I have these two data structures:
a = np.array([1,2,3])
ts = pd.TimeSeries([1,2,3])
What I want to get at the end is:
1 2 3
2 4 6
3 6 9
You can use the outer product:
In [490]: np.outer(a, ts)
Out[490]:
array([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
Or align one of them vertically first:
In [491]: a * ts[:, None]
Out[491]:
array([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
Note that the strange index just makes it a column vector:
In [493]: ts[:, None]
Out[493]:
array([[1],
[2],
[3]])
by adding an extra length-1 dimension to the shape:
In [494]: ts[:, None].shape
Out[494]: (3, 1)
>>> np.outer(a, ts)
array([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
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