sklearn.datasets.make_blobs  
sklearn.datasets.make_blobs(n_samples=100, n_features=2, *, centers=None, cluster_std=1.0, center_box=- 10.0, 10.0, shuffle=True, random_state=None, return_centers=False) [source]
 
Generate isotropic Gaussian blobs for clustering. Read more in the User Guide.  Parameters 
 
n_samplesint or array-like, default=100 

If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster.  Changed in version v0.20: one can now pass an array-like to the n_samples parameter   
n_featuresint, default=2 

The number of features for each sample.  
centersint or ndarray of shape (n_centers, n_features), default=None 

The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.  
cluster_stdfloat or array-like of float, default=1.0 

The standard deviation of the clusters.  
center_boxtuple of float (min, max), default=(-10.0, 10.0) 

The bounding box for each cluster center when centers are generated at random.  
shufflebool, default=True 

Shuffle the samples.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.  
return_centersbool, default=False 

If True, then return the centers of each cluster  New in version 0.23.     Returns 
 
Xndarray of shape (n_samples, n_features) 

The generated samples.  
yndarray of shape (n_samples,) 

The integer labels for cluster membership of each sample.  
centersndarray of shape (n_centers, n_features) 

The centers of each cluster. Only returned if return_centers=True.      See also  
make_classification


A more intricate variant.    Examples >>> from sklearn.datasets import make_blobs
>>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])
>>> X, y = make_blobs(n_samples=[3, 3, 4], centers=None, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])
 
 Examples using sklearn.datasets.make_blobs
 
  Release Highlights for scikit-learn 0.23  

  Probability calibration of classifiers  

  Probability Calibration for 3-class classification  

  Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification  

  An example of K-Means++ initialization  

  A demo of the mean-shift clustering algorithm  

  Demonstration of k-means assumptions  

  Demo of affinity propagation clustering algorithm  

  Demo of DBSCAN clustering algorithm  

  Inductive Clustering  

  Compare BIRCH and MiniBatchKMeans  

  Comparison of the K-Means and MiniBatchKMeans clustering algorithms  

  Comparing different hierarchical linkage methods on toy datasets  

  Selecting the number of clusters with silhouette analysis on KMeans clustering  

  Comparing different clustering algorithms on toy datasets  

  Plot randomly generated classification dataset  

  SGD: Maximum margin separating hyperplane  

  Plot multinomial and One-vs-Rest Logistic Regression  

  Comparing anomaly detection algorithms for outlier detection on toy datasets  

  Demonstrating the different strategies of KBinsDiscretizer  

  SVM: Maximum margin separating hyperplane  

  SVM Tie Breaking Example  

  Plot the support vectors in LinearSVC  

  SVM: Separating hyperplane for unbalanced classes