numpy.full() in Python (original) (raw)

Last Updated : 09 Mar, 2022

numpy.full(shape, fill_value, dtype = None, order = ‘C’) : Return a new array with the same shape and type as a given array filled with a fill_value.
Parameters :

shape : Number of rows order : C_contiguous or F_contiguous dtype : [optional, float(by Default)] Data type of returned array.
fill_value : [bool, optional] Value to fill in the array.

Returns :

ndarray

import numpy as geek

a = geek.full([ 2 , 2 ], 67 , dtype = int )

print ( "\nMatrix a : \n" , a)

c = geek.full([ 3 , 3 ], 10.1 )

print ( "\nMatrix c : \n" , c)

Output :

Matrix a : [[67 67] [67 67]]

Matrix c : [[ 10.1 10.1 10.1] [ 10.1 10.1 10.1] [ 10.1 10.1 10.1]]

References :
https://docs.scipy.org/doc/numpy/reference/generated/numpy.full.html#numpy.full
Note :
These NumPy-Python programs won’t run on online IDE’s, so run them on your systems to explore them
.

Similar Reads

• 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 : 3 min read
• numpy.floor() in Python The numpy.floor() function returns the largest integer less than or equal to each element in the input array. It effectively rounds numbers down to the nearest whole number. Let's understand with an example: [GFGTABS] Python import numpy as np a = [0.5, 1.5, 2.5, 3, 4.5, 10.1] res = np.floor(a) prin 1 min read
• 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 3 min read
• numpy.ndarray.fill() in Python numpy.ndarray.fill() method is used to fill the numpy array with a scalar value. If we have to initialize a numpy array with an identical value then we use numpy.ndarray.fill(). Suppose we have to create a NumPy array a of length n, each element of which is v. Then we use this function as a.fill(v). 2 min read
• 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 2 min read
• numpy.index() in Python numpy.core.defchararray.index(arr, substring, start=0, end=None): Finds the lowest index of the sub-string in the specified range But if substring is not found, it raises ValueError. Parameters: arr : array-like or string to be searched. substring : substring to search for. start, end : [int, option 1 min read
• numpy.isinf() in Python The numpy.isinf() function tests element-wise whether it is +ve or -ve infinity or not return the result as a boolean array. Syntax: numpy.isinf(array [, out]) Parameters : array : [array_like]Input array or object whose elements, we need to test for infinity out : [ndarray, optional]Output array pl 2 min read
• numpy.iinfo() function – Python numpy.iinfo() function shows machine limits for integer types. Syntax : numpy.iinfo(dtype) Parameters : dtype : [integer type, dtype, or instance] The kind of integer data type to get information about. Return : Machine limits for integer types. Code #1 : # Python program explaining # numpy.iinfo() 1 min read
• numpy.who function - Python numpy.who() function print the NumPy arrays in the given dictionary. Syntax : numpy.who(vardict = None) Parameters : vardict : [dict, optional] A dictionary possibly containing ndarrays. Return : Returns ‘None’. If there is no dictionary passed in or vardict is None then returns NumPy arrays in the 1 min read
• numpy.load() in Python numpy.load() function return the input array from a disk file with npy extension(.npy). Syntax : numpy.load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII') Parameters: file : : file-like object, string, or pathlib.Path.The file to read. File-like objects must support the 2 min read