Data exploration aka exploratory data analysis or EDA. The whys and the how-to using a few lines of Python code (and a few great Python packages)

The data science pipeline begins in data exploration, also known as exploratory data analysis or EDA.
In this step of the data analysis, we better understand the data.
Understanding which variables are important and which are not.
Identifying missing values, outliers, or human error.
Understanding the correlation and relationships between variables or their absence.
EDA optimizes the observations into a dataset and reduces potential mistakes later in the process.