{
 "metadata": {
  "name": "",
  "signature": "sha256:96e35271160898d64aa86dee2d180782faca482fa6789d8b0c6bdabad643d7fa"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "CSV Example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python provides many useful functions to allow us to open data stored in files. For example, the csv module is one useful module for dealing with many forms of data found in the wild. There are many others."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "----------------"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import csv\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's get a list of available files\n",
      "%ls -1 *.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "sunspot_num.csv\r\n",
        "WDI_Data_US_China.csv\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Sunspot Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Open up the sunspot data and read out the header\n",
      "sd = open(\"sunspot_num.csv\", \"rb\")\n",
      "csv_reader = csv.DictReader(sd)\n",
      "csv_reader.fieldnames"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "['YEAR', 'MON', 'SSN', 'DEV']"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Looks like a simple set of data of monthly counts\n",
      "# of sunspot activity. Read the rest of the data and \n",
      "# plot the sunspot data\n",
      "year = []\n",
      "spot = []\n",
      "for row in csv_reader:\n",
      "    year.append(float(row[\"YEAR\"]) + (float(row[\"MON\"]) - 1)/12)\n",
      "    spot.append(float(row[\"SSN\"]))\n",
      "    \n",
      "print \"Found %d points\" % len(year)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Found 3184 points\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot the data\n",
      "plt.figure(figsize=(8,4))\n",
      "plt.plot(year, spot)\n",
      "plt.title(\"Sunspot Count\")\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAEKCAYAAADUwrbCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQRAEQRAEQRAEQRCE9vP/SEbMPR1bdW4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x451d690>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Savings Rates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Open up the World Bank Data\n",
      "# I had the full data set for the demo, but for the repository, I only \n",
      "# included a scrubbed subset of the data I was using. If you'd like to\n",
      "# use the full data set, you can find it at the link I provide in the\n",
      "# \"Resources\" section.\n",
      "#wd = open(\"WDI_Data.csv\", \"rb\")\n",
      "wd = open(\"WDI_Data_US_China.csv\", \"rb\")\n",
      "csv_reader = csv.DictReader(wd)\n",
      "csv_reader.fieldnames"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "['Country Name',\n",
        " 'Country Code',\n",
        " 'Indicator Name',\n",
        " 'Indicator Code',\n",
        " '1960',\n",
        " '1961',\n",
        " '1962',\n",
        " '1963',\n",
        " '1964',\n",
        " '1965',\n",
        " '1966',\n",
        " '1967',\n",
        " '1968',\n",
        " '1969',\n",
        " '1970',\n",
        " '1971',\n",
        " '1972',\n",
        " '1973',\n",
        " '1974',\n",
        " '1975',\n",
        " '1976',\n",
        " '1977',\n",
        " '1978',\n",
        " '1979',\n",
        " '1980',\n",
        " '1981',\n",
        " '1982',\n",
        " '1983',\n",
        " '1984',\n",
        " '1985',\n",
        " '1986',\n",
        " '1987',\n",
        " '1988',\n",
        " '1989',\n",
        " '1990',\n",
        " '1991',\n",
        " '1992',\n",
        " '1993',\n",
        " '1994',\n",
        " '1995',\n",
        " '1996',\n",
        " '1997',\n",
        " '1998',\n",
        " '1999',\n",
        " '2000',\n",
        " '2001',\n",
        " '2002',\n",
        " '2003',\n",
        " '2004',\n",
        " '2005',\n",
        " '2006',\n",
        " '2007',\n",
        " '2008',\n",
        " '2009',\n",
        " '2010',\n",
        " '2011',\n",
        " '2012',\n",
        " '2013']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Looks like rows of data where each row represent an indicator\n",
      "# for a particular country. And we have data that can range\n",
      "# from 1960 to 2013. Let's gather all the rows for USA and China.\n",
      "# Be patient. This requires a little work. We will convert\n",
      "# each row into a dictionary that can be access by field name.\n",
      "usa = []\n",
      "china = []\n",
      "for row in csv_reader:\n",
      "    if row[\"Country Code\"] == \"USA\":\n",
      "        usa.append({f: row[f] for f in csv_reader.fieldnames})\n",
      "    elif row[\"Country Code\"] == \"CHN\":\n",
      "        china.append({f: row[f] for f in csv_reader.fieldnames})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How many indicators are there?\n",
      "print len(usa), len(china)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1334 1334\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Whoa! That's a lot of data. Let's find all the rows that\n",
      "# have something to do with savings.\n",
      "usa_saving = []\n",
      "for row in usa:\n",
      "    if row[\"Indicator Name\"].lower().find(\"savings\") != -1:\n",
      "        usa_saving.append(row)\n",
      "\n",
      "china_saving = []\n",
      "for row in china:\n",
      "    if row[\"Indicator Name\"].lower().find(\"savings\") != -1:\n",
      "        china_saving.append(row)\n",
      "        \n",
      "print len(usa_saving), len(china_saving)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "30 30\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# That's better. Let's print out the savings indicators.\n",
