A RetroSearch Logo

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Search Query:

Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/../api/pandas.arrays.BooleanArray.html below:

pandas.arrays.BooleanArray — pandas 2.3.1 documentation

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

Array of boolean (True/False) data with missing values.

This is a pandas Extension array for boolean data, under the hood represented by 2 numpy arrays: a boolean array with the data and a boolean array with the mask (True indicating missing).

BooleanArray implements Kleene logic (sometimes called three-value logic) for logical operations. See Kleene logical operations for more.

To construct an BooleanArray from generic array-like input, use pandas.array() specifying dtype="boolean" (see examples below).

Warning

BooleanArray is considered experimental. The implementation and parts of the API may change without warning.

Parameters:
valuesnumpy.ndarray

A 1-d boolean-dtype array with the data.

masknumpy.ndarray

A 1-d boolean-dtype array indicating missing values (True indicates missing).

copybool, default False

Whether to copy the values and mask arrays.

Attributes

Methods

Returns:
BooleanArray

Examples

Create an BooleanArray with pandas.array():

>>> pd.array([True, False, None], dtype="boolean")
<BooleanArray>
[True, False, <NA>]
Length: 3, dtype: boolean

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4