pandas 相当于 np.where

Question

np.where has the semantics of a vectorized if/else (similar to Apache Spark's when / otherwise DataFrame method). I know that I can use np.where on pandas.Series , but pandas often defines its own API to use instead of raw numpy functions, which is usually more convenient with pd.Series / pd.DataFrame .

Sure enough, I found pandas.DataFrame.where . However, at first glance, it has completely different semantics. I could not find a way to rewrite the most basic example of np.where using pandas where :

# df is pd.DataFrame
# how to write this using df.where?
df['C'] = np.where((df['A']<0) | (df['B']>0), df['A']+df['B'], df['A']/df['B'])

Am I missing something obvious? Or is pandas' where intended for a completely different use case, despite same name as np.where ?

Answer 1

Try:

(df['A'] + df['B']).where((df['A'] < 0) | (df['B'] > 0), df['A'] / df['B'])

The difference between the numpy where and DataFrame where is that the default values are supplied by the DataFrame that the where method is being called on ( docs ).

Ie

np.where(m, A, B)

is roughly equivalent to

A.where(m, B)

If you wanted a similar call signature using pandas, you could take advantage of the way method calls work in Python :

pd.DataFrame.where(cond=(df['A'] < 0) | (df['B'] > 0), self=df['A'] + df['B'], other=df['A'] / df['B'])

or without kwargs (Note: that the positional order of arguments is different from the numpy where argument order ):

pd.DataFrame.where(df['A'] + df['B'], (df['A'] < 0) | (df['B'] > 0), df['A'] / df['B'])

Answer 2

I prefer using pandas' mask over where since it is less counterintuitive (at least for me).

(df['A']/df['B']).mask(df['A']<0) | (df['B']>0), df['A']+df['B'])

Here, column A and B are added where the condition holds, otherwise their ratio stays untouched.