pandas.DataFrame.to_numpy

DataFrame.to_numpy(dtype=None, copy=False)

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 and float32, the results dtype will be float32. 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 that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.

Returns:

array : 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)