notebooks/02_numpy_and_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Agenda\n",
    "* Numpy\n",
    "* Pandas\n",
    "* Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Introduction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Download this notebook for our code-along:\n",
    "\n",
    "From our class GitHub repository \"notebooks\" directory, download the file \"02_numpy_and_pandas.ipynb\".  Move this to a convenient place where you are grouping your class related materials.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introduction to Numpy (\"num-pi\")\n",
    "\n",
    "* Overview\n",
    "* ndarray\n",
    "* Indexing and Slicing\n",
    "\n",
    "More info: [http://wiki.scipy.org/Tentative_NumPy_Tutorial](http://wiki.scipy.org/Tentative_NumPy_Tutorial)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Numpy Overview\n",
    "\n",
    "* Why Python for Data?\n",
    "    * It makes our data science life easier!!\n",
    "        * Numpy brings *decades* of C math into Python!\n",
    "        * Numpy provides a wrapper for extensive C/C++/Fortran codebases, used for data analysis functionality\n",
    "        * NDAarray allows easy vectorized math and broadcasting (i.e. functions for vector elements of different shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "## from numpy import * #Load all the numpy packages\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### A Note About \"import as\"\n",
    "\n",
    "    import * \n",
    "This loads all submodules. Note: this is a waste of memory when incorporated into deployed code. \n",
    "We'll use it here by example - it's fine to use for learning purposes and legibility.\n",
    "\n",
    "As we'll see later, the proper convention is to use:\n",
    "\n",
    "    import numpy as np\n",
    "    \n",
    "And then to specifically call needed methods:\n",
    "\n",
    "![](http://note.io/1juA60F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Creating ndarrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "An array object represents a multidimensional, homogeneous array of fixed-size items. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Creating arrays\n",
    "a = np.zeros((3))\n",
    "b = np.ones((2,3))\n",
    "c = np.random.randint(1,10,(2,3,4))\n",
    "d = np.arange(0,11,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "What are these functions?\n",
    "\n",
    "    arange?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a: ', array([ 0.,  0.,  0.]))\n",
      "('b: ', array([[ 1.,  1.,  1.],\n",
      "       [ 1.,  1.,  1.]]))\n",
      "('c: ', array([[[5, 8, 6, 4],\n",
      "        [3, 4, 5, 5],\n",
      "        [3, 7, 3, 6]],\n",
      "\n",
      "       [[2, 1, 4, 7],\n",
      "        [2, 8, 1, 9],\n",
      "        [6, 4, 4, 6]]]))\n",
      "('d: ', array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]))\n"
     ]
    }
   ],
   "source": [
    "# Note the way each array is printed:\n",
    "print(\"a: \",a)\n",
    "print(\"b: \",b)\n",
    "print(\"c: \",c)\n",
    "print(\"d: \",d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "## Arithmetic in arrays is element wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ">>> a = np.array( [20,30,40,50] )\n",
    ">>> b = np.arange( 4 )\n",
    ">>> b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 29, 38, 47])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ">>> c = a-b\n",
    ">>> c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 4, 9])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ">>> b**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Indexing, Slicing and Iterating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# one-dimensional arrays work like lists:\n",
    "a = np.arange(10)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  9, 16])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Multidimensional arrays use tuples with commas for indexing\n",
    "# with (row,column) conventions beginning, as always in Python, from 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "b = np.random.randint(1,100,(4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[78, 50, 38, 58],\n",
       "       [52, 51, 46, 75],\n",
       "       [15, 51,  5, 28],\n",
       "       [68, 93, 68, 87]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n"
     ]
    }
   ],
   "source": [
    "# Guess the output\n",
    "print(b[2,3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78\n"
     ]
    }
   ],
   "source": [
    "# Guess the output\n",
    "print(b[0,0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 51, 51])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Guess the output\n",
    "b[0:3,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 51, 51, 93])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Guess the output\n",
    "b[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52, 51, 46, 75],\n",
       "       [15, 51,  5, 28]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Guess the output\n",
    "b[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introduction to Pandas\n",
