diff --git a/src/modelskill/comparison/_collection.py b/src/modelskill/comparison/_collection.py
index 884abe81c..fb91e1969 100644
--- a/src/modelskill/comparison/_collection.py
+++ b/src/modelskill/comparison/_collection.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+from collections.abc import Collection, Sequence
 from copy import deepcopy
 import os
 from pathlib import Path
@@ -12,7 +13,6 @@
     Union,
     Optional,
     Mapping,
-    Iterable,
     overload,
     Hashable,
     Tuple,
@@ -88,7 +88,7 @@ class ComparerCollection(Mapping):
 
     plotter = ComparerCollectionPlotter
 
-    def __init__(self, comparers: Iterable[Comparer]) -> None:
+    def __init__(self, comparers: Collection[Comparer]) -> None:
         self._comparers: Dict[str, Comparer] = {}
 
         names = [c.name for c in comparers]
@@ -215,13 +215,13 @@ def rename(self, mapping: Dict[str, str]) -> "ComparerCollection":
         return ComparerCollection(cmps)
 
     @overload
-    def __getitem__(self, x: slice | Iterable[Hashable]) -> ComparerCollection: ...
+    def __getitem__(self, x: slice | Collection[Hashable]) -> ComparerCollection: ...
 
     @overload
     def __getitem__(self, x: int | Hashable) -> Comparer: ...
 
     def __getitem__(
-        self, x: int | Hashable | slice | Iterable[Hashable]
+        self, x: int | Hashable | slice | Collection[Hashable]
     ) -> Comparer | ComparerCollection:
         if isinstance(x, str):
             return self._comparers[x]
@@ -234,7 +234,7 @@ def __getitem__(
             name = _get_name(x, self.obs_names)
             return self._comparers[name]
 
-        if isinstance(x, Iterable):
+        if isinstance(x, Collection):
             cmps = [self[i] for i in x]
             return ComparerCollection(cmps)
 
@@ -422,8 +422,8 @@ def query(self, query: str) -> "ComparerCollection":
 
     def skill(
         self,
-        by: str | Iterable[str] | None = None,
-        metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
+        by: str | Collection[str] | None = None,
+        metrics: Sequence[str] | Sequence[Callable] | str | Callable | None = None,
         observed: bool = False,
     ) -> SkillTable:
         """Aggregated skill assessment of model(s)
@@ -492,14 +492,14 @@ def skill(
         df = cc._to_long_dataframe(attrs_keys=attrs_keys, observed=observed)
 
         res = _groupby_df(df, by=agg_cols, metrics=pmetrics)
-        mtr_cols = [m.__name__ for m in pmetrics]  # type: ignore
+        mtr_cols = [m.__name__ for m in pmetrics]
         res = res.dropna(subset=mtr_cols, how="all")  # TODO: ok to remove empty?
         res = self._append_xy_to_res(res, cc)
-        res = cc._add_as_col_if_not_in_index(df, skilldf=res)  # type: ignore
+        res = cc._add_as_col_if_not_in_index(df, skilldf=res)
         return SkillTable(res)
 
     def _to_long_dataframe(
-        self, attrs_keys: Iterable[str] | None = None, observed: bool = False
+        self, attrs_keys: Collection[str] | None = None, observed: bool = False
     ) -> pd.DataFrame:
         """Return a copy of the data as a long-format pandas DataFrame (for groupby operations)"""
         frames = []
@@ -570,8 +570,8 @@ def gridded_skill(
         self,
         bins: int = 5,
         binsize: float | None = None,
-        by: str | Iterable[str] | None = None,
-        metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
+        by: str | Collection[str] | None = None,
+        metrics: Sequence[str] | Sequence[Callable] | str | Callable | None = None,
         n_min: Optional[int] = None,
         **kwargs: Any,
     ) -> SkillGrid:
diff --git a/src/modelskill/comparison/_comparer_plotter.py b/src/modelskill/comparison/_comparer_plotter.py
index 5467eafc2..8144de67b 100644
--- a/src/modelskill/comparison/_comparer_plotter.py
+++ b/src/modelskill/comparison/_comparer_plotter.py
@@ -16,7 +16,7 @@
     import matplotlib.axes
     from ._comparison import Comparer
 
