Verbs reference¶
Overview¶
When you type mlr {something} myfile.dat
, the {something}
part is called a verb. It specifies how you want to transform your data. (See also Command overview for a breakdown.) The following is an alphabetical list of verbs with their descriptions.
The verbs put
and filter
are special in that they have a rich expression language (domain-specific language, or “DSL”). More information about them can be found at DSL reference.
Here’s a comparison of verbs and put
/filter
DSL expressions:
Example:
$ mlr stats1 -a sum -f x -g a data/small
a=pan,x_sum=0.346790
a=eks,x_sum=1.140079
a=wye,x_sum=0.777892
Verbs are coded in C
They run a bit faster
They take fewer keystrokes
There is less to learn
Their customization is limited to each verb’s options
Example:
$ mlr put -q '@x_sum[$a] += $x; end{emit @x_sum, "a"}' data/small
a=pan,x_sum=0.346790
a=eks,x_sum=1.140079
a=wye,x_sum=0.777892
You get to write your own DSL expressions
They run a bit slower
They take more keystrokes
There is more to learn
They are highly customizable
altkv¶
Map list of values to alternating key/value pairs.
$ mlr altkv -h
Usage: mlr altkv [no options]
Given fields with values of the form a,b,c,d,e,f emits a=b,c=d,e=f pairs.
$ echo 'a,b,c,d,e,f' | mlr altkv
a=b,c=d,e=f
$ echo 'a,b,c,d,e,f,g' | mlr altkv
a=b,c=d,e=f,4=g
bar¶
Cheesy bar-charting.
$ mlr bar -h
Usage: mlr bar [options]
Replaces a numeric field with a number of asterisks, allowing for cheesy
bar plots. These align best with --opprint or --oxtab output format.
Options:
-f {a,b,c} Field names to convert to bars.
-c {character} Fill character: default '*'.
-x {character} Out-of-bounds character: default '#'.
-b {character} Blank character: default '.'.
--lo {lo} Lower-limit value for min-width bar: default '0.000000'.
--hi {hi} Upper-limit value for max-width bar: default '100.000000'.
-w {n} Bar-field width: default '40'.
--auto Automatically computes limits, ignoring --lo and --hi.
Holds all records in memory before producing any output.
$ 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 bar --lo 0 --hi 1 -f x,y data/small
a b i x y
pan pan 1 *************........................... *****************************...........
eks pan 2 ******************************.......... ********************....................
wye wye 3 ********................................ *************...........................
eks wye 4 ***************......................... *****...................................
wye pan 5 **********************.................. **********************************......
$ mlr --opprint bar --lo 0.4 --hi 0.6 -f x,y data/small
a b i x y
pan pan 1 #....................................... ***************************************#
eks pan 2 ***************************************# ************************................
wye wye 3 #....................................... #.......................................
eks wye 4 #....................................... #.......................................
wye pan 5 **********************************...... ***************************************#
$ mlr --opprint bar --auto -f x,y data/small
a b i x y
pan pan 1 [0.204603]**********..............................[0.75868] [0.134189]********************************........[0.863624]
eks pan 2 [0.204603]***************************************#[0.75868] [0.134189]*********************...................[0.863624]
wye wye 3 [0.204603]#.......................................[0.75868] [0.134189]***********.............................[0.863624]
eks wye 4 [0.204603]************............................[0.75868] [0.134189]#.......................................[0.863624]
wye pan 5 [0.204603]**************************..............[0.75868] [0.134189]***************************************#[0.863624]
bootstrap¶
$ mlr bootstrap --help
Usage: mlr bootstrap [options]
Emits an n-sample, with replacement, of the input records.
Options:
-n {number} Number of samples to output. Defaults to number of input records.
Must be non-negative.
See also mlr sample and mlr shuffle.
