gdp_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from __future__ import unicode_literals\n",
    "from IPython.core.display import HTML\n",
    "from string import Template\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#reading in the Excel file\n",
    "gdp_data = pd.read_excel('datasets/gdp_Download-GDPcurrent-USD-countries.xls', skiprows=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#creating a time-series\n",
    "time_series = []\n",
    "for column in gdp_data.columns:\n",
    "    if type(column) == int:\n",
    "        time_series.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#creating the country set\n",
    "country_set = set(gdp_data.Country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#creating the shortened versions of the indicators\n",
    "meanings = {'Exports of goods and services':'Ex', 'General government final consumption expenditure':'G', 'Gross Domestic Product (GDP)':'GDP', 'Imports of goods and services':'Im', 'Household consumption expenditure (including Non-profit institutions serving households)':'C', 'Gross capital formation':'I'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''converts the long-form of the column name to the shortened version as per the meanings dict'''\n",
    "def convertname(x):\n",
    "    if x in meanings.keys():\n",
    "        return meanings[x]\n",
    "    else:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#an annonymous function that converts names in the IndicatorName columun into its shortened version per the meanings dict\n",
    "converted_name = gdp_data.IndicatorName.map(lambda x: convertname(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#creates a new column to the end of the dataframe that is a concatenation of the country name and the shortened indicator name\n",
    "gdp_data['Concat'] = gdp_data['Country']+ \" \" + converted_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#sets the index column of the dataframe to the Concat column for ease of filtering\n",
    "gdp_data.set_index('Concat', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fills in all NaN values with 0's\n",
    "gdp_data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#converts all float data-types to integers\n",
    "# for column in gdp_data.columns:\n",
    "#     if gdp_data[column].dtype == float:\n",
    "#         gdp_data[column] = gdp_data[column].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#removes the index name as a header\n",
    "gdp_data.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#drops all rows that do not do not have abbreviated indicator names from the dataframe\n",
    "abbv_list = ['C','I','G','Ex','Im', 'GDP']\n",
    "for indicator in gdp_data.index:\n",
    "    split_indicator = indicator.split()\n",
    "    if split_indicator[-1].strip() not in abbv_list:\n",
    "        gdp_data.drop(indicator, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#removing the Country and IndicatorName columns\n",
    "remove_cols = ['IndicatorName', 'Country']\n",
    "gdp_data.drop(remove_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([u'Canada', u'Sao Tome and Principe', u'Turkmenistan', u'Yugoslavia (Former)', u'Lithuania', u'Cambodia', u'Switzerland', u'Ethiopia', u'Aruba', u'Swaziland', u'Argentina', u'Cameroon', u'Burkina Faso', u'Ghana', u'Saudi Arabia', u'Japan', u'State of Palestine', u'Slovenia', u'Guatemala', u'Bosnia and Herzegovina', u'Kuwait', u'Russian Federation', u'Jordan', u'Dominica', u'Liberia', u'Maldives', u'Jamaica', u'Oman', u'Cabo Verde', u'Albania', u'Gabon', u'Monaco', u'Samoa', u'New Zealand', u'Yemen', u'Andorra', u'Greenland', u\"Lao People's DR\", u'United Arab Emirates', u'Czechoslovakia (Former)', u'India', u'Azerbaijan', u'Lesotho', u'Kenya', u'Tajikistan', u'Turkey', u'Afghanistan', u'Micronesia (FS