csvkit

Display CSV file in a readable table format

$ csvlook [data.csv]

$ csvstat [data.csv]

$ csvcut -c [col1,col2] [data.csv]

$ csvsort -c [column] [data.csv]

$ csvsql --query "[SELECT * FROM data WHERE id > 100]" [data.csv]

$ csvstack [file1.csv] [file2.csv]

csvkit is a suite of utilities for working with CSV files

csvkit is a comprehensive suite of command-line tools for working with CSV files. It brings database-like operations to tabular data without requiring a database, following Unix philosophy principles.
The tools handle CSV quoting and escaping correctly, avoiding the pitfalls of using awk, sed, or cut directly on CSV data. They support various input encodings and delimiters, making them versatile for real-world data processing.
csvkit is particularly useful for data journalism, quick data exploration, ETL processes, and as part of data pipelines. All tools can read from stdin and write to stdout for easy chaining.

in2csv

Convert various formats (Excel, JSON) to CSV.

Display CSV in a human-readable table format.

Generate statistics for CSV columns.

Select and reorder columns.

Filter rows by column values.

Sort rows by columns.

Join two CSV files on common columns.

Concatenate CSV files vertically.

Query CSV files using SQL.

Convert CSV to JSON.

Validate and fix CSV formatting issues.

Convert CSV to other delimited formats.

Some operations load entire files into memory. Type inference can sometimes misclassify data. Performance may be slower than specialized tools for very large files. Requires Python installation.

csvkit was created by Christopher Groskopf and first released in 2011. It was designed to provide data journalists and analysts with powerful command-line tools for processing CSV data, becoming a standard toolkit in the data science community.

miller(1), xsv(1), jq(1), awk(1)