    "\n",
    "**Pandas, Know it! Love it! We will use it extensively.**\n",
    "\n",
    "Will allow us to use short lines of code to do important manipulation and viewing of our data\n",
    "* Object Creation\n",
    "* Viewing data\n",
    "* Selection\n",
    "* Missing data\n",
    "* Grouping\n",
    "* Reshaping\n",
    "* Time series\n",
    "* Plotting\n",
    "* i/o\n",
    " \n",
    "\n",
    "_pandas.pydata.org_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas Overview\n",
    "\n",
    "_Source: [pandas.pydata.org](http://pandas.pydata.org/pandas-docs/stable/10min.html)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-01', '2014-01-02', '2014-01-03', '2014-01-04',\n",
       "               '2014-01-05', '2014-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20140101',periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'A', u'B', u'C', u'D'], dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "z = pd.DataFrame(index = df.index, columns = df.columns)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
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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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index, columns, underlying numpy data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014-01-01 00:00:00</th>\n",
       "      <th>2014-01-02 00:00:00</th>\n",
       "      <th>2014-01-03 00:00:00</th>\n",
       "      <th>2014-01-04 00:00:00</th>\n",
       "      <th>2014-01-05 00:00:00</th>\n",
       "      <th>2014-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>0.493560</td>\n",
       "      <td>-0.616204</td>\n",
       "      <td>1.495474</td>\n",
       "      <td>0.087313</td>\n",
       "      <td>0.558734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.064400</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>-0.293773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.864159</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.621983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.146882</td>\n",
       "      <td>-1.852307</td>\n",
       "      <td>0.509120</td>\n",
       "      <td>0.534279</td>\n",
       "      <td>-0.907806</td>\n",
       "      <td>-1.414901</td>\n",
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       "   2014-01-01  2014-01-02  2014-01-03  2014-01-04  2014-01-05  2014-01-06\n",
       "A   -1.852417    0.493560   -0.616204    1.495474    0.087313    0.558734\n",
       "B   -0.064400    1.292923    0.725380   -0.529946   -0.146817   -0.293773\n",
       "C   -0.864159    0.442482    0.053441   -0.700288    0.954461   -0.621983\n",
       "D   -0.146882   -1.852307    0.509120    0.534279   -0.907806   -1.414901"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
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      "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>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A          B  C  D    E\n",
       "0  1 2013-01-02  1  3  foo\n",
       "1  1 2013-01-02  1  3  foo\n",
       "2  1 2013-01-02  1  3  foo\n",
       "3  1 2013-01-02  1  3  foo"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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' : 'foo' })\n",
    "    \n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With specific dtypes\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Viewing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-01', '2014-01-02', '2014-01-03', '2014-01-04',\n",
       "               '2014-01-05', '2014-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.027743</td>\n",
       "      <td>0.163895</td>\n",
       "      <td>-0.122674</td>\n",
       "      <td>-0.546416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.149468</td>\n",
       "      <td>0.697066</td>\n",
       "      <td>0.727102</td>\n",
       "      <td>1.003186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.440325</td>\n",
       "      <td>-0.257034</td>\n",
       "      <td>-0.680712</td>\n",
       "      <td>-1.288127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.290436</td>\n",
       "      <td>-0.105609</td>\n",
       "      <td>-0.284271</td>\n",
       "      <td>-0.527344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.542441</td>\n",
       "      <td>0.527935</td>\n",
       "      <td>0.345222</td>\n",
       "      <td>0.345119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>0.534279</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   0.027743  0.163895 -0.122674 -0.546416\n",
       "std    1.149468  0.697066  0.727102  1.003186\n",
       "min   -1.852417 -0.529946 -0.864159 -1.852307\n",
       "25%   -0.440325 -0.257034 -0.680712 -1.288127\n",
       "50%    0.290436 -0.105609 -0.284271 -0.527344\n",
       "75%    0.542441  0.527935  0.345222  0.345119\n",
       "max    1.495474  1.292923  0.954461  0.534279"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jim_byers/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort(columns='B')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2014-01-01 -1.852417 -0.064400\n",
