These are as discussed in File formats, with the exception of --right
which makes pretty-printed output right-aligned:
$ mlr --opprint cat data/small
a b i x y
pan pan 1 0.3467901443380824 0.7268028627434533
eks pan 2 0.7586799647899636 0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006 0.8636244699032729
$ mlr --opprint --right cat data/small
a b i x y
pan pan 1 0.3467901443380824 0.7268028627434533
eks pan 2 0.7586799647899636 0.5221511083334797
wye wye 3 0.20460330576630303 0.33831852551664776
eks wye 4 0.38139939387114097 0.13418874328430463
wye pan 5 0.5732889198020006 0.8636244699032729
Additional notes:
Use --csv, --pprint, etc. when the input and output formats are the same.
Use --icsv --opprint, etc. when you want format conversion as part of what Miller does to your data.
DKVP (key-value-pair) format is the default for input and output. So,
--oxtab is the same as --idkvp --oxtab.
Pro-tip: Please use either --format1, or --iformat1
--oformat2. If you use --format1 --oformat2 then what happens is
that flags are set up for input and output for format1, some of which
are overwritten for output in format2. For technical reasons, having
--oformat2 clobber all the output-related effects of
--format1 also removes some flexibility from the command-line
interface. See also
https://github.com/johnkerl/miller/issues/180 and
https://github.com/johnkerl/miller/issues/199.
In-place mode
Use the mlr -I flag to process files in-place. For example,
mlr -I --csv cut -x -f unwanted_column_name mydata/*.csv will remove
unwanted_column_name from all your *.csv files in your
mydata/ subdirectory.
By default, Miller output goes to the screen (or you can redirect a file
using > or to another process using |). With -I,
for each file name on the command line, output is written to a temporary file
in the same directory. Miller writes its output into that temp file, which is
then renamed over the original. Then, processing continues on the next file.
Each file is processed in isolation: if the output format is CSV, CSV headers
will be present in each output file; statistics are only over each file's own
records; and so on.
Please see here
for examples.
Compression
Options:
--prepipe {command}
The prepipe command is anything which reads from standard input and produces data acceptable to
Miller. Nominally this allows you to use whichever decompression utilities you have installed on your
system, on a per-file basis. If the command has flags, quote them: e.g. mlr --prepipe 'zcat -cf'. Examples:
# These two produce the same output:
$ gunzip < myfile1.csv.gz | mlr cut -f hostname,uptime
$ mlr --prepipe gunzip cut -f hostname,uptime myfile1.csv.gz
# With multiple input files you need --prepipe:
$ mlr --prepipe gunzip cut -f hostname,uptime myfile1.csv.gz myfile2.csv.gz
$ mlr --prepipe gunzip --idkvp --oxtab cut -f hostname,uptime myfile1.dat.gz myfile2.dat.gz
# Similar to the above, but with compressed output as well as input:
$ gunzip < myfile1.csv.gz | mlr cut -f hostname,uptime | gzip > outfile.csv.gz
$ mlr --prepipe gunzip cut -f hostname,uptime myfile1.csv.gz | gzip > outfile.csv.gz
$ mlr --prepipe gunzip cut -f hostname,uptime myfile1.csv.gz myfile2.csv.gz | gzip > outfile.csv.gz
# Similar to the above, but with different compression tools for input and output:
$ gunzip < myfile1.csv.gz | mlr cut -f hostname,uptime | xz -z > outfile.csv.xz
$ xz -cd < myfile1.csv.xz | mlr cut -f hostname,uptime | gzip > outfile.csv.xz
$ mlr --prepipe 'xz -cd' cut -f hostname,uptime myfile1.csv.xz myfile2.csv.xz | xz -z > outfile.csv.xz
... etc.
Record/field/pair separators
Miller has record separators IRS and ORS, field
separators IFS and OFS, and pair separators IPS and
OPS. For example, in the DKVP line a=1,b=2,c=3, the record
separator is newline, field separator is comma, and pair separator is the
equals sign. These are the default values.
Options:
You can change a separator from input to output via e.g. --ifs =
--ofs :. Or, you can specify that the same separator is to be used for
input and output via e.g. --fs :.
The pair separator is only relevant to DKVP format.
Pretty-print and xtab formats ignore the separator arguments altogether.
The --repifs means that multiple successive occurrences of the
field separator count as one. For example, in CSV data we often signify nulls
by empty strings, e.g. 2,9,,,,,6,5,4. On the other hand, if the field
separator is a space, it might be more natural to parse 2 4 5 the
same as 2 4 5: --repifs --ifs ' ' lets this happen. In fact,
the --ipprint option above is internally implemented in terms of
--repifs.
Just write out the desired separator, e.g. --ofs '|'. But you
may use the symbolic names newline, space, tab,
pipe, or semicolon if you like.
