How do I stack vectors of different lengths in NumPy?
numpy, python
Solution
Short answer: you can't. NumPy does not support jagged arrays natively.
Long answer:
>>> a = ones((3,))
>>> b = ones((2,))
>>> c = array([a, b])
>>> c
array([[ 1. 1. 1.], [ 1. 1.]], dtype=object)
gives an array that may or may not behave as you expect. E.g. it doesn't support basic methods like `sum` or `reshape`, and you should treat this much as you'd treat the ordinary Python list `[a, b]` (iterate over it to perform operations instead of using vectorized idioms).
Several possible workarounds exist; the easiest is to coerce `a` and `b` to a common length, perhaps using masked arrays or NaN to signal that some indices are invalid in some rows. E.g. here's `b` as a masked array:
>>> ma.array(np.resize(b, a.shape[0]), mask=[False, False, True])
masked_array(data = [1.0 1.0 --],
mask = [False False True],
fill_value = 1e+20)
This can be stacked with `a` as follows:
>>> ma.vstack([a, ma.array(np.resize(b, a.shape[0]), mask=[False, False, True])])
masked_array(data =
[[1.0 1.0 1.0]
[1.0 1.0 --]],
mask =
[[False False False]
[False False True]],
fill_value = 1e+20)
(For some purposes, `scipy.sparse` may also be interesting.)
Problem
How do I stack column-wise `n` vectors of shape `(x,)` where x could be any number? For example, ``` from numpy import * a = ones((3,)) b = ones((2,)) c = vstack((a,b)) # <-- gives an error c = vstack((a[:,newaxis],b[:,newaxis])) #<-- also gives an error ``` `hstack` works fine but concatenates along the wrong dimension.