Python Data Visualization Transcripts
Chapter: Course Conclusion
Lecture: My workflow
0:00 Now that we've gone through all of these tools, I'll talk through how I typically work. Normally, what I do is I start my analysis with Seaborn.
0:10 I'll do quick exploratory analysis. Seaborn really makes it easy for me to switch between
0:17 the different types of plots that I normally use and then sometimes if I need to I can customize it with matplot lib.
0:24 If I need to have more interactivity, I'll use plotly and typically I'll alternate between Seaborn and plotly to figure out which
0:33 visualization works best for my needs. If I actually need to build more analysis, more interactive analysis and have started with plotly or Seaborn,
0:44 then I'll move to streamlit, where I can have more complex interaction and filtering and share with others.
0:51 But ultimately this data visualization stack works for me. But you need to spend time developing your own,
0:58 playing around with the tools and understanding what works for the types of problems that you encounter on a daily basis.