calling apply() on an empty pandas DataFrame

Question

I'm having a problem with the apply() method of the pandas DataFrame. My issue is that apply() can return either a Series or a DataFrame, depending on the return type of the input function; however, when the frame is empty, apply() (almost) always returns a DataFrame. So I can't write code that expects a Series. Here's an example:

import pandas as pd

def area_from_row(row):
    return row['width'] * row['height']

def add_area_column(frame):
    # I know I can multiply the columns directly, but my actual function is
    # more complicated.
    frame['area'] = frame.apply(area_from_row, axis=1)

# This works as expected.
non_empty_frame = pd.DataFrame(data=[[2, 3]], columns=['width', 'height'])
add_area_column(non_empty_frame)

# This fails!
empty_frame = pd.DataFrame(data=None, columns=['width', 'height'])
add_area_column(empty_frame)

Is there a standard way of dealing with this? I can do the following, but it's silly:

def area_from_row(row):
    # The way we respond to an empty row tells pandas whether we're a
    # reduction or not.
    if not len(row):
        return None
    return row['width'] * row['height']

(I'm using pandas 0.11.0, but I checked this on 0.12.0-1100-g0c30665 as well.)

Answer 1

You can set the result_type parameter in apply to 'reduce'.

From the documentation,

By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

And then,

‘reduce’ : returns a Series if possible rather than expanding list-like results. This is the opposite of ‘expand’.

In your code, update here:

def add_area_column(frame):
    # I know I can multiply the columns directly, but my actual function is
    # more complicated.
    frame['area'] = frame.apply(area_from_row, axis=1, result_type='reduce')