EDA in Python
Python enables comprehensive exploratory data analysis.
Descriptive Statistics
df.describe() gives summary statistics. df.corr() computes correlation matrix.
value_counts() shows frequency distribution. describe(percentiles=[.25, .75]) customizes output.
Visual EDA
Histograms: df['col'].hist(). Box plots: df.boxplot(). Scatter matrix: pd.plotting.scatter_matrix().
Correlation heatmap: sns.heatmap(df.corr()).
Missing Data Analysis
Missingno provides visualization: ms.matrix(df). Bar chart: ms.bar(df).
Analyze patterns to inform imputation strategy.
Key Takeaways
- describe() provides comprehensive summary
- Visual EDA reveals distributions and relationships
- Missing data analysis informs cleaning strategy