of)', u'Bangladesh', u'Mauritania', u'Iran (Islamic Republic of)', u'Sudan (Former)', u'Saint Lucia', u'San Marino', u'French Polynesia', u'France', u'Syrian Arab Republic', u'Bermuda', u'Slovakia', u'Somalia', u'Peru', u'Vanuatu', u'Nauru', u'Seychelles', u'Norway', u'Malawi', u'Cook Islands', u'Benin', u'Cuba', u'Montenegro', u'Saint Kitts and Nevis', u'Togo', u'China', u'Armenia', u'Republic of Korea', u'Dominican Republic', u'Bolivia (Plurinational State of)', u'Ukraine', u'Bahrain', u'Tonga', u'Finland', u'Libya', u'Yemen Arab Republic (Former)', u'Cayman Islands', u'Central African Republic', u'Mauritius', u'Liechtenstein', u'Australia', u'British Virgin Islands', u'Mali', u'Sweden', u'D.P.R. of Korea', u'Bulgaria', u'Yemen Democratic (Former)', u'United States', u'Romania', u'Angola', u'Chad', u'South Africa', u'St. Vincent and the Grenadines', u'Cyprus', u'Brunei Darussalam', u'Qatar', u'Malaysia', u'Austria', u'Mozambique', u'Uganda', u'Hungary', u'Niger', u'Brazil', u'Guinea', u'Panama', u'Republic of Moldova', u'Costa Rica', u'Luxembourg', u'Bahamas', u'Ireland', u'Pakistan', u'Palau', u'Nigeria', u'Ecuador', u'Czech Republic', u'Viet Nam', u'Belarus', u'Algeria', u'El Salvador', u'Tuvalu', u'Solomon Islands', u'Marshall Islands', u'Chile', u'Puerto Rico', u'Belgium', u'Kiribati', u'Former Netherlands Antilles', u'Belize', u'Sierra Leone', u'Georgia', u'China, Hong Kong SAR', u'Gambia', u'Philippines', u'D.R. of the Congo', u'China, Macao SAR', u'Morocco', u'Uruguay', u'Croatia', u'Mongolia', u'Guinea-Bissau', u'Thailand', u'Namibia', u'Grenada', u'Haiti', u'TFYR of Macedonia', u'Venezuela (Bolivarian Republic of)', u'Iraq', u'Portugal', u'Estonia', u'Kosovo', u'USSR (Former)', u'Mexico', u'Lebanon', u'Uzbekistan', u'Tunisia', u'Djibouti', u'Rwanda', u'Antigua and Barbuda', u'Spain', u'Colombia', u'Burundi', u'Fiji', u'Barbados', u'Madagascar', u'Italy', u'Bhutan', u'Sudan', u'Nepal', u'Malta', u'Netherlands', u'Suriname', u'Ethiopia (Former)', u'Anguilla', u'Turks and Caicos Islands', u'U.R. of Tanzania: Mainland', u'United Kingdom', u'Israel', u'Indonesia', u'Iceland', u'Zambia', u'Senegal', u'Papua New Guinea', u'Trinidad and Tobago', u'Zimbabwe', u'Germany', u'Denmark', u'Kazakhstan', u'Poland', u'Eritrea', u'Kyrgyzstan', u'Montserrat', u'New Caledonia', u'Sri Lanka', u'Latvia', u'South Sudan', u'Guyana', u\"C\\xf4te d'Ivoire\", u'Honduras', u'Myanmar', u'Equatorial Guinea', u'Egypt', u'Nicaragua', u'Singapore', u'Serbia', u'Botswana', u'Timor-Leste', u'Congo', u'Sint Maarten (Dutch part)', u'Greece', u'Paraguay', u'Cura\\xe7ao', u'Comoros', u'Zanzibar'])\n",
      "\n",
      "\n",
      "Please type in the name of the country or countries you want to investigate (separate with \", \"): United States, Canada, Italy, Romania, Armenia\n",
      "[u'United States', u'Canada', u'Italy', u'Romania', u'Armenia']\n",
      "\n",
      "\n",
      "For Consumer/Personal consumption, type C\n",
      "For Investment/Business consumption, type I\n",
      "For Government consumption, type G\n",
      "For Total Exports, type Ex\n",
      "For Total Imports, type Im\n",
      "For Aggregate consumption, type GDP\n",
      "\n",
      "Please type in the abbrieviate name of the GDP Indicator.(separate with \", \"): Ex, GDP\n",
      "[u'Ex', u'GDP']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#declaring chart variables\n",
    "\n",
    "#reminder of which countries are available\n",
    "print country_set\n",
    "print '\\n'\n",
    "\n",
    "#user selects countries to investigate\n",
    "user_country_select = raw_input('Please type in the name of the country or countries you want to investigate (separate with \", \"): ')\n",
    "user_country_list = list(user_country_select.split(', '))\n",
    "print user_country_list \n",
    "print '\\n'\n",
    "\n",