Number formatting
The command-line option --ofmt {format string} is the global
number format for commands which generate numeric output, e.g.
stats1, stats2, histogram, and step, as
well as mlr put. Examples:
--ofmt %.9le --ofmt %.6lf --ofmt %.0lf
These are just C printf formats applied to double-precision
numbers. Please don’t use %s or %d. Additionally, if
you use leading width (e.g. %18.12lf) then the output will contain
embedded whitespace, which may not be what you want if you pipe the output to
something else, particularly CSV. I use Miller’s pretty-print format
(mlr --opprint) to column-align numerical data.
To apply formatting to a single field, overriding the global
ofmt, use fmtnum function within mlr put. For example:
$ echo 'x=3.1,y=4.3' | mlr put '$z=fmtnum($x*$y,"%08lf")'
x=3.1,y=4.3,z=13.330000
$ echo 'x=0xffff,y=0xff' | mlr put '$z=fmtnum(int($x*$y),"%08llx")'
x=0xffff,y=0xff,z=00feff01
Input conversion from hexadecimal is done automatically on fields handled
by mlr put and mlr filter as long as the field value begins
with "0x". To apply output conversion to hexadecimal on a single column, you
may use fmtnum, or the keystroke-saving hexfmt function.
Example:
$ echo 'x=0xffff,y=0xff' | mlr put '$z=hexfmt($x*$y)'
x=0xffff,y=0xff,z=0xfeff01
You can, if you like, instead simply chain commands together using the
then keyword:
mlr cut --complement -f os_version then sort -f hostname,uptime *.dat
(You can precede the very first verb with then, if you like, for symmetry.)
Here’s a performance comparison:
% cat piped.sh
mlr cut -x -f i,y data/big | mlr sort -n y > /dev/null
% time sh piped.sh
real 0m2.828s
user 0m3.183s
sys 0m0.137s
% cat chained.sh
mlr cut -x -f i,y then sort -n y data/big > /dev/null
% time sh chained.sh
real 0m2.082s
user 0m1.933s
sys 0m0.137s
There are two reasons to use then-chaining: one is for performance, although I
don’t expect this to be a win in all cases. Using then-chaining avoids
redundant string-parsing and string-formatting at each pipeline step: instead
input records are parsed once, they are fed through each pipeline stage in
memory, and then output records are formatted once. On the other hand, Miller
is single-threaded, while modern systems are usually multi-processor, and when
streaming-data programs operate through pipes, each one can use a CPU. Rest
assured you get the same results either way.
The other reason to use then-chaining is for simplicity: you don’t
have re-type formatting flags (e.g. --csv --fs tab) at every
pipeline stage.
Auxiliary commands
There are a few nearly-standalone programs which have nothing to do with the rest of Miller, do not
participate in record streams, and do not deal with file formats. They might as well be little standalone executables
but they’re delivered within the main Miller executable for convenience.
$ mlr aux-list
Available subcommands:
aux-list
lecat
termcvt
hex
unhex
netbsd-strptime
For more information, please invoke mlr {subcommand} --help
$ mlr lecat --help
Usage: mlr lecat [options] {zero or more file names}
Simply echoes input, but flags CR characters in red and LF characters in green.
If zero file names are supplied, standard input is read.
Options:
--mono: don't try to colorize the output
-h or --help: print this message
$ mlr termcvt --help
Usage: mlr termcvt [option] {zero or more file names}
Option (exactly one is required):
--cr2crlf
--lf2crlf
--crlf2cr
--crlf2lf
--cr2lf
--lf2cr
-I in-place processing (default is to write to stdout)
-h or --help: print this message
Zero file names means read from standard input.
Output is always to standard output; files are not written in-place.
$ mlr hex --help
Usage: mlr hex [options] {zero or more file names}
Simple hex-dump.
If zero file names are supplied, standard input is read.
Options:
-r: print only raw hex without leading offset indicators or trailing ASCII dump.
-h or --help: print this message
$ mlr unhex --help
Usage: mlr unhex [option] {zero or more file names}
Options:
-h or --help: print this message
Zero file names means read from standard input.
Output is always to standard output; files are not written in-place.
Miller’s input and output are all string-oriented: there is (as of
August 2015 anyway) no support for binary record packing. In this sense,
everything is a string in and out of Miller. During processing, field names
are always strings, even if they have names like "3"; field values are usually
strings. Field values’ ability to be interpreted as a non-string type
only has meaning when comparison or function operations are done on them. And
it is an error condition if Miller encounters non-numeric (or otherwise
mistyped) data in a field in which it has been asked to do numeric (or
otherwise type-specific) operations.
Field values are treated as numeric for the following:
Miller’s types for function processing are empty-null (empty
string), absent-null (reads of unset right-hand sides, or fall-through
non-explicit return values from user-defined functions), error,
string, float (double-precision), int (64-bit signed), and
boolean.
On input, string values representable as numbers, e.g. "3" or "3.1", are
treated as int or float, respectively. If a record has x=1,y=2 then
mlr put '$z=$x+$y' will produce x=1,y=2,z=3, and mlr put
'$z=$x.$y' does not give an error simply because the dot operator has been
generalized to stringify non-strings. To coerce back to string for processing,
use the string function: mlr put '$z=string($x).string($y)'
will produce x=1,y=2,z=12.
