pandas-parallel-apply


Namepandas-parallel-apply JSON
Version 1.4.3 PyPI version JSON
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home_pagehttps://gitlab.com/mihaicristianpirvu/pandas-parallel-apply
SummaryWrapper for df and df[col].apply parallelized
upload_time2022-05-20 13:36:35
maintainer
docs_urlNone
author
requires_python>=3.8
licenseWTFPL
keywords
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requirements No requirements were recorded.
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            # 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.



            

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    "description": "# pandas-parallel-apply\n\n`df.apply(fn)`, `df[col].apply(fn)` and `series.apply(fn)` wrappers with tqdm included\n\n## Installation\n\n`pip install pandas-parallel-apply`\n\n## Examples\nSee `examples/` for usage on some dummy dataframe and series.\n\n## Usage\n\n## 1. Procedural\n\n### Apply on each row of a dataframe\n\n`df.apply(fn)` -> `apply_on_df_parallel(df: pd.DataFrame, fn: Callable, n_cores: int, pbar: bool = True)`\n\n### Apply on a column of a dataframe and return the Series\n\n`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)`\n\n### Apply on a series and return the modified Series\n`series.apply(fn)` -> `apply_on_seris_parallel(series: pd.Series, fn: Callable, n_cores: int, pbar: bool = True)\n\n### Switches for boolean parallel/non-parallel\n\n`apply_on_df/df_col/series_maybe_parallel(*, parallel: bool, n_cores: int, pbar: bool = True)`\n\n## 2. Object Oriented Programming\n\n### Apply on each row of a dataframe\n\n`df.apply(fn)` -> `DataFrameParallel(df, n_cores: int, pbar: bool = True).apply(fn)`\n\n### Apply on a column of a dataframe and return the Series\n`df[col].apply(fn, axis=1)` -> `DataFrameParallel(df, n_cores: int, pbar: bool=True)[col].apply(fn, axis=1)`\n\n### Apply on a series\n`series.apply(fn)` -> `SeriesParallel(series, n_cores: int, pbar: bool=True).apply(fn)`\n\nThat's all.\n\n\n",
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