    "print 'For Consumer/Personal consumption, type C' + '\\n',\\\n",
    "'For Investment/Business consumption, type I' + '\\n',\\\n",
    "'For Government consumption, type G' + '\\n',\\\n",
    "'For Total Exports, type Ex' + '\\n',\\\n",
    "'For Total Imports, type Im' + '\\n',\\\n",
    "'For Aggregate consumption, type GDP'+ '\\n'\n",
    "\n",
    "user_indicator_select = raw_input('Please type in the abbrieviate name of the GDP Indicator.(separate with \", \"): ')\n",
    "user_indicator_list = list(user_indicator_select.split(', '))\n",
    "print user_indicator_list\n",
    "print '\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[u'United States Ex', u'Canada Ex', u'Italy Ex', u'Romania Ex', u'Armenia Ex'], [u'United States GDP', u'Canada GDP', u'Italy GDP', u'Romania GDP', u'Armenia GDP']]\n"
     ]
    }
   ],
   "source": [
    "#grouping the indicators and countries\n",
    "gdp_request_grouping = []\n",
    "\n",
    "for indicator in user_indicator_list:\n",
    "    inner_grouping = []\n",
    "    for country in user_country_list:\n",
    "        indicator_country = country +\" \"+ indicator\n",
    "        if indicator == 'C':\n",
    "            inner_grouping.append(indicator_country)\n",
    "        elif indicator == 'I':\n",
    "            inner_grouping.append(indicator_country)\n",
    "        elif indicator == 'G':\n",
    "            inner_grouping.append(indicator_country)\n",
    "        elif indicator == 'Ex':\n",
    "            inner_grouping.append(indicator_country)\n",
    "        elif indicator == 'Im':\n",
    "            inner_grouping.append(indicator_country)\n",
    "        elif indicator == 'GDP':\n",
    "            inner_grouping.append(indicator_country)\n",
    "        else:\n",
    "            print 'oops'\n",
    "    gdp_request_grouping.append(inner_grouping)\n",
    "\n",
    "print gdp_request_grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
       "      <th>1976</th>\n",
       "      <th>1977</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States Ex</th>\n",
       "      <td>5.970000e+10</td>\n",
       "      <td>6.300000e+10</td>\n",
       "      <td>7.080000e+10</td>\n",
       "      <td>9.530000e+10</td>\n",
       "      <td>1.267000e+11</td>\n",
       "      <td>1.387000e+11</td>\n",
       "      <td>1.495000e+11</td>\n",
       "      <td>1.593000e+11</td>\n",
       "      <td>1.869000e+11</td>\n",
       "      <td>2.301000e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.308900e+12</td>\n",
       "      <td>1.476320e+12</td>\n",
       "      <td>1.664620e+12</td>\n",
       "      <td>1.841940e+12</td>\n",
       "      <td>1.587740e+12</td>\n",
       "      <td>1.852330e+12</td>\n",
       "      <td>2.106370e+12</td>\n",
       "      <td>2.198182e+12</td>\n",
       "      <td>2.263254e+12</td>\n",
       "      <td>2.341933e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada Ex</th>\n",
       "      <td>1.926851e+10</td>\n",
       "      <td>2.091564e+10</td>\n",
       "      <td>2.407413e+10</td>\n",
       "      <td>2.990430e+10</td>\n",
       "      <td>3.862719e+10</td>\n",
       "      <td>3.831190e+10</td>\n",
       "      <td>4.494299e+10</td>\n",
       "      <td>4.819629e+10</td>\n",
       "      <td>5.379827e+10</td>\n",
       "      <td>6.418693e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>4.302796e+11</td>\n",
       "      <td>4.647426e+11</td>\n",
       "      <td>5.003383e+11</td>\n",
       "      <td>5.316942e+11</td>\n",
       "      <td>3.898948e+11</td>\n",
       "      <td>4.690637e+11</td>\n",
       "      <td>5.466925e+11</td>\n",
       "      <td>5.535793e+11</td>\n",
       "      <td>5.552631e+11</td>\n",
       "      <td>5.644229e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy Ex</th>\n",
       "      <td>1.719227e+10</td>\n",
