This week it got intense in comparison to last week. I had to attend to other commitments and a result spent less time of these topics.
After generation, access and manipulation of data, the next course of action was to look at presentation in data visualization and for this week focus is on:
- Pandas Built-in Data Visualization
- Matplotlib
- Seaborn
What new skills have you learned? 📚
Pandas Built-in Data Visualization
- Pandas has built-in capabilities for data visualization that is built off Matplotlib but baked into Pandas for easier use.
- Here is a Pandas Built-in data-viz notebook with some of the plots
that you can come up with.
- Fun fact, learnt how to display the legend outside of the plot.
Matplotlib
- This is library of data visualization with Python. Created by John Hunter. in an effort to replicate MatLab’s (another programming language) plotting capabilities in Python.
- Key learnings I took on Matplotlib concepts:
- Creating Multiplots on Same Canvas
- Line-marks and various types and customizations
- subplots()
- Figure size, aspect ratio and DPI
- Setting colors, linewidths, linetypes
Seaborn
- By definition; Seaborn is a Python visualization library based on Matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
- Got to learn on seaborn concepts and generated some great looking
visualizations in the process.
- According to tips dataset, smokers 🚬 tip more than non-smokers 😃
What has been easy?
Data plotting commands come in oneliners and once I grasped these, it was well within my scope to generate them.
I found it enjoyable working with data visualizations as they provide insights into datasets that I didn’t anticipate on.
A lot of resources are available freely online.
What has been difficult?
Getting time to practice, The concepts so far touch the surface, these libraries are ocean deep with possibilities.
How have you used the problem solving strategies to overcome challenges so far?
Yet to try.. 👞
So that was the second week.. 🔏