Skip to main content

A nifty data processing framework, based on data packages

Project description

logo DataFlows

Travis Coveralls PyPI - Python Version Gitter chat

DataFlows is a simple and intuitive way of building data processing flows.

  • It's built for small-to-medium-data processing - data that fits on your hard drive, but is too big to load in Excel or as-is into Python, and not big enough to require spinning up a Hadoop cluster...
  • It's built upon the foundation of the Frictionless Data project - which means that all data produced by these flows is easily reusable by others.
  • It's a pattern not a heavy-weight framework: if you already have a bunch of download and extract scripts this will be a natural fit

Read more in the Features section below.

QuickStart

Install dataflows via pip install.

(If you are using minimal UNIX OS, run first sudo apt install build-essential)

Then use the command-line interface to bootstrap a basic processing script for any remote data file:

# Install from PyPi
$ pip install dataflows

# Inspect a remote CSV file
$ dataflows init https://raw.githubusercontent.com/datahq/dataflows/master/data/academy.csv
Writing processing code into academy_csv.py
Running academy_csv.py
academy:
#     Year           Ceremony  Award                                 Winner  Name                            Film
      (string)      (integer)  (string)                            (string)  (string)                        (string)
----  ----------  -----------  --------------------------------  ----------  ------------------------------  -------------------
1     1927/1928             1  Actor                                         Richard Barthelmess             The Noose
2     1927/1928             1  Actor                                      1  Emil Jannings                   The Last Command
3     1927/1928             1  Actress                                       Louise Dresser                  A Ship Comes In
4     1927/1928             1  Actress                                    1  Janet Gaynor                    7th Heaven
5     1927/1928             1  Actress                                       Gloria Swanson                  Sadie Thompson
6     1927/1928             1  Art Direction                                 Rochus Gliese                   Sunrise
7     1927/1928             1  Art Direction                              1  William Cameron Menzies         The Dove; Tempest
...

# dataflows create a local package of the data and a reusable processing script which you can tinker with
$ tree
.
├── academy_csv
│   ├── academy.csv
│   └── datapackage.json
└── academy_csv.py

1 directory, 3 files

# Resulting 'Data Package' is super easy to use in Python
[adam] ~/code/budgetkey-apps/budgetkey-app-main-page/tmp (master=) $ python
Python 3.6.1 (default, Mar 27 2017, 00:25:54)
[GCC 4.2.1 Compatible Apple LLVM 8.0.0 (clang-800.0.42.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from datapackage import Package
>>> pkg = Package('academy_csv/datapackage.json')
>>> it = pkg.resources[0].iter(keyed=True)
>>> next(it)
{'Year': '1927/1928', 'Ceremony': 1, 'Award': 'Actor', 'Winner': None, 'Name': 'Richard Barthelmess', 'Film': 'The Noose'}
>>> next(it)
{'Year': '1927/1928', 'Ceremony': 1, 'Award': 'Actor', 'Winner': '1', 'Name': 'Emil Jannings', 'Film': 'The Last Command'}

# You now run `academy_csv.py` to repeat the process
# And obviously modify it to add data modification steps

Features

  • Trivial to get started and easy to scale up
  • Set up and run from command line in seconds ...
    • dataflows init => flow.py
    • python flow.py
  • Validate input (and esp source) quickly (non-zero length, right structure, etc.)
  • Supports caching data from source and even between steps
    • so that we can run and test quickly (retrieving is slow)
  • Immediate test is run: and look at output ...
    • Log, debug, rerun
  • Degrades to simple python
  • Conventions over configuration
  • Log exceptions and / or terminate
  • The input to each stage is a Data Package or Data Resource (not a previous task)
    • Data package based and compatible
  • Processors can be a function (or a class) processing row-by-row, resource-by-resource or a full package
  • A pre-existing decent contrib library of Readers (Collectors) and Processors and Writers

Learn more

Dive into the Tutorial to get a deeper glimpse into everything that dataflows can do. Also review this list of Built-in Processors, which also includes an API reference for each one of them.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dataflows-0.2.8.tar.gz (36.1 kB view details)

Uploaded Source

Built Distribution

dataflows-0.2.8-py2.py3-none-any.whl (51.3 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file dataflows-0.2.8.tar.gz.

File metadata

  • Download URL: dataflows-0.2.8.tar.gz
  • Upload date:
  • Size: 36.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.8

File hashes

Hashes for dataflows-0.2.8.tar.gz
Algorithm Hash digest
SHA256 6d117ab62ccb6f3ab7780db20af015a6e563c4953b75154e97792502c56d783b
MD5 ed918e8aa4661dfea853ca5b35f20503
BLAKE2b-256 2481219571e62de864f67320fefea286b3247c013bed7a818c03dbbb883e58f8

See more details on using hashes here.

Provenance

File details

Details for the file dataflows-0.2.8-py2.py3-none-any.whl.

File metadata

  • Download URL: dataflows-0.2.8-py2.py3-none-any.whl
  • Upload date:
  • Size: 51.3 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/49.6.0 requests-toolbelt/0.9.1 tqdm/4.49.0 CPython/3.7.8

File hashes

Hashes for dataflows-0.2.8-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 e14bc26012ce2db6cd5996011ae59ba7d3690a28c2fb807537ad702a8760b492
MD5 e39097cebb9ae9890b2733d6d32681bd
BLAKE2b-256 6faf3d7f9bd24bbc9e51cf9e943cd8339e54d7fe95266d9807fd6c7c5e373903

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page