Example summarize

Demonstrates how to use the summarize function to get a quick summary of your dataset.

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]

print(__doc__)

# Author: Taylor Smith <taylor.smith@alkaline-ml.com>

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)