       "      <td>1.942104e+10</td>\n",
       "      <td>2.369204e+10</td>\n",
       "      <td>2.825918e+10</td>\n",
       "      <td>3.696322e+10</td>\n",
       "      <td>4.264851e+10</td>\n",
       "      <td>4.503907e+10</td>\n",
       "      <td>5.506099e+10</td>\n",
       "      <td>6.828815e+10</td>\n",
       "      <td>8.810804e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>4.567006e+11</td>\n",
       "      <td>5.094873e+11</td>\n",
       "      <td>6.042048e+11</td>\n",
       "      <td>6.446730e+11</td>\n",
       "      <td>4.911200e+11</td>\n",
       "      <td>5.352641e+11</td>\n",
       "      <td>6.147434e+11</td>\n",
       "      <td>5.925448e+11</td>\n",
       "      <td>6.161083e+11</td>\n",
       "      <td>6.331233e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania Ex</th>\n",
       "      <td>3.286258e+08</td>\n",
       "      <td>3.609320e+08</td>\n",
       "      <td>4.586351e+08</td>\n",
       "      <td>2.517398e+09</td>\n",
       "      <td>3.393985e+09</td>\n",
       "      <td>3.648643e+09</td>\n",
       "      <td>3.737667e+09</td>\n",
       "      <td>4.103098e+09</td>\n",
       "      <td>5.091164e+09</td>\n",
       "      <td>6.352882e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.280953e+10</td>\n",
       "      <td>3.960518e+10</td>\n",
       "      <td>4.999549e+10</td>\n",
       "      <td>5.607386e+10</td>\n",
       "      <td>4.582640e+10</td>\n",
       "      <td>5.426640e+10</td>\n",
       "      <td>6.829798e+10</td>\n",
       "      <td>6.431258e+10</td>\n",
       "      <td>7.613694e+10</td>\n",
       "      <td>8.186610e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia Ex</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.411788e+09</td>\n",
       "      <td>1.491343e+09</td>\n",
       "      <td>1.766287e+09</td>\n",
       "      <td>1.754800e+09</td>\n",
       "      <td>1.338223e+09</td>\n",
       "      <td>1.928926e+09</td>\n",
       "      <td>2.409447e+09</td>\n",
       "      <td>2.447144e+09</td>\n",
       "      <td>2.815363e+09</td>\n",
       "      <td>3.401771e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          1970          1971          1972          1973  \\\n",
       "United States Ex  5.970000e+10  6.300000e+10  7.080000e+10  9.530000e+10   \n",
       "Canada Ex         1.926851e+10  2.091564e+10  2.407413e+10  2.990430e+10   \n",
       "Italy Ex          1.719227e+10  1.942104e+10  2.369204e+10  2.825918e+10   \n",
       "Romania Ex        3.286258e+08  3.609320e+08  4.586351e+08  2.517398e+09   \n",
       "Armenia Ex        0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "                          1974          1975          1976          1977  \\\n",
       "United States Ex  1.267000e+11  1.387000e+11  1.495000e+11  1.593000e+11   \n",
       "Canada Ex         3.862719e+10  3.831190e+10  4.494299e+10  4.819629e+10   \n",
       "Italy Ex          3.696322e+10  4.264851e+10  4.503907e+10  5.506099e+10   \n",
       "Romania Ex        3.393985e+09  3.648643e+09  3.737667e+09  4.103098e+09   \n",
       "Armenia Ex        0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "\n",
       "                          1978          1979      ...               2005  \\\n",
       "United States Ex  1.869000e+11  2.301000e+11      ...       1.308900e+12   \n",
       "Canada Ex         5.379827e+10  6.418693e+10      ...       4.302796e+11   \n",
       "Italy Ex          6.828815e+10  8.810804e+10      ...       4.567006e+11   \n",
       "Romania Ex        5.091164e+09  6.352882e+09      ...       3.280953e+10   \n",
       "Armenia Ex        0.000000e+00  0.000000e+00      ...       1.411788e+09   \n",
       "\n",
       "                          2006          2007          2008          2009  \\\n",
