# Numpy MaskedArray.anom() function | Python

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.anom() ` function Compute the anomalies (deviations from the arithmetic mean) along the given axis.It returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis.

Syntax : `numpy.MaskedArray.anom(axis=None, dtype=None)`

Parameters:
axis : [int or None] Axis over which the anomalies are taken.
dtype : [ dtype, optional] Type to use in computing the variance.

Return : [ndarray]an array of anomalies.

Code #1 :

 `# Python program explaining ` `# numpy.MaskedArray.anom() 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 third entry as invalid.  ` `mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``0``, ``0``, ``1``, ``0``, ``0``]) ` `print` `(``"Masked array : "``, mask_arr) ` ` `  `# applying MaskedArray.anom methods to mask array ` `out_arr ``=` `mask_arr.anom() ` `print` `(``"Output anomalies array : "``, out_arr) `

Output:

```Input array :  [ 1  2  3 -1  5]
Masked array :  [1 2 -- -1 5]
Output anomalies array :  [-0.75 0.25 -- -2.75 3.25]
```

Code #2 :

 `# Python program explaining ` `# numpy.MaskedArray.anom() 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([``10``, ``20``, ``30``, ``40``, ``50``]) ` `print` `(``"Input array : "``, in_arr) ` ` `  `# Now we are creating a masked array by making  ` `# first and third entry as invalid.  ` `mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[``1``, ``0``, ``1``, ``0``, ``0``]) ` `print` `(``"Masked array : "``, mask_arr) ` ` `  `# applying MaskedArray.anom methods to mask array ` `out_arr ``=` `mask_arr.anom() ` `print` `(``"Output anomalies array : "``, out_arr) `

Output:

```nput array :  [10 20 30 40 50]
Masked array :  [-- 20 -- 40 50]
Output anomalies array :  [-- -16.666666666666664 -- 3.3333333333333357 13.333333333333336]
```

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