feat!: Expedited removal of certain deprecated functionality

crates/polars-time/src/upsample.rs

@@ -43,7 +43,6 @@ pub trait PolarsUpsample {
         by: I,
         time_column: &str,
         every: Duration,
-        offset: Duration,
     ) -> PolarsResult<DataFrame>;
 
     /// Upsample a [`DataFrame`] at a regular frequency.
@@ -85,7 +84,6 @@ pub trait PolarsUpsample {
         by: I,
         time_column: &str,
         every: Duration,
-        offset: Duration,
     ) -> PolarsResult<DataFrame>;
 }
 
@@ -95,27 +93,23 @@ impl PolarsUpsample for DataFrame {
         by: I,
         time_column: &str,
         every: Duration,
-        offset: Duration,
     ) -> PolarsResult<DataFrame> {
         let by = by.into_vec();
         let time_type = self.column(time_column)?.dtype();
-        ensure_duration_matches_data_type(offset, time_type, "offset")?;
         ensure_duration_matches_data_type(every, time_type, "every")?;
-        upsample_impl(self, by, time_column, every, offset, false)
+        upsample_impl(self, by, time_column, every, false)
     }
 
     fn upsample_stable<I: IntoVec<String>>(
         &self,
         by: I,
         time_column: &str,
         every: Duration,
-        offset: Duration,
     ) -> PolarsResult<DataFrame> {
         let by = by.into_vec();
         let time_type = self.column(time_column)?.dtype();
-        ensure_duration_matches_data_type(offset, time_type, "offset")?;
         ensure_duration_matches_data_type(every, time_type, "every")?;
-        upsample_impl(self, by, time_column, every, offset, true)
+        upsample_impl(self, by, time_column, every, true)
     }
 }
 
@@ -124,7 +118,6 @@ fn upsample_impl(
     by: Vec<String>,
     index_column: &str,
     every: Duration,
-    offset: Duration,
     stable: bool,
 ) -> PolarsResult<DataFrame> {
     let s = source.column(index_column)?;
@@ -137,7 +130,7 @@ fn upsample_impl(
                 .unwrap()
         })
         .unwrap();
-        let mut out = upsample_impl(&df, by, index_column, every, offset, stable)?;
+        let mut out = upsample_impl(&df, by, index_column, every, stable)?;
         out.apply(index_column, |s| s.cast(time_type).unwrap())
             .unwrap();
         Ok(out)
@@ -154,7 +147,7 @@ fn upsample_impl(
                 .unwrap()
         })
         .unwrap();
-        let mut out = upsample_impl(&df, by, index_column, every, offset, stable)?;
+        let mut out = upsample_impl(&df, by, index_column, every, stable)?;
         out.apply(index_column, |s| s.cast(time_type).unwrap())
             .unwrap();
         Ok(out)
@@ -165,13 +158,13 @@ fn upsample_impl(
                 .unwrap()
         })
         .unwrap();
-        let mut out = upsample_impl(&df, by, index_column, every, offset, stable)?;
+        let mut out = upsample_impl(&df, by, index_column, every, stable)?;
         out.apply(index_column, |s| s.cast(time_type).unwrap())
             .unwrap();
         Ok(out)
     } else if by.is_empty() {
         let index_column = source.column(index_column)?;
-        upsample_single_impl(source, index_column, every, offset)
+        upsample_single_impl(source, index_column, every)
     } else {
         let gb = if stable {
             source.group_by_stable(by)
@@ -181,7 +174,7 @@ fn upsample_impl(
         // don't parallelize this, this may SO on large data.
         gb?.apply(|df| {
             let index_column = df.column(index_column)?;
-            upsample_single_impl(&df, index_column, every, offset)
+            upsample_single_impl(&df, index_column, every)
         })
     }
 }
@@ -190,7 +183,6 @@ fn upsample_single_impl(
     source: &DataFrame,
     index_column: &Series,
     every: Duration,
-    offset: Duration,
 ) -> PolarsResult<DataFrame> {
     let index_col_name = index_column.name();
 
@@ -208,11 +200,6 @@ fn upsample_single_impl(
                         Some(tz) => Some(parse_time_zone(tz)?),
                         _ => None,
                     };
-                    let first = match tu {
-                        TimeUnit::Nanoseconds => offset.add_ns(first, tz.as_ref())?,
-                        TimeUnit::Microseconds => offset.add_us(first, tz.as_ref())?,
-                        TimeUnit::Milliseconds => offset.add_ms(first, tz.as_ref())?,
-                    };
                     let range = datetime_range_impl(
                         index_col_name,
                         first,

docs/src/rust/user-guide/transformations/time-series/resampling.rs

@@ -31,15 +31,15 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     // --8<-- [start:upsample]
     let out1 = df
         .clone()
-        .upsample::<[String; 0]>([], "time", Duration::parse("15m"), Duration::parse("0"))?
+        .upsample::<[String; 0]>([], "time", Duration::parse("15m"))?
         .fill_null(FillNullStrategy::Forward(None))?;
     println!("{}", &out1);
     // --8<-- [end:upsample]
 
