From 1573a48d581e15e59855bfd6b1e6e84af5d2770c Mon Sep 17 00:00:00 2001 From: virtualarchitectures Date: Mon, 9 Dec 2019 20:23:07 +0000 Subject: [PATCH] Added Jupyter notebook for CSO JSON-Stat analysis --- CSO_Ireland_JSONStat4Py.ipynb | 1262 +++++++++++++++++++++++++++++++++ 1 file changed, 1262 insertions(+) create mode 100644 CSO_Ireland_JSONStat4Py.ipynb diff --git a/CSO_Ireland_JSONStat4Py.ipynb b/CSO_Ireland_JSONStat4Py.ipynb new file mode 100644 index 0000000..8e23529 --- /dev/null +++ b/CSO_Ireland_JSONStat4Py.ipynb @@ -0,0 +1,1262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessing the Central Statistics Office (CSO) API with JSON-stat for Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates how to access the Statbank API for Central Statistics Office (CSO) Ireland.\n", + "The [Statbank API](https://www.cso.ie/webserviceclient/) utilises the [JSON-stat](https://www.cso.ie/webserviceclient/) format for encoding statistical information. This can be accessed using the [jsonstat.py](https://github.com/26fe/jsonstat.py) library for Python by [Giovanni F](http://www.26fe.com/). Further documentation is available online: [jsonstat.py](https://jsonstatpy.readthedocs.io/en/latest/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path\n", + "import jsonstat\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a cache directory to store copies of downloaded data for faster development and consistency" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'D:\\\\GitHub Repositories\\\\Python\\\\Test_Data\\\\CSO_Test_DATA'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cache_dir = path.abspath(path.join(\"..\", \"Test_Data\", \"CSO_Test_DATA\"))\n", + "jsonstat.cache_dir(cache_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Identify CSO JSON table to download by table number" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#Provide table identifier\n", + "table_id = \"QLF08\"\n", + "\n", + "#Provide base url\n", + "base_uri = 'https://statbank.cso.ie/StatbankServices/StatbankServices.svc/jsonservice/responseinstance/'\n", + "uri = base_uri + table_id\n", + "\n", + "#Specify name for cached file\n", + "filename = \"cso_ie_\" + table_id + \".json\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load data into a collection" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JsonstatCollection contains the following JsonStatDataSet:\n", + "+-----+-----------+\n", + "| pos | dataset |\n", + "+-----+-----------+\n", + "| 0 | 'dataset' |\n", + "+-----+-----------+\n" + ] + } + ], + "source": [ + "collection = jsonstat.from_url(uri, filename)\n", + "print(collection)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Select the first dataset in the collection and print its description" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name: 'dataset'\n", + "label: 'Persons aged 15 years and over by Region, Quarter and Statistic'\n", + "source: 'Persons aged 15 years and over by Region, Quarter and Statistic'\n", + "size: 1800\n", + "+-----+-----------+-----------+------+--------+\n", + "| pos | id | label | size | role |\n", + "+-----+-----------+-----------+------+--------+\n", + "| 0 | Region | Region | 12 | |\n", + "| 1 | Quarter | Quarter | 30 | time |\n", + "| 2 | Statistic | Statistic | 5 | metric |\n", + "+-----+-----------+-----------+------+--------+\n" + ] + } + ], + "source": [ + "dataset = collection.dataset(0)\n", + "print(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Print dimensions of the dataset for reveiw" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-----+---------+------------------------+\n", + "| pos | idx | label |\n", + "+-----+---------+------------------------+\n", + "| 0 | '-' | 'State' |\n", + "| 1 | 'IE04' | 'Northern and Western' |\n", + "| 2 | 'IE041' | 'Border' |\n", + "| 3 | 'IE042' | 'West' |\n", + "| 4 | 'IE05' | 'Southern' |\n", + "| 5 | 'IE051' | 'Mid-West' |\n", + "| 6 | 'IE052' | 'South-East' |\n", + "| 7 | 'IE053' | 'South-West' |\n", + "| 8 | 'IE06' | 'Eastern and Midland' |\n", + "| 9 | 'IE061' | 'Dublin' |\n", + "| 10 | 'IE062' | 'Mid-East' |\n", + "| 11 | 'IE063' | 'Midland' |\n", + "+-----+---------+------------------------+\n", + "+-----+----------+----------+\n", + "| pos | idx | label |\n", + "+-----+----------+----------+\n", + "| 0 | '2012Q1' | '2012Q1' |\n", + "| 1 | '2012Q2' | '2012Q2' |\n", + "| 2 | '2012Q3' | '2012Q3' |\n", + "| 3 | '2012Q4' | '2012Q4' |\n", + "| 4 | '2013Q1' | '2013Q1' |\n", + "| 5 | '2013Q2' | '2013Q2' |\n", + "| 6 | '2013Q3' | '2013Q3' |\n", + "| 7 | '2013Q4' | '2013Q4' |\n", + "| 8 | '2014Q1' | '2014Q1' |\n", + "| 9 | '2014Q2' | '2014Q2' |\n", + "| 10 | '2014Q3' | '2014Q3' |\n", + "| 11 | '2014Q4' | '2014Q4' |\n", + "| 12 | '2015Q1' | '2015Q1' |\n", + "| 13 | '2015Q2' | '2015Q2' |\n", + "| 14 | '2015Q3' | '2015Q3' |\n", + "| 15 | '2015Q4' | '2015Q4' |\n", + "| 16 | '2016Q1' | '2016Q1' |\n", + "| 17 | '2016Q2' | '2016Q2' |\n", + "| 18 | '2016Q3' | '2016Q3' |\n", + "| 19 | '2016Q4' | '2016Q4' |\n", + "| 20 | '2017Q1' | '2017Q1' |\n", + "| 21 | '2017Q2' | '2017Q2' |\n", + "| 22 | '2017Q3' | '2017Q3' |\n", + "| 23 | '2017Q4' | '2017Q4' |\n", + "| 24 | '2018Q1' | '2018Q1' |\n", + "| 25 | '2018Q2' | '2018Q2' |\n", + "| 26 | '2018Q3' | '2018Q3' |\n", + "| 27 | '2018Q4' | '2018Q4' |\n", + "| 28 | '2019Q1' | '2019Q1' |\n", + "| 29 | '2019Q2' | '2019Q2' |\n", + "+-----+----------+----------+\n", + "+-----+------------+-------------------------------------------------------------+\n", + "| pos | idx | label |\n", + "+-----+------------+-------------------------------------------------------------+\n", + "| 0 | 'QLF08C01' | 'Persons aged 15 years and over in Employment (Thousand)' |\n", + "| 1 | 'QLF08C02' | 'Unemployed Persons aged 15 years and over (Thousand)' |\n", + "| 2 | 'QLF08C03' | 'Persons aged 15 years and over in Labour Force (Thousand)' |\n", + "| 3 | 'QLF08C04' | 'ILO Unemployment Rate (15 - 74 years) (%)' |\n", + "| 4 | 'QLF08C05' | 'ILO Participation Rate (15 years and over) (%)' |\n", + "+-----+------------+-------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "print(dataset.dimension('Region'))\n", + "print(dataset.dimension('Quarter'))\n", + "print(dataset.dimension('Statistic'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Return a value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case we'll select the value associated with the statistic for the number of unemployed people (thousands) in Dublin for the first quarter of 2019." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "JsonStatValue(idx=1491, value=32.1, status=None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.data(Region='Dublin', Quarter='2019Q1', Statistic='QLF08C02')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32.1" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.value(Region='Dublin', Quarter='2019Q1', Statistic='QLF08C02')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Convert dataset to a Pandas data frame" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RegionQuarterStatisticValue
0State2012Q1Persons aged 15 years and over in Employment (...1863.2
1State2012Q1Unemployed Persons aged 15 years and over (Tho...348.1
2State2012Q1Persons aged 15 years and over in Labour Force...2211.3
3State2012Q1ILO Unemployment Rate (15 - 74 years) (%)15.8
4State2012Q1ILO Participation Rate (15 years and over) (%)61.4
5State2012Q2Persons aged 15 years and over in Employment (...1878.0
6State2012Q2Unemployed Persons aged 15 years and over (Tho...352.7
7State2012Q2Persons aged 15 years and over in Labour Force...2230.7
8State2012Q2ILO Unemployment Rate (15 - 74 years) (%)15.9
9State2012Q2ILO Participation Rate (15 years and over) (%)61.9
\n", + "
" + ], + "text/plain": [ + " Region Quarter Statistic Value\n", + "0 State 2012Q1 Persons aged 15 years and over in Employment (... 1863.2\n", + "1 State 2012Q1 Unemployed Persons aged 15 years and over (Tho... 348.1\n", + "2 State 2012Q1 Persons aged 15 years and over in Labour Force... 2211.3\n", + "3 State 2012Q1 ILO Unemployment Rate (15 - 74 years) (%) 15.8\n", + "4 State 2012Q1 ILO Participation Rate (15 years and over) (%) 61.4\n", + "5 State 2012Q2 Persons aged 15 years and over in Employment (... 1878.0\n", + "6 State 2012Q2 Unemployed Persons aged 15 years and over (Tho... 352.7\n", + "7 State 2012Q2 Persons aged 15 years and over in Labour Force... 2230.7\n", + "8 State 2012Q2 ILO Unemployment Rate (15 - 74 years) (%) 15.9\n", + "9 State 2012Q2 ILO Participation Rate (15 years and over) (%) 61.9" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dataset = dataset.to_data_frame()\n", + "df_dataset.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract a subset of the data for unemployment figures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case we'll select the subset of data for the number of unemployed people (thousands) in Dublin for all quarters available in the dataset. Quarter will be used as the index for the data frame. The region and statistic chose for the subset need to be specified by their index (idx)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RegionQuarterStatisticValue
