typ: Remove pin on pandas-stubs

Author: has2k1

plotnine/geoms/geom_rug.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import typing
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 
@@ -10,7 +10,7 @@
 from .geom import geom
 from .geom_path import geom_path
 
-if typing.TYPE_CHECKING:
+if TYPE_CHECKING:
     from typing import Any
 
     import pandas as pd
@@ -90,29 +90,29 @@ def draw_group(
         yheight = (ymax - ymin) * params["length"]
 
         # Please the type checker
-        x: FloatArray
-        y: FloatArray
+        x = cast("FloatArray", data["x"].to_numpy())
+        y = cast("FloatArray", data["y"].to_numpy())
 
         if has_x:
             if "b" in sides:
-                x = np.repeat(data["x"].to_numpy(), 2)
+                x = np.repeat(x, 2)
                 y = np.tile([ymin, ymin + yheight], n)
                 rugs.extend(make_line_segments(x, y, ispath=False))
 
             if "t" in sides:
-                x = np.repeat(data["x"].to_numpy(), 2)
+                x = np.repeat(x, 2)
                 y = np.tile([ymax - yheight, ymax], n)
                 rugs.extend(make_line_segments(x, y, ispath=False))
 
         if has_y:
             if "l" in sides:
                 x = np.tile([xmin, xmin + xheight], n)
-                y = np.repeat(data["y"].to_numpy(), 2)
+                y = np.repeat(y, 2)
                 rugs.extend(make_line_segments(x, y, ispath=False))
 
             if "r" in sides:
                 x = np.tile([xmax - xheight, xmax], n)
-                y = np.repeat(data["y"].to_numpy(), 2)
+                y = np.repeat(y, 2)
                 rugs.extend(make_line_segments(x, y, ispath=False))
 
         color = to_rgba(data["color"], data["alpha"])

plotnine/stats/stat_density_2d.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 import pandas as pd
@@ -92,17 +92,19 @@ def compute_group(self, data, scales):
         group = data["group"].iloc[0]
         range_x = scales.x.dimension()
         range_y = scales.y.dimension()
-        x = np.linspace(range_x[0], range_x[1], params["n"])
-        y = np.linspace(range_y[0], range_y[1], params["n"])
+        _x = np.linspace(range_x[0], range_x[1], params["n"])
+        _y = np.linspace(range_y[0], range_y[1], params["n"])
 
         # The grid must have a "similar" shape (n, p) to the var_data
-        X, Y = np.meshgrid(x, y)
-        var_data = np.array([data["x"].to_numpy(), data["y"].to_numpy()]).T
+        X, Y = np.meshgrid(_x, _y)
+        x = cast("FloatArrayLike", data["x"].to_numpy())
+        y = cast("FloatArrayLike", data["y"].to_numpy())
+        var_data = np.array([x, y]).T
         grid = np.array([X.flatten(), Y.flatten()]).T
         density = kde(var_data, grid, package, **kde_params)
 
         if params["contour"]:
-            Z = density.reshape(len(x), len(y))
+            Z = density.reshape(len(_x), len(_y))
             data = contour_lines(X, Y, Z, params["levels"])
             # Each piece should have a distinct group
             groups = str(group) + "-00" + data["piece"].astype(str)

plotnine/stats/stat_pointdensity.py

@@ -1,3 +1,5 @@
+from typing import TYPE_CHECKING, cast
+
 import numpy as np
 import pandas as pd
 
@@ -6,6 +8,9 @@
 from .density import get_var_type, kde
 from .stat import stat
 
+if TYPE_CHECKING:
+    from plotnine.typing import FloatArray
+
 
 @document
 class stat_pointdensity(stat):
@@ -67,8 +72,10 @@ def setup_params(self, data):
     def compute_group(self, data, scales):
         package = self.params["package"]
         kde_params = self.params["kde_params"]
+        x = cast("FloatArray", data["x"].to_numpy())
+        y = cast("FloatArray", data["y"].to_numpy())
 
