pandas.Series.to_numpy — pandas 2.2.3 documentation (original) (raw)

Series.to_numpy(dtype=None, copy=False, na_value=<no_default>, **kwargs)[source]#

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

Parameters:

dtypestr or numpy.dtype, optional

The dtype to pass to numpy.asarray().

copybool, default False

Whether to ensure that the returned value is 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.

na_valueAny, optional

The value to use for missing values. The default value depends on dtype and the type of the array.

**kwargs

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

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.

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'), Timestamp('2000-01-02 00:00:00+0100', tz='CET')], 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...'], dtype='datetime64[ns]')