Apply a custom function on columns in a pandas dataframe

Question

I want to do something equivalent to

Select x,y,z from data where f(x, Y);

And f is my customized function that looks into the values of specific columns in a row and returns True or False. I tried the following:

df = df.ix[_is_detection_in_window(df['Product'], df['CreatedDate'])== True]

But I get

TypeError: 'Series' objects are mutable, thus they cannot be hashed

I think it does not iterate over the rows.
I also tried:

 i = 0

   for index, row in df.iterrows():

           if _is_detection_in_window(row['Product'], row['CreatedDate']):

                   print 'in range '

                   new_df.iloc[i] = row

                   i+= 1

   df = new_df

but I get :

IndexError: single positional indexer is out-of-bounds

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Answer 1

It seems like your function doesn't accept Series, but that can be changed using np.vectorize:

v = np.vectorize(_is_detection_in_window)
df = df.loc[v(df['Product'], df['CreatedDate'])]

Furthermore, you should refrain from using .ix which is now deprecated as of v20.

Apply a custom function on columns in a pandas dataframe

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