Finding the longest sequence of dates in a dataframe - pandas

Question

I'd like to know how to find the longest unbroken sequence of dates (formatted as 2016-11-27) in a publish_date column (dates are not the index, though I suppose they could be).
There are a number of stack overflow questions which are similar, but AFAICT all proposed answers return the size of the longest sequence, which is not what I'm after.
I want to know e.g. that the stretch from 2017-01-01 to 2017-06-01 had no missing dates and was the longest such streak.

Answer 1

Here is an example of how you can do this:
import pandas as pd
import datetime
# initialize data
data = {'a': [1,2,3,4,5,6,7],
'date': ['2017-01-01', '2017-01-03', '2017-01-05', '2017-01-06', '2017-01-07', '2017-01-09', '2017-01-31']}
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
# create mask that indicates sequential pair of days (except the first date)
df['mask'] = 1
df.loc[df['date'] - datetime.timedelta(days=1) == df['date'].shift(),'mask'] = 0
# convert mask to numbers - each sequence have its own number
df['mask'] = df['mask'].cumsum()
# find largest sequence number and get this sequence
res = df.loc[df['mask'] == df['mask'].value_counts().idxmax(), 'date']
# extract min and max dates if you need
min_date = res.min()
max_date = res.max()
# print result
print('min_date: {}'.format(min_date))
print('max_date: {}'.format(max_date))
print('result:')
print(res)
The result will be:
min_date: 2017-01-05 00:00:00
max_date: 2017-01-07 00:00:00
result:
2 2017-01-05
3 2017-01-06
4 2017-01-07