diff --git a/08_dimensionality_reduction.ipynb b/08_dimensionality_reduction.ipynb
index c7f1797..272c8ea 100644
--- a/08_dimensionality_reduction.ipynb
+++ b/08_dimensionality_reduction.ipynb
@@ -184,7 +184,7 @@
    "source": [
     "from sklearn.decomposition import PCA\n",
     "\n",
-    "pca = PCA(n_components = 2)\n",
+    "pca = PCA(n_components=2)\n",
     "X2D = pca.fit_transform(X)"
    ]
   },
@@ -761,6 +761,13 @@
     "# MNIST compression"
    ]
   },
+  {
+   "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 set `as_frame=True`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 31,
@@ -769,7 +776,7 @@
    "source": [
     "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.uint8)"
    ]
   },
@@ -863,7 +870,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pca = PCA(n_components = 154)\n",
+    "pca = PCA(n_components=154)\n",
     "X_reduced = pca.fit_transform(X_train)\n",
     "X_recovered = pca.inverse_transform(X_reduced)"
    ]
@@ -1101,15 +1108,15 @@
     "\n",
     "for n_components in (2, 10, 154):\n",
     "    print(\"n_components =\", n_components)\n",
-    "    regular_pca = PCA(n_components=n_components)\n",
+    "    regular_pca = PCA(n_components=n_components, svd_solver=\"full\")\n",
     "    inc_pca = IncrementalPCA(n_components=n_components, batch_size=500)\n",
     "    rnd_pca = PCA(n_components=n_components, random_state=42, svd_solver=\"randomized\")\n",
     "\n",
-    "    for pca in (regular_pca, inc_pca, rnd_pca):\n",
+    "    for name, pca in ((\"PCA\", regular_pca), (\"Inc PCA\", inc_pca), (\"Rnd PCA\", rnd_pca)):\n",
     "        t1 = time.time()\n",
     "        pca.fit(X_train)\n",
     "        t2 = time.time()\n",
-    "        print(\"    {}: {:.1f} seconds\".format(pca.__class__.__name__, t2 - t1))"
+    "        print(\"    {}: {:.1f} seconds\".format(name, t2 - t1))"
    ]
   },
   {
@@ -1130,12 +1137,12 @@
     "sizes = [1000, 10000, 20000, 30000, 40000, 50000, 70000, 100000, 200000, 500000]\n",
     "for n_samples in sizes:\n",
     "    X = np.random.randn(n_samples, 5)\n",
-    "    pca = PCA(n_components = 2, svd_solver=\"randomized\", random_state=42)\n",
+    "    pca = PCA(n_components=2, svd_solver=\"randomized\", random_state=42)\n",
     "    t1 = time.time()\n",
     "    pca.fit(X)\n",
     "    t2 = time.time()\n",
     "    times_rpca.append(t2 - t1)\n",
-    "    pca = PCA(n_components = 2)\n",
+    "    pca = PCA(n_components=2, svd_solver=\"full\")\n",
     "    t1 = time.time()\n",
     "    pca.fit(X)\n",
     "    t2 = time.time()\n",
@@ -1169,12 +1176,12 @@
     "sizes = [1000, 2000, 3000, 4000, 5000, 6000]\n",
     "for n_features in sizes:\n",
     "    X = np.random.randn(2000, n_features)\n",
-    "    pca = PCA(n_components = 2, random_state=42, svd_solver=\"randomized\")\n",
+    "    pca = PCA(n_components=2, random_state=42, svd_solver=\"randomized\")\n",
     "    t1 = time.time()\n",
     "    pca.fit(X)\n",
     "    t2 = time.time()\n",
     "    times_rpca.append(t2 - t1)\n",
-    "    pca = PCA(n_components = 2)\n",
+    "    pca = PCA(n_components=2, svd_solver=\"full\")\n",
     "    t1 = time.time()\n",
     "    pca.fit(X)\n",
     "    t2 = time.time()\n",
@@ -2252,7 +2259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,