snippsat/gist:dee922a8ce65be549934

## gistfile1.txt
{
 "metadata": {
  "name": "work_data_csv"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "data = '''\\\nBob,34,121,13,69000\nZoe,19,63,2,23300\nAmy,45,59,5,45000\nFrank,56,93,12,57000\nKaka,23,55,7,45000\nUdo,27,65,3,39000\nVera,31,71,8,45000\nBetty,22,77,5,45000'''",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\nfrom StringIO import StringIO",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data = pd.read_csv(StringIO(data), sep=',', \n        names=['name', 'age', 'weight', 'seniority', 'pay'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n      <td> 121</td>\n      <td> 13</td>\n      <td> 69000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n      <td>  63</td>\n      <td>  2</td>\n      <td> 23300</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n      <td>  59</td>\n      <td>  5</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n      <td>  93</td>\n      <td> 12</td>\n      <td> 57000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td>  55</td>\n      <td>  7</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n      <td>  65</td>\n      <td>  3</td>\n      <td> 39000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n      <td>  71</td>\n      <td>  8</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n      <td>  77</td>\n      <td>  5</td>\n      <td> 45000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": "    name  age  weight  seniority    pay\n0    Bob   34     121         13  69000\n1    Zoe   19      63          2  23300\n2    Amy   45      59          5  45000\n3  Frank   56      93         12  57000\n4   Kaka   23      55          7  45000\n5    Udo   27      65          3  39000\n6   Vera   31      71          8  45000\n7  Betty   22      77          5  45000"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.pay.max()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": "69000"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.pay.min()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": "23300"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.pay.mean()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "46037.5"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print('Minimum pay = {}\\nMaximum pay = {}\\nAverage pay in department = ${:0.2f}'\\\n      .format(work_data.pay.min(),work_data.pay.max(),work_data.pay.mean()))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Minimum pay = 23300\nMaximum pay = 69000\nAverage pay in department = $46037.50\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.age.plot()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "<matplotlib.axes._subplots.AxesSubplot at 0xa9bf2b0>"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np\nimport plotly.plotly as py\nimport plotly.tools as tls\nfrom plotly.graph_objs import *\npy.sign_in(\"snippsat\", \"xqwfpvkkuf\")\nfrom ggplot import *\nimport seaborn as sns",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.age",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": "0    34\n1    19\n2    45\n3    56\n4    23\n5    27\n6    31\n7    22\nName: age, dtype: int64"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data[['name', 'age']]",
     "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>name</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": "    name  age\n0    Bob   34\n1    Zoe   19\n2    Amy   45\n3  Frank   56\n4   Kaka   23\n5    Udo   27\n6   Vera   31\n7  Betty   22"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "name_age = work_data[['name', 'age']]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "name_age.plot(x=work_data.name,figsize=(15, 5))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": "<matplotlib.axes._subplots.AxesSubplot at 0x13ba1390>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9KBGRJQiCgHP5dXjlg645ZgCwaFIi/mNFOg8puU8BCg/MfFgNXbsBO74vEjsOERGRU+v1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\nnZcr1fbWEQQCgJAAL6cfQcBDTOyYK40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tzugBUfTpHMr54stMz9zNVzmnbbLuisorfPjZ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\nH6dIQDJPzPTWAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x13ba1230>"
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "name_age",
     "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>name</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": "    name  age\n0    Bob   34\n1    Zoe   19\n2    Amy   45\n3  Frank   56\n4   Kaka   23\n5    Udo   27\n6   Vera   31\n7  Betty   22"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "p = name_age.plot(x=work_data.name)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YmHYVeYBhwd6MHuhLXGoeO+IzuWaUf7u2Z64S0wupqDZwzSh/dDaqj5aJyxSm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\n3krD/7pPAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x13bdbd30>"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import matplotlylib",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = plt.figure()\nnames = work_data.name\nx = range(len(names))\ny = work_data.age\nplt.plot(x, y)\nplt.xticks(x, names)\nplt.show()\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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R693Z4OfOH/5ykA/25aBoNNx983CLqS+m1GmR//jjjykqKuL+++/HyckJrVZL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\n",
