Reputation: 177
I have following arrays.
In [1]: a = np.array([["aa", "bb", "cc"], ["cc", "bb", "aa"]])
In [2]: a
Out[2]:
array([['aa', 'bb', 'cc'],
['cc', 'bb', 'aa']],
dtype='|S2')
In [3]: b = np.array([[11, 12, 13], [21, 22, 23]])
In [4]: b
Out[4]:
array([[11, 12, 13],
[21, 22, 23]])
Relation between a and b can be described conceptually as b[0].aa = 11 b[0].bb = 12. b[0].cc = 13. i.e. row in 'a' are keys and row in 'b' are values of a single dict. First rows will represent
{'aa': 11, 'bb': 12, 'cc': 13}
Now we are given with keys of these dicts.
In [5]: c = np.array(["bb", "aa"])
In [6]: c
Out[6]:
array(['bb', 'aa'],
dtype='|S2')
Now, what is the best way of accessing b array given 'c' which will give axis of each row as value in 'a'. One way to do is
In [7]: cond_list = [a[:, 0] == c, a[:, 1] == c, a[:, 2] == c]
In [8]: choice_list = [b[:, 0], b[:, 1], b[:, 2]]
In [9]: np.select(cond_list, choice_list)
Out[9]: array([12, 23])
Is there a better way of doing it? What if number of axis in a and b are not known before hand?
Upvotes: 2
Views: 1258
Reputation: 879143
In [13]: a==c[:,np.newaxis]
Out[13]:
array([[False, True, False],
[False, False, True]], dtype=bool)
In [14]: b[a==c[:,np.newaxis]]
Out[14]: array([12, 23])
Upvotes: 2