What Is EDA?
Exploratory Data Analysis is the process of examining a dataset to understand its structure, spot anomalies, test assumptions, and discover patterns — all before building any model. EDA is where data science intuition is built.
A Structured EDA Workflow
- 1. Shape and types — df.shape, df.dtypes, df.info()
- 2. Missing values — df.isnull().sum()
- 3. Distributions — histograms, value_counts()
- 4. Outliers — box plots, IQR method, z-scores
- 5. Relationships — correlation matrix, scatter plots
- 6. Group comparisons — groupby + aggregation
EDA is iterative, not linear. Each finding may lead you back to an earlier step to investigate further.
Key Takeaway
EDA is the most important step in any data science project. Skipping it leads to flawed models and misleading conclusions.