       "United States Ex  1.476320e+12  1.664620e+12  1.841940e+12  1.587740e+12   \n",
       "Canada Ex         4.647426e+11  5.003383e+11  5.316942e+11  3.898948e+11   \n",
       "Italy Ex          5.094873e+11  6.042048e+11  6.446730e+11  4.911200e+11   \n",
       "Romania Ex        3.960518e+10  4.999549e+10  5.607386e+10  4.582640e+10   \n",
       "Armenia Ex        1.491343e+09  1.766287e+09  1.754800e+09  1.338223e+09   \n",
       "\n",
       "                          2010          2011          2012          2013  \\\n",
       "United States Ex  1.852330e+12  2.106370e+12  2.198182e+12  2.263254e+12   \n",
       "Canada Ex         4.690637e+11  5.466925e+11  5.535793e+11  5.552631e+11   \n",
       "Italy Ex          5.352641e+11  6.147434e+11  5.925448e+11  6.161083e+11   \n",
       "Romania Ex        5.426640e+10  6.829798e+10  6.431258e+10  7.613694e+10   \n",
       "Armenia Ex        1.928926e+09  2.409447e+09  2.447144e+09  2.815363e+09   \n",
       "\n",
       "                          2014  \n",
       "United States Ex  2.341933e+12  \n",
       "Canada Ex         5.644229e+11  \n",
       "Italy Ex          6.331233e+11  \n",
       "Romania Ex        8.186610e+10  \n",
       "Armenia Ex        3.401771e+09  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1 = gdp_request_grouping[0][0]\n",
    "series2 = gdp_request_grouping[0][1]\n",
    "series3 = gdp_request_grouping[0][2]\n",
    "series4 = gdp_request_grouping[0][3]\n",
    "series5 = gdp_request_grouping[0][4]\n",
    "gdp_data.loc[[series1, series2, series3, series4, series5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oe/PCERERaeWihfHiNTSE9tpaDS+1sEySmX3yFoWIiEixaEphPCUzLarJyYy7v5TPQERE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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9b8c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('GDP Time Series')\n",
    "plt.plot(gdp_data.columns, gdp_data.loc[series1], color='red')\n",
    "plt.plot(gdp_data.columns, gdp_data.loc[series2], color='blue')\n",
    "plt.plot(gdp_data.columns, gdp_data.loc[series3], color='green')\n",
    "plt.plot(gdp_data.columns, gdp_data.loc[series4], color='gray')\n",
    "plt.plot(gdp_data.columns, gdp_data.loc[series5], color='yellow')\n",
    "\n",
    "plt.scatter(time_series, gdp_data.loc[series1], color='red')\n",
    "plt.scatter(time_series, gdp_data.loc[series2], color='blue')\n",
    "plt.scatter(time_series, gdp_data.loc[series3], color='green')\n",
    "plt.scatter(time_series, gdp_data.loc[series4], color='gray')\n",
    "plt.scatter(time_series, gdp_data.loc[series5], color='yellow')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Local Currency as Expressed in USD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#converting the series data into an array of dictionaries for D3\n",
    "gdp_data_array_of_dicts = gdp_data.loc[[series1, series2, series3, series4, series5]].to_dict(orient='records')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script src=\"https://d3js.org/d3.v4.min.js\"></script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading in the D3 library\n",
    "HTML('<script src=\"https://d3js.org/d3.v4.min.js\"></script>')\n",
    "# HTML('''\n",
    "# <script src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.17/d3.min.js\"></script>\n",
    "# ''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#css style text\n",
    "css_text = '''\n",
    ".links line {\n",
    "  stroke: #999;\n",
    "  stroke-opacity: 0.6;\n",
    "}\n",
    "\n",
    ".nodes circle {\n",
    "  stroke: #fff;\n",
    "  stroke-width: 1.5px;\n",
    "}\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#javascript for animated path multi-series line chart\n",
    "js_text_template = Template('''\n",
    "var svg = d3.select(\"#$graphdiv\"),\n",
