Python range() and zip() object type
I understand how functions like range() and zip() can be used in a for loop. However I expected range() to output a list – much like seq in the unix shell. If I run the following code:
Python
Python: Adding element to list while iterating
I know that it is not allowed to remove elements while iterating a list, but is it allowed to add elements to a python list while iterating. Here is an example:
Why does Python’s itertools.permutations contain duplicates? (When the original list has duplicates)
It is universally agreed that a list of n distinct symbols has n! permutations. However, when the symbols are not distinct, the most common convention, in mathematics and elsewhere, seems to be to count only distinct permutations. Thus the permutations of the list [1, 1, 2] are usually considered to be
[1, 1, 2], [1, 2, 1], [2, 1, 1]. Indeed, the following C++ code prints precisely those three:
‘WSGIRequest’ object has no attribute ‘user’ Django admin
When I trying to access the admin page it gives me the following error:
How to SSH and run commands in EC2 using boto3?
I want to be able to ssh into an EC2 instance, and run some shell commands in it, like this.
Google search using python script
Could anyone help me on how to write a python script that searches google and prints the links of top results. Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the … Read more
Replace n with
I’m parsing text from file with Python. I have to replace all newlines (n) with
cause this text will build html-content. For example, here is some line from file:
seaborn distplot / displot with multiple distributions
I am using seaborn to plot a distribution plot. I would like to plot multiple distributions on the same plot in different colors:
Type hinting / annotation (PEP 484) for numpy.ndarray
Has anyone implemented type hinting for the specific numpy.ndarray class?
Flattening a list of NumPy arrays?
It appears that I have data in the format of a list of NumPy arrays (type() = np.ndarray):