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DS-1000 / 351 / I have data of sample 1 and sample 2 (`a` and `b`) – size is different for…

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Problem:
I have data of sample 1 and sample 2 (`a` and `b`) – size is different for sample 1 and sample 2. I want to do a weighted (take n into account) two-tailed t-test.
I tried using the scipy.stat module by creating my numbers with np.random.normal, since it only takes data and not stat values like mean and std dev (is there any way to use these values directly). But it didn't work since the data arrays has to be of equal size.
For some reason, nans might be in original data, and we want to omit them.
Any help on how to get the p-value would be highly appreciated.
A:
<code>
import numpy as np
import scipy.stats
a = np.random.randn(40)
b = 4*np.random.randn(50)
</code>
p_value = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Numpy

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Source and history

upstream_id / Original problem ID
351
task_id
b1c15694-9e2d-5ca6-9e56-0e60f46158d5
task_key
default--test--351
task_revision_id
1
config / split
default / test

Official source

initial import