       "2014-01-02  0.493560  1.292923\n",
       "2014-01-03 -0.616204  0.725380\n",
       "2014-01-04  1.495474 -0.529946\n",
       "2014-01-05  0.087313 -0.146817\n",
       "2014-01-06  0.558734 -0.293773"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.852417\n",
       "B   -0.064400\n",
       "C   -0.864159\n",
       "D   -0.146882\n",
       "Name: 2014-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By label\n",
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2014-01-01 -1.852417 -0.064400\n",
       "2014-01-02  0.493560  1.292923\n",
       "2014-01-03 -0.616204  0.725380\n",
       "2014-01-04  1.495474 -0.529946\n",
       "2014-01-05  0.087313 -0.146817\n",
       "2014-01-06  0.558734 -0.293773"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multi-axis by label\n",
    "df.loc[:,['A','B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>1.292923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>0.725380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>-0.529946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B\n",
       "2014-01-02  1.292923\n",
       "2014-01-03  0.725380\n",
       "2014-01-04 -0.529946"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Date Range\n",
    "df.loc['20140102':'20140104',['B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2929234858887562"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fast access to scalar\n",
    "df.at[dates[1],'B']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.027743</td>\n",
       "      <td>0.163895</td>\n",
       "      <td>-0.122674</td>\n",
       "      <td>-0.546416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.149468</td>\n",
       "      <td>0.697066</td>\n",
       "      <td>0.727102</td>\n",
       "      <td>1.003186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.440325</td>\n",
       "      <td>-0.257034</td>\n",
       "      <td>-0.680712</td>\n",
       "      <td>-1.288127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.290436</td>\n",
       "      <td>-0.105609</td>\n",
       "      <td>-0.284271</td>\n",
       "      <td>-0.527344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.542441</td>\n",
       "      <td>0.527935</td>\n",
       "      <td>0.345222</td>\n",
       "      <td>0.345119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>0.534279</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   0.027743  0.163895 -0.122674 -0.546416\n",
       "std    1.149468  0.697066  0.727102  1.003186\n",
       "min   -1.852417 -0.529946 -0.864159 -1.852307\n",
       "25%   -0.440325 -0.257034 -0.680712 -1.288127\n",
       "50%    0.290436 -0.105609 -0.284271 -0.527344\n",
       "75%    0.542441  0.527935  0.345222  0.345119\n",
       "max    1.495474  1.292923  0.954461  0.534279"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(A    0.027743\n",
       " B    0.163895\n",
       " C   -0.122674\n",
       " D   -0.546416\n",
       " dtype: float64, 2014-01-01   -0.731965\n",
       " 2014-01-02    0.094165\n",
       " 2014-01-03    0.167934\n",
       " 2014-01-04    0.199880\n",
       " 2014-01-05   -0.003212\n",
       " 2014-01-06   -0.442981\n",
       " Freq: D, dtype: float64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(),df.mean(1) # Operation on two different axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Boolean Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.06440</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.72538</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        B         C         D\n",
       "2014-01-01 -1.852417 -0.06440 -0.864159 -0.146882\n",
       "2014-01-03 -0.616204  0.72538  0.053441  0.509120"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A < 0] # Basically a 'where' operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-01-01     True\n",
       "2014-01-02    False\n",
       "2014-01-03     True\n",
       "2014-01-04    False\n",
       "2014-01-05    False\n",
       "2014-01-06    False\n",
       "Freq: D, Name: A, dtype: bool"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.A < 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.72538</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.50912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        B         C        D\n",
       "2014-01-03 -0.616204  0.72538  0.053441  0.50912"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Two conditions added together\n",
    "df[(df.A < 0) & (df.B > .5) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Two conditions ORed together\n",
    "df[(df.A < -1) | (df.D > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.04395911, -1.47305205, -0.23126949, -1.11665082],\n",
       "       [ 0.9447397 , -0.60450232,  0.55677749, -0.77687019],\n",
       "       [ 0.08640536,  0.42821086, -0.02543862, -0.65067117],\n",
       "       [ 1.1729843 ,  1.49750043,  1.70720118, -3.78520453],\n",
       "       [-0.28600257,  0.23568711, -1.53532043, -1.00410235],\n",
       "       [-0.38195294, -0.42503652, -1.46167607,  0.09055646],\n",