-import numpy as np  # type: ignore
+import numpy as np
 
 from .. import metrics as mtr
 from ..utils import _get_idx
@@ -115,7 +115,7 @@ def timeseries(
             return ax
 
         elif backend == "plotly":  # pragma: no cover
-            import plotly.graph_objects as go  # type: ignore
+            import plotly.graph_objects as go
 
             mod_scatter_list = []
             for j in range(cmp.n_models):
@@ -746,7 +746,14 @@ def taylor(
         df = df.rename(columns={"_std_obs": "obs_std", "_std_mod": "std"})
 
         pts = [
-            TaylorPoint(name=r.model, obs_std=r.obs_std, std=r.std, cc=r.cc, marker=marker, marker_size=marker_size)
+            TaylorPoint(
+                name=r.model,
+                obs_std=r.obs_std,
+                std=r.std,
+                cc=r.cc,
+                marker=marker,
+                marker_size=marker_size,
+            )
             for r in df.itertuples()
         ]
 
diff --git a/src/modelskill/comparison/_comparison.py b/src/modelskill/comparison/_comparison.py
index f8fedef75..5b768cad3 100644
--- a/src/modelskill/comparison/_comparison.py
+++ b/src/modelskill/comparison/_comparison.py
@@ -1,4 +1,5 @@
 from __future__ import annotations
+from collections.abc import Collection, Sequence
 from dataclasses import dataclass
 from pathlib import Path
 from typing import (
@@ -10,8 +11,6 @@
     Mapping,
     Optional,
     Union,
-    Iterable,
-    Sequence,
     TYPE_CHECKING,
 )
 import warnings
@@ -128,7 +127,7 @@ def _is_model(da: xr.DataArray) -> bool:
     return str(da.attrs["kind"]) == "model"
 
 
-def _validate_metrics(metrics: Iterable[Any]) -> None:
+def _validate_metrics(metrics: Collection[Any]) -> None:
     for m in metrics:
         if isinstance(m, str):
             if not mtr.is_valid_metric(m):
@@ -195,8 +194,8 @@ def all(self) -> Sequence[str]:
     def parse(
         items: Sequence[str],
         obs_item: str | int | None = None,
-        mod_items: Optional[Iterable[str | int]] = None,
-        aux_items: Optional[Iterable[str | int]] = None,
+        mod_items: Collection[str | int] | None = None,
+        aux_items: Collection[str | int] | None = None,
         x_item: str | int | None = None,
         y_item: str | int | None = None,
     ) -> ItemSelection:
@@ -284,18 +283,18 @@ def _area_is_polygon(area: Any) -> bool:
 
 
 def _inside_polygon(polygon: Any, xy: np.ndarray) -> np.ndarray:
-    import matplotlib.path as mp  # type: ignore
+    import matplotlib.path as mp
 
     if polygon.ndim == 1:
         polygon = np.column_stack((polygon[0::2], polygon[1::2]))
-    return mp.Path(polygon).contains_points(xy)  # type: ignore
+    return mp.Path(polygon).contains_points(xy)
 
