CODEWITHSUNDEEP
pythonpandasdata-science
Sun
Sun

Reputation: 3564

Pandas dataframe timedelta is giving exceptions

I am trying to get the next month first date based on billDate in a dataframe.

I did this:

import pandas as pd
import datetime
from datetime import timedelta
dt = pd.to_datetime('15/4/2019', errors='coerce')
print(dt)
print((dt.replace(day=1) + datetime.timedelta(days=32)).replace(day=1))

It is working perfectly, and the output is :

2019-04-15 00:00:00
2019-05-01 00:00:00

Now, I am applying same logic in my dataframe in the below code

df[comNewColName] = (pd.to_datetime(df['billDate'], errors='coerce').replace(day=1) + datetime.timedelta(days=32)).replace(day=1)

But I am getting error like this:

---> 69                 df[comNewColName] = (pd.to_datetime(df['billDate'], errors='coerce').replace(day=1) + datetime.timedelta(days=32)).replace(day=1)
     70                 '''print(df[['billDate']])'''
     71                 '''df = df.assign(Product=lambda x: (x['Field_1'] * x['Field_2'] * x['Field_3']))'''

TypeError: replace() got an unexpected keyword argument 'day'

Upvotes: 0

Views: 281

Answers (1)

jezrael
jezrael

Reputation: 862511

You can use Series.to_period for month periods, add 1 for next month and then convert back to datetimes by Series.dt.to_timestamp:

print (df)
    billDate
0  15/4/2019
1  30/4/2019
2  15/8/2019

df['billDate'] = (pd.to_datetime(df['billDate'], errors='coerce', dayfirst=True)
                   .dt.to_period('m')
                   .add(1)
                   .dt.to_timestamp())
print (df)
    billDate
0 2019-05-01
1 2019-05-01
2 2019-09-01

Upvotes: 2

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