Q:

Multiplying columns with missing values in Python (pandas)

I have a dataset with multiple columns which i need to multiply. One of these columns have missing values in them, what I would like is that when I am multiplying the columns, the missing values are skipped, and the columns which do have values in them are used for the result.
For example,
A B C

1 2 1

2 3 NaN

1 4 NaN

1 1 2

For each row, i would like the result to be column D with following values:-
2

6

4

2

I have tried .fillna(), .notnull(), .isnull() and .dropna() but I did not get the desired result.
Thanks in advance
Edit:
I had tried:

df['D'] = df['A'].fillna()*df['B']*df['C']
df

A:

Here's an example of utilizing .fillna():
import pandas as pd
import numpy as np

data = pd.DataFrame({"a":[3,6,7],"b":[2,5,7],"c":[5,np.nan,np.nan]})

A quick look at data:
a   b   c
3   2   5.0
6   5   NaN
7   7   NaN

Then utilize .fillna():
data.fillna(1).prod(axis=1)

Result:
30.0
30.0
49.0

I noticed that you used .fillna(). If you could include attempted code, it would help us debug your code for a more precise solution.

