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As far as I can see it, it isn't covered in the indexing, slicing and iterating scipy tutorial, so let me ask it here:
Say I've
x = np.array([[1,2],[3,4],[5,6],[7,8],[9,0]])
x: array([[1, 2],
[3, 4],
[5, 6],
[7, 8],
[9, 0]])
How do I slice the array in order get both the first and last rows:
y: array([[1, 2],
[3, 4],
[7, 8],
[9, 0]])
483
Slicing in python means extracting data from one given index to another given index, however, NumPy slicing is slightly different. Slicing can be done with the help of (:) . A NumPy array slicing object is constructed by giving start , stop , and step parameters to the built-in slicing function.
hsplit() function. The hsplit() function is used to split an array into multiple sub-arrays horizontally (column-wise). hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension.
Slice Two-dimensional Numpy Arrays To slice elements from two-dimensional arrays, you need to specify both a row index and a column index as [row_index, column_index] . For example, you can use the index [1,2] to query the element at the second row, third column in precip_2002_2013 .
Use the array_split() method, pass in the array you want to split and the number of splits you want to do.
I don't know if there's a slick way to do that. You could list the indices explicitly, of course:
>>> x[[0,1,-2,-1]]
array([[1, 2],
[3, 4],
[7, 8],
[9, 0]])
Or use r_ to help, which would probably be more convenient if we wanted more rows from the head or tail:
>>> x[np.r_[0:2, -2:0]]
array([[1, 2],
[3, 4],
[7, 8],
[9, 0]])
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