# Convert inputs to arrays with at least three dimensions in Numpy

To convert inputs to arrays with at least three dimensions, use the ma.atleast_3d() method in Python Numpy. The parameters are one or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved.

The function returns an array, or list of arrays, each with a.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape (N,) becomes a view of shape (1, N, 1), and a 2-D array of shape (M, N) becomes a view of shape (M, N, 1). It is applied to both the _data and the _mask, if any.

## Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...", arr)
print("Array type...", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...",arr.ndim)


Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]])
print("Our Masked Array", maskArr)
print("Our Masked Array type...", maskArr.dtype)

Get the dimensions of the Masked Array −

print("Our Masked Array Dimensions...",maskArr.ndim)


Get the shape of the Masked Array −

print("Our Masked Array Shape...",maskArr.shape)

Get the number of elements of the Masked Array −

print("Elements in the Masked Array...",maskArr.size)


To convert inputs to arrays with at least three dimensions, use the ma.atleast_3d() method in Python Numpy −

print("Result...",np.atleast_3d(1, maskArr))

## Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
print("Array...", arr)
print("Array type...", arr.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",arr.ndim)

# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]])
print("Our Masked Array", maskArr)
print("Our Masked Array type...", maskArr.dtype)

# Get the dimensions of the Masked Array
print("Our Masked Array Dimensions...",maskArr.ndim)

# Get the shape of the Masked Array
print("Our Masked Array Shape...",maskArr.shape)

# Get the number of elements of the Masked Array
print("Elements in the Masked Array...",maskArr.size)

# To convert inputs to arrays with at least three dimensions, use the ma.atleast_3d() method in Python Numpy
print("Result...",np.atleast_3d(1, maskArr))

## Output

Array...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- -- 81]
[-- 33 39]
[73 -- 51]
[62 -- 67]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(4, 3)

Elements in the Masked Array...
12

Result...
[array([[[1]]]), masked_array(
data=[[[--],
[--],
[81]],
[[--],
[33],
[39]],
[[73],
[--],
[51]],
[[62],
[--],
[67]]],

mask=[[[ True],
[ True],
[False]],
[[ True],
[False],
[False]],
[[False],
[ True],
[False]],
[[False],
[ True],
[False]]],
fill_value=999999)]

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