0Dublin2012Q1Unemployed Persons aged 15 years and over (Tho...85.1
1Dublin2012Q2Unemployed Persons aged 15 years and over (Tho...79.7
2Dublin2012Q3Unemployed Persons aged 15 years and over (Tho...84.9
3Dublin2012Q4Unemployed Persons aged 15 years and over (Tho...72.2
4Dublin2013Q1Unemployed Persons aged 15 years and over (Tho...72.1
5Dublin2013Q2Unemployed Persons aged 15 years and over (Tho...79.5
6Dublin2013Q3Unemployed Persons aged 15 years and over (Tho...70.2
7Dublin2013Q4Unemployed Persons aged 15 years and over (Tho...66.6
8Dublin2014Q1Unemployed Persons aged 15 years and over (Tho...69.5
9Dublin2014Q2Unemployed Persons aged 15 years and over (Tho...68.0
10Dublin2014Q3Unemployed Persons aged 15 years and over (Tho...70.7
11Dublin2014Q4Unemployed Persons aged 15 years and over (Tho...59.2
12Dublin2015Q1Unemployed Persons aged 15 years and over (Tho...60.1
13Dublin2015Q2Unemployed Persons aged 15 years and over (Tho...55.5
14Dublin2015Q3Unemployed Persons aged 15 years and over (Tho...56.2
15Dublin2015Q4Unemployed Persons aged 15 years and over (Tho...52.6
16Dublin2016Q1Unemployed Persons aged 15 years and over (Tho...48.6
17Dublin2016Q2Unemployed Persons aged 15 years and over (Tho...58.1
18Dublin2016Q3Unemployed Persons aged 15 years and over (Tho...55.6
19Dublin2016Q4Unemployed Persons aged 15 years and over (Tho...45.1
20Dublin2017Q1Unemployed Persons aged 15 years and over (Tho...44.7
21Dublin2017Q2Unemployed Persons aged 15 years and over (Tho...46.1
22Dublin2017Q3Unemployed Persons aged 15 years and over (Tho...44.8
23Dublin2017Q4Unemployed Persons aged 15 years and over (Tho...43.4
24Dublin2018Q1Unemployed Persons aged 15 years and over (Tho...37.8
25Dublin2018Q2Unemployed Persons aged 15 years and over (Tho...38.5
26Dublin2018Q3Unemployed Persons aged 15 years and over (Tho...38.9
27Dublin2018Q4Unemployed Persons aged 15 years and over (Tho...36.4
28Dublin2019Q1Unemployed Persons aged 15 years and over (Tho...32.1
29Dublin2019Q2Unemployed Persons aged 15 years and over (Tho...32.7
\n", + "
" + ], + "text/plain": [ + " Region Quarter Statistic Value\n", + "0 Dublin 2012Q1 Unemployed Persons aged 15 years and over (Tho... 85.1\n", + "1 Dublin 2012Q2 Unemployed Persons aged 15 years and over (Tho... 79.7\n", + "2 Dublin 2012Q3 Unemployed Persons aged 15 years and over (Tho... 84.9\n", + "3 Dublin 2012Q4 Unemployed Persons aged 15 years and over (Tho... 72.2\n", + "4 Dublin 2013Q1 Unemployed Persons aged 15 years and over (Tho... 72.1\n", + "5 Dublin 2013Q2 Unemployed Persons aged 15 years and over (Tho... 79.5\n", + "6 Dublin 2013Q3 Unemployed Persons aged 15 years and over (Tho... 70.2\n", + "7 Dublin 2013Q4 Unemployed Persons aged 15 years and over (Tho... 66.6\n", + "8 Dublin 2014Q1 Unemployed Persons aged 15 years and over (Tho... 69.5\n", + "9 Dublin 2014Q2 Unemployed Persons aged 15 years and over (Tho... 68.0\n", + "10 Dublin 2014Q3 Unemployed Persons aged 15 years and over (Tho... 70.7\n", + "11 Dublin 2014Q4 Unemployed Persons aged 15 years and over (Tho... 59.2\n", + "12 Dublin 2015Q1 Unemployed Persons aged 15 years and over (Tho... 60.1\n", + "13 Dublin 2015Q2 Unemployed Persons aged 15 years and over (Tho... 55.5\n", + "14 Dublin 2015Q3 Unemployed Persons aged 15 years and over (Tho... 56.2\n", + "15 Dublin 2015Q4 Unemployed Persons aged 15 years and over (Tho... 52.6\n", + "16 Dublin 2016Q1 Unemployed Persons aged 15 years and over (Tho... 48.6\n", + "17 Dublin 2016Q2 Unemployed Persons aged 15 years and over (Tho... 58.1\n", + "18 Dublin 2016Q3 Unemployed Persons aged 15 years and over (Tho... 55.6\n", + "19 Dublin 2016Q4 Unemployed Persons aged 15 years and over (Tho... 45.1\n", + "20 Dublin 2017Q1 Unemployed Persons aged 15 years and over (Tho... 44.7\n", + "21 Dublin 2017Q2 Unemployed Persons aged 15 years and over (Tho... 46.1\n", + "22 Dublin 2017Q3 Unemployed Persons aged 15 years and over (Tho... 44.8\n", + "23 Dublin 2017Q4 Unemployed Persons aged 15 years and over (Tho... 43.4\n", + "24 Dublin 2018Q1 Unemployed Persons aged 15 years and over (Tho... 37.8\n", + "25 Dublin 2018Q2 Unemployed Persons aged 15 years and over (Tho... 38.5\n", + "26 Dublin 2018Q3 Unemployed Persons aged 15 years and over (Tho... 38.9\n", + "27 Dublin 2018Q4 Unemployed Persons aged 15 years and over (Tho... 36.4\n", + "28 Dublin 2019Q1 Unemployed Persons aged 15 years and over (Tho... 32.1\n", + "29 Dublin 2019Q2 Unemployed Persons aged 15 years and over (Tho... 32.7" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dublin_unemployed = dataset.to_data_frame(blocked_dims={'Region':'IE061', 'Statistic':'QLF08C02'})\n", + "df_dublin_unemployed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get summary statistics for the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Value
