{
 "metadata": {
  "name": "",
  "signature": "sha256:7a2e72e28d410a6c0411432c917e21cbf8ddb67b8bb90a602542188d5c877156"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Common data formats:\n",
      "\n",
      "* JSON\n",
      "* csv\n",
      "* matlab\n",
      "* excel spreadsheets\n",
      "* binary formats\n",
      "* XML\n",
      "* HDF\n",
      "* SQL"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "http://stats.stackexchange.com/questions/27237/what-are-the-most-useful-sources-of-economics-data\n",
      "\n",
      "Will use here:\n",
      "\n",
      "https://research.stlouisfed.org/fred2/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#JSON (Javascript object notation)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://www.json.org/\n",
      "\n",
      "* easy for humans to read and write\n",
      "* easy for machines to parse and generate\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Using FRED requires a personal key\n",
      "# To avoid exposing my key, I have it in a separate file\n",
      "from keys import fred_key"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_id = 'GNPCA'\n",
      "request_url = 'http://api.stlouisfed.org/fred/series?series_id=' + series_id + '&api_key=' + fred_key + '&file_type=json'\n",
      "\n",
      "import urllib2\n",
      "f = urllib2.urlopen(request_url)\n",
      "data = f.read()\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "'{\"realtime_start\":\"2016-04-07\",\"realtime_end\":\"2016-04-07\",\"seriess\":[{\"id\":\"GNPCA\",\"realtime_start\":\"2016-04-07\",\"realtime_end\":\"2016-04-07\",\"title\":\"Real Gross National Product\",\"observation_start\":\"1929-01-01\",\"observation_end\":\"2015-01-01\",\"frequency\":\"Annual\",\"frequency_short\":\"A\",\"units\":\"Billions of Chained 2009 Dollars\",\"units_short\":\"Bil. of Chn. 2009 $\",\"seasonal_adjustment\":\"Not Seasonally Adjusted\",\"seasonal_adjustment_short\":\"NSA\",\"last_updated\":\"2016-03-25 07:56:03-05\",\"popularity\":35,\"notes\":\"BEA Account Code: A001RX1\"}]}'"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "json_data = json.loads(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(json_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "[u'seriess', u'realtime_start', u'realtime_end']"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data[u'realtime_start'], json_data[u'realtime_end']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "(u'2016-04-07', u'2016-04-07')"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data[u'seriess']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "[{u'frequency': u'Annual',\n",
        "  u'frequency_short': u'A',\n",
        "  u'id': u'GNPCA',\n",
        "  u'last_updated': u'2016-03-25 07:56:03-05',\n",
        "  u'notes': u'BEA Account Code: A001RX1',\n",
        "  u'observation_end': u'2015-01-01',\n",
        "  u'observation_start': u'1929-01-01',\n",
        "  u'popularity': 35,\n",
        "  u'realtime_end': u'2016-04-07',\n",
        "  u'realtime_start': u'2016-04-07',\n",
        "  u'seasonal_adjustment': u'Not Seasonally Adjusted',\n",
        "  u'seasonal_adjustment_short': u'NSA',\n",
        "  u'title': u'Real Gross National Product',\n",
        "  u'units': u'Billions of Chained 2009 Dollars',\n",
        "  u'units_short': u'Bil. of Chn. 2009 $'}]"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other series:\n",
      "https://research.stlouisfed.org/fred2/tags/series \n",
      "    \n",
      "\n",
      "API docs:    \n",
      "https://research.stlouisfed.org/docs/api/fred/\n",
      "\n",
      "Now let's get some data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "request_url = 'http://api.stlouisfed.org/fred/series/observations?series_id=' + series_id + '&api_key=' + fred_key + '&file_type=json'\n",
      "\n",
      "import urllib2\n",
      "f = urllib2.urlopen(request_url)\n",
      "data = f.read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data = json.loads(data)\n",
      "json_data.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "[u'count',\n",
        " u'order_by',\n",
        " u'observation_start',\n",
        " u'file_type',\n",
        " u'observation_end',\n",
        " u'realtime_end',\n",
        " u'sort_order',\n",
        " u'limit',\n",
        " u'observations',\n",
        " u'offset',\n",
        " u'units',\n",
        " u'output_type',\n",
        " u'realtime_start']"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data[u'count'],json_data[u'order_by'], json_data[u'observation_start'], json_data[ u'file_type'], json_data[ u'observation_end']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "(87, u'observation_date', u'1776-07-04', u'json', u'9999-12-31')"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data[u'realtime_start'], json_data[u'realtime_end'], json_data[u'sort_order'], json_data[u'limit'], json_data[u'offset']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "(u'2016-04-07', u'2016-04-07', u'asc', 100000, 0)"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_data[u'output_type'],  json_data[u'units']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "(1, u'lin')"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values = []\n",
      "\n",
      "for o in json_data['observations']:\n",
