Data Analytics Simplified
Welcome to Data Analytics Simplified, a blog dedicated to helping you streamline data workflows, automate processes, and scale your infrastructure—without the headaches. Whether you’re battling messy spreadsheets, inefficient pipelines, or trying to get the most out of your data analytics investments, you’re in the right place.
I’ll share proven strategies, tips, and frameworks from my experience in data engineering and analytics, focusing on:
Data doesn’t have to be overwhelming. With the right approach, you can declutter, optimize, and build a solid foundation for data science and analytics.
Let’s get to work.
Replit is a free tool that makes it easy to write Flask code and deploy it instantly. They handle of all the underlying infrastructure, allowing you to focus on building and refining your app without worrying about setup and maintenance.
A window function allows you to concisely compare rows in a single table.
In this post, I’ll walk through how to convert a Pandas column that is in seconds and convert it to a datetime or a formatted string.
This is a little Flask web app I made to get recommendations for things to do when traveling.
The Pandas package in Python allows you to generate a list of dates dynamically and then extract their attributes with various datetime functions.
This is a little trick I used to append new rows to a Pandas DataFrame. This method is similar to appending a new item to a list.
A Data Engineer’s primary focus is to assist companies in scaling their reporting capabilities beyond the limitations of spreadsheets. Automated systems are implemented to replace manual processes and import data from various sources, which is then transformed for easy visualization or use in data science models.
Pandas allow for almost anything as a column header and I’ll show you how to get your columns parquet and database ready.
Having consistent schemas between two Pandas DataFrames is essential when saving to Parquet and for merging operations.