396 lines
15 KiB
Python
396 lines
15 KiB
Python
from __future__ import annotations
|
|
|
|
from typing import TYPE_CHECKING
|
|
from typing import Any
|
|
from typing import Sequence
|
|
|
|
from narwhals._compliant import EagerExpr
|
|
from narwhals._expression_parsing import evaluate_output_names_and_aliases
|
|
from narwhals._pandas_like.group_by import PandasLikeGroupBy
|
|
from narwhals._pandas_like.series import PandasLikeSeries
|
|
from narwhals.exceptions import ColumnNotFoundError
|
|
from narwhals.utils import generate_temporary_column_name
|
|
|
|
if TYPE_CHECKING:
|
|
from typing_extensions import Self
|
|
|
|
from narwhals._compliant.typing import AliasNames
|
|
from narwhals._compliant.typing import EvalNames
|
|
from narwhals._compliant.typing import EvalSeries
|
|
from narwhals._expression_parsing import ExprMetadata
|
|
from narwhals._pandas_like.dataframe import PandasLikeDataFrame
|
|
from narwhals._pandas_like.namespace import PandasLikeNamespace
|
|
from narwhals.typing import FillNullStrategy
|
|
from narwhals.typing import NonNestedLiteral
|
|
from narwhals.typing import RankMethod
|
|
from narwhals.utils import Implementation
|
|
from narwhals.utils import Version
|
|
from narwhals.utils import _FullContext
|
|
|
|
WINDOW_FUNCTIONS_TO_PANDAS_EQUIVALENT = {
|
|
"cum_sum": "cumsum",
|
|
"cum_min": "cummin",
|
|
"cum_max": "cummax",
|
|
"cum_prod": "cumprod",
|
|
# Pandas cumcount starts counting from 0 while Polars starts from 1
|
|
# Pandas cumcount counts nulls while Polars does not
|
|
# So, instead of using "cumcount" we use "cumsum" on notna() to get the same result
|
|
"cum_count": "cumsum",
|
|
"rolling_sum": "sum",
|
|
"rolling_mean": "mean",
|
|
"rolling_std": "std",
|
|
"rolling_var": "var",
|
|
"shift": "shift",
|
|
"rank": "rank",
|
|
"diff": "diff",
|
|
"fill_null": "fillna",
|
|
}
|
|
|
|
|
|
def window_kwargs_to_pandas_equivalent(
|
|
function_name: str, kwargs: dict[str, object]
|
|
) -> dict[str, object]:
|
|
if function_name == "shift":
|
|
pandas_kwargs: dict[str, object] = {"periods": kwargs["n"]}
|
|
elif function_name == "rank":
|
|
_method = kwargs["method"]
|
|
pandas_kwargs = {
|
|
"method": "first" if _method == "ordinal" else _method,
|
|
"ascending": not kwargs["descending"],
|
|
"na_option": "keep",
|
|
"pct": False,
|
|
}
|
|
elif function_name.startswith("cum_"): # Cumulative operation
|
|
pandas_kwargs = {"skipna": True}
|
|
elif function_name.startswith("rolling_"): # Rolling operation
|
|
pandas_kwargs = {
|
|
"min_periods": kwargs["min_samples"],
|
|
"window": kwargs["window_size"],
|
|
"center": kwargs["center"],
|
|
}
|
|
else: # e.g. std, var
|
|
pandas_kwargs = kwargs
|
|
return pandas_kwargs
|
|
|
|
|
|
class PandasLikeExpr(EagerExpr["PandasLikeDataFrame", PandasLikeSeries]):
|
|
def __init__(
|
|
self,
|
|
call: EvalSeries[PandasLikeDataFrame, PandasLikeSeries],
|
|
*,
|
|
depth: int,
|
|
function_name: str,
|
|
evaluate_output_names: EvalNames[PandasLikeDataFrame],
|
|
alias_output_names: AliasNames | None,
|
|
implementation: Implementation,
|
|
backend_version: tuple[int, ...],
|
|
version: Version,
|
|
call_kwargs: dict[str, Any] | None = None,
|
|
) -> None:
|
|
self._call = call
|
|
self._depth = depth
|
|
self._function_name = function_name
|
|
self._evaluate_output_names = evaluate_output_names
|
|
self._alias_output_names = alias_output_names
|
|
self._implementation = implementation
|
|
self._backend_version = backend_version
|
|
self._version = version
|
|
self._call_kwargs = call_kwargs or {}
|
|
self._metadata: ExprMetadata | None = None
|
|
|
|
def __narwhals_namespace__(self) -> PandasLikeNamespace:
|
|
from narwhals._pandas_like.namespace import PandasLikeNamespace
|
|
|
|
return PandasLikeNamespace(
|
|
self._implementation, self._backend_version, version=self._version
|
|
)
|
|
|
|
def __narwhals_expr__(self) -> None: ...
