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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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