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. Usedtype=object to return an ndarray of pandas Timestamp objects, each withthe 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)

Ordtype=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 ornumpy.dtype,optional) – The dtype to pass tonumpy.asarray().

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

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

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

  • **kwargs – Additional keywords passed through to theto_numpy methodof the underlying array (for extension arrays).

Returns:

A NumPy ndarray representing the values in thisSeries or Index.

Return type:

numpy.ndarray

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