.. _sphx_glr_auto_examples_exploration_ex_summarize.py: ================= Example summarize ================= Demonstrates how to use the ``summarize`` function to get a quick summary of your dataset. .. raw:: html
.. rst-class:: sphx-glr-script-out Out:: sepal length (cm) ... x5 Mean 5.843333 ... NaN Median 5.800000 ... NaN Max 7.900000 ... NaN Min 4.300000 ... NaN Variance 0.685694 ... NaN Skewness 0.311753 ... NaN Kurtosis -0.573568 ... NaN Least Freq. NaN ... Level1 Most Freq. NaN ... Level1 Class Balance NaN ... 1 Num Levels NaN ... 1 Arity NaN ... 0.00666667 Missing 0.000000 ... 0 [13 rows x 6 columns] | .. code-block:: python print(__doc__) # Author: Taylor Smith from skoot.exploration import summarize from skoot.datasets import load_iris_df # ############################################################################# # load data iris = load_iris_df(include_tgt=True) # add a feature of nothing but a single level of strings. This is to # demonstrate that the summary will report on even uninformative features iris["x5"] = "Level1" # print the summary of the dataset print(summarize(iris)) **Total running time of the script:** ( 0 minutes 0.035 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: ex_summarize.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: ex_summarize.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_