dierk
8f7399de58
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>
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
from __future__ import annotations
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import pandas as pd
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from sqlalchemy import (
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Column, Integer, Float, String, DateTime, Date, Boolean, Numeric, Text
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)
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from .config import ColumnMapping
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def _pandas_dtype_to_sqla(series: pd.Series, varchar_length: int):
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dtype = series.dtype
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if pd.api.types.is_bool_dtype(dtype):
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return Boolean()
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if pd.api.types.is_integer_dtype(dtype):
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return Integer()
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if pd.api.types.is_float_dtype(dtype):
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return Float()
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if pd.api.types.is_datetime64_any_dtype(dtype):
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return DateTime()
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# object columns: check if they look like dates
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if dtype == object:
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sample = series.dropna().head(100)
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if len(sample) > 0:
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try:
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pd.to_datetime(sample)
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return DateTime()
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except Exception:
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pass
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max_len = int(series.dropna().astype(str).str.len().max()) if len(series.dropna()) > 0 else 1
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return String(max(max_len + 10, varchar_length))
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return Text()
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def _override_to_sqla(dtype_str: str):
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"""Convert a user-supplied type string like 'VARCHAR(100)' to a SQLAlchemy type."""
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s = dtype_str.upper().strip()
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if s.startswith("VARCHAR"):
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length = int(s.split("(")[1].rstrip(")")) if "(" in s else 255
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return String(length)
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if s in ("TEXT", "CLOB"):
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return Text()
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if s in ("INTEGER", "INT", "NUMBER"):
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return Integer()
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if s.startswith("NUMBER") or s.startswith("NUMERIC") or s.startswith("DECIMAL"):
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if "(" in s:
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parts = s.split("(")[1].rstrip(")").split(",")
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p, sc = int(parts[0]), int(parts[1]) if len(parts) > 1 else 0
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return Numeric(precision=p, scale=sc)
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return Numeric()
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if s in ("FLOAT", "REAL", "DOUBLE"):
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return Float()
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if s in ("DATETIME", "TIMESTAMP"):
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return DateTime()
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if s == "DATE":
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return Date()
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if s in ("BOOLEAN", "BOOL"):
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return Boolean()
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raise ValueError(f"Unknown dtype override: {dtype_str!r}")
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def build_columns(df: pd.DataFrame, column_configs: list[ColumnMapping], varchar_length: int) -> list[Column]:
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override_map = {c.target or c.source: c.dtype for c in column_configs if c.dtype and not c.skip}
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columns = []
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for col in df.columns:
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col_name = str(col)
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if col_name in override_map and override_map[col_name]:
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sqla_type = _override_to_sqla(override_map[col_name])
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else:
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sqla_type = _pandas_dtype_to_sqla(df[col], varchar_length)
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columns.append(Column(col_name, sqla_type))
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return columns
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