On input, string values representable as boolean (e.g. "true",
"false") are not automatically treated as boolean. (This is
because "true" and "false" are ordinary words, and auto
string-to-boolean on a column consisting of words would result in some strings
mixed with some booleans.) Use the boolean function to coerce: e.g.
giving the record x=1,y=2,w=false to mlr put '$z=($x<$y) ||
boolean($w)'.
Functions take types as described in mlr --help-all-functions:
for example, log10 takes float input and produces float output,
gmt2sec maps string to int, and sec2gmt maps int to string.
All math functions described in mlr --help-all-functions take
integer as well as float input.
Null data: empty and absent
One of Miller’s key features is its support for heterogeneous
data. For example, take mlr sort: if you try to sort on field
hostname when not all records in the data stream have a field
named hostname, it is not an error (although you could pre-filter the
data stream using mlr having-fields --at-least hostname then sort
...). Rather, records lacking one or more sort keys are simply output
contiguously by mlr sort.
Miller has two kinds of null data:
Empty (key present, value empty): a field name is present in a
record (or in an out-of-stream variable) with empty value: e.g. x=,y=2
in the data input stream, or assignment $x="" or @x="" in
mlr put.
Absent (key not present): a field name is not present, e.g. input
record is x=1,y=2 and a put or filter expression
refers to $z. Or, reading an out-of-stream variable which hasn’t
been assigned a value yet, e.g. mlr put -q '@sum += $x; end{emit
@sum}' or mlr put -q '@sum[$a][$b] += $x; end{emit @sum, "a",
"b"}'.
You can test these programatically using the functions
is_empty/is_not_empty, is_absent/is_present, and
is_null/is_not_null. For the last pair, note that null means
either empty or absent.
Rules for null-handling:
Records with one or more empty sort-field values sort after records with
all sort-field values present:
Functions/operators which have one or more empty arguments produce empty output: e.g.
$ echo 'x=2,y=3' | mlr put '$a=$x+$y'
x=2,y=3,a=5
$ echo 'x=,y=3' | mlr put '$a=$x+$y'
x=,y=3,a=
$ echo 'x=,y=3' | mlr put '$a=log($x);$b=log($y)'
x=,y=3,a=,b=1.098612
with the exception that the min and max functions are
special: if one argument is non-null, it wins:
$ echo 'x=,y=3' | mlr put '$a=min($x,$y);$b=max($x,$y)'
x=,y=3,a=3,b=3
Functions of absent variables (e.g. mlr put '$y =
log10($nonesuch)') evaluate to absent, and arithmetic/bitwise/boolean
operators with both operands being absent evaluate to absent.
Arithmetic operators with one absent operand return the other operand.
More specifically, absent values act like zero for addition/subtraction, and
one for multiplication: Furthermore, any expression which evaluates to
absent is not stored in the left-hand side of an assignment statement :
$ echo 'x=2,y=3' | mlr put '$a=$u+$v; $b=$u+$y; $c=$x+$y'
x=2,y=3,b=3,c=5
$ echo 'x=2,y=3' | mlr put '$a=min($x,$v);$b=max($u,$y);$c=min($u,$v)'
x=2,y=3,a=2,b=3
Likewise, for assignment to maps, absent-valued keys or values result
in a skipped assignment.
The reasoning is as follows:
Empty values are explicit in the data so they should explicitly affect accumulations:
mlr put '@sum += $x'
should accumulate numeric x values into the sum but an empty
x, when encountered in the input data stream, should make the sum
non-numeric. To work around this you can use the
is_not_null function as follows:
mlr put 'is_not_null($x) { @sum += $x }'
Absent stream-record values should not break accumulations, since Miller
by design handles heterogenous data: the running @sum in
mlr put '@sum += $x'
should not be invalidated for records which have no x.
Absent out-of-stream-variable values are precisely what allow you to write
mlr put '@sum += $x'. Otherwise you would have to write
mlr put 'begin{@sum = 0}; @sum += $x' —
which is tolerable — but for
mlr put 'begin{...}; @sum[$a][$b] += $x'
you’d have to pre-initialize @sum for all values of $a and $b in your
input data stream, which is intolerable.
The penalty for the absent feature is that misspelled variables can be hard to find:
e.g. in mlr put 'begin{@sumx = 10}; ...; update @sumx somehow per-record; ...; end {@something = @sum * 2}'
the accumulator is spelt @sumx in the begin-block but @sum in the end-block, where since it
is absent, @sum*2 evaluates to 2. See also the section on
errors and transparency.
