{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sympy import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "init_printing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from math import ceil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv_nzstatsdate(obj, attr):\n",
    "    s1=obj.loc[:,attr].apply(lambda x:pd.to_datetime(datetime(int(x), int(ceil(frac(x)*100)),1)))\n",
    "    obj.drop(attr, axis=1, inplace=True)\n",
    "    obj.loc[:,attr]=s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(r'C:\\Users\\Glenn\\Documents\\Stats\\Oct 2017\\gdp-mar17-alltables-csv\\gdp-mar17-alltables-csv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Series_reference', u'Period', u'Data_value', u'STATUS', u'UNITS',\n",
       "       u'MAGNTUDE', u'Subject', u'Group', u'Series_title_1', u'Series_title_2',\n",
       "       u'Series_title_3', u'Series_title_4', u'Series_title_5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_reference</th>\n",
       "      <th>Period</th>\n",
       "      <th>Data_value</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>UNITS</th>\n",
       "      <th>MAGNTUDE</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Series_title_1</th>\n",
       "      <th>Series_title_2</th>\n",
       "      <th>Series_title_3</th>\n",
       "      <th>Series_title_4</th>\n",
       "      <th>Series_title_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SNEA.SG00NAC00B15</td>\n",
       "      <td>1972.03</td>\n",
       "      <td>6990.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, Key aggregates, Nominal, Actual, Total</td>\n",
       "      <td>Gross Domestic Product - expenditure measure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SNEA.SG00NAC00B15</td>\n",
       "      <td>1973.03</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, Key aggregates, Nominal, Actual, Total</td>\n",
       "      <td>Gross Domestic Product - expenditure measure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SNEA.SG00NAC00B15</td>\n",
       "      <td>1974.03</td>\n",
       "      <td>9361.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, Key aggregates, Nominal, Actual, Total</td>\n",
       "      <td>Gross Domestic Product - expenditure measure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SNEA.SG00NAC00B15</td>\n",
       "      <td>1975.03</td>\n",
       "      <td>10203.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, Key aggregates, Nominal, Actual, Total</td>\n",
       "      <td>Gross Domestic Product - expenditure measure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SNEA.SG00NAC00B15</td>\n",
       "      <td>1976.03</td>\n",
       "      <td>11506.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, Key aggregates, Nominal, Actual, Total</td>\n",
       "      <td>Gross Domestic Product - expenditure measure</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Series_reference   Period  Data_value STATUS    UNITS  MAGNTUDE  \\\n",
       "0  SNEA.SG00NAC00B15  1972.03      6990.0  FINAL  Dollars         6   \n",
       "1  SNEA.SG00NAC00B15  1973.03      8080.0  FINAL  Dollars         6   \n",
       "2  SNEA.SG00NAC00B15  1974.03      9361.0  FINAL  Dollars         6   \n",
       "3  SNEA.SG00NAC00B15  1975.03     10203.0  FINAL  Dollars         6   \n",
       "4  SNEA.SG00NAC00B15  1976.03     11506.0  FINAL  Dollars         6   \n",
       "\n",
       "                              Subject  \\\n",
       "0  National Accounts - SNA 2008 - SNE   \n",
       "1  National Accounts - SNA 2008 - SNE   \n",
       "2  National Accounts - SNA 2008 - SNE   \n",
       "3  National Accounts - SNA 2008 - SNE   \n",
       "4  National Accounts - SNA 2008 - SNE   \n",
       "\n",
       "                                            Group  \\\n",
       "0  Series, Key aggregates, Nominal, Actual, Total   \n",
       "1  Series, Key aggregates, Nominal, Actual, Total   \n",
       "2  Series, Key aggregates, Nominal, Actual, Total   \n",
       "3  Series, Key aggregates, Nominal, Actual, Total   \n",
       "4  Series, Key aggregates, Nominal, Actual, Total   \n",
       "\n",
       "                                 Series_title_1 Series_title_2 Series_title_3  \\\n",
       "0  Gross Domestic Product - expenditure measure            NaN            NaN   \n",
       "1  Gross Domestic Product - expenditure measure            NaN            NaN   \n",
       "2  Gross Domestic Product - expenditure measure            NaN            NaN   \n",
       "3  Gross Domestic Product - expenditure measure            NaN            NaN   \n",
       "4  Gross Domestic Product - expenditure measure            NaN            NaN   \n",
       "\n",
       "   Series_title_4  Series_title_5  \n",
       "0             NaN             NaN  \n",
       "1             NaN             NaN  \n",
       "2             NaN             NaN  \n",
       "3             NaN             NaN  \n",
       "4             NaN             NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_reference</th>\n",
       "      <th>Period</th>\n",
       "      <th>Data_value</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>UNITS</th>\n",
       "      <th>MAGNTUDE</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Series_title_1</th>\n",
       "      <th>Series_title_2</th>\n",
       "      <th>Series_title_3</th>\n",
       "      <th>Series_title_4</th>\n",
       "      <th>Series_title_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62059</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>397.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62060</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1987.09</td>\n",