       "text": "<matplotlib.figure.Figure at 0x12ff94d0>"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "py.plot_mpl(fig)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": "u'https://plot.ly/~snippsat/134'"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pay_data = Data([\n    Scatter(\n        x=work_data.name.tolist(),\n        y=work_data.pay.tolist()\n    )\n])\npy.iplot(pay_data, filename='Pay-bar')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~snippsat/168.embed\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": "<plotly.tools.PlotlyDisplay at 0x12ff9790>"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.age.tolist()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": "[34, 19, 45, 56, 23, 27, 31, 22]"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": " pd.set_option('display.mpl_style', 'default')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "name_age",
     "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>name</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": "    name  age\n0    Bob   34\n1    Zoe   19\n2    Amy   45\n3  Frank   56\n4   Kaka   23\n5    Udo   27\n6   Vera   31\n7  Betty   22"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "name_age.sort(['age'], ascending=False)",
     "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>name</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": "    name  age\n3  Frank   56\n2    Amy   45\n0    Bob   34\n6   Vera   31\n5    Udo   27\n4   Kaka   23\n7  Betty   22\n1    Zoe   19"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data[['name', 'pay']].sort('pay')",
     "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>name</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 23300</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 39000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 45000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 57000</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 69000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": "    name    pay\n1    Zoe  23300\n5    Udo  39000\n2    Amy  45000\n4   Kaka  45000\n6   Vera  45000\n7  Betty  45000\n3  Frank  57000\n0    Bob  69000"
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "m = work_data.groupby(['age', 'pay'])",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a = work_data['pay']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a.add(10000)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": "0    79000\n1    33300\n2    55000\n3    67000\n4    55000\n5    49000\n6    55000\n7    55000\nName: pay, dtype: int64"
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data['pay'].add(10000)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": "0    89000\n1    43300\n2    65000\n3    77000\n4    65000\n5    59000\n6    65000\n7    65000\nName: pay, dtype: int64"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# All get 10000 extra in salary\nwork_data['pay'] = work_data['pay'].add(10000)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "(work_data[work_data['pay'] > 50000])",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n      <td> 121</td>\n      <td> 13</td>\n      <td> 69000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n      <td>  93</td>\n      <td> 12</td>\n      <td> 57000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": "    name  age  weight  seniority    pay\n0    Bob   34     121         13  69000\n3  Frank   56      93         12  57000"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# Peploe that get paid more than 50000 and are under\nwork_data[ (work_data['pay'] > 50000) & (work_data['age'] <  30) ]",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td> 55</td>\n      <td> 7</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n      <td> 77</td>\n      <td> 5</td>\n      <td> 55000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": "    name  age  weight  seniority    pay\n4   Kaka   23      55          7  55000\n7  Betty   22      77          5  55000"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "(work_data[work_data['age'] < 30])",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n      <td> 63</td>\n      <td> 2</td>\n      <td> 33300</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td> 55</td>\n      <td> 7</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n      <td> 65</td>\n      <td> 3</td>\n      <td> 49000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n      <td> 77</td>\n      <td> 5</td>\n      <td> 55000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 143,
       "text": "    name  age  weight  seniority    pay\n1    Zoe   19      63          2  33300\n4   Kaka   23      55          7  55000\n5    Udo   27      65          3  49000\n7  Betty   22      77          5  55000"
