[FIXED] Apply conditional statement to specific rows in Python

Issue

I have a dataset where I would like a certain statement to only be applied to specific rows in my dataset. I would like this to only be applied to rows that contain [type] == 'aa', and apply second statement for rows that contain [type] == 'bb'

Data

location    type    mig1        de          mig2        re
ny          aa      8/1/2021    10/1/2021   1/1/2022    2/1/2022
ny          aa      8/1/2021    10/1/2021   1/1/2022    2/1/2022
ca          aa      8/1/2021    10/1/2021   1/1/2022    2/1/2022
tx          bb      9/1/2021    11/1/2021   2/1/2022    3/1/2022

Desired

 #Date 8/1/2022 is input, which yields:

    location    type    mig1        de          mig2        re
    ny          aa      03/1/2022   05/1/2022   8/1/2022    7/1/2022
    ny          aa      03/1/2022   05/1/2022   8/1/2022    7/1/2022
    ca          aa      03/1/2022   05/1/2022   8/1/2022    7/1/2022
    tx          bb      03/1/2021   11/1/2021   08/1/2022   3/1/2022



Logic - we see that the shift applies to all columns when the type == aa
        when the type value == bb, a different statement is applied

Doing

#takes input value

datevalue = pd.to_datetime(input("Enter shift: "))

#shifts dates from the datevalue input - However I would like d variable to only be applied to the rows that contain [type] == 'aa'

#apply e variable to rows that contain [type] = 'bb'

d = {
    'mig1': pd.DateOffset(months=5),
    'de': pd.DateOffset(months=3),
    're': pd.DateOffset(months=1),
}

e = {
        'mig1': pd.DateOffset(months=5),
        
    }


s = pd.Series(d).rsub(datevalue)
df.assign(**{**s, 'mig2': datevalue})

Any suggestion is appreciated

Solution

Try:

datevalue = pd.to_datetime(input("Enter shift: "), dayfirst=False)  # 08/01/2022

new_values = pd.Series(d).rsub(datevalue) \
                         .append(pd.Series({'mig2': datevalue})) \
                         .dt.strftime('%-m/%-d/%Y')
df.update(pd.DataFrame([new_values], index=df[df['type'] == 'aa'].index))

# do the same for type='bb' and 'e' dict
new_values = pd.Series(e).rsub(datevalue) \
                         .append(pd.Series({'mig2': datevalue})) \
                         .dt.strftime('%-m/%-d/%Y')
df.update(pd.DataFrame([new_values], index=df[df['type'] == 'bb'].index))

Output:

>>> df
  location type      mig1         de      mig2        re
0       ny   aa  3/1/2022   5/1/2022  8/1/2022  7/1/2022
1       ny   aa  3/1/2022   5/1/2022  8/1/2022  7/1/2022
2       ca   aa  3/1/2022   5/1/2022  8/1/2022  7/1/2022
3       tx   bb  3/1/2022  11/1/2021  8/1/2022  3/1/2022

Answered By - Corralien