NumPy ndarray nbytes() Method Last Updated : 12 Jul, 2025 Comments Improve Suggest changes Like Article Like Report The NumPy ndarray nbytes() function return the total bytes consumed by the elements of the array. NotesIt returns the total bytes consumed by the total element of the array, but it does not calculate memory consumed by non-element attributes of the array object.The size of the data element depends on its attribute.It calculates the total bytes consumed by multiplying the shape of the array and the size of one array element.Syntax Syntax: numpy.ndarray.nbytes(arr) Parameters arr : [array_like] Input array. Return [int] Total bytes consumed by the elements of the array.Examples Let's see an example of how to use ndarray nbytes() method of NumPy library in Python. Example 1 Python3 import numpy as np arr = np.zeros((1, 2, 3), dtype = np.complex128) bytes_consumed = arr.nbytes print (bytes_consumed) Output : 96Example 2 Python3 # Python program explaining # numpy.ndarray.nbytes() function # importing numpy as geek import numpy as geek arr = geek.random.rand(10000, 50) gfg = arr.nbytes print (gfg) Output : 4000000 Comment S sanjoy_62 Follow 0 Improve S sanjoy_62 Follow 0 Improve Article Tags : Machine Learning Python-numpy Python numpy-ndarray python 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 Tutorial4 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