Visualizations with Python

I’ve recently started experimenting with the programming language Python. My main goal is to apply Python to data science projects when needed. I’m still in the super early stages of learning the basic concepts, working with packages, and just seeing the broader picture of what it has to offer, but so far it’s been relatively straightforward and clean. Working with Jupyter Notebooks is a huge factor in what makes it so convenient and user-friendly. Jupyter is basically an IDE that is online and has a very similar feel to Google Docs, Sheets, etc. and allows users to create in a more comfortable, convenient environment. I really love it so far. Anyways, below is a small dashboard I created. The data and assignment were both from a course on Udemy which served as my introduction to Python, although at this point I’m looking for more project-based tutorials. The visualizations are running off of a package called Seaborn, which is incredibly flexible and intuitive. Seaborn works on top of Matlplotlib, which is one of the core packages that comes with Python and allows for the calculations necessary for a lot of data science projects. Finally, if you want to review my code and let me know what you think, check it out at GitHub.

A visualization of critic ratings, audience ratings, and budgets, all using the Seaborn visualization package for Python.
Please follow and like us:

Working with Tableau

Recently I’ve been working a lot with the data visualization tool Tableau. It’s exceptional in its ability to provide detailed filtering, granularity, and flexibility. In fact, I’m coming to find that if the data I’m working with is clean enough and set up correctly, there’s not much I can’t do. With that in mind, I’ve decided to share a workbook I’ve created for a class I’m taking. The data is basically just made up sales data, but the exercises show the level of flexibility you can achieve. Anyways, check it out.

 

Please follow and like us: