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# `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)

  Generate isotropic Gaussian blobs for clustering.

  Read more in the User Guide.

  Parameters:
    n_samples int 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_features int, default=2

      The number of features for each sample.

    centers int or array-like 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_std float or array-like of float, default=1.0

      The standard deviation of the clusters.

    center_box tuple of float (min, max), default=(-10.0, 10.0)

      The bounding box for each cluster center when centers are generated at random.

    shuffle bool, default=True

      Shuffle the samples.

    random_state int, 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_centers bool, default=False

      If True, then return the centers of each cluster.

      New in version 0.23.

  Returns:
    X ndarray of shape (n_samples, n_features)

      The generated samples.

    y ndarray of shape (n_samples,)

      The integer labels for cluster membership of each sample.

    centers ndarray 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 1.1

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

Probability Calibration for 3-class classification

Probability Calibration for 3-class classification

Probability calibration of classifiers

Probability calibration of classifiers

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

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

A demo of the mean-shift clustering algorithm

A demo of the mean-shift clustering algorithm

An example of K-Means++ initialization

An example of K-Means++ initialization

Bisecting K-Means and Regular K-Means Performance Comparison

Bisecting K-Means and Regular K-Means Performance Comparison

Compare BIRCH and MiniBatchKMeans

Compare BIRCH and MiniBatchKMeans

Comparing different clustering algorithms on toy datasets

Comparing different clustering algorithms on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Comparison of the K-Means and MiniBatchKMeans clustering algorithms

Demo of DBSCAN clustering algorithm

Demo of DBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

Demo of affinity propagation clustering algorithm

Demo of affinity propagation clustering algorithm

Demonstration of k-means assumptions

Demonstration of k-means assumptions

Inductive Clustering

Inductive Clustering

Selecting the number of clusters with silhouette analysis on KMeans clustering

Selecting the number of clusters with silhouette analysis on KMeans clustering

Plot randomly generated classification dataset

Plot randomly generated classification dataset

GMM Initialization Methods

GMM Initialization Methods

Plot multinomial and One-vs-Rest Logistic Regression

Plot multinomial and One-vs-Rest Logistic Regression

SGD: Maximum margin separating hyperplane

SGD: Maximum margin separating hyperplane

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

Demonstrating the different strategies of KBinsDiscretizer

Demonstrating the different strategies of KBinsDiscretizer

Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC

SVM Tie Breaking Example

SVM Tie Breaking Example

SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes