Miller Share Help Try It

Like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON

Category

Text

License

open source

Platforms

Windows macOS Linux

Released

2015

Path

c:\miller\mlr.exe

Benefits

Use named fields without needing to count positional indices, using familiar formats such as CSV, TSV, JSON, JSON Lines, and positionally-indexed. Then, on the fly, you can add new fields which are functions of existing fields, drop fields, sort, aggregate statistically, pretty-print, and more.

Examples

mlr.exe --itsv --otsv put '$PostalCode = substr($PostalCode, 0,4)' d:\MLS-ZipPlus4.txt >d:\MLS-clean.txt

Try it

• Convert iris.csv to JSON
  Miller's verb chain is read-format / verb / write-format. --icsv tells Miller the input is CSV; --ojson sets the output to JSON. The 'cat' verb passes every row through unchanged, which combined with the format swap means a clean CSV-to-JSON transformation with no field reshaping. Output is JSON Lines (one record per line); tryit.cfm pretty-prints it centrally.
• Filter iris.csv to a single species
  The 'filter' verb runs a Miller DSL expression against every record and keeps only those that evaluate truthy. The expression $species == "setosa" (loaded from a sibling .mlr file via 'filter -f ...') selects iris-setosa rows by named field. Output is CSV with the same column shape as the input. The file-based approach is used because Miller's DSL requires double-quoted string literals AND Java's ProcessBuilder on Windows mangles embedded double quotes in argv; loading the expression from a file sidesteps shell quoting entirely (same trick as the JQ CSV demo).
• Per-species aggregate stats
  Use stats1 to compute one-pass summary statistics. -a mean,stddev,min,max picks the accumulators; -f sepal_length,petal_length picks the value columns; -g species groups the rows so the output has one summary record per distinct species. The chained --icsv --ojson reads CSV in and writes JSON out so the grouped breakdown is easy to scan.

Agree to terms to run demos.

# Miller - Help

Source: https://github.com/johnkerl/miller

```
Usage: mlr [flags] {verb} [verb-dependent options ...] {zero or more file names}

If zero file names are provided, standard input is read, e.g.
  mlr --csv sort -f shape example.csv

Output of one verb may be chained as input to another using "then", e.g.
  mlr --csv stats1 -a min,mean,max -f quantity then sort -f color example.csv

Please see 'mlr help topics' for more information.
Please also see https://miller.readthedocs.io
```