Pandas Updating Weekend Date To Nearest Business Day

I have a dataframe that currently looks like this: raw_data = {'AllDate':['2017-04-05','2017-04-06','2017-04-07','2017-04-08','2017-04-09']} import pandas as pd df = pd.DataFrame(r

Solution 1:

This works best (adding to the answer posted by Zhe):

import pandas as pd
import time
from datetime import datetime,timedelta
df = pd.DataFrame({'AllDate':['2017-04-05','2017-04-06','2017-04-07','2017-04-08','2017-04-09']})
df['WeekDate'] = [x if x.weekday() not in [5,6] else x - timedelta(days = (x.weekday()-4)) for x in pd.to_datetime(df['AllDate'])]

Solution 2:

Try:

import pandas as pd
import time

df = pd.DataFrame({
    'AllDate':['2017-04-05','2017-04-06','2017-04-07','2017-04-08','2017-04-09']
})
df['WeekDate'] = [
    x if x.weekday() notin [5,6] elseNonefor x in pd.to_datetime(df['AllDate'])
]
print(df.ffill())

Solution 3:

Here's perhaps a simpler answer that comes up a lot dealing with timeseries, etc. Key is the offset objects available in Pandas tseries

df = pd.DataFrame({"AllDate": ["2017-04-01", "2017-04-02", "2017-04-03", "2017-04-04", "2017-04-09"]})
df["AllDate"] = pd.to_datetime(df["AllDate"])
df["PrevBusDate"] = df["AllDate"].apply(pd.tseries.offsets.BusinessDay().rollback)

df.head()
...
>>>    AllDate PrevBusDate
0   2017-04-01  2017-03-31
1   2017-04-02  2017-03-31
2   2017-04-03  2017-04-03
3   2017-04-04  2017-04-04
4   2017-04-09  2017-04-07

NB: Don't have to convert the 'AllDate' column if you don't want to. Can simply generate the offsets and work with them however you like, eg:

[pd.tseries.offsets.BusinessDay().rollback(d) for d in pd.to_datetime(df["AllDate"])]