     // --8<-- [start:upsample2]
     let out2 = df
         .clone()
-        .upsample::<[String; 0]>([], "time", Duration::parse("15m"), Duration::parse("0"))?
+        .upsample::<[String; 0]>([], "time", Duration::parse("15m"))?
         .lazy()
         .with_columns([col("values").interpolate(InterpolationMethod::Linear)])
         .collect()?

py-polars/polars/dataframe/frame.py

@@ -6003,15 +6003,13 @@ def upsample(
         time_column: str,
         *,
         every: str | timedelta,
-        offset: str | timedelta | None = None,
         group_by: str | Sequence[str] | None = None,
         maintain_order: bool = False,
     ) -> Self:
         """
         Upsample a DataFrame at a regular frequency.
 
-        The `every` and `offset` arguments are created with
-        the following string language:
+        The `every` argument is created with the following string language:
 
         - 1ns   (1 nanosecond)
         - 1us   (1 microsecond)
@@ -6042,12 +6040,6 @@ def upsample(
             Note that this column has to be sorted for the output to make sense.
         every
             Interval will start 'every' duration.
-        offset
-            Change the start of the date_range by this offset.
-
-            .. deprecated:: 0.20.19
-                This argument is deprecated and will be removed in the next breaking
-                release. Instead, chain `upsample` with `dt.offset_by`.
         group_by
             First group by these columns and then upsample for every group.
         maintain_order
@@ -6094,24 +6086,15 @@ def upsample(
         │ 2021-06-01 00:00:00 ┆ B      ┆ 3      │
         └─────────────────────┴────────┴────────┘
         """
-        if offset is not None:
-            issue_deprecation_warning(
-                "`offset` is deprecated and will be removed in the next breaking release. "
-                "Instead, chain `upsample` with `dt.offset_by`.",
-                version="0.20.19",
-            )
         if group_by is None:
             group_by = []
         if isinstance(group_by, str):
             group_by = [group_by]
-        if offset is None:
-            offset = "0ns"
 
         every = parse_as_duration_string(every)
-        offset = parse_as_duration_string(offset)
 
         return self._from_pydf(
-            self._df.upsample(group_by, time_column, every, offset, maintain_order)
+            self._df.upsample(group_by, time_column, every, maintain_order)
         )
 
     def join_asof(

py-polars/polars/expr/expr.py

@@ -10263,7 +10263,7 @@ def ewm_mean(
         alpha: float | None = None,
         adjust: bool = True,
         min_periods: int = 1,
-        ignore_nulls: bool | None = None,
+        ignore_nulls: bool = False,
     ) -> Self:
         r"""
         Exponentially-weighted moving average.
@@ -10306,15 +10306,15 @@ def ewm_mean(
         ignore_nulls
             Ignore missing values when calculating weights.
 
-                - When `ignore_nulls=False`, weights are based on absolute
+                - When `ignore_nulls=False` (default), weights are based on absolute
                   positions.
                   For example, the weights of :math:`x_0` and :math:`x_2` used in
                   calculating the final weighted average of
                   [:math:`x_0`, None, :math:`x_2`] are
                   :math:`(1-\alpha)^2` and :math:`1` if `adjust=True`, and
                   :math:`(1-\alpha)^2` and :math:`\alpha` if `adjust=False`.
 
-                - When `ignore_nulls=True` (current default), weights are based
+                - When `ignore_nulls=True`, weights are based
                   on relative positions. For example, the weights of
                   :math:`x_0` and :math:`x_2` used in calculating the final weighted
                   average of [:math:`x_0`, None, :math:`x_2`] are
@@ -10336,16 +10336,6 @@ def ewm_mean(
         │ 2.428571 │
         └──────────┘
         """
-        if ignore_nulls is None:
-            issue_deprecation_warning(
-                "The default value for `ignore_nulls` for `ewm` methods"
-                " will change from True to False in the next breaking release."
-                " Explicitly set `ignore_nulls=True` to keep the existing behavior"
-                " and silence this warning.",
-                version="0.20.11",
-            )
-            ignore_nulls = True
-
         alpha = _prepare_alpha(com, span, half_life, alpha)
         return self._from_pyexpr(
             self._pyexpr.ewm_mean(alpha, adjust, min_periods, ignore_nulls)
@@ -10465,7 +10455,7 @@ def ewm_std(
         adjust: bool = True,
         bias: bool = False,
         min_periods: int = 1,
-        ignore_nulls: bool | None = None,
+        ignore_nulls: bool = False,
     ) -> Self:
         r"""
         Exponentially-weighted moving standard deviation.
@@ -10511,15 +10501,15 @@ def ewm_std(
         ignore_nulls
             Ignore missing values when calculating weights.
 
