pandas.DataFrame.to_numpy¶
-
DataFrame.
to_numpy
(self, dtype=None, copy=False)[source]¶ Convert the DataFrame to a NumPy array.
New in version 0.24.0.
By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are
float16
andfloat32
, the results dtype will befloat32
. This may require copying data and coercing values, which may be expensive.Parameters: - dtype : str or numpy.dtype, optional
The dtype to pass to
numpy.asarray()
- copy : bool, default False
Whether to ensure that the returned value is a not a view on another array. Note that
copy=False
does not ensure thatto_numpy()
is no-copy. Rather,copy=True
ensure that a copy is made, even if not strictly necessary.
Returns: - numpy.ndarray
See also
Series.to_numpy
- Similar method for Series.
Examples
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy() array([[1, 3], [2, 4]])
With heterogenous data, the lowest common type will have to be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]}) >>> df.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['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)