Tips and tricks, abnormally in the programming world, can be very useful. Sometimes a little hack can be both time and life-saving. A minor adjustment or add-on can sometimes prove to be a Godsend and can be a real abundance booster. So, here are some of my admired tips and tricks I’ve used and aggregate calm in the form of this article. Some may be fairly known and some may be new, but I’m sure they’ll come in pretty handy the next time you work on a data assay project.

Profiling the ‘pandas’ dataframe

Profilingis a action that helps us accept our data, and Pandas Profiling is a python amalgamation that does absolutely that. It’s a simple and fast way to accomplish basic data assay of a Pandas Dataframe. The pandas df.describe()and df.info()functions are commonly used as a first step in the EDA process. However, it only gives a very basic overview of the data and doesn’t help much in the case of large data sets. The Pandas Profiling function, on the other hand, extends the pandas DataFrame with df.profile_report() for quick data analysis. It displays a lot of advice with a single line of code and that too in an alternate HTML report.

For a given dataset the pandas profiling amalgamation computes the afterward statistics: