Plotting a referendum - Results

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 Electoral Commission has made Referendum results available online. Note that the data is available under the Open Government License v2.0.

The following assumes that the .csv file is in the current directory and called EU-referendum-result-data.csv.

Having got the data, we construct a pandas DataFrame before pickling the data, so we can easily access it when we need to.

import pandas as pd

d = pd.read_csv('./EU-referendum-result-data.csv')
d = d.set_index('Area_Code').ix[:, 'Area':].sort_index()

d.to_pickle('results.pkl')

The data comes in a friendly format, so we don’t really need to perform many conversions to it. All we do is index the data by Area Code and throw away the columns which appear before Area before sorting the data by its index.

The DataFrame now contains the voting records for 382 areas, including the following information (d.info()):

Area                       382 non-null object
Electorate                 382 non-null int64
ExpectedBallots            382 non-null int64
VerifiedBallotPapers       382 non-null int64
Pct_Turnout                382 non-null float64
Votes_Cast                 382 non-null int64
Valid_Votes                382 non-null int64
Remain                     382 non-null int64
Leave                      382 non-null int64
Rejected_Ballots           382 non-null int64
No_official_mark           382 non-null int64
Voting_for_both_answers    382 non-null int64
Writing_or_mark            382 non-null int64
Unmarked_or_void           382 non-null int64
Pct_Remain                 382 non-null float64
Pct_Leave                  382 non-null float64
Pct_Rejected               382 non-null float64

The final results can be checked to be correct with:

>>> d[['Remain', 'Leave']].sum()
Remain    16141241
Leave     17410742
dtype: int64

Leave wins by just 1 269 501 votes (try d[['Remain', 'Leave']].sum().diff()), or just 3.78% of the votes (d[['Remain', 'Leave']].sum().diff().Leave / d['Valid_Votes'].sum() * 100).