pandas.Series.to_numpy¶
-
Series.
to_numpy
(dtype=None, copy=False)[source]¶ A NumPy ndarray representing the values in this Series or Index.
New in version 0.24.0.
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 a not a view on another array. Note that
copy=False
does not ensure thatto_numpy()
is no-copy. Rather,copy=True
ensure that a copy is made, even if not strictly necessary.
Returns: - numpy.ndarray
See also
Series.array
- Get the actual data stored within.
Index.array
- Get the actual data stored within.
DataFrame.to_numpy
- Similar method for DataFrame.
Notes
The returned array will be the same up to equality (values equal in self will be equal in the returned array; likewise for values that are not equal). When self contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series,
to_numpy()
will return a NumPy array and the categorical dtype will be lost.For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming
copy=False
). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that).For extension types,
to_numpy()
may require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data,Series.array
should be used instead.This table lays out the different dtypes and default return types of
to_numpy()
for various dtypes within pandas.dtype array type category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps) Examples
>>> ser = pd.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 pandasTimestamp
objects, each with the correcttz
.>>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> ser.to_numpy(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
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]") ... # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'], dtype='datetime64[ns]')