Note
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Is Single Value#
This notebook provides an overview for using and understanding the Is Single Value check.
Structure:
What is the Is Single Value check#
The IsSingleValue
check checks if there are columns which have only a single unique
value in all rows.
Imports#
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from deepchecks.tabular.checks import IsSingleValue
Load Data#
iris = load_iris()
X = iris.data
Run the check#
IsSingleValue().run(pd.DataFrame(X))
If None
is given as a value, it will be ignored (this can be changed with ignore_nan
set to False
):
df = pd.DataFrame({'a': [3, 4, 1], 'b': [2, 2, 2], 'c': [None, None, None], 'd': ['a', 4, 6]})
sv = IsSingleValue()
sv.run(df)
# Ignoring NaN values:
IsSingleValue(ignore_nan=True).run(pd.DataFrame({
'a': [3, np.nan],
'b': [2, 2],
'c': [None, np.nan],
'd': ['a', 4]
}))
Ignoring specific columns by name is also possible:
sv_ignore = IsSingleValue(ignore_columns=['b', 'c'])
sv_ignore.run(df)
Total running time of the script: (0 minutes 0.109 seconds)