    "    //*****width = +svg.attr(\"width\"),\n",
    "    //*****height = +svg.attr(\"height\");\n",
    "    width = 720 - margin.left - margin.right,\n",
    "    height = 375 - margin.top - margin.bottom;\n",
    "\n",
    "var color = d3.scaleOrdinal(d3.schemeCategory20);\n",
    "\n",
    "var simulation = d3.forceSimulation()\n",
    "    .force(\"link\", d3.forceLink().id(function(d) { return d.id; }))\n",
    "    .force(\"charge\", d3.forceManyBody())\n",
    "    .force(\"center\", d3.forceCenter(width / 2, height / 2));\n",
    "\n",
    "//*****d3.json(\"miserables.json\", function(error, graph) {\n",
    "//*****  if (error) throw error;\n",
    "\n",
    "var data = $python_data ;\n",
    "\n",
    "  var link = svg.append(\"g\")\n",
    "      .attr(\"class\", \"links\")\n",
    "    .selectAll(\"line\")\n",
    "    .data(graph.links)\n",
    "    .enter().append(\"line\")\n",
    "      .attr(\"stroke-width\", function(d) { return Math.sqrt(d.value); });\n",
    "\n",
    "  var node = svg.append(\"g\")\n",
    "      .attr(\"class\", \"nodes\")\n",
    "    .selectAll(\"circle\")\n",
    "    .data(graph.nodes)\n",
    "    .enter().append(\"circle\")\n",
    "      .attr(\"r\", 5)\n",
    "      .attr(\"fill\", function(d) { return color(d.group); })\n",
    "      .call(d3.drag()\n",
    "          .on(\"start\", dragstarted)\n",
    "          .on(\"drag\", dragged)\n",
    "          .on(\"end\", dragended));\n",
    "\n",
    "  node.append(\"title\")\n",
    "      .text(function(d) { return d.id; });\n",
    "\n",
    "  simulation\n",
    "      .nodes(graph.nodes)\n",
    "      .on(\"tick\", ticked);\n",
    "\n",
    "  simulation.force(\"link\")\n",
    "      .links(graph.links);\n",
    "\n",
    "  function ticked() {\n",
    "    link\n",
    "        .attr(\"x1\", function(d) { return d.source.x; })\n",
    "        .attr(\"y1\", function(d) { return d.source.y; })\n",
    "        .attr(\"x2\", function(d) { return d.target.x; })\n",
    "        .attr(\"y2\", function(d) { return d.target.y; });\n",
    "\n",
    "    node\n",
    "        .attr(\"cx\", function(d) { return d.x; })\n",
    "        .attr(\"cy\", function(d) { return d.y; });\n",
    "  }\n",
    "//*****});\n",
    "\n",
    "function dragstarted(d) {\n",
    "  if (!d3.event.active) simulation.alphaTarget(0.3).restart();\n",
    "  d.fx = d.x;\n",
    "  d.fy = d.y;\n",
    "}\n",
    "\n",
    "function dragged(d) {\n",
    "  d.fx = d3.event.x;\n",
    "  d.fy = d3.event.y;\n",
    "}\n",
    "\n",
    "function dragended(d) {\n",
    "  if (!d3.event.active) simulation.alphaTarget(0);\n",
    "  d.fx = null;\n",
    "  d.fy = null;\n",
    "}\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style> \n",
       ".links line {\n",
       "  stroke: #999;\n",
       "  stroke-opacity: 0.6;\n",
       "}\n",
       "\n",
       ".nodes circle {\n",
       "  stroke: #fff;\n",
       "  stroke-width: 1.5px;\n",
       "}\n",
       " </style>\n",
       "<div id=\"graph-div\"></div>\n",
       "<script> \n",
       "var svg = d3.select(\"#graph-div\"),\n",
       "    //*****width = +svg.attr(\"width\"),\n",
       "    //*****height = +svg.attr(\"height\");\n",
       "    width = 720 - margin.left - margin.right,\n",
       "    height = 375 - margin.top - margin.bottom;\n",
       "\n",
       "var color = d3.scaleOrdinal(d3.schemeCategory20);\n",
       "\n",
       "var simulation = d3.forceSimulation()\n",
       "    .force(\"link\", d3.forceLink().id(function(d) { return d.id; }))\n",
       "    .force(\"charge\", d3.forceManyBody())\n",
       "    .force(\"center\", d3.forceCenter(width / 2, height / 2));\n",
       "\n",
       "//*****d3.json(\"miserables.json\", function(error, graph) {\n",
       "//*****  if (error) throw error;\n",
       "\n",