Since absent plus absent is absent (and likewise for other operators),
accumulations such as @sum += $x work correctly on heterogenous data,
as do within-record formulas if both operands are absent. If one operand is
present, you may get behavior you don’t desire. To work around this
— namely, to set an output field only for records which have all the
inputs present — you can use a pattern-action block with
is_present:
If you’re interested in a formal description of how empty and absent
fields participate in arithmetic, here’s a table for plus (other
arithmetic/boolean/bitwise operators are similar):
You can use the following backslash escapes for strings such as between the double quotes in contexts such as
mlr filter '$name =~ "..."',
mlr put '$name = $othername . "..."',
mlr put '$name = sub($name, "...", "..."), etc.:
\a: ASCII code 0x07 (alarm/bell)
\b: ASCII code 0x08 (backspace)
\f: ASCII code 0x0c (formfeed)
\n: ASCII code 0x0a (LF/linefeed/newline)
\r: ASCII code 0x0d (CR/carriage return)
\t: ASCII code 0x09 (tab)
\v: ASCII code 0x0b (vertical tab)
\\: backslash
\": double quote
\123: Octal 123, etc. for \000 up to \377\x7f: Hexadecimal 7f, etc. for \x00 up to \xff
See also https://en.wikipedia.org/wiki/Escape_sequences_in_C.
These replacements apply only to strings you key in for the DSL expressions for filter and put:
that is, if you type \t in a string literal for a filter/put expression, it will be turned into a tab character. If you want a backslash followed by a t, then please type \\t.
However, these replacements are not done automatically within your data stream. If you wish to make these
replacements, you can do, for example, for a field named field, mlr put '$field = gsub($field, "\\t",
"\t")'. If you need to make such a replacement for all fields in your data, you should probably simply use the
system sed command.
Regular expressions
Miller lets you use regular expressions (of type POSIX.2) in the following contexts:
In mlr filter with =~ or !=~, e.g. mlr
filter '$url =~ "http.*com"'
In mlr put with sub or gsub, e.g. mlr put
'$url = sub($url, "http.*com", "")'
In mlr having-fields, e.g. mlr having-fields
--any-matching '^sda[0-9]'
In mlr cut, e.g. mlr cut -r -f '^status$,^sda[0-9]'
In mlr rename, e.g. mlr rename -r '^(sda[0-9]).*$,dev/\1'
In mlr grep, e.g. mlr --csv grep 00188555487 myfiles*.csv
Points demonstrated by the above examples:
There are no implicit start-of-string or end-of-string anchors; please
use ^ and/or $ explicitly.
Miller regexes are wrapped with double quotes rather than slashes.
The i after the ending double quote indicates a case-insensitive
regex.
Capture groups are wrapped with (...) rather than
\(...\); use \( and \) to match against parentheses.
For filter and put, if the regular expression is a string
literal (the normal case), it is precompiled at process start and reused
thereafter, which is efficient. If the regular expression is a more complex
expression, including string concatenation using ., or a column name
(in which case you can take regular expressions from input data!), then regexes
are compiled on each record which works but is less efficient. As well, in this
case there is no way to specify case-insensitive matching.
Example:
Regex captures of the form \0 through \9 are supported as
follows:
Captures have in-function context for sub and gsub.
For example, the first \1,\2 pair belong to the first sub and
the second \1,\2 pair belong to the second sub:
Captures endure for the entirety of a put for the =~
and !=~ operators. For example, here the \1,\2 are set by the
=~ operator and are used by both subsequent assignment statements:
The captures are not retained across multiple puts. For example, here the
\1,\2 won’t be expanded from the regex capture:
mlr put '$a =~ "(..)_(....)' then {... something else ...} then put '$b = "left_\1"; $c = "right_\2"'
Captures are ignored in filter for the =~ and
!=~ operators. For example, there is no mechanism provided to refer to
the first (..) as \1 or to the second (....) as
\2 in the following filter statement:
mlr filter '$a =~ "(..)_(....)'
Up to nine matches are supported: \1 through \9, while
\0 is the entire match string; \15 is treated as \1
followed by an unrelated 5.
Arithmetic
Input scanning
Numbers in Miller are double-precision float or 64-bit signed integers.
Anything scannable as int, e.g 123 or 0xabcd, is treated as
an integer; otherwise, input scannable as float (4.56 or 8e9)
is treated as float; everything else is a string.
If you want all numbers to be treated as floats, then you may use
float() in your filter/put expressions (e.g. replacing $c = $a *
$b with $c = float($a) * float($b)) — or, more simply, use
mlr filter -F and mlr put -F which forces all numeric input,
whether from expression literals or field values, to float. Likewise mlr
stats1 -F and mlr step -F force integerable accumulators (such as
count) to be done in floating-point.
Conversion by math routines
For most math functions, integers are cast to float on input, and produce
float output: e.g. exp(0) = 1.0 rather than 1. The
following, however, produce integer output if their inputs are integers:
+-*///%absceilfloormaxminroundroundmsgn. As well, stats1 -a min, stats1 -a
max, stats1 -a sum, step -a delta, and step -a
rsum produce integer output if their inputs are integers.
Conversion by arithmetic operators
The sum, difference, and product of integers is again integer, except for
when that would overflow a 64-bit integer at which point Miller converts the
result to float.
The short of it is that Miller does this transparently for you so you
needn’t think about it.
Implementation details of this, for the interested: integer adds and
subtracts overflow by at most one bit so it suffices to check sign-changes.
Thus, Miller allows you to add and subtract arbitrary 64-bit signed integers,
converting only to float precisely when the result is less than -263
or greater than 263-1. Multiplies, on the other hand, can overflow
by a word size and a sign-change technique does not suffice to detect overflow.
