sklearn.datasets.load_digits  
sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=False) [source]
 
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   Read more in the User Guide.  Parameters 
 
n_classint, default=10 

The number of classes to return. Between 0 and 10.  
return_X_ybool, 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_framebool, 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 
 
dataBunch 

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 

 New in version 0.18.   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
   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
 
  Recognizing hand-written digits  

  Feature agglomeration  

  Various Agglomerative Clustering on a 2D embedding of digits  

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

  The Digit Dataset  

  Early stopping of Gradient Boosting  

  Recursive feature elimination  

  Comparing various online solvers  

  L1 Penalty and Sparsity in Logistic Regression  

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

  The Johnson-Lindenstrauss bound for embedding with random projections  

  Explicit feature map approximation for RBF kernels  

  Plotting Validation Curves  

  Parameter estimation using grid search with cross-validation  

  Comparing randomized search and grid search for hyperparameter estimation  

  Balance model complexity and cross-validated score  

  Plotting Learning Curves  

  Kernel Density Estimation  

  Caching nearest neighbors  

  Dimensionality Reduction with Neighborhood Components Analysis  

  Restricted Boltzmann Machine features for digit classification  

  Compare Stochastic learning strategies for MLPClassifier  

  Pipelining: chaining a PCA and a logistic regression  

  Selecting dimensionality reduction with Pipeline and GridSearchCV  

  Label Propagation digits: Demonstrating performance  

  Label Propagation digits active learning  

  Digits Classification Exercise  

  Cross-validation on Digits Dataset Exercise