      "    values.append(float(o['value']))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Should use something like the pandas module to ensure the data is consistent (e.g. the time series is equally spaced, ordered and there are no mising time values)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f72f0036ad0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DzGyxmS00s+5p5Z3NbF587La08kZmNiGWzzKz/Wv6DYpI9nGH88+HE0+Ec85JOhops6Ma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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f72f0d173d0>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('../shared/gnpca.txt', 'w')\n",
      "\n",
      "for v in values:\n",
      "    f.write(str(v) + '\\n')\n",
      "    \n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other sets:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_id = 'UNRATE'\n",
      "\n",
      "request_url = 'http://api.stlouisfed.org/fred/series/observations?series_id=' + series_id + '&api_key=' + fred_key + '&file_type=json'\n",
      "\n",
      "f = urllib2.urlopen(request_url)\n",
      "data = f.read()\n",
      "json_data = json.loads(data)\n",
      "\n",
      "values = []\n",
      "for o in json_data['observations']:\n",
      "    values.append(float(o['value']))\n",
      "    \n",
      "plot(values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f72e9dabc10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jtRa5jps/+qjxhGPlGF7kCQ8eDBxwANcF+cMfeGZu2BOK0qmuNrKBrOCkDno2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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f72f0bfd190>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('../shared/unrate.txt', 'w')\n",
      "\n",
      "for v in values:\n",
      "    f.write(str(v) + '\\n')\n",
      "    \n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series_id = 'GS10'\n",
      "\n",
      "request_url = 'http://api.stlouisfed.org/fred/series/observations?series_id=' + series_id + '&api_key=' + fred_key + '&file_type=json'\n",
      "\n",
      "f = urllib2.urlopen(request_url)\n",
      "data = f.read()\n",
      "json_data = json.loads(data)\n",
      "\n",
      "values = []\n",
      "for o in json_data['observations']:\n",
      "    values.append(float(o['value']))\n",
      "    \n",
      "plot(values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f72e9ce65d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9+2sMfvDgwOcNHKjhN3/FvXr2VCfAe5bxtGlaWnn27NC/B+I+Iu58ZIxZDuDY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\nIyjqhBCSRlDUCSEkjfh/MCgyUgpCxlsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f72e9dca5d0>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open('../shared/gs10.txt', 'w')\n",
      "\n",
      "for v in values:\n",
      "    f.write(str(v) + '\\n')\n",
      "    \n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#CSV (Comma Separated Values)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "request_url = 'http://ichart.finance.yahoo.com/table.csv?s=^GSPC&ignore=.csv'\n",
      "\n",
      "f = urllib2.urlopen(request_url)\n",
      "data = f.read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "parsed_csv = csv.reader(data.split('\\n'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(parsed_csv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "_csv.reader"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parsed_csv.next()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close']"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parsed_csv.next()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "['2016-04-06',\n",
        " '2045.560059',\n",
        " '2067.330078',\n",
        " '2043.089966',\n",
        " '2066.659912',\n",
        " '3750800000',\n",
        " '2066.659912']"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parsed_csv.next()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "['2016-04-05',\n",
        " '2062.50',\n",
        " '2062.50',\n",
        " '2042.560059',\n",
        " '2045.170044',\n",
        " '4154920000',\n",
        " '2045.170044']"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "highs = []\n",
      "for row in parsed_csv:\n",
      "    if len(row) > 0:\n",
      "        highs.append(float(row[2]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(highs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f72f0a464d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F2WcfeOGF5s/ZbTdYuLA08YgUm1oQIq00d27oYho+PFu2aFEocw8LAC5aFFoS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\nQkREEilBiIhIIiUIERFJ9P8BLCkYnHhcFXwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f72f0dca590>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "https://finance.yahoo.com/echarts?s=^GSPC+Interactive#{%22range%22:%22max%22,%22allowChartStacking%22:true}\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(highs[::-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f72f0bf08d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hwsz2E4mbxkGINKG+Hm691T9F1NR3jH33TV70N270+zqXHA0NPoE4p+QgojsI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\nBCEiIpGUIEREJJIShIiIRFKCEBGRSP8fA8Aqu+WzSTcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f72f0a5e750>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#MATLAB matrix format"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A long list of datasets:\n",
      "http://www.models.life.ku.dk/datasets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib\n",
      "urllib.urlretrieve('http://www.eigenvector.com/data/Corn/corn.mat', 'corn.mat')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "('corn.mat', <httplib.HTTPMessage instance at 0x7f72f30060e0>)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://www.eigenvector.com/data/Corn/index.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.io import loadmat\n",
