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featurebyte.SourceTable.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 columns.

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

Get a summary of a view.

>>> catalog.get_table("GROCERYINVOICE").describe(
...     from_timestamp=datetime(2022, 1, 1),
...     to_timestamp=datetime(2022, 12, 31),
... )
                            GroceryInvoiceGuid                   GroceryCustomerGuid                      Timestamp            record_available_at     Amount
dtype                                  VARCHAR                               VARCHAR                      TIMESTAMP                      TIMESTAMP      FLOAT
unique                                   25422                                   471                          25399                           5908       6734
%missing                                   0.0                                   0.0                            0.0                            0.0        0.0
%empty                                       0                                     0                            NaN                            NaN        NaN
entropy                               6.214608                              5.784261                            NaN                            NaN        NaN
top       018f0163-249b-4cbc-ab4d-e933ce3786c1  c5820998-e779-4d62-ab8b-79ef0dfd841b                            NaN                            NaN        NaN
freq                                       1.0                                 692.0                            NaN                            NaN        NaN
mean                                       NaN                                   NaN                            NaN                            NaN  19.966062
std                                        NaN                                   NaN                            NaN                            NaN  25.027878
min                                        NaN                                   NaN  2022-01-01T00:24:14.000000000  2022-01-01T01:01:00.000000000        0.0
25%                                        NaN                                   NaN                            NaN                            NaN     4.5325
50%                                        NaN                                   NaN                            NaN                            NaN     10.725
75%                                        NaN                                   NaN                            NaN                            NaN      24.99
max                                        NaN                                   NaN  2022-12-30T22:37:57.000000000  2022-12-30T23:01:00.000000000     360.84

See Also