pandas-parallel-apply 0.2.0

Wrapper for df, series and df[col].apply parallelized

pandas-parallel-apply

df.apply(fn), df[col].apply(fn) and series.apply(fn) wrappers with tqdm included

Installation

pip install pandas-parallel-apply

Examples

See examples/ for usage on some dummy dataframe and series.

Usage

1. Procedural

Apply on each row of a dataframe

df.apply(fn) -> apply_on_df_parallel(df: pd.DataFrame, fn: Callable, n_cores: int, pbar: bool = True)

Apply on a column of a dataframe and return the Series

df[col].apply(fn, axis=1) -> apply_on_df_col_parallel(df: pd.DataFrame, col_name: str, fn: Callable, n_cores: int, pbar: bool = True)

Apply on a series and return the modified Series

series.apply(fn) -> `apply_on_seris_parallel(series: pd.Series, fn: Callable, n_cores: int, pbar: bool = True)

Switches for boolean parallel/non-parallel

apply_on_df/df_col/series_maybe_parallel(*, parallel: bool, n_cores: int, pbar: bool = True)

2. Object Oriented Programming

Apply on each row of a dataframe

df.apply(fn) -> DataFrameParallel(df, n_cores: int, pbar: bool = True).apply(fn)

Apply on a column of a dataframe and return the Series

df[col].apply(fn, axis=1) -> DataFrameParallel(df, n_cores: int, pbar: bool=True)[col].apply(fn, axis=1)

Apply on a series

series.apply(fn) -> SeriesParallel(series, n_cores: int, pbar: bool=True).apply(fn)

That's all.