data-science-ipython-notebooks/pandas/pandas.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Basics"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import Series, DataFrame\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Series\n",
      "\n",
      "A Series is a one-dimensional array-like object containing an array of data and an associated array of data labels.  The data can be any NumPy data type and the labels are the Series' index."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a Series:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_1 = Series([1, 1, 2, -3, -5, 8, 13])\n",
      "ser_1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "0     1\n",
        "1     1\n",
        "2     2\n",
        "3    -3\n",
        "4    -5\n",
        "5     8\n",
        "6    13\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the array representation of a Series:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_1.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([ 1,  1,  2, -3, -5,  8, 13])"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the index of the Series:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_1.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64')"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a Series with a custom index:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2 = Series([1, 1, 2, -3, -5], index=['a', 'b', 'c', 'd', 'e'])\n",
      "ser_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "a    1\n",
        "b    1\n",
        "c    2\n",
        "d   -3\n",
        "e   -5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get a value from a Series:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2[4] == ser_2['e']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get a set of values from a Series by passing in a list:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2[['c', 'a', 'b']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "c    2\n",
        "a    1\n",
        "b    1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get values great than 0:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2[ser_2 > 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "a    1\n",
        "b    1\n",
        "c    2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Scalar multiply:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_2 * 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "a     2\n",
        "b     2\n",
        "c     4\n",
        "d    -6\n",
        "e   -10\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Apply a numpy math function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "np.exp(ser_2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "a    2.718282\n",
        "b    2.718282\n",
        "c    7.389056\n",
        "d    0.049787\n",
        "e    0.006738\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A Series is like a fixed-length, ordered dict.  \n",
      "\n",
      "Create a series by passing in a dict:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dict_1 = {'foo' : 100, 'bar' : 200, 'baz' : 300}\n",
      "ser_3 = Series(dict_1)\n",
      "ser_3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "bar    200\n",
        "baz    300\n",
        "foo    100\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Re-order a Series by passing in an index (indices not found are NaN):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index = ['foo', 'bar', 'baz', 'qux']\n",
      "ser_4 = Series(dict_1, index=index)\n",
      "ser_4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "foo    100\n",
        "bar    200\n",
        "baz    300\n",
        "qux    NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check for NaN with the pandas method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.isnull(ser_4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "foo    False\n",
        "bar    False\n",
        "baz    False\n",
        "qux     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check for NaN with the Series method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_4.isnull()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "foo    False\n",
        "bar    False\n",
        "baz    False\n",
        "qux     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series automatically aligns differently indexed data in arithmetic operations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_3 + ser_4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "bar    400\n",
        "baz    600\n",
        "foo    200\n",
        "qux    NaN\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Name a Series:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_4.name = 'foobarbazqux'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Name a Series index:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_4.index.name = 'label'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "label\n",
        "foo      100\n",
        "bar      200\n",
        "baz      300\n",
        "qux      NaN\n",
        "Name: foobarbazqux, dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Rename a Series' index in place:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ser_4.index = ['fo', 'br', 'bz', 'qx']\n",
      "ser_4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "fo    100\n",
        "br    200\n",
        "bz    300\n",
        "qx    NaN\n",
        "Name: foobarbazqux, dtype: float64"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}