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Introducing the Socket Python SDK
The initial version of the Socket Python SDK is now on PyPI, enabling developers to more easily interact with the Socket REST API in Python projects.
Python library for denormalizing nested dicts or json objects to tables and back
Python library for denormalizing/flattening lists of complex objects to tables/data frames, with roundtripping
Given YAML/JSON/JSON-Lines such as:
- id: S001
name: Lord of the Rings
genres:
- fantasy
creator:
name: JRR Tolkein
from_country: England
books:
- id: S001.1
name: Fellowship of the Ring
price: 5.99
summary: Hobbits
- id: S001.2
name: The Two Towers
price: 5.99
summary: More hobbits
- id: S001.3
name: Return of the King
price: 6.99
summary: Yet more hobbits
- id: S002
name: The Culture Series
genres:
- scifi
creator:
name: Ian M Banks
from_country: Scotland
books:
- id: S002.1
name: Consider Phlebas
price: 5.99
- id: S002.2
name: Player of Games
price: 5.99
Denormalize using jfl
command:
jfl flatten -C creator=flat -C books=multivalued -i examples/books1.yaml -o examples/books1-flattened.tsv
id | name | genres | creator_name | creator_from_country | books_name | books_summary | books_price | books_id | creator_genres |
---|---|---|---|---|---|---|---|---|---|
S001 | Lord of the Rings | [fantasy] | JRR Tolkein | England | [Fellowship of the Ring|The Two Towers|Return of the King] | [Hobbits|More hobbits|Yet more hobbits] | [5.99|5.99|6.99] | [S001.1|S001.2|S001.3] | |
S002 | The Culture Series | [scifi] | Ian M Banks | Scotland | [Consider Phlebas|Player of Games] | [5.99|5.99] | [S002.1|S002.2] |
Convert back to JSON/YAML:
jfl unflatten -C creator=flat -C books=multivalued -i examples/books1.tsv -o examples/books1.yaml
This library also allows complex fields to be directly serialized as json or yaml (the default is to append _json
to the key). For example:
jfl flatten -C creator=json -C books=json -i examples/books1.yaml -o examples/books1-jsonified.tsv
id | name | genres | creator_json | books_json |
---|---|---|---|---|
S001 | Lord of the Rings | [fantasy] | {"name": "JRR Tolkein", "from_country": "England"} | [{"id": "S001.1", "name": "Fellowship of the Ring", "summary": "Hobbits", "price": 5.99}, {"id": "S001.2", "name": "The Two Towers", "summary": "More hobbits", "price": 5.99}, {"id": "S001.3", "name": "Return of the King", "summary": "Yet more hobbits", "price": 6.99}] |
S002 | The Culture Series | [scifi] | {"name": "Ian M Banks", "from_country": "Scotland"} | [{"id": "S002.1", "name": "Consider Phlebas", "price": 5.99}, {"id": "S002.2", "name": "Player of Games", "price": 5.99}] |
S003 | Book of the New Sun | [scifi, fantasy] | {"name": "Gene Wolfe", "genres": ["scifi", "fantasy"], "from_country": "USA"} | [{"id": "S003.1", "name": "Shadow of the Torturer"}, {"id": "S003.2", "name": "Claw of the Conciliator", "price": 6.99}] |
S004 | Example with single book | {"name": "Ms Writer", "genres": ["romance"], "from_country": "USA"} | [{"id": "S004.1", "name": "Blah"}] | |
S005 | Example with no books | {"name": "Mr Unproductive", "genres": ["romance", "scifi", "fantasy"], "from_country": "USA"} |
See
The primary use case is to go from a rich normalized data model (as python objects, JSON, or YAML) to a flatter representation that is amenable to processing with:
The target denormalized format is a list of rows / a data matrix, where each cell is either an atom or a list of atoms.
creator
and from_country
becomes creator_from_country
books
is a list of book objects, and name
is a key on book, then books_name
is a list of names of each bookbooks_name
is matched to the first element of books_price
, etcDocumentation coming soon: see test folder for now
https://github.com/wnameless/json-flattener
FAQs
Python library for denormalizing nested dicts or json objects to tables and back
We found that json-flattener demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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