REF: simplify internals.construction (#38400)

Author: jbrockmendel

pandas/core/internals/construction.py

@@ -525,59 +525,49 @@ def to_arrays(
  if columns is not None:
  return [[]] * len(columns), columns
  return [], [] # columns if columns is not None else []
- if isinstance(data[0], (list, tuple)):
- return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
- elif isinstance(data[0], abc.Mapping):
- return _list_of_dict_to_arrays(
- data, columns, coerce_float=coerce_float, dtype=dtype
- )
- elif isinstance(data[0], ABCSeries):
- return _list_of_series_to_arrays(
- data, columns, coerce_float=coerce_float, dtype=dtype
- )
+
  elif isinstance(data[0], Categorical):
  if columns is None:
  columns = ibase.default_index(len(data))
  return data, columns
- elif (
- isinstance(data, (np.ndarray, ABCSeries, Index))
- and data.dtype.names is not None
- ):
 
+ elif isinstance(data, np.ndarray) and data.dtype.names is not None:
+ # e.g. recarray
  columns = list(data.dtype.names)
  arrays = [data[k] for k in columns]
  return arrays, columns
+
+ if isinstance(data[0], (list, tuple)):
+ content, columns = _list_to_arrays(data, columns)
+ elif isinstance(data[0], abc.Mapping):
+ content, columns = _list_of_dict_to_arrays(data, columns)
+ elif isinstance(data[0], ABCSeries):
+ content, columns = _list_of_series_to_arrays(data, columns)
  else:
  # last ditch effort
  data = [tuple(x) for x in data]
- return _list_to_arrays(data, columns, coerce_float=coerce_float, dtype=dtype)
+ content, columns = _list_to_arrays(data, columns)
+
+ content, columns = _finalize_columns_and_data(content, columns, dtype, coerce_float)
+ return content, columns
 
 
 def _list_to_arrays(
  data: List[Scalar],
  columns: Union[Index, List],
- coerce_float: bool = False,
- dtype: Optional[DtypeObj] = None,
 ) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
- if len(data) > 0 and isinstance(data[0], tuple):
- content = list(lib.to_object_array_tuples(data).T)
+ # Note: we already check len(data) > 0 before getting hre
+ if isinstance(data[0], tuple):
+ content = lib.to_object_array_tuples(data)
  else:
  # list of lists
- content = list(lib.to_object_array(data).T)
- # gh-26429 do not raise user-facing AssertionError
- try:
- columns = _validate_or_indexify_columns(content, columns)
- result = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
- except AssertionError as e:
- raise ValueError(e) from e
- return result, columns
+ content = lib.to_object_array(data)
+ return content, columns
 
 
 def _list_of_series_to_arrays(
  data: List,
  columns: Union[Index, List],
- coerce_float: bool = False,
- dtype: Optional[DtypeObj] = None,
 ) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
  if columns is None:
  # We know pass_data is non-empty because data[0] is a Series
@@ -600,22 +590,14 @@ def _list_of_series_to_arrays(
  values = extract_array(s, extract_numpy=True)
  aligned_values.append(algorithms.take_1d(values, indexer))
 
- values = np.vstack(aligned_values)
+ content = np.vstack(aligned_values)
 
- if values.dtype == np.object_:
- content = list(values.T)
- columns = _validate_or_indexify_columns(content, columns)
- content = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
- return content, columns
- else:
- return values.T, columns
+ return content, columns
 
 
 def _list_of_dict_to_arrays(
  data: List[Dict],
  columns: Union[Index, List],
- coerce_float: bool = False,
- dtype: Optional[DtypeObj] = None,
 ) -> Tuple[List[Scalar], Union[Index, List[Axis]]]:
  """
  Convert list of dicts to numpy arrays
@@ -630,8 +612,6 @@ def _list_of_dict_to_arrays(
  data : iterable
  collection of records (OrderedDict, dict)
  columns: iterables or None
- coerce_float : bool
- dtype : np.dtype
 
  Returns
  -------
@@ -647,9 +627,29 @@ def _list_of_dict_to_arrays(
  # classes
  data = [(type(d) is dict) and d or dict(d) for d in data]
 
- content = list(lib.dicts_to_array(data, list(columns)).T)
- columns = _validate_or_indexify_columns(content, columns)
- content = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
+ content = lib.dicts_to_array(data, list(columns))
+ return content, columns
+
+
+def _finalize_columns_and_data(
+ content: np.ndarray,
+ columns: Optional[Union[Index, List]],
+ dtype: Optional[DtypeObj],
+ coerce_float: bool,
+) -> Tuple[List[np.ndarray], Union[Index, List[Axis]]]:
+ """
+ Ensure we have valid columns, cast object dtypes if possible.
+ """
+ content = list(content.T)
+
+ try:
+ columns = _validate_or_indexify_columns(content, columns)
+ except AssertionError as err:
+ # GH#26429 do not raise user-facing AssertionError
+ raise ValueError(err) from err
+
+ if len(content) and content[0].dtype == np.object_:
+ content = _convert_object_array(content, dtype=dtype, coerce_float=coerce_float)
  return content, columns