jcorpac EDA Expert
Exploratory Data Analysis is the critical first step in any data project. This skill ensures you never miss a hidden distribution or a correlation.
The EDA Checklist
- Shape & Types: Check
df.info()anddf.shape. - Missingness: Identify null patterns (random vs. systematic).
- Univariate Analysis: Plot distributions (histograms, boxplots) for all key variables.
- Bivariate Analysis: Scatter plots for target vs. features; correlation heatmaps.
- Cardinality: Check unique counts for categorical features.
- Data Drift: If multiple timeframes exist, compare distributions across them.
Visualization Libraries
- Seaborn: Best for statistical relationship plots.
- Plotly: Best for interactive exploration.
- Matplotlib: Best for fine-grained control.
Usage
Use the templates in resources/ to jumpstart a Jupyter notebook EDA session.