Instead Miller tests whether the floating-point product exceeds the
representable integer range. Now, 64-bit integers have 64-bit precision while
IEEE-doubles have only 52-bit mantissas — so, there are 53 bits including
implicit leading one. The following experiment explicitly demonstrates the
resolution at this range:
64-bit integer 64-bit integer Casted to double Back to 64-bit
in hex in decimal integer
0x7ffffffffffff9ff 9223372036854774271 9223372036854773760.000000 0x7ffffffffffff800
0x7ffffffffffffa00 9223372036854774272 9223372036854773760.000000 0x7ffffffffffff800
0x7ffffffffffffbff 9223372036854774783 9223372036854774784.000000 0x7ffffffffffffc00
0x7ffffffffffffc00 9223372036854774784 9223372036854774784.000000 0x7ffffffffffffc00
0x7ffffffffffffdff 9223372036854775295 9223372036854774784.000000 0x7ffffffffffffc00
0x7ffffffffffffe00 9223372036854775296 9223372036854775808.000000 0x8000000000000000
0x7ffffffffffffffe 9223372036854775806 9223372036854775808.000000 0x8000000000000000
0x7fffffffffffffff 9223372036854775807 9223372036854775808.000000 0x8000000000000000
That is, one cannot check an integer product to see if it is precisely
greater than 263-1 or less than -263 using either integer
arithmetic (it may have already overflowed) or using double-precision (due to
granularity). Instead Miller checks for overflow in 64-bit integer
multiplication by seeing whether the absolute value of the double-precision
product exceeds the largest representable IEEE double less than 263,
which we see from the listing above is 9223372036854774784. (An alternative
would be to do all integer multiplies using handcrafted multi-word 128-bit
arithmetic. This approach is not taken.)
Quotient of integers is floating-point: 7/2 is 3.5.
Integer division is done with //: 7//2 is 3.
This rounds toward the negative.
Remainders are non-negative.
On-line help
Examples:
$ mlr --help
Usage: mlr [I/O options] {verb} [verb-dependent options ...] {zero or more file names}
Command-line-syntax examples:
mlr --csv cut -f hostname,uptime mydata.csv
mlr --tsv --rs lf filter '$status != "down" && $upsec >= 10000' *.tsv
mlr --nidx put '$sum = $7 < 0.0 ? 3.5 : $7 + 2.1*$8' *.dat
grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group
mlr join -j account_id -f accounts.dat then group-by account_name balances.dat
mlr --json put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*.json
mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/*
mlr stats2 -a linreg-pca -f u,v -g shape data/*
mlr put -q '@sum[$a][$b] += $x; end {emit @sum, "a", "b"}' data/*
mlr --from estimates.tbl put '
for (k,v in $*) {
if (is_numeric(v) && k =~ "^[t-z].*$") {
$sum += v; $count += 1
}
}
$mean = $sum / $count # no assignment if count unset'
mlr --from infile.dat put -f analyze.mlr
mlr --from infile.dat put 'tee > "./taps/data-".$a."-".$b, $*'
mlr --from infile.dat put 'tee | "gzip > ./taps/data-".$a."-".$b.".gz", $*'
mlr --from infile.dat put -q '@v=$*; dump | "jq .[]"'
mlr --from infile.dat put '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'
Data-format examples:
DKVP: delimited key-value pairs (Miller default format)
+---------------------+
| apple=1,bat=2,cog=3 | Record 1: "apple" => "1", "bat" => "2", "cog" => "3"
| dish=7,egg=8,flint | Record 2: "dish" => "7", "egg" => "8", "3" => "flint"
+---------------------+
NIDX: implicitly numerically indexed (Unix-toolkit style)
+---------------------+
| the quick brown | Record 1: "1" => "the", "2" => "quick", "3" => "brown"
| fox jumped | Record 2: "1" => "fox", "2" => "jumped"
+---------------------+
CSV/CSV-lite: comma-separated values with separate header line
+---------------------+
| apple,bat,cog |
| 1,2,3 | Record 1: "apple => "1", "bat" => "2", "cog" => "3"
| 4,5,6 | Record 2: "apple" => "4", "bat" => "5", "cog" => "6"
+---------------------+
Tabular JSON: nested objects are supported, although arrays within them are not:
+---------------------+
| { |
| "apple": 1, | Record 1: "apple" => "1", "bat" => "2", "cog" => "3"
| "bat": 2, |
| "cog": 3 |
| } |
| { |
| "dish": { | Record 2: "dish:egg" => "7", "dish:flint" => "8", "garlic" => ""
| "egg": 7, |
| "flint": 8 |
| }, |
| "garlic": "" |
| } |
+---------------------+
PPRINT: pretty-printed tabular
+---------------------+
| apple bat cog |
| 1 2 3 | Record 1: "apple => "1", "bat" => "2", "cog" => "3"
| 4 5 6 | Record 2: "apple" => "4", "bat" => "5", "cog" => "6"
+---------------------+
XTAB: pretty-printed transposed tabular
+---------------------+
| apple 1 | Record 1: "apple" => "1", "bat" => "2", "cog" => "3"
| bat 2 |
| cog 3 |
| |
| dish 7 | Record 2: "dish" => "7", "egg" => "8"
| egg 8 |
+---------------------+
Markdown tabular (supported for output only):
+-----------------------+
| | apple | bat | cog | |
| | --- | --- | --- | |
| | 1 | 2 | 3 | | Record 1: "apple => "1", "bat" => "2", "cog" => "3"
| | 4 | 5 | 6 | | Record 2: "apple" => "4", "bat" => "5", "cog" => "6"
+-----------------------+
Help options:
-h or --help Show this message.