-        var_data = np.array([data["x"].to_numpy(), data["y"].to_numpy()]).T
+        var_data = np.array([x, y]).T
         density = kde(var_data, var_data, package, **kde_params)
 
         data = pd.DataFrame(

plotnine/stats/stat_qq.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 import pandas as pd
@@ -76,7 +76,7 @@ class stat_qq(stat):
     }
 
     def compute_group(self, data, scales):
-        sample = data["sample"].sort_values().to_numpy()
+        sample = cast("FloatArray", data["sample"].sort_values().to_numpy())
         theoretical = theoretical_qq(
             sample,
             self.params["distribution"],

plotnine/stats/stat_qq_line.py

@@ -1,3 +1,5 @@
+from typing import TYPE_CHECKING, cast
+
 import numpy as np
 import pandas as pd
 
@@ -6,6 +8,9 @@
 from .stat import stat
 from .stat_qq import theoretical_qq
 
+if TYPE_CHECKING:
+    from plotnine.typing import FloatArray
+
 
 @document
 class stat_qq_line(stat):
@@ -75,7 +80,7 @@ def compute_group(self, data, scales):
         dparams = self.params["dparams"]
 
         # Compute theoretical values
-        sample = data["sample"].sort_values().to_numpy()
+        sample = cast("FloatArray", data["sample"].sort_values().to_numpy())
         theoretical = theoretical_qq(
             sample,
             self.params["distribution"],

plotnine/stats/stat_sina.py

@@ -1,3 +1,5 @@
+from typing import TYPE_CHECKING, cast
+
 import numpy as np
 import pandas as pd
 
@@ -9,6 +11,9 @@
 from .stat import stat
 from .stat_density import compute_density
 
+if TYPE_CHECKING:
+    from plotnine.typing import FloatArray, IntArray
+
 
 @document
 class stat_sina(stat):
@@ -254,19 +259,18 @@ def compute_group(self, data, scales):
     def finish_layer(self, data):
         # Rescale x in case positions have been adjusted
         style = self.params["style"]
-        x_mean = data["x"].to_numpy()
+        x_mean = cast("FloatArray", data["x"].to_numpy())
         x_mod = (data["xmax"] - data["xmin"]) / data["width"]
         data["x"] = data["x"] + data["x_diff"] * x_mod
-        x = data["x"].to_numpy()
-        even = data["group"].to_numpy() % 2 == 0
+        group = cast("IntArray", data["group"].to_numpy())
+        x = cast("FloatArray", data["x"].to_numpy())
+        even = group % 2 == 0
 
         def mirror_x(bool_idx):
             """
             Mirror x locations along the mean value
             """
-            data.loc[bool_idx, "x"] = (
-                2 * x_mean[bool_idx] - data.loc[bool_idx, "x"]
-            )
+            data.loc[bool_idx, "x"] = 2 * x_mean[bool_idx] - x[bool_idx]
 
         match style:
             case "left":

plotnine/stats/stat_summary_bin.py

@@ -1,3 +1,5 @@
+from typing import TYPE_CHECKING, cast
+
 import numpy as np
 import pandas as pd
 
@@ -9,6 +11,9 @@
 from .stat import stat
 from .stat_summary import make_summary_fun
 
+if TYPE_CHECKING:
+    from plotnine.typing import IntArray
+
 
 @document
 class stat_summary_bin(stat):
@@ -157,7 +162,8 @@ def func_wrapper(data: pd.DataFrame) -> pd.DataFrame:
         # This is a plyr::ddply
         out = groupby_apply(data, "bin", func_wrapper)
         centers = (breaks[:-1] + breaks[1:]) * 0.5
-        bin_centers = centers[out["bin"].to_numpy()]
+        bin = cast("IntArray", out["bin"].to_numpy())
+        bin_centers = centers[bin]
         out["x"] = bin_centers
         out["bin"] += 1
         if isinstance(scales.x, scale_discrete):

pyproject.toml

@@ -83,7 +83,7 @@ dev = [
 typing = [
     "pyright==1.1.404",
     "ipython",
-    "pandas-stubs==v2.3.2.250827",
+    "pandas-stubs",
 ]
 
 [project.urls]