# Numpy MaskedArray.atleast_3d() function | Python

`numpy.MaskedArray.atleast_3d() ` function is used to convert inputs to masked arrays with at least three dimension.Scalar, 1-dimensional and 2-dimensional arrays are converted to 3-dimensional arrays, whilst higher-dimensional inputs are preserved.

Syntax : `numpy.ma.atleast_3d(*arys)`

Parameters:
arys:[ array_like] One or more input arrays.

Return : [ ndarray] An array, or list of arrays, each with ` arr.ndim >= 3`

Code #1 :

 `# Python program explaining ` `# numpy.MaskedArray.atleast_3d() method  ` `   `  `# importing numpy as geek   ` `# and numpy.ma module as ma  ` `import` `numpy as geek  ` `import` `numpy.ma as ma  ` `   `  `# creating input arrays   ` `in_arr1 ``=` `geek.array([ ``3``, ``-``1``, ``5``, ``-``3``]) ` `print` `(``"1st Input array : "``, in_arr1) ` ` `  `in_arr2 ``=` `geek.array(``2``) ` `print` `(``"2nd Input array : "``, in_arr2) ` ` `  `in_arr3 ``=` `geek.array([[``1``, ``2``], [ ``3``, ``-``1``], [ ``5``, ``-``3``]]) ` `print` `(``"3rd Input array : "``, in_arr3)  ` `   `  `# Now we are creating  masked array.  ` `# by making  entry as invalid.   ` `mask_arr1 ``=` `ma.masked_array(in_arr1, mask ``=``[ ``1``, ``0``, ``1``, ``0``])  ` `print` `(``"1st Masked array : "``, mask_arr1) ` ` `  `mask_arr2 ``=` `ma.masked_array(in_arr2, mask ``=` `0``)  ` `print` `(``"2nd Masked array : "``, mask_arr2) ` ` `  `mask_arr3 ``=` `ma.masked_array(in_arr3, mask ``=``[[ ``1``, ``0``], [ ``0``, ``1``], [ ``0``, ``0``]])  ` `print` `(``"3rd Masked array : "``, mask_arr3) ` `   `  `# applying MaskedArray.atleast_3d methods  ` `# to masked array  ` `out_arr ``=` `ma.atleast_2d(mask_arr1, mask_arr2, mask_arr3)  ` `print` `(``"Output masked array : "``, out_arr)  `

Output:

```1st Input array :  [ 3 -1  5 -3]
2nd Input array :  2
3rd Input array :  [[ 1  2]
[ 3 -1]
[ 5 -3]]
1st Masked array :  [-- -1 -- -3]
2nd Masked array :  2
3rd Masked array :  [[-- 2]
[3 --]
[5 -3]]
Output masked array :  [masked_array(data=[[--, -1, --, -3]],
mask=[[ True, False,  True, False]],
fill_value=999999), masked_array(data=[[2]],
mask=[[False]],
fill_value=999999), masked_array(
data=[[--, 2],
[3, --],
[5, -3]],
mask=[[ True, False],
[False,  True],
[False, False]],
fill_value=999999)]

```

Code #2 :

 `# Python program explaining ` `# numpy.MaskedArray.atleast_3d() 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([[[ ``2e8``, ``3e``-``5``]], [[ ``-``45.0``, ``2e5``]]]) ` `print` `(``"Input array : "``, in_arr) ` `     `  `# Now we are creating a masked array.  ` `# by making one entry as invalid.   ` `mask_arr ``=` `ma.masked_array(in_arr, mask ``=``[[[ ``1``, ``0``]], [[ ``0``, ``0``]]])  ` `print` `(``"3D Masked array : "``, mask_arr)  ` `    `  `# applying MaskedArray.atleast_3d methods  ` `# to masked array ` `out_arr ``=` `ma.atleast_3d(mask_arr)  ` `print` `(``"Output masked array : "``, out_arr) `

Output:

```Input array :  [[[ 2.0e+08  3.0e-05]]

[[-4.5e+01  2.0e+05]]]
3D Masked array :  [[[-- 3e-05]]

[[-45.0 200000.0]]]
Output masked array :  [[[-- 3e-05]]

[[-45.0 200000.0]]]
```

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