pandas-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 minutes pandas\n",
    "Source: [http://pandas.pydata.org/pandas-docs/version/0.15.2/10min.html](http://pandas.pydata.org/pandas-docs/version/0.15.2/10min.html)\n",
    "\n",
    "This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook\n",
    "\n",
    "Customarily, we import as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the [Data Structure Intro section](http://pandas.pydata.org/pandas-docs/version/0.15.2/dsintro.html#dsintro)\n",
    "\n",
    "Creating a Series by passing a list of values, letting pandas create a default integer index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.732062</td>\n",
       "      <td>-0.872924</td>\n",
       "      <td>-0.029987</td>\n",
       "      <td>-0.428751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.223329</td>\n",
       "      <td>1.104988</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>1.367178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.430158</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>-1.618767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.489252</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>1.582406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.972740</td>\n",
       "      <td>-0.174383</td>\n",
       "      <td>1.023746</td>\n",
       "      <td>1.307901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.642879</td>\n",
       "      <td>-1.502393</td>\n",
       "      <td>1.080234</td>\n",
       "      <td>-1.233327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.732062 -0.872924 -0.029987 -0.428751\n",
       "2013-01-02  0.223329  1.104988 -1.143353  1.367178\n",
       "2013-01-03  0.430158  0.754639 -0.506686 -1.618767\n",
       "2013-01-04  0.489252  2.327406  1.459776  1.582406\n",
       "2013-01-05 -0.972740 -0.174383  1.023746  1.307901\n",
       "2013-01-06  0.642879 -1.502393  1.080234 -1.233327"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a DataFrame by passing a dict of objects that can be converted to series-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\n",
    "        'A': 1.,\n",
    "        'B': pd.Timestamp('20130102'),\n",
    "        'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "        'D': np.array([3] * 4, dtype='int32'),\n",
    "        'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "        'F': 'foo'\n",
    "    })\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having specific [dtypes](http://pandas.pydata.org/pandas-docs/version/0.15.2/basics.html#basics-dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "Name: A, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the [Basics section](http://pandas.pydata.org/pandas-docs/version/0.15.2/basics.html#basics)\n",
    "\n",
    "See the top & bottom rows of the frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.732062</td>\n",
       "      <td>-0.872924</td>\n",
       "      <td>-0.029987</td>\n",
       "      <td>-0.428751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.223329</td>\n",
       "      <td>1.104988</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>1.367178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.430158</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>-1.618767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.732062 -0.872924 -0.029987 -0.428751\n",
       "2013-01-02  0.223329  1.104988 -1.143353  1.367178\n",
       "2013-01-03  0.430158  0.754639 -0.506686 -1.618767"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.489252</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>1.582406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.972740</td>\n",
       "      <td>-0.174383</td>\n",
       "      <td>1.023746</td>\n",
       "      <td>1.307901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.642879</td>\n",
       "      <td>-1.502393</td>\n",
       "      <td>1.080234</td>\n",
       "      <td>-1.233327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04  0.489252  2.327406  1.459776  1.582406\n",
       "2013-01-05 -0.972740 -0.174383  1.023746  1.307901\n",
       "2013-01-06  0.642879 -1.502393  1.080234 -1.233327"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the index,columns, and the underlying numpy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.73206226, -0.87292427, -0.02998722, -0.42875054],\n",
       "       [ 0.22332874,  1.10498803, -1.14335343,  1.36717802],\n",
       "       [ 0.43015796,  0.75463924, -0.50668561, -1.61876721],\n",
       "       [ 0.48925244,  2.32740612,  1.45977631,  1.58240583],\n",
       "       [-0.97274017, -0.17438326,  1.0237459 ,  1.30790104],\n",
       "       [ 0.64287938, -1.5023928 ,  1.08023424, -1.23332652]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe shows a quick statistic summary of your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>0.257490</td>\n",
       "      <td>0.272889</td>\n",
       "      <td>0.313955</td>\n",
       "      <td>0.162773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.627998</td>\n",
       "      <td>1.400720</td>\n",
       "      <td>1.031444</td>\n",
       "      <td>1.431792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.972740</td>\n",
       "      <td>-1.502393</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>-1.618767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.275036</td>\n",
       "      <td>-0.698289</td>\n",
       "      <td>-0.387511</td>\n",
       "      <td>-1.032183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.459705</td>\n",
       "      <td>0.290128</td>\n",
       "      <td>0.496879</td>\n",
       "      <td>0.439575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.604473</td>\n",
       "      <td>1.017401</td>\n",
       "      <td>1.066112</td>\n",
       "      <td>1.352359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.732062</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>1.582406</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   0.257490  0.272889  0.313955  0.162773\n",
       "std    0.627998  1.400720  1.031444  1.431792\n",
       "min   -0.972740 -1.502393 -1.143353 -1.618767\n",
