pandas.Index.to_numpy#

Index.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 that to_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: The NumPy ndarray holding the values from this Series or Index. The dtype of the array may differ. See Notes.

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 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]")
... 
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'],
      dtype='datetime64[ns]')