vinum
q
vinum | q | |
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5 | 46 | |
65 | 10,126 | |
- | - | |
0.0 | 2.1 | |
almost 3 years ago | 14 days ago | |
C++ | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 only |
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vinum
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Practical SQL for Data Analysis(what you can do without Pandas)
Following similar observations I was wondering if one can actually execute SQL queries inside of Python process with the access to native Python functions and Numpy as UDFs. Thanks to Apache Arrow one can mix C++ and Python operators without need to copy the data and essentially combine DataFrame API with SQL, all while within the confines of the same Python process.
https://github.com/dmitrykoval/vinum
Vinum allows users to write queries which may invoke any Numpy or Python functions as UDFs available to the interpreter.
- Vinum is a SQL query processor for Python, designed for data analysis workflows
- dmitrykoval/vinum Vinum is a SQL query processor for Python, designed for data analysis workflows and in-memory analytics.
- Vinum – SQL Processor for Python with native Python, Numpy UDF support.
- Show HN: Vinum – SQL Processor for Python with Native Python, Numpy UDF Support
q
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I wrote this iCalendar (.ics) command-line utility to turn common calendar exports into more broadly compatible CSV files.
CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller
- Segítség kérés Excel automatizáláshoz
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Show HN: ClickHouse-local – a small tool for serverless data analytics
I think they're talking about https://github.com/harelba/q, which is not very fast.
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sqly - execute SQL against CSV / JSON with shell
Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.
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Q – Run SQL Directly on CSV or TSV Files
Hi, author of q here.
Regarding the error you got, q currently does not autodetect headers, so you'd need to add -H as a flag in order to use the "country" column name. You're absolutely correct on failing-fast here - It's a bug which i'll fix.
In general regarding speed - q supports automatic caching of the CSV files (through the "-C readwrite" flag). Once it's activated, it will write the data into another file (with a .qsql extension), and will use it automatically in further queries in order to speed things considerably.
Effectively, the .qsql files are regular sqlite3 files (with some metadata), and q can be used to query them directly (or any regular sqlite3 file), including the ability to seamlessly join between multiple sqlite3 files.
http://harelba.github.io/q/#auto-caching-examples
- PostgreSQL alternative for Large amounts of data
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q VS trdsql - a user suggested alternative
2 projects | 25 Jun 2022
- One-liner for running queries against CSV files with SQLite
What are some alternatives?
siuba - Python library for using dplyr like syntax with pandas and SQL
textql - Execute SQL against structured text like CSV or TSV
NumCpp - C++ implementation of the Python Numpy library
csvq - SQL-like query language for csv
turbodbc - Turbodbc is a Python module to access relational databases via the Open Database Connectivity (ODBC) interface. The module complies with the Python Database API Specification 2.0.
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
datatable - A Python package for manipulating 2-dimensional tabular data structures
InquirerPy - :snake: Python port of Inquirer.js (A collection of common interactive command-line user interfaces)
duckdb - DuckDB is an in-process SQL OLAP Database Management System
xsv - A fast CSV command line toolkit written in Rust.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
ledger - Double-entry accounting system with a command-line reporting interface