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from __future__ import annotations
from typing import TYPE_CHECKING, Any
from narwhals._compliant.any_namespace import DateTimeNamespace
from narwhals._duration import parse_interval_string
from narwhals._pandas_like.utils import (
UNIT_DICT,
PandasLikeSeriesNamespace,
calculate_timestamp_date,
calculate_timestamp_datetime,
get_dtype_backend,
int_dtype_mapper,
is_pyarrow_dtype_backend,
)
if TYPE_CHECKING:
from narwhals._pandas_like.series import PandasLikeSeries
from narwhals.typing import TimeUnit
class PandasLikeSeriesDateTimeNamespace(
PandasLikeSeriesNamespace, DateTimeNamespace["PandasLikeSeries"]
):
def date(self) -> PandasLikeSeries:
result = self.with_native(self.native.dt.date)
if str(result.dtype).lower() == "object":
msg = (
"Accessing `date` on the default pandas backend "
"will return a Series of type `object`."
"\nThis differs from polars API and will prevent `.dt` chaining. "
"Please switch to the `pyarrow` backend:"
'\ndf.convert_dtypes(dtype_backend="pyarrow")'
)
raise NotImplementedError(msg)
return result
def year(self) -> PandasLikeSeries:
return self.with_native(self.native.dt.year)
def month(self) -> PandasLikeSeries:
return self.with_native(self.native.dt.month)
def day(self) -> PandasLikeSeries:
return self.with_native(self.native.dt.day)
def hour(self) -> PandasLikeSeries:
return self.with_native(self.native.dt.hour)
def minute(self) -> PandasLikeSeries:
return self.with_native(self.native.dt.minute)
def second(self) -> PandasLikeSeries:
return self.with_native(self.native.dt.second)
def millisecond(self) -> PandasLikeSeries:
return self.microsecond() // 1000
def microsecond(self) -> PandasLikeSeries:
if self.backend_version < (3, 0, 0) and self._is_pyarrow():
# crazy workaround for https://github.com/pandas-dev/pandas/issues/59154
import pyarrow.compute as pc # ignore-banned-import()
from narwhals._arrow.utils import lit
arr_ns = self.native.array
arr = arr_ns.__arrow_array__()
result_arr = pc.add(
pc.multiply(pc.millisecond(arr), lit(1_000)), pc.microsecond(arr)
)
result = type(self.native)(type(arr_ns)(result_arr), name=self.native.name)
return self.with_native(result)
return self.with_native(self.native.dt.microsecond)
def nanosecond(self) -> PandasLikeSeries:
return self.microsecond() * 1_000 + self.native.dt.nanosecond
def ordinal_day(self) -> PandasLikeSeries:
year_start = self.native.dt.year
result = (
self.native.to_numpy().astype("datetime64[D]")
- (year_start.to_numpy() - 1970).astype("datetime64[Y]")
).astype("int32") + 1
dtype = "Int64[pyarrow]" if self._is_pyarrow() else "int32"
return self.with_native(
type(self.native)(result, dtype=dtype, name=year_start.name)
)
def weekday(self) -> PandasLikeSeries:
# Pandas is 0-6 while Polars is 1-7
return self.with_native(self.native.dt.weekday) + 1
def _is_pyarrow(self) -> bool:
return is_pyarrow_dtype_backend(self.native.dtype, self.implementation)
def _get_total_seconds(self) -> Any:
if hasattr(self.native.dt, "total_seconds"):
return self.native.dt.total_seconds()
else: # pragma: no cover
return (
self.native.dt.days * 86400
+ self.native.dt.seconds
+ (self.native.dt.microseconds / 1e6)
+ (self.native.dt.nanoseconds / 1e9)
)
def total_minutes(self) -> PandasLikeSeries:
s = self._get_total_seconds()
# this calculates the sign of each series element
s_sign = 2 * (s > 0).astype(int_dtype_mapper(s.dtype)) - 1
s_abs = s.abs() // 60
if ~s.isna().any():
s_abs = s_abs.astype(int_dtype_mapper(s.dtype))
return self.with_native(s_abs * s_sign)
def total_seconds(self) -> PandasLikeSeries:
s = self._get_total_seconds()
# this calculates the sign of each series element
s_sign = 2 * (s > 0).astype(int_dtype_mapper(s.dtype)) - 1
s_abs = s.abs() // 1
if ~s.isna().any():
s_abs = s_abs.astype(int_dtype_mapper(s.dtype))
return self.with_native(s_abs * s_sign)
def total_milliseconds(self) -> PandasLikeSeries:
