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.
Leverage Google Maps API or Nominatim in Python to return complete address information that you can use for geo charts.
In this post, I’ll show you how to extract every number from a string in Python using regular expressions.
The default print view for a Pandas DataFrame can be limiting for larger datasets and can get in the way of a thorough review of the data.
In this post, I’ll show you how to extract emojis from a string in Python, count the frequency, then plot them using Plotly.
In this post, I’ll show you how to add a timezone to a naive Datetime object.
Python Pandas allows for a lot of flexibility when naming your which can cause trouble if you are trying to import the data into a structured database like SQL or BigQuery. In this post, I’ll show you how to format your Pandas columns to make them compliant with structured databases.
Pandas has a function pd.read_html() to get an HTML table from a website in one line of code.
Google Colab is Google’s version of a Jupyter Notebook and takes advantage of the same features you find in other Google Apps to make Python coding easy.
In this post, I’ll walk you through exporting tickets, users, and organizations from Zendesk using the API and Python for data analysis.