       "       [ 1.77305363, -0.70536741,  1.43643196,  1.41475157],\n",
       "       [ 1.32074725,  0.34090157,  1.50595862, -0.56409384],\n",
       "       [-1.78048411, -0.52681978,  1.89095182, -0.78942382],\n",
       "       [ 0.2513967 ,  0.16786423, -1.83040754, -0.28591861]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(10,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.715391</td>\n",
       "      <td>-0.615844</td>\n",
       "      <td>1.035251</td>\n",
       "      <td>0.748857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.645622</td>\n",
       "      <td>0.368765</td>\n",
       "      <td>-1.258206</td>\n",
       "      <td>0.191632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.147788</td>\n",
       "      <td>0.317607</td>\n",
       "      <td>-1.965764</td>\n",
       "      <td>-0.124198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.595805</td>\n",
       "      <td>-0.402327</td>\n",
       "      <td>0.260561</td>\n",
       "      <td>-0.425883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.862880</td>\n",
       "      <td>0.772479</td>\n",
       "      <td>-0.187875</td>\n",
       "      <td>0.726583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.359919</td>\n",
       "      <td>-0.773547</td>\n",
       "      <td>-0.928463</td>\n",
       "      <td>-0.454038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.633747</td>\n",
       "      <td>-1.159134</td>\n",
       "      <td>1.301220</td>\n",
       "      <td>-0.542743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.544222</td>\n",
       "      <td>-0.714093</td>\n",
       "      <td>0.006704</td>\n",
       "      <td>2.144965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.304869</td>\n",
       "      <td>0.256355</td>\n",
       "      <td>-1.386836</td>\n",
       "      <td>0.639978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.355813</td>\n",
       "      <td>-0.738924</td>\n",
       "      <td>0.357239</td>\n",
       "      <td>0.921883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.715391 -0.615844  1.035251  0.748857\n",
       "1  0.645622  0.368765 -1.258206  0.191632\n",
       "2  1.147788  0.317607 -1.965764 -0.124198\n",
       "3 -1.595805 -0.402327  0.260561 -0.425883\n",
       "4  0.862880  0.772479 -0.187875  0.726583\n",
       "5  0.359919 -0.773547 -0.928463 -0.454038\n",
       "6 -0.633747 -1.159134  1.301220 -0.542743\n",
       "7  1.544222 -0.714093  0.006704  2.144965\n",
       "8 -1.304869  0.256355 -1.386836  0.639978\n",
       "9  0.355813 -0.738924  0.357239  0.921883"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concatenating pandas objects together\n",
    "df2 = pd.DataFrame(np.random.randn(10,4))\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0  0.715391 -0.615844  1.035251  0.748857\n",
       " 1  0.645622  0.368765 -1.258206  0.191632\n",
       " 2  1.147788  0.317607 -1.965764 -0.124198,\n",
       "           0         1         2         3\n",
       " 3 -1.595805 -0.402327  0.260561 -0.425883\n",
       " 4  0.862880  0.772479 -0.187875  0.726583\n",
       " 5  0.359919 -0.773547 -0.928463 -0.454038\n",
       " 6 -0.633747 -1.159134  1.301220 -0.542743,\n",
       "           0         1         2         3\n",
       " 7  1.544222 -0.714093  0.006704  2.144965\n",
       " 8 -1.304869  0.256355 -1.386836  0.639978\n",
       " 9  0.355813 -0.738924  0.357239  0.921883]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Break it into pieces\n",
    "pieces = [df2[:3], df2[3:7],df2[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>0.715391</td>\n",
       "      <td>-0.615844</td>\n",
       "      <td>1.035251</td>\n",
       "      <td>0.748857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.645622</td>\n",
       "      <td>0.368765</td>\n",
       "      <td>-1.258206</td>\n",
       "      <td>0.191632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.147788</td>\n",
       "      <td>0.317607</td>\n",
       "      <td>-1.965764</td>\n",
       "      <td>-0.124198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.595805</td>\n",
       "      <td>-0.402327</td>\n",
       "      <td>0.260561</td>\n",
       "      <td>-0.425883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.862880</td>\n",
       "      <td>0.772479</td>\n",
       "      <td>-0.187875</td>\n",
       "      <td>0.726583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.359919</td>\n",
       "      <td>-0.773547</td>\n",
       "      <td>-0.928463</td>\n",
       "      <td>-0.454038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.633747</td>\n",
       "      <td>-1.159134</td>\n",
       "      <td>1.301220</td>\n",
       "      <td>-0.542743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.544222</td>\n",
       "      <td>-0.714093</td>\n",
       "      <td>0.006704</td>\n",
       "      <td>2.144965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.304869</td>\n",
       "      <td>0.256355</td>\n",
       "      <td>-1.386836</td>\n",
