I'm hoping to replace values in all columns within a df using integers from a specified column. Using the df below I want to use the values in Code and replace them in all other columns.
df = pd.DataFrame({
'Place' : ['X','Y','X','Y','X','Y','X','Y'],
'Number' : ['A','B','C','D','F','G','H','I'],
'Code' : [1,2,3,0,1,2,5,4],
'Value' : ['','','','','','','','']
})
df[:] = df['Code'].apply(lambda x: x if np.isreal(x) else 0).astype(int)
print(df)
Intended Output:
Place Number Code Value
0 1 1 1 1
1 2 2 2 2
2 3 3 3 3
3 0 0 0 0
4 1 1 1 1
5 2 2 2 2
6 5 5 5 5
7 4 4 4 4
Use reindex, ffill, bfill
df[['Code']].reindex(columns=df.columns).ffill(1).bfill(1).astype(int)
Out[256]:
Place Number Code Value
0 1 1 1 1
1 2 2 2 2
2 3 3 3 3
3 0 0 0 0
4 1 1 1 1
5 2 2 2 2
6 5 5 5 5
7 4 4 4 4
Numpy solution
df[:] = np.transpose([df.Code] * df.shape[1])
Out[314]:
Place Number Code Value
0 1 1 1 1
1 2 2 2 2
2 3 3 3 3
3 0 0 0 0
4 1 1 1 1
5 2 2 2 2
6 5 5 5 5
7 4 4 4 4
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With