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YAML-configurable Pandas pipelines.

Project description


YAML-configurable Pandas pipelines.

The pdpipewrench package reads input data, generates pipeline stages, and writes output data entirely from the information supplied in a YAML configuration file. In addition, custom-made or module-specific functions may be wrapped into pipeline stages as specified in the YAML. Keyword arguments to such functions are also specified in YAML, which sidesteps the problem of hard coding parameters into numerous *.py files for different datasets, each slightly different than the last.


pip install pdpipewrench


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.


All aspects of a pipeline are 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 PDPIPEWRENCHDIR is not specified, then then it will be set to the current working directory. The file config.yaml should be put in the PDPIPEWRENCHDIR, and data to be processed should be in that directory or its subdirectories.


The directory structure of this example is as follows:


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

    file: raw/products*.csv
        - items
        - prices
        - inventory
    index_col: items

    file: output/*_processed.csv


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

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

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

The module contains:

    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

import custom_functions
import pdpipewrench as pdpw

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

# generate the pipeline from `config.yaml`.
line = pdpw.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)
(dfs_in, dfs_out) =

Running 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 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, 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:

items prices inventory color
foo 19 5 red
bar 24 3 green
baz 22 7 blue

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

items prices inventory
foo 19 5
bar 24 3
baz 22 7

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.

items prices
foo 20.5
bar 25.5
baz 23.5

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)

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