flatten-tool
ndjson.github.io
flatten-tool | ndjson.github.io | |
---|---|---|
2 | 17 | |
101 | 23 | |
0.0% | - | |
5.4 | 0.0 | |
about 2 months ago | 9 months ago | |
Python | CSS | |
MIT License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
flatten-tool
-
Select, put and delete data from JSON, TOML, YAML, XML and CSV files
* https://flatten-tool.readthedocs.io/en/latest/
It's maintained by Open Data Services Coop, where we use it as a component in several of our web & data pipeline tools for working with data that is published in a Data Standard.
- Show HN: Transform a CSV into a JSON and vice versa
ndjson.github.io
-
What the fuck
However, since every JSON document can be represented in a single line, something like newline-delimited JSON / JSON Lines feels like it would've been more suitable for that kind of data.
- The XML spec is 25 years old today
-
Consider Using CSV
No one uses that format for streamed json, see ndson and jsonl
http://ndjson.org/
The size complaint is overblown, as repeated fields are compressed away.
As other folks rightfully commented, csv is a mine field. One should assume every CSV file is broken in some way. They also don't enumerate any of the downsides of CSV.
What people should consider is using formats like Avro or Parquet that carry their schema with them so the data can be loaded and analyzed without have to manually deal with column meaning.
-
DevTool Intro: The Algolia CLI!
What is ndjson? Newline delimited JSON is the format the Algolia CLI reads from and writes to files. This means that any command that passes ndjson formatted data as output or accepts it as input can be piped together with an Algolia CLI command! We’ll see more of this in the next example
-
On read of JSON file it loads the entire JSON into memory.
You might consider using json-lines format (also known as newline-delimited JSON), in which each line is a separate JSON document so they can be loaded individually.
-
How to format it as json?
The format you're getting is known as Newline-Delimited JSON. Instead of trying to parse the whole input and pass that to the JSON Decoder, you can use something like bufio.Scanner to get and parse it line by line.
-
Arrow2 0.12.0 released - including almost complete support for Parquet
This is in oposition to NDJSON, which allows to split records without deserializing JSON itself, via e.g. read_lines. fwiw CSV suffers from the same problem as JSON - generally not possible to break into records without deserializing. It is worse than NDJSON because the character \n may appear at any position within an item, thus forbidding read_lines.
-
Processing large JSON files in Python without running out of memory
I've always seen it referred to as ndjson
-
Speeding up Go's builtin JSON encoder up to 55% for large arrays of objects
I think this would be fine, as long as the CSV layer was still parsable using the RFC 4180, then you could still use a normal CSV parser to parse the CSV layer and a normal JSON parser to parse the JSON layer. My worry with your example is that it is nether format, so it will need custom serialisation and deserialisation logic as it is essentially a bran new format.
https://datatracker.ietf.org/doc/html/rfc4180
If you’re looking for line-oriented JSON, another option would be ndjson: http://ndjson.org/
- IETF should keep XMPP as IM standard, instead of Matrix
What are some alternatives?
flatterer - Opinionated JSON to CSV/XLSX/SQLITE/PARQUET converter. Flattens JSON fast.
ndjson - Streaming line delimited json parser + serializer
jsonmatic - ⚗️ Transform a CSV (spreadsheet) into a JSON.
miller - Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
yj - CLI - Convert between YAML, TOML, JSON, and HCL. Preserves map order.
babashka - A Clojure babushka for the grey areas of Bash (native fast-starting Clojure scripting environment) [Moved to: https://github.com/babashka/babashka]
datasette - An open source multi-tool for exploring and publishing data
grop - helper script for the `gron | grep | gron -u` workflow
brackit - Query processor with proven optimizations, ready to use for your JSON store to query semi-structured data with JSONiq. Can also be used as an ad-hoc in-memory query processor.
csv2sqlite