diff --git a/Changelog.md b/Changelog.md index 3c8afb6..c80dc6b 100644 --- a/Changelog.md +++ b/Changelog.md @@ -1,6 +1,6 @@ # Unreleased (YYYY-MM-DD) -- ... +- Add support for dask dataframes # 1.5.0 (2024-04-17) diff --git a/docker/Dockerfile b/docker/Dockerfile index deb5a0c..93b3be6 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.8-slim +FROM python:3.9-slim # Install curl to install poetry RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt : \ diff --git a/pandas_paddles/__init__.py b/pandas_paddles/__init__.py index a6352e3..949906c 100644 --- a/pandas_paddles/__init__.py +++ b/pandas_paddles/__init__.py @@ -259,13 +259,16 @@ __version__ = "1.6.0-dev" __all__ = ["C", "DF", "I", "S", "report", "paddles"] -from .contexts import DataframeContext, SeriesContext + from .axis import ColumnSelectionComposer, IndexSelectionComposer from .pipe import report from . import paddles +try: + from .dask import DF, S +except ImportError as e: + print("Using pandas-only...", e) + from .pandas import DF, S C = ColumnSelectionComposer() -DF = DataframeContext() I = IndexSelectionComposer() -S = SeriesContext() diff --git a/pandas_paddles/axis.py b/pandas_paddles/axis.py index 3f3b006..47a8bb7 100644 --- a/pandas_paddles/axis.py +++ b/pandas_paddles/axis.py @@ -1,6 +1,7 @@ """Select axis labels (columns or index) of a data frame.""" import operator -from typing import Any, Callable, List, Optional, Sequence +from typing import Any, Callable, Optional, Sequence +import typing try: from typing import Literal except ImportError: @@ -9,8 +10,11 @@ import numpy as np import pandas as pd +Indices = "Indices" +AnyDataframe = "AnyDataframe" +if typing.TYPE_CHECKING: + from .types import AnyDataframe, Indices -Indices = List[int] class Selection: @@ -40,7 +44,7 @@ def __init__(self, included:Optional[Indices]=None, excluded:Optional[Indices]=N self.included: Optional[Indices] = included self.excluded: Optional[Indices] = excluded - def apply(self, axis:Literal["columns", "index"], df: pd.DataFrame): + def apply(self, axis:Literal["columns", "index"], df: AnyDataframe): labels = getattr(df, axis) included = self.included if included is None: @@ -99,7 +103,7 @@ def union_indices(left: Indices, right: Indices) -> Indices: # Column selection operator closures class BaseOp: """API definition of the closure object.""" - def __call__(self, axis: Literal["columns", "index"], df: pd.DataFrame) -> Selection: + def __call__(self, axis: Literal["columns", "index"], df: AnyDataframe) -> Selection: """Evaluate operator on data frame from context.""" raise NotImplementedError("Must be implemented in sub-class.") @@ -191,7 +195,7 @@ def pp(a): return f'(level={self.level}).{self.meth}({pp_args})' return f'.{self.meth}({pp_args})' - def __call__(self, axis, df: pd.DataFrame) -> Selection: + def __call__(self, axis, df: AnyDataframe) -> Selection: labels = getattr(df, axis) if self.level is None: str_accessor = labels.str @@ -205,7 +209,7 @@ def __call__(self, axis, df: pd.DataFrame) -> Selection: class EllipsisOp(BaseOp): """Select all labels (i.e. columns or rows).""" - def __call__(self, axis, df: pd.DataFrame) -> Selection: + def __call__(self, axis, df: AnyDataframe) -> Selection: labels = getattr(df, axis) return Selection(mask=np.ones(len(labels), dtype=bool)) @@ -238,7 +242,7 @@ def _pprint(self, axis: str) -> str: return f"{self.left._pprint(axis)} {op_name} {self.right._pprint(axis)}" - def __call__(self, axis, df: pd.DataFrame) -> Selection: + def __call__(self, axis, df: AnyDataframe) -> Selection: sel_left = self.left(axis, df) sel_right = self.right(axis, df) @@ -271,7 +275,7 @@ def _pprint(self, axis: str) -> str: right = ")" return f"{op_name}{left}{self.wrapped._pprint(axis)}{right}" - def __call__(self, axis, df: pd.DataFrame) -> Selection: + def __call__(self, axis, df: AnyDataframe) -> Selection: sel = self.wrapped(axis, df) return self.op(sel) @@ -389,7 +393,7 @@ def __invert__(self): op=operator.invert, )) - def __call__(self, df:pd.DataFrame) -> pd.Index: + def __call__(self, df: AnyDataframe) -> pd.Index: """Evaluate the wrapped operations.""" selection = self.op(self.axis, df) return selection.apply(self.axis, df) diff --git a/pandas_paddles/contexts.py b/pandas_paddles/contexts.py index ecff1b7..058deb9 100644 --- a/pandas_paddles/contexts.py +++ b/pandas_paddles/contexts.py @@ -10,7 +10,7 @@ from . import operator_helpers -def _add_dunder_operators(cls): +def add_dunder_operators(cls): """Dress class with all sensible comparison operations. The class must implement a ``_operator_proxy`` method. @@ -21,20 +21,29 @@ def _add_dunder_operators(cls): """ # Fix the closure of `op_wrap` to the current value of `op`. Without # `fix_closure()` all created methods point to the last `op` value. + + cls_lookup_candidates = [pd.Series] + for wrapped_cls in cls.wrapped_cls: + if wrapped_cls in cls_lookup_candidates: + continue + cls_lookup_candidates.append(wrapped_cls) + + def fix_closure(op): def op_wrap(self, *args, **kwargs): return self._operator_proxy(op)(*args, **kwargs) # Update method metadata to improve usablility op_wrap.__name__ = op - orig_doc = None - orig_annot = None - for pd_cls in {pd.Series} | {cls.wrapped_cls}: - if hasattr(pd_cls, op): - a = getattr(pd_cls, op) + for orig_cls in cls_lookup_candidates: + if hasattr(orig_cls, op): + a = getattr(orig_cls, op) if not a.__doc__: continue op_wrap.__doc__ = a.__doc__ - op_wrap.__annotations__ = a.__annotations__ + try: + op_wrap.__annotations__ = a.__annotations__ + except AttributeError: + pass break return op_wrap @@ -51,7 +60,7 @@ def op_wrap(self, *args, **kwargs): return cls -def _get_obj_attr_doc(obj_or_class: Any, attr: str): +def get_obj_attr_doc(obj_or_class: Any, attr: str): """Get doc-string for attribute ``attr`` of ``obj_or_class`` if it exists.""" if isinstance(attr, str): a = getattr(obj_or_class, attr, None) @@ -62,7 +71,7 @@ def _get_obj_attr_doc(obj_or_class: Any, attr: str): class ClosureFactoryBase: """Abstract base-class for generating DataFrame and Series context closures.""" - wrapped_cls: ClassVar[Type] = type('NotABaseOfAnything', (), {}) + wrapped_cls: ClassVar[Tuple[Type]] = (type('NotABaseOfAnything', (), {}),) wrapped_s: str = "X" def __init__(self, closures: Optional[Iterable[ClosureBase]]=None): @@ -110,7 +119,7 @@ def __getitem__(self, key: str) -> "ClosureFactoryBase": def _operator_proxy(self, op_name: str) -> Callable: """Generate proxy function for built-in operators. - Used by :func:`_add_dunder_operators` + Used by :func:`add_dunder_operators` """ def op_wrapper(*args, **kwargs): return type(self)(self._closures + (MethodClosure(op_name, type(self), *args, **kwargs),)) @@ -169,178 +178,3 @@ def to_node(x): cur = new return cur.root - - -@_add_dunder_operators # This is necessary to overload all dunder operators. -class DataframeContext(ClosureFactoryBase): - """Build callable to access columns and operators. - - Use the global instance like:: - - from pandas_paddles import DF - df.loc[DF["x"] < 3] - - All operations (item/attribute access, method calls) are passed to the - data frame of the