      "corn = loadmat('corn.mat')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(corn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "dict"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corn.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "['mp6nbs',\n",
        " 'information',\n",
        " 'propvals',\n",
        " '__globals__',\n",
        " 'm5nbs',\n",
        " 'mp6spec',\n",
        " 'm5spec',\n",
        " 'mp5spec',\n",
        " '__header__',\n",
        " '__version__',\n",
        " 'mp5nbs']"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corn['mp6spec'][0][0][7]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "array([[-0.0227014, -0.0228025, -0.0228795, ...,  0.675079 ,  0.674679 ,\n",
        "         0.674056 ],\n",
        "       [-0.0219211, -0.0220554, -0.0221607, ...,  0.682942 ,  0.682648 ,\n",
        "         0.682164 ],\n",
        "       [-0.0208596, -0.0209931, -0.0211072, ...,  0.652276 ,  0.651984 ,\n",
        "         0.651517 ],\n",
        "       ..., \n",
        "       [-0.0178645, -0.0179813, -0.0180715, ...,  0.695484 ,  0.695075 ,\n",
        "         0.694381 ],\n",
        "       [-0.0067957, -0.0068881, -0.0069559, ...,  0.690173 ,  0.689855 ,\n",
        "         0.689125 ],\n",
        "       [-0.0152611, -0.0153799, -0.0154608, ...,  0.703188 ,  0.70277  ,\n",
        "         0.702071 ]])"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(corn['mp6spec'][0][0][7][0,:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f72f08f7190>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T/aef1n3e3//uy0937RrfvePUpo2P6rn6at9YvjaVlb5xzSWXNF1s+SJqAmgLzE97vSB1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\nPr3epAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f72f09e6e90>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Excel sheets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Various ways to open excel sheets in python:\n",
      "    \n",
      "http://stackoverflow.com/questions/3239207/how-can-i-open-an-excel-file-in-python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Binary formats"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data can be encoded \"raw\" in a file. i.e. the file just contains bytes that are copied directly into memory. The user must know the format and data type of the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib = [1,1,2,3,5,8,13]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import struct\n",
      "enc = struct.pack('i', 10)\n",
      "enc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "'\\n\\x00\\x00\\x00'"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hex(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "'0xa'"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "chr(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "'\\n'"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(enc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ord('\\n'), ord('a')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "(10, 97)"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'i'*len(fib)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "'iiiiiii'"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes = struct.pack('i'*len(fib), *fib)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "'\\x01\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00\\x05\\x00\\x00\\x00\\x08\\x00\\x00\\x00\\r\\x00\\x00\\x00'"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib = struct.unpack('iiiiiii', bytes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "(1, 1, 2, 3, 5, 8, 13)"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib_f = struct.unpack('fffffff', bytes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib_f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "(1.401298464324817e-45,\n",
        " 1.401298464324817e-45,\n",
        " 2.802596928649634e-45,\n",
        " 4.203895392974451e-45,\n",
        " 7.006492321624085e-45,\n",
        " 1.1210387714598537e-44,\n",
        " 1.8216880036222622e-44)"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes = struct.pack('f'*len(fib), *fib)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "'\\x00\\x00\\x80?\\x00\\x00\\x80?\\x00\\x00\\x00@\\x00\\x00@@\\x00\\x00\\xa0@\\x00\\x00\\x00A\\x00\\x00PA'"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib_f = struct.unpack('fffffff', bytes)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fib_f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "(1.0, 1.0, 2.0, 3.0, 5.0, 8.0, 13.0)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#XML"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Earthquake XML.ipynb](Earthquake XML.ipynb)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#HDF (Hierarchical Data Format)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "https://www.hdfgroup.org/\n",
      "\n",
      "Common format for \"big data\". Specifically designed for large multi-dimensional data sets.\n",
      "\n",
      "https://www.hdfgroup.org/tools/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#SQL (Structured Query Language)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Several options depending on need:\n",
      "\n",
      "https://docs.python.org/2/library/sqlite3.html\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---------------------------------"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By: Andr\u00e9s Cabrera mantaraya36@gmail.com\n",
      "\n",
      "For Course MAT 240F at UCSB\n",
      "\n",
      "This ipython notebook is licensed under the CC-BY-NC-SA license: http://creativecommons.org/licenses/by-nc-sa/4.0/\n",
      "\n",
      "![http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}