Python Pandas - Arithmetic Operations on Series Object

Pandas Series is one of the primary data structures, that stores the one-dimensional labeled data. The data can be any type, such as integers, floats, or strings. One of the primary advantages of using a Pandas Series is the ability to perform arithmetic operations in a vectorized manner. This means arithmetic operations on Series are performed without needing a loop through elements manually.

In this tutorial, we will learn how to apply arithmetic operations like addition(+), subtraction(-), multiplication(*), and division(/) to a single Series or between two Series objects.

Arithmetic Operations on a Series with Scalar Value

Arithmetic operations on a Pandas Series object can be directly applied to an entire Series elements, which means the operation is executed element-wise across all values. This is very similar to how operations work with NumPy arrays.

Following is the list of commonly used arithmetic operations on Pandas Series −

Example

The following example demonstrates how to applies the all arithmetical operations on a Series object with the scalar values.

 import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) # Display the Input Series print('Input Series\n',s) # Apply all Arithmetic Operation and Display the Results print('\nAddition:\n',s+2) print('\nSubtraction:\n', s-2) print('\nMultiplication:\n', s * 2) print('\nDivision:\n', s/2) print('\nExponentiation:\n', s**2) print('\nModulus:\n', s%2) print('\nFloor Division:\n', s//2) 

Following is the output of the above code −

 Input Series a 1 b 2 c 3 d 4 e 5 dtype: int64 Addition: a 3 b 4 c 5 d 6 e 7 dtype: int64 Subtraction: a -1 b 0 c 1 d 2 e 3 dtype: int64 Multiplication: a 2 b 4 c 6 d 8 e 10 dtype: int64 Division: a 0.5 b 1.0 c 1.5 d 2.0 e 2.5 dtype: float64 Exponentiation: a 1 b 4 c 9 d 16 e 25 dtype: int64 Modulus: a 1 b 0 c 1 d 0 e 1 dtype: int64 Floor Division: a 0 b 1 c 1 d 2 e 2 dtype: int64 

Arithmetic Operations Between Two Series

You can perform arithmetical operations between two series objects. Pandas automatically aligns the data by index labels. If one of the Series object does not have an index but not the other, then the resultant value for that index will be NaN.

Example

This example demonstrates applying the arithmetic operations on two series objects.

 import pandas as pd s1 = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) s2 = pd.Series([9, 8, 6, 5], index=['x','a','b','c']) # Apply all Arithmetic Operations and Display the Results print('\nAddition:\n',s1+s2) print('\nSubtraction:\n', s1-s2) print('\nMultiplication:\n', s1 * s2) print('\nDivision:\n', s1/s2) 

Following is the output of the above code −

 Addition: a 9.0 b 8.0 c 8.0 d NaN e NaN x NaN dtype: float64 Subtraction: a -7.0 b -4.0 c -2.0 d NaN e NaN x NaN dtype: float64 Multiplication: a 8.0 b 12.0 c 15.0 d NaN e NaN x NaN dtype: float64 Division: a 0.125000 b 0.333333 c 0.600000 d NaN e NaN x NaN dtype: float64