semvec/pypi-packages huggingface dataset:
redissimple 0.11
A library to use Redis Package
License: MIT

# Redis-simple

Redis-simple is a Python package that contains handy functions for interacting with Redis Library. 

## Installation and updating
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install RedisSimple like below. 
Rerun this command to check for and install  updates .
```bash
pip install redissimple
```

## Usage
Features:
* functions.get (tenant_id, entity_name, entity_id)  --> Get the data stored for provided key in the Redis Cache.
* functions.set    --> Set the key value pair in the Redis cache
* functions.remove      --> Remove the key from Redis Cache

##
How to create distribution
* Delete existing dist folder on project root directory
* Execute below command on project root directory
** python3 setup.py sdist

How to upload distribution to pypi
python3 -m twine upload --repository pypi dist/*

#### Demo of some of the features:
```python

```
multiprocesspandas 1.1.5
Extends Pandas to run apply methods for  dataframe, series and groups on multiple cores at same time.
License: MIT
Keywords: Pandas,Multiprocessing,Pandas Multiprocessing,Parallel,Parallize Pandas

# MultiprocessPandas

MultiprocessPandas package extends functionality of Pandas to easily run operations on multiple cores i.e. parallelize the operations. The current version of the package provides capability to parallelize **_apply()_** methods on DataFrames, Series and DataFrameGroupBy .

Importing the applyparallel module will add **_apply_parallel()_** method to DataFrame, Series and DataFrameGroupBy, which will allow you to run operation on multiple cores.

## Installation

The package can be pulled from GitHub or can be installed from PyPi directly.

To install using pip

```python
    pip install multiprocesspandas
```

## Setting up the Library

To use the library, you have to import applyparallel module. Import will attach required methods to pandas, and you can call them directly on Pandas data objects.

```python
    from multiprocesspandas import applyparallel
```

## Usage

Once imported, the library adds functionality to call **_apply_parallel()_** method on your DataFrame, Series or DataFrameGroupBy . The methods accepts a function that has to be applied, and two named arguments:

- **_static_data_** (External Data required by passed function, defaults to None)
- **_num_processes_** (Defaults to maximum available cores on your CPU)
- **_axis_** (Only for DataFrames, defaults to 0 i.e. rows. For columns, set axis=1.

**\*Note:** Any extra module required by the passed function must be re-imported again inside the function.\*

### Usage with DataFrameGroupBy

```python
    def func(x):
        import pandas as pd
        return pd.Series([x['C'].mean()])

    df.groupby(["A","B"]).apply_parallel(func, num_processes=30)
```

If you need some external data inside **func()**, it has to be passed and received as position arguments or keyword arguments.

```python
    data1 = pd.Series([1,2,3])
    data2 = 20

    def func(x, data1, data2):
        import pandas as pd
        output = data1 - x['C'].mean()
        return output * data2

    df.groupby(["A","B"]).apply_parallel(func, data1=data1, data2=data2, num_processes=30)
```

### Usage with DataFrame

Usage with DataFrames is very similar to the one with DataFrameGroupBy, however you have to pass an extra argument 'axis' which tells whether to apply function on the rows or the columns.

```python
    def func(x):
        return x.mean()

    df.apply_parallel(func, num_processes=30, axis=1)
```

External data can be passed in same way as we did in DataFrameGroupBy

```python
    data = pd.Series([1,2,3])

    def func(x, data):
        return data.sum() + x.mean()

    df.apply_parallel(func, data=data, num_processes=30)
```

### Usage with Series

Usage with Series is very similar to the usage with DataFrames and DataFrameGroupBy.

```python
    data = pd.Series([1,2,3])

    def func(x, data):
	    return data-x

    series.apply_parallel(func, data=data, num_processes=30)
```