Data exploration is by far one of the most important aspects of any data analysis task. The initial probing and preliminary checks that we perform, using the vast catalog of visualization tools, give us actionable insights into the nature of data. However, the choice of visualization tool at times is more complicated than the task itself. On the one hand, we have libraries that are easier to use but are not so helpful in showing complex relationships in data. Then there are others that render interactivity but have a considerable learning curve. Fortunately, some open-source libraries have been created that try to address this pain point effectively.

In this article, we’ll look at two such libraries, namely pandas_bokeh and cufflinks. We’ll learn how to create plotly and bokeh charts with the basic pandas plotting syntax, which we all are comfortable with. Since the article’s emphasis is on the syntax rather than the types of plots, we’ll limit ourselves to the five basic charts, i.e., line charts, bar charts, histograms, scatter plots, and pie charts. We’ll create each of these charts first with pandas plotting library and then recreate them in plotly and bokeh, albeit with a twist.

#2021 jun tutorials #overviews #data visualization #pandas #python #get interactive plots directly with pandas