With its powerful and flexible functionalities, Pandas has become an indispensable tool for data scientists and analysts.
Referring to the statistics reported by PyPI, can you imagine that Padnas receives over 3M downloads daily?
Of course, this statistic gives very little information about the number of users. Nonetheless, it does highlight its widespread popularity.
While many of its users are already aware of its fundamental features, there are still many hidden gems in pandas that you probably aren’t aware of.
Thus, in this blog, I’ll share six interesting things you can do with Pandas, that can supercharge your data analysis.
Let’s begin
#1) Display plots in a DataFrame column
Jupyter is a web-based IDE. Thus, whenever we print a DataFrame, it is rendered using HTML and CSS.
This allows us to format the output just like any other web page.
One interesting way to format this is by embedding inline plots which appear as a column of a dataframe, also called sparklines, to get something like this: