.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot/plot_ames.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_plot_ames.py: Ames Housing Dataset Visualization ==================================== .. GENERATED FROM PYTHON SOURCE LINES 5-16 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/plot/images/sphx_glr_plot_ames_001.png :alt: Target distribution :srcset: /auto_examples/plot/images/sphx_glr_plot_ames_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/plot/images/sphx_glr_plot_ames_002.png :alt: Continuous Feature vs Target, F=8.08E-01, F=7.23E-01, F=7.01E-01, F=6.81E-01, F=6.60E-01, F=6.38E-01, F=6.05E-01, F=6.02E-01, F=5.81E-01, F=4.98E-01 :srcset: /auto_examples/plot/images/sphx_glr_plot_ames_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/plot/images/sphx_glr_plot_ames_003.png :alt: Categorical Feature vs Target, F=5.64E-01, F=3.70E-01, F=3.37E-01, F=3.30E-01, F=2.89E-01, F=2.76E-01, F=2.56E-01, F=2.30E-01, F=2.29E-01, F=2.23E-01 :srcset: /auto_examples/plot/images/sphx_glr_plot_ames_003.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) /home/circleci/project/dabl/preprocessing.py:172: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format. pd.to_datetime(series[:10]) Target looks like regression /home/circleci/project/dabl/plot/utils.py:704: UserWarning: Dropped 2 outliers in column SalePrice. warn("Dropped {} outliers in column {}.".format( /home/circleci/project/dabl/plot/supervised.py:652: UserWarning: Discarding 2 outliers in target column. warn(f"Discarding {n_outliers} outliers in target column.", /home/circleci/project/dabl/plot/supervised.py:111: UserWarning: Showing only top 10 continuous features. warn("Showing only top 10 continuous features.") Showing only top 10 of 41 categorical features /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) /home/circleci/project/dabl/plot/supervised.py:214: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. medians = X_new.groupby(col)[target_col].median() /home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. grouped_vals = vals.groupby(grouper) | .. code-block:: Python # sphinx_gallery_thumbnail_number = 3 from dabl import plot from dabl.datasets import load_ames import matplotlib.pyplot as plt # load the ames housing dataset # returns a plain dataframe data = load_ames() plot(data, target_col='SalePrice') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 15.298 seconds) .. _sphx_glr_download_auto_examples_plot_plot_ames.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ames.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_ames.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_