# numpy.zeros() in Python

numpy.zeros(shape, dtype = None, order = ‘C’) : Return a new array of 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 : \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 Program illustrating ` `# numpy.zeros method ` ` `  `import` `numpy as geek ` ` `  `# manipulation with data-types ` `b ``=` `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-ID. Please run them on your systems to explore the working.

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