--version Show the software version.
{verb name} --help Show verb-specific help.
--help-all-verbs Show help on all verbs.
-l or --list-all-verbs List only verb names.
-L List only verb names, one per line.
-f or --help-all-functions Show help on all built-in functions.
-F Show a bare listing of built-in functions by name.
-k or --help-all-keywords Show help on all keywords.
-K Show a bare listing of keywords by name.
Verbs:
altkv bar bootstrap cat check clean-whitespace count-distinct count-similar
cut decimate fill-down filter format-values fraction grep group-by
group-like having-fields head histogram join label least-frequent
merge-fields most-frequent nest nothing put regularize remove-empty-columns
rename reorder repeat reshape sample sec2gmt sec2gmtdate seqgen shuffle
skip-trivial-records sort stats1 stats2 step tac tail tee top uniq
unsparsify
Functions for the filter and put verbs:
+ + - - * / // .+ .+ .- .- .* ./ .// % ** | ^ & ~ << >> bitcount == != =~
!=~ > >= < <= && || ^^ ! ? : . gsub regextract regextract_or_else strlen sub
ssub substr tolower toupper capitalize lstrip rstrip strip
collapse_whitespace clean_whitespace system abs acos acosh asin asinh atan
atan2 atanh cbrt ceil cos cosh erf erfc exp expm1 floor invqnorm log log10
log1p logifit madd max mexp min mmul msub pow qnorm round roundm sgn sin
sinh sqrt tan tanh urand urandrange urand32 urandint dhms2fsec dhms2sec
fsec2dhms fsec2hms gmt2sec localtime2sec hms2fsec hms2sec sec2dhms sec2gmt
sec2gmt sec2gmtdate sec2localtime sec2localtime sec2localdate sec2hms
strftime strftime_local strptime strptime_local systime is_absent is_bool
is_boolean is_empty is_empty_map is_float is_int is_map is_nonempty_map
is_not_empty is_not_map is_not_null is_null is_numeric is_present is_string
asserting_absent asserting_bool asserting_boolean asserting_empty
asserting_empty_map asserting_float asserting_int asserting_map
asserting_nonempty_map asserting_not_empty asserting_not_map
asserting_not_null asserting_null asserting_numeric asserting_present
asserting_string boolean float fmtnum hexfmt int string typeof depth haskey
joink joinkv joinv leafcount length mapdiff mapexcept mapselect mapsum
splitkv splitkvx splitnv splitnvx
Please use "mlr --help-function {function name}" for function-specific help.
Data-format options, for input, output, or both:
--idkvp --odkvp --dkvp Delimited key-value pairs, e.g "a=1,b=2"
(this is Miller's default format).
--inidx --onidx --nidx Implicitly-integer-indexed fields
(Unix-toolkit style).
-T Synonymous with "--nidx --fs tab".
--icsv --ocsv --csv Comma-separated value (or tab-separated
with --fs tab, etc.)
--itsv --otsv --tsv Keystroke-savers for "--icsv --ifs tab",
"--ocsv --ofs tab", "--csv --fs tab".
--iasv --oasv --asv Similar but using ASCII FS 0x1f and RS 0x1e
--iusv --ousv --usv Similar but using Unicode FS U+241F (UTF-8 0xe2909f)
and RS U+241E (UTF-8 0xe2909e)
--icsvlite --ocsvlite --csvlite Comma-separated value (or tab-separated
with --fs tab, etc.). The 'lite' CSV does not handle
RFC-CSV double-quoting rules; is slightly faster;
and handles heterogeneity in the input stream via
empty newline followed by new header line. See also
http://johnkerl.org/miller/doc/file-formats.html#CSV/TSV/etc.
--itsvlite --otsvlite --tsvlite Keystroke-savers for "--icsvlite --ifs tab",
"--ocsvlite --ofs tab", "--csvlite --fs tab".
-t Synonymous with --tsvlite.
--iasvlite --oasvlite --asvlite Similar to --itsvlite et al. but using ASCII FS 0x1f and RS 0x1e
--iusvlite --ousvlite --usvlite Similar to --itsvlite et al. but using Unicode FS U+241F (UTF-8 0xe2909f)
and RS U+241E (UTF-8 0xe2909e)
--ipprint --opprint --pprint Pretty-printed tabular (produces no
output until all input is in).
--right Right-justifies all fields for PPRINT output.
--barred Prints a border around PPRINT output
(only available for output).
--omd Markdown-tabular (only available for output).
--ixtab --oxtab --xtab Pretty-printed vertical-tabular.
--xvright Right-justifies values for XTAB format.
--ijson --ojson --json JSON tabular: sequence or list of one-level
maps: {...}{...} or [{...},{...}].
--json-map-arrays-on-input JSON arrays are unmillerable. --json-map-arrays-on-input
--json-skip-arrays-on-input is the default: arrays are converted to integer-indexed
--json-fatal-arrays-on-input maps. The other two options cause them to be skipped, or
to be treated as errors. Please use the jq tool for full
JSON (pre)processing.
--jvstack Put one key-value pair per line for JSON
output.
--jlistwrap Wrap JSON output in outermost [ ].
--jknquoteint Do not quote non-string map keys in JSON output.
--jvquoteall Quote map values in JSON output, even if they're
numeric.
--jflatsep {string} Separator for flattening multi-level JSON keys,
e.g. '{"a":{"b":3}}' becomes a:b => 3 for
non-JSON formats. Defaults to :.
-p is a keystroke-saver for --nidx --fs space --repifs
Examples: --csv for CSV-formatted input and output; --idkvp --opprint for
DKVP-formatted input and pretty-printed output.
Please use --iformat1 --oformat2 rather than --format1 --oformat2.
The latter sets up input and output flags for format1, not all of which
are overridden in all cases by setting output format to format2.
Comments in data:
--skip-comments Ignore commented lines (prefixed by "#")
within the input.
--skip-comments-with {string} Ignore commented lines within input, with
specified prefix.
--pass-comments Immediately print commented lines (prefixed by "#")
within the input.
--pass-comments-with {string} Immediately print commented lines within input, with
specified prefix.
Notes:
* Comments are only honored at the start of a line.
* In the absence of any of the above four options, comments are data like
any other text.
* When pass-comments is used, comment lines are written to standard output
immediately upon being read; they are not part of the record stream.
Results may be counterintuitive. A suggestion is to place comments at the
start of data files.
Format-conversion keystroke-saver options, for input, output, or both:
As keystroke-savers for format-conversion you may use the following:
--c2t --c2d --c2n --c2j --c2x --c2p --c2m
--t2c --t2d --t2n --t2j --t2x --t2p --t2m
--d2c --d2t --d2n --d2j --d2x --d2p --d2m
--n2c --n2t --n2d --n2j --n2x --n2p --n2m
--j2c --j2t --j2d --j2n --j2x --j2p --j2m
--x2c --x2t --x2d --x2n --x2j --x2p --x2m
--p2c --p2t --p2d --p2n --p2j --p2x --p2m
The letters c t d n j x p m refer to formats CSV, TSV, DKVP, NIDX, JSON, XTAB,
PPRINT, and markdown, respectively. Note that markdown format is available for
output only.
Compressed-data options:
--prepipe {command} This allows Miller to handle compressed inputs. You can do
without this for single input files, e.g. "gunzip < myfile.csv.gz | mlr ...".
However, when multiple input files are present, between-file separations are
lost; also, the FILENAME variable doesn't iterate. Using --prepipe you can
specify an action to be taken on each input file. This pre-pipe command must
be able to read from standard input; it will be invoked with
{command} < {filename}.
Examples:
mlr --prepipe 'gunzip'
mlr --prepipe 'zcat -cf'
mlr --prepipe 'xz -cd'
mlr --prepipe cat
Note that this feature is quite general and is not limited to decompression
utilities. You can use it to apply per-file filters of your choice.
For output compression (or other) utilities, simply pipe the output:
mlr ... | {your compression command}
Separator options, for input, output, or both:
--rs --irs --ors Record separators, e.g. 'lf' or '\r\n'
--fs --ifs --ofs --repifs Field separators, e.g. comma
--ps --ips --ops Pair separators, e.g. equals sign
Notes about line endings:
* Default line endings (--irs and --ors) are "auto" which means autodetect from
the input file format, as long as the input file(s) have lines ending in either
LF (also known as linefeed, '\n', 0x0a, Unix-style) or CRLF (also known as
carriage-return/linefeed pairs, '\r\n', 0x0d 0x0a, Windows style).
* If both irs and ors are auto (which is the default) then LF input will lead to LF
output and CRLF input will lead to CRLF output, regardless of the platform you're
running on.
* The line-ending autodetector triggers on the first line ending detected in the input
stream. E.g. if you specify a CRLF-terminated file on the command line followed by an
LF-terminated file then autodetected line endings will be CRLF.
* If you use --ors {something else} with (default or explicitly specified) --irs auto
then line endings are autodetected on input and set to what you specify on output.
* If you use --irs {something else} with (default or explicitly specified) --ors auto
then the output line endings used are LF on Unix/Linux/BSD/MacOSX, and CRLF on Windows.
Notes about all other separators:
* IPS/OPS are only used for DKVP and XTAB formats, since only in these formats
do key-value pairs appear juxtaposed.
