| |
| """Statistical Analysis Plugin""" |
| import pandas as pd |
| from typing import Dict, Any |
|
|
| class StatisticalAnalyzer: |
| """Perform statistical analysis on data.""" |
| def analyze(self, df: pd.DataFrame) -> Dict[str, Any]: |
| """Generate comprehensive statistical summary.""" |
| analysis = {"shape": {"rows": len(df), "columns": len(df.columns)}, "columns": {}} |
| for col in df.columns: |
| col_analysis = {"name": col, "dtype": str(df[col].dtype)} |
| col_analysis["missing_percent"] = float(df[col].isna().mean() * 100) |
| if pd.api.types.is_numeric_dtype(df[col]): |
| col_analysis.update({ |
| "mean": float(df[col].mean()), |
| "std": float(df[col].std()), |
| "min": float(df[col].min()), |
| "max": float(df[col].max()), |
| "median": float(df[col].median()) |
| }) |
| elif pd.api.types.is_datetime64_any_dtype(df[col]): |
| col_analysis.update({"min_date": str(df[col].min()), "max_date": str(df[col].max())}) |
| else: |
| col_analysis.update({"unique_values": int(df[col].nunique())}) |
| analysis["columns"][col] = col_analysis |
| return analysis |
|
|