 
 def _matched_data_to_xarray(
     df: pd.DataFrame,
     obs_item: int | str | None = None,
-    mod_items: Optional[Iterable[str | int]] = None,
-    aux_items: Optional[Iterable[str | int]] = None,
+    mod_items: Collection[str | int] | None = None,
+    aux_items: Collection[str | int] | None = None,
     name: Optional[str] = None,
     x: Optional[float] = None,
     y: Optional[float] = None,
@@ -464,10 +463,10 @@ def __init__(
     @staticmethod
     def from_matched_data(
         data: xr.Dataset | pd.DataFrame,
-        raw_mod_data: Optional[Dict[str, PointModelResult | TrackModelResult]] = None,
+        raw_mod_data: Optional[dict[str, PointModelResult | TrackModelResult]] = None,
         obs_item: str | int | None = None,
-        mod_items: Optional[Iterable[str | int]] = None,
-        aux_items: Optional[Iterable[str | int]] = None,
+        mod_items: Optional[Collection[str | int]] = None,
+        aux_items: Optional[Collection[str | int]] = None,
         name: Optional[str] = None,
         weight: float = 1.0,
         x: Optional[float] = None,
@@ -477,7 +476,41 @@ def from_matched_data(
         y_item: str | int | None = None,
         quantity: Optional[Quantity] = None,
     ) -> "Comparer":
-        """Initialize from compared data"""
+        """Create a Comparer from data that is already matched (aligned).
+
+        Parameters
+        ----------
+        data : [pd.DataFrame, xr.Dataset]
+            DataFrame (or xarray.Dataset)
+        raw_mod_data : dict of modelskill.PointModelResult, optional
+            Raw model data. If None, observation and modeldata must be provided.
+        obs_item : [str, int], optional
+            Name or index of observation item, by default first item
+        mod_items : Collection of [str, int], optional
+            Names or indicies of model items, if None all remaining columns are model items, by default None
+        aux_items : Collection of [str, int], optional
+            Names or indicies of auxiliary items, by default None
+        name : str, optional
+            Name of the comparer, by default None (will be set to obs_item)
+        x : float, optional
+            x-coordinate of observation, by default None
+        y : float, optional
+            y-coordinate of observation, by default None
+        z : float, optional
+            z-coordinate of observation, by default None
+        x_item: [str, int], optional,
+            Name of x item, only relevant for track data
+        y_item: [str, int], optional
+            Name of y item, only relevant for track data
+        quantity : Quantity, optional
+            Quantity of the observation and model results, by default Quantity(name="Undefined", unit="Undefined")
+
+        Returns
+        -------
+        Comparer
+            A Comparer object with matched observation and model data
+        """
+
         if not isinstance(data, xr.Dataset):
             # TODO: handle raw_mod_data by accessing data.attrs["kind"] and only remove nan after
             data = _matched_data_to_xarray(
@@ -818,7 +851,7 @@ def sel(
             raw_mod_data = {m: raw_mod_data[m] for m in mod_names}
         if (start is not None) or (end is not None):
             # TODO: can this be done without to_index? (simplify)
-            d = d.sel(time=d.time.to_index().to_frame().loc[start:end].index)  # type: ignore
+            d = d.sel(time=d.time.to_index().to_frame().loc[start:end].index)  # type: ignore[misc]
 
             # Note: if user asks for a specific time, we also filter raw
             raw_mod_data = {
@@ -892,7 +925,7 @@ def query(self, query: str) -> "Comparer":
         return Comparer.from_matched_data(d, self.raw_mod_data)
 
     def _to_long_dataframe(
-        self, attrs_keys: Iterable[str] | None = None
+        self, attrs_keys: Collection[str] | None = None
     ) -> pd.DataFrame:
         """Return a copy of the data as a long-format pandas DataFrame (for groupby operations)"""
 
@@ -927,8 +960,8 @@ def _to_long_dataframe(
 
     def skill(
         self,
-        by: str | Iterable[str] | None = None,
-        metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
+        by: str | Collection[str] | None = None,
+        metrics: Sequence[str] | Sequence[Callable] | str | Callable | None = None,
     ) -> SkillTable:
         """Skill assessment of model(s)
 
