/state_super_majorities

Primary LanguageJupyter Notebook

Analyzing State Supermajorities

  • The data for this analysis comes from Ballotpedia and contains data that classifies states according to the degree of "monopoly" a political party has over the state's government and how long that state's monopoly has been in place.
  • I encourage you to check out the analysis here which is where the data is source from.
  • For the remainder of this report, I will use the word "Trifecta" instead of "Monopoly".

Ballotpedia classifies states in different types of Trifectas. Below is Ballotpedia's definition for Trifectas:

State government trifecta is a term to describe single-party government, when one political party holds the governorship and majorities in both chambers of the state legislature.

Below I collect the dataset from Ballotpedia's website

from pathlib import Path
import pandas as pd
import geopandas as gpd
from datetime import datetime
import matplotlib.pyplot as plt
from IPython.display import display
import matplotlib.patches as mpatches

df = pd.read_html('https://ballotpedia.org/State_government_trifectas')[4]
df.head()
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Trifecta status by state
State Trifecta status Year of last status change Unnamed: 3_level_1
0 Alabama Republican trifecta 2011 NaN
1 Alaska Divided government 2015 NaN
2 Arizona Republican trifecta 2009 NaN
3 Arkansas Republican trifecta 2015 NaN
4 California Democratic trifecta 2011 NaN

Next I...

  • Drop unecessary columns
  • Add a column that counts the number years since the status of the state's trifecta has changed
df.columns = df.columns.droplevel().str.lower().str.replace(' ', '_')
df = df[[x for x in df.columns if 'unnamed' not in x]]
df = df.assign(years_with_trifecta=datetime.now().year - df.year_of_last_status_change.astype(int))
display(df.head())
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state trifecta_status year_of_last_status_change years_with_trifecta
0 Alabama Republican trifecta 2011 11
1 Alaska Divided government 2015 7
2 Arizona Republican trifecta 2009 13
3 Arkansas Republican trifecta 2015 7
4 California Democratic trifecta 2011 11

Next I filter out states with a "Diveded government" status because they different bodies of the state government are controlled by different political parties

df = df.query('~trifecta_status.str.contains("Divided")')

How many states have a current trifecta?

print('States with trifecta:', df.shape[0])
States with trifecta: 37

state_abbreviations = pd.read_html('https://www.bu.edu/brand/guidelines/editorial-style/us-state-abbreviations/', header=0)[0]
abbreviations = {}
nones = state_abbreviations.apply(lambda row: {row.iloc[0]: row.iloc[-1]}, axis=1).apply(lambda x: abbreviations.update(x))
df = df.assign(abbreviation=df.state.map(abbreviations))

fig, ax = plt.subplots(figsize=(15,10))
shapefile_path = str(Path('.') / 'shapefiles' /'cb_2018_us_state_20m.shp')
geo_file = gpd.read_file(shapefile_path).assign(supermajority=lambda x: x.STUSPS.isin(df.abbreviation.to_list()).astype(int))
geo_file.plot(column='supermajority', ax=ax, cmap='PiYG')
ax.set_xlim(right=-25)
ax.set_title('Super Majorities', fontsize=20)
green_patch = mpatches.Patch(color='#276419', label='Has a supermajority')
pink_patch = mpatches.Patch(color='#8e0152', label='No supermajority')
plt.legend(handles=[green_patch, pink_patch], fontsize=15);

png

How long on average have states held their current trifectas?

average_years_trifecta = round(df.years_with_trifecta.mean(), 2)
df.years_with_trifecta.hist(figsize=(15,6))
plt.title(f'Trifecta duration\n(Average Years with trifecta: {average_years_trifecta})', fontsize=15);

png

How many trifecta does each party have?

df.trifecta_status.value_counts().plot(kind='bar', figsize=(15,5))
plt.xticks(rotation=0, fontsize=15)
plt.title('Number of trifectas by political party', fontsize=25);

png

What is the average amount of years each party has held their trifecta?

fig, ax = plt.subplots(1,2, figsize=(15,6))

df.groupby('trifecta_status').years_with_trifecta.mean().plot(kind='bar', ax=ax[0])
for label in ax[0].get_xticklabels():
    label.set_rotation(0)
    label.set_fontsize(12)
    
df.groupby('trifecta_status').years_with_trifecta.median().plot(kind='bar', ax=ax[1])
for label in ax[1].get_xticklabels():
    label.set_rotation(0)
    label.set_fontsize(12)

ax[0].set_xlabel('')
ax[1].set_xlabel('')
ax[0].set_title('Mean years with trifecta', fontsize=15)
ax[1].set_title('Median years with trifecta', fontsize=15);

png