Pandas .replace inside a for i in range()-CodePudding

I don't know why my .replace wont advance to the next value in my for loop.

Code:

import pandas as pd
import numpy as np

df1 = pd.read_csv("df1.csv")
df2 = pd.read_csv("df2.csv")


print(df1)
print(df2)

df3 = pd.merge(df1, df2, on='ID', how='left')

print(df3)

C = len(df3.index)

for i in range(C):
    if pd.isnull(df3.at[i, 'Leg']) == True:
        df3.replace((df3.at[i, 'Leg']), (df3.at[i, 'ID']), inplace=True)
        df3.replace((df3.at[i, 'SAP']), (df3.at[i, 'ID']), inplace=True)

print(df3)

Result:

My replaced values in the 'Leg' and 'SAP' columns don't advance with my for loop. I want any NaN values to read the same as the corresponding value in the 'ID' column. Without changing any other values in any other columns.

Image as Text:

    ID   Desc  QTY
0  100  Test1    1
1  200  Test2    7
2  300  Test3    1
3  400  Test4    3
4  500  Test5    4
5  600  Test6    9

    ID    Leg     SAP
0  100  165-8   142.0
1  200    NaN   655.0
2  300    NaN     NaN
3  400  21-56  4343.0
4  500   65-5     NaN
5  600  556-4   233.0

    ID   Desc  QTY    Leg     SAP
0  100  Test1    1  165-8   142.0
1  200  Test2    7    NaN   655.0
2  300  Test3    1    NaN     NaN
3  400  Test4    3  21-56  4343.0
4  500  Test5    4   65-5     NaN
5  600  Test6    9  556-4   233.0

    ID   Desc  QTY    Leg     SAP
0  100  Test1    1  165-8   142.0
1  200  Test2    7    200   200.0
2  300  Test3    1    200   200.0
3  400  Test4    3  21-56  4343.0
4  500  Test5    4   65-5   200.0
5  600  Test6    9  556-4   233.0

Expected Result:

    ID   Desc  QTY    Leg     SAP
0  100  Test1    1  165-8   142.0
1  200  Test2    7    200   655.0
2  300  Test3    1    300   300.0
3  400  Test4    3  21-56  4343.0
4  500  Test5    4   65-5   500.0
5  600  Test6    9  556-4   233.0

CodePudding user response：

You can try:

df3["Leg"] = df3["Leg"].combine_first(df3["ID"])
df3["SAP"] = df3["SAP"].combine_first(df3["ID"])
print(df3)

Prints:

    ID   Desc  QTY    Leg     SAP
0  100  Test1    1  165-8   142.0
1  200  Test2    7    200   655.0
2  300  Test3    1    300   300.0
3  400  Test4    3  21-56  4343.0
4  500  Test5    4   65-5   500.0
5  600  Test6    9  556-4   233.0

Or:

df3[["Leg", "SAP"]] = (
    df3[["ID", "Leg", "ID", "SAP"]].ffill(axis=1).iloc[:, [1, 3]]
)