numpy.ix_() function | Python Last Updated : 22 Apr, 2020 Comments Improve Suggest changes 2 Likes Like Report numpy.ix_() function construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions. Syntax : numpy.ix_(args) Parameters : args : [1-D sequences] Each sequence should be of integer or boolean type. Return : [tuple of ndarrays] N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh. Code #1 : Python3 # Python program explaining # numpy.ix_() function # importing numpy as geek import numpy as geek gfg = geek.ix_([0, 1], [2, 4]) print (gfg) Output : (array([[0], [1]]), array([[2, 4]])) Code #2 : Python3 # Python program explaining # numpy.ix_() function # importing numpy as geek import numpy as geek arr = geek.arange(10).reshape(2, 5) print("Initial array : \n", arr) ixgrid = geek.ix_([0, 1], [2, 4]) print("New array : \n", arr[ixgrid]) Output : Initial array : [[0 1 2 3 4] [5 6 7 8 9]] New array : [[2 4] [7 9]] Create Quiz Comment S sanjoy_62 Follow 2 Improve S sanjoy_62 Follow 2 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 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 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 Learning7 min readMachine Learning PracticeMachine Learning Interview Questions and Answers15+ min read100+ Machine Learning Projects with Source Code5 min read Like