pybokeh/rmagic

## rmagic
{
 "metadata": {
  "name": "",
  "signature": "sha256:b72bb58631224f7343dd661386ef15970cba7dd7ff25e3fb6f44ad4abb241b17"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Obtain stock ticker data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas.io.data as web\n",
      "from datetime import datetime\n",
      "import pandas as pd\n",
      "\n",
      "ticker = 'HMC'\n",
      "start = datetime(2014, 1, 1)\n",
      "end = datetime(2014, 12, 19)\n",
      "\n",
      "df=web.DataReader(ticker, 'yahoo', start, end)\n",
      "df.reset_index(level=0, inplace=True)  # R's data frame doesn't like it when the index is a date so convert date to column"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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>Date</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",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2014-01-02</td>\n",
        "      <td> 41.10</td>\n",
        "      <td> 41.13</td>\n",
        "      <td> 40.60</td>\n",
        "      <td> 40.69</td>\n",
        "      <td> 179400</td>\n",
        "      <td> 40.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2014-01-03</td>\n",
        "      <td> 40.68</td>\n",
        "      <td> 40.82</td>\n",
        "      <td> 40.50</td>\n",
        "      <td> 40.69</td>\n",
        "      <td> 191900</td>\n",
        "      <td> 40.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2014-01-06</td>\n",
        "      <td> 41.08</td>\n",
        "      <td> 41.38</td>\n",
        "      <td> 40.66</td>\n",
        "      <td> 40.80</td>\n",
        "      <td> 277000</td>\n",
        "      <td> 40.80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2014-01-07</td>\n",
        "      <td> 40.67</td>\n",
        "      <td> 40.81</td>\n",
        "      <td> 40.56</td>\n",
        "      <td> 40.71</td>\n",
        "      <td> 207000</td>\n",
        "      <td> 40.71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2014-01-08</td>\n",
        "      <td> 40.72</td>\n",
        "      <td> 40.72</td>\n",
        "      <td> 40.50</td>\n",
        "      <td> 40.65</td>\n",
        "      <td> 258100</td>\n",
        "      <td> 40.65</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "        Date   Open   High    Low  Close  Volume  Adj Close\n",
        "0 2014-01-02  41.10  41.13  40.60  40.69  179400      40.69\n",
        "1 2014-01-03  40.68  40.82  40.50  40.69  191900      40.69\n",
        "2 2014-01-06  41.08  41.38  40.66  40.80  277000      40.80\n",
        "3 2014-01-07  40.67  40.81  40.56  40.71  207000      40.71\n",
        "4 2014-01-08  40.72  40.72  40.50  40.65  258100      40.65"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Load the rpy2 extension so that we can use RMagic"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Pass the pandas dataframe to R as an input"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R -i df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -w 480 -h 300 -u px # instead of px, you can also choose 'in', 'cm', or 'mm'\n",
      "df = as.data.frame(df)  # ensure dataframe that was passed in from Python will be R's dataframe type\n",
      "library(ggplot2)\n",
      "p = ggplot(df, aes(df$Date, df$Adj.Close))\n",
      "p = p + \n",
      "    geom_line() +\n",
      "    ggtitle(\"HMC Closing Price\") +\n",
      "    xlab(\"Date\") +\n",
      "    ylab(\"Price\")\n",
      "print(p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h7y5OG9U62ueXLwywpgIB+Jdy85rhra54rr7u7/GLwfhFhDPMlIMA5z6MTsHg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\nXtzkUwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "library(forecast)\n",
      "HoltWinters(df$Adj.Close)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Error in decompose(ts(x[1L:wind], start = start(x), frequency = f), seasonal) : \n",
        "  time series has no or less than 2 periods\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "hwm = HoltWinters(df$Adj.Close, gamma=FALSE)\n",
      "hwf = forecast.HoltWinters(hwm, h=20)\n",
      "summary(hwf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(summary(hwf))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<DataFrame - Python:0x08E26D50 / R:0x095AD9A8>\n",
        "[FloatVe..., FloatVe..., FloatVe..., FloatVe..., FloatVe...]\n",
        "  Point Forecast: <class 'rpy2.robjects.vectors.FloatVector'>\n",
        "  <FloatVector - Python:0x08E26A08 / R:0x082BF9E8>\n",
        "[30.309197, 30.288394, 30.267591, ..., 29.955544, 29.934741, 29.913938]\n",
        "  Lo 80: <class 'rpy2.robjects.vectors.FloatVector'>\n",
        "  <FloatVector - Python:0x08E263A0 / R:0x0875ADD8>\n",
        "[29.793228, 29.557593, 29.371186, ..., 27.709635, 27.623839, 27.539461]\n",
        "  Hi 80: <class 'rpy2.robjects.vectors.FloatVector'>\n",
        "  <FloatVector - Python:0x08E26800 / R:0x08ED1B38>\n",
        "[30.825166, 31.019194, 31.163995, ..., 32.201454, 32.245643, 32.288415]\n",
        "  Lo 95: <class 'rpy2.robjects.vectors.FloatVector'>\n",
        "  <FloatVector - Python:0x08E26300 / R:0x0778FE98>\n",
        "[29.520090, 29.170731, 28.896658, ..., 26.520722, 26.400521, 26.282489]\n",
        "  Hi 95: <class 'rpy2.robjects.vectors.FloatVector'>\n",
        "  <FloatVector - Python:0x05D5E850 / R:0x07D84198>\n",
        "[31.098303, 31.406057, 31.638523, ..., 33.390366, 33.468961, 33.545387]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%R print(head(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<DataFrame - Python:0x08E21198 / R:0x0869EED8>\n",
        "[POSIXct, Array, Array, ..., Array, Array, Array]\n",
        "  Date: <class 'rpy2.robjects.vectors.POSIXct'>\n",
        "  <POSIXct - Python:0x08E1A918 / R:0x09FBCDF8>\n",
        "[1388638800.000000, 1388725200.000000, 1388984400.000000, 1389070800.000000, 1389157200.000000, 1389243600.000000]\n",
        "  Open: <class 'rpy2.robjects.vectors.Array'>\n",
        "  <Array - Python:0x08E1A210 / R:0x09FBCDA0>\n",
        "[41.100000, 40.680000, 41.080000, 40.670000, 40.720000, 40.700000]\n",
        "  High: <class 'rpy2.robjects.vectors.Array'>\n",
        "  <Array - Python:0x08CCEC60 / R:0x09FBCD48>\n",
        "[41.130000, 40.820000, 41.380000, 40.810000, 40.720000, 40.750000]\n",
        "  ...\n",
        "  Date: <class 'rpy2.robjects.vectors.Array'>\n",
        "  <Array - Python:0x067E1210 / R:0x09FBCC98>\n",
        "[40.690000, 40.690000, 40.800000, 40.710000, 40.650000, 40.730000]\n",
        "  Open: <class 'rpy2.robjects.vectors.Array'>\n",
        "  <Array - Python:0x08CCF2B0 / R:0x0847A1D0>\n",
        "[  179400,   191900,   277000,   207000,   258100,   170800]\n",
        "  High: <class 'rpy2.robjects.vectors.Array'>\n",
        "  <Array - Python:0x08E125F8 / R:0x09FBCC40>\n",
        "[40.690000, 40.690000, 40.800000, 40.710000, 40.650000, 40.730000]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "plot.forecast(hwf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ddzt27AgLC/P396eexqTGidP7//HHH6pLRFpZWY0fP/6RRx559913hUKhUCjU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\no0z4heQmGkkAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}