Python | Pandas Series.prod() Last Updated : 10 Oct, 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 Series.prod() method is used to get the product of the values for the requested axis. Syntax: Series.prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0) Parameters: axis : {index (0)} skipna[boolean, default True] : Exclude NA/null values. If an entire row/column is NA, the result will be NA level[int or level name, default None] : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. numeric_only[boolean, default None] : Include only float, int, boolean data. If None, will attempt to use everything, then use only numeric data Returns: Return the product of the values for the requested axis Code #1: By default, product of an empty or all-NA Series is 1. Python3 1== # importing pandas module import pandas as pd # min_count = 0 is the default pd.Series([]).prod() # When passed min_count = 1, # product of an empty series will be NaN pd.Series([]).prod(min_count = 1) Output: 1.0 nan Code #2: Python3 1== # importing pandas module import pandas as pd # applying prod() on a list of series val = pd.Series([12, 5, 7]).prod() val Output: 420 Comment More info S Shivam_k Follow Improve Article Tags : Machine Learning Python-pandas Python pandas-series Python pandas-series-methods python +1 More Explore Machine Learning BasicsIntroduction to Machine Learning8 min readTypes of Machine Learning13 min readWhat is Machine Learning Pipeline?7 min readApplications of Machine Learning3 min readPython for Machine LearningMachine Learning with Python Tutorial5 min readNumPy Tutorial - Python Library3 min readPandas Tutorial6 min readData Preprocessing in Python4 min readEDA - Exploratory Data Analysis in Python6 min readFeature EngineeringWhat is Feature Engineering?5 min readIntroduction to Dimensionality Reduction4 min readFeature Selection Techniques in Machine Learning6 min readSupervised LearningSupervised Machine Learning7 min readLinear Regression in Machine learning15+ min readLogistic Regression in Machine Learning11 min readDecision Tree in Machine Learning9 min readRandom Forest Algorithm in Machine Learning5 min readK-Nearest Neighbor(KNN) Algorithm8 min readSupport Vector Machine (SVM) Algorithm9 min readNaive Bayes Classifiers7 min readUnsupervised LearningWhat is Unsupervised Learning5 min readK means Clustering â Introduction6 min readHierarchical Clustering in Machine Learning6 min readDBSCAN Clustering in ML - Density based clustering6 min readApriori Algorithm6 min readFrequent Pattern Growth Algorithm5 min readECLAT Algorithm - ML5 min readPrincipal Component Analysis(PCA)7 min readModel Evaluation and TuningEvaluation Metrics in Machine Learning9 min readRegularization in Machine Learning5 min readCross Validation in Machine Learning5 min readHyperparameter Tuning7 min readML | Underfitting and Overfitting5 min readBias and Variance in Machine Learning10 min readAdvanced TechniquesReinforcement Learning8 min readSemi-Supervised Learning in ML5 min readSelf-Supervised Learning (SSL)6 min readEnsemble Learning8 min readMachine Learning PracticeMachine Learning Interview Questions and Answers15+ min read100+ Machine Learning Projects with Source Code [2025]6 min read Like