to_numpy(self, dtype: 'npt.DTypeLike | None' = None, copy: 'bool' = False, na_value: 'Scalar' = <no_default>) -> 'np.ndarray'
By default converts to an object-dtype NumPy array. Specify the dtype
and :None:None:`na_value` keywords to customize the conversion.
The numpy dtype to convert to.
Whether to ensure that the returned value is a not a view on the 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. This is typically only possible when no missing values are present and dtype
is the equivalent numpy dtype.
Scalar missing value indicator to use in numpy array. Defaults to the native missing value indicator of this array (pd.NA).
Convert to a NumPy Array.
An object-dtype is the default result
This example is valid syntax, but we were not able to check execution>>> a = pd.array([True, False, pd.NA], dtype="boolean")
... a.to_numpy() array([True, False, <NA>], dtype=object)
When no missing values are present, an equivalent dtype can be used.
This example is valid syntax, but we were not able to check execution>>> pd.array([True, False], dtype="boolean").to_numpy(dtype="bool") array([ True, False])This example is valid syntax, but we were not able to check execution
>>> pd.array([1, 2], dtype="Int64").to_numpy("int64") array([1, 2])
However, requesting such dtype will raise a ValueError if missing values are present and the default missing value NA
is used.
>>> a = pd.array([True, False, pd.NA], dtype="boolean")This example is valid syntax, but we were not able to check execution
... a <BooleanArray> [True, False, <NA>] Length: 3, dtype: boolean
>>> a.to_numpy(dtype="bool") Traceback (most recent call last): ... ValueError: cannot convert to bool numpy array in presence of missing values
Specify a valid :None:None:`na_value` instead
>>> a.to_numpy(dtype="bool", na_value=False) array([ True, False, False])See :
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