alberta_life_oil_price.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from sklearn import datasets, linear_model\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_path = \"C:\\Users\\harriet.huang\\Documents\\Machine Learning\\Meetup\\oil_and_alberta\\data\"\n",
      "os.chdir(data_path)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oil_price = pd.read_csv(\"oil_price.csv\")\n",
      "\n",
      "print ('Summary Statistics \\n')\n",
      "print oil_price.describe()\n",
      "print oil_price.iloc[1,1]\n",
      "print oil_price.head()\n",
      "\n",
      "#=== set date data type ===#\n",
      "oil_price['DATE'] = pd.to_datetime(oil_price['DATE'])\n",
      "\n",
      "print type(pd.to_datetime(oil_price['DATE']).iloc[1])\n",
      "print type(oil_price['DATE'].iloc[1])\n",
      "print oil_price.head()\n",
      "\n",
      "#=== set date as data index ===#\n",
      "oil_price.index = pd.DatetimeIndex(oil_price['DATE'])\n",
      "\n",
      "#=== represent NULL/missing value ===#\n",
      "print ('Find the missing value: \\n')\n",
      "print oil_price[oil_price['VALUE']!=\".\"].describe()\n",
      "oil_price['VALUE'].replace(\".\", np.nan, regex=True)\n",
      "print oil_price[oil_price['VALUE']!=\".\"].describe()\n",
      "\n",
      "\n",
      "\n",
      "#=== convert price to number ===#\n",
      "oil_price.loc[oil_price['VALUE'] != \".\", 'VALUE'] = oil_price[oil_price['VALUE'] != \".\"]['VALUE'].astype(float)\n",
      "oil_price['VALUE'] = oil_price['VALUE'].convert_objects(convert_numeric=True)\n",
      "print oil_price['VALUE'].dtypes\n",
      "print oil_price['DATE'].dtypes\n",
      "\n",
      "#=== convert date to age variable ===#\n",
      "start = oil_price['DATE'].min(axis=1)\n",
      "oil_price['AGE'] = (oil_price['DATE'] - start)\n",
      "oil_price['AGE'] = (oil_price['AGE']/np.timedelta64(1, 'D')) # extract the integer value of days\n",
      "\n",
      "#=== take non-missing value ===#\n",
      "oil_price = oil_price[np.isfinite(oil_price['VALUE'])]\n",
      "oil_price.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Summary Statistics \n",
        "\n",
        "              DATE VALUE\n",
        "count          521   521\n",
        "unique         521   466\n",
        "top     2014-11-04     .\n",
        "freq             1    17\n",
        "93.66\n",
        "         DATE  VALUE\n",
        "0  2014-01-02  95.14\n",
        "1  2014-01-03  93.66\n",
        "2  2014-01-06  93.12\n",
        "3  2014-01-07  93.31\n",
        "4  2014-01-08  91.90\n",
        "<class 'pandas.tslib.Timestamp'>\n",
        "<class 'pandas.tslib.Timestamp'>\n",
        "        DATE  VALUE\n",
        "0 2014-01-02  95.14\n",
        "1 2014-01-03  93.66\n",
        "2 2014-01-06  93.12\n",
        "3 2014-01-07  93.31\n",
        "4 2014-01-08  91.90\n",
        "Find the missing value: \n",
        "\n",
        "                       DATE  VALUE\n",
        "count                   504    504\n",
        "unique                  504    465\n",
        "first   2014-01-02 00:00:00    NaN\n",
        "last    2015-12-31 00:00:00    NaN\n",
        "top     2015-09-24 00:00:00  60.01\n",
        "freq                      1      4\n",
        "                       DATE  VALUE\n",
        "count                   504    504\n",
        "unique                  504    465\n",
        "first   2014-01-02 00:00:00    NaN\n",
        "last    2015-12-31 00:00:00    NaN\n",
        "top     2015-09-24 00:00:00  60.01\n",
        "freq                      1      4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "float64\n",
        "datetime64[ns]\n"
       ]
      },
      {
       "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>DATE</th>\n",
        "      <th>VALUE</th>\n",
        "      <th>AGE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-01-02</th>\n",
        "      <td>2014-01-02</td>\n",
        "      <td> 95.14</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-03</th>\n",
        "      <td>2014-01-03</td>\n",
        "      <td> 93.66</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-06</th>\n",
        "      <td>2014-01-06</td>\n",
        "      <td> 93.12</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-07</th>\n",
        "      <td>2014-01-07</td>\n",
        "      <td> 93.31</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-01-08</th>\n",
        "      <td>2014-01-08</td>\n",
        "      <td> 91.90</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "                 DATE  VALUE  AGE\n",
        "2014-01-02 2014-01-02  95.14    0\n",
        "2014-01-03 2014-01-03  93.66    1\n",
        "2014-01-06 2014-01-06  93.12    4\n",
        "2014-01-07 2014-01-07  93.31    5\n",
        "2014-01-08 2014-01-08  91.90    6"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oil_price.plot()\n",
      "plt.suptitle('Crude Oil Prices from 2014-01-01 to 2016-01-01')\n",
      "plt.ylabel('Price')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "<matplotlib.text.Text at 0xe2975c0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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twLEiMhc4KliPHKXfDnKVqOnce2+YOzdcLeURhTqNgkZwndkkLHfQ4UnKfgSO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\nFYkvhgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe184b38>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fit a straighline trend"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lm = linear_model.LinearRegression()\n",
      "lm.fit(oil_price['AGE'][:,np.newaxis], oil_price['VALUE'])\n",
      "\n",
      "plt.plot(oil_price['VALUE'])\n",
      "plt.plot(lm.predict(oil_price['AGE'][:,np.newaxis]))\n",
      "plt.suptitle('Oil Price with a Straight-Line Trend')\n",
      "plt.xlabel('Days since 2014-01-02')\n",
      "plt.ylabel('Oil Price')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "<matplotlib.text.Text at 0x19d450f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Xqc6kjpNYvGsxbSe0pVyBcjQq1ShF18qXz6ZHNSbYWY7CJKhRqUa83eJtOk3u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\nmiuXW962TTV3bvc7AtUOHVR37FC9eNF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       "text": [
        "<matplotlib.figure.Figure at 0x195bfda0>"
       ]
      }
     ],
     "prompt_number": 114
    }
   ],
   "metadata": {}
  }
 ]
}