testing whether a Numpy array contains a given row

You can use .tolist()

>>> a = np.array([[1,2],[10,20],[100,200]])
>>> [1,2] in a.tolist()
True
>>> [1,20] in a.tolist()
False
>>> [1,20] in a.tolist()
False
>>> [1,42] in a.tolist()
False
>>> [42,1] in a.tolist()
False

Or use a view:

>>> any((a[:]==[1,2]).all(1))
True
>>> any((a[:]==[1,20]).all(1))
False

Or generate over the numpy list (potentially VERY SLOW):

any(([1,2] == x).all() for x in a)     # stops on first occurrence 

Or use numpy logic functions:

any(np.equal(a,[1,2]).all(1))

If you time these:

import numpy as np
import time

n=300000
a=np.arange(n*3).reshape(n,3)
b=a.tolist()

t1,t2,t3=a[n//100][0],a[n//2][0],a[-10][0]

tests=[ ('early hit',[t1, t1+1, t1+2]),
        ('middle hit',[t2,t2+1,t2+2]),
        ('late hit', [t3,t3+1,t3+2]),
        ('miss',[0,2,0])]

fmt="\t{:20}{:.5f} seconds and is {}"     

for test, tgt in tests:
    print('\n{}: {} in {:,} elements:'.format(test,tgt,n))

    name="view"
    t1=time.time()
    result=(a[...]==tgt).all(1).any()
    t2=time.time()
    print(fmt.format(name,t2-t1,result))

    name="python list"
    t1=time.time()
    result = True if tgt in b else False
    t2=time.time()
    print(fmt.format(name,t2-t1,result))

    name="gen over numpy"
    t1=time.time()
    result=any((tgt == x).all() for x in a)
    t2=time.time()
    print(fmt.format(name,t2-t1,result))

    name="logic equal"
    t1=time.time()
    np.equal(a,tgt).all(1).any()
    t2=time.time()
    print(fmt.format(name,t2-t1,result))

You can see that hit or miss, the numpy routines are the same speed to search the array. The Python in operator is potentially a lot faster for an early hit, and the generator is just bad news if you have to go all the way through the array.

Here are the results for 300,000 x 3 element array:

early hit: [9000, 9001, 9002] in 300,000 elements:
    view                0.01002 seconds and is True
    python list         0.00305 seconds and is True
    gen over numpy      0.06470 seconds and is True
    logic equal         0.00909 seconds and is True

middle hit: [450000, 450001, 450002] in 300,000 elements:
    view                0.00915 seconds and is True
    python list         0.15458 seconds and is True
    gen over numpy      3.24386 seconds and is True
    logic equal         0.00937 seconds and is True

late hit: [899970, 899971, 899972] in 300,000 elements:
    view                0.00936 seconds and is True
    python list         0.30604 seconds and is True
    gen over numpy      6.47660 seconds and is True
    logic equal         0.00965 seconds and is True

miss: [0, 2, 0] in 300,000 elements:
    view                0.00936 seconds and is False
    python list         0.01287 seconds and is False
    gen over numpy      6.49190 seconds and is False
    logic equal         0.00965 seconds and is False

And for 3,000,000 x 3 array:

early hit: [90000, 90001, 90002] in 3,000,000 elements:
    view                0.10128 seconds and is True
    python list         0.02982 seconds and is True
    gen over numpy      0.66057 seconds and is True
    logic equal         0.09128 seconds and is True

middle hit: [4500000, 4500001, 4500002] in 3,000,000 elements:
    view                0.09331 seconds and is True
    python list         1.48180 seconds and is True
    gen over numpy      32.69874 seconds and is True
    logic equal         0.09438 seconds and is True

late hit: [8999970, 8999971, 8999972] in 3,000,000 elements:
    view                0.09868 seconds and is True
    python list         3.01236 seconds and is True
    gen over numpy      65.15087 seconds and is True
    logic equal         0.09591 seconds and is True

miss: [0, 2, 0] in 3,000,000 elements:
    view                0.09588 seconds and is False
    python list         0.12904 seconds and is False
    gen over numpy      64.46789 seconds and is False
    logic equal         0.09671 seconds and is False

Which seems to indicate that np.equal is the fastest pure numpy way to do this…