Pushing the docs to dev/ for branch: main, commit 433600e68fbb12e72d8c5e0707916f5603bb7057

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -68,6 +68,20 @@
 )
 hist_no_interact.fit(X, y)
 
+# %%
+# Faster parser in :func:`~datasets.fetch_openml`
+# -----------------------------------------------
+# :func:`~datasets.fetch_openml` now supports a new `"pandas"` parser that is
+# more memory and CPU efficient. In v1.4, the default will change to
+# `parser="auto"` which will automatically use the `"pandas"` parser for dense
+# data and `"liac-arff"` for sparse data.
+from sklearn.datasets import fetch_openml
+
+X, y = fetch_openml(
+    "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
+)
+X.head()
+
 # %%
 # Experimental Array API support in :class:`~discriminant_analysis.LinearDiscriminantAnalysis`
 # --------------------------------------------------------------------------------------------

dev/_downloads/138e7c706c17949c3098ff8074b03ce7/plot_release_highlights_1_2_0.py

@@ -54,6 +54,24 @@
         "from sklearn.datasets import load_diabetes\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nX, y = load_diabetes(return_X_y=True, as_frame=True)\n\nhist_no_interact = HistGradientBoostingRegressor(\n    interaction_cst=[[i] for i in range(X.shape[1])], random_state=0\n)\nhist_no_interact.fit(X, y)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Faster parser in :func:`~datasets.fetch_openml`\n:func:`~datasets.fetch_openml` now supports a new `\"pandas\"` parser that is\nmore memory and CPU efficient. In v1.4, the default will change to\n`parser=\"auto\"` which will automatically use the `\"pandas\"` parser for dense\ndata and `\"liac-arff\"` for sparse data.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import fetch_openml\n\nX, y = fetch_openml(\n    \"titanic\", version=1, as_frame=True, return_X_y=True, parser=\"pandas\"\n)\nX.head()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},