pandas.arrays.FloatingArray#

class pandas.arrays.FloatingArray(values, mask, copy=False)[source]#

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 pandas.array() with one of the float dtypes (see examples).

See Nullable integer data type for more.

Parameters:

valuesnumpy.ndarray: A 1-d float-dtype array.
masknumpy.ndarray: A 1-d boolean-dtype array indicating missing values.
copybool, default False: Whether to copy the values and mask.

Attributes

None

Methods

None

Returns:

FloatingArray

See also

array: Create an array.
Float32Dtype: Float32 dtype for FloatingArray.
Float64Dtype: Float64 dtype for FloatingArray.
Series: One-dimensional labeled array capable of holding data.
DataFrame: Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Examples

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.

>>> pd.array([0.1, None, 0.3], dtype="Float32")
<FloatingArray>
[0.1, <NA>, 0.3]
Length: 3, dtype: Float32