Class StructAccessor (0.11.0)

Version 0.11.0

• 1.25.0 (latest)
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

StructAccessor(
    data=None,
    index: vendored_pandas_typing.Axes | None = None,
    dtype: typing.Optional[
        bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype
    ] = None,
    name: str | None = None,
    copy: typing.Optional[bool] = None,
    *,
    session: typing.Optional[bigframes.session.Session] = None
)

Accessor object for structured data properties of the Series values.

Methods

explode

explode() -> bigframes.dataframe.DataFrame

Extract all child fields of a struct as a DataFrame.

Examples:

>>> import <xref uid="bigframes.pandas">bigframes.pandas</xref> as bpd
>>> import pyarrow as pa
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series(
...     [
...         {"version": 1, "project": "pandas"},
...         {"version": 2, "project": "pandas"},
...         {"version": 1, "project": "numpy"},
...     ],
...     dtype=bpd.ArrowDtype(pa.struct(
...         [("version", pa.int64()), ("project", pa.string())]
...     ))
... )

Extract all child fields.

>>> s.struct.explode()
   version project
0        1  pandas
1        2  pandas
2        1   numpy
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
DataFrame	The data corresponding to all child fields.

field

field(name_or_index: str | int) -> bigframes.series.Series

Extract a child field of a struct as a Series.

Examples:

>>> import <xref uid="bigframes.pandas">bigframes.pandas</xref> as bpd
>>> import pyarrow as pa
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series(
...     [
...         {"version": 1, "project": "pandas"},
...         {"version": 2, "project": "pandas"},
...         {"version": 1, "project": "numpy"},
...     ],
...     dtype=bpd.ArrowDtype(pa.struct(
...         [("version", pa.int64()), ("project", pa.string())]
...     ))
... )

Extract by field name.

>>> s.struct.field("project")
0    pandas
1    pandas
2     numpy
Name: project, dtype: string

Extract by field index.

>>> s.struct.field(0)
0    1
1    2
2    1
Name: version, dtype: Int64

Returns
Type	Description
Series	The data corresponding to the selected child field.