pandas_profiling.config.Univariate

pydantic model pandas_profiling.config.Univariate

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

• bool (pandas_profiling.config.BoolVars)

• cat (pandas_profiling.config.CatVars)

• file (pandas_profiling.config.FileVars)

• image (pandas_profiling.config.ImageVars)

• num (pandas_profiling.config.NumVars)

• path (pandas_profiling.config.PathVars)

• timeseries (pandas_profiling.config.TimeseriesVars)

• url (pandas_profiling.config.UrlVars)

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 file: FileVars = FileVars(active=False)

field image: ImageVars = ImageVars(active=False, exif=True, hash=True)

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 path: PathVars = PathVars(active=False)

field timeseries: TimeseriesVars = TimeseriesVars(active=False, sortby=None, autocorrelation=0.7, lags=[1, 7, 12, 24, 30], significance=0.05, pacf_acf_lag=100)

field url: UrlVars = UrlVars(active=False)