pandas-pipeline-graphviz-0.1.5.tar.gz

# Pandas pipeline in graphviz Python package to build a nice explanative schema of a data processing pipeline in pandas. It's heavily inspired by [dask's `.visualize` method](https://docs.dask.org/en/latest/graphviz.html), but improved with 2 useful features: - visualize columns names in data nodes - highlight created columns at each task Here is an example from the [examples folder](examples):  ## Installation ### Pip Install with pip: ```bash $ pip install pandas-pipeline-graphviz ``` ### Manual installation Install manually: - git clone - use `python setup.py` ## Usage ### Disclaimer #### ⚠️ WARNING — it's a hack! There are no reliable methods in python to get variables names, either as input or as output. The methods used in this package are quite _hacky_, as discussed in this [stackoverflow thread](https://stackoverflow.com/questions/2749796/how-to-get-the-original-variable-name-of-variable-passed-to-a-function). To build the graph, this package makes use of: - `globals()` **to get the names of input dataframes**, doing a comparison between the input dataframes and all the variables available in the global variables. - `inspect.stack()` **to get the name of the output dataframe**, gathering the code lines calling the function and parsing it to find the output. Currently it supports only single-output transformations. Both methods should be considered as experimental and the behavior of the decorator is expected to break easily if it's not used as presented in the [examples](examples). #### Conditions for use - do not use several decorators on your function, only this decorator, otherwise it will break the output dataframe name detection through `inspect.stack()` - use only single output transformation functions, i.e. functions which return only 1 dataframe. ### Examples See [examples folder](examples) in the repository.