semvec/pypi-packages huggingface dataset:
flypipe 5.0.0
Flypipe
Requires Python: >=3.8

<div style="text-align: center;">
  <img src="docs/images/logo_white.png#only-dark" alt="Flypipe" width="500px"/>
</div>

Flypipe is a Python framework to simplify development, management and maintenance of transformation pipelines, which are 
commonly used in the data, feature and ML model space.

Each transformation is implemented in a small, composable function, a special decorator is then used to define it as a 
Flypipe node, which is the primary model Flypipe uses. Metadata on the node decorator allows for multiple nodes to be 
linked together into a Directed Acyclic Graph (DAG). 

As each node (transformation) is connected to its ancestors, we can easily view the pipeline graphically in a html page 
(`my_graph.html()`) or execute it by invoking `my_graph.run()`

![Flypipe Graph Pipeline](docs/images/flypipe_pipelines.png)

## Installation

```
pip install flypipe
```

Package published in [PyPI](https://pypi.org/project/flypipe/).

## Example Usage

```python
import pandas as pd
from flypipe.node import node


@node(
  type="pandas"
)
def t0():
  return pd.DataFrame(data={"fruit": ["mango", "lemon"]})


@node(
  type="pandas",
  dependencies=[t0.select("fruit").alias("df")]
)
def t1(df):
  categories = {'mango': 'sweet', 'lemon': 'sour'}
  df['flavour'] = df['fruit']
  df = df.replace({'flavour': categories})
  return df

df = t1.run()
```
```python
>>> print(df)
+----+---------+-----------+
|    | fruit   | flavour   |
+====+=========+===========+
|  0 | mango   | sweet     |
+----+---------+-----------+
|  1 | lemon   | sour      |
+----+---------+-----------+
```


## What Flypipe aims to facilitate?

- Free open-source tool for data transformations
- Facilitate streaming pipeline development (improved use of caches)  
- Increase pipeline stability (better use of unittests)
- End-to-end transformation lineage
- Create development standards for Data Engineers, Machine Learning Engineers and Data Scientists
- Improve re-usability of transformations in different pipelines & contexts via composable nodes
- Faster integration and portability of pipelines to different contexts with different available technology stacks:
  - Flexibility to use and mix up pyspark/pandas on spark/pandas in transformations seamlessly
  - As a simple wheel package, it's very lightweight and unopinionated about runtime environment. This allows for it to 
  be easily integrated into Databricks and independently of Databricks. 
- Low latency for on-demand feature generation and predictions
- Framework level optimisations and dynamic transformations help to make even complex transformation pipelines low 
latency. This in turn allows for on-demand feature generation/predictions.

## Commonly used

<p float="left">
  <img src="docs/images/databricks_logo.png" alt="Databricks" style="height:100px;"/>
  <img src="docs/images/python.png" alt="Python" style="height:80px;"/>
</p>

## Source Code

API code is available at [https://github.com/flypipe/flypipe](https://github.com/flypipe/flypipe).

## Documentation

Full documentation is available at [https://flypipe.github.io/flypipe/](https://flypipe.github.io/flypipe/).