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featurebyte.Table.describe

describe(
size: int=0,
seed: int=1234,
from_timestamp: Union[datetime, str, NoneType]=None,
to_timestamp: Union[datetime, str, NoneType]=None,
after_cleaning: bool=False
) -> DataFrame

Description

Returns descriptive statistics of the table. By default, the statistics are computed before any cleaning operations that were defined at the table level.

Parameters

  • size: int
    default: 0
    Maximum number of rows to sample. If 0, all rows will be used.

  • seed: int
    default: 1234
    Seed to use for random sampling.

  • from_timestamp: Union[datetime, str, NoneType]
    Start of date range to sample from.

  • to_timestamp: Union[datetime, str, NoneType]
    End of date range to sample from.

  • after_cleaning: bool
    default: False
    Whether to apply cleaning operations.

Returns

  • DataFrame
    Summary of the table.

Examples

Describe a table without cleaning operations

>>> event_table = catalog.get_table("GROCERYINVOICE")
>>> description = event_table.describe(
...   from_timestamp=datetime(2020, 1, 1),
...   to_timestamp=datetime(2020, 1, 31),
... )

Describe a table after cleaning operations have been applied.

>>> event_table = catalog.get_table("GROCERYINVOICE")
>>> event_table["Amount"].update_critical_data_info(
...   cleaning_operations=[
...     fb.MissingValueImputation(imputed_value=0),
...   ]
... )

>>> description = event_table.describe(
...   from_timestamp=datetime(2020, 1, 1),
...   to_timestamp=datetime(2020, 1, 31),
...   after_cleaning=True
... )