003458  16.235255 -2.376958  34.964767\n",
       "996  2002-09-23  23.977122  16.555848 -1.746319  35.222469\n",
       "997  2002-09-24  24.738345  14.692118 -3.112445  36.535400\n",
       "998  2002-09-25  23.977210  14.979042 -3.557790  36.189400\n",
       "999  2002-09-26  23.442975  14.431692 -4.008666  35.876610\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HDF5\n",
    "Reading and writing to [HDFStores](http://pandas.pydata.org/pandas-docs/stable/io.html#io-hdf5)\n",
    "\n",
    "Writing to a HDF5 Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading from a HDF5 Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.067377</td>\n",
       "      <td>-0.035638</td>\n",
       "      <td>-0.021100</td>\n",
       "      <td>-0.752226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.341526</td>\n",
       "      <td>0.867899</td>\n",
       "      <td>0.147792</td>\n",
       "      <td>-2.190171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.118822</td>\n",
       "      <td>1.501776</td>\n",
       "      <td>-0.472641</td>\n",
       "      <td>-1.448117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.727838</td>\n",
       "      <td>0.889072</td>\n",
       "      <td>-2.704155</td>\n",
       "      <td>-2.934499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.531075</td>\n",
       "      <td>1.073829</td>\n",
       "      <td>-2.847804</td>\n",
       "      <td>-2.517378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.091479</td>\n",
       "      <td>1.478376</td>\n",
       "      <td>-3.029743</td>\n",
       "      <td>-0.906359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>1.492265</td>\n",
       "      <td>2.113159</td>\n",
       "      <td>-3.549888</td>\n",
       "      <td>-2.117010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>1.851265</td>\n",
       "      <td>2.703705</td>\n",
       "      <td>-3.141079</td>\n",
       "      <td>0.079449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>2.213547</td>\n",
       "      <td>3.685811</td>\n",
       "      <td>-3.029953</td>\n",
       "      <td>1.620792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>2.028055</td>\n",
       "      <td>2.034899</td>\n",
       "      <td>-2.115068</td>\n",
       "      <td>3.566303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>2.562459</td>\n",
       "      <td>1.562458</td>\n",
       "      <td>-1.266220</td>\n",
       "      <td>3.125902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>3.563649</td>\n",
       "      <td>2.535037</td>\n",
       "      <td>-1.609729</td>\n",
       "      <td>1.636481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>3.723012</td>\n",
       "      <td>3.932004</td>\n",
       "      <td>-1.801761</td>\n",
       "      <td>3.817881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>5.097816</td>\n",
       "      <td>4.143041</td>\n",
       "      <td>-2.183664</td>\n",
       "      <td>2.784085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>5.415682</td>\n",
       "      <td>2.042796</td>\n",
       "      <td>-1.186153</td>\n",
       "      <td>3.828601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>5.875182</td>\n",
       "      <td>0.968725</td>\n",
       "      <td>-1.122289</td>\n",
       "      <td>3.771920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>5.352968</td>\n",
       "      <td>-0.637527</td>\n",
       "      <td>-2.741543</td>\n",
       "      <td>2.230048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>5.403275</td>\n",
       "      <td>0.561341</td>\n",
       "      <td>-1.943048</td>\n",
       "      <td>1.291451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>5.898989</td>\n",
       "      <td>0.260370</td>\n",
       "      <td>0.010189</td>\n",
       "      <td>2.686019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>6.446354</td>\n",
       "      <td>0.214986</td>\n",
       "      <td>-0.621627</td>\n",
       "      <td>2.265080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>6.093474</td>\n",
       "      <td>0.696028</td>\n",
       "      <td>-0.604788</td>\n",
       "      <td>2.150748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>4.816564</td>\n",
       "      <td>2.795033</td>\n",
       "      <td>-1.992706</td>\n",
       "      <td>2.617393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>4.484142</td>\n",
