431 pandas: powerful Python data analysis toolkit, Release 0.25.3 4.5 Reshaping and pivot tables 4.5.1 Reshaping by pivoting DataFrame objects Data is often stored in so-called stacked or record format:

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2374 4.5.1 Registering custom accessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2374 types, your needs may not be fully satisfied. Pandas offers a few options for extending pandas. 4.5.1 Registering custom accessors Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor()

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2370 4.5.1 Registering custom accessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2370 types, your needs may not be fully satisfied. Pandas offers a few options for extending pandas. 4.5.1 Registering custom accessors Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor()

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2468 4.5.1 Registering custom accessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2468 types, your needs may not be fully satisfied. pandas offers a few options for extending pandas. 4.5.1 Registering custom accessors Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor()

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2468 4.5.1 Registering custom accessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2468 types, your needs may not be fully satisfied. pandas offers a few options for extending pandas. 4.5.1 Registering custom accessors Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor()

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1733 4.5.1 pandas.array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Series, Index, or as a column in a DataFrame. array(data, dtype, numpy.dtype, ...) Create an array. 4.5.1 pandas.array pandas.array(data: Sequence[object], dtype: Union[str, numpy.dtype, pan- das.core.dtypes

style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2655 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pre-commit to automatically run black, flake8, isort, and related code checks when you make a git commit. 4.5.1 Patterns We use a flake8 plugin, pandas-dev-flaker, to check our codebase for unwanted patterns.

style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2734 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pre-commit to automatically run black, flake8, isort, and related code checks when you make a git commit. 4.5.1 Patterns We use a flake8 plugin, pandas-dev-flaker, to check our codebase for unwanted patterns.

style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2734 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pre-commit to automatically run black, flake8, isort, and related code checks when you make a git commit. 4.5.1 Patterns We use a flake8 plugin, pandas-dev-flaker, to check our codebase for unwanted patterns.

style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2827 4.5.1 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. pandas code style guide 2827 pandas: powerful Python data analysis toolkit, Release 1.4.2 4.5.1 Patterns We use a flake8 plugin, pandas-dev-flaker, to check our codebase for unwanted patterns.