Numpy where function multiple conditions

I have worked out this simple example

import numpy as np

ar = np.array([3,4,5,14,2,4,3,7])

print [X for X in list(ar) if (X >= 3 and X <= 6)]

>>> 
[3, 4, 5, 4, 3]

Try:

np.intersect1d(np.where(dists >= r)[0],np.where(dists <= r + dr)[0])

The accepted answer explained the problem well enough. However, the more Numpythonic approach for applying multiple conditions is to use numpy logical functions. In this case, you can use np.logical_and:

np.where(np.logical_and(np.greater_equal(dists,r),np.greater_equal(dists,r + dr)))

I like to use np.vectorize for such tasks. Consider the following:

>>> # function which returns True when constraints are satisfied.
>>> func = lambda d: d >= r and d<= (r+dr) 
>>>
>>> # Apply constraints element-wise to the dists array.
>>> result = np.vectorize(func)(dists) 
>>>
>>> result = np.where(result) # Get output.

You can also use np.argwhere instead of np.where for clear output.

This should work:

dists[((dists >= r) & (dists <= r+dr))]

Try:

import numpy as np
dist = np.array([1,2,3,4,5])
r = 2
dr = 3
np.where(np.logical_and(dist> r, dist<=r+dr))

Output: (array([2, 3, 4]),)

You can see Logic functions for more details.

One interesting thing to point here; the usual way of using OR and AND too will work in this case, but with a small change. Instead of “and” and instead of “or”, rather use Ampersand(&) and Pipe Operator(|) and it will work.

When we use ‘and’:

ar = np.array([3,4,5,14,2,4,3,7])
np.where((ar>3) and (ar<6), 'yo', ar)

Output:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

When we use Ampersand(&):

ar = np.array([3,4,5,14,2,4,3,7])
np.where((ar>3) & (ar<6), 'yo', ar)

Output:
array(['3', 'yo', 'yo', '14', '2', 'yo', '3', '7'], dtype='<U11')

And this is same in the case when we are trying to apply multiple filters in case of pandas Dataframe. Now the reasoning behind this has to do something with Logical Operators and Bitwise Operators and for more understanding about same, I’d suggest to go through this answer or similar Q/A in stackoverflow.

UPDATE

A user asked, why is there a need for giving (ar>3) and (ar<6) inside the parenthesis. Well here’s the thing. Before I start talking about what’s happening here, one needs to know about Operator precedence in Python.

Similar to what BODMAS is about, python also gives precedence to what should be performed first. Items inside the parenthesis are performed first and then the bitwise operator comes to work. I’ll show below what happens in both the cases when you do use and not use “(“, “)”.

Case1:

np.where( ar>3 & ar<6, 'yo', ar)
np.where( np.array([3,4,5,14,2,4,3,7])>3 & np.array([3,4,5,14,2,4,3,7])<6, 'yo', ar)

Since there are no brackets here, the bitwise operator(&) is getting confused here that what are you even asking it to get logical AND of, because in the operator precedence table if you see, & is given precedence over < or > operators. Here’s the table from from lowest precedence to highest precedence.

It’s not even performing the < and > operation and being asked to perform a logical AND operation. So that’s why it gives that error.

One can check out the following link to learn more about: operator
precedence

Now to Case 2:

If you do use the bracket, you clearly see what happens.

np.where( (ar>3) & (ar<6), 'yo', ar)
np.where( (array([False,  True,  True,  True, False,  True, False,  True])) & (array([ True,  True,  True, False,  True,  True,  True, False])), 'yo', ar)

Two arrays of True and False. And you can easily perform logical AND operation on them. Which gives you:

np.where( array([False,  True,  True, False, False,  True, False, False]),  'yo', ar)

And rest you know, np.where, for given cases, wherever True, assigns first value(i.e. here ‘yo’) and if False, the other(i.e. here, keeping the original).

That’s all. I hope I explained the query well.

To get np.where() working with multiple conditions, just do the following:

np.where((condition 1) & (condition 2)) # for and
np.where((condition 1) | (condition 2)) # for or

I know this repeats some other answers, but I put this simple answer here for people still wondering, "Why am I getting that annoying error message about The truth value of an array with more than one element is ambiguous" who are confused by the very verbose and complex answers that are addressing the somewhat specialized nature of the original post.

Now, as to why numpy breaks when you use and instead of &, I will not try to answer that here. It just does 🙂 See other answers here for explanation. It seems like something they should just fix instead of forcing it for consistency IMHO. Or at least they should make a better error message. 🙂