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.
Since Pandas allows you to have mixed data type columns, converting them to a string data type can be essential when exporting the data.
Using a dictionary to set the data types for a Pandas DataFrame gives you greater control over the schema.
Comparing columns in a DataFrame is essential when trying to concatenate two Pandas DataFrames with a lot of columns.
In this post, I’ll walk you through pulling Jira issues from the API using JQL with Python Pandas.
Installing Spark on your local machine can be a pain. In this post, I’ll show you how to install Spark on Google Colab so that you can easily get going with PySpark.
Google Data Studio makes it so easy to take address data and convert it into an interactive map.
Leverage Google Maps API or Nominatim in Python to return complete address information that you can use for geo charts.