context. - - This is useful in combination with :attr:`~pandas.DataFrame.loc`, - :attr:`~pandas.DataFrame.iloc`, :meth:`~pandas.DataFrame.assign()` and - other methods that accept callables taking the data frame to act on as - single argument. - - Examples - -------- - Usage with :attr:`~pandas.DataFrame.loc` or - :attr:`~pandas.DataFrame.iloc`:: - - df = pd.DataFrame({"x": [1, 2, 3, 4]}) - df.loc[DF["x"] <= 2] - # Out: - # x - # 0 1 - # 1 2 - - Usage with :meth:`~pandas.DataFrame.assign()`:: - - df.assign(y = DF["x"] * 2) - # Out: - # x y - # 0 1 2 - # 1 2 4 - # 2 3 6 - # 3 4 8 - """ - wrapped_cls = pd.DataFrame - wrapped_s = "DF" - def _get_doc(self) -> Optional[str]: - doc = super()._get_doc() - # Assume DataFrame-level function for 1-level accessor - if len(self._closures) == 1 and isinstance(self._closures[-1].name, str): - doc = _get_obj_attr_doc(self.wrapped_cls, self._closures[-1].name) or doc - # Check for typed Series accessors for 3+-level accessor - elif len(self._closures) >= 3 and self._closures[-2].name in ('dt', 'str'): - doc = ( - _get_obj_attr_doc( - getattr(pd.Series, self._closures[-2].name), - self._closures[-1].name, - ) - or doc - ) - # Check for Series-level function for 2+-level accessor - elif len(self._closures) > 1: - doc = _get_obj_attr_doc(pd.Series, self._closures[-1].name) or doc - - return doc - - - -@_add_dunder_operators # This is necessary to overload all dunder operators. -class SeriesContext(ClosureFactoryBase): - """Build callable for series attributes and operators. - - Use the global instance like:: - - from pandas_paddles import S - s[S < 0] - - All operations (item/attribute access, method calls) are passed to the - series of the context. - - This is useful in combination with :attr:`~pandas.Series.loc`, - :attr:`~pandas.Series.iloc`, and other methods that accept callables - taking the series to act on as argument, e.g., `.agg()` after a - group-by. - - Examples - -------- - Usage with ``[]``, ``.loc[]`` or ``.iloc[]``:: - - from pandas_paddles import S - s = pd.Series(range(10)) - s[S <= 2] - # Out: - # 0 0 - # 1 1 - # 2 2 - # dtype: int64 - - Aggregating a single ``groupby``ed column with - ``groupby(...)[col].agg()``:: - - df = pd.DataFrame({ - "x": [1, 2, 3, 4], - "y": ["a", "b", "b", "a"], - "z": [0.1, 0.5, 0.6, 0.9], - }) - df.groupby("y")["x"].agg(S.max() - S.min()) - # Out: - # y - # a 3 - # b 1 - # Name: x, dtype: int64 - - Appying multiple aggregations to a single column:: - - df.groupby("y")["x"].agg([ - S.max() - S.min(), - S.mean(), - ]) - # Out: - # S.max() - S.min() S.min() - # y - # a 3 1 - # b 1 2 - - Aggregating multiple columns (**Note:** You must wrap the - ``S``-expressions in a ``list`` even when using only one expression!):: - - df.groupby("y").agg([S.min()]) - # Out: - # x z - # S.min() S.min() - # y - # a 1 0.1 - # b 2 0.5 - - Multiple ``S``-expressions work the same:: - - df.groupby("y").agg([S.min(), S.mean()]) - # Out: - # x z - # S.min() S.mean() S.min() S.mean() - # y - # a 1 2.5 0.1 0.50 - # b 2 2.5 0.5 0.55 - - ``S``-expressions can alsoe be passed in a ``dict`` argument to - ``.agg()`` (Again, they always need to be wrapped in a ``list``!):: - - df.groupby("y").agg({ - "x": [S.min(), S.mean()], - "z": [S.max(), S.max() - S.min()], - }) - # Out: - # x z - # S.min() S.mean() S.max() S.max() - S.min() - # y - # a 1 2.5 0.9 0.8 - # b 2 2.5 0.6 0.1 - """ - wrapped_cls = pd.Series - wrapped_s = "S" - def _get_doc(self) -> Optional[str]: - doc = super()._get_doc() - # Assume Series-level function for 1-level accessor - if