pandas.DataFrame.to_numpy — pandas 3.0.0rc0+33.g1fd184de2a documentation (original) (raw)

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 NumPy dtype of all types in the DataFrame. For example, if the dtypes arefloat16 and float32, the results dtype will be float32. 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.

Returns:

numpy.ndarray

The NumPy array representing the values in the DataFrame.

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)