count30.000000
mean56.830000
std15.874924
min32.100000
25%44.725000
50%55.900000
75%70.025000
max85.100000
\n", + "
" + ], + "text/plain": [ + " Value\n", + "count 30.000000\n", + "mean 56.830000\n", + "std 15.874924\n", + "min 32.100000\n", + "25% 44.725000\n", + "50% 55.900000\n", + "75% 70.025000\n", + "max 85.100000" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dublin_unemployed.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot unemployment in Dublin" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qvuUc6Zo5FOfnfDHOL4RKGh7O6zF3f2oOkVZfme/E6RZaiJMutaEyp+BcYNyH87O7J06Nrjb/BvyPOCldb8cJBvG6E+fn+mac96PmPh0W53nzBE778LN69AhKzUr5q6pVOG3TicrqeDnO9YGdwAvAz1T1LXfaP3DOrS04xyQ2N3l978v3cL48i3HO0cbkNK/vnPbgHPOdOKMcnYrznsfuy18asa0WZblQOgFxung9oKo1f6K3OSIyEOdnbO+2+rO1IxKR6gGdJ7nXhTo9EfkqcJWqXtraZamLBfAOSJw+wDNxaji9cAY++FhVb6l3wVYmzhBrv8XpshXvyOnGdFoWwDsgEUnDaX45DqdJYx5OE0+brdG6bZB7cJocvuK2pRpj6mEB3Bhj2im7iGmMMe1Uiyb26dGjhw4ePLglN2mMMe1efn7+PlXNqfl6iwbwwYMHs3Tp0oZnNMYYc5iI1HoXrzWhGGNMO2UB3Bhj2ikL4MYY00516tFJjDGOUCjEjh07qKioL2mhSbZgMEj//v3x+2tNQnoMC+DGGHbs2EFmZiaDBw/GHcjGtDBVZf/+/ezYsYMhQ4bEtYw1oRhjqKiooHv37ha8W5GI0L1790b9CrIAbowBsODdBjT2PWjRAF5aabftG2NMorRoAD9QHKW8KvFBfMHqCt5aYRdfjGnPtmzZwrhx44567Y477uDXv/51i5dl4cKFzJkzJ6nbGDx4MPv2xTNMQN1aNIBHVZmXn9hUwzsPRHj6gzJeWVpO1BJzGWM6kRYN4OlBD/NXVLC3sK7RqxpHVXnqg1IiUSirVHbuT8x6jTFty2mnncYPf/hDpk2bxsiRI3n//fcBiEQifP/732fq1Knk5ubyl784g+csXLiQU089lUsvvZSRI0dy22238fjjjzNt2jTGjx/Pxo0bAbjmmmu46aabOPnkkxk5ciSvvHLsyIcHDhzgggsuIDc3l+nTp7Ny5Uqi0SgjRoygoKAAgGg0yvDhw9m3bx8FBQVcdNFFTJ06lalTp7JokTNY1v79+5k9ezaTJk3ixhtvJBGZYFu0G2G3dA9eD8z9sIx/O7uxg2AfK39jiLU7wpw1McgbyytYtytM/x7WM9KY5njqg1K270tsZWhADy+XzUhv1jrC4TBLlizh1Vdf5c477+Ttt9/m4YcfpkuXLnzyySdUVlZy0kknMXv2bABWrFjB2rVryc7OZujQoVx//fUsWbKE3//+99x3333ce++9gNN08+6777Jx40ZmzpzJhg0bjtruz372MyZNmsSLL77IO++8wze/+U2WL1/OlVdeyeOPP84tt9zC22+/zYQJE+jRowdXXHEFt956KzNmzGDbtm2cddZZrF27ljvvvJMZM2Zw++23M2/ePB588MFmHQ9o4QDu9cDZk1N5cUk5n38Z4rh+8XVWr01lSHlmURkDeni5cHoqn2yoYt3OEKePDyawxMaYllJXD4zq1y+88EIApkyZwpYtWwB48803WblyJXPnzgWgsLCQ9evXEwgEmDp1Kn36OMOmDhs27HBgHz9+PAsWLDi8/ksvvRSPx8OIESMYOnQon3/++VHb/+CDD3juuecAOP3009m/fz+FhYVcd911nH/++dxyyy387W9/49prrwXg7bffZs2aNYeXLyoqori4mPfee4/nn3eG4zz33HPp1q1b0w+Wq8Wrq7MmBnl/bSVPf1DGf1+ShcfTtK5Lr+aXc7A0yg2zM/F4hJF9fXy2PYSqWncoY5qhuTXlpurevTsHDx486rUDBw4cvqklJSUFAK/XSzjsjOmsqtx3332cddZZRy23cOHCw/MDeDyew889Hs/h5eHYL46az2tr6hARBgwYQK9evXjnnXdYvHgxjz/+OOA0p3z00UekpqbWulwitXg/8IBPuPiENHbsj/DB2somrWPPoQhvLq9g+sgAw/s4tfiRfX0Ulyu7D0UTWVxjTAvJyMigT58+zJ8/H3CC9+uvv86MGTPqXOass87i/vvvJxQKAbBu3TpKS0sbtd1nn32WaDTKxo0b2bRpE6NGjTpq+imnnHI4OC9cuJAePXqQlZUFwPXXX8+VV17JpZdeitfrBWD27Nn88Y9/PLz88uXLj1nPa6+9dsyXVVO0yo08U4b5GdHHx4tLyimrbFzAdS5cluHzwsUnph1+fURfJ5Cv3xlKaFmNMS3n73//O3fddRcTJ07k9NNP52c/+xnDhg2rc/7rr7+eMWPGMHnyZMaNG8eNN954VO06HqNGjeLUU0/l7LPP5oEHHiAYPLoZ9o477mDp0qXk5uZy22238dhjjx2edt5551FSUnK4+QTgD3/4w+H5x4wZwwMPPAA4benvvfcekydP5s0332TgwIGNKmdtWnRMzLy8PK0e0GFrQZhfPFvErAlBLjkprYElj1i+uYo/vVbCpSelMWvCkQOtqnz/sUMc18/P9bMyEl52YzqytWvXMnr06NYuRou75pprmDNnDhdffHGTll+6dCm33nrr4V4xiVDbeyEi+aqaV3PeVruVflCOjxOPCzB/VQV7DsV3xbsq7NS++2Z7mTku5ahpIsKIPn7W7QwnpHuOMcbU55577uGiiy7i7rvvbrUytGoulK9NT8PvhWc/LItr/tc/rWB/cZTLT07D5z32YsDIvj4OlkbZV2zt4MaYhj366KNNrn3fdtttbN26td42+mRr1QDeJc3DOVNSWbElxJrt9bddFxRFeH1ZOVOHB+rsfjiyr9OpZt3OxrWBGWNq721hWlZj34NWz0Z4Zm6QHlkenl5URiRad+GfWVSGR46+cFlTn2wv6SliFzKNaaRgMMj+/fstiLei6nzgNS+i1qfVb1v0+4RLTkjj/jdKeG9NJTPHHVv4VVurWL45xIXTU8nOqPs7xyPCiL4+q4Eb00j9+/dnx44dh28NN62jekSeeLV6AAeYNNTPqL4+Xl5SzrThAdKDR4J0KOJcuOzV1XNUr5O6jOzrZ/nmEAdLonSrJ9gbY47w+/1xjwJj2o42EeFEhK/PSKO0Qnll6dHZCt9aXsHewiiXz0iv9cJlTSP7uO3gu6wZxRjTscUVwEXkVhH5TERWi8iTIhIUkSEislhE1ovI0yISaE5BBvTwMWNMCgtWV7LroNOt8EBxhHn55Uwa4mfswPjypgzo4SXoh3VfWjOKMaZjazCAi0g/4D+APFUdB3iBy4BfAr9T1RHAQeBbzS3MBdNSCfiEZxc53Qqf+bAcVfj6jPhv9PF4hOF9/Ky3GrgxpoOLtwnFB6SKiA9IA3YBpwNz3emPARc0tzBZaR7OnRJk1bYQcz8qI39jFedMSaV7prdR6xnZ18eug1GKyqw/uDGm42owgKvql8CvgW04