data-analysis | data analysis functions

I am trying to write a function that will calculate the mean and SD for a variable from a multiply imputed dataframe (mids). The code works fine outside of the function (as shown in two examples below), but will produce unreliable results when placed inside of a function. The function seems to keep giving results for bmi despite calling upon chl.

Any insight into this issue is appreciated. Eventually I would like this function to be able to calculate means and SDs for multiple variables at once (i.e., bmi and chl) but that is likely a separate question.

What am I doing wrong? Can anyone help me? Or give me specific keywords for google search (I'm sure I'm not the first)? Have been dealing with this problem for over 8h now, cant find something on the internet.

Full Notebook Link (problem at the end): Kaggle Notebook

My code:

I have been working on using python and computer vision to detect the board state of a gameboard in a game called Go. Based on the data collected here, I planned to base my implementation off of this paper's algorithm(s). However, I ran into trouble when I got to section 3.1.2 in the paper and had to compute a Hough Transform on my image. I tried using OpenCV's Hough Line function, but got an image so full of lines I couldn't see the original image.

I tried various line thicknesses, and different thresholds values for previous functions but I always seemed to end up with either way too many lines or practically no lines at all. For example, when using the top image, I got the image below it with the code I pasted at the very bottom

I assume that the though HoughLines function just produces so many lines that it covers the screen, but I can't seem to get a normal amount of lines. I'm not sure if this bit will be useful but I have to go to extremely high values of threshold compared to any tutorial or example I can find online to avoid an almost completely red screen, but even then only like 5 lines show up. I could just not use the HoughLines function but the next step of the paper depends on this result and so I either have to solve this or find a completely different implementation of this. Any help is appreciated on this. Thanks!

As I go through online tutorials and\or articles in general, when I encounter a plot that uses the Seaborn distplot plot I re-create it using either histplot or displot.

I do this because distplot is deprecated and I want to re-write the code using newer standards.

I am going through this article: https://www.kite.com/blog/python/data-analysis-visualization-python/

and there is a section using distplot whose output I cannot replicate.

This is the section of code that I am trying to replicate:

TLDR: I would like some suggestions on how I can improve my code.

I'm learning data science from datacamp, I have an beginner-intermediate knowledge about coding. This is a data-analysis project I did today and am not happy with my code since it feels jumbled and inefficient.

In the below code I'm supposed to find number of apps in each category and then make a new dataframe with category, number of apps, avg price and avg rating. I did a shit job so would like some helpful tips

Ok, so I was looking through some data analysis (very basic) projects. I came across this line-

I want to plot the best fit line to every Iris class per feature histogram plot. I have tried the solutions from these examples: 1 and 2, but dont get the result i want.

This is how the histogram looks like now, and how I want them to look, but with an best fit line per class.

Here is the code that I have used to achive this.

I am running the following slurm script on a cluster computing system.