       "      <td>2.215204</td>\n",
       "      <td>-2.216621</td>\n",
       "      <td>4.585891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>6.218927</td>\n",
       "      <td>2.262602</td>\n",
       "      <td>-2.310701</td>\n",
       "      <td>3.788079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>7.490060</td>\n",
       "      <td>1.679666</td>\n",
       "      <td>-2.538864</td>\n",
       "      <td>2.202103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>6.878468</td>\n",
       "      <td>0.292143</td>\n",
       "      <td>-2.203920</td>\n",
       "      <td>3.033513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>8.265415</td>\n",
       "      <td>-1.160655</td>\n",
       "      <td>-2.457205</td>\n",
       "      <td>1.806101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>8.800961</td>\n",
       "      <td>0.506590</td>\n",
       "      <td>-1.682888</td>\n",
       "      <td>2.444211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>9.069957</td>\n",
       "      <td>0.362689</td>\n",
       "      <td>-1.158481</td>\n",
       "      <td>5.313443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>9.342882</td>\n",
       "      <td>0.817050</td>\n",
       "      <td>-1.591316</td>\n",
       "      <td>3.924335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td>28.436666</td>\n",
       "      <td>12.185562</td>\n",
       "      <td>0.713590</td>\n",
       "      <td>33.470501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>27.263426</td>\n",
       "      <td>12.515911</td>\n",
       "      <td>-0.883309</td>\n",
       "      <td>33.574743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>26.365767</td>\n",
       "      <td>12.070123</td>\n",
       "      <td>-1.070257</td>\n",
       "      <td>33.533270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>24.797556</td>\n",
       "      <td>12.432728</td>\n",
       "      <td>-2.800671</td>\n",
       "      <td>31.792798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>24.063454</td>\n",
       "      <td>12.938684</td>\n",
       "      <td>-2.392343</td>\n",
       "      <td>31.930125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>24.246746</td>\n",
       "      <td>13.716965</td>\n",
       "      <td>-1.796432</td>\n",
       "      <td>32.215315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>25.025802</td>\n",
       "      <td>15.046681</td>\n",
       "      <td>0.176345</td>\n",
       "      <td>31.351909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>25.722266</td>\n",
       "      <td>15.208363</td>\n",
       "      <td>-2.028918</td>\n",
       "      <td>30.888133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>27.017412</td>\n",
       "      <td>16.800099</td>\n",
       "      <td>-3.470389</td>\n",
       "      <td>30.260138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>28.739792</td>\n",
       "      <td>17.267548</td>\n",
       "      <td>-4.587964</td>\n",
       "      <td>30.492176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>28.691180</td>\n",
       "      <td>17.406774</td>\n",
       "      <td>-3.824464</td>\n",
       "      <td>30.611210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>28.051428</td>\n",
       "      <td>16.879897</td>\n",
       "      <td>-3.696306</td>\n",
       "      <td>30.545629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>26.684984</td>\n",
       "      <td>16.739758</td>\n",
       "      <td>-2.998523</td>\n",
       "      <td>31.096066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>26.921334</td>\n",
       "      <td>16.768293</td>\n",
       "      <td>-2.056697</td>\n",
       "      <td>30.499331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>27.003199</td>\n",
       "      <td>14.581637</td>\n",
       "      <td>-2.688241</td>\n",
       "      <td>30.131872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>25.007810</td>\n",
       "      <td>13.499850</td>\n",
       "      <td>-3.161634</td>\n",
       "      <td>32.054496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>24.787813</td>\n",
       "      <td>11.329320</td>\n",
       "      <td>-2.826720</td>\n",
       "      <td>33.077085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>26.903225</td>\n",
       "      <td>12.068355</td>\n",
       "      <td>-2.648789</td>\n",