       "      <td>360.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62061</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1987.12</td>\n",
       "      <td>399.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62062</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1988.03</td>\n",
       "      <td>443.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62063</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1988.06</td>\n",
       "      <td>462.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Series_reference   Period  Data_value STATUS    UNITS  MAGNTUDE  \\\n",
       "62059  SNEQ.SG02RSC15P30VE  1987.06       397.0  FINAL  Dollars         6   \n",
       "62060  SNEQ.SG02RSC15P30VE  1987.09       360.0  FINAL  Dollars         6   \n",
       "62061  SNEQ.SG02RSC15P30VE  1987.12       399.0  FINAL  Dollars         6   \n",
       "62062  SNEQ.SG02RSC15P30VE  1988.03       443.0  FINAL  Dollars         6   \n",
       "62063  SNEQ.SG02RSC15P30VE  1988.06       462.0  FINAL  Dollars         6   \n",
       "\n",
       "                                  Subject  \\\n",
       "62059  National Accounts - SNA 2008 - SNE   \n",
       "62060  National Accounts - SNA 2008 - SNE   \n",
       "62061  National Accounts - SNA 2008 - SNE   \n",
       "62062  National Accounts - SNA 2008 - SNE   \n",
       "62063  National Accounts - SNA 2008 - SNE   \n",
       "\n",
       "                                                   Group  \\\n",
       "62059  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "62060  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "62061  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "62062  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "62063  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "\n",
       "                      Series_title_1  \\\n",
       "62059  Final Consumption Expenditure   \n",
       "62060  Final Consumption Expenditure   \n",
       "62061  Final Consumption Expenditure   \n",
       "62062  Final Consumption Expenditure   \n",
       "62063  Final Consumption Expenditure   \n",
       "\n",
       "                                  Series_title_2 Series_title_3  \\\n",
       "62059  Resident Household's Expenditure Overseas     Households   \n",
       "62060  Resident Household's Expenditure Overseas     Households   \n",
       "62061  Resident Household's Expenditure Overseas     Households   \n",
       "62062  Resident Household's Expenditure Overseas     Households   \n",
       "62063  Resident Household's Expenditure Overseas     Households   \n",
       "\n",
       "       Series_title_4  Series_title_5  \n",
       "62059             NaN             NaN  \n",
       "62060             NaN             NaN  \n",
       "62061             NaN             NaN  \n",
       "62062             NaN             NaN  \n",
       "62063             NaN             NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1[df1.Series_reference.isin(['SNEQ.SG02RSC15P30WE','SNEQ.SG02RSC15P30VE','SNEQ.SG02RSC15P30ZE'])]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_reference</th>\n",
       "      <th>Period</th>\n",
       "      <th>Data_value</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>UNITS</th>\n",
       "      <th>MAGNTUDE</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Group</th>\n",
       "      <th>Series_title_1</th>\n",
       "      <th>Series_title_2</th>\n",
       "      <th>Series_title_3</th>\n",
       "      <th>Series_title_4</th>\n",
       "      <th>Series_title_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62059</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>397.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62179</th>\n",
       "      <td>SNEQ.SG02RSC15P30WE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>855.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Non Resident's Expenditure in New Zealand</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62299</th>\n",
       "      <td>SNEQ.SG02RSC15P30ZE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>14949.0</td>\n",
       "      <td>FINAL</td>\n",
       "      <td>Dollars</td>\n",
       "      <td>6</td>\n",
       "      <td>National Accounts - SNA 2008 - SNE</td>\n",
       "      <td>Series, GDP(E), Chain volume, Seasonally adjus...</td>\n",
       "      <td>Final Consumption Expenditure</td>\n",
       "      <td>Final Consumption Expenditure of Resident Hous...</td>\n",
       "      <td>Households</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Series_reference   Period  Data_value STATUS    UNITS  MAGNTUDE  \\\n",
       "62059  SNEQ.SG02RSC15P30VE  1987.06       397.0  FINAL  Dollars         6   \n",
       "62179  SNEQ.SG02RSC15P30WE  1987.06       855.0  FINAL  Dollars         6   \n",
       "62299  SNEQ.SG02RSC15P30ZE  1987.06     14949.0  FINAL  Dollars         6   \n",
       "\n",
       "                                  Subject  \\\n",
       "62059  National Accounts - SNA 2008 - SNE   \n",
       "62179  National Accounts - SNA 2008 - SNE   \n",
       "62299  National Accounts - SNA 2008 - SNE   \n",
       "\n",
       "                                                   Group  \\\n",
       "62059  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "62179  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "62299  Series, GDP(E), Chain volume, Seasonally adjus...   \n",
       "\n",
       "                      Series_title_1  \\\n",
