knoopx/nu-shell

nu-shell

Nushell treats all data as structured tables — lists of records with rows and columns. Every file and command output becomes a table you can filter, transform, and combine.

File I/O & Parsing

Files auto-detect from extension. Pipe command output into tables:

open data.csv                # CSV → table
df -h | detect columns       # Command output → table
$data | save -f output.csv   # Write (overwrite with -f)

Core Pipeline Commands

Filtering & Selecting

open data.csv | where rating > 4.0 and status == "active"
ls | sort-by size | reverse | first 10

• select col1 col2 — keep specific columns (keeps table shape)
• reject col — drop a column
• get col — extract as a list (not a table)

Transforming Data

# Map rows
$items | each { |row| { ...$row, tax: ($row.price * 0.1) } }

# Transform a column
$table | update price { |x| $x * 1.1 }

# Add or update columns
$table | insert new_col ($in.old_col * 2)

# Rename
$table | rename old_name new_name

Combining Data

$first | append $b          # Stack rows
$first | merge $second      # Side-by-side columns

String Operations

where name =~ "pattern"         # Regex match
str upcase / str downcase       # Case conversion
str trim / str kebab-case       # Formatting
str join "," $list              # Join list with separator
$"My value is ($expr)"          # Interpolation

Flow Control & Variables

let x = (open data.csv); mut count = 0
if ($x | length) > 0 { print "has" } else { print "empty" }
for row in $items { process $row }
match $value { "A" => do_a, "B" => do_b, _ => default_action }
try { open nonexistent.txt } catch { |err| print $"Error: ($err.msg)" }

# Capture external command output
do { ^my-command arg1 } | complete   # Returns .exit_code, .stdout, .stderr

Best Practices

• Prefer internal commands: Built-ins return structured data. Only use ^ prefix for external binaries when necessary.
• Collect before save: Use collect | save --force file to avoid read/write conflicts.
• Type safety: Empty cells parse as empty strings, not null. Filter empties before numeric conversion: where column != "" | into int.
• Prefer filters over loops: Use where, each, reduce instead of for/while — they stream and parallelize better.