|
|
|
|
@classmethod
|
|
def from_column_names(
|
|
cls: type[Self],
|
|
evaluate_column_names: EvalNames[PandasLikeDataFrame],
|
|
/,
|
|
*,
|
|
context: _FullContext,
|
|
function_name: str = "",
|
|
) -> Self:
|
|
def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
|
|
try:
|
|
return [
|
|
PandasLikeSeries(
|
|
df._native_frame[column_name],
|
|
implementation=df._implementation,
|
|
backend_version=df._backend_version,
|
|
version=df._version,
|
|
)
|
|
for column_name in evaluate_column_names(df)
|
|
]
|
|
except KeyError as e:
|
|
missing_columns = [
|
|
x for x in evaluate_column_names(df) if x not in df.columns
|
|
]
|
|
raise ColumnNotFoundError.from_missing_and_available_column_names(
|
|
missing_columns=missing_columns,
|
|
available_columns=df.columns,
|
|
) from e
|
|
|
|
return cls(
|
|
func,
|
|
depth=0,
|
|
function_name=function_name,
|
|
evaluate_output_names=evaluate_column_names,
|
|
alias_output_names=None,
|
|
implementation=context._implementation,
|
|
backend_version=context._backend_version,
|
|
version=context._version,
|
|
)
|
|
|
|
@classmethod
|
|
def from_column_indices(
|
|
cls: type[Self], *column_indices: int, context: _FullContext
|
|
) -> Self:
|
|
def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
|
|
return [
|
|
PandasLikeSeries(
|
|
df._native_frame.iloc[:, column_index],
|
|
implementation=df._implementation,
|
|
backend_version=df._backend_version,
|
|
version=df._version,
|
|
)
|
|
for column_index in column_indices
|
|
]
|
|
|
|
return cls(
|
|
func,
|
|
depth=0,
|
|
function_name="nth",
|
|
evaluate_output_names=lambda df: [df.columns[i] for i in column_indices],
|
|
alias_output_names=None,
|
|
implementation=context._implementation,
|
|
backend_version=context._backend_version,
|
|
version=context._version,
|
|
)
|
|
|
|
def ewm_mean(
|
|
self,
|
|
*,
|
|
com: float | None,
|
|
span: float | None,
|
|
half_life: float | None,
|
|
alpha: float | None,
|
|
adjust: bool,
|
|
min_samples: int,
|
|
ignore_nulls: bool,
|
|
) -> Self:
|
|
return self._reuse_series(
|
|
"ewm_mean",
|
|
com=com,
|
|
span=span,
|
|
half_life=half_life,
|
|
alpha=alpha,
|
|
adjust=adjust,
|
|
min_samples=min_samples,
|
|
ignore_nulls=ignore_nulls,
|
|
)
|
|
|
|
def cum_sum(self, *, reverse: bool) -> Self:
|
|
return self._reuse_series("cum_sum", call_kwargs={"reverse": reverse})
|
|
|
|
def shift(self, n: int) -> Self:
|
|
return self._reuse_series("shift", call_kwargs={"n": n})
|
|
|
|
def over( # noqa: C901, PLR0915
|
|
self,
|
|
partition_by: Sequence[str],
|
|
order_by: Sequence[str] | None,
|
|
) -> Self:
|
|
if not partition_by:
