numpy.ravel_multi_index() function | Python Last Updated : 22 Apr, 2020 Comments Improve Suggest changes Like Article Like Report numpy.ravel_multi_index() function converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index. Syntax : numpy.ravel_multi_index(multi_index, dims, mode = 'raise', order = 'C) Parameters : multi_index : [tuple of array_like] A tuple of integer arrays, one array for each dimension. dims : [tuple of ints] The shape of array into which the indices from multi_index apply. mode : [{‘raise’, ‘wrap’, ‘clip’}, optional] Specifies how out-of-bounds indices are handled. Can specify either one mode or a tuple of modes, one mode per index. ‘raise’ – raise an error (default) ‘wrap’ – wrap around ‘clip’ – clip to the range In ‘clip’ mode, a negative index that would normally wrap will clip to 0 instead. order : [{‘C’, ‘F’}, optional] Determines whether the multi-index should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order. Return : [ndarray] An array of indices into the flattened version of an array of dimensions dims. Code #1 : Python3 # Python program explaining # numpy.ravel_multi_index() function # importing numpy as geek import numpy as geek arr = geek.array([[3, 6, 6], [4, 5, 1]]) gfg = geek.ravel_multi_index(arr, (7, 6)) print(gfg) Output : [22 41 37] Code #2 : Python3 # Python program explaining # numpy.ravel_multi_index() function # importing numpy as geek import numpy as geek arr = geek.array([[3, 6, 6], [4, 5, 1]]) gfg = geek.ravel_multi_index(arr, (7, 6), order = 'F') print(gfg) Output : [31 41 13] Code #3 : Python3 # Python program explaining # numpy.ravel_multi_index() function # importing numpy as geek import numpy as geek arr = geek.array([[3, 6, 6], [4, 5, 1]]) gfg = geek.ravel_multi_index(arr, (7, 6), mode = 'clip') print(gfg) Output : [22 41 37] Create Quiz Comment S sanjoy_62 Follow 0 Improve S sanjoy_62 Follow 0 Improve Article Tags : Machine Learning Python-numpy Python numpy-arrayManipulation python Explore Machine Learning BasicsIntroduction to Machine Learning8 min readTypes of Machine Learning7 min readWhat is Machine Learning Pipeline?6 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 Learning4 min readSupervised LearningSupervised Machine Learning7 min readLinear Regression in Machine learning14 min readLogistic Regression in Machine Learning10 min readDecision Tree in Machine Learning8 min readRandom Forest Algorithm in Machine Learning5 min readK-Nearest Neighbor(KNN) Algorithm8 min readSupport Vector Machine (SVM) Algorithm9 min readNaive Bayes Classifiers6 min readUnsupervised LearningWhat is Unsupervised Learning5 min readK means Clustering – 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 Tuning5 min readUnderfitting and Overfitting in ML3 min readBias and Variance in Machine Learning6 min readAdvanced TechniquesReinforcement Learning9 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 Code5 min read Like