       "      <td>0.639978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.355813</td>\n",
       "      <td>-0.738924</td>\n",
       "      <td>0.357239</td>\n",
       "      <td>0.921883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.715391 -0.615844  1.035251  0.748857\n",
       "1  0.645622  0.368765 -1.258206  0.191632\n",
       "2  1.147788  0.317607 -1.965764 -0.124198\n",
       "3 -1.595805 -0.402327  0.260561 -0.425883\n",
       "4  0.862880  0.772479 -0.187875  0.726583\n",
       "5  0.359919 -0.773547 -0.928463 -0.454038\n",
       "6 -0.633747 -1.159134  1.301220 -0.542743\n",
       "7  1.544222 -0.714093  0.006704  2.144965\n",
       "8 -1.304869  0.256355 -1.386836  0.639978\n",
       "9  0.355813 -0.738924  0.357239  0.921883"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Also can \"Join\" and \"Append\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df_posA = df.copy() # Without \"copy\" it would act on the dataset\n",
    "\n",
    "df_posA[df_posA.A < 0] = -1*df_posA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-01</th>\n",
       "      <td>1.852417</td>\n",
       "      <td>0.064400</td>\n",
       "      <td>0.864159</td>\n",
       "      <td>0.146882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>0.616204</td>\n",
       "      <td>-0.725380</td>\n",
       "      <td>-0.053441</td>\n",
       "      <td>-0.509120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2014-01-01  1.852417  0.064400  0.864159  0.146882\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307\n",
       "2014-01-03  0.616204 -0.725380 -0.053441 -0.509120\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_posA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#Setting new column aligns data by index\n",
    "s1 = pd.Series([1,2,3,4,5,6],index=pd.date_range('20140102',periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-01-02    1\n",
       "2014-01-03    2\n",
       "2014-01-04    3\n",
       "2014-01-05    4\n",
       "2014-01-06    5\n",
       "2014-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "df['F'] = s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882 NaN\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307   1\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120   2\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279   3\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806   4\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901   5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Add a column with missing data\n",
    "df1 = df.reindex(index=dates[0:4],columns=list(df.columns) + ['E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df1.loc[dates[0]:dates[1],'E'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F   E\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882 NaN   1\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307   1   1\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120   2 NaN\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279   3 NaN"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</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>2014-01-04</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2014-01-01  False  False  False  False   True  False\n",
       "2014-01-02  False  False  False  False  False  False\n",
       "2014-01-03  False  False  False  False  False   True\n",
       "2014-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find where values are null\n",
    "pd.isnull(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Applying functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>0.493560</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.442482</td>\n",
       "      <td>-1.852307</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.616204</td>\n",
       "      <td>0.725380</td>\n",
       "      <td>0.053441</td>\n",
       "      <td>0.509120</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>1.495474</td>\n",
       "      <td>-0.529946</td>\n",
       "      <td>-0.700288</td>\n",
       "      <td>0.534279</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>0.087313</td>\n",
       "      <td>-0.146817</td>\n",
       "      <td>0.954461</td>\n",
       "      <td>-0.907806</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.558734</td>\n",
       "      <td>-0.293773</td>\n",
       "      <td>-0.621983</td>\n",
       "      <td>-1.414901</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882 NaN\n",
       "2014-01-02  0.493560  1.292923  0.442482 -1.852307   1\n",
       "2014-01-03 -0.616204  0.725380  0.053441  0.509120   2\n",
       "2014-01-04  1.495474 -0.529946 -0.700288  0.534279   3\n",
       "2014-01-05  0.087313 -0.146817  0.954461 -0.907806   4\n",
       "2014-01-06  0.558734 -0.293773 -0.621983 -1.414901   5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>2014-01-01</th>\n",
       "      <td>-1.852417</td>\n",
       "      <td>-0.064400</td>\n",
       "      <td>-0.864159</td>\n",
       "      <td>-0.146882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>-1.358857</td>\n",
       "      <td>1.228523</td>\n",
