feat: remove Python 3.9 support

.github/workflows/ci.yml

@@ -31,7 +31,6 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-        - "3.9"
         - "3.10"
         - "3.11"
         - "3.12"
@@ -78,7 +77,7 @@ jobs:
 
     - uses: actions/setup-python@v6
       with:
-        python-version: '3.9'
+        python-version: '3.10'
 
     - name: Setup uv
       uses: astral-sh/setup-uv@v7

docs/user-guide/notebooks/SVGHistogram.ipynb

@@ -235,7 +235,7 @@
     "\n",
     "    boxes = []\n",
     "    for height, left_edge, right_edge in zip(\n",
-    "        norm_vals, norm_edges[:-1], norm_edges[1:]\n",
+    "        norm_vals, norm_edges[:-1], norm_edges[1:], strict=True\n",
     "    ):\n",
     "        boxes.append(\n",
     "            rect.pad(\n",
@@ -340,10 +340,13 @@
     "\n",
     "    boxes = []\n",
     "    for r, up_edge, bottom_edge in zip(\n",
-    "        range(len(norm_edges[1])), norm_edges[1][:-1], norm_edges[1][1:]\n",
+    "        range(len(norm_edges[1])), norm_edges[1][:-1], norm_edges[1][1:], strict=True\n",
     "    ):\n",
     "        for c, left_edge, right_edge in zip(\n",
-    "            range(len(norm_edges[0])), norm_edges[0][:-1], norm_edges[0][1:]\n",
+    "            range(len(norm_edges[0])),\n",
+    "            norm_edges[0][:-1],\n",
+    "            norm_edges[0][1:],\n",
+    "            strict=True,\n",
     "        ):\n",
     "            opacity = 1 - norm_vals[r][c]\n",
     "            boxes.append(\n",

pyproject.toml

@@ -21,7 +21,6 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -38,11 +37,11 @@ keywords = [
     "boost-histogram",
     "dask-histogram",
 ]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
     "boost-histogram>=1.5,<1.7",
     "histoprint>=2.2.0",
-    'numpy>=1.19.3',
+    'numpy>=1.21.3',
     'typing-extensions>=4;python_version<"3.11"',
 ]
 dynamic = ["version"]
@@ -60,16 +59,16 @@ Changelog = "https://hist.readthedocs.io/en/latest/changelog.html"
 [project.optional-dependencies]
 # Keep in sync with dependency-groups below
 mpl = [
-    "matplotlib >=3.3.3",
-    "mplhep >=0.3.17",
+    "matplotlib >=3.10.0",
+    "mplhep >=0.3.31",
 ]
 plot = [
-    "matplotlib >=3.3.3",
-    "mplhep >=0.3.17",
+    "matplotlib >=3.10.0",
+    "mplhep >=0.3.31",
 ]
 fit = [
-    "scipy >=1.5.4",
-    "iminuit >=2",
+    "scipy >=1.7.0",
+    "iminuit >=2.9.0",
 ]
 dask = [
     "dask[dataframe] >=2022,<2025",
@@ -78,12 +77,12 @@ dask = [
 
 [dependency-groups]
 plot = [
-    "matplotlib >=3.3.3",
-    "mplhep >=0.3.17",
+    "matplotlib >=3.10.0",
+    "mplhep >=0.3.31",
 ]
 fit = [
-    "scipy >=1.5.4",
-    "iminuit >=2; python_version<'3.14'",
+    "scipy >=1.7.0",
+    "iminuit >=2.9.0",
 ]
 dask = [
     "dask[dataframe] >=2022,<2025",
@@ -141,7 +140,7 @@ filterwarnings = [
 [tool.mypy]
 warn_unused_configs = true
 files = "src"
-python_version = "3.9"
+python_version = "3.10"
 strict = true
 enable_error_code = ["ignore-without-code", "truthy-bool", "redundant-expr"]
 warn_unreachable = true
@@ -161,7 +160,7 @@ ignore_missing_imports = true
 
