Dynamic Reverse Conversion (#205)

* first commit

* second commit

* Remove parquet_compatible from PandasArrowConverter

* Remove parquet_compatible from PandasArrowConverter

* - Replace function-based logic with class-based methods (__call__, _target_dtype_name, etc.)\n- Ensure consistent handling of dtype conversion from pyarrow to NumPy/Pandas

* - Replace function-based logic with class-based methods (__call__, _target_dtype_name, etc.)
- Ensure consistent handling of dtype conversion from pyarrow to NumPy/Pandas

pandas_pyarrow/pda_converter.py

@@ -7,9 +7,6 @@
 
 class PandasArrowConverter:
     """PandasArrowConverter manages the conversion of Pandas DataFrame data types to Arrow data types.
-
-    :param parquet_compatible: if True, column names will be converted to parquet compatible names. Default is False.
-    **disclaimer**: not yet implemented
     :param custom_mapper: dictionary with key as the source data type and value as the target data type.
     Will override default mapping
     :param default_target_type: Optional string specifying the default data type to use if no mapping is found for a
@@ -23,11 +20,9 @@ class PandasArrowConverter:
 
     def __init__(
         self,
-        parquet_compatible: Optional[bool] = False,
         custom_mapper: Optional[Dict[str, str]] = None,
         default_target_type: Optional[str] = "string[pyarrow]",
     ):
-        self.parquet_compatible = parquet_compatible
         self.additional_mapper_dicts = custom_mapper or {}
         self.defaults_dtype = default_target_type
         self._mapper = create_mapper() | self.additional_mapper_dicts

pandas_pyarrow/reverse_converter.py

@@ -1,20 +1,58 @@
-from pandas_pyarrow.mappers import reverse_create_mapper
+# reverse_converter.py
+
+from typing import Dict, List, Optional
+
+from .mappers import reverse_create_mapper
 
 import numpy as np
 import pandas as pd
 
 
+class ReversePandasArrowConverter:
+    """
+    ReversePandasArrowConverter manages the conversion of pyarrow-backed Pandas DataFrame dtypes
+    back to their Numpy/Pandas equivalents.
+    :param custom_mapper: Dictionary with key as the string-representation of the
+        Arrow-backed dtype, and value as the desired target dtype (e.g. "object", "int64", etc.).
+        This overrides default mapping returned by reverse_create_mapper().
+    :param default_target_type: Optional string specifying the default dtype to use
+        if no mapping is found for a specific dtype. Default is "object".
+    Methods
+    -------
+    - __call__(df: pd.DataFrame) -> pd.DataFrame:
+        Converts pyarrow-backed dtypes in the given Pandas DataFrame to Numpy/Pandas dtypes
+        and returns the converted DataFrame.
+    """
+
+    def __init__(
+        self,
+        custom_mapper: Optional[Dict[str, str]] = None,
+        default_target_type: Optional[str] = "object",
+    ):
+        self._mapper = reverse_create_mapper() | (custom_mapper or {})
+        self._default_target_type = default_target_type
+
+    def __call__(self, df: pd.DataFrame) -> pd.DataFrame:
+        dtype_names: List[str] = df.dtypes.astype(str).tolist()
+        target_dtype_names = self._map_dtype_names(dtype_names)
+        col_to_dtype = dict(zip(df.columns, target_dtype_names))
+
+        new_df = pd.DataFrame(df.values, columns=df.columns, dtype="object").fillna(np.nan).astype(col_to_dtype)
+        return new_df
+
+    def _target_dtype_name(self, dtype_name: str) -> str:
+        if "pyarrow" not in dtype_name:
+            return dtype_name
+
+        if "bool" in dtype_name:
+            return "bool"
+
+        return self._mapper.get(dtype_name, self._default_target_type)
+
+    def _map_dtype_names(self, dtype_names: List[str]) -> List[str]:
+        return [self._target_dtype_name(dtype_name) for dtype_name in dtype_names]
+
+
 def convert_to_numpy(df: pd.DataFrame) -> pd.DataFrame:
-    values = df.values
-    r_mapper = reverse_create_mapper()
-    pyarrow_types = df.dtypes
-    numpy_types = dict()
-    for col, dtype in pyarrow_types.items():
-        if "pyarrow" in repr(dtype):
-            numpy_types[col] = r_mapper[repr(dtype)] if "bool" not in repr(dtype) else bool
-
-        else:
-            numpy_types[col] = dtype
-
-    new_df = pd.DataFrame(values, columns=df.columns, dtype=object).fillna(np.nan).astype(numpy_types)
-    return new_df
+    converter = ReversePandasArrowConverter()
+    return converter(df)