pandas.DataFrame.to_numpy#

DataFrame.to_numpy(dtype=None,copy=False,na_value=<no_default>)[source]#

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPydtype of all types in the DataFrame. For example, if the dtypes arefloat16 andfloat32, the results dtype will befloat32.This may require copying data and coercing values, which may beexpensive.

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 dtypes of the DataFrame columns.

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 heterogeneous data, the lowest common type will have tobe 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 willhave 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)