numpy.nanmin() in Python

numpy.nonzero() in Python

Last Updated : 28 Nov, 2018

numpy.nonzero()function is used to Compute the indices of the elements that are non-zero. It returns a tuple of arrays, one for each dimension of arr, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values in the array can be obtained with arr[nonzero(arr)] . To group the indices by element, rather than dimension we can use transpose(nonzero(arr)).

Syntax : numpy.nonzero(arr) Parameters : arr : [array_like] Input array. Return : [tuple_of_arrays] Indices of elements that are non-zero.

Code #1 : Working

Python

# Python program explaining
# nonzero() function

import numpy as geek
arr = geek.array([[0, 8, 0], [7, 0, 0], [-5, 0, 1]])

print ("Input  array : \n", arr)
  
out_tpl = geek.nonzero(arr)
print ("Indices of non zero elements : ", out_tpl)

Output :

Input array : [[ 0 8 0] [ 7 0 0] [-5 0 1]] Indices of non zero elements : (array([0, 1, 2, 2], dtype=int64), array([1, 0, 0, 2], dtype=int64))

Code #2 :

Python

# Python program for getting
# The corresponding non-zero values:
out_arr = arr[geek.nonzero(arr)]

print ("Output array of non-zero number: ", out_arr)

Output :

Output array of non-zero number:  [ 8  7 -5  1]

Code #3 :

Python

# Python program for grouping the indices
# by element, rather than dimension

out_ind = geek.transpose(geek.nonzero(arr))

print ("indices of non-zero number: \n", out_ind)

Output :

indices of non-zero number: 
 [[0 1]
 [1 0]
 [2 0]
 [2 2]]

numpy.nanmin() in Python

jana_sayantan

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.nanprod() in Python

numpy.nanprod() function is used when we want to compute the product of array elements over a given axis treating NaNs as ones. One is returned for slices that are all-NaN or empty. Syntax : numpy.nanprod(arr, axis=None, dtype=None, out=None, keepdims='class numpy._globals._NoValue'). Parameters : a

numpy.isnan() in Python

The numpy.isnan() function tests element-wise whether it is NaN or not and returns the result as a boolean array. Syntax :Â numpy.isnan(array [, out]) Parameters :Â array : [array_like]Input array or object whose elements, we need to test for infinity out : [ndarray, optional]Output array placed wit

numpy.flatnonzero() in Python

numpy.flatnonzero()function is used to Compute indices that are non-zero in the flattened version of arr. Syntax : numpy.flatnonzero(arr) Parameters : arr : [array_like] Input array. Return : ndarray Output array, containing the indices of the elements of arr.ravel() that are non-zero. Code #1 : Wor

numpy.nansum() in Python

numpy.nansum()function is used when we want to compute the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. Syntax : numpy.nansum(arr, axis=None, dtype=None, out=None, keepdims='no value') Parameters : arr : [array_like] Array containing numbers whose sum is desired. If

numpy.nanmin() in Python

numpy.nanmin()function is used when to returns minimum value of an array or along any specific mentioned axis of the array, ignoring any Nan value. Syntax : numpy.nanmin(arr, axis=None, out=None) Parameters : arr :Input array. axis :Axis along which we want the min value. Otherwise, it will consider

Numpy count_nonzero method | Python

numpy.count_nonzero() function counts the number of non-zero values in the array arr. Syntax : numpy.count_nonzero(arr, axis=None) Parameters : arr : [array_like] The array for which to count non-zeros. axis : [int or tuple, optional] Axis or tuple of axes along which to count non-zeros. Default is

numpy.isneginf() in Python

The numpy.isneginf() function tests element-wise whether it is negative infinity or not, and returns the result as a boolean array.Â Syntax :Â Â numpy.isneginf(array, y = None) Parameters :Â array : [array_like]Input array or object whose elements, we need to test for infinity. y : [array_like]A boole

numpy.isposinf() in Python

The numpy.isposinf() function tests element-wise whether it is positive infinity or not and returns the result as a boolean array.Â Syntax :Â numpy.isposinf(array, y = None) Parameters:Â Â array : [array_like]Input array or object whose elements, we need to test for infinity. y : [array_like]A boolea

numpy.negative() in Python

numpy.negative() function is used when we want to compute the negative of array elements. It returns element-wise negative value of an array or negative value of a scalar. Syntax : numpy.negative(arr, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, ext

numpy.isfinite() in Python

The numpy.isfinite() function tests element-wise whether it is finite or not(not infinity or not Not a Number) and return the result as a boolean array. Syntax :Â numpy.isfinite(array [, out]) Parameters :Â array : [array_like]Input array or object whose elements, we need to test for infinity out :