# Pandas apply and map

Contents

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