Pandas DataFrame应用函数，多个参数

Question

I have a Pandas dataframe and one of the columns is a string. I imported a function from an external module to do some RegEx checking and reduce this string to a short classification.

This works:

df['PageCLass'] = df['PageClass'].apply(lambda x: PageClassify.page_classify(x))

However what I would really like to do is incorporate another column 'Rev' in the dataframe which happens to be either be a float or NaN into the checking.

When I did this:

df['PageCLass'] = df['PageClass'].apply(lambda x: PageClassify.page_classify(x,df['Rev']))

and I was doing logical checks inside the classification function on the 2nd argument, I got this error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

What I am looking for is a way to capture the 2nd argument value by value, just as lambda x: captures the first argument value by value.

Answer 1

The method above is ok I guess if it worked... In my opinion it does not answer the question because you're concatenating two arguments into one.

A way to do this to allow you to pass two arguments to apply:

df['PageCLass'] = df[['PageClass','Rev']].apply(lambda x: PageClassify.page_classify(*x), axis=1)

I don't know what the page_classify method looks like but if it takes two arguments the above should work. Does this work for you?

Answer 2

Assuming you want to just do this row by row, the following should work:

df['PageCLass'] = (df['PageClass'] + df['Rev'].apply(str)).apply(lambda x: PageClassify.page_classify(x))

Here, you are simply concatenating the two dataframe columns together and then you can apply the function to each row in the new column. If you need to check the values of PageClass and Rev as separate arguments, you could also add a delimiter (eg '\\t') to the concatenation and then simply split on that inside the function:

df['PageCLass'] = (df['PageClass'] + '\t' + df['Rev'].apply(str)).apply(lambda x: PageClassify.page_classify(x))

Hope this helps!