pandas.Series.to_numpy#

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 tonumpy.asarray().

copybool, default False

Whether to ensure that the returned value is not a view onanother array. Note thatcopy=False does notensure thatto_numpy() is no-copy. Rather,copy=True ensure thata copy is made, even if not strictly necessary.

na_valueAny, optional

The value to use for missing values. The default value dependsondtype and the type of the array.

**kwargs

Additional keywords passed through to theto_numpy methodof the underlying array (for extension arrays).

Returns:

numpy.ndarray

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 equalinself will be equal in the returned array; likewise for valuesthat are not equal). Whenself contains an ExtensionArray, thedtype may be different. For example, for a category-dtype Series,to_numpy() will return a NumPy array and the categorical dtypewill be lost.

For NumPy dtypes, this will be a reference to the actual data storedin this Series or Index (assumingcopy=False). Modifying the resultin place will modify the data stored in the Series or Index (not thatwe recommend doing that).

For extension types,to_numpy()may require copying data andcoercing the result to a NumPy type (possibly object), which may beexpensive. 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 thedtype to control how datetime-aware data is represented.Usedtype=object to return an ndarray of pandasTimestampobjects, 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)

Ordtype='datetime64[ns]' to return an ndarray of nativedatetime64 values. The values are converted to UTC and the timezoneinfo is dropped.

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