[Python] Find date range overlap in python

I am trying to find a more efficient way of finding overlapping data ranges (start/end dates provided per row) in a dataframe based on a specific column (id). Dataframe is sorted on the 'from' column. I think there is a way to avoid the double apply function as I did:

import pandas as pd
from datetime import datetime

df = pd.DataFrame(columns=['id','from','to'], index=range(5), \
data=[[878,'2006-01-01','2007-10-01'],
[878,'2007-10-02','2008-12-01'],
[878,'2008-12-02','2010-04-03'],
[879,'2010-04-04','2199-05-11'],
[879,'2016-05-12','2199-12-31']])

df['from'] = pd.to_datetime(df['from'])
df['to'] = pd.to_datetime(df['to'])


id from to
0 878 2006-01-01 2007-10-01
1 878 2007-10-02 2008-12-01
2 878 2008-12-02 2010-04-03
3 879 2010-04-04 2199-05-11
4 879 2016-05-12 2199-12-31


I used the "apply" function to loop on all groups and within each group, I use "apply" per row:

def check_date_by_id(df):

df['prevFrom'] = df['from'].shift()
df['prevTo'] = df['to'].shift()

def check_date_by_row(x):

if pd.isnull(x.prevFrom) or pd.isnull(x.prevTo):
x['overlap'] = False
return x

latest_start = max(x['from'], x.prevFrom)
earliest_end = min(x['to'], x.prevTo)
x['overlap'] = int((earliest_end - latest_start).days) + 1 > 0
return x

return df.apply(check_date_by_row, axis=1).drop(['prevFrom','prevTo'], axis=1)

df.groupby('id').apply(check_date_by_id)

id from to overlap
0 878 2006-01-01 2007-10-01 False
1 878 2007-10-02 2008-12-01 False
2 878 2008-12-02 2010-04-03 False
3 879 2010-04-04 2199-05-11 False
4 879 2016-05-12 2199-12-31 True


My code was inspired from the following links :

• 
  Efficient date range overlap calculation in python?
  
  
• 
  How to find range overlap in python?
  

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