xarray.CFTimeIndex.to_numpy#
- CFTimeIndex.to_numpy(dtype=None, copy=False, na_value=_NoDefault.no_default, **kwargs)[source]#
A NumPy ndarray representing the values in this Series or Index.
- Parameters
dtype (
str
ornumpy.dtype
, optional) – The dtype to pass tonumpy.asarray()
.copy (
bool
, defaultFalse
) – Whether to ensure that the returned value is not a view on another array. Note thatcopy=False
does not ensure thatto_numpy()
is no-copy. Rather,copy=True
ensure that a copy is made, even if not strictly necessary.na_value (
Any
, 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
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'), 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]')