pandas.Series.to_numpy — pandas 0.24.0rc1 documentation (original) (raw)
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 |
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 ofto_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 pandas Timestampobjects, each with the correct tz.
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]')