Skip to content
geeksforgeeks
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • Tutorials
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
  • Practice
    • Build your AI Agent
    • GfG 160
    • Problem of the Day
    • Practice Coding Problems
    • GfG SDE Sheet
  • Contests
    • Accenture Hackathon (Ending Soon!)
    • GfG Weekly [Rated Contest]
    • Job-A-Thon Hiring Challenge
    • All Contests and Events
  • Data preprocessing
  • Data Manipulation
  • Data Analysis using Pandas
  • EDA
  • Pandas Exercise
  • Pandas AI
  • Numpy
  • Matplotlib
  • Plotly
  • Data Analysis
  • Machine Learning
  • Data science
Open In App
Next Article:
Python | Pandas dataframe.product()
Next article icon

Python | Pandas dataframe.prod()

Last Updated : 22 Nov, 2018
Comments
Improve
Suggest changes
Like Article
Like
Report

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas dataframe.prod() function return the value of the product for the requested axis. It multiplies all the element together on the requested axis. By default the index axis is selected.

Syntax: DataFrame.prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result.
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
min_count : The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns : prod : Series or DataFrame (if level specified)

Example #1: Use prod() function to find product of all the elements over the column axis in the dataframe.




# importing pandas as pd
import pandas as pd
  
# Creating the dataframe 
df = pd.DataFrame({"A":[1, 5, 3, 4, 2], 
                   "B":[3, 2, 4, 3, 4],
                   "C":[2, 2, 7, 3, 4], 
                   "D":[4, 3, 6, 12, 7]})
  
# Print the dataframe
df 
 
 
                          
 
 
 
 
 
Let’s use the dataframe.prod() function to find the product of each element in the dataframe over the column axis.
 
 
 
 
 
 
                                      
 
                                    
                                     
 
 
                                    
 
 
                                    
                                     
                                                                      
 
 
 
 
 
 
  
  
 
# find the product over the column axis 
 
df.prod(axis = 1) 
 
 		 
  
 
 
 
 
 
                          
 
 
                          
 
 
 
 
Output : 
 
  
 Example #2:  Use prod() function to find the product of any axis in the dataframe. The dataframe contains NaN values.
 
 
 
 
 
 
                                      
 
                                    
                                     
 
 
                                    
 
 
                                    
                                     
                                                                      
 
 
 
 
 
 
  
  
 
# importing pandas as pd 
 
import pandas as pd 
 
  
 
# Creating the first dataframe  
 
df = pd.DataFrame({"A":[1, 5, 3, 4, 2], 
 
                   "B":[3, None, 4, 3, 4],  
 
                   "C":[2, 2, 7, None, 4],  
 
                   "D":[None, 3, 6, 12, 7]}) 
 
  
 
# using prod() function to raise each element  
 
# in df1 to the power of corresponding element in df2 
 
df.prod(axis = 1, skipna = True) 
 
 		 
  
 
 
 
 
 
                          
 
 
                          
 
 
 
 
Output : 
 
 

                                                                                      

                                                                                
                                          
                                                                                  
                                                                  
                              
                                
                                                                            Next Article                                                                                                                                                            
                                          Python | Pandas dataframe.product()                                      
                                  
                                                          
                          
                              
                                          
                                              
                                                  
                                                  
                                                      
S
          
                        
          
                                                  
                                                  
                                                      Shubham__Ranjan                                                
                                                  
                                                  
              
              
  
                                            
                                                                                        
                                          
                                                                                            
                                                                                                                                                  
                                                                                                                            
                                      Improve                                                                        
                                                                        
                          
                          
                                                              
                                      
Article Tags : 
                                      
                                                            
  •                   Python              
    •               
  •                   Pandas-DataFrame-Methods              
    •               
  •                   Python pandas-dataFrame              
    •               
  •                   Python-pandas              
    •                                     
                                                                      
                                                                                          
                                      
Practice Tags : 
                                      
                                              
  • python
    •                                     
                                                                      
                                                      
                          
                                                                                                                                 
                                  
Similar Reads
                                  
                                      
  •                       
    Python | Pandas dataframe.product()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.product() function return the value of the product for the requested
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas dataframe.pow()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.pow() function calculates the exponential power of dataframe and othe
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas Dataframe.pop()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.Pandas Pop() method is common in most of the data structures but pop() method is a litt
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas dataframe.mul()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.Pandas dataframe.mul() function return multiplication of dataframe and other element- w
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas dataframe.div()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.Pandas dataframe.div() is used to find the floating division of the dataframe and other
                      
                      
                                                3 min read                  
                      
  •                       
    Python | Pandas dataframe.rmul()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.rmul() function is used for finding the multiplication of dataframe a
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas dataframe.eval()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.eval() function is used to evaluate an expression in the context of t
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas dataframe.melt()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.melt() function unpivots a DataFrame from wide format to long format,
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas DataFrame.axes                      
                          
    Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure o
                      
                      
                                                2 min read                  
                      
  •                       
    Python | Pandas dataframe.rdiv()                      
                          
    Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.rdiv() function compute Floating division of dataframe and other, ele
                      
                      
                                                2 min read                  
                      
                                 
                              
                                                                                
                                  
                                                                                                            
                              
                            
                              
                                                                                      
                                
                              
                                                                                    
                      
                  
              
                  
          
              
                  
                      
                                        
                   
                            
                                  
                              
                                  
              
          
              
                        
  	  	  	
  		
  					
  			
  			
  					
  	
    	  	
  		
  					
  			
  			
  			
  				
  	
  	  	        	  	
  		
  					
  			
  			    	  	
  		
  		   		
  	
  
                    
          
      
      
          
              
              
          
      
  
    
                        	
    
      
                
          
                    
              
                
                                  
                        
        
      
    
      
        
        
            
                                  geeksforgeeks-footer-logo                                
                    
                      
                                               
                                               
Corporate & Communications Address:
                                              
                        A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)                    
                      
                    
                                               
                                                  
                          
Registered Address:
                          K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305                      
                      
                                        
                
                
                                            
                                                                
                                                                
                                                                
                                                                
                                    
                
                    GFG App on Play Store                    GFG App on App Store                
                
                  Advertise with us                
             
          
            
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
        
         
                              
            
                @GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved            
            
            
        
    
  
       
                We use cookies to ensure you have the best browsing experience on our website. By using our site, you          acknowledge that you have read and understood our          Cookie Policy &          Privacy Policy                      
        
     Lightbox       
                      
              
                  
                      
Improvement
                      
                                                
                  
                  
                      
                                          
                                                
                  
                  
                    
                      
                        
Suggest Changes
                        
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
                      
                      
                        geeksforgeeks-suggest-icon                      
                    
                    
                      
                        
Create Improvement
                        
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
                      
                      
                      geeksforgeeks-improvement-icon                      
                    
                    
                  
              
              
                                    
                    
                      
                                              
                      
Suggest Changes
                    
                    
                                              
                  
                  
                          
min 4 words, max Words Limit:1000
                          
                          
                                                    
                                
Thank You!
                                
Your suggestions are valuable to us.
                          
                  
                                     
                      
                                        
              
                        
                                                        
       
         
             
                 
What kind of Experience do you want to share?
                 
             
                          
                                      
                         
                     
                     
                         Interview Experiences                     
                                                       
                         
                     
                     
                         Admission Experiences                     
                                                       
                         
                     
                     
                         Career Journeys                     
                                                       
                         
                     
                     
                         Work Experiences                     
                                                       
                         
                     
                     
                         Campus Experiences                     
                                                       
                         
                     
                     
                         Competitive Exam Experiences