feat: add centralized logging to all modules; fix import formatting; correct datefmt typo

Author: JCbral04

README.md

@@ -4,6 +4,8 @@
 
 A production-ready Python library for data cleaning, anomaly detection, report generation, and SQL query analysis. Built on **pandas** and **numpy**, it provides typed, well-documented APIs suitable for pipelines, notebooks, and automated reporting workflows.
 
+> ⚠️ **Limitation:** `QueryOptimizer` performs static heuristic analysis using regex patterns. It does not parse SQL with a full AST engine. Complex queries (nested CTEs, window functions, or dialect-specific syntax) may produce incomplete results. For production-grade SQL parsing, consider [sqlglot](https://github.com/tobymao/sqlglot).
+
 ## Features
 
 | Module | Class | Purpose |

src/_logging.py

@@ -0,0 +1,18 @@
+"""Centralized logging configuration for the toolkit."""
+
+import logging as lg
+
+
+def configure_logging(level: int = lg.INFO) -> None:
+    """Configure basic logging with a specified level.
+
+    Parameters:
+    -----------
+    Level : int
+        Logging level (default: INFO).
+    """
+    lg.basicConfig(
+        level=level,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )

src/anomaly_detector.py

@@ -2,6 +2,14 @@
 
 from __future__ import annotations
 
+import logging as lg
+
+from src._logging import configure_logging
+
+configure_logging()
+_logger = lg.getLogger(__name__)
+
+
 from typing import Literal
 
 import numpy as np

src/data_cleaner.py

@@ -2,6 +2,13 @@
 
 from __future__ import annotations
 
+import logging as lg
+
+from src._logging import configure_logging
+
+configure_logging()
+_logger = lg.getLogger(__name__)
+
 from typing import Any, Literal
 
 import numpy as np
@@ -34,6 +41,11 @@ class DataCleaner:
     """
 
     def __init__(self, df: pd.DataFrame) -> None:
+        _logger.debug(
+            "Initializing DataCleaner with %d rows, %d columns",
+            len(df),
+            len(df.columns),
+        )
         if not isinstance(df, pd.DataFrame):
             raise DataCleanerError(
                 f"Expected pandas DataFrame, got {type(df).__name__}."
@@ -119,6 +131,12 @@ def handle_missing_values(
                 else:
                     self._df[col] = self._df[col].fillna(mode_vals.iloc[0])
 
+        _logger.info(
+            "handle_missing_values: strategy=%s, columns=%s, rows=%d",
+            strategy,
+            columns,
+            len(self._df),
+        )
         return self.data
 
     def remove_duplicates(

src/query_optimizer.py

@@ -1,7 +1,13 @@
 """SQL query analysis and optimization utilities."""
-
 from __future__ import annotations
 
+import logging as lg
+
+from src._logging import configure_logging
+
+configure_logging()
+_logger = lg.getLogger(__name__)
+
 import re
 from dataclasses import dataclass, field
 

src/report_generator.py

@@ -2,6 +2,13 @@
 
 from __future__ import annotations
 
+import logging as lg
+
+from src._logging import configure_logging
+
+configure_logging()
+_logger = lg.getLogger(__name__)
+
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Literal