       "var data = [{\"1970\": 59700000000.0, \"1971\": 63000000000.0, \"1972\": 70800000000.0, \"1973\": 95300000000.0, \"1974\": 126700000000.0, \"1975\": 138700000000.0, \"1976\": 149500000000.0, \"1977\": 159300000000.0, \"1978\": 186900000000.0, \"1979\": 230100000000.0, \"1980\": 280800000000.0, \"1981\": 305200000000.0, \"1982\": 283200000000.0, \"1983\": 277000000000.0, \"1984\": 302400000000.0, \"1985\": 303200000000.0, \"1986\": 321000000000.0, \"1987\": 363900000000.0, \"1988\": 444600000000.0, \"1989\": 504300000000.0, \"1990\": 551900000000.0, \"1991\": 594900000000.0, \"1992\": 633000000000.0, \"1993\": 654800000000.0, \"1994\": 720900000000.0, \"1995\": 812810000000.0, \"1996\": 867590000000.0, \"1997\": 953800000000.0, \"1998\": 952980000000.0, \"1999\": 991980000000.0, \"2000\": 1096830000000.0, \"2001\": 1026710000000.0, \"2002\": 1002510000000.0, \"2003\": 1040280000000.0, \"2004\": 1181510000000.0, \"2005\": 1308900000000.0, \"2006\": 1476320000000.0, \"2007\": 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       "\n",
       "  var link = svg.append(\"g\")\n",
       "      .attr(\"class\", \"links\")\n",
       "    .selectAll(\"line\")\n",
       "    .data(graph.links)\n",
       "    .enter().append(\"line\")\n",
       "      .attr(\"stroke-width\", function(d) { return Math.sqrt(d.value); });\n",
       "\n",
       "  var node = svg.append(\"g\")\n",
       "      .attr(\"class\", \"nodes\")\n",
       "    .selectAll(\"circle\")\n",
       "    .data(graph.nodes)\n",
       "    .enter().append(\"circle\")\n",
       "      .attr(\"r\", 5)\n",
       "      .attr(\"fill\", function(d) { return color(d.group); })\n",
       "      .call(d3.drag()\n",
       "          .on(\"start\", dragstarted)\n",
       "          .on(\"drag\", dragged)\n",
       "          .on(\"end\", dragended));\n",
       "\n",
       "  node.append(\"title\")\n",
       "      .text(function(d) { return d.id; });\n",
       "\n",
       "  simulation\n",
       "      .nodes(graph.nodes)\n",
       "      .on(\"tick\", ticked);\n",
       "\n",
       "  simulation.force(\"link\")\n",
       "      .links(graph.links);\n",
       "\n",
       "  function ticked() {\n",
       "    link\n",
       "        .attr(\"x1\", function(d) { return d.source.x; })\n",
       "        .attr(\"y1\", function(d) { return d.source.y; })\n",
       "        .attr(\"x2\", function(d) { return d.target.x; })\n",
       "        .attr(\"y2\", function(d) { return d.target.y; });\n",
       "\n",
       "    node\n",
       "        .attr(\"cx\", function(d) { return d.x; })\n",
       "        .attr(\"cy\", function(d) { return d.y; });\n",
       "  }\n",
       "//*****});\n",
       "\n",
       "function dragstarted(d) {\n",
       "  if (!d3.event.active) simulation.alphaTarget(0.3).restart();\n",
       "  d.fx = d.x;\n",
       "  d.fy = d.y;\n",
       "}\n",
       "\n",
       "function dragged(d) {\n",
       "  d.fx = d3.event.x;\n",
       "  d.fy = d3.event.y;\n",
       "}\n",
       "\n",
       "function dragended(d) {\n",
       "  if (!d3.event.active) simulation.alphaTarget(0);\n",
       "  d.fx = null;\n",
       "  d.fy = null;\n",
       "}\n",
       " </script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_template = Template('''\n",
    "<style> $css_text </style>\n",
    "<div id=\"graph-div\"></div>\n",
    "<script> $js_text </script>\n",
    "''')\n",
    "js_text = js_text_template.substitute({'python_data': json.dumps(gdp_data_array_of_dicts),\n",
    "                                       'graphdiv': 'graph-div'})\n",
    "HTML(html_template.substitute({'css_text': css_text, 'js_text': js_text}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
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 },
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 "nbformat_minor": 1
}