modin.pandas.Series.to_numpy¶

Series.to_numpy(dtype: npt.DTypeLike | None = None, copy: bool = False, na_value: object = _NoDefault.no_default, **kwargs: Any) → np.ndarray[source]¶

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

Parameters:

  • dtype (str or numpy.dtype, optional) – The dtype to pass to numpy.asarray().

  • copy (bool, default False) – This argument is ignored in Snowflake backend. The data from Snowflake will be retrieved into the client, and a numpy array containing this data will be returned.

  • 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).

Return type:

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 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.

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)
Copy

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='UTC+01:00'),
       Timestamp('2000-01-02 00:00:00+0100', tz='UTC+01:00')],
      dtype=object)
Copy

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]')
Copy
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