Check if values in multiple columns are not null in pandas?

Question

Let's say I have a dataframe that looks like this

       A        B        C
0    NaN      NaN   lizard
1    NaN  penguin  giraffe
2  horse   turtle      NaN

What I want to do is add a column (D) whose value is either that of column A if it exists otherwise it should be a combination of values in column B and C providing they are both not null/none. If column A is null and either of B or C is null, then D should be null/none.

I can do as such in regards to column A by using numpy's where function as such:

df['D'] = np.where(df['A'].notnull, df['A'], np.nan)

However I don't know how I would also add this column if both B and C aren't null/none.

Ideally the data frame would then look like this:

       A        B        C               D
0    NaN      NaN   lizard             NaN
1    NaN  penguin  giraffe  penguingiraffe
2  horse   turtle      NaN           horse

Your np.where approach is fine if you replace np.nan with df["B"]+df["C"]. — Henry Yik
– Henry Yik, Commented Oct 27, 2021 at 14:31

Shubham Sharma · Accepted Answer · 2021-10-27 14:24:37Z

2

Concat the columns B and C, then fill the NaN values in column A and assign this result to column D

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

       A        B        C               D
0    NaN      NaN   lizard             NaN
1    NaN  penguin  giraffe  penguingiraffe
2  horse   turtle      NaN           horse

answered Oct 27, 2021 at 14:24

Shubham Sharma

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