# 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

- Wes McKinney,
*Python for Data Analysis*( O’Reilly, 2012) - Difference between map, applymap and apply methods in Pandas