Add pylint dependency, fix a couple of files (partially) to show impact

py-polars/build.requirements.txt

@@ -20,6 +20,7 @@ isort~=5.10.1
 mypy==0.961
 ghp-import==2.1.0
 flake8==4.0.1
+pylint==2.14.5
 sphinx==4.2.0
 pydata-sphinx-theme==0.6.3
 sphinx-panels==0.6.0

py-polars/polars/__init__.py

@@ -1,3 +1,4 @@
+import os
 import warnings
 
 try:
@@ -10,7 +11,7 @@ def version() -> str:
  # this is only useful for documentation
  warnings.warn("polars binary missing!")
 
-import polars.testing as testing
+from polars import testing
 from polars.cfg import Config, toggle_string_cache # We do not export in __all__
 from polars.convert import (
  from_arrow,
@@ -238,6 +239,5 @@ def version() -> str:
 
 __version__ = version()
 
-import os
 
 os.environ["POLARS_ALLOW_EXTENSION"] = "true"

py-polars/polars/convert.py

@@ -165,8 +165,8 @@ def from_records(
  DeprecationWarning,
  )
  return DataFrame._from_numpy(data, columns=columns, orient=orient)
- else:
-  return DataFrame._from_records(data, columns=columns, orient=orient)
+
+ return DataFrame._from_records(data, columns=columns, orient=orient)
 
 
 def from_numpy(
@@ -281,10 +281,10 @@ def from_arrow(
  raise ImportError("'pyarrow' is required when using from_arrow().")
  if isinstance(a, pa.Table):
  return DataFrame._from_arrow(a, rechunk=rechunk)
- elif isinstance(a, (pa.Array, pa.ChunkedArray)):
+ if isinstance(a, (pa.Array, pa.ChunkedArray)):
  return Series._from_arrow("", a, rechunk)
- else:
-  raise ValueError(f"Expected Arrow Table or Array, got {type(a)}.")
+
+ raise ValueError(f"Expected Arrow Table or Array, got {type(a)}.")
 
 
 @overload
@@ -369,7 +369,7 @@ def from_pandas(
 
  if isinstance(df, (pd.Series, pd.DatetimeIndex)):
  return Series._from_pandas("", df, nan_to_none=nan_to_none)
- elif isinstance(df, pd.DataFrame):
+ if isinstance(df, pd.DataFrame):
  return DataFrame._from_pandas(df, rechunk=rechunk, nan_to_none=nan_to_none)
- else:
-  raise ValueError(f"Expected pandas DataFrame or Series, got {type(df)}.")
+
+ raise ValueError(f"Expected pandas DataFrame or Series, got {type(df)}.")

py-polars/polars/datatypes.py

@@ -126,13 +126,13 @@ def __eq__(self, other: type[DataType]) -> bool: # type: ignore[override]
  # List[i64] == List[f32] == False
 
  # allow comparing object instances to class
- if type(other) is type and issubclass(other, List):
+ if isinstance(other, type) and issubclass(other, List): # type: ignore[redundant-expr]
  return True
  if isinstance(other, List):
  if self.inner is None or other.inner is None:
  return True
- else:
-  return self.inner == other.inner
+
+ return self.inner == other.inner
  else:
  return False
 
@@ -421,26 +421,26 @@ def dtype_to_ctype(dtype: PolarsDataType) -> type[_SimpleCData]:
  try:
  return _DTYPE_TO_CTYPE[dtype]
  except KeyError: # pragma: no cover
- raise NotImplementedError
+ raise NotImplementedError from None
 
 
 def dtype_to_ffiname(dtype: PolarsDataType) -> str:
  try:
  return _DTYPE_TO_FFINAME[dtype]
  except KeyError: # pragma: no cover
- raise NotImplementedError
+ raise NotImplementedError from None
 
 
 def dtype_to_py_type(dtype: PolarsDataType) -> type:
  try:
  return _DTYPE_TO_PY_TYPE[dtype]
  except KeyError: # pragma: no cover
- raise NotImplementedError
+ raise NotImplementedError from None
 