gbsQyAcOqWp1O8UOoF9ty4vIDSKyVESWxnOF+4zcID27eHjj0wpysjycNbHx6WGr+4Ov32XNKMaYjiueJpRuwPnAEKAvkA6cXcustXYgVdUHVTVPVfNycnIaLJDPK3z9pDQCPrj85DT8vsanXxyU4yPga/nEVtGocxG2oMhGBjLGJF883QjPBDaragGAiDwPnAh0FRGfWwvvD+xMVKFyBwe497puTQre4HwJDOvt44sW7g++aluIl5aUU1ga5Runtk5OZWNM5xFPG/g2YLqIpImTjfwMYA2wAKhOInA18FIiC9bU4F1tRB8/X+6PUFrRcu3gC1Y5+c3zN1URreeuUmOMSYR42sAX41ysXAascpd5EPgh8J8isgHoDjycxHI22si+PhTYsLtlauF7DkX4bHuIgTleisuVddb+boxJsrh6oajqz1T1OFUdp6pXqWqlqm5S1WmqOlxVL1HVpg2vkyRDevnweVousdXCzyrweuDG2RkEfJC/oapFtmuM6bzaxJ2YyRDwCYN7+VrkQmZlSPnw8yomDQnQs4uX8YMCLLNmFGNMknXYAA5OM8rWgggVVckNpEvWV1JWqcwc7wwykTcsQFG5WjdGY0xSdewA3sdPVGFjEtvBVZUFqyvpl+1lhJuHZfwgPwEfLLVmFGNMEnXoAD6stw+PJDex1cbdYbbvizBzfApOJx1I8QvjB/mtGcUYk1QdOoAHA8KgHG9SL2QuWF1JakA4fuTRY3ROsWYUY0ySdegADjCir58te8JUhRNfEy4qi5K/sYoTRwUI+o/ut547KOA0o2y0ZhRjTHJ0+AA+sq+PcBQ270l8Tfj9NZVEonBaLaMIHW5G2WjNKMaY5OjwAXx4bx9C4vuDR6LKu59VMrq/j97das+WaM0oxphk6vABPD3ooV93b8Lzg6/YEuJgabTWMTyrWTOKMSaZOnwABxjV18fG3WHCkcQ1ZSxcXUF2hofcwXWPFGTNKMaYZOoUAXxEXz9VYdhakJimjF0HIqzdEeaUsSl4PfUn3bJmFGNMsnSOAF490HGC2sEXfFaBzwMnj05pcN7qZpR8a0YxxiRYpwjgWWke+nTzJCSAV1QpH31eyZRhAbLSGj58KX5h3EA/+daMYoxJsE4RwMHJD75hV6jZQfTjdZVUhGDm+PiHerPcKMaYZOg0AXxkPx8VIdi+r+nDnVXnPRmY42Vor/gHWh4/2JpRjDGJ12kC+Ig+Tm+R5gy0sG5nmJ0HIswcFzyc9yQeQbcZxXKjGGMSqdME8OwMDzlZHtY1Iz/4gtWVpKUIU4cHGr1s3rAAhWXaYiMEGWM6vk4TwMG5rX79zjBRbXwt+FBplOWbq5hxXAop/saP1zl+cAC/11LMGmMSp1MF8BF9/ZRWKrsONL4d/L3PKohG4dRxDXcdrE3QUswaYxKsUwXwkX2b1h88HFHeW1PJ2IF+enaJ/+JlTdaMYoxJpE4VwHtkeuiW3vj+4J9urqKwTJnZxNp3tcPNKNYbxRiTAJ0qgIsII/r6WLczhDaiHXzh6kp6ZHkYN7DuvCfxCFpuFGNMAvlauwAtbVRfH0vWVzF/ZSVd0oWAT/B7BZ8X9zH43df8PthXFGXdzjAXn5CKp4G8J/GYMizAsk0hNuwOM7Jv874QjDGdW6cL4KP7+/F64OlFZXEv4/fCSXHkPYlH7uAAfm8pSzdWWQA3xjRLpwvgOV28/OaarpRVKlURJRyGUESpcv+HI1AVVkIRJeS+1i/bS0YwMa1Nsc0ol81Iw9OIG4KMMSZWpwvg4AzykB5/KpOEO9yMssuaUYwxTdepLmK2FblubxTLjWKMaQ4L4K2guhklf2NVk+4KNcYYiCOAi8goEVke81ckIreISLaIvCUi693/3VqiwB3FlOqbetphitnV26p48M0SKkL25WNMa2owgKvqF6o6UVUnAlOAMuAF4DZgvqqOAOa7z02c2mszyntrKrhvXgmfbKhizbbEDhRtjGmcxjahnAFsVNWtwPnAY+7rjwEXJLJgHV11itlPNlRRXB5t7eI0SFV54eMy/rGwjDED/KT4Yc0OC+DGtKbGBvDLgCfdx71UdReA+79nIgvWGZw9OZWKKuXPr5UQCrfd5ohQRPnr26W8uqyCU8ak8J1zMhjV18+a7RbAjWlNcQdwEQkA5wHPNmYDInKDiCwVkaUFBQWNLV+HNqSXj+vOyGDD7jCPvFOa0AualSFl3c5Qs9dZWhHl3n8Vs2R9FRdOT+XKU9PweoQxA/wUFEUpKGr6CEfGmOZpTD/ws4FlqrrHfb5HRPqo6i4R6QPsrW0hVX0QeBAgLy+v7VYzW0ne8AAFRak8/3E5OV08fO34tGavs7g8yu9fKWZrQYS+2V7OmRJk6rBAo1MB7CuK8Id5xRQURrn+zHSOH3nkbtQx/Z3+62u3h8gZ2/QMjcaYpmtME8rlHGk+AXgZuNp9fDXwUqIK1dl8ZVKQk0en8Gp+BYvWVjZrXQdLovzfi0XsPBDhq1NTUYW/vlXKfz9ZyKK1lYQj8X2Hbtkb5u7niigsU249L/Oo4A3Qu5uHruli7eDGtKK4auAikgbMAm6Mefke4BkR+RawDbgk8cXrHESEK05JY19xhH+8W0p2pofR/Rt/h+bewgi/fbmYkoooN8/JZFQ/P3PygizfFGJefjmPLijlX0vL+cqkICcdl4LfV3uNfMUWp5tgZqqH752bSZ/sY2vYIk4zyvLNIaJRTUiiL2NM40hj0qo2V15eni5durTFttfelFVGuef5Yg6VRvnRhV1l96AAACAASURBVFm1Bs667Ngf5t5/FROJws1zMhnc8+jvZlVl1VYnkG/aE6FLmnDWpFROGXP0EHELVlXw5AdlDOzh5bvnZtIlre4faYvXVfLXt0v58UVZDOnVKbMyGNMiRCRfVfNqvm53YrYhaSkebp6Tgd8Lv59XTFFZfN0LN+0J838vFiMC378g65jgDU6NOXdwgNsuzOI/z8ukd1cvzywq40f/PMRry8opq4zy7IdlPPF+GbmD/Hz/gqx6gzdw+FfCWmtGMaZVWA28Ddq8J8yvXyqiX3cv3zs/i0AdTR3gBM8/vVpMVpqHW8/LJCcr/lr7hl0h5uVXsHpbCK8HIlGYOS7FyZIYZ5PInU8Xkh4Uvnd+VtzbNcY0jtXA25EhvXx868wMtuyJ8Lf5JXV2BVy+uYo/zCumR5aXH3wtq1HBG2B4Hz83z8nkpxdnMWVYgMtmpHH5yfEHb4AxA/xs3BWm0m6rN6bFWQBvoyYPDXDxiankbwzxwsflx0z/6ItK7n+9hAHdvXz/gky6pjf9rRzU08e3Z2VwRm4QaWR+8jH9/YSjsH6XNaMY09IsgLdhsyYEOW1sCq9/WsF7ayoOv75gVQV/m1/KyL4+/vO8LNITNNhEU4zo68PnhTXb219SLmPaO+s60IaJCJednMa+4iiPv1tG9wwvW/aGeXFJOROH+LlhVkadXQFbSsAnDO/ts9vqjWkFVgNv47we4YbZGfTN9nLfq8W8uKSc6SMD3Di79YN3tTED/Hx5IEJhnL1mjDGJYQG8HUgNCN89N5OeXTycmZvCtWek4/O2jeANR99Wb4xpOdaE0k5kZ3j4n8u7tnYxajUgx0tG0LmtfvqolIYXMMYkhNXATbN5RDiun5NetiXvKzCms7MAbhJizAAfhWXKzoOWXtaYlmIB3CTEmAHV7eDWndCYlmIB3CRE90wvPbt4LL2sMS3IArhJmDED/Kz7MhR3znFjTPNYADcJM6a/n8qwkx3RGJN8FsBNwozq50MEuyvTmBZiAdwkTFqKhyE9vdYObkwLsQBuEmrMAD9b9kYorbDb6o1JNgvgJqFG9/ejCl98ae3gxiSbBXCTUEN7+UjxY80oxrQAC+AmoXxeYVRfv13INKYFWAA3CTdmgJ+CoigFRXZbvTHJZAHcJJyllzWmZVgANwnXu5uHruli7eDGJJkFcJNwIsKYAX4+3xEmGrXb6o1JFgvgJinG9PdTWqls22ft4MYkiwVwkxSj3XZw641iTPJYADdJkZXmoX93u63emGSyAG6SZkx/Pxt3hakMWTu4MckQVwAXka4iMldEPheRtSJygohki8hbIrLe/d8t2YU17cvoAT7CUVi/y2rhxiRDvDXw3wOvq+pxwARgLXAbMF9VRwDz3efGHDaijx+fB9Y0Ypi1ypCyrSBsgyMbEwdfQzOISBZwCnANgKpWAVUicj5wmjvbY8BC4IfJKKRpn1L8wvA+vgYvZO4tjLBqa4iVW6tY92WYcBSuOjWNU8YGW6ikxrRPDQZwYChQADwiIhOAfOBmoJeq7gJQ1V0i0rO2hUXkBuAGgIEDByak0Kb9GDPAz/Mfl1NYFqVLmvODLxRR1u8Ms2prFau2hdhzyEk927urh9PGp7B1b4RnPixjzAA/PbK8rVl8Y9q0eAK4D5gMfFdVF4vI72lEc4mqPgg8