3 Unique Visualization Capabilities in Python
Learning a new programming language is difficult and time-consuming task especially if you were never familiar with any of its concept in the past.
As a novice python explorer in a Data Science boot camp, building a new project literally means spending 80% of my time in the same process over and over again:
SEARCHING…. SEARHCHING…. SEARCHING…
At the end of the project, I just realize that none of this outcome would have been possible without Google. It is almost at a point which I feel bad about myself thinking that
“Will I ever be able to write my own code without the endless searching process?”
But no worries on that, I can assure that EVERYONE starts off coding this way and in fact, even the most experienced programmers also frequently search on the web for getting their coding done. Simply you are not the only one!
I decided to share 3 of the unique visualization capabilities in Python which I’ve went over while working on the most recent project. Although I haven’t actually used them, I found these could be potentially utilized later when I start to work on upcoming projects.
- Violin Plot
Code Used:
Violin plot is a great visualization tool when we want to obtain a rough idea of multiple sectors and each sector’s number of observations at the same time. Although it may not be suitable for looking at detailed numbers at a sight, it would surely bring an insightful view of how you should focus your analysis on.
2. Correlogram (Heatmap)
Code Used:
Correlogram is also a handy visualization tool when we want to compare relation of numeric values of all the columns within a data frame which can be obatined by using heatmap from seaborn. It will grab basic relation such as mpg of a car will more likely decrease as weight of a car increases. (denoted as -0.88 on the above chart)
3. Diverging Bars
Code Used:
Last but not least, diverging bar is an amazing visualization tool when we wish to compare values of a single metric for a given subject. It will generate an in-depth visualization to compare the performance of all the subjects extremely conveniently.
We’ve explored some of the interesting visualization tools in python which would be a great asset for being an experienced data scientist. Data visualization is an essential instrument for data scientists to share their insights with the audiences in an effective manner.
One day, I wish to write all these codes without having to reference them every five seconds. Until then, I will have to add this bookmark this post on my chrome browser. I wish this could find handy for all of you as well.
Source:
https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
