Initial implementation of generic Excel-to-DB import tool

Supports .xls and .xlsx, Oracle and PostgreSQL via SQLAlchemy.
Includes CLI (run/inspect/generate-config), YAML config, auto schema
detection, and append/replace/upsert modes.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

excel_import/schema.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+import pandas as pd
+from sqlalchemy import (
+    Column, Integer, Float, String, DateTime, Date, Boolean, Numeric, Text
+)
+
+from .config import ColumnMapping
+
+
+def _pandas_dtype_to_sqla(series: pd.Series, varchar_length: int):
+    dtype = series.dtype
+    if pd.api.types.is_bool_dtype(dtype):
+        return Boolean()
+    if pd.api.types.is_integer_dtype(dtype):
+        return Integer()
+    if pd.api.types.is_float_dtype(dtype):
+        return Float()
+    if pd.api.types.is_datetime64_any_dtype(dtype):
+        return DateTime()
+    # object columns: check if they look like dates
+    if dtype == object:
+        sample = series.dropna().head(100)
+        if len(sample) > 0:
+            try:
+                pd.to_datetime(sample)
+                return DateTime()
+            except Exception:
+                pass
+        max_len = int(series.dropna().astype(str).str.len().max()) if len(series.dropna()) > 0 else 1
+        return String(max(max_len + 10, varchar_length))
+    return Text()
+
+
+def _override_to_sqla(dtype_str: str):
+    """Convert a user-supplied type string like 'VARCHAR(100)' to a SQLAlchemy type."""
+    s = dtype_str.upper().strip()
+    if s.startswith("VARCHAR"):
+        length = int(s.split("(")[1].rstrip(")")) if "(" in s else 255
+        return String(length)
+    if s in ("TEXT", "CLOB"):
+        return Text()
+    if s in ("INTEGER", "INT", "NUMBER"):
+        return Integer()
+    if s.startswith("NUMBER") or s.startswith("NUMERIC") or s.startswith("DECIMAL"):
+        if "(" in s:
+            parts = s.split("(")[1].rstrip(")").split(",")
+            p, sc = int(parts[0]), int(parts[1]) if len(parts) > 1 else 0
+            return Numeric(precision=p, scale=sc)
+        return Numeric()
+    if s in ("FLOAT", "REAL", "DOUBLE"):
+        return Float()
+    if s in ("DATETIME", "TIMESTAMP"):
+        return DateTime()
+    if s == "DATE":
+        return Date()
+    if s in ("BOOLEAN", "BOOL"):
+        return Boolean()
+    raise ValueError(f"Unknown dtype override: {dtype_str!r}")
+
+
+def build_columns(df: pd.DataFrame, column_configs: list[ColumnMapping], varchar_length: int) -> list[Column]:
+    override_map = {c.target or c.source: c.dtype for c in column_configs if c.dtype and not c.skip}
+
+    columns = []
+    for col in df.columns:
+        col_name = str(col)
+        if col_name in override_map and override_map[col_name]:
+            sqla_type = _override_to_sqla(override_map[col_name])
+        else:
+            sqla_type = _pandas_dtype_to_sqla(df[col], varchar_length)
+        columns.append(Column(col_name, sqla_type))
+    return columns