|
|
# e.g. `nw.col('a').cum_sum().order_by(key)`
|
|
# We can always easily support this as it doesn't require grouping.
|
|
assert order_by is not None # noqa: S101 # help type-check
|
|
|
|
def func(df: PandasLikeDataFrame) -> Sequence[PandasLikeSeries]:
|
|
token = generate_temporary_column_name(8, df.columns)
|
|
df = df.with_row_index(token).sort(
|
|
*order_by, descending=False, nulls_last=False
|
|
)
|
|
results = self(df.drop([token], strict=True))
|
|
sorting_indices = df.get_column(token)
|
|
for s in results:
|
|
s._scatter_in_place(sorting_indices, s)
|
|
return results
|
|
elif not self._is_elementary():
|
|
msg = (
|
|
"Only elementary expressions are supported for `.over` in pandas-like backends.\n\n"
|
|
"Please see: "
|
|
"https://narwhals-dev.github.io/narwhals/pandas_like_concepts/improve_group_by_operation/"
|
|
)
|
|
raise NotImplementedError(msg)
|
|
else:
|
|
function_name = PandasLikeGroupBy._leaf_name(self)
|
|
pandas_function_name = WINDOW_FUNCTIONS_TO_PANDAS_EQUIVALENT.get(
|
|
function_name, PandasLikeGroupBy._REMAP_AGGS.get(function_name)
|
|
)
|
|
if pandas_function_name is None:
|
|
msg = (
|
|
f"Unsupported function: {function_name} in `over` context.\n\n"
|
|
f"Supported functions are {', '.join(WINDOW_FUNCTIONS_TO_PANDAS_EQUIVALENT)}\n"
|
|
f"and {', '.join(PandasLikeGroupBy._REMAP_AGGS)}."
|
|
)
|
|
raise NotImplementedError(msg)
|
|
pandas_kwargs = window_kwargs_to_pandas_equivalent(
|
|
function_name, self._call_kwargs
|
|
)
|
|
|
|
def func(df: PandasLikeDataFrame) -> Sequence[PandasLikeSeries]: # noqa: C901, PLR0912
|
|
output_names, aliases = evaluate_output_names_and_aliases(self, df, [])
|
|
if function_name == "cum_count":
|
|
plx = self.__narwhals_namespace__()
|
|
df = df.with_columns(~plx.col(*output_names).is_null())
|
|
|
|
if function_name.startswith("cum_"):
|
|
reverse = self._call_kwargs["reverse"]
|
|
else:
|
|
assert "reverse" not in self._call_kwargs # noqa: S101
|
|
reverse = False
|
|
|
|
if order_by:
|
|
columns = list(set(partition_by).union(output_names).union(order_by))
|
|
token = generate_temporary_column_name(8, columns)
|
|
df = (
|
|
df.simple_select(*columns)
|
|
.with_row_index(token)
|
|
.sort(*order_by, descending=reverse, nulls_last=reverse)
|
|
)
|
|
sorting_indices = df.get_column(token)
|
|
elif reverse:
|
|
columns = list(set(partition_by).union(output_names))
|
|
df = df.simple_select(*columns)._gather_slice(slice(None, None, -1))
|
|
grouped = df._native_frame.groupby(partition_by)
|
|
if function_name.startswith("rolling"):
|
|
rolling = grouped[list(output_names)].rolling(**pandas_kwargs)
|
|
assert pandas_function_name is not None # help mypy # noqa: S101
|
|
if pandas_function_name in {"std", "var"}:
|
|
res_native = getattr(rolling, pandas_function_name)(
|
|
ddof=self._call_kwargs["ddof"]
|
|
)
|
|
else:
|
|
res_native = getattr(rolling, pandas_function_name)()
|
|
elif function_name == "fill_null":
|
|
df_grouped = grouped[list(output_names)]
|
|
if self._call_kwargs["strategy"] == "forward":
|
|
res_native = df_grouped.ffill(limit=self._call_kwargs["limit"])
|
|
elif self._call_kwargs["strategy"] == "backward":
|
|
res_native = df_grouped.bfill(limit=self._call_kwargs["limit"])
|
|
else: # pragma: no cover
|
|
# This is deprecated in pandas. Indeed, `nw.col('a').fill_null(3).over('b')`
|
|
# does not seem very useful, and DuckDB doesn't support it either.
|
|
msg = "`fill_null` with `over` without `strategy` specified is not supported."