@@ -1051,8 +1084,8 @@ def gridded_skill(
         self,
         bins: int = 5,
         binsize: float | None = None,
-        by: str | Iterable[str] | None = None,
-        metrics: Iterable[str] | Iterable[Callable] | str | Callable | None = None,
+        by: str | Collection[str] | None = None,
+        metrics: Sequence[str] | Sequence[Callable] | str | Callable | None = None,
         n_min: int | None = None,
         **kwargs: Any,
     ):
@@ -1176,12 +1209,12 @@ def remove_bias(
             for j in range(cmp.n_models):
                 mod_name = cmp.mod_names[j]
                 mod_ts = cmp.raw_mod_data[mod_name]
-                with xr.set_options(keep_attrs=True):  # type: ignore
+                with xr.set_options(keep_attrs=True):
                     mod_ts.data[mod_name].values = mod_ts.values - bias[j]
                     cmp.data[mod_name].values = cmp.data[mod_name].values - bias[j]
         elif correct == "Observation":
             # what if multiple models?
-            with xr.set_options(keep_attrs=True):  # type: ignore
+            with xr.set_options(keep_attrs=True):
                 cmp.data[cmp._obs_str].values = cmp.data[cmp._obs_str].values + bias
         else:
             raise ValueError(
diff --git a/src/modelskill/comparison/_utils.py b/src/modelskill/comparison/_utils.py
index f8d399d1a..762959a70 100644
--- a/src/modelskill/comparison/_utils.py
+++ b/src/modelskill/comparison/_utils.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
-from typing import Callable, Optional, Iterable, List, Tuple, Union
+from collections.abc import Collection
+from typing import Callable, Optional, List, Tuple, Union
 from datetime import datetime
 import numpy as np
 import pandas as pd
@@ -139,7 +140,7 @@ def _add_dt_to_df(df: pd.DataFrame, by: List[str]) -> Tuple[pd.DataFrame, List[s
 
 
 def _parse_groupby(
-    by: str | Iterable[str] | None, *, n_mod: int, n_qnt: int
+    by: str | Collection[str] | None, *, n_mod: int, n_qnt: int
 ) -> List[str | pd.Grouper]:
     if by is None:
         cols: List[str | pd.Grouper]
@@ -153,7 +154,7 @@ def _parse_groupby(
 
     if isinstance(by, str):
         cols = [by]
-    elif isinstance(by, Iterable):
+    elif isinstance(by, Collection):
         cols = list(by)
 
     res = []
diff --git a/src/modelskill/matching.py b/src/modelskill/matching.py
index b182f0ad7..fe7aa4d2b 100644
--- a/src/modelskill/matching.py
+++ b/src/modelskill/matching.py
@@ -4,7 +4,6 @@
 from pathlib import Path
 from typing import (
     Collection,
-    Iterable,
     Literal,
     Mapping,
     Optional,
@@ -75,8 +74,8 @@ def from_matched(
     data: Union[str, Path, pd.DataFrame, mikeio.Dfs0, mikeio.Dataset],
     *,
     obs_item: str | int | None = 0,
-    mod_items: Optional[Iterable[str | int]] = None,
-    aux_items: Optional[Iterable[str | int]] = None,
+    mod_items: Optional[Collection[str | int]] = None,
+    aux_items: Optional[Collection[str | int]] = None,
     quantity: Optional[Quantity] = None,
     name: Optional[str] = None,
     weight: float = 1.0,
@@ -95,9 +94,9 @@ def from_matched(
         with columns obs_item, mod_items, aux_items
     obs_item : [str, int], optional
         Name or index of observation item, by default first item
-    mod_items : Iterable[str, int], optional
+    mod_items : Collection of [str, int], optional
         Names or indicies of model items, if None all remaining columns are model items, by default None
-    aux_items : Iterable[str, int], optional
+    aux_items : Collection of [str, int], optional
         Names or indicies of auxiliary items, by default None
     quantity : Quantity, optional
         Quantity of the observation and model results, by default Quantity(name="Undefined", unit="Undefined")
@@ -185,7 +184,7 @@ def match(
 
 @overload
 def match(
-    obs: Iterable[ObsTypes],
+    obs: Sequence[ObsTypes],
     mod: MRTypes | Sequence[MRTypes],
     *,
     max_model_gap: Optional[float] = None,
@@ -340,7 +339,7 @@ def _match_single_obs(
     return Comparer(matched_data=matched_data, raw_mod_data=raw_mod_data)
 