       "62059  Final Consumption Expenditure   \n",
       "62179  Final Consumption Expenditure   \n",
       "62299  Final Consumption Expenditure   \n",
       "\n",
       "                                          Series_title_2 Series_title_3  \\\n",
       "62059          Resident Household's Expenditure Overseas     Households   \n",
       "62179          Non Resident's Expenditure in New Zealand     Households   \n",
       "62299  Final Consumption Expenditure of Resident Hous...     Households   \n",
       "\n",
       "       Series_title_4  Series_title_5  \n",
       "62059             NaN             NaN  \n",
       "62179             NaN             NaN  \n",
       "62299             NaN             NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop_duplicates('Series_title_2').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_reference</th>\n",
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       "      <th>Series_title_2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62059</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>397.0</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62179</th>\n",
       "      <td>SNEQ.SG02RSC15P30WE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>855.0</td>\n",
       "      <td>Non Resident's Expenditure in New Zealand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62299</th>\n",
       "      <td>SNEQ.SG02RSC15P30ZE</td>\n",
       "      <td>1987.06</td>\n",
       "      <td>14949.0</td>\n",
       "      <td>Final Consumption Expenditure of Resident Hous...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Series_reference   Period  Data_value  \\\n",
       "62059  SNEQ.SG02RSC15P30VE  1987.06       397.0   \n",
       "62179  SNEQ.SG02RSC15P30WE  1987.06       855.0   \n",
       "62299  SNEQ.SG02RSC15P30ZE  1987.06     14949.0   \n",
       "\n",
       "                                          Series_title_2  \n",
       "62059          Resident Household's Expenditure Overseas  \n",
       "62179          Non Resident's Expenditure in New Zealand  \n",
       "62299  Final Consumption Expenditure of Resident Hous...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[['Series_reference','Period', 'Data_value', 'Series_title_2']]\n",
    "df3.drop_duplicates('Series_reference').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62059    1987.06\n",
       "62060    1987.09\n",
       "62061    1987.12\n",
       "62062    1988.03\n",
       "62063    1988.06\n",
       "Name: Period, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df3.Period.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62059     6.0\n",
       "62060     9.0\n",
       "62061    12.0\n",
       "62062     3.0\n",
       "62063     6.0\n",
       "Name: Period, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.Period.apply(lambda x:ceil(frac(x)*100)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Glenn\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\Glenn\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.py:288: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "C:\\Users\\Glenn\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.py:465: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "conv_nzstatsdate(df3,'Period')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series_reference</th>\n",
       "      <th>Data_value</th>\n",
       "      <th>Series_title_2</th>\n",
       "      <th>Period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62059</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>397.0</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>1987-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62060</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>360.0</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>1987-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62061</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>399.0</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>1987-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62062</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>443.0</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>1988-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62063</th>\n",
       "      <td>SNEQ.SG02RSC15P30VE</td>\n",
       "      <td>462.0</td>\n",
       "      <td>Resident Household's Expenditure Overseas</td>\n",
       "      <td>1988-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Series_reference  Data_value  \\\n",
       "62059  SNEQ.SG02RSC15P30VE       397.0   \n",
       "62060  SNEQ.SG02RSC15P30VE       360.0   \n",
       "62061  SNEQ.SG02RSC15P30VE       399.0   \n",
       "62062  SNEQ.SG02RSC15P30VE       443.0   \n",
       "62063  SNEQ.SG02RSC15P30VE       462.0   \n",
       "\n",
       "                                  Series_title_2     Period  \n",
       "62059  Resident Household's Expenditure Overseas 1987-06-01  \n",
       "62060  Resident Household's Expenditure Overseas 1987-09-01  \n",
       "62061  Resident Household's Expenditure Overseas 1987-12-01  \n",
       "62062  Resident Household's Expenditure Overseas 1988-03-01  \n",
       "62063  Resident Household's Expenditure Overseas 1988-06-01  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Series_title_2</th>\n",
       "      <th>Final Consumption Expenditure of Resident Household</th>\n",
       "      <th>Non Resident's Expenditure in New Zealand</th>\n",
       "      <th>Resident Household's Expenditure Overseas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-06-01</th>\n",
