The numpy.zeros() function returns a new array of given shape and type, with zeros. Syntax:
numpy.zeros(shape, dtype = None, order = 'C')
Parameters :
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.
Returns :
ndarray of zeros having given shape, order and datatype.
Code 1 :
Python
# Python Program illustrating# numpy.zeros methodimport numpy as geekb = geek.zeros(2, dtype = int)print("Matrix b : \n", b)a = geek.zeros([2, 2], dtype = int)print("\nMatrix a : \n", a)c = geek.zeros([3, 3])print("\nMatrix c : \n", c) |
Output :
Matrix b : [0 0] Matrix a : [[0 0] [0 0]] Matrix c : [[ 0. 0. 0.] [ 0. 0. 0.] [ 0. 0. 0.]]
Code 2 : Manipulating data types
Python
# Python Program illustrating# numpy.zeros methodimport numpy as geek# manipulation with data-typesb = geek.zeros((2,), dtype=[('x', 'float'), ('y', 'int')])print(b) |
Output :
[(0.0, 0) (0.0, 0)]
Reference : https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.zeros.html#numpy.zeros Note : zeros, unlike zeros and empty, does not set the array values to zero or random values respectively.Also, these codes won’t run on online IDE’s. Please run them on your systems to explore the working. This article is contributed by Mohit Gupta_OMG 😀. If you like Lazyroar and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to review-team@geeksforgeeks.org. See your article appearing on the Lazyroar main page and help other Geeks. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
