pandas-parallel-apply-1.3.0.tar.gz

# 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.