-                - When `ignore_nulls=False`, weights are based on absolute
+                - When `ignore_nulls=False` (default), weights are based on absolute
                   positions.
                   For example, the weights of :math:`x_0` and :math:`x_2` used in
                   calculating the final weighted average of
                   [:math:`x_0`, None, :math:`x_2`] are
                   :math:`(1-\alpha)^2` and :math:`1` if `adjust=True`, and
                   :math:`(1-\alpha)^2` and :math:`\alpha` if `adjust=False`.
 
-                - When `ignore_nulls=True` (current default), weights are based
+                - When `ignore_nulls=True`, weights are based
                   on relative positions. For example, the weights of
                   :math:`x_0` and :math:`x_2` used in calculating the final weighted
                   average of [:math:`x_0`, None, :math:`x_2`] are
@@ -10541,16 +10531,6 @@ def ewm_std(
         │ 0.963624 │
         └──────────┘
         """
-        if ignore_nulls is None:
-            issue_deprecation_warning(
-                "The default value for `ignore_nulls` for `ewm` methods"
-                " will change from True to False in the next breaking release."
-                " Explicitly set `ignore_nulls=True` to keep the existing behavior"
-                " and silence this warning.",
-                version="0.20.11",
-            )
-            ignore_nulls = True
-
         alpha = _prepare_alpha(com, span, half_life, alpha)
         return self._from_pyexpr(
             self._pyexpr.ewm_std(alpha, adjust, bias, min_periods, ignore_nulls)
@@ -10566,7 +10546,7 @@ def ewm_var(
         adjust: bool = True,
         bias: bool = False,
         min_periods: int = 1,
-        ignore_nulls: bool | None = None,
+        ignore_nulls: bool = False,
     ) -> Self:
         r"""
         Exponentially-weighted moving variance.
@@ -10612,15 +10592,15 @@ def ewm_var(
         ignore_nulls
             Ignore missing values when calculating weights.
 
-                - When `ignore_nulls=False`, weights are based on absolute
+                - When `ignore_nulls=False` (default), weights are based on absolute
                   positions.
                   For example, the weights of :math:`x_0` and :math:`x_2` used in
                   calculating the final weighted average of
                   [:math:`x_0`, None, :math:`x_2`] are
                   :math:`(1-\alpha)^2` and :math:`1` if `adjust=True`, and
                   :math:`(1-\alpha)^2` and :math:`\alpha` if `adjust=False`.
 
-                - When `ignore_nulls=True` (current default), weights are based
+                - When `ignore_nulls=True`, weights are based
                   on relative positions. For example, the weights of
                   :math:`x_0` and :math:`x_2` used in calculating the final weighted
                   average of [:math:`x_0`, None, :math:`x_2`] are
@@ -10642,16 +10622,6 @@ def ewm_var(
         │ 0.928571 │
         └──────────┘
         """
-        if ignore_nulls is None:
-            issue_deprecation_warning(
-                "The default value for `ignore_nulls` for `ewm` methods"
-                " will change from True to False in the next breaking release."
-                " Explicitly set `ignore_nulls=True` to keep the existing behavior"
-                " and silence this warning.",
-                version="0.20.11",
-            )
-            ignore_nulls = True
-
         alpha = _prepare_alpha(com, span, half_life, alpha)
         return self._from_pyexpr(
             self._pyexpr.ewm_var(alpha, adjust, bias, min_periods, ignore_nulls)

py-polars/polars/functions/lazy.py

@@ -17,7 +17,7 @@
 from polars._utils.unstable import issue_unstable_warning, unstable
 from polars._utils.various import extend_bool
 from polars._utils.wrap import wrap_df, wrap_expr
-from polars.datatypes import DTYPE_TEMPORAL_UNITS, Date, Datetime, Int64, UInt32
+from polars.datatypes import DTYPE_TEMPORAL_UNITS, Date, Datetime, Int64
 
 with contextlib.suppress(ImportError):  # Module not available when building docs
     import polars.polars as plr
@@ -209,17 +209,6 @@ def cum_count(*columns: str, reverse: bool = False) -> Expr:
     │ 2   │
     └─────┘
     """
-    if not columns:
-        issue_deprecation_warning(
-            "`pl.cum_count()` is deprecated. The same result can be achieved using"
-            " `pl.int_range(1, pl.len() + 1, dtype=pl.UInt32)`,"
-            " or `int_range(pl.len(), 0, -1, dtype=pl.UInt32)` when `reverse=True`.",
-            version="0.20.5",
-        )
-        if reverse:
-            return F.int_range(F.len(), 0, step=-1, dtype=UInt32).alias("cum_count")
-        else:
-            return F.int_range(1, F.len() + 1, dtype=UInt32).alias("cum_count")
     return F.col(*columns).cum_count(reverse=reverse)