CJCXl2e/izuZMf39PE85i9dVEgwIq7aGWLsjRGUIfF44rp+fmeOCjB/kp2cXJ1jvL45wx1OFPLqglP88LxOPSCvvhTFtUzwBfAewQ1UXu8/n4gTwPSLSx6199wH2JquQpv0akOMlIyg8+2E5ANkZHqaPTGH8ID/H9fOT4j82OHfP9HLpSWn8fWEZ766uZOZ4a0oxpjYNBnBV3S0i20VklKp+AZwBrHH/rgbucf+/lNSSmnbJI8JVp6ZTUBRh3CA/fbt5kThq1DNGp7BsU4i5H5UxduCR2rkx5oh4e6F8F3hcRFYCE4H/xQncs0RkPTDLfW7MMSYPC3DWpFT6ZfviCt7g5FO56rR0vB7h0XdKiVqvFGOOEU8TCqq6HMirZdIZiS2OMUdkZ3i4bEYaj7xTyjsrKzlzgjWlGBPL7sQ0bdoJowLkDvLz/Mdl7D7UcRJjrd5WxUNvlVBUZoM/m6azAG7atOqmFL9PeHR+aYdJTzsvv4Il66v4+bOFbNlrA0CbprEAbtq8rukerjg5jY17wry1oqK1i9NshWVRNu4Kc/yIAF6P8MsXivjoi8rWLpZphyyAm3Zh2ogAk4b4eXFJOTsPtO+mlE83VaHAOVNS+cnFWQzr7eNv80t5elEpkQ7yC8O0DAvgpl0QEa48NZ2gX3jknZJ2HejyN1bRu6uHvtleMlM93DInkzNyU3h7RSW/f6WYkgprFzfxsQBu2o2sNA9XnJLGlr0R3vi0fTalFJdHWbczzJRhgcOv+bzCZTPSueb0dNbvCvOLuUXs2Gft4qZhFsBNuzJ1eAp5wwK8/Ek5O/a3vyC3fHMVUXX6xtd00nEp/OCCLMIR5e7ni8jfWNUKJTTtiQVw0+5ccUoaaSnCI/NLCUcabkopr1LWbA/x8iflPPBGSau2oS/bFCIny8OA7rXfWTqkl4+fXNyFAd19PPBGCS98XGY3MZk6xXUjjzFtSWaqhytPTef+10t4bVkFX52aeniaqlJQFGXDrjAbd4fZtCfMl/sjKCCA3wdb9ob50UVZh7MjtpTSiihrd4Q4c0Kw3jtSu6Z7+K8LMnnyvTJeXVbB9v0Rrj8znbQUq2+Zo1kAN+3S5KEBpo0IMC+/nJ5dPBwsjbJxtxO0i8udGmtqQBjSy8ukoakM7+1jSC8fewsj/OqFIv74ajHfOz+r1mRaybJiS4hIFKYMPbb5pCa/V7jqtDQG5Hh5+oMy7n6uiJvnZFp6XXMUC+Cm3bri5DS++DLEX98uBaBnFw/jBvoZ3tvH0N4++nbz4vEcHaAH5fi4YXYGf3q1hIffLuGmszKOmSdZlm2qIjvDw+Ce8QVhEWHmuCD9sr384ZVi5uVXcPXM9CSX0rQnFsBNu5Ue9PBf52extzDC0F4+MlPja2KYMDjA12ek8dQHZTz3UTmXnJSW5JJCRZXy2fYQp41NiTuhV7WRff2MGxRg1dYqVNMavbzpuKxRzbRrfbp5mTA4EHfwrnZGbpDTx6fw5ooKFq5OfpfElVurCEdq730Sj9xBfgrLlG372vdNTCaxLICbTuvrJ6WRO8jPE++XsWprcrvsLdtURZc0YVjvpv3oHT/IjwCrttY/vqjpXCyAm07L4xG+PTuDAd29/OXNErYn6eaZypCyamuISUMDTR4eLjPVw+BeXlZusb7h5ggL4KZTC/qF75yTSWpAuG9eCYdKE38b+2fbQlSFOeruy6bIHRRgy96IpaA1h1kAN51etwwP/3FuJmVVUe6bV0xFKLE3zuRvqiIjKIzo07w+A7mD/Ciwaps1oxiHBXBjgAE9fNw4O4Pt+yP89a2ShOUdD4WVlVuqmDTESR3bvDJ66ZouSW+vN+2HBXBjXOMHBbji5DRWbAnxzKKyhKxzzY4QFSGYPMzf7HWJCOMHBfhsWyiuFAKm47MAbkyM08YFmTUhyPxVlcxf2fzuhfkbq0hLEY7r1/wADk4zSkUI1u9qf4m8TOJZADemhotPSGXiED9PLypjRTN6fYQjyootISYM9uPzJubmm9H9/fi8Tr9yYyyAG1ODxyNcf2YGA3t4eeitEnYdbNrNM59/GaKsUpvd+yRWit+pzVt/cAMWwI2pVYpf+LezMwn4hPtfL2lSz5Rlm0Kk+GFM/8Q0n1QbP8jPnkNR9hyyuzI7OwvgxtQhO8PDt2dlsPtQhH8sLEUbkZc7ElU+3VTFhMEB/L7E5i4ZP8j5QrBauLEAbkw9Rvf3c8G0VJasr2LB6vhHjl+/K0xJhTI5jtSxjZWT5aVPN6+1gxsL4MY05CuTg0wY7OeZRWVs3B1frXfZxioCPhg3MLHNJ9VyB/lZtzNMeZV1J+zMLIAb0wCPCNeenk52hocH3ihp8Fb2qCrLNlUxbqA/aQNG5A72E4nCmu3WjNKZWQA3Jg7pQQ83fSWD0grloQbu1Ny0O0xhmTY5dWw8hvX2kZZid2V2dnEFcBHZIiKrRGS5iCx1X8sWkbdEZL37v1tyi2pM6xrYw8c3Tknn8y/DvLSkvM758jdW4fM6yaeSxesRxg7ws3JryAY97sQaUwOfqaoTVTXPfX4bMF9VRwDz3efGdGgnjU7h5NEpvLqsotabfFSVZZtCjB3gJzWQ3JFzcgf5KS5Xtu617oSdVXOaUM4HHnMfPwZc0PziGNP2XX5yGgNzvDz8dikFhUcHzy17IxwoiSal90lNYwf6EcGaUTqxeAO4Am+KSL6I3OC+1ktVdwG4/3vWtqCI3CAiS0VkaUFBQfNLbEwr8/uEm87KQATuf6OEqvCRJoz8TVV4PTBhcHJ6n8TKTPUwtJePldYfvNOKN4CfpKqTgbOBfxeRU+LdgKo+qKp5qpqXk5PTpEIa09bkZHm5/sx0tu+L8MR7pYDbfLKxiuP6+UkPtkz/gNxBfrYWRJIyEIVp++I6y1R1p/t/L/ACMA3YIyJ9ANz/e5NVSGPaovGDAszJC7Lo8yreX1PB9v0RCoqiTElA6th45dpdmZ1agwFcRNJFJLP6MTAbWA28DFztznY18FKyCmlMW/XVvFTG9PfxxPtl/OuTckRg4pDkt39X69fdS3aGx9rBO6l4auC9gA9EZAWwBJinqq8D9wCzRGQ9MMt9bkyn4vEI18/KICvVw/LNIUb19ZGZ2nK3VziDPPhZsz1EyAZ56HQaPNNUdZOqTnD/xqrqL9zX96vqGao6wv1/IPnFNabtyUz1cONZGfi9MH1kSotvf/wgP5VhWL/TBnnobJo3yqoxBoChvXz89rpupLTCJ+q4fn78Xli5pYoxA1qu/d20PruV3pgECfoFkeTevFOb6kEeVm4NNSrlrWn/LIAb0wHkDvZTUBRl9yHrTtiZWAA3pgM4MsiD9UbpTCyAG9MBdM/00i/by8ot1h+8M7EAbkwHkTvIz4bdYcoqrRmls7AAbkwHUT3Iw2c2yEOnYQHcmA5iaC8f6Slit9V3ItYP3JgOwuMRxg30s3pbiGhU8XgS16VRVVm7I8wby8tZtzPMpCEBTh+fwrDevlbpOmkcFsCN6UDGD/azeH0Vm/dGGNa7+R/vcERZsr6Kt1ZUsGN/hC5pQt6wACu2hPhkQxX9u3s5fXwK00akJG38T1M3C+DGdCDjBhwZ5KE5AbysMsp7ayqZv7KCQ6VK32wv18xMZ9rIAH6vUBFSFq+rZMGqSv6+sIy5H5Vz0nEpnDYuhZ5dvAncI1MfC+DGdCDpQQ/De/v4dHOIvOFhemR5CTaiZry/OMLbKyp4f20llSEY3d/H1TODjB3gP6qpJOgXTh0b5JQxKazfFWbBqkreWVXB2ysqGDfQz8zxKYwd6MdjzStJJS15621eXp4uXbq0xbZnTGf01ooKnllUdvh5ZqrQI9NDjyyv+999nOUhO8ODzyts3RvmjeUV5G+sQgSmDg8wa2KQgT3ir+MdKo3y3mcVvLemksIyJSfLw2njUjjpuJQWG+CioxKR/JjxiI+8bgHcmI4lqs5AxwVFEfYVRdlXHGWf+/hASZRITDdxEchKFQrLlKAfThkb5IzxKWRnNr0ZJBxRPt1UxTurK9mwK0yKD2aMTuGMCUFysqx5pSksgBtjiEaVg6VRJ7AXRdlXHGF/cZT+3b3MGJ1CWkpia8rb9oV5e0UFS9ZXEVWYPDTAWRODDOllrbeNYQHcGNNqDpZEeWdVBe9+Vkl5lTKij4/ZE4PkDrZ28nhYADfGtLqKKuWDtZW8taKCAyVRenX1MGtCkBNGpRDwWSCviwVwY0ybEYkqyzZW8cbyCrYWRMgICjPHpTBzfLBFh6RrL+oK4NYQZYxpcV6PMHVECnnDA6zbGebN5RX8a2kFby6v4AcXZjWq90tnZl91xphWIyKM6ufnu+dm8j+XdcHvE55ZVGYjC8XJArgxpk3ok+3lq1NT+eLLsCXkipMFcGNMm3HKmBR6dvEw96NyIlGrhTfEArgxps3weYWLTkhj18EIi9ZWtnZx2jwL4MaYNmXSED