scikit-learn · jeremiedbb · Apr 11, 2025 · Mar 26, 2025 · Mar 31, 2025 · Apr 11, 2025
diff --git a/examples/applications/plot_species_distribution_modeling.py b/examples/applications/plot_species_distribution_modeling.py
@@ -194,7 +194,7 @@ def plot_species_distribution(
         Z = np.ones((data.Ny, data.Nx), dtype=np.float64)
 
         # We'll predict only for the land points.
-        idx = np.where(land_reference > -9999)
+        idx = (land_reference > -9999).nonzero()
         coverages_land = data.coverages[:, idx[0], idx[1]].T
 
         pred = clf.decision_function((coverages_land - mean) / std)

diff --git a/examples/applications/plot_stock_market.py b/examples/applications/plot_stock_market.py
@@ -213,7 +213,7 @@
 )
 
 # Plot the edges
-start_idx, end_idx = np.where(non_zero)
+start_idx, end_idx = non_zero.nonzero()
 # a sequence of (*line0*, *line1*, *line2*), where::
 #            linen = (x0, y0), (x1, y1), ... (xm, ym)
 segments = [

diff --git a/examples/bicluster/plot_bicluster_newsgroups.py b/examples/bicluster/plot_bicluster_newsgroups.py
@@ -147,7 +147,7 @@ def bicluster_ncut(i):
 
     # words
     out_of_cluster_docs = cocluster.row_labels_ != cluster
-    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
+    out_of_cluster_docs = out_of_cluster_docs.nonzero()[0]
     word_col = X[:, cluster_words]
     word_scores = np.array(
         word_col[cluster_docs, :].sum(axis=0)

diff --git a/examples/cluster/plot_hdbscan.py b/examples/cluster/plot_hdbscan.py
@@ -40,7 +40,7 @@ def plot(X, labels, probabilities=None, parameters=None, ground_truth=False, ax=
             # Black used for noise.
             col = [0, 0, 0, 1]
 
-        class_index = np.where(labels == k)[0]
+        class_index = (labels == k).nonzero()[0]
         for ci in class_index:
             ax.plot(
                 X[ci, 0],

diff --git a/examples/decomposition/plot_sparse_coding.py b/examples/decomposition/plot_sparse_coding.py
@@ -106,7 +106,7 @@ def ricker_matrix(width, resolution, n_components):
         dictionary=D, transform_algorithm="threshold", transform_alpha=20
     )
     x = coder.transform(y.reshape(1, -1))
-    _, idx = np.where(x != 0)
+    _, idx = (x != 0).nonzero()
     x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y, rcond=None)
     x = np.ravel(np.dot(x, D))
     squared_error = np.sum((y - x) ** 2)

diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py
@@ -65,7 +65,7 @@
 
 # Plot the training points
 for i, n, c in zip(range(2), class_names, plot_colors):
-    idx = np.where(y == i)
+    idx = (y == i).nonzero()
     plt.scatter(
         X[idx, 0],
         X[idx, 1],

diff --git a/examples/linear_model/plot_sgd_iris.py b/examples/linear_model/plot_sgd_iris.py
@@ -55,7 +55,7 @@
 
 # Plot also the training points
 for i, color in zip(clf.classes_, colors):
-    idx = np.where(y == i)
+    idx = (y == i).nonzero()
     plt.scatter(
         X[idx, 0],
         X[idx, 1],

diff --git a/examples/manifold/plot_mds.py b/examples/manifold/plot_mds.py
@@ -89,7 +89,7 @@
 plt.legend(scatterpoints=1, loc="best", shadow=False)
 
 # Plot the edges
-start_idx, end_idx = np.where(X_mds)
+start_idx, end_idx = X_mds.nonzero()
 # a sequence of (*line0*, *line1*, *line2*), where::
 #            linen = (x0, y0), (x1, y1), ... (xm, ym)
 segments = [

diff --git a/examples/miscellaneous/plot_multilabel.py b/examples/miscellaneous/plot_multilabel.py
@@ -71,8 +71,8 @@ def plot_subfigure(X, Y, subplot, title, transform):
     plt.subplot(2, 2, subplot)
     plt.title(title)
 
-    zero_class = np.where(Y[:, 0])
-    one_class = np.where(Y[:, 1])
+    zero_class = (Y[:, 0]).nonzero()
+    one_class = (Y[:, 1]).nonzero()
     plt.scatter(X[:, 0], X[:, 1], s=40, c="gray", edgecolors=(0, 0, 0))
     plt.scatter(
         X[zero_class, 0],

diff --git a/examples/semi_supervised/plot_label_propagation_digits_active_learning.py b/examples/semi_supervised/plot_label_propagation_digits_active_learning.py
@@ -108,7 +108,7 @@
             sub.axis("off")
 
         # labeling 5 points, remote from labeled set
-        (delete_index,) = np.where(unlabeled_indices == image_index)
+        (delete_index,) = (unlabeled_indices == image_index).nonzero()
         delete_indices = np.concatenate((delete_indices, delete_index))
 
     unlabeled_indices = np.delete(unlabeled_indices, delete_indices)

diff --git a/examples/semi_supervised/plot_label_propagation_structure.py b/examples/semi_supervised/plot_label_propagation_structure.py
@@ -78,8 +78,8 @@
 # when the label was unknown.
 output_labels = label_spread.transduction_
 output_label_array = np.asarray(output_labels)
-outer_numbers = np.where(output_label_array == outer)[0]
-inner_numbers = np.where(output_label_array == inner)[0]
+outer_numbers = (output_label_array == outer).nonzero()[0]
+inner_numbers = (output_label_array == inner).nonzero()[0]
 
 plt.figure(figsize=(4, 4))
 plt.scatter(

diff --git a/examples/svm/plot_linearsvc_support_vectors.py b/examples/svm/plot_linearsvc_support_vectors.py
@@ -31,7 +31,7 @@
     # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
     # The support vectors are the samples that lie within the margin
     # boundaries, whose size is conventionally constrained to 1
-    support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]
+    support_vector_indices = (np.abs(decision_function) <= 1 + 1e-15).nonzero()[0]
     support_vectors = X[support_vector_indices]
 
     plt.subplot(1, 2, i + 1)

diff --git a/examples/tree/plot_iris_dtc.py b/examples/tree/plot_iris_dtc.py
@@ -63,7 +63,7 @@
 
     # Plot the training points
     for i, color in zip(range(n_classes), plot_colors):
-        idx = np.where(y == i)
+        idx = np.asarray(y == i).nonzero()
         plt.scatter(
             X[idx, 0],
             X[idx, 1],