Pandas - DataFrame Arithmetic Functions

The Pandas package contains a number of arithmetic functions which provides all the functionality required for various arithmetic operations on a DataFrame. Below mentioned are the most frequently used arithmetic functions.

Function	Description
add()	Add arguments element-wise.
sub()	Subtract arguments, element-wise.

Lets discuss these functions in detail:

Basic Arithmetic Operations

Basic arithmetic operations can be performed on a given DataFrame, element-wise using add(), sub(), mul() and div() functions. The syntax for using this function is given below:

Syntax

DataFrame.add(other, axis='columns', level=None, fill_value=None)
DataFrame.sub(other, axis='columns', level=None, fill_value=None)
DataFrame.mul(other, axis='columns', level=None, fill_value=None)
DataFrame.div(other, axis='columns', level=None, fill_value=None)

Parameters

`other`	`Required.` Specify any single or multiple element data structure, or list-like object.
`axis`	`Optional.` Specify whether to compare by the index (0 or 'index') or columns (1 or 'columns'). For Series input, axis to match Series index on. Default is 'columns'.
`level`	`Optional.` Specify int or label to broadcast across a level, matching Index values on the passed MultiIndex level. Default is None.
`fill_value`	`Optional.` Specify value to fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment. If data in both corresponding DataFrame locations is missing the result will be missing. Default is None.