DataFrame.to_numpy() ⚬ Entries

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DataFrame.to_numpy() ⚬｜Documentation｜1st｜20251021185351-00-⌔

pandas.DataFrame.to_numpy — pandas 2.3.3 documentation#pandas.DataFrame.to_numpy

DataFrame.to_numpy(dtype=None, copy=False, na_value=<no_default>)

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 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 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.

na_value: Any, 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.

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)

Printed 2026-06-28.

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