s = self._get_total_seconds() * 1e3
# this calculates the sign of each series element
s_sign = 2 * (s > 0).astype(int_dtype_mapper(s.dtype)) - 1
s_abs = s.abs() // 1
if ~s.isna().any():
s_abs = s_abs.astype(int_dtype_mapper(s.dtype))
return self.with_native(s_abs * s_sign)
def total_microseconds(self) -> PandasLikeSeries:
s = self._get_total_seconds() * 1e6
# this calculates the sign of each series element
s_sign = 2 * (s > 0).astype(int_dtype_mapper(s.dtype)) - 1
s_abs = s.abs() // 1
if ~s.isna().any():
s_abs = s_abs.astype(int_dtype_mapper(s.dtype))
return self.with_native(s_abs * s_sign)
def total_nanoseconds(self) -> PandasLikeSeries:
s = self._get_total_seconds() * 1e9
# this calculates the sign of each series element
s_sign = 2 * (s > 0).astype(int_dtype_mapper(s.dtype)) - 1
s_abs = s.abs() // 1
if ~s.isna().any():
s_abs = s_abs.astype(int_dtype_mapper(s.dtype))
return self.with_native(s_abs * s_sign)
def to_string(self, format: str) -> PandasLikeSeries:
# Polars' parser treats `'%.f'` as pandas does `'.%f'`
# PyArrow interprets `'%S'` as "seconds, plus fractional seconds"
# and doesn't support `%f`
if not self._is_pyarrow():
format = format.replace("%S%.f", "%S.%f")
else:
format = format.replace("%S.%f", "%S").replace("%S%.f", "%S")
return self.with_native(self.native.dt.strftime(format))
def replace_time_zone(self, time_zone: str | None) -> PandasLikeSeries:
de_zone = self.native.dt.tz_localize(None)
result = de_zone.dt.tz_localize(time_zone) if time_zone is not None else de_zone
return self.with_native(result)
def convert_time_zone(self, time_zone: str) -> PandasLikeSeries:
if self.compliant.dtype.time_zone is None: # type: ignore[attr-defined]
result = self.native.dt.tz_localize("UTC").dt.tz_convert(time_zone)
else:
result = self.native.dt.tz_convert(time_zone)
return self.with_native(result)
def timestamp(self, time_unit: TimeUnit) -> PandasLikeSeries:
s = self.native
dtype = self.compliant.dtype
mask_na = s.isna()
dtypes = self.version.dtypes
if dtype == dtypes.Date:
# Date is only supported in pandas dtypes if pyarrow-backed
s_cast = s.astype("Int32[pyarrow]")
result = calculate_timestamp_date(s_cast, time_unit)
elif isinstance(dtype, dtypes.Datetime):
fn = (
s.view
if (self.implementation.is_pandas() and self.backend_version < (2,))
else s.astype
)
s_cast = fn("Int64[pyarrow]") if self._is_pyarrow() else fn("int64")
result = calculate_timestamp_datetime(s_cast, dtype.time_unit, time_unit)
else:
msg = "Input should be either of Date or Datetime type"
raise TypeError(msg)
result[mask_na] = None
return self.with_native(result)
def truncate(self, every: str) -> PandasLikeSeries:
multiple, unit = parse_interval_string(every)
native = self.native
if self.implementation.is_cudf():
if multiple != 1:
msg = f"Only multiple `1` is supported for cuDF, got: {multiple}."
raise NotImplementedError(msg)
return self.with_native(self.native.dt.floor(UNIT_DICT.get(unit, unit)))
dtype_backend = get_dtype_backend(native.dtype, self.compliant._implementation)
if unit in {"mo", "q", "y"}:
if self.implementation.is_cudf():
msg = f"Truncating to {unit} is not supported yet for cuDF."
raise NotImplementedError(msg)
if dtype_backend == "pyarrow":
import pyarrow.compute as pc # ignore-banned-import
from narwhals._arrow.utils import UNITS_DICT
ca = native.array._pa_array
result_arr = pc.floor_temporal(ca, multiple, UNITS_DICT[unit])
else:
if unit == "q":
multiple *= 3
np_unit = "M"
elif unit == "mo":
np_unit = "M"
else:
np_unit = "Y"
arr = native.values
arr_dtype = arr.dtype
result_arr = arr.astype(f"datetime64[{multiple}{np_unit}]").astype(
arr_dtype
)
result_native = native.__class__(
result_arr, dtype=native.dtype, index=native.index, name=native.name
)
return self.with_native(result_native)
return self.with_native(
self.native.dt.floor(f"{multiple}{UNIT_DICT.get(unit, unit)}")
)
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