|
|
raise NotImplementedError(msg)
|
|
elif function_name == "len":
|
|
if len(output_names) != 1: # pragma: no cover
|
|
msg = "Safety check failed, please report a bug."
|
|
raise AssertionError(msg)
|
|
res_native = grouped.transform("size").to_frame(aliases[0])
|
|
else:
|
|
res_native = grouped[list(output_names)].transform(
|
|
pandas_function_name, **pandas_kwargs
|
|
)
|
|
result_frame = df._with_native(res_native).rename(
|
|
dict(zip(output_names, aliases))
|
|
)
|
|
results = [result_frame.get_column(name) for name in aliases]
|
|
if order_by:
|
|
for s in results:
|
|
s._scatter_in_place(sorting_indices, s)
|
|
return results
|
|
if reverse:
|
|
return [s._gather_slice(slice(None, None, -1)) for s in results]
|
|
return results
|
|
|
|
return self.__class__(
|
|
func,
|
|
depth=self._depth + 1,
|
|
function_name=self._function_name + "->over",
|
|
evaluate_output_names=self._evaluate_output_names,
|
|
alias_output_names=self._alias_output_names,
|
|
implementation=self._implementation,
|
|
backend_version=self._backend_version,
|
|
version=self._version,
|
|
)
|
|
|
|
def cum_count(self, *, reverse: bool) -> Self:
|
|
return self._reuse_series("cum_count", call_kwargs={"reverse": reverse})
|
|
|
|
def cum_min(self, *, reverse: bool) -> Self:
|
|
return self._reuse_series("cum_min", call_kwargs={"reverse": reverse})
|
|
|
|
def cum_max(self, *, reverse: bool) -> Self:
|
|
return self._reuse_series("cum_max", call_kwargs={"reverse": reverse})
|
|
|
|
def cum_prod(self, *, reverse: bool) -> Self:
|
|
return self._reuse_series("cum_prod", call_kwargs={"reverse": reverse})
|
|
|
|
def fill_null(
|
|
self,
|
|
value: Self | NonNestedLiteral,
|
|
strategy: FillNullStrategy | None,
|
|
limit: int | None,
|
|
) -> Self:
|
|
return self._reuse_series(
|
|
"fill_null", call_kwargs={"strategy": strategy, "limit": limit}, value=value
|
|
)
|
|
|
|
def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self:
|
|
return self._reuse_series(
|
|
"rolling_sum",
|
|
call_kwargs={
|
|
"window_size": window_size,
|
|
"min_samples": min_samples,
|
|
"center": center,
|
|
},
|
|
)
|
|
|
|
def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self:
|
|
return self._reuse_series(
|
|
"rolling_mean",
|
|
call_kwargs={
|
|
"window_size": window_size,
|
|
"min_samples": min_samples,
|
|
"center": center,
|
|
},
|
|
)
|
|
|
|
def rolling_std(
|
|
self, window_size: int, *, min_samples: int, center: bool, ddof: int
|
|
) -> Self:
|
|
return self._reuse_series(
|
|
"rolling_std",
|
|
call_kwargs={
|
|
"window_size": window_size,
|
|
"min_samples": min_samples,
|
|
"center": center,
|
|
"ddof": ddof,
|
|
},
|
|
)
|
|
|
|
def rolling_var(
|
|
self, window_size: int, *, min_samples: int, center: bool, ddof: int
|
|
) -> Self:
|
|
return self._reuse_series(
|
|
"rolling_var",
|
|
call_kwargs={
|
|
"window_size": window_size,
|
|
"min_samples": min_samples,
|
|
"center": center,
|
|
"ddof": ddof,
|
|
},
|
|
)
|
|
|
|
def rank(self, method: RankMethod, *, descending: bool) -> Self:
|
|
return self._reuse_series(
|
|
"rank", call_kwargs={"method": method, "descending": descending}
|
|
)
|