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Is it ok to use map() in python unit tests [closed]

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Is it ok / good style to use python built in functions like map() in unit tests?
for example instead of writing an individual assert for all test cases something like:
def double_x(x):
return x*2
def test_double_x():
orig_vals = [1, 2, 3, 4, 5, 6]
expected_vals = [2, 4, 6, 8, 10, 12]
assert list(map(double_x, orig_vals)) == expected_vals

There's no problem, but like anywhere else, a list comprehension may be preferable.
assert [double_x(x) for x in orig-vals] == expected_vals
Individual assertions, though, may make it easier to identify the failed test.
for x, y in zip(orig_vals, expected_vals):
z = double_x(x)
assert z == y, f'double_x({x}) returned {z}, not {y} as expected'

How to increasingly iterate on multiple iterators? [closed]

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Can someone help me writing a code in order to increasingly iterate on multiple iterators.
As an example imagine that I have several lists (for example : [1, 9] and [2, 3, 4]) and I want to get an iterable from those lists yielding 1, 2, 3, 4 and 9. Of course the idea is to use more big and complex iterators (otherwise it is easy to just merge and sort).
Thank you !

heapq is exactly designed for what you want : It creates an iterator, so you don't have to handle huge lists. Here is the code for a simple example :
import heapq
a = [1, 4, 7, 10]
b = [2, 5, 6, 11]
for c in heapq.merge(a, b):
print(c)
Of course, it works only if your lists are sorted, you have to sort them before if they are not sorted.

Random Question Sequence From Array Python [closed]

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I have 5 arrays with 6 questions in each one.
I need the script to pick 2 questions from each array and create an input() function. The part I can't seem to think of is how to make an output for a correct answer for the questions. I understand how a specified input would work but what about randomized.

I think you're looking for something like this:
randomNumber1=***some generated number (0 thru 6)
randomNumber2=***some generated number (0 thru 6)
array1=['what is the meaning of life', 'how far away is the sun',...]
array2=['what did is your favorite color', 'how many pennies are in 1 dollar'...]
q1=array1[randomNumber1]
q2=array2[randomNumber2]
input1=input(q1)
input2=input(q2)
#stores answers in a dictionary
answers={q1:input1, q2:input2}

I do not think the random module has the function that you want.
But it is easy to build one if you like. Python is easy.
Does this work?
import random
from typing import Iterable
def get_sub_random_list(sub_length: int, iterable: Iterable) -> list:
iterable_copy = list(iterable)
result = []
for __ in range(sub_length):
length = len(iterable_copy)
if length == 0:
raise ValueError(f"the iterable should longer than {sub_length}")
index = random.choice(range(length))
result.append(iterable_copy[index])
del iterable_copy[index]
return result
example:
>>> get_sub_random_list(1, [1, 2, 3, 4, 5, 6])
[5]
>>> get_sub_random_list(6, [1, 2, 3, 4, 5, 6])
[4, 1, 5, 2, 6, 3]
The complexity is O(n+m): n is the length of iterable, and the m is the the times of the loop.

np.where() solution explanation [closed]

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I am going through the exercises here: https://www.machinelearningplus.com/python/101-pandas-exercises-python/
Problem #16 has a solution (#1) using np.where() that I am having trouble understanding.
import pandas as pd
import numpy as np
print('pandas: {}'.format(pd.__version__))
print('NumPy: {}'.format(np.__version__))
print('-----')
ser1 = pd.Series([10, 9, 6, 5, 3, 1, 12, 8, 13])
ser2 = pd.Series([1, 3, 10, 13])
# Get the positions of items of 'ser2' in 'ser1' as a list.
# Solution 1
list1 = [np.where(i == ser1)[0].tolist()[0] for i in ser2]
print(list1)
print()
# Solution 2
list2 = [pd.Index(ser1).get_loc(i) for i in ser2]
print(list2)
I have looked up np.where() here:
# https://stackoverflow.com/questions/34667282/numpy-where-detailed-step-by-step-explanation-examples
# https://thispointer.com/numpy-where-tutorial-examples-python/
# https://www.geeksforgeeks.org/numpy-where-in-python/
To be precise, I am not understanding the function and placement of both
bracketed zero's ( [0] ).

np.where outputs a tuple (output of numpy.where(condition) is not an array, but a tuple of arrays: why?), so you'd have to index it (hence the first [0]), then, the output is a numpy array of elements. There is only one in this case, so the second [0] works. the tolist() is completely redundant though
It'd be better to extend list1 with the found indexes, because this code fails when an element occurs more than once:
list1 = []
[list1.extend(np.where(i == ser1)[0]) for i in ser2]
print(list1)
print()
Not the best code imo.
tip, just check the output of stuff yourself, and you would have figured this out. just run np.where(i==ser1) and you'd have seen it returns a tuple, and you need to index it. etc.

Python: The Replacements [closed]

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I'm currently trying to complete Computer Science Circles online but I am stuck on part 14: Methods. Here is the question.
Using index and other list methods, write a function replace(list, X, Y) which replaces all occurrences of X in list with Y. For example, if L = [3, 1, 4, 1, 5, 9] then replace(L, 1, 7) would change the contents of L to [3, 7, 4, 7, 5, 9]. To make this exercise a challenge, you are not allowed to use [].
Note: you don't need to use return.
I would probably be able to do this if we were allowed to use square brackets.
Here is what I have so far.
def replace(L, X, Y):
while X in L:
var = L.index(X)
var = Y
return(L)

I'll give some tips since this is an exercise.
1) You already found out the index where you're supposed to replace one element with another. What other way is there to replace a value in a given index? Check all the methods of list.
2) A list comprehension also allows an elegant solution:
[...???... for value in list]
You'll need to figure out what the expression should be, and how to make the comprehension modify your original list, not just create a new one.