       "      <td>34.210246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>25.777238</td>\n",
       "      <td>11.535393</td>\n",
       "      <td>-1.952559</td>\n",
       "      <td>35.667764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>24.381155</td>\n",
       "      <td>10.885129</td>\n",
       "      <td>-4.349392</td>\n",
       "      <td>33.810491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>24.914723</td>\n",
       "      <td>10.461896</td>\n",
       "      <td>-4.523927</td>\n",
       "      <td>33.943282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>25.144380</td>\n",
       "      <td>12.115287</td>\n",
       "      <td>-5.210419</td>\n",
       "      <td>34.402361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>26.253174</td>\n",
       "      <td>11.910041</td>\n",
       "      <td>-4.889495</td>\n",
       "      <td>36.015240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>25.736717</td>\n",
       "      <td>13.275022</td>\n",
       "      <td>-2.518543</td>\n",
       "      <td>35.721396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>24.350343</td>\n",
       "      <td>14.154565</td>\n",
       "      <td>-2.495837</td>\n",
       "      <td>36.593416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>25.003458</td>\n",
       "      <td>16.235255</td>\n",
       "      <td>-2.376958</td>\n",
       "      <td>34.964767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>23.977122</td>\n",
       "      <td>16.555848</td>\n",
       "      <td>-1.746319</td>\n",
       "      <td>35.222469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>24.738345</td>\n",
       "      <td>14.692118</td>\n",
       "      <td>-3.112445</td>\n",
       "      <td>36.535400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>23.977210</td>\n",
       "      <td>14.979042</td>\n",
       "      <td>-3.557790</td>\n",
       "      <td>36.189400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>23.442975</td>\n",
       "      <td>14.431692</td>\n",
       "      <td>-4.008666</td>\n",
       "      <td>35.876610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A          B         C          D\n",
       "2000-01-01  -0.067377  -0.035638 -0.021100  -0.752226\n",
       "2000-01-02  -0.341526   0.867899  0.147792  -2.190171\n",
       "2000-01-03  -0.118822   1.501776 -0.472641  -1.448117\n",
       "2000-01-04  -0.727838   0.889072 -2.704155  -2.934499\n",
       "2000-01-05   0.531075   1.073829 -2.847804  -2.517378\n",
       "2000-01-06   0.091479   1.478376 -3.029743  -0.906359\n",
       "2000-01-07   1.492265   2.113159 -3.549888  -2.117010\n",
       "2000-01-08   1.851265   2.703705 -3.141079   0.079449\n",
       "2000-01-09   2.213547   3.685811 -3.029953   1.620792\n",
       "2000-01-10   2.028055   2.034899 -2.115068   3.566303\n",
       "2000-01-11   2.562459   1.562458 -1.266220   3.125902\n",
       "2000-01-12   3.563649   2.535037 -1.609729   1.636481\n",
       "2000-01-13   3.723012   3.932004 -1.801761   3.817881\n",
       "2000-01-14   5.097816   4.143041 -2.183664   2.784085\n",
       "2000-01-15   5.415682   2.042796 -1.186153   3.828601\n",
       "2000-01-16   5.875182   0.968725 -1.122289   3.771920\n",
       "2000-01-17   5.352968  -0.637527 -2.741543   2.230048\n",
       "2000-01-18   5.403275   0.561341 -1.943048   1.291451\n",
       "2000-01-19   5.898989   0.260370  0.010189   2.686019\n",
       "2000-01-20   6.446354   0.214986 -0.621627   2.265080\n",
       "2000-01-21   6.093474   0.696028 -0.604788   2.150748\n",
       "2000-01-22   4.816564   2.795033 -1.992706   2.617393\n",
       "2000-01-23   4.484142   2.215204 -2.216621   4.585891\n",
       "2000-01-24   6.218927   2.262602 -2.310701   3.788079\n",
       "2000-01-25   7.490060   1.679666 -2.538864   2.202103\n",
       "2000-01-26   6.878468   0.292143 -2.203920   3.033513\n",
       "2000-01-27   8.265415  -1.160655 -2.457205   1.806101\n",
       "2000-01-28   8.800961   0.506590 -1.682888   2.444211\n",
       "2000-01-29   9.069957   0.362689 -1.158481   5.313443\n",
       "2000-01-30   9.342882   0.817050 -1.591316   3.924335\n",
       "...               ...        ...       ...        ...\n",
       "2002-08-28  28.436666  12.185562  0.713590  33.470501\n",
       "2002-08-29  27.263426  12.515911 -0.883309  33.574743\n",