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n      <td> 121</td>\n      <td> 13</td>\n      <td> 79000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n      <td>  63</td>\n      <td>  2</td>\n      <td> 33300</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n      <td>  59</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n      <td>  93</td>\n      <td> 12</td>\n      <td> 67000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td>  55</td>\n      <td>  7</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n      <td>  65</td>\n      <td>  3</td>\n      <td> 49000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n      <td>  71</td>\n      <td>  8</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n      <td>  77</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": "    name  age  weight  seniority    pay\n0    Bob   34     121         13  79000\n1    Zoe   19      63          2  33300\n2    Amy   45      59          5  55000\n3  Frank   56      93         12  67000\n4   Kaka   23      55          7  55000\n5    Udo   27      65          3  49000\n6   Vera   31      71          8  55000\n7  Betty   22      77          5  55000"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.head()",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n      <td> 121</td>\n      <td> 13</td>\n      <td> 79000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n      <td>  63</td>\n      <td>  2</td>\n      <td> 33300</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n      <td>  59</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n      <td>  93</td>\n      <td> 12</td>\n      <td> 67000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td>  55</td>\n      <td>  7</td>\n      <td> 55000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": "    name  age  weight  seniority    pay\n0    Bob   34     121         13  79000\n1    Zoe   19      63          2  33300\n2    Amy   45      59          5  55000\n3  Frank   56      93         12  67000\n4   Kaka   23      55          7  55000"
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data.sort('age', ascending=False, inplace=True)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n      <td> 121</td>\n      <td> 13</td>\n      <td> 79000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n      <td>  63</td>\n      <td>  2</td>\n      <td> 33300</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n      <td>  59</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n      <td>  93</td>\n      <td> 12</td>\n      <td> 67000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td>  55</td>\n      <td>  7</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n      <td>  65</td>\n      <td>  3</td>\n      <td> 49000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n      <td>  71</td>\n      <td>  8</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n      <td>  77</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": "    name  age  weight  seniority    pay\n0    Bob   34     121         13  79000\n1    Zoe   19      63          2  33300\n2    Amy   45      59          5  55000\n3  Frank   56      93         12  67000\n4   Kaka   23      55          7  55000\n5    Udo   27      65          3  49000\n6   Vera   31      71          8  55000\n7  Betty   22      77          5  55000"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "trace1 = Scatter(\n    x=work_data.name.tolist(),\n    y=work_data.age.tolist(),\n    name='Age'\n)\ntrace2 = Scatter(\n    x=work_data.name.tolist(),\n    y=work_data.pay.tolist(),\n    name='Pay',\n    yaxis='y2'\n)\ndata = Data([trace1, trace2])\nlayout = Layout(\n    title='Work data',\n    yaxis=YAxis(\n        title='Age'\n    ),\n    yaxis2=YAxis(\n        title='Pay',\n        titlefont=Font(\n            color='rgb(148, 103, 189)'\n        ),\n        tickfont=Font(\n            color='rgb(148, 103, 189)'\n        ),\n        overlaying='y',\n        side='right'\n    )\n)\nfig = Figure(data=data, layout=layout)\npy.iplot(fig, filename='pay_age')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~snippsat/192.embed\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": "<plotly.tools.PlotlyDisplay at 0x13754ff0>"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "work_data",
     "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>name</th>\n      <th>age</th>\n      <th>weight</th>\n      <th>seniority</th>\n      <th>pay</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>   Bob</td>\n      <td> 34</td>\n      <td> 121</td>\n      <td> 13</td>\n      <td> 79000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>   Zoe</td>\n      <td> 19</td>\n      <td>  63</td>\n      <td>  2</td>\n      <td> 33300</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>   Amy</td>\n      <td> 45</td>\n      <td>  59</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> Frank</td>\n      <td> 56</td>\n      <td>  93</td>\n      <td> 12</td>\n      <td> 67000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>  Kaka</td>\n      <td> 23</td>\n      <td>  55</td>\n      <td>  7</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>   Udo</td>\n      <td> 27</td>\n      <td>  65</td>\n      <td>  3</td>\n      <td> 49000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>  Vera</td>\n      <td> 31</td>\n      <td>  71</td>\n      <td>  8</td>\n      <td> 55000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td> Betty</td>\n      <td> 22</td>\n      <td>  77</td>\n      <td>  5</td>\n      <td> 55000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": "    name  age  weight  seniority    pay\n0    Bob   34     121         13  79000\n1    Zoe   19      63          2  33300\n2    Amy   45      59          5  55000\n3  Frank   56      93         12  67000\n4   Kaka   23      55          7  55000\n5    Udo   27      65          3  49000\n6   Vera   31      71          8  55000\n7  Betty   22      77          5  55000"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": "Dett er en test."
    }
   ],
   "metadata": {}
  }
 ]
}