QuickDataScience | Quick & Easy Data Science

Exploring the dataset is both fun and tedious but an inevitable step for the Machine Learning journey. The challenge always stands for correctness, completeness and timely analysis of the data.  To overcome these issues lot of libraries are present, having their advantages and disadvantages. We have already discussed a few of them( Pandas profiling , dtale , autoviz , lux , sweetviz ) in previous articles. Today, we would like to present a new library for Exploratory Data Analysis --- Dora.  Saying only an EDA library would not be justified as it does not help explore the dataset but also helps to adjust data for the modelling purpose.

We had a look over the basics of EDA in our previous article  EDA - Exploratory Data Analysis . So now let's move ahead and look at how we can automate the process and the various APIs used for the same. We will be focusing on the 7 major libraries that can be used for the same. These are our personal favourites & we prefer to use them most of the time.  We will look into the libraries' & will cover the install, load, and analyse parts for each separately.  D-tale Pandas - Profiling Lux Sweetviz Autoviz ExploriPy Dora