datapipeliner

dvc-ready data pipelines

Provides data pipelines that are especially suited to incoporation into DVC. Allows model and data processing experiment production support.

Data pipelines are described by a YAML configuration file. The pipelines can be parameterized by tags names. These can be programatically controlled via a dvc params.yaml file - which allows for data processing experiements to be integrated into a dvc experiment.

The YAML file can describe standard pdpipe stages as well as custom stages defined in a .py file. Parameters to the stages are defined in the YAML file.

Installation

pip install datapipeliner

Requirements

This package manages YAML configurations with confuse, which itself depends on pyYAML. Pipeline stages and pipelines are generated with pdpipe, and engarde is an optional dependency for verify_all-, verify_any-, and engarde-type stages.

Details

The pipeline is defined in config.yaml. This file contains information about sources, files from which the data is drawn, pipelines and their stages, and the sinks, files to which the transformed data is written. Custom-made functions may be defined in a standard *.py file/module, which must take a pandas.DataFrame as input and return a pandas.DataFrame as output. Pipeline stages are generated from these custom functions by specifying them and their keyword arguments in config.yaml.

The file config.yaml controls all aspects of the pipeline, from data discovery, to pipeline stages, to data output. If the environment variable DATAPIPELINERDIR is not specified, then then it will be set to the current working directory. The file config.yaml should be put in the DATAPIEPLINEDIR, and data to be processed should be in that directory or its subdirectories.

Example

The directory structure of this example is as follows:

example/
    config.yaml
    custom_functions.py
    example.py
    raw
        products_storeA.csv
        products_storeB.csv
    output
        products_storeA_processed.csv
        products_storeB_processed.csv

The contents of config.yaml is as follows (paths are relative to the location of config.yaml, i.e. the DATAPIPELINERDIR):

sources:
  example_source:
    file: raw/products*.csv
    kwargs:
      usecols:
        - items
        - prices
        - inventory
    index_col: items

sinks:
  example_sink:
    file: output/*_processed.csv

pipelines:
  example_pipeline:

  - type: transform
      function: add_to_col
      tag: add
      kwargs:
        col_name: prices
        val: 1.5
      staging:
        desc: Adds $1.5 to column 'prices'
        exmsg: Couldn't add to 'prices'.

    - type: pdpipe
      function: ColDrop
      kwargs:
        columns: inventory
      staging:
        exraise: false

    - type: verify_all
      check: high_enough
      tag: verify
      kwargs:
        col_name: prices
        val: 19
      staging:
        desc: Checks whether all 'prices' are over $19.

The module custom_functions.py contains:

custom_functions.py

    def add_to_col(df, col_name, val):
        df.loc[:, col_name] = df.loc[:, col_name] + val
        return df

    def high_enough(df, col_name, val):
        return df.loc[:, col_name] > val

Finally, the contents of the file example.py:

import custom_functions
import datapipeliner as dpp

src = dpp.Source("example_source")  # generate the source from `config.yaml`
snk = dpp.Sink("example_sink")  # generate the sink from `config.yaml`.

# generate the pipeline from `config.yaml`.
line = dpp.Line("example_pipeline", custom_functions)

# connect the source and sink to the pipeline, print what the pipeline will do, then run
# the pipeline, writing the output to disk. capture the input/output dataframes if desired.
pipeline = line.connect(src, snk)
print(pipeline)
(dfs_in, dfs_out) = line.run()

Running example.py generates src, snk, and line objects. Then, the src and snk are connected to an internal pipeline, which is a pdpipe.PdPipeLine object. When this pipeline is printed, the following output is displayed:

A pdpipe pipeline:
[ 0]  Adds $1.5 to column 'prices'
[ 1]  Drop columns inventory
[ 2]  Checks whether all 'prices' are over $19.

The function of this pipeline is apparent from the descriptions of each stage. Some stages have custom descriptions specified in the desc key of config.yaml. Stages of type pdpipe have their descriptions auto-generated from the keyword arguments.

The command line.run() pulls data from src, passes it through pipeline, and drains it to snk. The returns dfs_in and dfs_out show that came in from src and what went to snk. In addition to line.run(), the first n stages of the pipeline can be tested on file m from the source with line.test(m,n).

Output from Example

This is .\raw\products_storeA.csv before it is drawn into the source:

This is .\raw\products_storeA.csv after it is drawn into the source with the argument usecols = ["items", "prices", "inventory"] specified in config.yaml:

The output from the pipeline is sent to .\products_storeA_processed.csv. The arguments specified by config.yaml have been applied. Namely, prices have been incremented by 1.5, the inventory column has been dropped, and then a check has been made that all prices are over 19.

If the verify_all step had failed, an exception would be raised, and the items that did not pass the check would be returned in the exception message. Say, for example, that the val argument was 21 instead of 19:

AssertionError: ('high_enough not true for all',
prices  items        
foo      20.5)

Direct Dataframe Injection

Additionally is is possible to call the pipeline directly with a data

import custom_functions
import datapipeliner as dpp
import pandas as pd

tags =  'add;verify'

df_in = pd.read_csv("myfile.csv")

# generate the pipeline from `config.yaml`.
line = dpp.Line("example_pipeline", custom_functions, tags)

df_out= line.runDataFrame(df_in)

Provenance

This project was created as a fork of the excellent pdpipewrench. A big thanks to blakeNaccarato / pdpipewrench.