diff --git a/conftest.py b/conftest.py
index 5c77aaeb..ee8dcf1c 100644
--- a/conftest.py
+++ b/conftest.py
@@ -7,7 +7,6 @@
 
 
 def pytest_collection_modifyitems(config, items):
-
     # numpy changed the str/repr formatting of numpy arrays in 1.14. We want to
     # run doctests only for numpy >= 1.14.
     skip_doctests = False
diff --git a/examples/cluster/plot_kmedoids_digits.py b/examples/cluster/plot_kmedoids_digits.py
index 11f7cc71..8bb7ab11 100644
--- a/examples/cluster/plot_kmedoids_digits.py
+++ b/examples/cluster/plot_kmedoids_digits.py
@@ -63,7 +63,6 @@
 plot_cols = 2
 
 for i, (model, description) in enumerate(selected_models):
-
     # Obtain labels for each point in mesh. Use last trained model.
     model.fit(reduced_data)
     Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
diff --git a/examples/plot_clara_digits.py b/examples/plot_clara_digits.py
index e1bb1f54..a703c948 100644
--- a/examples/plot_clara_digits.py
+++ b/examples/plot_clara_digits.py
@@ -79,7 +79,6 @@
 plot_cols = 2
 
 for i, (model, description) in enumerate(selected_models):
-
     # Obtain labels for each point in mesh. Use last trained model.
     init_time = time.time()
     model.fit(reduced_data)
diff --git a/examples/plot_kmedoids.py b/examples/plot_kmedoids.py
index 514ac41d..88856743 100644
--- a/examples/plot_kmedoids.py
+++ b/examples/plot_kmedoids.py
@@ -37,7 +37,6 @@
     plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))
 ]
 for k, col in zip(unique_labels, colors):
-
     class_member_mask = labels == k
 
     xy = X[class_member_mask]
diff --git a/sklearn_extra/robust/robust_weighted_estimator.py b/sklearn_extra/robust/robust_weighted_estimator.py
index e0165671..76140398 100644
--- a/sklearn_extra/robust/robust_weighted_estimator.py
+++ b/sklearn_extra/robust/robust_weighted_estimator.py
@@ -308,7 +308,6 @@ def fit(self, X, y=None):
 
         # Optimization algorithm
         for epoch in range(self.max_iter):
-
             if self._estimator_type == "classifier":
                 # If in classification, use decision_function
                 pred = base_estimator.decision_function(X)
@@ -1016,7 +1015,6 @@ def __init__(
         verbose=0,
         random_state=None,
     ):
-
         self.weighting = weighting
         self.max_iter = max_iter
         self.c = c
diff --git a/sklearn_extra/robust/tests/test_mean_estimators.py b/sklearn_extra/robust/tests/test_mean_estimators.py
index 0bce4dab..2f005662 100644
--- a/sklearn_extra/robust/tests/test_mean_estimators.py
+++ b/sklearn_extra/robust/tests/test_mean_estimators.py
@@ -8,6 +8,7 @@
 
 sample = rng.normal(size=100)
 
+
 # Check good in normal case
 def test_normal():
     assert np.abs(median_of_means(sample, 3, rng)) < 1e-1
diff --git a/sklearn_extra/robust/tests/test_robust_weighted_estimator.py b/sklearn_extra/robust/tests/test_robust_weighted_estimator.py
index 346849d3..aaecc603 100644
--- a/sklearn_extra/robust/tests/test_robust_weighted_estimator.py
+++ b/sklearn_extra/robust/tests/test_robust_weighted_estimator.py
@@ -118,6 +118,7 @@ def test_corrupted_classif(loss, weighting, k, c, multi_class):
     random_state=rng,
 )
 
+
 # check binary throw an error
 def test_robust_estimator_unsupported_loss():
     model = RobustWeightedClassifier(multi_class="binary")
@@ -259,7 +260,6 @@ def test_corrupted_regression(loss, weighting, k, c):
 @pytest.mark.parametrize("loss", regression_losses)
 @pytest.mark.parametrize("weighting", weightings)
 def test_corrupted_regression_multidim(loss, weighting):
-
     n = 1000
     d = 10
 
@@ -299,6 +299,7 @@ def test_regression_corrupted_weights(weighting):
 y_r = X_r + 0.1 * rng.normal(size=1000)
 X_r = X_r.reshape(-1, 1)
 
+
 # Check that the fit is close to SGD when in extremal parameter cases
 @pytest.mark.parametrize("loss", regression_losses)
 @pytest.mark.parametrize("weighting", weightings)
@@ -383,6 +384,7 @@ def test_corrupted_cluster(weighting, k, c):
     n_samples=100, centers=np.array([[-1, -1], [1, 1]]), random_state=rng
 )
 
+
 # Check that the fit is close to KMeans when in extremal parameter cases
 @pytest.mark.parametrize("weighting", weightings)
 def test_not_robust_cluster(weighting):