* IRS/ORS are ignored for XTAB format. Nominally IFS and OFS are newlines;
XTAB records are separated by two or more consecutive IFS/OFS -- i.e.
a blank line. Everything above about --irs/--ors/--rs auto becomes --ifs/--ofs/--fs
auto for XTAB format. (XTAB's default IFS/OFS are "auto".)
* OFS must be single-character for PPRINT format. This is because it is used
with repetition for alignment; multi-character separators would make
alignment impossible.
* OPS may be multi-character for XTAB format, in which case alignment is
disabled.
* TSV is simply CSV using tab as field separator ("--fs tab").
* FS/PS are ignored for markdown format; RS is used.
* All FS and PS options are ignored for JSON format, since they are not relevant
to the JSON format.
* You can specify separators in any of the following ways, shown by example:
- Type them out, quoting as necessary for shell escapes, e.g.
"--fs '|' --ips :"
- C-style escape sequences, e.g. "--rs '\r\n' --fs '\t'".
- To avoid backslashing, you can use any of the following names:
cr crcr newline lf lflf crlf crlfcrlf tab space comma pipe slash colon semicolon equals
* Default separators by format:
File format RS FS PS
gen N/A (N/A) (N/A)
dkvp auto , =
json auto (N/A) (N/A)
nidx auto space (N/A)
csv auto , (N/A)
csvlite auto , (N/A)
markdown auto (N/A) (N/A)
pprint auto space (N/A)
xtab (N/A) auto space
Relevant to CSV/CSV-lite input only:
--implicit-csv-header Use 1,2,3,... as field labels, rather than from line 1
of input files. Tip: combine with "label" to recreate
missing headers.
--allow-ragged-csv-input|--ragged If a data line has fewer fields than the header line,
fill remaining keys with empty string. If a data line has more
fields than the header line, use integer field labels as in
the implicit-header case.
--headerless-csv-output Print only CSV data lines.
-N Keystroke-saver for --implicit-csv-header --headerless-csv-output.
Double-quoting for CSV output:
--quote-all Wrap all fields in double quotes
--quote-none Do not wrap any fields in double quotes, even if they have
OFS or ORS in them
--quote-minimal Wrap fields in double quotes only if they have OFS or ORS
in them (default)
--quote-numeric Wrap fields in double quotes only if they have numbers
in them
--quote-original Wrap fields in double quotes if and only if they were
quoted on input. This isn't sticky for computed fields:
e.g. if fields a and b were quoted on input and you do
"put '$c = $a . $b'" then field c won't inherit a or b's
was-quoted-on-input flag.
Numerical formatting:
--ofmt {format} E.g. %.18lf, %.0lf. Please use sprintf-style codes for
double-precision. Applies to verbs which compute new
values, e.g. put, stats1, stats2. See also the fmtnum
function within mlr put (mlr --help-all-functions).
Defaults to %lf.
Other options:
--seed {n} with n of the form 12345678 or 0xcafefeed. For put/filter
urand()/urandint()/urand32().
--nr-progress-mod {m}, with m a positive integer: print filename and record
count to stderr every m input records.
--from {filename} Use this to specify an input file before the verb(s),
rather than after. May be used more than once. Example:
"mlr --from a.dat --from b.dat cat" is the same as
"mlr cat a.dat b.dat".
-n Process no input files, nor standard input either. Useful
for mlr put with begin/end statements only. (Same as --from
/dev/null.) Also useful in "mlr -n put -v '...'" for
analyzing abstract syntax trees (if that's your thing).
-I Process files in-place. For each file name on the command
line, output is written to a temp file in the same
directory, which is then renamed over the original. Each
file is processed in isolation: if the output format is
CSV, CSV headers will be present in each output file;
statistics are only over each file's own records; and so on.
Then-chaining:
Output of one verb may be chained as input to another using "then", e.g.
mlr stats1 -a min,mean,max -f flag,u,v -g color then sort -f color
Auxiliary commands:
Miller has a few otherwise-standalone executables packaged within it.
They do not participate in any other parts of Miller.
Available subcommands:
aux-list
lecat
termcvt
hex
unhex
netbsd-strptime
For more information, please invoke mlr {subcommand} --help
For more information please see http://johnkerl.org/miller/doc and/or
http://github.com/johnkerl/miller. This is Miller version v5.6.3.
$ mlr sort --help
Usage: mlr sort {flags}
Flags:
-f {comma-separated field names} Lexical ascending
-n {comma-separated field names} Numerical ascending; nulls sort last
-nf {comma-separated field names} Same as -n
-r {comma-separated field names} Lexical descending
-nr {comma-separated field names} Numerical descending; nulls sort first
Sorts records primarily by the first specified field, secondarily by the second
field, and so on. (Any records not having all specified sort keys will appear
at the end of the output, in the order they were encountered, regardless of the
specified sort order.) The sort is stable: records that compare equal will sort
in the order they were encountered in the input record stream.
Example:
mlr sort -f a,b -nr x,y,z
which is the same as:
mlr sort -f a -f b -nr x -nr y -nr z