import pandas as pd import numpy as np import matplotlib.pyplot as plt # Create index of periods (as date_range only allows range of 584 years due to nanosecond resolution) idx = pd.period_range('0001-01-01', '2020-12-31', freq='D') # Create dataframe from index of periods. For weekday, Monday=0 and Sunday=6. df = pd.DataFrame({'date': idx, 'weekday_num': idx.weekday, 'weekday': None, 'monthday': idx.day}) # Names are easier to with than numbers: map the day of week to weekday name weekday_map_dict = {0: 'monday', 1: 'tuesday', 2: 'wednesday', 3: 'thursday', 4: 'friday', 5: 'saturday', 6: 'sunday'} df['weekday'] = df['weekday_num'].map(weekday_map_dict)

daycombos = df.groupby(['weekday_num','monthday', 'weekday']).size().to_frame('frequency').reset_index().sort_values(by=['frequency'], ascending=False)

daycombos[['weekday', 'monthday', 'frequency']][daycombos.monthday.eq(13)]

daycombos[['weekday', 'frequency']][daycombos.monthday.eq(13)].plot.bar(x='weekday', y='frequency', legend=False, title='Frequency of the 13th by weekday')

# What are the most common day/date combinations? daycombos[['weekday', 'monthday', 'frequency']].head(30)