       "25%    0.275036 -0.698289 -0.387511 -1.032183\n",
       "50%    0.459705  0.290128  0.496879  0.439575\n",
       "75%    0.604473  1.017401  1.066112  1.352359\n",
       "max    0.732062  2.327406  1.459776  1.582406"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.732062</td>\n",
       "      <td>0.223329</td>\n",
       "      <td>0.430158</td>\n",
       "      <td>0.489252</td>\n",
       "      <td>-0.972740</td>\n",
       "      <td>0.642879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.872924</td>\n",
       "      <td>1.104988</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>-0.174383</td>\n",
       "      <td>-1.502393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.029987</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>1.023746</td>\n",
       "      <td>1.080234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.428751</td>\n",
       "      <td>1.367178</td>\n",
       "      <td>-1.618767</td>\n",
       "      <td>1.582406</td>\n",
       "      <td>1.307901</td>\n",
       "      <td>-1.233327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A    0.732062    0.223329    0.430158    0.489252   -0.972740    0.642879\n",
       "B   -0.872924    1.104988    0.754639    2.327406   -0.174383   -1.502393\n",
       "C   -0.029987   -1.143353   -0.506686    1.459776    1.023746    1.080234\n",
       "D   -0.428751    1.367178   -1.618767    1.582406    1.307901   -1.233327"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.428751</td>\n",
       "      <td>-0.029987</td>\n",
       "      <td>-0.872924</td>\n",
       "      <td>0.732062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.367178</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>1.104988</td>\n",
       "      <td>0.223329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.618767</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>0.430158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1.582406</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>0.489252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.307901</td>\n",
       "      <td>1.023746</td>\n",
       "      <td>-0.174383</td>\n",
       "      <td>-0.972740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-1.233327</td>\n",
       "      <td>1.080234</td>\n",
       "      <td>-1.502393</td>\n",
       "      <td>0.642879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01 -0.428751 -0.029987 -0.872924  0.732062\n",
       "2013-01-02  1.367178 -1.143353  1.104988  0.223329\n",
       "2013-01-03 -1.618767 -0.506686  0.754639  0.430158\n",
       "2013-01-04  1.582406  1.459776  2.327406  0.489252\n",
       "2013-01-05  1.307901  1.023746 -0.174383 -0.972740\n",
       "2013-01-06 -1.233327  1.080234 -1.502393  0.642879"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.642879</td>\n",
       "      <td>-1.502393</td>\n",
       "      <td>1.080234</td>\n",
       "      <td>-1.233327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.732062</td>\n",
       "      <td>-0.872924</td>\n",
       "      <td>-0.029987</td>\n",
       "      <td>-0.428751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.972740</td>\n",
       "      <td>-0.174383</td>\n",
       "      <td>1.023746</td>\n",
       "      <td>1.307901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.430158</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>-1.618767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.223329</td>\n",
       "      <td>1.104988</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>1.367178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.489252</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>1.582406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-06  0.642879 -1.502393  1.080234 -1.233327\n",
       "2013-01-01  0.732062 -0.872924 -0.029987 -0.428751\n",
       "2013-01-05 -0.972740 -0.174383  1.023746  1.307901\n",
       "2013-01-03  0.430158  0.754639 -0.506686 -1.618767\n",
       "2013-01-02  0.223329  1.104988 -1.143353  1.367178\n",
       "2013-01-04  0.489252  2.327406  1.459776  1.582406"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection\n",
    "\n",
    "**Note:** While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc, .iloc and .ix.\n",
    "\n",
    "See the indexing documentation [Indexing and Selecing Data](http://pandas.pydata.org/pandas-docs/version/0.15.2/indexing.html#indexing) and [MultiIndex / Advanced Indexing](http://pandas.pydata.org/pandas-docs/version/0.15.2/advanced.html#advanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single column, which yields a Series, equivalent to df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.732062\n",
       "2013-01-02    0.223329\n",
       "2013-01-03    0.430158\n",
       "2013-01-04    0.489252\n",
       "2013-01-05   -0.972740\n",
       "2013-01-06    0.642879\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.732062</td>\n",
       "      <td>-0.872924</td>\n",
       "      <td>-0.029987</td>\n",
       "      <td>-0.428751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.223329</td>\n",
       "      <td>1.104988</td>\n",
       "      <td>-1.143353</td>\n",
       "      <td>1.367178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.430158</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>-1.618767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.732062 -0.872924 -0.029987 -0.428751\n",
       "2013-01-02  0.223329  1.104988 -1.143353  1.367178\n",
       "2013-01-03  0.430158  0.754639 -0.506686 -1.618767"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.430158</td>\n",
       "      <td>0.754639</td>\n",
       "      <td>-0.506686</td>\n",
       "      <td>-1.618767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.489252</td>\n",
       "      <td>2.327406</td>\n",
       "      <td>1.459776</td>\n",
       "      <td>1.582406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-03  0.430158  0.754639 -0.506686 -1.618767\n",
       "2013-01-04  0.489252  2.327406  1.459776  1.582406"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20130103':'20130104']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}