The canonical use for bootstrap sampling is to put error bars on statistical quantities, such as mean. For example:
$ mlr --opprint stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color u_mean u_count
yellow 0.497129 1413
red 0.492560 4641
purple 0.494005 1142
green 0.504861 1109
blue 0.517717 1470
orange 0.490532 303
$ mlr --opprint bootstrap then stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color u_mean u_count
yellow 0.500651 1380
purple 0.501556 1111
green 0.503272 1068
red 0.493895 4702
blue 0.512529 1496
orange 0.521030 321
$ mlr --opprint bootstrap then stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color u_mean u_count
yellow 0.498046 1485
blue 0.513576 1417
red 0.492870 4595
orange 0.507697 307
green 0.496803 1075
purple 0.486337 1199
$ mlr --opprint bootstrap then stats1 -a mean,count -f u -g color data/colored-shapes.dkvp
color u_mean u_count
blue 0.522921 1447
red 0.490717 4617
yellow 0.496450 1419
purple 0.496523 1192
green 0.507569 1111
orange 0.468014 292
cat¶
Most useful for format conversions (see File formats, and concatenating multiple same-schema CSV files to have the same header:
$ mlr cat -h
Usage: mlr cat [options]
Passes input records directly to output. Most useful for format conversion.
Options:
-n Prepend field "n" to each record with record-counter starting at 1
-g {comma-separated field name(s)} When used with -n/-N, writes record-counters
keyed by specified field name(s).
-v Write a low-level record-structure dump to stderr.
-N {name} Prepend field {name} to each record with record-counter starting at 1
$ cat data/a.csv
a,b,c
1,2,3
4,5,6
$ cat data/b.csv
a,b,c
7,8,9
$ mlr --csv cat data/a.csv data/b.csv
a,b,c
1,2,3
4,5,6
7,8,9
$ mlr --icsv --oxtab cat data/a.csv data/b.csv
a 1
b 2
c 3
a 4
b 5
c 6
a 7
b 8
c 9
$ mlr --csv cat -n data/a.csv data/b.csv
n,a,b,c
1,1,2,3
2,4,5,6
3,7,8,9
$ 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 cat -n -g a data/small
n a b i x y
1 pan pan 1 0.3467901443380824 0.7268028627434533
1 eks pan 2 0.7586799647899636 0.5221511083334797
1 wye wye 3 0.20460330576630303 0.33831852551664776
2 eks wye 4 0.38139939387114097 0.13418874328430463
2 wye pan 5 0.5732889198020006 0.8636244699032729
check¶
$ mlr check --help
Usage: mlr check
Consumes records without printing any output.
Useful for doing a well-formatted check on input data.
clean-whitespace¶
$ mlr clean-whitespace --help
Usage: mlr clean-whitespace [options]
For each record, for each field in the record, whitespace-cleans the keys and
values. Whitespace-cleaning entails stripping leading and trailing whitespace,
and replacing multiple whitespace with singles. For finer-grained control,
please see the DSL functions lstrip, rstrip, strip, collapse_whitespace,
and clean_whitespace.
Options:
-k|--keys-only Do not touch values.
-v|--values-only Do not touch keys.
It is an error to specify -k as well as -v -- to clean keys and values,
leave off -k as well as -v.
$ mlr --icsv --ojson cat data/clean-whitespace.csv
{ " Name ": " Ann Simons", " Preference ": " blue " }
{ " Name ": "Bob Wang ", " Preference ": " red " }
{ " Name ": " Carol Vee", " Preference ": " yellow" }
$ mlr --icsv --ojson clean-whitespace -k data/clean-whitespace.csv
{ "Name": " Ann Simons", "Preference": " blue " }
{ "Name": "Bob Wang ", "Preference": " red " }
{ "Name": " Carol Vee", "Preference": " yellow" }
$ mlr --icsv --ojson clean-whitespace -v data/clean-whitespace.csv
{ " Name ": "Ann Simons", " Preference ": "blue" }
{ " Name ": "Bob Wang", " Preference ": "red" }
{ " Name ": "Carol Vee", " Preference ": "yellow" }
$ mlr --icsv --ojson clean-whitespace data/clean-whitespace.csv
{ "Name": "Ann Simons", "Preference": "blue" }
{ "Name": "Bob Wang", "Preference": "red" }
{ "Name": "Carol Vee", "Preference": "yellow" }
Function links:
count¶
$ mlr count --help
Usage: mlr count [options]
Prints number of records, optionally grouped by distinct values for specified field names.
