Especially, when you want it to be publication-ready.
As per my experience, we could utilize seaborn (static plots) and Plotly (interactive plots) for the majority of exploratory analysis tasks with very few lines of codes and avoiding complexity.Īfter going through different plotting tools, especially in Python, I have observed that still there are challenges one would face while implementing plots using the Matplotlib and Seaborn library. So, I tried several libraries like Matplotlib, Seaborn, Bokeh and Plotly. So, I thought let’s see whether python visualization tools offer similar flexibility or not like what ggplot2 does. I have observed a significant improvement in python data analysis tools specifically, data manipulation, plotting and machine learning. Recently, I also started implementing the same using python due to recent advancements in this language libraries. When comes to visualization my all-time favourite is ggplot2 library (R’s plotting library: R is a statistical programming language) which is one of the popular plotting tools. In the data analysis part of the task, I have to often perform exploratory analysis. I work in the transportation domain, thus I’m fortunate that I get to work with lots of data. I’m a PhD student in the Department of Civil Engineering at IIT Guwahati. This helps us present the data in pictorial or graphical format. The visualization is an important part of any data analysis.
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