.. _sphx_glr_auto_examples_preprocessing_ex_binning.py: =================== Binning transformer =================== Demonstrates how to bin continuous features in your dataset. .. raw:: html
.. image:: /auto_examples/preprocessing/images/sphx_glr_ex_binning_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: a ... b_binned 0 5.1 ... (3.30, 3.90] 1 4.9 ... (2.70, 3.30] 2 4.7 ... (2.70, 3.30] 3 4.6 ... (2.70, 3.30] 4 5.0 ... (3.30, 3.90] [5 rows x 6 columns] | .. code-block:: python print(__doc__) # Author: Taylor Smith from matplotlib import pyplot as plt from skoot.datasets import load_iris_df from skoot.preprocessing import BinningTransformer # ############################################################################# # load data iris = load_iris_df(include_tgt=False, names=["a", "b", "c", "d"]) binner = BinningTransformer(cols=["a", "b"], return_bin_label=True, strategy="uniform", overwrite=False, n_bins=4) # print the head of the binned dataset print(binner.fit_transform(iris).head()) # ############################################################################# # Show where the boundaries reside a_lower = binner.bins_["a"].lower_bounds[1:] # skip the -np.inf plt.hist(iris["a"].values) # plot vertical lines where bins are for bound in a_lower: plt.axvline(bound, ls="--") plt.title("Iris feature 'a' + bin markers") plt.show() **Total running time of the script:** ( 0 minutes 0.059 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: ex_binning.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: ex_binning.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_