Plotting a referendum - Income

Following the shock decision by the UK to leave the EU, many ideas were put forward as to the reasons behind why so many people voted the ways that they did.

In an effort to learn more about data handling in python, using pandas, matplotlib and other fun stuff I scoured the internet for data and set about plotting graphs.

Overview
Sourcing data
- Results
- Postcodes
- The CAP
- Immigration
- Income
- Unemployment
Mapping data
Scatter plots

The income data downloaded from Nomis is already very structured and succinct, as we only chose the data we wanted to use. As such we don’t need to do much to it before pickling it.

We rename the columns, set the electoral wards as index and throw away a few rows which were constructed from the comments in the initial csv file.

"""
Data available from:
    NOMIS website.
    Dataset: Annual Survey of Hours and Earnings
        -> annual survey of hours and earnings - resident analysis
    Geography: local authorities: district / unitary (as of April 2015)
    Date: 2015
    Pay and hours: Annual pay - gross
    Sex & full/part-time: full-time workers
    Variable: median
    Include Area codes
Assumes resulting filename is: 'nomis-media-income.csv'
Data is released under the Open Government Licence:
        http://www.nationalarchives.gov.uk/doc/open-government-licence/
"""

import pandas as pd

inc = pd.read_csv('./raw/nomis-median-income.csv',header=7, na_values=['#','-'])
inc.columns = ['Area','Code','Income','Income_Conf']
inc.set_index('Code',inplace=True)
inc.drop('Column Total', inplace=True)
inc.sort_index(inplace=True)
inc = inc.dropna(subset=['Area'])

inc.to_pickle('./data/income.pkl')

File also available here.