 
 def is_polars_dtype(data_type: Any) -> bool:
  return (
- type(data_type) is type
+ isinstance(data_type, type)
  and issubclass(data_type, DataType)
  or isinstance(data_type, DataType)
  )
@@ -453,7 +453,7 @@ def py_type_to_dtype(data_type: Any) -> type[DataType]:
  try:
  return _PY_TYPE_TO_DTYPE[data_type]
  except KeyError: # pragma: no cover
- raise NotImplementedError
+ raise NotImplementedError from None
 
 
 def py_type_to_arrow_type(dtype: type[Any]) -> pa.lib.DataType:
@@ -463,7 +463,7 @@ def py_type_to_arrow_type(dtype: type[Any]) -> pa.lib.DataType:
  try:
  return _PY_TYPE_TO_ARROW_TYPE[dtype]
  except KeyError: # pragma: no cover
- raise ValueError(f"Cannot parse dtype {dtype} into Arrow dtype.")
+ raise ValueError(f"Cannot parse dtype {dtype} into Arrow dtype.") from None
 
 
 def dtype_to_arrow_type(dtype: PolarsDataType) -> pa.lib.DataType:
@@ -473,7 +473,7 @@ def dtype_to_arrow_type(dtype: PolarsDataType) -> pa.lib.DataType:
  try:
  return _DTYPE_TO_ARROW_TYPE[dtype]
  except KeyError: # pragma: no cover
- raise ValueError(f"Cannot parse dtype {dtype} into Arrow dtype.")
+ raise ValueError(f"Cannot parse dtype {dtype} into Arrow dtype.") from None
 
 
 def supported_numpy_char_code(dtype: str) -> bool:
@@ -484,7 +484,7 @@ def numpy_char_code_to_dtype(dtype: str) -> type[DataType]:
  try:
  return _NUMPY_CHAR_CODE_TO_DTYPE[dtype]
  except KeyError: # pragma: no cover
- raise NotImplementedError
+ raise NotImplementedError from None
 
 
 def maybe_cast(
@@ -495,7 +495,7 @@ def maybe_cast(
 
  if isinstance(el, datetime):
  return _datetime_to_pl_timestamp(el, time_unit)
- elif isinstance(el, timedelta):
+ if isinstance(el, timedelta):
  return _timedelta_to_pl_timedelta(el, time_unit)
  py_type = dtype_to_py_type(dtype)
  if not isinstance(el, py_type):

py-polars/polars/datatypes_constructor.py

@@ -73,7 +73,7 @@ def polars_type_to_constructor(
  try:
  return _POLARS_TYPE_TO_CONSTRUCTOR[dtype]
  except KeyError: # pragma: no cover
- raise ValueError(f"Cannot construct PySeries for type {dtype}.")
+ raise ValueError(f"Cannot construct PySeries for type {dtype}.") from None
 
 
 if _NUMPY_AVAILABLE and not _DOCUMENTING:
@@ -101,8 +101,8 @@ def numpy_type_to_constructor(dtype: type[np.dtype]) -> Callable[..., PySeries]:
  return _NUMPY_TYPE_TO_CONSTRUCTOR[dtype]
  except KeyError:
  return PySeries.new_object
- except NameError: # pragma: no cover
- raise ImportError("'numpy' is required for this functionality.")
+ except NameError as err: # pragma: no cover
+ raise ImportError("'numpy' is required for this functionality.") from err
 
 
 if not _DOCUMENTING:

py-polars/polars/io.py

@@ -4,8 +4,6 @@
 from pathlib import Path
 from typing import Any, BinaryIO, Callable, Mapping, TextIO, cast
 
-from polars.utils import format_path, handle_projection_columns
-
 try:
  import pyarrow as pa
 
