# numpy.zeros () in Python

numpy.zeros (shape, dtype = None, order = & # 39; C & # 39;): return a new array of the given shape and type with zeros.
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 program illustrating ` ` # numpy.zeros method `   ` import ` ` numpy as geek `   ` b ` ` = ` ` geek.zeros (` ` 2 ` `, dtype ` ` = ` ` int ` `) ` ` print ` ` (` ` "Matrix b:" ` `, b) `   ` a ` ` = ` ` geek.zeros ([` ` 2 ` `, ` ` 2 ` `], dtype ` ` = ` ` int ` `) ` ` print ` ` (` ` "Matrix a:" ` `, a) `   ` c ` ` = ` ` geek.zeros ([` ` 3 ` `, ` ` 3 ` `]) ` ` print ` ` (` ` "Matrix c:" ` `, 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: Type management data

 ` # Python program illustrating ` ` # numpy.zeros method `   ` import ` ` numpy as geek `   ` # manipulating data types ` ` b ` ` = ` ` geek.zeros ((` ` 2 ` `,), dtype ` ` = ` ` [ (` ` ’x’ ` `, ` `’ float’ ` `), (` ` ’y’ ` ` , ` ` ’int’ ` `)]) ` ` print ` ` (b) `

Output:

` [(0.0, 0) (0.0, 0)] `

https://docs.scipy.org/doc/numpy- dev / reference / generated / numpy.zeros.html # numpy.zeros
Note: zeros, unlike zeros and nulls, do not set array values ​​to zero or random values respectively. Also, these codes will not work with an online ID. Please run them on your systems to see how they work.

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