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How can I join two numpy ndarrays to accomplish the following in a fast way, using optimized numpy, without any looping?
>>> a = np.random.rand(2,2)
>>> a
array([[ 0.09028802, 0.2274419 ],
[ 0.35402772, 0.87834376]])
>>> b = np.random.rand(2,2)
>>> b
array([[ 0.4776325 , 0.73690098],
[ 0.69181444, 0.672248 ]])
>>> c = ???
>>> c
array([[ 0.09028802, 0.2274419, 0.4776325 , 0.73690098],
[ 0.09028802, 0.2274419, 0.69181444, 0.672248 ],
[ 0.35402772, 0.87834376, 0.4776325 , 0.73690098],
[ 0.35402772, 0.87834376, 0.69181444, 0.672248 ]])
645
Joining NumPy Arrays Joining means putting contents of two or more arrays in a single array. In SQL we join tables based on a key, whereas in NumPy we join arrays by axes. We pass a sequence of arrays that we want to join to the concatenate() function, along with the axis.
The concat() method is used to merge two or more arrays. This method does not change the existing arrays, but instead returns a new array.
It's faster to append list first and convert to array than appending NumPy arrays. NumPy automatically converts lists, usually, so I removed the unneeded array() conversions.
What is the difference between NumPy append and concatenate ? My observation is that concatenate is a bit faster and append flattens the array if axis is not specified. np. append is defined in turns of np.
Not the prettiest, but you could combine hstack, repeat, and tile:
>>> a = np.arange(4).reshape(2,2)
>>> b = a+10
>>> a
array([[0, 1],
[2, 3]])
>>> b
array([[10, 11],
[12, 13]])
>>> np.hstack([np.repeat(a,len(a),0),np.tile(b,(len(b),1))])
array([[ 0, 1, 10, 11],
[ 0, 1, 12, 13],
[ 2, 3, 10, 11],
[ 2, 3, 12, 13]])
Or for a 3x3 case:
>>> a = np.arange(9).reshape(3,3)
>>> b = a+10
>>> np.hstack([np.repeat(a,len(a),0),np.tile(b,(len(b),1))])
array([[ 0, 1, 2, 10, 11, 12],
[ 0, 1, 2, 13, 14, 15],
[ 0, 1, 2, 16, 17, 18],
[ 3, 4, 5, 10, 11, 12],
[ 3, 4, 5, 13, 14, 15],
[ 3, 4, 5, 16, 17, 18],
[ 6, 7, 8, 10, 11, 12],
[ 6, 7, 8, 13, 14, 15],
[ 6, 7, 8, 16, 17, 18]])
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