In this post I wanted to look at the distribution of donation sizes. I was curious if one of the candidates was better at getting small donations versus large contributions from organizations.

Data is from the FEC

import pandas as pd
import plotly.plotly as py
import cufflinks as cf

cf.set_config_file(theme='polar')
cf.go_online()

Preparing The Data

df = pd.read_csv("P00000001-ALL.csv", index_col=False)

Filtering Out Refunds

df = df[df.contb_receipt_amt > 0]

Create A DataFrame Filtered To The Final Major Candidates

df = df[df.cand_nm.isin(['Trump, Donald J.','Clinton, Hillary Rodham'])]

What Is The Most Common Contribution Size?

bins = [0, 5, 10, 25, 50, 75, 100, 500, 1000, 10000, 100000, 1000000, 10000000, 100000000]
group_names = ['0 to 5', '5 to 10', '10 to 25', '25 to 50', '50 to 75', '75 to 100', '100 to 500', 
                '500 to 1000', '1000 to 10000', '10000 to 100000', '100000 to 1000000', 
               '1000000 to 10000000', '10000000 to 100000000']

categories = pd.cut(df['contb_receipt_amt'], bins, labels=group_names)
df['categories'] = pd.cut(df['contb_receipt_amt'], bins, labels=group_names)
df['cat_str'] = df['categories'].astype('str')

df['cat_str'].value_counts().iplot(kind='bar', yTitle='Number of Contributions',
                                   title='Distribution of Contribution Sizes', filename='2016-fec-donation-sizes')

What Is The Most Common Contribution Size By Candidate?

r = df[['cat_str', 'cand_nm']].groupby('cat_str').cand_nm.value_counts()
r = r.unstack().reset_index().set_index('cat_str')
r = r.reindex(index = group_names)
r.iplot(kind='bar', barmode='stack', filename='2016-fec-contribution-size-comparison', 
        yTitle='Number of Contributions', xTitle='Contribution Range In USD')

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The Jupyter Notebook for this work can be found on GitHub.