archive,category,category-data-science,category-59,theme-stockholm,stockholm-core-1.1,woocommerce-no-js,select-theme-ver-5.1.8,ajax_fade,page_not_loaded,wpb-js-composer js-comp-ver-6.0.5,vc_responsive

A Quick Guide for Combining Data in Pandas Using merge(), .join(), concat(), and .append()

For one of my projects, I was stuck (and confused) with which combination methods to use to combine my dataframes. Granted that I am not superb at coding, and my brain can be foggy after 8 hours of data cleaning. Hence, I put this quick guide together to remind myself (& hopefully bring newbies some valuable info) on the differences between merge() / .join() / concat() / .append(). So, here we go:

READ MORE Continue Reading

Data Cleaning: Another Fancy Way to call Ourselves Data Janitors

A major part of my first data project was making sure that the messy data that I got was properly cleaned. I had 5 days of time to do this project, and I spent 4 days of cleaning it. They weren’t kidding when they said data scientists spend 60-80% of their time on cleaning and organizing data. That’s why many blog posts (such as this one) called ourselves data janitors. 

READ MORE Continue Reading