 
-def _get_global_start_end(idxs: Iterable[pd.DatetimeIndex]) -> Period:
+def _get_global_start_end(idxs: Collection[pd.DatetimeIndex]) -> Period:
     assert all([len(x) > 0 for x in idxs])
 
     starts = [x[0] for x in idxs]
diff --git a/src/modelskill/metrics.py b/src/modelskill/metrics.py
index 30e4b02bc..95a86d30c 100644
--- a/src/modelskill/metrics.py
+++ b/src/modelskill/metrics.py
@@ -5,10 +5,10 @@
 
 import sys
 import warnings
+from collections.abc import Sequence
 from typing import (
     Any,
     Callable,
-    Iterable,
     List,
     Literal,
     Optional,
@@ -1164,7 +1164,7 @@ def get_metric(metric: Union[str, Callable]) -> Callable:
 
 
 def _parse_metric(
-    metric: str | Iterable[str] | Callable | Iterable[Callable] | None,
+    metric: str | Sequence[str] | Callable | Sequence[Callable] | None,
     *,
     directional: bool = False,
 ) -> List[Callable]:
@@ -1179,7 +1179,7 @@ def _parse_metric(
         metrics: list = [metric]
     elif callable(metric):
         metrics = [metric]
-    elif isinstance(metric, Iterable):
+    elif isinstance(metric, Sequence):
         metrics = list(metric)
 
     parsed_metrics = []
diff --git a/src/modelskill/model/dfsu.py b/src/modelskill/model/dfsu.py
index 511e829d6..c81b32d49 100644
--- a/src/modelskill/model/dfsu.py
+++ b/src/modelskill/model/dfsu.py
@@ -109,7 +109,7 @@ def time(self) -> pd.DatetimeIndex:
         return pd.DatetimeIndex(self.data.time)
 
     def _in_domain(self, x: float, y: float) -> bool:
-        return self.data.geometry.contains([x, y])  # type: ignore
+        return self.data.geometry.contains([x, y])
 
     def extract(
         self, observation: Observation, spatial_method: Optional[str] = None
@@ -194,7 +194,7 @@ def _extract_point(
             if isinstance(self.data, mikeio.Dataset):
                 ds_model = self.data.isel(element=elemids)
             else:  # Dfsu
-                ds_model = self.data.read(elements=elemids, items=self.sel_items.all)  # type: ignore
+                ds_model = self.data.read(elements=elemids, items=self.sel_items.all)
         else:
             if z is not None:
                 raise NotImplementedError(
diff --git a/src/modelskill/plotting/_scatter.py b/src/modelskill/plotting/_scatter.py
index de2cd700a..d92aa4837 100644
--- a/src/modelskill/plotting/_scatter.py
+++ b/src/modelskill/plotting/_scatter.py
@@ -222,7 +222,7 @@ def scatter(
         df = pd.DataFrame({"obs": x, "model": y})
         cmp = from_matched(df)
         metrics = None if skill_table is True else skill_table
-        skill = cmp.skill(metrics=metrics)
+        skill = cmp.skill(metrics=metrics)  # type: ignore[arg-type]
         skill_scores = skill.to_dict("records")[0]
 
     return PLOTTING_BACKENDS[backend](
diff --git a/src/modelskill/plotting/_spatial_overview.py b/src/modelskill/plotting/_spatial_overview.py
index 156af04c1..2492aced0 100644
--- a/src/modelskill/plotting/_spatial_overview.py
+++ b/src/modelskill/plotting/_spatial_overview.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
-from typing import Optional, Iterable, Tuple, TYPE_CHECKING
+from collections.abc import Sequence
+from typing import Optional, Tuple, TYPE_CHECKING
 
 if TYPE_CHECKING:
     import matplotlib.axes
@@ -13,12 +14,12 @@
 
 
 def spatial_overview(
-    obs: Observation | Iterable[Observation],
+    obs: Observation | Sequence[Observation],
     mod: Optional[
         DfsuModelResult
         | GeometryFM2D
-        | Iterable[DfsuModelResult]
-        | Iterable[GeometryFM2D]
+        | Sequence[DfsuModelResult]
+        | Sequence[GeometryFM2D]
     ] = None,
     ax=None,
     figsize: Optional[Tuple] = None,
@@ -61,8 +62,8 @@ def spatial_overview(
     ms.plotting.spatial_overview([o1, o2], mr)
     ```
     """
-    obs = [] if obs is None else list(obs) if isinstance(obs, Iterable) else [obs]  # type: ignore
-    mods = [] if mod is None else list(mod) if isinstance(mod, Iterable) else [mod]  # type: ignore
+    obs = [] if obs is None else list(obs) if isinstance(obs, Sequence) else [obs]
+    mods = [] if mod is None else list(mod) if isinstance(mod, Sequence) else [mod]
 
     ax = _get_ax(ax=ax, figsize=figsize)
 
