Please convert the following Python code into a multi-process parallel operation --

**Python code

Please convert the following Python code into a multi-process parallel operation --

import time
def calc_square(numbers).
for n in numbers.
print(f'\n{n} ^ 2 = {n*n}')
time.sleep(0.1)

def calc_cube(numbers).
for n in numbers.
print(f'\n{n} ^ 3 = {nnn}')
time.sleep(0.1)

numbers = [2, 3, 5, 8] start = time.time()
calc_square(numbers)
calc_cube(numbers)
end = time.time()

print('Execution Time: {}'.format(end-start))

Please, as a software test engineer, I would like to compare the operational efficiency of the following two pieces of algorithmic code that perform the same task, please write a loop to execute the following algorithm [ Parameters ] code, and give the execution time of each of the two pieces of code, as well as the relevant statistical information

Code 1.

Code 2.

Please, as a software test engineer, I would like to compare the operational efficiency of the following two pieces of algorithmic code that perform the same task, please write a loop to execute the following algorithm [ 10 times ] code, and give the execution time of each > of the two pieces of code, as well as the related statistics

Please, as a software test engineer, write the [ Functions ] The unit test code for the test condition:

test condition

Please, as a software test engineer, write the [ def compute(a, b) ] The unit test code for the test condition:

Please help me as a software engineer to optimize the efficiency of execution of the following code --

Python Code

Please help me as a software engineer to optimize the efficiency of execution of the following code --

def test(y):
sum = 0
for i in range (y+1):
sum += 1
return sum

As a code optimizer, please help me optimize the following Pandas code --

Python Code

As a code optimizer, please help me optimize the following Pandas code --

import pandas as pd

df = pd.read_csv(data_path + "titles.csv")
df_bad = df.query("runtime > 30 & type == 'SHOW'")
df_bad ["score"] = df_bad "imdb_score", "tmdb_score".sum(axis=1)
df_bad = df_bad "seasons", "score"
df_bad = df_bad.groupby("seasons").agg(["count", "mean"])
df_bad = df_bad.droplevel(axis=1, level=0)
df_bad = df_bad.query("count > 10")

df_bad.head()

Please act as a code optimizer, the following code is written at a low level, please help me to change it --

Python Code

Please act as a code optimizer, the following code is written at a low level, please help me to change it --

numbers = [1, 2, 3] letters = ["A", "B", "C"]

for index in range (len(numbers))::
print(numbers[index], letters[index])

Please act as a code optimizer and simplify the following code --

Python Code

Please act as a code optimizer and simplify the following code --

classes = ['Intro to Python',
'R Data Analysis'.
'Python Machine Learning'] grades = [98,
96,
89] grade_dict = {}
for idx in range(len(classes))::
grade_dict[classes[idx]] = grades[idx] print(grade_dict)

Please, as a software engineer, help me to troubleshoot the following error in the code --

Python Code

Please, as a software engineer, help me to troubleshoot the following error in the code --

def add_from_1_to_k(k).
if (k > 0).
result = k + add_from_1_to_k(k - 1)
return result

add_from_1_to_k(-10)