numpy.any() in Python

numpy.empty() in Python

Last Updated : 29 Nov, 2018

numpy.empty(shape, dtype = float, order = ‘C’) : Return a new array of given shape and type, with random values.
Parameters :

-> shape : Number of rows
-> order : C_contiguous or F_contiguous
-> dtype : [optional, float(by Default)] Data type of returned array.

# Python Programming illustrating 
# numpy.empty method 
  
import numpy as geek 
  
b = geek.empty(2, dtype = int) 
print("Matrix b : \n", b) 
  
a = geek.empty([2, 2], dtype = int) 
print("\nMatrix a : \n", a) 
  
c = geek.empty([3, 3]) 
print("\nMatrix c : \n", c) 

Output :

Matrix b : 
 [         0 1079574528]

Matrix a : 
 [[0 0]
 [0 0]]

Matrix a : 
 [[ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]

Note : empty, unlike zeros, does not set the array values to zero, and may therefore be marginally faster.
Also, these codes won’t run on online-ID. Please run them on your systems to explore the working

numpy.any() in Python

M

Mohit Gupta_OMG

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.empty_like() in Python

numpy.empty_like(a, dtype = None, order = 'K', subok = True) : Return a new array with the same shape and type as a given array. Parameters : shape : Number of rows order : C_contiguous or F_contiguous dtype : [optional, float(by Default)] Data type of returned array. subok : [bool, optional] to mak

numpy.any() in Python

The numpy.any() function tests whether any array elements along the mentioned axis evaluate to True. Syntax : numpy.any(a, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters : array :[array_like]Input array or object whose elements, we need to test. axis : [i

numpy.all() in Python

The numpy.all() function tests whether all array elements along the mentioned axis evaluate to True. Syntax: numpy.all(array, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters : array :[array_like]Input array or object whose elements, we need to test. axis :

numpy.matlib.empty() function | Python

numpy.matlib.empty() function return a new matrix of given shape and type, without initializing entries. Syntax : numpy.matlib.empty(shape, dtype = None, order = 'C') Parameters : shape : [int or tuple of int] Shape of the empty matrix. dtype : [data-type, optional] Desired output data-type. order :

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 : numpy.isnan(array [, out]) Parameters : array : [array_like]Input array or object whose elements, we need to test for infinity out : [ndarray, optional]Output array placed with

Python | Numpy np.legzero() method

np.legzero() method can be used instead of np.zeros for creating a array whose elements are 0. Syntax : np.legzero() Return : Return array([0]) Example #1 : # Python program explaining # numpy.legzero() method # import numpy and legzero import numpy as np from numpy.polynomial.legendre import legzer

Python | Numpy np.lagzero() method

np.lagzero() method can be used instead of np.zeros for creating a array whose elements are 0. Syntax : np.lagzero() Return : Return array([0]) Example #1 : # Python program explaining # numpy.lagzero() method # import numpy and lagzero import numpy as np from numpy.polynomial.laguerre import lagzer

numpy.zeros() in Python

numpy.zeros() function creates a new array of specified shapes and types, filled with zeros. It is beneficial when you need a placeholder array to initialize variables or store intermediate results. We can create 1D array using numpy.zeros(). Let's understand with the help of an example: [GFGTABS] P

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.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