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Stack arrays in sequence vertically (row wise) in Numpy
To stack arrays in sequence vertically (row wise), use the ma.row_stack() method in Python Numpy. This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit. Returns the array formed by stacking the given arrays, will be at least 2-D.
This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations. It is applied to both the _data and the _mask, if any.
The parameters are the arrays that must have the same shape along all but the first axis. 1-D arrays must have the same length.
Steps
At first, import the required library −
import numpy as np import numpy.ma as ma
Create a new array using the array() method −
arr = np.array([[200], [300], [400], [500]])
print("Array...
", arr)Type of array −
print("
Array type...
", arr.dtype)Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)To stack arrays in sequence vertically (row wise), use the ma.row_stack() method −
resArr = np.ma.row_stack (arr)
Resultant Array −
print("
Result...
", resArr)Example
# Python ma.MaskedArray - Stack arrays in sequence vertically (row wise)
import numpy as np
import numpy.ma as ma
# Create a new array using the array() method
arr = np.array([[200], [300], [400], [500]])
print("Array...
", arr)
# Type of array
print("
Array type...
", arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",arr.ndim)
# To stack arrays in sequence vertically (row wise), use the ma.row_stack() method in Python Numpy
resArr = np.ma.row_stack (arr)
# Resultant Array
print("
Result...
", resArr)Output
Array... [[200] [300] [400] [500]] Array type... int64 Array Dimensions... 2 Result... [[200] [300] [400] [500]]
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