Is Single Value#

Imports#

import pandas as pd
from sklearn.datasets import load_iris

from deepchecks.tabular.checks import IsSingleValue

Load Data#

iris = load_iris()
X = iris.data
df = pd.DataFrame({'a':[3,4,1], 'b':[2,2,2], 'c':[None, None, None], 'd':['a', 4, 6]})
df

	a	b	c	d
0	3	2	None	a
1	4	2	None	4
2	1	2	None	6

See functionality#

IsSingleValue().run(pd.DataFrame(X))

Out:

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:886: UserWarning:

Received a "pandas.DataFrame" instance. It is recommended to pass a "deepchecks.tabular.Dataset" instance by doing "Dataset(dataframe)"

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:581: UserWarning:

It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred

Single Value in Column

IsSingleValue().run(pd.DataFrame({'a':[3,4], 'b':[2,2], 'c':[None, None], 'd':['a', 4]}))

Out:

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:886: UserWarning:

Received a "pandas.DataFrame" instance. It is recommended to pass a "deepchecks.tabular.Dataset" instance by doing "Dataset(dataframe)"

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:581: UserWarning:

It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred

Single Value in Column

sv = IsSingleValue()
sv.run(df)

Out:

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:886: UserWarning:

Received a "pandas.DataFrame" instance. It is recommended to pass a "deepchecks.tabular.Dataset" instance by doing "Dataset(dataframe)"

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:581: UserWarning:

It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred

Single Value in Column

sv_ignore = IsSingleValue(ignore_columns=['b','c'])
sv_ignore.run(df)

Out:

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:886: UserWarning:

Received a "pandas.DataFrame" instance. It is recommended to pass a "deepchecks.tabular.Dataset" instance by doing "Dataset(dataframe)"

/home/runner/work/deepchecks/deepchecks/deepchecks/tabular/dataset.py:581: UserWarning:

It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred

Single Value in Column

Total running time of the script: ( 0 minutes 0.231 seconds)

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Feature Label Correlation

Mixed Data Types