bigframes.geopandas.GeoSeries.to_numpy#

GeoSeries.to_numpy(dtype=None, copy=False, na_value=<no_default>, *, allow_large_results=None, **kwargs) → ndarray#

A NumPy ndarray representing the values in this Series or Index.

Examples:

>>> ser = bpd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)

Specify the dtype to control how datetime-aware data is represented. Use dtype=object to return an ndarray of pandas Timestamp objects, each with the correct tz.

>>> ser = bpd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('1999-12-31 23:00:00+0000', tz='UTC'),
       Timestamp('2000-01-01 23:00:00+0000', tz='UTC')], dtype=object)

Or dtype=datetime64[ns] to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped.

>>> ser.to_numpy(dtype="datetime64[ns]")
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00.000000000'],
      dtype='datetime64[ns]')
Parameters:

  • dtype (str or numpy.dtype, optional) – The dtype to pass to numpy.asarray().

  • copy (bool, default False) – Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.

  • na_value (Any, optional) – The value to use for missing values. The default value depends on dtype and the type of the array.

  • allow_large_results (bool, default None) – If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

  • **kwargs – Additional keywords passed through to the to_numpy method of the underlying array (for extension arrays).

Returns:

A NumPy ndarray representing the values in this Series or Index.

Return type:

numpy.ndarray

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