The Python programming language has the ability to approximate data. That is, to scientifically approximate functions and rounding of numbers to specific and precise ones. Many mathematical functions in Python look concise and ergonomic, such as range, vector, and others.

random functions allow you to run the algorithm through all possible values ​​of variables/arrays. Random number approximation functions to an integer (randint) create portability of working with code.
Compact assignment functions =, instead of := in C/C++, allow you to not focus on the logical operation. Working with indents (Tab) allows you to not clutter your code with brackets {} to highlight the beginning and end of a function.

Jupyter Notebook makes it easier to work with functions because it has an extended range of libraries underneath it. Even machine learning with large data samples takes only minutes to run in code.
You don't have to think about the layout of files in the folder where the project is launched, you can store everything in one place (.ipynb file).

A = matrix_gen(10)
for i in range(10):
    for j in range(10): 
        print('{0:8.5f}'.format(A[i,j]), end = ' ') 
    print()
print()

x = opinion_gen(10)
for i in x:
    print('{0:8.2f}'.format(i), end = ' ') 
print()

Formatted output of tabular data is based on the format function with integer limits and values ​​after the decimal point specified. The differences from other object-oriented programming languages ​​are small, but pleasant from the point of view of solving mathematically complex problems.