      "for s in usa_saving:\n",
      "    print s[\"Indicator Name\"], \", \", s[\"Indicator Code\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Adjusted net savings, excluding particulate emission damage (% of GNI) ,  NY.ADJ.SVNX.GN.ZS\n",
        "Adjusted net savings, excluding particulate emission damage (current US$) ,  NY.ADJ.SVNX.CD\n",
        "Adjusted net savings, including particulate emission damage (% of GNI) ,  NY.ADJ.SVNG.GN.ZS\n",
        "Adjusted net savings, including particulate emission damage (current US$) ,  NY.ADJ.SVNG.CD\n",
        "Adjusted savings: carbon dioxide damage (% of GNI) ,  NY.ADJ.DCO2.GN.ZS\n",
        "Adjusted savings: carbon dioxide damage (current US$) ,  NY.ADJ.DCO2.CD\n",
        "Adjusted savings: consumption of fixed capital (% of GNI) ,  NY.ADJ.DKAP.GN.ZS\n",
        "Adjusted savings: consumption of fixed capital (current US$) ,  NY.ADJ.DKAP.CD\n",
        "Adjusted savings: education expenditure (% of GNI) ,  NY.ADJ.AEDU.GN.ZS\n",
        "Adjusted savings: education expenditure (current US$) ,  NY.ADJ.AEDU.CD\n",
        "Adjusted savings: energy depletion (% of GNI) ,  NY.ADJ.DNGY.GN.ZS\n",
        "Adjusted savings: energy depletion (current US$) ,  NY.ADJ.DNGY.CD\n",
        "Adjusted savings: gross savings (% of GNI) ,  NY.ADJ.ICTR.GN.ZS\n",
        "Adjusted savings: mineral depletion (% of GNI) ,  NY.ADJ.DMIN.GN.ZS\n",
        "Adjusted savings: mineral depletion (current US$) ,  NY.ADJ.DMIN.CD\n",
        "Adjusted savings: natural resources depletion (% of GNI) ,  NY.ADJ.DRES.GN.ZS\n",
        "Adjusted savings: net forest depletion (% of GNI) ,  NY.ADJ.DFOR.GN.ZS\n",
        "Adjusted savings: net forest depletion (current US$) ,  NY.ADJ.DFOR.CD\n",
        "Adjusted savings: net national savings (% of GNI) ,  NY.ADJ.NNAT.GN.ZS\n",
        "Adjusted savings: net national savings (current US$) ,  NY.ADJ.NNAT.CD\n",
        "Adjusted savings: particulate emission damage (% of GNI) ,  NY.ADJ.DPEM.GN.ZS\n",
        "Adjusted savings: particulate emission damage (current US$) ,  NY.ADJ.DPEM.CD\n",
        "Gross domestic savings (% of GDP) ,  NY.GDS.TOTL.ZS\n",
        "Gross domestic savings (constant LCU) ,  NY.GDS.TOTL.KN\n",
        "Gross domestic savings (current LCU) ,  NY.GDS.TOTL.CN\n",
        "Gross domestic savings (current US$) ,  NY.GDS.TOTL.CD\n",
        "Gross savings (% of GDP) ,  NY.GNS.ICTR.ZS\n",
        "Gross savings (% of GNI) ,  NY.GNS.ICTR.GN.ZS\n",
        "Gross savings (current LCU) ,  NY.GNS.ICTR.CN\n",
        "Gross savings (current US$) ,  NY.GNS.ICTR.CD\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's look at \"Gross savings (% of GDP)\"\n",
      "# Using the Indicator Code\n",
      "usa_year = []\n",
      "usa_rate = []\n",
      "for s in usa_saving:\n",
      "    if s[\"Indicator Code\"] == \"NY.GNS.ICTR.ZS\":\n",
      "        for Y in range(1960, 2014):\n",
      "            # Ignore years without data\n",
      "            if len(s[str(Y)]):\n",
      "                usa_year.append(Y)\n",
      "                usa_rate.append(float(s[str(Y)]))\n",
      "\n",
      "china_year = []\n",
      "china_rate = []\n",
      "for s in china_saving:\n",
      "    if s[\"Indicator Code\"] == \"NY.GNS.ICTR.ZS\":\n",
      "        for Y in range(1960, 2014):\n",
      "            # Ignore years without data\n",
      "            if len(s[str(Y)]):\n",
      "                china_year.append(Y)\n",
      "                china_rate.append(float(s[str(Y)]))\n",
      "                \n",
      "plt.figure(figsize=(8,4))\n",
      "plt.title(\"Gross savings (% of GDP)\")\n",
      "plt.plot(usa_year, usa_rate, china_year, china_rate)\n",
      "plt.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAegAAAEKCAYAAADZ3MaFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSimllFJKKaWUUkoppZRSSinlRP8PPPqA++Ut7dAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x525ebd0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Resources"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data collected from:\n",
      "<html>\n",
      "<table>\n",
      "<tr><td>sunspot_num.csv</td><td>http://solarscience.msfc.nasa.gov/SunspotCycle.shtml</td></tr>\n",
      "<trd><td>WDI_Data_US_China.csv</td><td>http://data.worldbank.org/data-catalog/world-development-indicators?cid=GPD_WDI</td></tr>\n",
      "</table>\n",
      "</html>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Copyright Notice"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<html>\n",
      "<p xmlns:dct=\"http://purl.org/dc/terms/\" xmlns:vcard=\"http://www.w3.org/2001/vcard-rdf/3.0#\">\n",
      "  <a rel=\"license\"\n",
      "     href=\"http://creativecommons.org/publicdomain/zero/1.0/\">\n",
      "    <img src=\"CC0-88x31.png\" style=\"border-style: none;\" alt=\"CC0\" />\n",
      "  </a>\n",
      "  <br />\n",
      "  To the extent possible under law,\n",
      "  <a rel=\"dct:publisher\"\n",
      "     href=\"http://charles.stanho.pe\">\n",
      "    <span property=\"dct:title\">Charles Stanhope</span></a>\n",
      "  has waived all copyright and related or neighboring rights to\n",
      "  <i><span property=\"dct:title\">CSV Example</span></i>.\n",
      "This work is published from:\n",
      "<span property=\"vcard:Country\" datatype=\"dct:ISO3166\"\n",
      "      content=\"US\" about=\"http://charles.stanho.pe\">\n",
      "  United States</span>.\n",
      "</p>\n",
      "</html>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}