@@ -22,6 +20,7 @@
 from polars.datatypes import DataType, Utf8
 from polars.internals import DataFrame, LazyFrame, _scan_ds
 from polars.internals.io import _prepare_file_arg
+from polars.utils import format_path, handle_projection_columns
 
 try:
  import connectorx as cx
@@ -967,10 +966,10 @@ def read_sql(
  protocol=protocol,
  )
  return cast(DataFrame, from_arrow(tbl))
- else:
-  raise ImportError(
-  "connectorx is not installed. Please run `pip install connectorx>=0.2.2`."
-  )
+
+ raise ImportError(
+ "connectorx is not installed. Please run `pip install connectorx>=0.2.2`."
+ )
 
 
 def read_excel(
@@ -1060,7 +1059,7 @@ def read_excel(
  except ImportError:
  raise ImportError(
  "xlsx2csv is not installed. Please run `pip install xlsx2csv`."
- )
+ ) from None
 
  if isinstance(file, (str, Path)):
  file = format_path(file)

py-polars/polars/testing.py

@@ -275,11 +275,11 @@ def _getattr_multi(obj: object, op: str) -> Any:
  get the attribute "str", and then the attribute "lengths"
  """
  op_list = op.split(".")
- return reduce(lambda o, m: getattr(o, m), op_list, obj)
+ return reduce(getattr, op_list, obj)
 
 
 def verify_series_and_expr_api(
- input: Series, expected: Series | None, op: str, *args: Any, **kwargs: Any
+ result: Series, expected: Series | None, op: str, *args: Any, **kwargs: Any
 ) -> None:
  """
  Small helper function to test element-wise functions for both the series and expressions api.
@@ -291,8 +291,8 @@ def verify_series_and_expr_api(
  >>> verify_series_and_expr_api(s, expected, "sort")
  """
  expr = _getattr_multi(col("*"), op)(*args, **kwargs)
- result_expr: Series = input.to_frame().select(expr)[:, 0] # type: ignore[assignment]
- result_series = _getattr_multi(input, op)(*args, **kwargs)
+ result_expr: Series = result.to_frame().select(expr)[:, 0] # type: ignore[assignment]
+ result_series = _getattr_multi(result, op)(*args, **kwargs)
  if expected is None:
  assert_series_equal(result_series, result_expr)
  else:
@@ -305,7 +305,7 @@ def is_categorical_dtype(data_type: Any) -> bool:
  Check if the input is a polars Categorical dtype.
  """
  return (
- type(data_type) is type
+ isinstance(data_type, type)
  and issubclass(data_type, Categorical)
  or isinstance(data_type, Categorical)
  )
@@ -409,23 +409,23 @@ def __post_init__(self) -> None:
  raise InvalidArgument(
  f"No strategy (currently) available for {self.dtype} type"
  )
+
+ # given a custom strategy, but no explicit dtype. infer one
+ # from the first non-None value that the strategy produces.
+ with warnings.catch_warnings():
+ # note: usually you should not call "example()" outside of an interactive shell, hence
+ # the warning. however, here it is reasonable to do so, so we catch and ignore it
+ warnings.simplefilter("ignore", NonInteractiveExampleWarning)
+ sample_value_iter = (self.strategy.example() for _ in range(100)) # type: ignore[union-attr]
+ sample_value_type = type(
+ next(e for e in sample_value_iter if e is not None)
+ )
+ if sample_value_type is not None:
+ self.dtype = py_type_to_dtype(sample_value_type)
  else:
- # given a custom strategy, but no explicit dtype. infer one
- # from the first non-None value that the strategy produces.
- with warnings.catch_warnings():
- # note: usually you should not call "example()" outside of an interactive shell, hence
- # the warning. however, here it is reasonable to do so, so we catch and ignore it
- warnings.simplefilter("ignore", NonInteractiveExampleWarning)
- sample_value_iter = (self.strategy.example() for _ in range(100)) # type: ignore[union-attr]
- sample_value_type = type(
- next(e for e in sample_value_iter if e is not None)
- )
- if sample_value_type is not None:
- self.dtype = py_type_to_dtype(sample_value_type)
- else:
- raise InvalidArgument(
- f"Unable to determine dtype for strategy {self.dtype} type"
- )
+ raise InvalidArgument(
+ f"Unable to determine dtype for strategy {self.dtype} type"
+ )
 
  def columns(
  cols: int | Sequence[str] | None = None,