Pandas apply and map

19 Nov, 2016

The way to apply a function to pandas data structures is not always obvious--several methods exist (apply, applymap, map) and their scope is different.

First there is two main structures (fortunately I'm not talking about Panel here):

• Series: one-dimensional labeled array
• DataFrame: 2-dimensional labeled data structure

The apply / map methods can work on different ways.

Element-wise

The function is called (mapped) for each individual element (value)--so it takes the element (each distinct value) as parameter.

• map for a Series: can be used with either a dict, a function, or a Series.
• applymap for a DataFrame: It is equivalent to calling map on all columns of the DataFrame.

By row / column

The function is called (applied) for an entire row or a column--so it takes a row or a column as parameter, in other words a Series.

• apply for a DataFrame that can be called with an axis parameter indicating to apply to column (0) or to row (1).
• apply can also be used with a Series: it will only work for the entire array when used with a numpy universal function ufunc. So it's not working element-wise, however when used with standard function it will work element-wise.

In short, apply works on row / column of a DataFrame, applymap works element-wise on a DataFrame, and map--and apply for most cases--works element-wise on a Series.

References / Further reading

1. Wes McKinney, Python for Data Analysis ( O'Reilly, 2012)
2. Difference between map, applymap and apply methods in Pandas

#python