@@ -79,7 +80,7 @@ def spatial_overview(
             g = m
 
             # TODO this is not supported for all model types
-        g.plot.outline(ax=ax)  # type: ignore
+        g.plot.outline(ax=ax)
 
     for o in obs:
         if isinstance(o, PointObservation):
@@ -101,7 +102,7 @@ def spatial_overview(
     for o in obs:
         if isinstance(o, PointObservation):
             # TODO adjust xlim to accomodate text
-            ax.annotate(o.name, (o.x + offset_x, o.y))  # type: ignore
+            ax.annotate(o.name, (o.x + offset_x, o.y))
 
     if not title:
         title = "Spatial coverage"
diff --git a/src/modelskill/plotting/_wind_rose.py b/src/modelskill/plotting/_wind_rose.py
index b10e018bf..01f82d676 100644
--- a/src/modelskill/plotting/_wind_rose.py
+++ b/src/modelskill/plotting/_wind_rose.py
@@ -116,10 +116,10 @@ def _dirhist2d(
     mask = data[:, 0] >= vmin
     calm = len(data[~mask]) / len(data)
     n = len(data)
-    counts, _, _ = np.histogram2d(  # type: ignore
+    counts, _, _ = np.histogram2d(
         data[mask][:, 0],
         data[mask][:, 1],
-        bins=[ui, thetai],  # type: ignore
+        bins=[ui, thetai],
     )
     density = counts / n
     return DirectionalHistogram(
@@ -552,7 +552,7 @@ def _add_legend_to_ax(
             -0.06,
             0.1,
             0.8,
-        )  # type: ignore
+        )
         loc = "lower left"
     else:
         bbox_to_anchor = (-0.13, -0.06, 0.1, 0.8)
diff --git a/src/modelskill/skill.py b/src/modelskill/skill.py
index d16bda8dd..0c4010c08 100644
--- a/src/modelskill/skill.py
+++ b/src/modelskill/skill.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 import warnings
-from typing import Any, Iterable, Collection, overload, Hashable, TYPE_CHECKING
+from collections.abc import Collection, Sequence
+from typing import Any, Iterable, overload, Hashable, TYPE_CHECKING
 import numpy as np
 import pandas as pd
 
@@ -14,7 +15,7 @@
 
 
 # TODO remove ?
-def _validate_multi_index(index, min_levels=2, max_levels=2):  # type: ignore
+def _validate_multi_index(index, min_levels=2, max_levels=2):
     errors = []
     if isinstance(index, pd.MultiIndex):
         if len(index.levels) < min_levels:
@@ -220,7 +221,7 @@ def grid(
         s = self.skillarray
         ser = s._ser
 
-        errors = _validate_multi_index(ser.index)  # type: ignore
+        errors = _validate_multi_index(ser.index)
         if len(errors) > 0:
             warnings.warn("plot_grid: " + "\n".join(errors))
             # TODO raise error?
@@ -282,11 +283,11 @@ def grid(
 
 
 class DeprecatedSkillPlotter:
-    def __init__(self, skilltable):  # type: ignore
+    def __init__(self, skilltable):
         self.skilltable = skilltable
 
     @staticmethod
-    def _deprecated_warning(method, field):  # type: ignore
+    def _deprecated_warning(method, field):
         warnings.warn(
             f"Selecting metric in plot functions like modelskill.skill().plot.{method}({field}) is deprecated and will be removed in a future version. Use modelskill.skill()['{field}'].plot.{method}() instead.",
             FutureWarning,
@@ -297,20 +298,20 @@ def __call__(self, *args: Any, **kwds: Any) -> Any:
             "It is not possible to call plot directly on SkillTable! Select metric first (which gives a plotable SkillArray)"
         )
 
