Now uses :None:attr:`pandas.NA` as the missing value rather than :None:attr:`numpy.nan`.
IntegerArray is currently experimental, and its API or internal implementation may change without warning.
We represent an IntegerArray with 2 numpy arrays:
data: contains a numpy integer array of the appropriate dtype
mask: a boolean array holding a mask on the data, True is missing
To construct an IntegerArray from generic array-like input, use pandas.array
with one of the integer dtypes (see examples).
See integer_na
for more.
A 1-d integer-dtype array.
A 1-d boolean-dtype array indicating missing values.
Whether to copy the :None:None:`values` and :None:None:`mask`.
Array of integer (optional missing) values.
Create an IntegerArray with pandas.array
.
>>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
... int_array <IntegerArray> [1, <NA>, 3] Length: 3, dtype: Int32
String aliases for the dtypes are also available. They are capitalized.
This example is valid syntax, but we were not able to check execution>>> pd.array([1, None, 3], dtype='Int32') <IntegerArray> [1, <NA>, 3] Length: 3, dtype: Int32This example is valid syntax, but we were not able to check execution
>>> pd.array([1, None, 3], dtype='UInt16') <IntegerArray> [1, <NA>, 3] Length: 3, dtype: UInt16See :
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