pandas.DataFrame.to_numpy#
- DataFrame.to_numpy(dtype=None, copy=False, na_value=_NoDefault.no_default)[source]#
Convert the DataFrame to a NumPy array.
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
- dtypestr or numpy.dtype, optional
The dtype to pass to
numpy.asarray()
.- copybool, default False
Whether to ensure that the returned value is 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.- na_valueAny, optional
The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.
New in version 1.1.0.
- 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 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)