-    def line(self, field, **kwargs):  # type: ignore
-        self._deprecated_warning("line", field)  # type: ignore
+    def line(self, field, **kwargs):
+        self._deprecated_warning("line", field)
         return self.skilltable[field].plot.line(**kwargs)
 
-    def bar(self, field, **kwargs):  # type: ignore
-        self._deprecated_warning("bar", field)  # type: ignore
+    def bar(self, field, **kwargs):
+        self._deprecated_warning("bar", field)
         return self.skilltable[field].plot.bar(**kwargs)
 
-    def barh(self, field, **kwargs):  # type: ignore
-        self._deprecated_warning("barh", field)  # type: ignore
+    def barh(self, field, **kwargs):
+        self._deprecated_warning("barh", field)
         return self.skilltable[field].plot.barh(**kwargs)
 
-    def grid(self, field, **kwargs):  # type: ignore
-        self._deprecated_warning("grid", field)  # type: ignore
+    def grid(self, field, **kwargs):
+        self._deprecated_warning("grid", field)
         return self.skilltable[field].plot.grid(**kwargs)
 
 
@@ -429,7 +430,7 @@ def __init__(self, data: pd.DataFrame):
             data if isinstance(data, pd.DataFrame) else data.to_dataframe()
         )
         # TODO remove in v1.1
-        self.plot = DeprecatedSkillPlotter(self)  # type: ignore
+        self.plot = DeprecatedSkillPlotter(self)
 
     # TODO: remove?
     @property
@@ -507,10 +508,10 @@ def _repr_html_(self) -> Any:
     def __getitem__(self, key: Hashable | int) -> SkillArray: ...
 
     @overload
-    def __getitem__(self, key: Iterable[Hashable]) -> SkillTable: ...
+    def __getitem__(self, key: Collection[Hashable]) -> SkillTable: ...
 
     def __getitem__(
-        self, key: Hashable | Iterable[Hashable]
+        self, key: Hashable | Collection[Hashable]
     ) -> SkillArray | SkillTable:
         if isinstance(key, int):
             key = list(self.data.columns)[key]
@@ -545,14 +546,14 @@ def __getattr__(self, item: str, *args, **kwargs) -> Any:
             # )
 
     @property
-    def iloc(self, *args, **kwargs):  # type: ignore
+    def iloc(self, *args, **kwargs):
         return self.data.iloc(*args, **kwargs)
 
     @property
-    def loc(self, *args, **kwargs):  # type: ignore
+    def loc(self, *args, **kwargs):
         return self.data.loc(*args, **kwargs)
 
-    def sort_index(self, *args, **kwargs) -> SkillTable:  # type: ignore
+    def sort_index(self, *args, **kwargs) -> SkillTable:
         """Sort by index (level) e.g. sorting by observation
 
         Wrapping pd.DataFrame.sort_index()
@@ -570,7 +571,7 @@ def sort_index(self, *args, **kwargs) -> SkillTable:  # type: ignore
         """
         return self.__class__(self.data.sort_index(*args, **kwargs))
 
-    def sort_values(self, *args, **kwargs) -> SkillTable:  # type: ignore
+    def sort_values(self, *args, **kwargs) -> SkillTable:
         """Sort by values e.g. sorting by rmse values
 
         Wrapping pd.DataFrame.sort_values()
@@ -589,7 +590,7 @@ def sort_values(self, *args, **kwargs) -> SkillTable:  # type: ignore
         """
         return self.__class__(self.data.sort_values(*args, **kwargs))
 
-    def swaplevel(self, *args, **kwargs) -> SkillTable:  # type: ignore
+    def swaplevel(self, *args, **kwargs) -> SkillTable:
         """Swap the levels of the MultiIndex e.g. swapping 'model' and 'observation'
 