 
 [tool.pylint]
-py-version = "3.9"
+py-version = "3.10"
 extension-pkg-allow-list = ["boost_histogram._core"]
 reports.output-format = "colorized"
 similarities.ignore-imports = "yes"

src/hist/_compat/builtins.py

@@ -1,26 +1,6 @@
 from __future__ import annotations
 
-import sys
-
-if sys.version_info < (3, 10):
-    import builtins
-    import itertools
-    from collections.abc import Iterator
-    from typing import Any
-
-    def zip(*iterables: Any, strict: bool = False) -> Iterator[tuple[Any, ...]]:
-        if strict:
-            marker = object()
-            for each in itertools.zip_longest(*iterables, fillvalue=marker):
-                for val in each:
-                    if val is marker:
-                        raise ValueError("zip() arguments are not the same length")
-                yield each
-        else:
-            yield from builtins.zip(*iterables)
-
-else:
-    from builtins import zip  # noqa: UP029
+from builtins import zip  # noqa: UP029
 
 __all__ = ["zip"]
 

src/hist/basehist.py

@@ -6,10 +6,9 @@
 import operator
 import typing
 import warnings
-from collections.abc import Generator, Iterator, Mapping, Sequence
+from collections.abc import Callable, Generator, Iterator, Mapping, Sequence
 from typing import (
     Any,
-    Callable,
     Protocol,
     SupportsIndex,
     Union,
@@ -45,9 +44,9 @@ def __lt__(self, __other: Any) -> bool: ...
 InnerIndexing = Union[
     SupportsIndex, str, Callable[[bh.axis.Axis], int], slice, "ellipsis"
 ]
-IndexingWithMapping = Union[InnerIndexing, Mapping[Union[int, str], InnerIndexing]]
-IndexingExpr = Union[IndexingWithMapping, tuple[IndexingWithMapping, ...]]
-AxisTypes = Union[AxisProtocol, tuple[int, float, float]]
+IndexingWithMapping = InnerIndexing | Mapping[int | str, InnerIndexing]
+IndexingExpr = IndexingWithMapping | tuple[IndexingWithMapping, ...]
+AxisTypes = AxisProtocol | tuple[int, float, float]
 
 
 # Workaround for bug in mplhep
@@ -234,11 +233,11 @@ def from_columns(
         for ax in axes:
             if isinstance(ax, str):
                 assert ax in data, f"{ax} must be present in data={list(data)}"
-                cats = set(data[ax])
+                cats = set(data[ax])  # type: ignore[arg-type]
                 if all(isinstance(a, str) for a in cats):
-                    axes_list.append(hist.axis.StrCategory(sorted(cats), name=ax))
+                    axes_list.append(hist.axis.StrCategory(sorted(cats), name=ax))  # type: ignore[arg-type]
                 elif all(isinstance(a, int) for a in cats):
-                    axes_list.append(hist.axis.IntCategory(sorted(cats), name=ax))
+                    axes_list.append(hist.axis.IntCategory(sorted(cats), name=ax))  # type: ignore[arg-type]
                 else:
                     raise TypeError(
                         f"{ax} must be all int or strings if axis not given"
@@ -252,7 +251,7 @@ def from_columns(
 
         self = cls(*axes_list, storage=storage)
         data_list = {x.name: data[x.name] for x in axes_list}
-        self.fill(**data_list, weight=weight_arr)
+        self.fill(**data_list, weight=weight_arr)  # type: ignore[arg-type]
         return self
 
