pandas_profiling.config.Univariate
- pydantic model pandas_profiling.config.Univariate[source]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- Fields
-
field bool:
BoolVars
= BoolVars(n_obs=3, imbalance_threshold=0.5, mappings={'t': True, 'f': False, 'yes': True, 'no': False, 'y': True, 'n': False, 'true': True, 'false': False})
-
field cat:
CatVars
= CatVars(length=True, characters=True, words=True, cardinality_threshold=50, imbalance_threshold=0.5, n_obs=5, chi_squared_threshold=0.999, coerce_str_to_date=False, redact=False, histogram_largest=50, stop_words=[])
-
field num:
NumVars
= NumVars(quantiles=[0.05, 0.25, 0.5, 0.75, 0.95], skewness_threshold=20, low_categorical_threshold=5, chi_squared_threshold=0.999)
-
field timeseries:
TimeseriesVars
= TimeseriesVars(active=False, sortby=None, autocorrelation=0.7, lags=[1, 7, 12, 24, 30], significance=0.05, pacf_acf_lag=100)