pandas_time_series_analysis.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Analysing time-series data with Python and Pandas"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "In this IPython Notebook we will use Pandas to analyse Apple (AAPL), Google(GOOGL) , and Microsoft(MSFT) stock prices.\n",
      "First, the necessary libraries need to be imported"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "mpl.rc('figure', figsize=(8, 7))\n",
      "import pandas.io.data\n",
      "from IPython.display import Image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Now, we will set a date range for the stock prices, and that range will be analysed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = datetime.datetime(2008,1,1)\n",
      "end = datetime.datetime(2014,1,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Next, we obtain the stock price data for each respective company."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Apple stock data\n",
      "aapl = pd.io.data.get_data_yahoo('AAPL', start=start, end=end)\n",
      "\n",
      "#Google stock data\n",
      "googl = pd.io.data.get_data_yahoo('GOOGL', start=start, end=end)\n",
      "\n",
      "#Microsft stock data\n",
      "msft = pd.io.data.get_data_yahoo('MSFT', start=start, end=end)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "To look at the first few rows for each data set, I'll use the head() method."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl.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>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008-01-02</th>\n",
        "      <td> 199.27</td>\n",
        "      <td> 200.26</td>\n",
        "      <td> 192.55</td>\n",
        "      <td> 194.84</td>\n",
        "      <td> 38542100</td>\n",
        "      <td> 186.23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-03</th>\n",
        "      <td> 195.41</td>\n",
        "      <td> 197.39</td>\n",
        "      <td> 192.69</td>\n",
        "      <td> 194.93</td>\n",
        "      <td> 30073800</td>\n",
        "      <td> 186.32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-04</th>\n",
        "      <td> 191.45</td>\n",
        "      <td> 193.00</td>\n",
        "      <td> 178.89</td>\n",
        "      <td> 180.05</td>\n",
        "      <td> 51994000</td>\n",
        "      <td> 172.09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-07</th>\n",
        "      <td> 181.25</td>\n",
        "      <td> 183.60</td>\n",
        "      <td> 170.23</td>\n",
        "      <td> 177.64</td>\n",
        "      <td> 74006900</td>\n",
        "      <td> 169.79</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-08</th>\n",
        "      <td> 180.14</td>\n",
        "      <td> 182.46</td>\n",
        "      <td> 170.80</td>\n",
        "      <td> 171.25</td>\n",
        "      <td> 54422000</td>\n",
        "      <td> 163.68</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "              Open    High     Low   Close    Volume  Adj Close\n",
        "Date                                                           \n",
        "2008-01-02  199.27  200.26  192.55  194.84  38542100     186.23\n",
        "2008-01-03  195.41  197.39  192.69  194.93  30073800     186.32\n",
        "2008-01-04  191.45  193.00  178.89  180.05  51994000     172.09\n",
        "2008-01-07  181.25  183.60  170.23  177.64  74006900     169.79\n",
        "2008-01-08  180.14  182.46  170.80  171.25  54422000     163.68\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "msft.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>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008-01-02</th>\n",
        "      <td> 35.79</td>\n",
        "      <td> 35.96</td>\n",
        "      <td> 35.00</td>\n",
        "      <td> 35.22</td>\n",
        "      <td> 63004200</td>\n",
        "      <td> 30.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-03</th>\n",
        "      <td> 35.22</td>\n",
        "      <td> 35.65</td>\n",
        "      <td> 34.86</td>\n",
        "      <td> 35.37</td>\n",
        "      <td> 49599600</td>\n",
        "      <td> 30.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-04</th>\n",
        "      <td> 35.19</td>\n",
        "      <td> 35.20</td>\n",
        "      <td> 34.09</td>\n",
        "      <td> 34.38</td>\n",
        "      <td> 72090800</td>\n",
        "      <td> 29.28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-07</th>\n",
        "      <td> 34.55</td>\n",
        "      <td> 34.80</td>\n",
        "      <td> 34.25</td>\n",
        "      <td> 34.61</td>\n",
        "      <td> 80164300</td>\n",
        "      <td> 29.48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-08</th>\n",
        "      <td> 34.71</td>\n",
        "      <td> 34.71</td>\n",
        "      <td> 33.40</td>\n",
        "      <td> 33.45</td>\n",
        "      <td> 79148300</td>\n",
        "      <td> 28.49</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "             Open   High    Low  Close    Volume  Adj Close\n",
        "Date                                                       \n",
        "2008-01-02  35.79  35.96  35.00  35.22  63004200      30.00\n",
        "2008-01-03  35.22  35.65  34.86  35.37  49599600      30.13\n",
        "2008-01-04  35.19  35.20  34.09  34.38  72090800      29.28\n",
        "2008-01-07  34.55  34.80  34.25  34.61  80164300      29.48\n",
        "2008-01-08  34.71  34.71  33.40  33.45  79148300      28.49\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "googl.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>Open</th>\n",
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
        "      <th>Volume</th>\n",
        "      <th>Adj Close</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008-01-02</th>\n",
        "      <td> 692.87</td>\n",
        "      <td> 697.37</td>\n",
        "      <td> 677.73</td>\n",
        "      <td> 685.19</td>\n",
        "      <td>  8605100</td>\n",
        "      <td> 342.94</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-03</th>\n",
        "      <td> 685.26</td>\n",
        "      <td> 686.85</td>\n",
        "      <td> 676.52</td>\n",
        "      <td> 685.33</td>\n",
        "      <td>  6498400</td>\n",
        "      <td> 343.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-04</th>\n",
        "      <td> 679.69</td>\n",
        "      <td> 680.96</td>\n",
        "      <td> 655.00</td>\n",
        "      <td> 657.00</td>\n",
        "      <td> 10708800</td>\n",
        "      <td> 328.83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-07</th>\n",
        "      <td> 653.94</td>\n",
        "      <td> 662.28</td>\n",
        "      <td> 637.35</td>\n",
        "      <td> 649.25</td>\n",
        "      <td> 12793900</td>\n",
        "      <td> 324.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-01-08</th>\n",
        "      <td> 653.00</td>\n",
        "      <td> 659.96</td>\n",
        "      <td> 631.00</td>\n",
        "      <td> 631.68</td>\n",
        "      <td> 10667500</td>\n",
        "      <td> 316.16</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "              Open    High     Low   Close    Volume  Adj Close\n",
        "Date                                                           \n",
        "2008-01-02  692.87  697.37  677.73  685.19   8605100     342.94\n",
        "2008-01-03  685.26  686.85  676.52  685.33   6498400     343.01\n",
        "2008-01-04  679.69  680.96  655.00  657.00  10708800     328.83\n",
        "2008-01-07  653.94  662.28  637.35  649.25  12793900     324.95\n",
        "2008-01-08  653.00  659.96  631.00  631.68  10667500     316.16\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "I'm interested in the closing price for each day, so I shall isolate this data for each dataset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "googl_close = googl['Close']\n",
      "aapl_close = aapl['Close']\n",
      "msft_close = msft['Close']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "To differentiate between the datasets when they are plotted, I shall give each dataset a label on the graph."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aapl_close.plot(label='AAPL')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10752f7d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "msft_close.plot(label='MSFT')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10752f7d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " googl_close.plot(label='GOOGL')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10752f7d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.legend.Legend at 0x10752ff50>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "And this last part is to show the graphs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "And the below Image is the output you will get."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image('http://i.imgur.com/MgNeBqa.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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x4sXq2rWr02EgF+THXuTGXuGWmwv+fzQ1VTqnaAqJ+vWlqKgCvWTDhg2VkJCg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\nZHzu52vXriEiIgK9e/e2cgkJsR5KQIhNKi0tRWVlpem5IAhwc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       "text": [
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 ]
}