Add, subtract, multiple and divide two Pandas Series Last Updated : 28 Jul, 2020 Summarize Comments Improve Suggest changes Share Like Article Like Report Let us see how to perform basic arithmetic operations like addition, subtraction, multiplication, and division on 2 Pandas Series. For all the 4 operations we will follow the basic algorithm : Import the Pandas module. Create 2 Pandas Series objects. Perform the required arithmetic operation using the respective arithmetic operator between the 2 Series and assign the result to another Series. Display the resultant Series. Addition of 2 Series python3 # importing the module import pandas as pd # creating 2 Pandas Series series1 = pd.Series([1, 2, 3, 4, 5]) series2 = pd.Series([6, 7, 8, 9, 10]) # adding the 2 Series series3 = series1 + series2 # displaying the result print(series3) Output : Subtraction of 2 Series python3 # importing the module import pandas as pd # creating 2 Pandas Series series1 = pd.Series([1, 2, 3, 4, 5]) series2 = pd.Series([6, 7, 8, 9, 10]) # subtracting the 2 Series series3 = series1 - series2 # displaying the result print(series3) Output : Multiplication of 2 Series python3 # importing the module import pandas as pd # creating 2 Pandas Series series1 = pd.Series([1, 2, 3, 4, 5]) series2 = pd.Series([6, 7, 8, 9, 10]) # multiplying the 2 Series series3 = series1 * series2 # displaying the result print(series3) Output : Division of 2 Series python3 # importing the module import pandas as pd # creating 2 Pandas Series series1 = pd.Series([1, 2, 3, 4, 5]) series2 = pd.Series([6, 7, 8, 9, 10]) # dividing the 2 Series series3 = series1 / series2 # displaying the result print(series3) Output : Comment More infoAdvertise with us Next Article Stack two Pandas series vertically and horizontally M mukulsomukesh Follow Improve Article Tags : Python Python-pandas Python pandas-series pandas-dataframe-program Practice Tags : python Similar Reads Add a Pandas series to another Pandas series Let us see how to add a Pandas series to another series in Python. This can be done using 2 ways: append()concat() Method 1: Using the append() function: It appends one series object at the end of another series object and returns an appended series. The attribute, ignore_index=True is used when we 2 min read Stack two Pandas series vertically and horizontally In this article we'll see how we can stack two Pandas series both vertically and horizontally. We stack these lists to combine some data in a DataFrame for a better visualization of the data, combining different data, etc. Now let's see with the help of examples how we can do this. Stacking Horizont 2 min read Pandas - Compute the Euclidean distance between two series There are many distance metrics that are used in various Machine Learning Algorithms. One of them is Euclidean Distance. Euclidean distance is the most used distance metric and it is simply a straight line distance between two points. Euclidean distance between points is given by the formula : \[d(x 2 min read Combine two Pandas series into a DataFrame In this post, we will learn how to combine two series into a DataFrame? Before starting let's see what a series is?Pandas Series is a one-dimensional labeled array capable of holding any data type. In other terms, Pandas Series is nothing but a column in an excel sheet. There are several ways to con 3 min read Python | Pandas Series.subtract() Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.subtract() function basically 2 min read How to Convert Pandas DataFrame columns to a Series? It is possible in pandas to convert columns of the pandas Data frame to series. Sometimes there is a need to converting columns of the data frame to another type like series for analyzing the data set. Case 1: Converting the first column of the data frame to Series Python3 # Importing pandas module 2 min read Like