     def project(self, *args: int | str) -> Self | float | bh.accumulators.Accumulator:
@@ -331,7 +330,7 @@ def fill_flattened(
                 user_args_broadcast = broadcast[:1]
                 user_kwargs_broadcast = {}
                 non_user_kwargs_broadcast = dict(
-                    zip(non_user_kwargs.keys(), broadcast[1:])
+                    zip(non_user_kwargs.keys(), broadcast[1:], strict=True)
                 )
             else:
                 # Result must be broadcast, so unpack and rebuild
@@ -342,22 +341,24 @@ def fill_flattened(
                 user_args_broadcast = ()
                 user_kwargs_broadcast = {
                     k: v
-                    for k, v in zip(destructured, broadcast[: len(destructured)])
+                    for k, v in zip(
+                        destructured, broadcast[: len(destructured)], strict=True
+                    )
                     if k in axis_names
                 }
                 non_user_kwargs_broadcast = dict(
-                    zip(non_user_kwargs, broadcast[len(destructured) :])
+                    zip(non_user_kwargs, broadcast[len(destructured) :], strict=True)
                 )
         # Multiple args: broadcast and flatten!
         else:
             inputs = (*args, *kwargs.values(), *non_user_kwargs.values())
             broadcast = interop.broadcast_and_flatten(inputs)
             user_args_broadcast = broadcast[: len(args)]
             user_kwargs_broadcast = dict(
-                zip(kwargs, broadcast[len(args) : len(args) + len(kwargs)])
+                zip(kwargs, broadcast[len(args) : len(args) + len(kwargs)], strict=True)
             )
             non_user_kwargs_broadcast = dict(
-                zip(non_user_kwargs, broadcast[len(args) + len(kwargs) :])
+                zip(non_user_kwargs, broadcast[len(args) + len(kwargs) :], strict=True)
             )
         return self.fill(
             *user_args_broadcast,
@@ -715,7 +716,7 @@ def stack(self, axis: int | str) -> hist.stack.Stack:
         stack_histograms: Iterator[BaseHist] = [  # type: ignore[assignment]
             self[{axis: i}] for i in range(len(self.axes[axis]))
         ]
-        for name, h in zip(self.axes[axis], stack_histograms):
+        for name, h in zip(self.axes[axis], stack_histograms, strict=True):
             h.name = name
 
         return hist.stack.Stack(*stack_histograms)

src/hist/interop.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-from collections.abc import Iterator, Sequence
-from typing import Any, Callable, Protocol, TypeVar, cast
+from collections.abc import Callable, Iterator, Sequence
+from typing import Any, Protocol, TypeVar, cast
 
 import numpy as np
 

src/hist/namedhist.py

@@ -98,19 +98,23 @@ def fill_flattened(  # type: ignore[override]
             # Partition into user and non-user args
             user_kwargs_broadcast = {
                 k: v
-                for k, v in zip(destructured, broadcast[: len(destructured)])
+                for k, v in zip(
+                    destructured, broadcast[: len(destructured)], strict=True
+                )
                 if k in axis_names
             }
             non_user_kwargs_broadcast = dict(
-                zip(non_user_kwargs, broadcast[len(destructured) :])
+                zip(non_user_kwargs, broadcast[len(destructured) :], strict=True)
             )
         # Multiple args: broadcast and flatten!
         else:
             inputs = (*kwargs.values(), *non_user_kwargs.values())
             broadcast = interop.broadcast_and_flatten(inputs)
-            user_kwargs_broadcast = dict(zip(kwargs, broadcast[: len(kwargs)]))
+            user_kwargs_broadcast = dict(
+                zip(kwargs, broadcast[: len(kwargs)], strict=True)
+            )
             non_user_kwargs_broadcast = dict(
-                zip(non_user_kwargs, broadcast[len(kwargs) :])
+                zip(non_user_kwargs, broadcast[len(kwargs) :], strict=True)
             )
         return self.fill(
             **user_kwargs_broadcast,

src/hist/plot.py

@@ -2,8 +2,8 @@
 
 import inspect
 import sys
-from collections.abc import Iterable
-from typing import Any, Callable, Literal, NamedTuple, Union
+from collections.abc import Callable, Iterable
+from typing import Any, Literal, NamedTuple, TypeAlias
 
 import numpy as np
 
@@ -56,10 +56,10 @@ class PullArtists(NamedTuple):
     patch_artist: list[matplotlib.patches.Rectangle]
 