len(self._closures) == 1 and isinstance(self._closures[-1].name, str): - doc = _get_obj_attr_doc(self.wrapped_cls, self._closures[-1].name) or doc - # Check for typed Series accessors - elif len(self._closures) > 1 and self._closures[0].name in ('dt', 'str'): - doc = ( - _get_obj_attr_doc( - getattr(pd.Series, self._closures[0].name), - self._closures[-1].name, - ) - or doc - ) - - return doc diff --git a/pandas_paddles/dask.py b/pandas_paddles/dask.py new file mode 100644 index 0000000..7ee66fc --- /dev/null +++ b/pandas_paddles/dask.py @@ -0,0 +1,18 @@ +"""Pandas and dask contexts""" + +from dask.dataframe import DataFrame +import pandas as pd + +from .pandas import PandasDataframeContext, S + + +from .contexts import ( + add_dunder_operators, + get_obj_attr_doc, +) + +@add_dunder_operators +class DaskDataframeContext(PandasDataframeContext): + wrapped_cls = (pd.DataFrame, DataFrame) + +DF = DaskDataframeContext() diff --git a/pandas_paddles/paddles.py b/pandas_paddles/paddles.py index f391b64..4578b84 100644 --- a/pandas_paddles/paddles.py +++ b/pandas_paddles/paddles.py @@ -8,7 +8,11 @@ import operator from typing import Any, Callable, Dict, Iterable, Literal, Union -from .contexts import DataframeContext +from .pandas import PandasDataframeContext +try: + from .dask import DF +except ImportError: + from .pandas import DF __all__ = [ "build_filter", @@ -16,10 +20,8 @@ "str_join", ] -DF = DataframeContext() - # Some typing hints -ColSpec = Union[str, DataframeContext] +ColSpec = Union[str, PandasDataframeContext] BinaryOp = Union[ Callable[[Any, Any], Any], Literal["and"], @@ -29,7 +31,7 @@ ] -def ensure_DF_expr(col: ColSpec) -> DataframeContext: +def ensure_DF_expr(col: ColSpec) -> PandasDataframeContext: """Convert column names to ``DF``-expressions when necessary. Parameters @@ -39,7 +41,7 @@ def ensure_DF_expr(col: ColSpec) -> DataframeContext: Returns ------- - DataframeContext + PandasDataframeContext The ``DF``-expression of ``col`` (if a ``str``) or just ``col``. Examples @@ -61,7 +63,7 @@ def ensure_DF_expr(col: ColSpec) -> DataframeContext: return col -def str_join(sep: str, col1: ColSpec, *cols: ColSpec) -> DataframeContext: +def str_join(sep: str, col1: ColSpec, *cols: ColSpec) -> PandasDataframeContext: """Create expression to join multiple columns in a string. This is similar to ``str.join`` @@ -80,7 +82,7 @@ def str_join(sep: str, col1: ColSpec, *cols: ColSpec) -> DataframeContext: Returns ------- - DataframeContext + PandasDataframeContext The ``DF``-expression, a callable taking a :class:`~pandas.DataFrame` as argument. @@ -119,9 +121,9 @@ def str_join(sep: str, col1: ColSpec, *cols: ColSpec) -> DataframeContext: def combine( - bool_expressions: Iterable[DataframeContext], + bool_expressions: Iterable[PandasDataframeContext], op: Callable[[Any, Any], Any] = operator.and_, -) -> DataframeContext: +) -> PandasDataframeContext: """Combine multiple DF-expressions to use in df.loc[]. The ``DF``-expressions must evaluate to a boolean array, e.g.,:: @@ -156,7 +158,7 @@ def combine( def build_filter( predicates: Dict[ColSpec, Any], op: BinaryOp = operator.and_ -) -> DataframeContext: +) -> PandasDataframeContext: """Build a filter expression from column-value pairs :: @@ -185,7 +187,7 @@ def build_filter( Returns ------- - DataframeContext + PandasDataframeContext The ``DF``-expression combining the predicates. """ expressions = (ensure_DF_expr(col) == val for col, val in predicates.items()) diff --git a/pandas_paddles/pandas.py b/pandas_paddles/pandas.py new file mode 100644 index 