FloatingArray is currently experimental, and its API or internal implementation may change without warning. Especially the behaviour regarding NaN (distinct from NA missing values) is subject to change.
We represent a FloatingArray with 2 numpy arrays:
data: contains a numpy float array of the appropriate dtype
mask: a boolean array holding a mask on the data, True is missing
To construct an FloatingArray from generic array-like input, use pandas.array
with one of the float dtypes (see examples).
See integer_na
for more.
A 1-d float-dtype array.
A 1-d boolean-dtype array indicating missing values.
Whether to copy the :None:None:`values` and :None:None:`mask`.
Array of floating (optional missing) values.
Create an FloatingArray with pandas.array
:
>>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype()) <FloatingArray> [0.1, <NA>, 0.3] Length: 3, dtype: Float32
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([0.1, None, 0.3], dtype="Float32") <FloatingArray> [0.1, <NA>, 0.3] Length: 3, dtype: Float32See :
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