glom

Restructuring data, the Python way

Real applications have real data, and real data nests. Objects inside of objects inside of lists of objects.

glom is a new and powerful way to handle real-world data, featuring:

• Path-based access for nested data structures
• Readable, meaningful error messages
• Declarative data transformation, using lightweight, Pythonic specifications
• Built-in data exploration and debugging features

All of that and more, available as a fully-documented, pure-Python package, tested on Python 2.7-3.7, as well as PyPy. Installation is as easy as:

  pip install glom

And when you install glom, you also get the glom command-line interface, letting you experiment at the console, but never limiting you to shell scripts:

Usage: glom [FLAGS] [spec [target]]

Command-line interface to the glom library, providing nested data access and data
restructuring with the power of Python.

Flags:

  --help / -h                     show this help message and exit
  --target-file TARGET_FILE       path to target data source (optional)
  --target-format TARGET_FORMAT   format of the source data (json or python) (defaults
                                  to 'json')
  --spec-file SPEC_FILE           path to glom spec definition (optional)
  --spec-format SPEC_FORMAT       format of the glom spec definition (json, python,
                                  python-full) (defaults to 'python')
  --indent INDENT                 number of spaces to indent the result, 0 to disable
                                  pretty-printing (defaults to 2)
  --debug                         interactively debug any errors that come up
  --inspect                       interactively explore the data

Anything you can do at the command line readily translates to Python code, so you've always got a path forward when complexity starts to ramp up.

Learn more

If all this seems interesting, continue exploring glom below:

• glom Tutorial
• Full API documentation at Read the Docs
• Original announcement blog post (2018-05-09)
• Frequently Asked Questions
• PyCon 2018 Lightning Talk (2018-05-11)

All of the links above are overflowing with examples, but should you find anything about the docs, or glom itself, lacking, please submit an issue!

In the meantime, just remember: When you've got nested data, glom it! ☄️