Bar plot ordered by categories in Pandas

categorical_bar_plot.py

import pandas as pd

judgements = ['strong', 'attribute mismatch', 'weak/related', 'unrelated', 'unjudged']
for key, report in reports.items(): 
    report['judgement'] = pd.Categorical(report['judgement'], categories=judgements, ordered=True)
    
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

fig, axes = plt.subplots(3, 1, figsize=(11, 11))
axes[0].bar(judgements, reports['Nov2019_baseln']['judgement'].value_counts().sort_index(), label='Nov2019_baseln', alpha=0.8, width=0.4)
axes[0].bar(judgements, reports['Nov2019_exp']['judgement'].value_counts().sort_index(), label='Nov2019_exp', alpha=0.7, align='edge', width=0.4)
axes[0].set_title('Nov2019')
axes[0].legend()

axes[1].bar(judgements, reports['Feb2020_baseln']['judgement'].value_counts().sort_index(), label='Feb2020_baseln', alpha=0.8, width=0.4)
axes[1].bar(judgements, reports['Feb2020_exp']['judgement'].value_counts().sort_index(), label='Feb2020_exp', alpha=0.7, align='edge', width=0.4)
axes[1].set_title('Feb2020')
axes[1].legend()


axes[2].bar(judgements, reports['Mar2020_baseln']['judgement'].value_counts().sort_index(), label='Mar2020_baseln', alpha=0.8, width=0.4)
axes[2].bar(judgements, reports['Mar2020_exp']['judgement'].value_counts().sort_index(), label='Mar2020_exp', alpha=0.7, align='edge', width=0.4)
axes[2].set_title('Mar2020')
axes[2].legend()


for i in range(len(axes)):
    axes[i].set_ylim([0, 3500])
    #axes[i].xaxis.set_major_locator(mdates.DayLocator(interval=2))
    # Format x-tick labels as 3-letter month name and day number
    #axes[i].xaxis.set_major_formatter(mdates.DateFormatter('%b %d'));
    #axes[i].xaxis.set_tick_params(rotation=45)

fig.suptitle('Model comparison', fontsize=16)
fig.tight_layout(pad=3.0)
fig.savefig('./figures/analysis.png', dpi=300)