pandas.Series.__array__¶
-
Series.
__array__
(dtype=None)[source]¶ Return the values as a NumPy array.
Users should not call this directly. Rather, it is invoked by
numpy.array()
andnumpy.asarray()
.Parameters: - dtype : str or numpy.dtype, optional
The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data.
Returns: - numpy.ndarray
The values in the series converted to a
numpy.ndarary
with the specified dtype.
See also
pandas.array
- Create a new array from data.
Series.array
- Zero-copy view to the array backing the Series.
Series.to_numpy
- Series method for similar behavior.
Examples
>>> ser = pd.Series([1, 2, 3]) >>> np.asarray(ser) array([1, 2, 3])
For timezone-aware data, the timezones may be retained with
dtype='object'
>>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> np.asarray(tzser, dtype="object") array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'), Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')], dtype=object)
Or the values may be localized to UTC and the tzinfo discared with
dtype='datetime64[ns]'
>>> np.asarray(tzser, dtype="datetime64[ns]") # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', ...], dtype='datetime64[ns]')