         Wrapping pd.DataFrame.swaplevel()
@@ -765,7 +766,7 @@ def round(self, decimals: int = 3) -> SkillTable:
     def style(
         self,
         decimals: int = 3,
-        metrics: Iterable[str] | None = None,
+        metrics: Sequence[str] | None = None,
         cmap: str = "OrRd",
         show_best: bool = True,
         **kwargs: Any,
@@ -844,7 +845,7 @@ def style(
 
         if len(bg_cols) > 0:
             sdf = sdf.background_gradient(subset=scols, cmap=cmap)
-            cmap_r = self._reverse_colormap(cmap)  # type: ignore
+            cmap_r = self._reverse_colormap(cmap)
             sdf = sdf.background_gradient(subset=lcols, cmap=cmap_r)
 
         if show_best:
@@ -856,7 +857,7 @@ def style(
 
         return sdf
 
-    def _reverse_colormap(self, cmap):  # type: ignore
+    def _reverse_colormap(self, cmap):
         cmap_r = cmap
         if isinstance(cmap, str):
             if cmap[-2:] == "_r":
@@ -901,14 +902,14 @@ def _style_max(self, s: pd.Series) -> list[str]:
 
     # TODO: remove plot_* methods in v1.1; warnings are not needed
     # as the refering method is also deprecated
-    def plot_line(self, **kwargs):  # type: ignore
-        return self.plot.line(**kwargs)  # type: ignore
+    def plot_line(self, **kwargs):
+        return self.plot.line(**kwargs)
 
-    def plot_bar(self, **kwargs):  # type: ignore
-        return self.plot.bar(**kwargs)  # type: ignore
+    def plot_bar(self, **kwargs):
+        return self.plot.bar(**kwargs)
 
-    def plot_barh(self, **kwargs):  # type: ignore
-        return self.plot.barh(**kwargs)  # type: ignore
+    def plot_barh(self, **kwargs):
+        return self.plot.barh(**kwargs)
 
-    def plot_grid(self, **kwargs):  # type: ignore
-        return self.plot.grid(**kwargs)  # type: ignore
+    def plot_grid(self, **kwargs):
+        return self.plot.grid(**kwargs)
diff --git a/src/modelskill/skill_grid.py b/src/modelskill/skill_grid.py
index 8af6cf07c..4c527a04a 100644
--- a/src/modelskill/skill_grid.py
+++ b/src/modelskill/skill_grid.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
-from typing import Any, Iterable, overload, Hashable, TYPE_CHECKING
+from collections.abc import Collection
+from typing import Any, overload, Hashable, TYPE_CHECKING
 import xarray as xr
 
 if TYPE_CHECKING:
@@ -13,12 +14,12 @@ class SkillGridMixin:
     @property
     def x(self) -> xr.DataArray:
         """x-coordinate values"""
-        return self.data.x  # type: ignore
+        return self.data.x
 
     @property
     def y(self) -> xr.DataArray:
         """y-coordinate values"""
-        return self.data.y  # type: ignore
+        return self.data.y
 
     @property
     def coords(self) -> Any:
@@ -141,10 +142,10 @@ def __repr__(self) -> str:
     def __getitem__(self, key: Hashable) -> SkillGridArray: ...
 
     @overload
-    def __getitem__(self, key: Iterable[Hashable]) -> SkillGrid: ...
+    def __getitem__(self, key: Collection[Hashable]) -> SkillGrid: ...
 
     def __getitem__(
-        self, key: Hashable | Iterable[Hashable]
+        self, key: Hashable | Collection[Hashable]
     ) -> SkillGridArray | SkillGrid:
         result = self.data[key]
         if isinstance(result, xr.DataArray):
diff --git a/src/modelskill/timeseries/_plotter.py b/src/modelskill/timeseries/_plotter.py
index e66374a48..391b168c0 100644
--- a/src/modelskill/timeseries/_plotter.py
+++ b/src/modelskill/timeseries/_plotter.py
@@ -97,7 +97,7 @@ def timeseries(self):
 
         Wraps plotly.express.line() function.
         """
-        import plotly.express as px  # type: ignore
+        import plotly.express as px
 
         fig = px.line(
             self._ts._values_as_series, color_discrete_sequence=[self._ts._color]
@@ -116,7 +116,7 @@ def hist(self, bins=100, **kwargs):
         **kwargs
             other keyword arguments to df.hist()
         """
-        import plotly.express as px  # type: ignore
+        import plotly.express as px
 
         fig = px.histogram(
             self._ts._values_as_series,