Replace Particular column values except first row in Dataframe In Python

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In Python, replace specific column values except the first row in a DataFrame.


I'm using pandas and have put some data into a DataFrame, as shown below. My data looks like this: that the Data2 and Data3 have the same repeated values, thus what I want to do is maintain one value for Data2 and Data3 for the same Date_x  column and replace the rest with zero.
(Below table is visible correctly in desktop mode)
Date_x Data2 Data3 Month Date_y
0 2021-07-03 2000 1400 7 2021-07-03
1 2021-07-03 2000 1400 7 2021-07-10
2 2021-07-03 2000 1400 7 2021-07-17
3 2021-07-03 2000 1400 7 2021-07-24
4 2021-07-03 2000 1400 7 2021-07-31
5 2021-08-07 1000 1500 8 2021-08-07
6 2021-08-07 1000 1500 8 2021-08-14
7 2021-08-07 1000 1500 8 2021-08-21
8 2021-08-07 1000 1500 8 2021-08-28
9 2021-09-04 3000 1600 9 2021-09-04
10 2021-09-04 3000 1600 9 2021-09-11
11 2021-09-04 3000 1600 9 2021-09-18
12 2021-09-04 3000 1600 9 2021-09-25
13 2021-10-02 4000 1700 10 2021-10-02
14 2021-10-02 4000 1700 10 2021-10-0

Steps for Code

  • First, I executed the block for DataFrame length.
  • The current Index value of Date x was then compared to the prior Index value of Date x.
  • If the condition is true, it replaces the Data2 and Data3 values in the DataFrame with 0.
  • If the condition fails, the case is passed.

Python Code

  for i in range(len(df_merged)-1):
    if df_merged['Date_x'][i] == df_merged['Date_x'][i+1]:
        df_merged['Data2'][i+1]=0
        df_merged['Data3'][i+1]=0
    else:
        pass

Final result after execution
(Below table is visible correctly in desktop mode)

 

 Date_x Data2 Data3 Month Date_y
0 2021-07-03 2000 1400 7 2021-07-03
1 2021-07-03 0 0 7 2021-07-10
2 2021-07-03 0 0 7 2021-07-17
3 2021-07-03 0 0 7 2021-07-24
4 2021-07-03 0 0 7 2021-07-31
5 2021-08-07 1000 1500 8 2021-08-07
6 2021-08-07 0 0 8 2021-08-14
7 2021-08-07 0 0 8 2021-08-21
8 2021-08-07 0 0 8 2021-08-28
9 2021-09-04 3000 1600 9 2021-09-04
10 2021-09-04 0 0 9 2021-09-11
11 2021-09-04 0 0 9 2021-09-18
12 2021-09-04 0 0 9 2021-09-25

Please if you have any question regarding the same or have any better solution please share it in comment section.