CODEWITHSUNDEEP
pythonnumpy
ulrich
ulrich

Reputation: 3587

mapping two numpy arrays

I have two numpy arrays A and B.

A = np.array ([[ 1  3] [ 2  3]  [ 2  1] ])

B = np.array([(1, 'Alpha'), (2, 'Beta'), (3, 'Gamma')]

How can I map A with B in order to get something like:

result = np.array ([[ 'Alpha'  'Gamma'] [ 'Beta'  'Gamma']  ['Beta'  'Alpha'] ])

I have tried map(B['f1'],A) but I am getting TypeError: 'numpy.ndarray' object is not callable

Upvotes: 5

Views: 4512

Answers (4)

B. M.
B. M.

Reputation: 18668

I assume that you want a numpy solution for efficiency. In this case, try to give your association table a more "numpythonic" appearance :

A = np.array ([[ 1,  3], [ 2,  3] , [ 2,  1] ])
B = np.array([(0,'before'),(1, 'Alpha'), (2, 'Beta'), (3, 'Gamma')])
C=np.array([b[1] for b in B])

Then the result is just : C.take(A).

Upvotes: 1

Divakar
Divakar

Reputation: 221774

Here's a NumPythonic vectorized approach -

B[:,1][(A == B[:,0].astype(int)[:,None,None]).argmax(0)]

Sample run on a generic case -

In [118]: A
Out[118]: 
array([[4, 3],
       [2, 3],
       [2, 4]])

In [119]: B
Out[119]: 
array([['3', 'Alpha'],
       ['4', 'Beta'],
       ['2', 'Gamma']], 
      dtype='|S5')

In [120]: B[:,1][(A == B[:,0].astype(int)[:,None,None]).argmax(0)]
Out[120]: 
array([['Beta', 'Alpha'],
       ['Gamma', 'Alpha'],
       ['Gamma', 'Beta']], 
      dtype='|S5')

Upvotes: 2

UlfR
UlfR

Reputation: 4395

Without using any specific numpy things you could do:

d = dict(B)
[[d.get(str(y)) for y in x] for x in A]

Upvotes: 2

Kasravnd
Kasravnd

Reputation: 107357

You can use a dictionary and a list comprehension :

>>> d=dict(B)
>>> np.array([[(d[str(i)]),d[str(j)]] for i,j in A])
array([['Alpha', 'Gamma'],
       ['Beta', 'Gamma'],
       ['Beta', 'Alpha']], 
      dtype='|S5')

Upvotes: 2

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