Create a date column and assign value from a condition based on an existing date column in pandas

Question

I have the following:

import pandas as pd

    file = pd.DataFrame()
    
        file['CASH RECIEVED DATE'] = ['2018-07-23', '2019-09-26', '2017-05-02']

and I need to create a column called Cash Received Date

file['Cash Received Date']

such as if [CASH_RECIEVED_DATE] is not null && [CASH RECIEVED_DATE] <= 2022-09-01 then [Cash Received Date] will be 2019-09-01, otherwise it will be the value of [CASH_RECIEVED_DATE], so the output would be:

    file['Cash Received Date'] = ['2019-09-01', '2019-09-26', '2019-09-01']

How do I achieve this by creating a function?

Many thanks,
Rafa

Asked By: user13410280

||

Source

Answer 1

def compare_date(x):
    if pd.to_datetime(x) > pd.to_datetime('2019-09-01'):
        return pd.to_datetime(x)
    else:
        return pd.to_datetime('2019-09-01')

file['Cash Received Date'] = file['CASH RECIEVED DATE'].apply(lambda x: compare_date(x))

gives file as :

  CASH RECIEVED DATE Cash Received Date
0         2018-07-23         2019-09-01
1         2019-09-26         2019-09-26
2         2017-05-02         2019-09-01

P.S. It’s best practice to give columns and dataframes more distinct names so as to prevent confusion with very similar or vague variable/column names.

Answered By: Frodnar

Answer 2

using .mask

first step is to ensure your values are datetime values.

df['Cash Received Date'] = pd.to_datetime(df['Cash Received Date'])

df['new_date'] = df['Cash Received Date'].mask(
                  df['Cash Received Date'].dropna().le('2019-09-01'),'2019-01-01')

  Cash Received Date    new_date
0         2019-09-01  2019-01-01
1         2019-09-26  2019-09-26
2         2019-09-01  2019-01-01

Answered By: Umar.H

Create a date column and assign value from a condition based on an existing date column in pandas

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