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# `sklearn.datasets`.load_digits

sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=False)

  Load and return the digits dataset (classification).

  Each datapoint is a 8x8 image of a digit.

  Classes | 10
  Samples per class | ~180
  Samples total | 1797
  Dimensionality | 64
  Features | integers 0-16

  This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

  Read more in the User Guide.

  Parameters:
    n_class int, default=10

      The number of classes to return. Between 0 and 10.

    return_X_y bool, default=False

      If True, returns `(data, target)` instead of a Bunch object. See below for more information about the `data` and `target` object.

      New in version 0.18.

    as_frame bool, default=False

      If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If `return_X_y` is True, then (`data`, `target`) will be pandas DataFrames or Series as described below.

      New in version 0.23.

  Returns:
    data `Bunch`

      Dictionary-like object, with the following attributes.

      data{ndarray, dataframe} of shape (1797, 64)

        The flattened data matrix. If `as_frame=True`, `data` will be a pandas DataFrame.

      target: {ndarray, Series} of shape (1797,)

        The classification target. If `as_frame=True`, `target` will be a pandas Series.

      feature_names: list

        The names of the dataset columns.

      target_names: list

        The names of target classes.

        New in version 0.20.

      frame: DataFrame of shape (1797, 65)

        Only present when `as_frame=True`. DataFrame with `data` and `target`.

        New in version 0.23.

      images: {ndarray} of shape (1797, 8, 8)

        The raw image data.

      DESCR: str

        The full description of the dataset.

    (data, target)tuple if `return_X_y` is True

      A tuple of two ndarrays by default. The first contains a 2D ndarray of shape (1797, 64) with each row representing one sample and each column representing the features. The second ndarray of shape (1797) contains the target samples. If `as_frame=True`, both arrays are pandas objects, i.e. `X` a dataframe and `y` a series.

      New in version 0.18.

  Examples

  To load the data and visualize the images:

      >>> from sklearn.datasets import load_digits
      >>> digits = load_digits()
      >>> print(digits.data.shape)
      (1797, 64)
      >>> import matplotlib.pyplot as plt
      >>> plt.gray()
      >>> plt.matshow(digits.images[0])
      <...>
      >>> plt.show()

## Examples using `sklearn.datasets.load_digits`

Release Highlights for scikit-learn 1.3

Release Highlights for scikit-learn 1.3

Recognizing hand-written digits

Recognizing hand-written digits

A demo of K-Means clustering on the handwritten digits data

A demo of K-Means clustering on the handwritten digits data

Feature agglomeration

Feature agglomeration

Various Agglomerative Clustering on a 2D embedding of digits

Various Agglomerative Clustering on a 2D embedding of digits

The Digit Dataset

The Digit Dataset

Recursive feature elimination

Recursive feature elimination

Comparing various online solvers

Comparing various online solvers

L1 Penalty and Sparsity in Logistic Regression

L1 Penalty and Sparsity in Logistic Regression

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…

Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...

Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels

The Johnson-Lindenstrauss bound for embedding with random projections

The Johnson-Lindenstrauss bound for embedding with random projections

Balance model complexity and cross-validated score

Balance model complexity and cross-validated score

Comparing randomized search and grid search for hyperparameter estimation

Comparing randomized search and grid search for hyperparameter estimation

Custom refit strategy of a grid search with cross-validation

Custom refit strategy of a grid search with cross-validation

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability

Plotting Validation Curves

Plotting Validation Curves

Caching nearest neighbors

Caching nearest neighbors

Dimensionality Reduction with Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Kernel Density Estimation

Kernel Density Estimation

Compare Stochastic learning strategies for MLPClassifier

Compare Stochastic learning strategies for MLPClassifier

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Pipelining: chaining a PCA and a logistic regression

Pipelining: chaining a PCA and a logistic regression

Selecting dimensionality reduction with Pipeline and GridSearchCV

Selecting dimensionality reduction with Pipeline and GridSearchCV

Label Propagation digits active learning

Label Propagation digits active learning

Label Propagation digits: Demonstrating performance

Label Propagation digits: Demonstrating performance

Cross-validation on Digits Dataset Exercise

Cross-validation on Digits Dataset Exercise

Digits Classification Exercise

Digits Classification Exercise