       "      <td>-0.421677</td>\n",
       "      <td>-1.999190</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-1.975061</td>\n",
       "      <td>1.953903</td>\n",
       "      <td>-0.368236</td>\n",
       "      <td>-1.490070</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>-0.479587</td>\n",
       "      <td>1.423957</td>\n",
       "      <td>-1.068524</td>\n",
       "      <td>-0.955791</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>-0.392274</td>\n",
       "      <td>1.277140</td>\n",
       "      <td>-0.114063</td>\n",
       "      <td>-1.863597</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.166460</td>\n",
       "      <td>0.983367</td>\n",
       "      <td>-0.736046</td>\n",
       "      <td>-3.278498</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D   F\n",
       "2014-01-01 -1.852417 -0.064400 -0.864159 -0.146882 NaN\n",
       "2014-01-02 -1.358857  1.228523 -0.421677 -1.999190   1\n",
       "2014-01-03 -1.975061  1.953903 -0.368236 -1.490070   3\n",
       "2014-01-04 -0.479587  1.423957 -1.068524 -0.955791   6\n",
       "2014-01-05 -0.392274  1.277140 -0.114063 -1.863597  10\n",
       "2014-01-06  0.166460  0.983367 -0.736046 -3.278498  15"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    3.347891\n",
       "B    1.822870\n",
       "C    1.818620\n",
       "D    2.386586\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Built in string methods\n",
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n",
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Grouping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "                       'foo', 'bar', 'foo', 'foo'],\n",
    "                       'B' : ['one', 'one', 'two', 'three',\n",
    "                             'two', 'two', 'one', 'three'],\n",
    "                       'C' : np.random.randn(8),\n",
    "                       'D' : np.random.randn(8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.019653</td>\n",
       "      <td>0.155704</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
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       "      <td>1.036166</td>\n",
       "      <td>-1.198918</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.203702</td>\n",
       "      <td>0.444151</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.480536</td>\n",
       "      <td>1.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-2.377876</td>\n",
       "      <td>0.582398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>0.833833</td>\n",
       "      <td>-0.162423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.106570</td>\n",
       "      <td>-0.319444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-1.510930</td>\n",
       "      <td>-0.445471</td>\n",
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       "     A      B         C         D\n",
       "0  foo    one -1.019653  0.155704\n",
       "1  bar    one  1.036166 -1.198918\n",
       "2  foo    two  0.203702  0.444151\n",
       "3  bar  three  0.480536  1.792000\n",
       "4  foo    two -2.377876  0.582398\n",
       "5  bar    two  0.833833 -0.162423\n",
       "6  foo    one  0.106570 -0.319444\n",
       "7  foo  three -1.510930 -0.445471"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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>1.036166</td>\n",
       "      <td>-1.198918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.480536</td>\n",
       "      <td>1.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.833833</td>\n",
       "      <td>-0.162423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.913084</td>\n",
       "      <td>-0.163740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-1.510930</td>\n",
       "      <td>-0.445471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-2.174174</td>\n",
       "      <td>1.026549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    1.036166 -1.198918\n",
       "    three  0.480536  1.792000\n",
       "    two    0.833833 -0.162423\n",
       "foo one   -0.913084 -0.163740\n",
       "    three -1.510930 -0.445471\n",
       "    two   -2.174174  1.026549"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# You can also stack or unstack levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "a = df3.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Pivot Tables\n",
    "# pd.pivot_table(df3,values=['C','D'],rows=['A'],cols=['B'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Time Series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# 100 Seconds starting on January 1st\n",
    "rng = pd.date_range('1/1/2014', periods=100, freq='S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Give each second a random value\n",
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-01-01 00:00:00    146\n",
       "2014-01-01 00:00:01     69\n",
       "2014-01-01 00:00:02    499\n",
       "2014-01-01 00:00:03    356\n",
       "2014-01-01 00:00:04    160\n",
       "2014-01-01 00:00:05    197\n",
       "2014-01-01 00:00:06    178\n",
       "2014-01-01 00:00:07    254\n",
       "2014-01-01 00:00:08    331\n",
       "2014-01-01 00:00:09    114\n",
       "2014-01-01 00:00:10    299\n",
       "2014-01-01 00:00:11    439\n",
       "2014-01-01 00:00:12    244\n",
       "2014-01-01 00:00:13    317\n",
       "2014-01-01 00:00:14    131\n",
       "2014-01-01 00:00:15    411\n",
       "2014-01-01 00:00:16    305\n",
       "2014-01-01 00:00:17    251\n",
       "2014-01-01 00:00:18    482\n",
       "2014-01-01 00:00:19    472\n",
       "2014-01-01 00:00:20    104\n",
       "2014-01-01 00:00:21     32\n",
       "2014-01-01 00:00:22    159\n",
       "2014-01-01 00:00:23    213\n",
       "2014-01-01 00:00:24    281\n",
       "2014-01-01 00:00:25    235\n",
       "2014-01-01 00:00:26     13\n",
       "2014-01-01 00:00:27    383\n",
       "2014-01-01 00:00:28    362\n",
       "2014-01-01 00:00:29    193\n",
       "                      ... \n",
       "2014-01-01 00:01:10    338\n",
       "2014-01-01 00:01:11    349\n",
       "2014-01-01 00:01:12    328\n",
       "2014-01-01 00:01:13    185\n",
       "2014-01-01 00:01:14    286\n",
       "2014-01-01 00:01:15    488\n",
       "2014-01-01 00:01:16    247\n",
       "2014-01-01 00:01:17    259\n",
       "2014-01-01 00:01:18    423\n",
       "2014-01-01 00:01:19    315\n",
       "2014-01-01 00:01:20    280\n",
       "2014-01-01 00:01:21    462\n",
       "2014-01-01 00:01:22    199\n",
       "2014-01-01 00:01:23    480\n",
       "2014-01-01 00:01:24    472\n",
       "2014-01-01 00:01:25    403\n",
       "2014-01-01 00:01:26    384\n",
       "2014-01-01 00:01:27     93\n",
       "2014-01-01 00:01:28    432\n",
       "2014-01-01 00:01:29    474\n",
       "2014-01-01 00:01:30    196\n",
       "2014-01-01 00:01:31    491\n",
       "2014-01-01 00:01:32    255\n",
       "2014-01-01 00:01:33    178\n",
       "2014-01-01 00:01:34    115\n",
       "2014-01-01 00:01:35    428\n",
       "2014-01-01 00:01:36     35\n",
       "2014-01-01 00:01:37    399\n",
       "2014-01-01 00:01:38    202\n",
       "2014-01-01 00:01:39    227\n",
       "Freq: S, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-01-01 00:00:00    241.483333\n",
       "2014-01-01 00:01:00    299.800000\n",
       "Freq: T, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Built in resampling\n",
    "ts.resample('1Min',how='mean') # Resample secondly to 1Minutely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Many additional time series features\n",
    "# ts. #use tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e69f510>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YJG1xdLQRke/SnE3RnV1GVQk9jeUibsSODn8KvbsbWLYM2LYNOP30ZOvLAnfcwcer/NrX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e67d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def randwalk(startdate,points):\n",
    "    ts = pd.Series(np.random.randn(points), index=pd.date_range(startdate, periods=points))\n",
    "    ts=ts.cumsum()\n",
    "    ts.plot()\n",
    "    return(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bjhmT2JwDN7uaeWByW8KBXURaish8EVksIstE5J7Q8UIRmSUipSIyU0QSbN0M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\nqqE1xsmOeXIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6cce50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using pandas to make a simple random walker by repeatedly running:\n",
    "a=randwalk('1/1/2012',1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Pandas plot function will print with labels as default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x111389a50>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e923410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JypOBU4gzvmfoy6NfUnxqPFVdXZU+pXxSt0icz0QlRlHN9TXJL8pP9k4dOxIN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\nZTJUqFTYGxHRRuBbm1q1Gj46qpdGjQb3paVhZO/e+GzECIOCXSyeCno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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e689d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(100, 4), index=ts.index,columns=['A', 'B', 'C', 'D'])\n",
    "df = df.cumsum()\n",
    "plt.figure();df.plot();plt.legend(loc='best') #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### I/O\n",
    "I/O is straightforward with, for example, pd.read_csv or df.to_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Next Steps\n",
    "\n",
    "**Recommended Resources**\n",
    "\n",
    "Name | Description\n",
    "--- | ---\n",
    "[Official Pandas Tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) | Wes & Company's selection of tutorials and lectures\n",
    "[Julia Evans Pandas Cookbook](https://github.com/jvns/pandas-cookbook) | Great resource with examples from weather, bikes and 311 calls\n",
    "[Learn Pandas Tutorials](https://bitbucket.org/hrojas/learn-pandas) | A great series of Pandas tutorials from Dave Rojas\n",
    "[Research Computing Python Data PYNBs](https://github.com/ResearchComputing/Meetup-Fall-2013/tree/master/python) | A super awesome set of python notebooks from a meetup-based course exclusively devoted to pandas"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}