Htykuut

I have two different lists and I have to apply np.dot function of numpy in python
how can I do that?

list1=

array([[      nan,       nan],

       [ 0.000829,  0.000326],

       [-0.000149, -0.00033 ],

       ...,

       [-0.000757, -0.000737],

       [-0.000795, -0.00068 ],

       [-0.000788, -0.00069 ]])

list2 =

array([[      nan,       nan],

       [      nan,       nan],

       [-0.000829, -0.000326],

       ...,

       [ 0.000763,  0.000738],

       [ 0.000757,  0.000737],

       [ 0.000795,  0.00068 ]])

these are two seperate list of lists

so I want to do it this way:

np.dot([-0.000149, -0.00033 ], [-0.000829, -0.000326])

so it is like

np.dot(list1[3], list2[3])

and it continue to choose one index from one list and one index from the other list
and that should return the one dimensional array, the problem is data, which is in two seperate list, so I need one index from list one and one index from the other list, i know it can be done through the loop, but not sure how is it possible,

i hope it is clear now

So your question is actually about how to loop through the lists and call np.dot on each corresponding pair. Here's one way to do it, using list comprehension and zip:

>>> import numpy as np

>>> list1 = np.array([[1,2],[3,4]])

>>> list2 = list1.copy()

>>> list_of_results = [np.dot(a,b) for a,b in zip(list1, list2)]

>>> list_of_results

[5, 25]

If you are not familiar with list comprehension, I advise you to look that up. But you could also do it with a simple for loop:

assert list1.shape == list2.shape, "List shapes do not match"

results = 

for inner_list_index in range(list1.shape[0]):

    a = list1[inner_list_index]

    b = list2[inner_list_index]

    res = np.dot(a,b)

    results = results.append(res)

This can be reduced to fewer lines:

>>> assert list1.shape[0] == list2.shape[0]

>>> results = 

>>> for i in range(list1.shape[0]):

...     results.append(np.dot(list1[i], list2[i]))

...

>>> results

[5, 25]

Note that both of these approaches return a normal list, not a numpy ndarray. This is because appending to numpy arrays is usually not too fast. You could use np.append() instead. Or just apply np.array() to the result if you need it as an np array again.