pandas_search_column_for_value.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Search A Pandas Column For A Value\n",
      "\n",
      "This snippit was written by [Chris R. Albon](http://www.chrisralbon.com/) and is part of his collection of [well-documented Python snippits](https://github.com/chrisalbon/code_py). All code is written in Python 3 in iPython notebook and offered under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/\"><img alt=\"Creative Commons License)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import modules\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "raw_data = {'first_name': ['Jason', 'Jason', 'Tina', 'Jake', 'Amy'], \n",
      "        'last_name': ['Miller', 'Miller', 'Ali', 'Milner', 'Cooze'], \n",
      "        'age': [42, 42, 36, 24, 73], \n",
      "        'preTestScore': [4, 4, 31, 2, 3],\n",
      "        'postTestScore': [25, 25, 57, 62, 70]}\n",
      "df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>first_name</th>\n",
        "      <th>last_name</th>\n",
        "      <th>age</th>\n",
        "      <th>preTestScore</th>\n",
        "      <th>postTestScore</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Jason</td>\n",
        "      <td> Miller</td>\n",
        "      <td> 42</td>\n",
        "      <td>  4</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Jason</td>\n",
        "      <td> Miller</td>\n",
        "      <td> 42</td>\n",
        "      <td>  4</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  Tina</td>\n",
        "      <td>    Ali</td>\n",
        "      <td> 36</td>\n",
        "      <td> 31</td>\n",
        "      <td> 57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  Jake</td>\n",
        "      <td> Milner</td>\n",
        "      <td> 24</td>\n",
        "      <td>  2</td>\n",
        "      <td> 62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   Amy</td>\n",
        "      <td>  Cooze</td>\n",
        "      <td> 73</td>\n",
        "      <td>  3</td>\n",
        "      <td> 70</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "  first_name last_name  age  preTestScore  postTestScore\n",
        "0      Jason    Miller   42             4             25\n",
        "1      Jason    Miller   42             4             25\n",
        "2       Tina       Ali   36            31             57\n",
        "3       Jake    Milner   24             2             62\n",
        "4        Amy     Cooze   73             3             70"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Find where a value exists in a column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# View preTestscore where postTestscore is greater than 50\n",
      "df['preTestScore'].where(df['postTestScore'] > 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0   NaN\n",
        "1   NaN\n",
        "2    31\n",
        "3     2\n",
        "4     3\n",
        "Name: preTestScore, dtype: float64"
       ]
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}