0000000..9dcefe4 --- /dev/null +++ b/pandas_paddles/pandas.py @@ -0,0 +1,196 @@ +"""Pandas-only contexts""" + +from typing import Optional + +import pandas as pd + +from .contexts import ( + add_dunder_operators, + get_obj_attr_doc, + ClosureFactoryBase, +) + + +@add_dunder_operators # This is necessary to overload all dunder operators. +class PandasDataframeContext(ClosureFactoryBase): + """Build callable to access columns and operators. + + Use the global instance like:: + + from pandas_paddles import DF + df.loc[DF["x"] < 3] + + All operations (item/attribute access, method calls) are passed to the + data frame of the context. + + This is useful in combination with :attr:`~pandas.DataFrame.loc`, + :attr:`~pandas.DataFrame.iloc`, :meth:`~pandas.DataFrame.assign()` and + other methods that accept callables taking the data frame to act on as + single argument. + + Examples + -------- + Usage with :attr:`~pandas.DataFrame.loc` or + :attr:`~pandas.DataFrame.iloc`:: + + df = pd.DataFrame({"x": [1, 2, 3, 4]}) + df.loc[DF["x"] <= 2] + # Out: + # x + # 0 1 + # 1 2 + + Usage with :meth:`~pandas.DataFrame.assign()`:: + + df.assign(y = DF["x"] * 2) + # Out: + # x y + # 0 1 2 + # 1 2 4 + # 2 3 6 + # 3 4 8 + """ + wrapped_cls = (pd.DataFrame,) + wrapped_s = "DF" + def _get_doc(self) -> Optional[str]: + doc = super()._get_doc() + # Assume DataFrame-level function for 1-level accessor + if len(self._closures) == 1 and isinstance(self._closures[-1].name, str): + for wrapped_cls in self.wrapped_cls: + wrapped_doc = get_obj_attr_doc(wrapped_cls, self._closures[-1].name) or doc + if wrapped_doc is not None: + break + doc = wrapped_doc + # Check for typed Series accessors for 3+-level accessor + elif len(self._closures) >= 3 and self._closures[-2].name in ('dt', 'str'): + doc = ( + get_obj_attr_doc( + getattr(pd.Series, self._closures[-2].name), + self._closures[-1].name, + ) + or doc + ) + # Check for Series-level function for 2+-level accessor + elif len(self._closures) > 1: + doc = get_obj_attr_doc(pd.Series, self._closures[-1].name) or doc + + return doc + + +@add_dunder_operators # This is necessary to overload all dunder operators. +class PandasSeriesContext(ClosureFactoryBase): + """Build callable for series attributes and operators. + + Use the global instance like:: + + from pandas_paddles import S + s[S < 0] + + All operations (item/attribute access, method calls) are passed to the + series of the context. + + This is useful in combination with :attr:`~pandas.Series.loc`, + :attr:`~pandas.Series.iloc`, and other methods that accept callables + taking the series to act on as argument, e.g., `.agg()` after a + group-by. + + Examples + -------- + Usage with ``[]``, ``.loc[]`` or ``.iloc[]``:: + + from pandas_paddles import S + s = pd.Series(range(10)) + s[S <= 2] + # Out: + # 0 0 + # 1 1 + # 2 2 + # dtype: int64 + + Aggregating a single ``groupby``ed column with + ``groupby(...)[col].agg()``:: + + df = pd.DataFrame({ + "x": [1, 2, 3, 4], + "y": ["a", "b", "b", "a"], + "z": [0.1, 0.5, 0.6, 0.9], + }) + df.groupby("y")["x"].agg(S.max() - S.min()) + # Out: + # y + # a 3 + # b 1 + # Name: x, dtype: int64 + + Appying multiple aggregations to a single column:: + + df.groupby("y")["x"].agg([ + S.max() - S.min(), + S.mean(), + ]) + # Out: + # S.max() - S.min() S.min() + # y + # a 3 1 + # b 1 2 + + Aggregating multiple columns (**Note:** You must wrap the + ``S``-expressions in a ``list`` even when using only one expression!):: + + df.groupby("y").agg([S.min()]) + # Out: + # x z + # S.min() S.min() + # y + # a 1 0.1 + # b 2 0.5 + + Multiple ``S``-expressions work the same:: + + df.groupby("y").agg([S.min(), S.mean()]) + # Out: + # x z + # S.min() S.mean() S.min() S.mean() + # y + # a 1 2.5 0.1 0.50 + # b 2 2.5 0.5 0.55 + + ``S``-expressions can alsoe be passed in a ``dict`` argument to + ``.agg()`` (Again, they always need to be wrapped in a ``list``!):: + + df.groupby("y").agg({ + "x": [S.min(), S.mean()], + "z": [S.max(), S.max() - S.min()], + }) + # Out: + # x z + # S.min() S.mean() S.max() S.max() - S.min() + # y + # a 1 2.5 0.9 0.8 + # b 2 2.5 0.6 0.1 + """ + wrapped_cls = (pd.Series,) + wrapped_s = "S" + def _get_doc(self) -> Optional[str]: + doc = super()._get_doc() + # Assume Series-level function for 1-level accessor + if len(self._closures) == 1 and isinstance(self._closures[-1].name, str): + for wrapped_cls in self.wrapped_cls: + wrapped_doc = get_obj_attr_doc(wrapped_cls, self._closures[-1].name) or doc + if wrapped_doc is not None: + break + doc = wrapped_doc + # Check for typed Series accessors + elif len(self._closures) > 1 and self._closures[0].name in ('dt', 'str'): + doc = ( + get_obj_attr_doc( + getattr(pd.Series, self._closures[0].name), + self._closures[-1].name, + ) + or doc + ) + + return doc + +DF = PandasDataframeContext() +S = PandasSeriesContext() diff --git a/pandas_paddles/pipe.py b/pandas_paddles/pipe.py index 8be9449..12ecbec 100644 --- a/pandas_paddles/pipe.py +++ b/pandas_paddles/pipe.py @@ -1,11 +1,10 @@ """Helpers for working with :meth:`pandas.DataFrame.pipe()`.""" import pandas as pd -from .contexts import DataframeContext - -# Here to prevent recursive import from __init__.py -# r -DF = DataframeContext() +try: + from .dask import DF +except ImportError: + from .pandas import DF def _generate_report(args, print_func, print_kwargs): def inner_report(df): diff --git a/pandas_paddles/types.py b/pandas_paddles/types.py new file mode 100644 index 0000000..6ffed80 --- /dev/null +++ b/pandas_paddles/types.py @@ -0,0 +1,4 @@ +from typing import List, TypeAlias, Union + +AnyDataframe: TypeAlias = Union["pandas.DataFrame", "dask.dataframe.DataFrame"] +Indices: TypeAlias = List[int] diff --git a/poetry.lock b/poetry.lock index 23f5022..1508792 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,168 +1,304 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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(>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-flake8", "pytest-mypy"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [extras] rtdbuild = ["pydata-sphinx-theme", "sphinx"] @@ -934,4 +1239,4 @@ rtdbuild = ["pydata-sphinx-theme", "sphinx"] [metadata] lock-version = "2.0" python-versions = "^3.7.0" -content-hash = "c87703a38c56a14a373d00928c9c3e41d07f39285d6235ea560c7d03eed582ae" +content-hash = "7bf8773ef80ec188a57abdfcb872e657c6f3f8c5be68112ba4e59eb4b1df9da5" diff --git a/pyproject.toml b/pyproject.toml index a7b2dde..5246ae9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,6 +42,7 @@ pandas-stubs = "~1.2" # docutils 0.21 cannot be installed with poetry # See https://github.com/python-poetry/poetry/issues/9293 docutils = "!=0.21" +dask = {version = ">=2024.5.2", python = "^3.9"} [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/tests/test_doc_passthrough.py b/tests/test_doc_passthrough.py index 7aba777..8424894 100644 --- a/tests/test_doc_passthrough.py +++ b/tests/test_doc_passthrough.py @@ -1,7 +1,7 @@ import pandas as pd import pytest -from pandas_paddles import DF, S +from pandas_paddles.pandas import DF, S @pytest.mark.parametrize( "df_sel,exp_doc",