       "      <td>14949.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-09-01</th>\n",
       "      <td>14957.0</td>\n",
       "      <td>948.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-12-01</th>\n",
       "      <td>15090.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-03-01</th>\n",
       "      <td>14913.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-06-01</th>\n",
       "      <td>14940.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Series_title_2  Final Consumption Expenditure of Resident Household  \\\n",
       "Period                                                                \n",
       "1987-06-01                                                14949.0     \n",
       "1987-09-01                                                14957.0     \n",
       "1987-12-01                                                15090.0     \n",
       "1988-03-01                                                14913.0     \n",
       "1988-06-01                                                14940.0     \n",
       "\n",
       "Series_title_2  Non Resident's Expenditure in New Zealand  \\\n",
       "Period                                                      \n",
       "1987-06-01                                          855.0   \n",
       "1987-09-01                                          948.0   \n",
       "1987-12-01                                          850.0   \n",
       "1988-03-01                                          860.0   \n",
       "1988-06-01                                          902.0   \n",
       "\n",
       "Series_title_2  Resident Household's Expenditure Overseas  \n",
       "Period                                                     \n",
       "1987-06-01                                          397.0  \n",
       "1987-09-01                                          360.0  \n",
       "1987-12-01                                          399.0  \n",
       "1988-03-01                                          443.0  \n",
       "1988-06-01                                          462.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=df3.pivot(index='Period', columns='Series_title_2', values='Data_value')\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Series_title_2</th>\n",
       "      <th>Final Consumption Expenditure of Resident Household</th>\n",
       "      <th>Non Resident's Expenditure in New Zealand</th>\n",
       "      <th>Resident Household's Expenditure Overseas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>33271.0</td>\n",
       "      <td>2758.0</td>\n",
       "      <td>1092.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>33944.0</td>\n",
       "      <td>2695.0</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>34401.0</td>\n",
       "      <td>2633.0</td>\n",
       "      <td>1213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>34540.0</td>\n",
       "      <td>2743.0</td>\n",
       "      <td>1261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>34973.0</td>\n",
       "      <td>2695.0</td>\n",
       "      <td>1304.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Series_title_2  Final Consumption Expenditure of Resident Household  \\\n",
       "Period                                                                \n",
       "2016-03-01                                                33271.0     \n",
       "2016-06-01                                                33944.0     \n",
       "2016-09-01                                                34401.0     \n",
       "2016-12-01                                                34540.0     \n",
       "2017-03-01                                                34973.0     \n",
       "\n",
       "Series_title_2  Non Resident's Expenditure in New Zealand  \\\n",
       "Period                                                      \n",
       "2016-03-01                                         2758.0   \n",
       "2016-06-01                                         2695.0   \n",
       "2016-09-01                                         2633.0   \n",
       "2016-12-01                                         2743.0   \n",
       "2017-03-01                                         2695.0   \n",
       "\n",
       "Series_title_2  Resident Household's Expenditure Overseas  \n",
       "Period                                                     \n",
       "2016-03-01                                         1092.0  \n",
       "2016-06-01                                         1111.0  \n",
       "2016-09-01                                         1213.0  \n",
       "2016-12-01                                         1261.0  \n",
       "2017-03-01                                         1304.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1091fda0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atWoRFRWVpm2gpOhO3Ka2ipiUPXv2UKNGDSpXrqxPs7GxoWvXrkRERBjVHxQU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f71fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df4.iloc[:,:2].plot(figsize=(8,6))\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10f717b8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RURn205i7uzvBwcFERETwzz//MHnyZEqWLGlxYVhQUBArVqygb9++BAUF4ebm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MqAMjP99eR1qV9taATGR+1neGPU5fVX+j/bknqof1S1IV2JHVzReH8JNXescN5vWTOwJKDcVuAO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109046d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4.iloc[:,:2].plot(figsize=(8,6))\n",
    "plt.legend(loc='best')cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}