Introduction to the Power of Exploratory Data Analysis with Python and Pandas

by Miguel Angel Sanchez Razo

21 Nov '23


Whether you are just getting started in data science or are a seasoned expert, one of the most critical activities is exploratory data analysis (EDA). EDA allows you to develop an intimate understanding of your data that is foundational to downstream modeling and analysis. It is the critical process of performing initial investigations on data to discover patterns, spot anomalies, test hypotheses, and check assumptions with summary statistics and graphical representations.

EDA is a crucial initial step when working with any new dataset. In this post, we’ll explore how to perform effective EDA using Python and the powerful Pandas data analysis library. Pandas provides data structures and instruments that make data exploration seamless. In this blog, we are going to explore some of the activities and tools that can be use to do EDA.








1. What is EDA?

EDA involves activities aimed at detecting patterns, identifying anomalies, testing assumptions, and conducting preliminary feature engineering and data preparation. Key aspects of EDA include:









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