       "2002-08-30  26.365767  12.070123 -1.070257  33.533270\n",
       "2002-08-31  24.797556  12.432728 -2.800671  31.792798\n",
       "2002-09-01  24.063454  12.938684 -2.392343  31.930125\n",
       "2002-09-02  24.246746  13.716965 -1.796432  32.215315\n",
       "2002-09-03  25.025802  15.046681  0.176345  31.351909\n",
       "2002-09-04  25.722266  15.208363 -2.028918  30.888133\n",
       "2002-09-05  27.017412  16.800099 -3.470389  30.260138\n",
       "2002-09-06  28.739792  17.267548 -4.587964  30.492176\n",
       "2002-09-07  28.691180  17.406774 -3.824464  30.611210\n",
       "2002-09-08  28.051428  16.879897 -3.696306  30.545629\n",
       "2002-09-09  26.684984  16.739758 -2.998523  31.096066\n",
       "2002-09-10  26.921334  16.768293 -2.056697  30.499331\n",
       "2002-09-11  27.003199  14.581637 -2.688241  30.131872\n",
       "2002-09-12  25.007810  13.499850 -3.161634  32.054496\n",
       "2002-09-13  24.787813  11.329320 -2.826720  33.077085\n",
       "2002-09-14  26.903225  12.068355 -2.648789  34.210246\n",
       "2002-09-15  25.777238  11.535393 -1.952559  35.667764\n",
       "2002-09-16  24.381155  10.885129 -4.349392  33.810491\n",
       "2002-09-17  24.914723  10.461896 -4.523927  33.943282\n",
       "2002-09-18  25.144380  12.115287 -5.210419  34.402361\n",
       "2002-09-19  26.253174  11.910041 -4.889495  36.015240\n",
       "2002-09-20  25.736717  13.275022 -2.518543  35.721396\n",
       "2002-09-21  24.350343  14.154565 -2.495837  36.593416\n",
       "2002-09-22  25.003458  16.235255 -2.376958  34.964767\n",
       "2002-09-23  23.977122  16.555848 -1.746319  35.222469\n",
       "2002-09-24  24.738345  14.692118 -3.112445  36.535400\n",
       "2002-09-25  23.977210  14.979042 -3.557790  36.189400\n",
       "2002-09-26  23.442975  14.431692 -4.008666  35.876610\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excel\n",
    "\n",
    "Reading and writing to [MS Excel](http://pandas.pydata.org/pandas-docs/stable/io.html#io-excel)\n",
    "\n",
    "Writing to an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading from an excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.067377</td>\n",
       "      <td>-0.035638</td>\n",
       "      <td>-0.021100</td>\n",
       "      <td>-0.752226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.341526</td>\n",
       "      <td>0.867899</td>\n",
       "      <td>0.147792</td>\n",
       "      <td>-2.190171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.118822</td>\n",
       "      <td>1.501776</td>\n",
       "      <td>-0.472641</td>\n",
       "      <td>-1.448117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.727838</td>\n",
       "      <td>0.889072</td>\n",
       "      <td>-2.704155</td>\n",
       "      <td>-2.934499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.531075</td>\n",
       "      <td>1.073829</td>\n",
       "      <td>-2.847804</td>\n",
       "      <td>-2.517378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.091479</td>\n",
       "      <td>1.478376</td>\n",
       "      <td>-3.029743</td>\n",
       "      <td>-0.906359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>1.492265</td>\n",
       "      <td>2.113159</td>\n",
       "      <td>-3.549888</td>\n",
       "      <td>-2.117010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>1.851265</td>\n",
       "      <td>2.703705</td>\n",
       "      <td>-3.141079</td>\n",
       "      <td>0.079449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>2.213547</td>\n",
       "      <td>3.685811</td>\n",
       "      <td>-3.029953</td>\n",
       "      <td>1.620792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>2.028055</td>\n",
       "      <td>2.034899</td>\n",
       "      <td>-2.115068</td>\n",
       "      <td>3.566303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>2.562459</td>\n",
       "      <td>1.562458</td>\n",
       "      <td>-1.266220</td>\n",
       "      <td>3.125902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>3.563649</td>\n",
       "      <td>2.535037</td>\n",
       "      <td>-1.609729</td>\n",
       "      <td>1.636481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>3.723012</td>\n",
       "      <td>3.932004</td>\n",
       "      <td>-1.801761</td>\n",
       "      <td>3.817881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>5.097816</td>\n",
       "      <td>4.143041</td>\n",
       "      <td>-2.183664</td>\n",
       "      <td>2.784085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>5.415682</td>\n",
       "      <td>2.042796</td>\n",
       "      <td>-1.186153</td>\n",
       "      <td>3.828601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>5.875182</td>\n",
       "      <td>0.968725</td>\n",
       "      <td>-1.122289</td>\n",
       "      <td>3.771920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>5.352968</td>\n",
       "      <td>-0.637527</td>\n",
       "      <td>-2.741543</td>\n",
       "      <td>2.230048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>5.403275</td>\n",
       "      <td>0.561341</td>\n",
       "      <td>-1.943048</td>\n",
       "      <td>1.291451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>5.898989</td>\n",
       "      <td>0.260370</td>\n",
       "      <td>0.010189</td>\n",
       "      <td>2.686019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>6.446354</td>\n",
       "      <td>0.214986</td>\n",
       "      <td>-0.621627</td>\n",
       "      <td>2.265080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>6.093474</td>\n",
       "      <td>0.696028</td>\n",
       "      <td>-0.604788</td>\n",
       "      <td>2.150748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>4.816564</td>\n",
       "      <td>2.795033</td>\n",
       "      <td>-1.992706</td>\n",
       "      <td>2.617393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>4.484142</td>\n",
       "      <td>2.215204</td>\n",
       "      <td>-2.216621</td>\n",
       "      <td>4.585891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>6.218927</td>\n",
       "      <td>2.262602</td>\n",
       "      <td>-2.310701</td>\n",
       "      <td>3.788079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>7.490060</td>\n",
       "      <td>1.679666</td>\n",
       "      <td>-2.538864</td>\n",
       "      <td>2.202103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>6.878468</td>\n",
       "      <td>0.292143</td>\n",
       "      <td>-2.203920</td>\n",
       "      <td>3.033513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>8.265415</td>\n",
       "      <td>-1.160655</td>\n",
       "      <td>-2.457205</td>\n",
       "      <td>1.806101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>8.800961</td>\n",
       "      <td>0.506590</td>\n",
       "      <td>-1.682888</td>\n",
       "      <td>2.444211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>9.069957</td>\n",
       "      <td>0.362689</td>\n",
       "      <td>-1.158481</td>\n",
       "      <td>5.313443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>9.342882</td>\n",
       "      <td>0.817050</td>\n",
       "      <td>-1.591316</td>\n",
       "      <td>3.924335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td>28.436666</td>\n",
       "      <td>12.185562</td>\n",
       "      <td>0.713590</td>\n",
       "      <td>33.470501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>27.263426</td>\n",
       "      <td>12.515911</td>\n",
       "      <td>-0.883309</td>\n",
       "      <td>33.574743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>26.365767</td>\n",
       "      <td>12.070123</td>\n",
       "      <td>-1.070257</td>\n",
       "      <td>33.533270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>24.797556</td>\n",
       "      <td>12.432728</td>\n",
       "      <td>-2.800671</td>\n",
       "      <td>31.792798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>24.063454</td>\n",
       "      <td>12.938684</td>\n",
       "      <td>-2.392343</td>\n",
       "      <td>31.930125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>24.246746</td>\n",
       "      <td>13.716965</td>\n",
       "      <td>-1.796432</td>\n",
       "      <td>32.215315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>25.025802</td>\n",
       "      <td>15.046681</td>\n",
       "      <td>0.176345</td>\n",
       "      <td>31.351909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>25.722266</td>\n",
       "      <td>15.208363</td>\n",
       "      <td>-2.028918</td>\n",
       "      <td>30.888133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>27.017412</td>\n",
       "      <td>16.800099</td>\n",
       "      <td>-3.470389</td>\n",
       "      <td>30.260138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>28.739792</td>\n",
       "      <td>17.267548</td>\n",
       "      <td>-4.587964</td>\n",
       "      <td>30.492176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>28.691180</td>\n",
       "      <td>17.406774</td>\n",
       "      <td>-3.824464</td>\n",
       "      <td>30.611210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>28.051428</td>\n",
       "      <td>16.879897</td>\n",
       "      <td>-3.696306</td>\n",
       "      <td>30.545629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>26.684984</td>\n",
       "      <td>16.739758</td>\n",
       "      <td>-2.998523</td>\n",
       "      <td>31.096066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>26.921334</td>\n",
       "      <td>16.768293</td>\n",
       "      <td>-2.056697</td>\n",
       "      <td>30.499331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>27.003199</td>\n",
       "      <td>14.581637</td>\n",
       "      <td>-2.688241</td>\n",
       "      <td>30.131872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>25.007810</td>\n",
       "      <td>13.499850</td>\n",
       "      <td>-3.161634</td>\n",
       "      <td>32.054496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>24.787813</td>\n",
       "      <td>11.329320</td>\n",
       "      <td>-2.826720</td>\n",
       "      <td>33.077085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>26.903225</td>\n",
       "      <td>12.068355</td>\n",
       "      <td>-2.648789</td>\n",
       "      <td>34.210246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>25.777238</td>\n",
       "      <td>11.535393</td>\n",
       "      <td>-1.952559</td>\n",
       "      <td>35.667764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>24.381155</td>\n",
       "      <td>10.885129</td>\n",
       "      <td>-4.349392</td>\n",
       "      <td>33.810491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>24.914723</td>\n",
       "      <td>10.461896</td>\n",
       "      <td>-4.523927</td>\n",
       "      <td>33.943282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>25.144380</td>\n",
       "      <td>12.115287</td>\n",
       "      <td>-5.210419</td>\n",
       "      <td>34.402361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>26.253174</td>\n",
       "      <td>11.910041</td>\n",
       "      <td>-4.889495</td>\n",
       "      <td>36.015240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>25.736717</td>\n",
       "      <td>13.275022</td>\n",
       "      <td>-2.518543</td>\n",
       "      <td>35.721396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>24.350343</td>\n",
       "      <td>14.154565</td>\n",
       "      <td>-2.495837</td>\n",
       "      <td>36.593416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>25.003458</td>\n",
       "      <td>16.235255</td>\n",
       "      <td>-2.376958</td>\n",
       "      <td>34.964767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>23.977122</td>\n",
       "      <td>16.555848</td>\n",
       "      <td>-1.746319</td>\n",
       "      <td>35.222469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>24.738345</td>\n",
       "      <td>14.692118</td>\n",
       "      <td>-3.112445</td>\n",
       "      <td>36.535400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>23.977210</td>\n",
       "      <td>14.979042</td>\n",
       "      <td>-3.557790</td>\n",
       "      <td>36.189400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>23.442975</td>\n",
       "      <td>14.431692</td>\n",
       "      <td>-4.008666</td>\n",
       "      <td>35.876610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A          B         C          D\n",
       "2000-01-01  -0.067377  -0.035638 -0.021100  -0.752226\n",
       "2000-01-02  -0.341526   0.867899  0.147792  -2.190171\n",
       "2000-01-03  -0.118822   1.501776 -0.472641  -1.448117\n",
       "2000-01-04  -0.727838   0.889072 -2.704155  -2.934499\n",
       "2000-01-05   0.531075   1.073829 -2.847804  -2.517378\n",
       "2000-01-06   0.091479   1.478376 -3.029743  -0.906359\n",
       "2000-01-07   1.492265   2.113159 -3.549888  -2.117010\n",
       "2000-01-08   1.851265   2.703705 -3.141079   0.079449\n",
       "2000-01-09   2.213547   3.685811 -3.029953   1.620792\n",
       "2000-01-10   2.028055   2.034899 -2.115068   3.566303\n",