/De/t4aUk5FSGrhdfHArgxpk0RES45KY2icuWNT8tbuzhtmgVwY0ybM7SXj7xhAd5cXsGhUhvjsy4WwI0xbdKF01OJROGlJVYLr4sFcGNMm5TTxcvM8Sks+rySHfvDrV2cNinuAC4iXhH5VERecZ8PEZHFIrJeRJ4WkUDyimmM6YzmTEklNSA895HVwmvTmBr4zcDamOe/BH6nqiOAg8C3ElkwY4xJD3o4d0qQ1dtCrNluN/fUFFcAF5H+wLnAX93nApwOzHVneQy4IBkFNMZ0bjPHB+mR5eHZD8uI2s09R4m3Bn4v8AOg+nJwd+CQqlY3TO0A+tW2oIjcICJLRWRpQUFBswprjOl8/F7hwuNT2bE/wsfrqlq7OG1KgwFcROYAe1U1P/blWmat9atRVR9U1TxVzcvJyWliMY0xnVne8ABDenp5YXEZlXZzz2Hx1MBPAs4TkS3AUzhNJ/cCXUWkOudjf2BnUkpojOn0RISLT0zjUKny9sqK1i5Om9FgAFfVH6lqf1UdDFwGvKOq3wAWABe7s10NvJS0UhpjOr2Rff1MHOLntWXlFJUl7+aeSFTZVxShtKLt30DUnKzpPwSeEpG7gE+BhxNTJGOMqd1FJ6Rxx1OF/OuTcr5xanqT1qGqFJU7QfrI4M5R53lxlIMlUSJRSA0I3zoznQmD224P6UYFcFVdCCx0H28CpiW+SMYYU7veXb2cMiaFdz+r5PTcIH26eeudvyKkbNkbZuOuMJv3htlbGGF/cZSqGvcFZaUKPbI8DO3lo8dwD90zPby3ppI/vVrCedNSOXdKEGmDgy/buEXGmHblq1NT+eiLSp77qIzvnJN5+HVVZX9xlI27w4f/tu+PUD24T59uHnp19TJ2oJ+cLC89Mj30yPLSPdNDiv/Y4Dx9VAr/WFjKS0vK2b4vzLVnZBCsZb7WZAHcGNOuZKZ6OHtyKi8sLue9NRVUVOnhgF1Y5kTrFL+TEOvcKUGG9fYxpKeP9GDjMocEfMJ1Z6QzoIeXuR+Vs+e5Iv797AxyutRf629JNiq9MabdqQorP328kINupsIeWR6G9fIxrI+PYb199Mv24vUkrra8ZnuIv7xZAsCNszMYM8CfsHXHo65R6S2AG2PapZ0HIuw+FGFoLx9d05Ofl29vYYQ/v1bCzoMRLj4hlVkTWq5dvK4AbtkIjTHtUt9sL5OHBlokeAP07OLltouymDTEz7MflvO3+aVUhVv3piIL4MYYE6egX7jxrAzOn5bKx+uq+NULRRwojrRaeSyAG2NMI3hEmJOXynfOyWDPoQh3zS1i3c7WyZRoAdwYY5pgwuAAP76oC6kB4bcvF7NiS8sn2rIAbowxTdQn28tPLs6iX3cvj8wv5UBJy95+bwHcGGOaIS3Fw7dnZRCOKH+bX9KiOcstgBtjTDP17url8pPT+eLLMG8sb7lsiRbAjTEmAU48LkDesAAvLSln856WGYTZArgxxiSAiHDVaWl0SfPw0FslVFQlvynFArgxxiRIWoqH62els684yhPvlyZ9exbAjTEmgUb08TNnSpCPvqhi8brKpG7LArgxxiTYuXmpDO/t45/vllJQmLw7NS2AG2NMgnk9wvWz0hER/vp2CZEkdS20AG6MMUnQPdPLlaemsWlPhFeWlidlGxbAjTEmSaaNSOHE4wLMy69ISr4UC+DGGJNEl89IJyfLw1/fKk34SPcWwI0xJomCAeHbszIoKo/yj3dLaewgOhWhuue3MTGNMSbJBvf0ccHxqTz3UTkfrK3k5DHBWucrrYiybV+EbQVhthZE2L4vzJ5DddfaLYAbY0wLmD0xyJrtIZ76oIzhffykBoRtBWG27YuwtSDM9n0R9hcfCdbZGR4G5niZNiKFh+pYp42JaYwxLeRQaZQ7ny6kvEqJuLFagJ5dPQzs4WNQjpcBPXwMzPGSETzSwl3XmJhWAzfGmBbSNd3DTWdlsHhdFf26exmY42VAdx/BQNMGR7YAbowxLWhUPz+j+vkTsi7rhWKMMe2UBXBjjGmnLIAbY0w71WAAF5GgiCwRkRUi8pmI3Om+PkREFovIehF5WkQCyS+uMcaYavHUwCuB01V1AjAR+IqITAd+CfxOVUcAB4FvJa+YxhhjamowgKujxH3qd/8UOB2Y677+GHBBUkpojDGmVnG1gYuIV0SWA3uBt4CNwCFVrR65cwfQr45lbxCRpSKytKCgIBFlNsYYQ5wBXFUjqjoR6A9MA0bXNlsdyz6oqnmqmpeTk9P0khpjjDlKo27kUdVDIrIQmA50FRGfWwvvD+xsaPn8/PwSEfmiSSVtX3oA+1q7EEnWGfYRbD87mva6n4Nqe7HBAC4iOUDIDd6pwJk4FzAXABcDTwFXAy/FUYgvarufv6MRkaUdfT87wz6C7WdH09H2M54aeB/gMRHx4jS5PKOqr4jIGuApEbkL+BR4OInlNMYYU0ODAVxVVwKTanl9E057uDHGmFbQ0ndiPtjC22stnWE/O8M+gu1nR9Oh9rNF84EbY4xJHMuFYowx7ZQFcGOMaafiSWY1QEQWiMhaN5nVze7r2SLylpvM6i0R6ea+fpyIfCQilSLyvYbW404TEfmpu651IvKuiOTGTP+FiGwXkRKSJIH7WWvyL3daQETuFZGNIrJBRF4RkYENHZ+2uJ8x6/OKyKci8kqc+1nn8WmL+ygiW0RklYgsF5GlMa/Xe87GzPeyiKxO9D4mcj9FZJS7f9V/RSJySzz72Z4+m+60W911rBaRJ0U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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "\n", + "df_dublin_unemployed.plot(kind='line',color='cornflowerblue',label ='Unemployed',x='Quarter',y='Value',ax=ax)\n", + "\n", + "plt.title(\"Unemployment in Dublin\" + \"\\n\" + \"Persons aged 15 years and over (Thousands)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extract employment figures for Dublin" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RegionQuarterStatisticValue
0Dublin2012Q1Persons aged 15 years and over in Employment (...543.5
1Dublin2012Q2Persons aged 15 years and over in Employment (...549.6
2Dublin2012Q3Persons aged 15 years and over in Employment (...552.0
3Dublin2012Q4Persons aged 15 years and over in Employment (...560.6
4Dublin2013Q1Persons aged 15 years and over in Employment (...550.3
5Dublin2013Q2Persons aged 15 years and over in Employment (...562.1
6Dublin2013Q3Persons aged 15 years and over in Employment (...576.3
7Dublin2013Q4Persons aged 15 years and over in Employment (...578.6
8Dublin2014Q1Persons aged 15 years and over in Employment (...581.1
9Dublin2014Q2Persons aged 15 years and over in Employment (...589.3
10Dublin2014Q3Persons aged 15 years and over in Employment (...593.9
11Dublin2014Q4Persons aged 15 years and over in Employment (...606.4
12Dublin2015Q1Persons aged 15 years and over in Employment (...600.9
13Dublin2015Q2Persons aged 15 years and over in Employment (...610.2
14Dublin2015Q3Persons aged 15 years and over in Employment (...622.7
15Dublin2015Q4Persons aged 15 years and over in Employment (...629.3
16Dublin2016Q1Persons aged 15 years and over in Employment (...633.8
