工具与资源github.com
TextQL：用 SQL 查询 CSV/TSV 文件
TextQL: Execute SQL Against CSV or TSV
2014 — 2018，它一次次回到首页
黑点是上首页（≥ 50 分）的那次投稿，空心点是没冲上去的投稿，点越大分数越高。
导读
TextQL 是一个命令行小工具，可以直接对 CSV 或 TSV 文件执行 SQL 查询，原理是把数据导入 SQLite 后运行查询。它让不写脚本也能快速筛选、分析表格数据变得非常简单。
为什么经久不衰：用 SQL 查询 CSV 是数据处理中反复出现的刚需，这类工具（TextQL、q、xsv 等）多年来持续被讨论，说明这个需求经久不衰，今天依然实用。
HN 评论精选
推荐同类工具 xsv 作为补充。
如果你经常处理 CSV，还可以看看 xsv——一个用于索引、切片、分析、拆分和连接 CSV 文件的命令行程序，作者是 Andrew Gallant（Go 和 Rust 社区知名的 burntsushi）。
kibwen查看英文原文
If you work with CSV a lot, then also check out xsv, "a command line program for indexing, slicing, analyzing, splitting and joining CSV files", from Andrew Gallant (burntsushi of Go and Rust fame): https://github.com/BurntSushi/xsv
指出更早的同类工具 q，功能几乎相同。
它看起来和 q 非常相似。q 已经存在很多年，社区很大，是一个可以直接对 CSV/TSV（以及任何表格文本文件）执行类 SQL 查询的命令行工具。
ravinizme查看英文原文
Seem very similar to q q is out there for years, with a very large community. q is a command line tool that allows direct execution of SQL-like queries on CSVs/TSVs (and any other tabular text files). http://harelba.github.io/q/
询问与 CSVKit 的差异，并给出 csvsql 的用法示例。
这个看起来非常简单易用。它和 CSVKit 有什么区别吗？CSVKit 里有个叫 csvsql 的工具，用法示例：csvsql --query "select name from data where age > 30" data.csv > new.csv。
ConceitedCode查看英文原文
This looks very simple and easy to use. Are there any differences with using this instead of CSVKit? https://csvkit.readthedocs.io/en/1.0.3/ It includes a tool called csvsql. Example usage - csvsql --query "select name from data where age > 30" data.csv > new.csv
指出 TextQL 缺少类型检测，并介绍 SQLite 虚拟表和 apsw 的自动导入方案。
它是把数据导入 SQLite 再在那里执行查询，我没看到任何类型检测的尝试。SQLite 有虚拟表功能，应用提供的代码可以提供底层数据，然后就能用 SQL 查询，这是处理外部数据格式最简单的方式。我是 SQLite 的 Python 封装 apsw 的作者，它提供了一个与官方兼容的命令行 shell，其中的 autoimport 命令可以自动推断分隔符和数据类型来导入 CSV 类数据。
rogerbinns查看英文原文
It is importing the data into SQLite and running the query there. I don't see any attempt at type detection. SQLite has virtual tables where app provided code can provide the underlying data and you can then use SQL queries against it. This is by far the easiest approach for foreign data formats. http://www.sqlite.org/vtab.html I'm the author of a Python wrapper for SQLite. It provides a shell compatible with the main SQLite one invocable from the command line (ie you don't need to go anywhere near Python) - http://rogerbinns.github.io/apsw/shell.html That shell has an autoimport command which automatically figures things out for csv like data (separators, data types etc) sqlite> .help autoimport .autoimport FILENAME ?TABLE? Imports filename creating a table and automatically working out separators and data types (alternative to .import command) The import command requires that you precisely pre-setup the table and schema, and set the data separators (eg commas or tabs). In many cases this information can be automatically deduced from the file contents which is what this command does. There must be at least two columns and two rows. If the table is not specified then the basename of the file will be used. Additionally the type of the contents of each column is also deduced - for example if it is a number or date. Empty values are turned into nulls. Dates are normalized into YYYY-MM-DD format and DateTime are normalized into ISO8601 format to allow easy sorting and searching. 4 digit years must be used to detect dates. US (swapped day and month) versus rest of the world is also detected providing there is at least one value that resolves the ambiguity. Care is taken to ensure that columns looking like numbers are only treated as numbers if they do not have unnecessary leading zeroes or plus signs. This is to avoid treating phone numbers and similar number like strings as integers. This command can take quite some time on large files as they are effectively imported twice. The first time is to determine the format and the types for each column while the second pass actually imports the data.
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