Cleaning Walmart coffee listings from 500 stores dataset

This time, I wanted to challenge myself and work with a CSV file that has some errors in it. I downloaded the Walmart yearly coffee sales dataset from Kaggle and decided to see how I can clean it up.
One of the columns that caught my eye was the weight column, which shows the weight of the coffee sold in grams. This is a useful column because it converts the pounds to kilograms, which is the standard unit outside of the US. However, I noticed that some of the cells had an A added to them, which made them invalid. For example, one cell had 453A instead of 453.
I tried to find a way to remove the A from the cells, but I couldn’t figure out how to do it with a formula or a conditional formatting. So I decided to use the replace errors function and replace the errors with 0. This way, I didn’t have to delete the whole row and I could still use the 0 value for further analysis.
I think this was a good exercise to learn some new functions and how to deal with data that is not perfect.