# Numpy MaskedArray.all() function | Python

• Last Updated : 27 Sep, 2019

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The `numpy.ma` module provides a convenient way to address this issue, by introducing masked arrays. Masked arrays are arrays that may have missing or invalid entries.

`numpy.MaskedArray.all()` function returns True if all elements evaluate to True.

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Syntax : `MaskedArray.all(axis=None, out=None, keepdims)`

Parameters:
axis : [int or None] Axis or axes along which a logical AND reduction is performed.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.
keepdims : [bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return : [ndarray, bool] A new boolean or array is returned unless out is specified, in which case a reference to out is returned.

Code #1 :

 `# Python program explaining``# numpy.MaskedArray.all() method `` ` `# importing numpy as geek ``# and numpy.ma module as ma``import` `numpy as geek``import` `numpy.ma as ma`` ` `# creating input array ``in_arr ``=` `geek.array([``1``, ``2``, ``3``, ``-``1``, ``5``])``print` `(``"Input array : "``, in_arr)`` ` ` ` `# applying MaskedArray.all methods to input array``out_arr ``=` `in_arr.``all``()``print` `(``"Output array : "``, out_arr)`` ` ` ` `# Now we are creating a masked array by ``# making third entry as invalid. ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``0``, ``0``, ``1``, ``0``, ``0``])``print` `(``"Masked array : "``, mask_arr)`` ` `# applying MaskedArray.all methods to mask array``out_arr ``=` `mask_arr.``all``()``print` `(``"Output array : "``, out_arr)`
Output:
```Input array :  [ 1  2  3 -1  5]
Output array :  True
Masked array :  [1 2 -- -1 5]
Output array :  True
```

Code #2 :

 `# Python program explaining``# numpy.MaskedArray.all() method `` ` `# importing numpy as geek ``# and numpy.ma module as ma``import` `numpy as geek``import` `numpy.ma as ma`` ` `# creating input array ``in_arr ``=` `geek.array([``1``, ``2``, ``3``, ``-``1``, ``5``])``print` `(``"Input array : "``, in_arr)`` ` `# Now we are creating a masked array by ``# making all entry as invalid. ``mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``1``, ``1``, ``1``, ``1``, ``1``])``print` `(``"Masked array : "``, mask_arr)`` ` `# applying MaskedArray.all methods to mask array``out_arr ``=` `mask_arr.``all``()``print` `(``"Output array : "``, out_arr)`
Output:
```Input array :  [ 1  2  3 -1  5]
Masked array :  [-- -- -- -- --]
Output array :  --
```

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