Fillna only if the condition of another column is met

I have a dataframe test that looks like this:

| sales |transactions|
|-------|------------|
|0.0    |NaN         |
|0.0    |NaN         |
|3802.29|NaN         |
|4520.35|8359        |

I’m looking for a way to fill the NaN values with 0 of only the rows that have 0 in the ‘sales’ column, without changing the other rows. I tried this:

test['transactions'] = test.apply(
        lambda row: 0 if row['sales'] == 0 else None,
        axis=1)

It works for those rows but the problem is that fills with NaN all the other rows

Output:

| sales |transactions|
|-------|------------|
|0.0    |0.0         |
|0.0    |0.0         |
|3802.29|NaN         |
|4520.35|NaN         |

Expected result:

| sales |transactions|
|-------|------------|
|0.0    |0.0         |
|0.0    |0.0         |
|3802.29|NaN         |
|4520.35|8359        |

Thank you in advance.

>Solution :

mask

Specifically, use the other argument in mask

df.assign(
    transactions=df.transactions.mask(df.sales == 0, other=0)
)

     sales  transactions
0     0.00           0.0
1     0.00           0.0
2  3802.29           NaN
3  4520.35        8359.0

In the event you have a transaction that isn’t null where sales are zero and don’t want to replace a non-null transaction with zero then do:

mask = df.sales == 0 & df.transactions.isna()
df.assign(
    transactions=df.transactions.mask(mask, other=0)

)