17Dublin2016Q2Persons aged 15 years and over in Employment (...641.3
18Dublin2016Q3Persons aged 15 years and over in Employment (...648.2
19Dublin2016Q4Persons aged 15 years and over in Employment (...653.5
20Dublin2017Q1Persons aged 15 years and over in Employment (...648.8
21Dublin2017Q2Persons aged 15 years and over in Employment (...649.6
22Dublin2017Q3Persons aged 15 years and over in Employment (...663.3
23Dublin2017Q4Persons aged 15 years and over in Employment (...675.5
24Dublin2018Q1Persons aged 15 years and over in Employment (...683.9
25Dublin2018Q2Persons aged 15 years and over in Employment (...695.1
26Dublin2018Q3Persons aged 15 years and over in Employment (...696.2
27Dublin2018Q4Persons aged 15 years and over in Employment (...701.4
28Dublin2019Q1Persons aged 15 years and over in Employment (...704.9
29Dublin2019Q2Persons aged 15 years and over in Employment (...716.7
\n", + "
" + ], + "text/plain": [ + " Region Quarter Statistic Value\n", + "0 Dublin 2012Q1 Persons aged 15 years and over in Employment (... 543.5\n", + "1 Dublin 2012Q2 Persons aged 15 years and over in Employment (... 549.6\n", + "2 Dublin 2012Q3 Persons aged 15 years and over in Employment (... 552.0\n", + "3 Dublin 2012Q4 Persons aged 15 years and over in Employment (... 560.6\n", + "4 Dublin 2013Q1 Persons aged 15 years and over in Employment (... 550.3\n", + "5 Dublin 2013Q2 Persons aged 15 years and over in Employment (... 562.1\n", + "6 Dublin 2013Q3 Persons aged 15 years and over in Employment (... 576.3\n", + "7 Dublin 2013Q4 Persons aged 15 years and over in Employment (... 578.6\n", + "8 Dublin 2014Q1 Persons aged 15 years and over in Employment (... 581.1\n", + "9 Dublin 2014Q2 Persons aged 15 years and over in Employment (... 589.3\n", + "10 Dublin 2014Q3 Persons aged 15 years and over in Employment (... 593.9\n", + "11 Dublin 2014Q4 Persons aged 15 years and over in Employment (... 606.4\n", + "12 Dublin 2015Q1 Persons aged 15 years and over in Employment (... 600.9\n", + "13 Dublin 2015Q2 Persons aged 15 years and over in Employment (... 610.2\n", + "14 Dublin 2015Q3 Persons aged 15 years and over in Employment (... 622.7\n", + "15 Dublin 2015Q4 Persons aged 15 years and over in Employment (... 629.3\n", + "16 Dublin 2016Q1 Persons aged 15 years and over in Employment (... 633.8\n", + "17 Dublin 2016Q2 Persons aged 15 years and over in Employment (... 641.3\n", + "18 Dublin 2016Q3 Persons aged 15 years and over in Employment (... 648.2\n", + "19 Dublin 2016Q4 Persons aged 15 years and over in Employment (... 653.5\n", + "20 Dublin 2017Q1 Persons aged 15 years and over in Employment (... 648.8\n", + "21 Dublin 2017Q2 Persons aged 15 years and over in Employment (... 649.6\n", + "22 Dublin 2017Q3 Persons aged 15 years and over in Employment (... 663.3\n", + "23 Dublin 2017Q4 Persons aged 15 years and over in Employment (... 675.5\n", + "24 Dublin 2018Q1 Persons aged 15 years and over in Employment (... 683.9\n", + "25 Dublin 2018Q2 Persons aged 15 years and over in Employment (... 695.1\n", + "26 Dublin 2018Q3 Persons aged 15 years and over in Employment (... 696.2\n", + "27 Dublin 2018Q4 Persons aged 15 years and over in Employment (... 701.4\n", + "28 Dublin 2019Q1 Persons aged 15 years and over in Employment (... 704.9\n", + "29 Dublin 2019Q2 Persons aged 15 years and over in Employment (... 716.7" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_dublin_employed = dataset.to_data_frame(blocked_dims={'Region':'IE061', 'Statistic':'QLF08C01'})\n", + "df_dublin_employed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot employment figures for Dublin" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "\n", + "df_dublin_employed.plot(kind='line',color='orange',label ='Employed',x='Quarter',y='Value',ax=ax)\n", + "\n", + "plt.title(\"Employment in Dublin\" + \"\\n\" + \"Persons aged 15 years and over (Thousands)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot employment and unemployment figures for Dublin together on the same axis for comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "\n", + "df_dublin_employed.plot(kind='line',color='orange',label='Employed',x='Quarter',y='Value',ax=ax)\n", + "df_dublin_unemployed.plot(kind='line',color='cornflowerblue',label='Unemployed',x='Quarter',y='Value',ax=ax)\n", + "\n", + "plt.title(\"Employment versus Unemployment in Dublin\" + \"\\n\" + \"Persons aged 15 years and over (Thousands)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}