pyspark.pandas.DataFrame.to_numpy¶
-
DataFrame.to_numpy() → numpy.ndarray¶ A NumPy ndarray representing the values in this DataFrame or Series.
Note
This method should only be used if the resulting NumPy ndarray is expected to be small, as all the data is loaded into the driver’s memory.
- Returns
- numpy.ndarray
Examples
>>> ps.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy() array([[1, 3], [2, 4]])
With heterogeneous data, the lowest common type will have to be used.
>>> ps.DataFrame({"A": [1, 2], "B": [3.0, 4.5]}).to_numpy() array([[1. , 3. ], [2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will have object dtype.
>>> df = ps.DataFrame({"A": [1, 2], "B": [3.0, 4.5], "C": pd.date_range('2000', periods=2)}) >>> df.to_numpy() array([[1, 3.0, Timestamp('2000-01-01 00:00:00')], [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
For Series,
>>> ps.Series(['a', 'b', 'a']).to_numpy() array(['a', 'b', 'a'], dtype=object)