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.

How to Use the Query Function to Clean and Combine Data for Power BI

In this post, I share how I used the query function to clean and prepare three csv files of sales data from different years. I learned how to rename, choose, filter, observe, add, mix, substitute, and verify data in the query editor. I also created a new table with all the three years combined. This helped me import the data to Power BI and create some amazing visualizations and insights. I hope you enjoy this post and learn something new!

Project 1 – PS4 Games Global sales

The blog post is about how the author created their first Power BI project using PS4 games global sales data from Kaggle. The author explains how they imported, transformed, and loaded the data into Power BI and then created various visuals to answer some questions about the games. The author also shares some of the insights they gained from the data and expresses their excitement about Power BI’s capabilities and features.