to_numpy(self, dtype: 'npt.DTypeLike | None' = None, copy: 'bool' = False, na_value=<no_default>, **kwargs) -> 'np.ndarray'
The returned array will be the same up to equality (values equal in :None:None:`self` will be equal in the returned array; likewise for values that are not equal). When :None:None:`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) ================== ================================
The dtype to pass to numpy.asarray
.
Whether to ensure that the returned value is not a view on another array. Note that copy=False
does not ensure that to_numpy()
is no-copy. Rather, copy=True
ensure that a copy is made, even if not strictly necessary.
The value to use for missing values. The default value depends on dtype
and the type of the array.
Additional keywords passed through to the to_numpy
method of the underlying array (for extension arrays).
A NumPy ndarray representing the values in this Series or Index.
DataFrame.to_numpy
Similar method for DataFrame.
Index.array
Get the actual data stored within.
Series.array
Get the actual data stored within.
>>> 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 Timestamp
objects, 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]")See :
... # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'], dtype='datetime64[ns]')
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