h3-py
spyql
| h3-py | spyql | |
|---|---|---|
| 1 | 23 | |
| 763 | 902 | |
| 1.6% | - | |
| 6.6 | 0.0 | |
| 1 day ago | over 1 year ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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h3-py
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Command-line data analytics made easy with SPyQL
Another advantage of SPyQL is that we can leverage the Python ecosystem. Let's try to do some more geographical statistics. Let's count towers by H3 cell (resolution 5) for Europe. First, we need to install the H3 lib:
spyql
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Fq: Jq for Binary Formats
I prefer a SQL-like format. It’s not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!
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Command-line data analytics made easy with SPyQL
SPyQL documentation: spyql.readthedocs.io
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This Week In Python
spyql – Query data on the command line with SQL-like SELECTs powered by Python expressions
- Command-line data analytics made easy
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Jc – JSONifies the output of many CLI tools
This is great!
I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.
I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.
[1] https://github.com/dcmoura/spyql
- The fastest command-line tools for querying large JSON datasets
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Working with more than 10gb csv
You can import the data into a PostgreSQL/MySQL/SQLite/... database and then query the database. However, even with the right choice of indexes, it might take a while to run queries on a table with hundreds of millions of records. You can easily import your data to these databases with SpyQL: $ spyql "SELECT * FROM csv TO sql(table=my_table_name) | sqlite3 my.db" (you would need to create the table my_table_name before running the command).
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ClickHouse Cloud is now in Public Beta
https://github.com/dcmoura/spyql/blob/master/notebooks/json_...
And ClickHouse looks like a normal relational database - there is no need for multiple components for different tiers (like in Druid), no need for manual partitioning into "daily", "hourly" tables (like you do in Spark and Bigquery), no need for lambda architecture... It's refreshing how something can be both simple and fast.
- A SQLite extension for reading large files line-by-line
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I want to convert a large JSON file into Tabular Format.
I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql
What are some alternatives?
leafmap - A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment
prql - PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
clean-architecture - Example project showing off clean/hexagonal architecture concepts in Python
malloy - Malloy is an experimental language for describing data relationships and transformations.
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
tresql - Shorthand SQL/JDBC wrapper language, providing nested results as JSON and more
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
Preql - An interpreted relational query language that compiles to SQL.
felicette - Satellite imagery for dummies.
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
matplotcli - Create matplotlib visualizations from the command-line
pxi - 🧚 pxi (pixie) is a small, fast, and magical command-line data processor similar to jq, mlr, and awk.