Pushing the docs to dev/ for branch: main, commit 73f14ab9f94174ca6c193fbcccc429155d342951

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 06232f874800925db347a87e63dba2a0
+config: b3ea042e8b7a43591ee3589aabcbd59b
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "from itertools import product\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndays = (\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\")\nhours = tuple(range(24))\nxticklabels = [f\"{day}\\n{hour}:00\" for day, hour in product(days, hours)]\nxtick_start, xtick_period = 6, 12\n\nfig, axs = plt.subplots(nrows=2, figsize=(8, 6), sharey=True, sharex=True)\naverage_bike_rentals = bikes.frame.groupby([\"year\", \"season\", \"weekday\", \"hour\"]).mean(\n    numeric_only=True\n)[\"count\"]\nfor ax, (idx, df) in zip(axs, average_bike_rentals.groupby(\"year\")):\n    df.groupby(\"season\").plot(ax=ax, legend=True)\n\n    # decorate the plot\n    ax.set_xticks(\n        np.linspace(\n            start=xtick_start,\n            stop=len(xticklabels),\n            num=len(xticklabels) // xtick_period,\n        )\n    )\n    ax.set_xticklabels(xticklabels[xtick_start::xtick_period])\n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"Average number of bike rentals\")\n    ax.set_title(\n        f\"Bike rental for {'2010 (train set)' if idx == 0.0 else '2011 (test set)'}\"\n    )\n    ax.set_ylim(0, 1_000)\n    ax.set_xlim(0, len(xticklabels))\n    ax.legend(loc=2)"
+        "from itertools import product\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndays = (\"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\")\nhours = tuple(range(24))\nxticklabels = [f\"{day}\\n{hour}:00\" for day, hour in product(days, hours)]\nxtick_start, xtick_period = 6, 12\n\nfig, axs = plt.subplots(nrows=2, figsize=(8, 6), sharey=True, sharex=True)\naverage_bike_rentals = bikes.frame.groupby(\n    [\"year\", \"season\", \"weekday\", \"hour\"], observed=True\n).mean(numeric_only=True)[\"count\"]\nfor ax, (idx, df) in zip(axs, average_bike_rentals.groupby(\"year\")):\n    df.groupby(\"season\", observed=True).plot(ax=ax, legend=True)\n\n    # decorate the plot\n    ax.set_xticks(\n        np.linspace(\n            start=xtick_start,\n            stop=len(xticklabels),\n            num=len(xticklabels) // xtick_period,\n        )\n    )\n    ax.set_xticklabels(xticklabels[xtick_start::xtick_period])\n    ax.set_xlabel(\"\")\n    ax.set_ylabel(\"Average number of bike rentals\")\n    ax.set_title(\n        f\"Bike rental for {'2010 (train set)' if idx == 0.0 else '2011 (test set)'}\"\n    )\n    ax.set_ylim(0, 1_000)\n    ax.set_xlim(0, len(xticklabels))\n    ax.legend(loc=2)"
       ]
     },
     {

dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb

@@ -110,11 +110,11 @@
 xtick_start, xtick_period = 6, 12
 
 fig, axs = plt.subplots(nrows=2, figsize=(8, 6), sharey=True, sharex=True)
-average_bike_rentals = bikes.frame.groupby(["year", "season", "weekday", "hour"]).mean(
-    numeric_only=True
-)["count"]
+average_bike_rentals = bikes.frame.groupby(
+    ["year", "season", "weekday", "hour"], observed=True
+).mean(numeric_only=True)["count"]
 for ax, (idx, df) in zip(axs, average_bike_rentals.groupby("year")):
-    df.groupby("season").plot(ax=ax, legend=True)
+    df.groupby("season", observed=True).plot(ax=ax, legend=True)
 
     # decorate the plot
     ax.set_xticks(