sklearn.datasets.load_iris  
sklearn.datasets.load_iris(*, return_X_y=False, as_frame=False) [source]
 
Load and return the iris dataset (classification). The iris dataset is a classic and very easy multi-class classification dataset.  
Classes 3  
Samples per class 50  
Samples total 150  
Dimensionality 4  
Features real, positive   Read more in the User Guide.  Parameters 
 
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 (150, 4) 

The data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, Series} of shape (150,)

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.  frame: DataFrame of shape (150, 5)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   DESCR: str

The full description of the dataset.  filename: str

The path to the location of the data.  New in version 0.20.     
(data, target)tuple if return_X_y is True 

 New in version 0.18.      Notes  Changed in version 0.20: Fixed two wrong data points according to Fisher’s paper. The new version is the same as in R, but not as in the UCI Machine Learning Repository.  Examples Let’s say you are interested in the samples 10, 25, and 50, and want to know their class name. >>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']
 
 Examples using sklearn.datasets.load_iris
 
  Release Highlights for scikit-learn 0.24  

  Release Highlights for scikit-learn 0.22  

  Plot classification probability  

  Plot Hierarchical Clustering Dendrogram  

  K-means Clustering  

  The Iris Dataset  

  Plot the decision surface of a decision tree on the iris dataset  

  Understanding the decision tree structure  

  PCA example with Iris Data-set  

  Incremental PCA  

  Comparison of LDA and PCA 2D projection of Iris dataset  

  Factor Analysis (with rotation) to visualize patterns  

  Plot the decision boundaries of a VotingClassifier  

  Early stopping of Gradient Boosting  

  Plot the decision surfaces of ensembles of trees on the iris dataset  

  Test with permutations the significance of a classification score  

  Univariate Feature Selection  

  GMM covariances  

  Gaussian process classification (GPC) on iris dataset  

  Regularization path of L1- Logistic Regression  

  Logistic Regression 3-class Classifier  

  Plot multi-class SGD on the iris dataset  

  Confusion matrix  

  Receiver Operating Characteristic (ROC) with cross validation  

  Nested versus non-nested cross-validation  

  Receiver Operating Characteristic (ROC)  

  Precision-Recall  

  Nearest Centroid Classification  

  Nearest Neighbors Classification  

  Comparing Nearest Neighbors with and without Neighborhood Components Analysis  

  Compare Stochastic learning strategies for MLPClassifier  

  Concatenating multiple feature extraction methods  

  Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset  

  SVM with custom kernel  

  SVM-Anova: SVM with univariate feature selection  

  Plot different SVM classifiers in the iris dataset  

  RBF SVM parameters  

  SVM Exercise