ml-finance-python

notebook.ipynb

(60012B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Quandl: ADP National Employment Report \n",
     "\n",
     "In this notebook, we'll take a look at data set , available on [Quantopian](https://www.quantopian.com/data). This dataset spans from 2001 through the current day. It contains the value for employment levels as provided by ADP, the payroll service provider. We access this data via the API provided by [Quandl](https://www.quandl.com). [More details](https://www.quandl.com/data/ADP/EMPL_SEC-Employment-by-Sector) on this dataset can be found on Quandl's website.\n",
     "\n",
     "### Blaze\n",
     "Before we dig into the data, we want to tell you about how  you generally access Quantopian partner data sets. These datasets are available using the [Blaze](http://blaze.pydata.org) library. Blaze provides the Quantopian user with a convenient interface to access very large datasets.\n",
     "\n",
     "Some of these sets (though not this one) are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
     "\n",
     "To learn more about using Blaze and generally accessing Quantopian partner data, clone [this tutorial notebook](https://www.quantopian.com/clone_notebook?id=561827d21777f45c97000054).\n",
     "\n",
     "With preamble in place, let's get started:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# import the dataset\n",
     "from quantopian.interactive.data.quandl import adp_empl_sec \n",
     "# Since this data is public domain and provided by Quandl for free, there is no _free version of this\n",
     "# data set, as found in the premium sets. This import gets you the entirety of this data set.\n",
     "\n",
     "# import data operations\n",
     "from odo import odo\n",
     "# import other libraries we will use\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>asof_date</th>\n",
        "      <th>total_private</th>\n",
        "      <th>goods_producing</th>\n",
        "      <th>service_providing</th>\n",
        "      <th>timestamp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>2001-04-30</td>\n",
        "      <td>111573.000000</td>\n",
        "      <td>24275.294168</td>\n",
        "      <td>87297.757625</td>\n",
        "      <td>2001-04-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2001-05-31</td>\n",
        "      <td>111398.132821</td>\n",
        "      <td>24140.307995</td>\n",
        "      <td>87257.824826</td>\n",
        "      <td>2001-05-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2001-06-30</td>\n",
        "      <td>111167.552706</td>\n",
        "      <td>23992.631803</td>\n",
        "      <td>87174.920903</td>\n",
        "      <td>2001-06-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>2001-07-31</td>\n",
        "      <td>110964.904589</td>\n",
        "      <td>23854.365510</td>\n",
        "      <td>87110.539079</td>\n",
        "      <td>2001-07-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2001-08-31</td>\n",
        "      <td>110719.120440</td>\n",
        "      <td>23699.879744</td>\n",
        "      <td>87019.240696</td>\n",
        "      <td>2001-08-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>2001-09-30</td>\n",
        "      <td>110457.629117</td>\n",
        "      <td>23562.811692</td>\n",
        "      <td>86894.817424</td>\n",
        "      <td>2001-09-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>2001-10-31</td>\n",
        "      <td>110078.236801</td>\n",
        "      <td>23390.394579</td>\n",
        "      <td>86687.842222</td>\n",
        "      <td>2001-10-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>2001-11-30</td>\n",
        "      <td>109716.868489</td>\n",
        "      <td>23206.664911</td>\n",
        "      <td>86510.203579</td>\n",
        "      <td>2001-11-30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>2001-12-31</td>\n",
        "      <td>109494.189038</td>\n",
        "      <td>23070.799900</td>\n",
        "      <td>86423.389138</td>\n",
        "      <td>2001-12-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>2002-01-31</td>\n",
        "      <td>109424.930934</td>\n",
        "      <td>22951.233078</td>\n",
        "      <td>86473.697856</td>\n",
        "      <td>2002-01-31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>2002-02-28</td>\n",
        "      <td>109334.446257</td>\n",
        "      <td>22861.907749</td>\n",
        "      <td>86472.538508</td>\n",
        "      <td>2002-02-28</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "text/plain": [
        "    asof_date  total_private  goods_producing  service_providing  timestamp\n",
        "0  2001-04-30  111573.000000     24275.294168       87297.757625 2001-04-30\n",
        "1  2001-05-31  111398.132821     24140.307995       87257.824826 2001-05-31\n",
        "2  2001-06-30  111167.552706     23992.631803       87174.920903 2001-06-30\n",
        "3  2001-07-31  110964.904589     23854.365510       87110.539079 2001-07-31\n",
        "4  2001-08-31  110719.120440     23699.879744       87019.240696 2001-08-31\n",
        "5  2001-09-30  110457.629117     23562.811692       86894.817424 2001-09-30\n",
        "6  2001-10-31  110078.236801     23390.394579       86687.842222 2001-10-31\n",
        "7  2001-11-30  109716.868489     23206.664911       86510.203579 2001-11-30\n",
        "8  2001-12-31  109494.189038     23070.799900       86423.389138 2001-12-31\n",
        "9  2002-01-31  109424.930934     22951.233078       86473.697856 2002-01-31\n",
        "..."
       ]
      },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "adp_empl_sec.sort('asof_date')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The data goes all the way back to 2001 and is updated monthly.\n",
     "\n",
     "Blaze provides us with the first 10 rows of the data for display. Just to confirm, let's just count the number of rows in the Blaze expression:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "176"
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       "text/plain": [
        "176"
       ]
      },
      "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "adp_empl_sec.count()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Let's go plot it for fun. This data set is definitely small enough to just put right into a Pandas DataFrame"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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GKlIiIiIiIiJlpCIlIiIiIiJSRipSIiIiIiIiZaQiJSIiIiIiUkYqUiIiIiIiImWkIiUi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\nxNiPfiElPdfRkURErmjByn0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+2schQ4aQnl5+HyaHDx9m3759REVFXXLp4B//XpbLCi88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\nRgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fad2560fa10>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "adp_df = odo(adp_empl_sec, pd.DataFrame)\n",
     "\n",
     "adp_df.plot(x='asof_date', y='total_private')\n",
     "plt.xlabel(\"As Of Date (asof_date)\")\n",
     "plt.ylabel(\"Employment Levels\")\n",
     "plt.title(\"ADP Employment Level Data\")\n",
     "plt.legend().set_visible(False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
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     "collapsed": true
    },
    "outputs": [],
    "source": []
   }
  ],
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