I'm using a function to determine if resources can be used again or not. This is the numpy array I'm using.

So this is main idea behind my function, the problem here is that it takes more than 60% of the whole program runtime and I would appreciate any advice about how to make it more efficient/faster. Thank you.

I'm using a function to determine if resources can be used again or not. This is the numpy array I'm using.

So this is main idea behind my function. The problem here is that it takes more than 60% of the whole program runtime, and I would appreciate any advice about how to make it more efficient/faster.

I'm using a function to determine if resources can be used again or not. This is the numpy array i'm using.

1. First step is to find all resource available on a date, for example (2019-05-16 from 8:30 to 17:30). To achieve this i used np.where like the example below:

    av_resource_np = resource_availability_np[np.where(
    (resource_availability_np[:,1] <= '2019-05-16')
    & (resource_availability_np[:,2] >= '2019-05-16')
    & (resource_availability_np[:,3] <= '17:30:00') 
    & (resource_availability_np[:,4] >= '08:30:00'))]
   

2. Here i try to find unique resource ids and the sum of their overload factor using np.unique()

    unique_id, count_nb = np.unique(av_resource_np[:,(0,5)], axis=0, return_counts=True)
    availability_mat = np.column_stack((unique_id, count_nb ))
   

Which yields the followig results:

So this is main idea behind my function, the problem here is that it takes more than 60% of the whole program runtime and i would appreciate any advice about how to make it more efficient/faster. Thank you.

I'm using a function to determine if resources can be used again or not. This is the numpy array I'm using.

1. First step is to find all resource available on a date, for example (2019-05-16 from 8:30 to 17:30). To achieve this I used np.where like the example below:

    av_resource_np = resource_availability_np[np.where(
    (resource_availability_np[:,1] <= '2019-05-16')
    & (resource_availability_np[:,2] >= '2019-05-16')
    & (resource_availability_np[:,3] <= '17:30:00') 
    & (resource_availability_np[:,4] >= '08:30:00'))]
   

2. Here I try to find unique resource ids and the sum of their overload factor using np.unique():

    unique_id, count_nb = np.unique(av_resource_np[:,(0,5)], axis=0, return_counts=True)
    availability_mat = np.column_stack((unique_id, count_nb ))
   

Which yields the following results:

So this is main idea behind my function, the problem here is that it takes more than 60% of the whole program runtime and I would appreciate any advice about how to make it more efficient/faster. Thank you.