Accessing Data Along Multiple Dimensions Arrays in Python Numpy

numpy.compress() in Python

Last Updated :08 Mar, 2024

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The numpy.compress() function returns selected slices of an array along mentioned axis, that satisfies an axis.

Syntax: numpy.compress(condition, array, axis = None, out = None)

Parameters :

condition :[array_like]Condition on the basis of which user extract elements.       Applying condition on input_array, if we print condition, it will return an arra      filled with either True or False. Array elements are extracted from the Indices having       True value.array     :Input array. User apply conditions on input_array elementsaxis      :[optional, int]Indicating which slice to select.          By Default, work on flattened array[1-D]out       :[optional, ndarray]Output_array with elements of input_array,                that satisfies condition

Return :

Copy of array with elements of input_array,that satisfies condition and along given axis

Python

# Python Program illustrating# numpy.compress methodimportnumpyasgeekarray=geek.arange(10).reshape(5,2)print("Original array :\n",array)a=geek.compress([0,1],array,axis=0)print("\nSliced array :\n",a)a=geek.compress([False,True],array,axis=0)print("\nSliced array :\n",a)

Output :

Original array :  [[0 1] [2 3] [4 5] [6 7] [8 9]]Sliced array :  [[2 3]]Sliced array :  [[2 3]]

Note : This codes won’t run on online-ID. Please run them on your systems to explore the working..

Accessing Data Along Multiple Dimensions Arrays in Python Numpy

Mohit Gupta

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Introduction

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Matrix in NumPy

Operations on NumPy Array

Reshaping NumPy Array

Indexing NumPy Array

Arithmetic operations on NumPyArray

Linear Algebra in NumPy Array

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