What is the most efficient way to check if a value exists in a NumPy array?

I have a very large NumPy array

1 40 3
4 50 4
5 60 7
5 49 6
6 70 8
8 80 9
8 72 1
9 90 7
.... 

I want to check to see if a value exists in the 1st column of the array. I've got a bunch of homegrown ways (e.g. iterating through each row and checking), but given the size of the array I'd like to find the most efficient method.

Thanks!

How about

if value in my_array[:, col_num]:
    do_whatever

Edit: I think __contains__ is implemented in such a way that this is the same as @detly's version

The most obvious to me would be:

np.any(my_array[:, 0] == value)

To check multiple values, you can use numpy.in1d(), which is an element-wise function version of the python keyword in. If your data is sorted, you can use numpy.searchsorted():

import numpy as np
data = np.array([1,4,5,5,6,8,8,9])
values = [2,3,4,6,7]
print np.in1d(values, data)

index = np.searchsorted(data, values)
print data[index] == values