       "2000-01-11   2.562459   1.562458 -1.266220   3.125902\n",
       "2000-01-12   3.563649   2.535037 -1.609729   1.636481\n",
       "2000-01-13   3.723012   3.932004 -1.801761   3.817881\n",
       "2000-01-14   5.097816   4.143041 -2.183664   2.784085\n",
       "2000-01-15   5.415682   2.042796 -1.186153   3.828601\n",
       "2000-01-16   5.875182   0.968725 -1.122289   3.771920\n",
       "2000-01-17   5.352968  -0.637527 -2.741543   2.230048\n",
       "2000-01-18   5.403275   0.561341 -1.943048   1.291451\n",
       "2000-01-19   5.898989   0.260370  0.010189   2.686019\n",
       "2000-01-20   6.446354   0.214986 -0.621627   2.265080\n",
       "2000-01-21   6.093474   0.696028 -0.604788   2.150748\n",
       "2000-01-22   4.816564   2.795033 -1.992706   2.617393\n",
       "2000-01-23   4.484142   2.215204 -2.216621   4.585891\n",
       "2000-01-24   6.218927   2.262602 -2.310701   3.788079\n",
       "2000-01-25   7.490060   1.679666 -2.538864   2.202103\n",
       "2000-01-26   6.878468   0.292143 -2.203920   3.033513\n",
       "2000-01-27   8.265415  -1.160655 -2.457205   1.806101\n",
       "2000-01-28   8.800961   0.506590 -1.682888   2.444211\n",
       "2000-01-29   9.069957   0.362689 -1.158481   5.313443\n",
       "2000-01-30   9.342882   0.817050 -1.591316   3.924335\n",
       "...               ...        ...       ...        ...\n",
       "2002-08-28  28.436666  12.185562  0.713590  33.470501\n",
       "2002-08-29  27.263426  12.515911 -0.883309  33.574743\n",
       "2002-08-30  26.365767  12.070123 -1.070257  33.533270\n",
       "2002-08-31  24.797556  12.432728 -2.800671  31.792798\n",
       "2002-09-01  24.063454  12.938684 -2.392343  31.930125\n",
       "2002-09-02  24.246746  13.716965 -1.796432  32.215315\n",
       "2002-09-03  25.025802  15.046681  0.176345  31.351909\n",
       "2002-09-04  25.722266  15.208363 -2.028918  30.888133\n",
       "2002-09-05  27.017412  16.800099 -3.470389  30.260138\n",
       "2002-09-06  28.739792  17.267548 -4.587964  30.492176\n",
       "2002-09-07  28.691180  17.406774 -3.824464  30.611210\n",
       "2002-09-08  28.051428  16.879897 -3.696306  30.545629\n",
       "2002-09-09  26.684984  16.739758 -2.998523  31.096066\n",
       "2002-09-10  26.921334  16.768293 -2.056697  30.499331\n",
       "2002-09-11  27.003199  14.581637 -2.688241  30.131872\n",
       "2002-09-12  25.007810  13.499850 -3.161634  32.054496\n",
       "2002-09-13  24.787813  11.329320 -2.826720  33.077085\n",
       "2002-09-14  26.903225  12.068355 -2.648789  34.210246\n",
       "2002-09-15  25.777238  11.535393 -1.952559  35.667764\n",
       "2002-09-16  24.381155  10.885129 -4.349392  33.810491\n",
       "2002-09-17  24.914723  10.461896 -4.523927  33.943282\n",
       "2002-09-18  25.144380  12.115287 -5.210419  34.402361\n",
       "2002-09-19  26.253174  11.910041 -4.889495  36.015240\n",
       "2002-09-20  25.736717  13.275022 -2.518543  35.721396\n",
       "2002-09-21  24.350343  14.154565 -2.495837  36.593416\n",
       "2002-09-22  25.003458  16.235255 -2.376958  34.964767\n",
       "2002-09-23  23.977122  16.555848 -1.746319  35.222469\n",
       "2002-09-24  24.738345  14.692118 -3.112445  36.535400\n",
       "2002-09-25  23.977210  14.979042 -3.557790  36.189400\n",
       "2002-09-26  23.442975  14.431692 -4.008666  35.876610\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gotchas\n",
    "If you are trying an operation and you see an exception like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-140-9cae3ab0f79f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I was true\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/takenory/.pyenv/versions/anaconda2-4.0.0/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    885\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m    886\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 887\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m    888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "if pd.Series([False, True, False]):\n",
    "    print(\"I was true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [Comparisons](http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-compare) for an explanation and what to do.\n",
    "\n",
    "See [Gotchas](http://pandas.pydata.org/pandas-docs/stable/gotchas.html#gotchas) as well."
   ]
  }
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