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Return a view of the masked array with axes transposed along given axis in NumPy
To return a view of the array with axes transposed in Python, use the ma.MaskedArray.transpose() method in Numpy. For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2-D array, this is a standard matrix transpose.
The axes can be,
None or no argument − reverses the order of the axes.
tuple of ints − i in the j-th place in the tuple means a’s i-th axis becomes a.transpose()’s j-th axis.
n ints − same as an n-tuple of the same ints
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([[49, 85, 45], [67, 33, 59]])
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 =[[0, 0, 1], [ 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)
Return a view of the array with axes transposed, use the ma.MaskedArray.transpose() −
print("
Result...
",maskArr.transpose((1, 0)))Example
# Python ma.MaskedArray - Return a view of the array with axes transposed along given axis
import numpy as np
import numpy.ma as ma
# Create an array with int elements using the numpy.array() method
arr = np.array([[78, 85, 51], [56, 33, 97]])
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 =[[0, 1, 0], [ 0, 0, 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 return a view of the array with axes transposed, use the ma.MaskedArray.transpose() method in Numpy
print("
Result...
",maskArr.transpose((1, 0)))Output
Array... [[78 85 51] [56 33 97]] Array type... int64 Array Dimensions... 2 Our Masked Array [[78 -- 51] [56 33 97]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (2, 3) Elements in the Masked Array... 6 Result... [[78 56] [-- 33] [51 97]]
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