diff --git a/examples/cluster/plot_linkage_comparison.py b/examples/cluster/plot_linkage_comparison.py
index dc009d0110f7c..793fee059d797 100644
--- a/examples/cluster/plot_linkage_comparison.py
+++ b/examples/cluster/plot_linkage_comparison.py
@@ -33,28 +33,28 @@
 from sklearn import cluster, datasets
 from sklearn.preprocessing import StandardScaler
 
-np.random.seed(0)
-
 # %%
 # Generate datasets. We choose the size big enough to see the scalability
 # of the algorithms, but not too big to avoid too long running times
 
 n_samples = 1500
-noisy_circles = datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05)
-noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05)
-blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
-no_structure = np.random.rand(n_samples, 2), None
+noisy_circles = datasets.make_circles(
+    n_samples=n_samples, factor=0.5, noise=0.05, random_state=170
+)
+noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05, random_state=170)
+blobs = datasets.make_blobs(n_samples=n_samples, random_state=170)
+rng = np.random.RandomState(170)
+no_structure = rng.rand(n_samples, 2), None
 
 # Anisotropicly distributed data
-random_state = 170
-X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
+X, y = datasets.make_blobs(n_samples=n_samples, random_state=170)
 transformation = [[0.6, -0.6], [-0.4, 0.8]]
 X_aniso = np.dot(X, transformation)
 aniso = (X_aniso, y)
 
 # blobs with varied variances
 varied = datasets.make_blobs(
-    n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=random_state
+    n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=170
 )
 
 # %%
diff --git a/examples/preprocessing/plot_all_scaling.py b/examples/preprocessing/plot_all_scaling.py
index 5a08976bd2653..f53c50e33875a 100644
--- a/examples/preprocessing/plot_all_scaling.py
+++ b/examples/preprocessing/plot_all_scaling.py
@@ -102,11 +102,15 @@
     ),
     (
         "Data after quantile transformation (uniform pdf)",
-        QuantileTransformer(output_distribution="uniform").fit_transform(X),
+        QuantileTransformer(
+            output_distribution="uniform", random_state=42
+        ).fit_transform(X),
     ),
     (
         "Data after quantile transformation (gaussian pdf)",
-        QuantileTransformer(output_distribution="normal").fit_transform(X),
+        QuantileTransformer(
+            output_distribution="normal", random_state=42
+        ).fit_transform(X),
     ),
     ("Data after sample-wise L2 normalizing", Normalizer().fit_transform(X)),
 ]
diff --git a/examples/preprocessing/plot_discretization_classification.py b/examples/preprocessing/plot_discretization_classification.py
index 71adf44474aa3..f3edcac0011d7 100644
--- a/examples/preprocessing/plot_discretization_classification.py
+++ b/examples/preprocessing/plot_discretization_classification.py
@@ -74,7 +74,7 @@ def get_name(estimator):
     (
         make_pipeline(
             StandardScaler(),
-            KBinsDiscretizer(encode="onehot"),
+            KBinsDiscretizer(encode="onehot", random_state=0),
             LogisticRegression(random_state=0),
         ),
         {
@@ -85,7 +85,7 @@ def get_name(estimator):
     (
         make_pipeline(
             StandardScaler(),
-            KBinsDiscretizer(encode="onehot"),
+            KBinsDiscretizer(encode="onehot", random_state=0),
             LinearSVC(random_state=0, dual="auto"),
         ),
         {