pandas.arrays.FloatingArray

class pandas.arrays.FloatingArray(values, mask, copy=False)

Array of floating (optional missing) values.

Note

By default, NaN is treated as missing (converted to NA). Set pd.options.future.distinguish_nan_and_na = True to keep NaN distinct from NA.

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 a 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.

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 a 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