Merge branch 'main' of https://github.com/modin-project/modin into is…

…sue7203

modin/conftest.py

@@ -250,6 +250,9 @@ def get_unique_base_execution():
 
 def pytest_configure(config):
  execution = config.option.execution
+ import ray
+
+ ray.init()
 
  if execution is None:
  return

modin/core/dataframe/pandas/metadata/dtypes.py

@@ -17,7 +17,6 @@
 
 from typing import TYPE_CHECKING, Callable, Optional, Union
 
-import numpy as np
 import pandas
 from pandas._typing import DtypeObj, IndexLabel
 from pandas.core.dtypes.cast import find_common_type
@@ -109,7 +108,7 @@ def __init__(
  self._know_all_names: bool = know_all_names
  # a common dtype for columns that are not present in 'known_dtypes' nor in 'cols_with_unknown_dtypes'
  self._remaining_dtype: Optional[DtypeObj] = remaining_dtype
- self._parent_df: Optional["PandasDataframe"] = parent_df
+ self._parent_df: Optional[PandasDataframe] = parent_df
  if columns_order is None:
  self._columns_order: Optional[dict[int, IndexLabel]] = None
  # try to compute '._columns_order' using 'parent_df'
@@ -1212,7 +1211,7 @@ def get_categories_dtype(
  )
 
 
-def extract_dtype(value) -> Union[DtypeObj, pandas.Series]:
+def extract_dtype(value) -> DtypeObj | pandas.Series:
  """
  Extract dtype(s) from the passed `value`.
 
@@ -1224,25 +1223,9 @@ def extract_dtype(value) -> Union[DtypeObj, pandas.Series]:
  -------
  DtypeObj or pandas.Series of DtypeObj
  """
- from modin.pandas.utils import is_scalar
-
- if hasattr(value, "dtype"):
- # If we're dealing with a numpy scalar (np.int, np.datetime64, ...)
- # we would like to get its internal dtype
- return value.dtype
- elif hasattr(value, "to_numpy"):
- # If we're dealing with a scalar that can be converted to numpy (for example pandas.Timestamp)
- # we would like to convert it and get its proper internal dtype
- return value.to_numpy().dtype
- elif hasattr(value, "dtypes"):
- return value.dtypes
- elif is_scalar(value):
- if value is None:
- # previous type was object instead of 'float64'
- return pandas.api.types.pandas_dtype(value)
- # TODO: backend is not taken into account
- # pd.api.types.pandas_dtype(pd.ArrowDtype(pa.array([1,2,3]).type))
- return pandas.api.types.pandas_dtype(type(value))
- else:
- # TODO: new way without numpy?
- return np.array(value).dtype
+ try:
+ dtype = pandas.api.types.pandas_dtype(value)
+ except TypeError:
+ dtype = pandas.Series(value).dtype
+
+ return dtype