In this article, we will learn how to create a Numpy array filled with all zeros, given the shape and type of array.
We can use Numpy.zeros() method to do this task. This method takes three parameters, discussed below –
shape : integer or sequence of integers
order : C_contiguous or F_contiguous
C-contiguous order in memory(last index varies the fastest)
C order means that operating row-rise on the array will be slightly quicker
FORTRAN-contiguous order in memory (first index varies the fastest).
F order means that column-wise operations will be faster.
dtype : [optional, float(byDeafult)] Data type of returned array.
Example #1:
# Python Program to create array with all zeros import numpy as geek a = geek.zeros(3, dtype = int) print("Matrix a : \n", a) b = geek.zeros([3, 3], dtype = int) print("\nMatrix b : \n", b) |
Output:
Matrix a : [0 0 0] Matrix b : [[0 0 0] [0 0 0] [0 0 0]]
Example #2:
# Python Program to create array with all zeros import numpy as geek c = geek.zeros([5, 3]) print("\nMatrix c : \n", c) d = geek.zeros([5, 2], dtype = float) print("\nMatrix d : \n", d) |
Output:
Matrix c : [[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]] Matrix d : [[ 0. 0.] [ 0. 0.] [ 0. 0.] [ 0. 0.] [ 0. 0.]]
