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FloatingDtypea
An ExtensionDtype to hold a single size of floating dtype.
These specific implementations are subclasses of the non-public
FloatingDtype. For example we have Float32Dtype to represent float32.
The attributes name & type are set when these subclasses are created.
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Return the array type associated with this dtype.
Returns
-------
type
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Safely cast the values to the given dtype.
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Array of floating (optional missing) values.
.. warning::
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
:func:`pandas.array` with one of the float dtypes (see examples).
See :ref:`integer_na` for more.
Parameters
----------
values : numpy.ndarray
A 1-d float-dtype array.
mask : numpy.ndarray
A 1-d boolean-dtype array indicating missing values.
copy : bool, default False
Whether to copy the `values` and `mask`.
Attributes
----------
None
Methods
-------
None
Returns
-------
FloatingArray
Examples
--------
Create an FloatingArray with :func:`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.
>>> pd.array([0.1, None, 0.3], dtype="Float32")
<FloatingArray>
[0.1, <NA>, 0.3]
Length: 3, dtype: Float32
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An ExtensionDtype for {dtype} data.
This dtype uses ``pd.NA`` as missing value indicator.
Attributes
----------
None
Methods
-------
None
Examples
--------
For Float32Dtype:
>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype())
>>> ser.dtype
Float32Dtype()
For Float64Dtype:
>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype())
>>> ser.dtype
Float64Dtype()
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