 
-MainAxisArtists = Union[FitResultArtists, Hist1DArtists]
+MainAxisArtists: TypeAlias = FitResultArtists | Hist1DArtists
 
-RatioArtists = Union[RatioErrorbarArtists, RatioBarArtists]
-RatiolikeArtists = Union[RatioArtists, PullArtists]
+RatioArtists = RatioErrorbarArtists | RatioBarArtists
+RatiolikeArtists = RatioArtists | PullArtists
 
 
 def __dir__() -> tuple[str, ...]:
@@ -631,7 +631,7 @@ def _plot_ratiolike(
             perr = np.sqrt(np.diagonal(pcov))
 
             fp_label = "Fit"
-            for name, value, error in zip(parnames, popt, perr):
+            for name, value, error in zip(parnames, popt, perr, strict=True):
                 fp_label += "\n  "
                 fp_label += fit_fmt.format(name=name, value=value, error=error)
             fp_kwargs["label"] = fp_label

src/hist/quick_construct.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-from collections.abc import Iterable
-from typing import TYPE_CHECKING, Any, Callable
+from collections.abc import Callable, Iterable
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 

src/hist/svgplots.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from typing import Any, Callable
+import itertools
+from collections.abc import Callable
+from typing import Any
 
 import numpy as np
 from boost_histogram.axis import Axis
@@ -70,7 +72,7 @@ def svg_hist_1d(h: hist.BaseHist) -> svg:
     (edges,) = h.axes.edges
     norm_edges = (edges - edges[0]) / (edges[-1] - edges[0])
     density = h.density()
-    max_dens: float = np.amax(density) or 1  # type: ignore[redundant-expr, unreachable]
+    max_dens: float = np.amax(density) or 1
     norm_vals: np.typing.NDArray[Any] = density / max_dens
 
     arr: np.typing.NDArray[np.float64] = np.empty(
@@ -121,7 +123,7 @@ def svg_hist_1d_c(h: hist.BaseHist) -> svg:
     (edges,) = h.axes.edges
     norm_edges = (edges - edges[0]) / (edges[-1] - edges[0]) * np.pi * 2
     density = h.density()
-    max_dens = np.amax(density) or 1  # type: ignore[redundant-expr, var-annotated, unreachable]
+    max_dens = np.amax(density) or 1
     norm_vals: np.typing.NDArray[Any] = density / max_dens
 
     arr: np.typing.NDArray[np.float64] = np.empty((2, len(norm_vals) * 2), dtype=float)
@@ -132,7 +134,7 @@ def svg_hist_1d_c(h: hist.BaseHist) -> svg:
     xs = arr[1] * np.cos(arr[0])
     ys = arr[1] * np.sin(arr[0])
 
-    points = " ".join(f"{x:3g},{y:.3g}" for x, y in zip(xs, ys))
+    points = " ".join(f"{x:3g},{y:.3g}" for x, y in zip(xs, ys, strict=True))
     bins = polygon(points=points, style="fill:none; stroke:currentColor;")
 
     center = circle(
@@ -155,13 +157,13 @@ def svg_hist_2d(h: hist.BaseHist) -> svg:
     ey = -(e1 - e1[0]) / (e1[-1] - e1[0]) * height
 
     density = h.density()
-    max_dens = np.amax(density) or 1  # type: ignore[redundant-expr, var-annotated, unreachable]
+    max_dens = np.amax(density) or 1
     norm_vals: np.typing.NDArray[Any] = density / max_dens
 
     boxes = []
-    for r, (up_edge, bottom_edge) in enumerate(zip(ey[:-1], ey[1:])):
+    for r, (up_edge, bottom_edge) in enumerate(itertools.pairwise(ey)):
         ht = up_edge - bottom_edge
-        for c, (left_edge, right_edge) in enumerate(zip(ex[:-1], ex[1:])):
+        for c, (left_edge, right_edge) in enumerate(itertools.pairwise(ex)):
             opacity = norm_vals[c, r]
             wt = left_edge - right_edge
             boxes.append(