Use as_frame=False when calling fetch_openml()

09_unsupervised_learning.ipynb

@@ -969,6 +969,13 @@
     "If the dataset does not fit in memory, the simplest option is to use the `memmap` class, just like we did for incremental PCA in the previous chapter. First let's load MNIST:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 46,
@@ -978,7 +985,7 @@
     "import urllib.request\n",
     "from sklearn.datasets import fetch_openml\n",
     "\n",
-    "mnist = fetch_openml('mnist_784', version=1)\n",
+    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n",
     "mnist.target = mnist.target.astype(np.int64)"
    ]
   },