Python

Python Tips and Tricks

Array enumeration

For numpy

a = np.array([[1, 2], [3, 4]])
for index, x in np.ndenumerate(a):
    print(index, x)

For usual python

a = np.array([[1, 2], [3, 4]])
for index, x in enumerate(a):
    print(index, x)

Zip

For iterating along the several list it is a good practice to use zip:

a = [1,2,3,4]
b = [5,6,7,8]
    for av, bv in zip(a, b):
        print(av)
        print(bv)

Multiprocessing

from multiprocessing import Pool
def run_with_mp_map(items, do_work, processes=None, chunksize=None):
    print(f"running using multiprocessing with {processes=}, {chunksize=}")
    start_t = time.perf_counter()
    with Pool(processes=processes) as pool:
        results = pool.imap(do_work, items, chunksize=chunksize)
    end_t = time.perf_counter()
    wall_duration = end_t - start_t
    print(f"it took: {wall_duration:.2f}s")
    return results

Context manager

 with open(filename, "w") as f:
        f.write("hello!\n")
    # close automatic, even if exception

PEP8 Checker

It is a good practice to follow PEP8 standard when writing code. In jupyter the PEP8 syntax checker may help highlight the errors. To load the checker into a jupyter notebook:

%load_ext pycodestyle_magic

To switch on the checker:

%pycodestyle_on

To switch off the checker:

%pycodestyle_off

Main function

It is a good practice to indicate whether your python file will be executed as a script by including "if __name__ == '__main__':" statement:

def main():
    pass
# Do Something:

if __name__ == '__main__':
   main()

Measure execution timing

import time
start = time.perf_counter()
time.sleep(1)
end = time.perf_counter()
print(end - start)