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 3.7+, as well as PyPy3. 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.
Examples
Without glom
>>> data = {'a': {'b': {'c': 'd'}}}
>>> data['a']['b']['c']
'd'
>>> data2 = {'a': {'b': None}}
>>> data2['a']['b']['c']
Traceback (most recent call last):
...
TypeError: 'NoneType' object is not subscriptable
With glom
>>> glom(data, 'a.b.c')
'd'
>>> glom(data2, 'a.b.c')
Traceback (most recent call last):
...
PathAccessError: could not access 'c', index 2 in path Path('a', 'b', 'c'), got error: ...
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!