Options:
-g {a,b,c} Field names for distinct count.
-n Show only the number of distinct values. Not interesting without -g.
-o {name} Field name for output count. Default "count".
$ mlr count data/medium
count=10000
$ mlr count -g a data/medium
a=pan,count=2081
a=eks,count=1965
a=wye,count=1966
a=zee,count=2047
a=hat,count=1941
$ mlr count -n -g a data/medium
count=5
$ mlr count -g b data/medium
b=pan,count=1942
b=wye,count=2057
b=zee,count=1943
b=eks,count=2008
b=hat,count=2050
$ mlr count -n -g b data/medium
count=5
$ mlr count -g a,b data/medium
a=pan,b=pan,count=427
a=eks,b=pan,count=371
a=wye,b=wye,count=377
a=eks,b=wye,count=407
a=wye,b=pan,count=392
a=zee,b=pan,count=389
a=eks,b=zee,count=357
a=zee,b=wye,count=455
a=hat,b=wye,count=423
a=pan,b=wye,count=395
a=zee,b=eks,count=391
a=hat,b=zee,count=385
a=hat,b=eks,count=389
a=wye,b=hat,count=426
a=pan,b=eks,count=429
a=eks,b=eks,count=413
a=hat,b=hat,count=381
a=hat,b=pan,count=363
a=zee,b=zee,count=403
a=pan,b=hat,count=417
a=pan,b=zee,count=413
a=zee,b=hat,count=409
a=wye,b=zee,count=385
a=eks,b=hat,count=417
a=wye,b=eks,count=386
count-distinct¶
$ mlr count-distinct --help
Usage: mlr count-distinct [options]
Prints number of records having distinct values for specified field names.
Same as uniq -c.
Options:
-f {a,b,c} Field names for distinct count.
-n Show only the number of distinct values. Not compatible with -u.
-o {name} Field name for output count. Default "count".
Ignored with -u.
-u Do unlashed counts for multiple field names. With -f a,b and
without -u, computes counts for distinct combinations of a
and b field values. With -f a,b and with -u, computes counts
for distinct a field values and counts for distinct b field
values separately.
$ mlr count-distinct -f a,b then sort -nr count data/medium
a=zee,b=wye,count=455
a=pan,b=eks,count=429
a=pan,b=pan,count=427
a=wye,b=hat,count=426
a=hat,b=wye,count=423
a=pan,b=hat,count=417
a=eks,b=hat,count=417
a=eks,b=eks,count=413
a=pan,b=zee,count=413
a=zee,b=hat,count=409
a=eks,b=wye,count=407
a=zee,b=zee,count=403
a=pan,b=wye,count=395
a=wye,b=pan,count=392
a=zee,b=eks,count=391
a=zee,b=pan,count=389
a=hat,b=eks,count=389
a=wye,b=eks,count=386
a=hat,b=zee,count=385
a=wye,b=zee,count=385
a=hat,b=hat,count=381
a=wye,b=wye,count=377
a=eks,b=pan,count=371
a=hat,b=pan,count=363
a=eks,b=zee,count=357
$ mlr count-distinct -u -f a,b data/medium
field=a,value=pan,count=2081
field=a,value=eks,count=1965
field=a,value=wye,count=1966
field=a,value=zee,count=2047
field=a,value=hat,count=1941
field=b,value=pan,count=1942
field=b,value=wye,count=2057
field=b,value=zee,count=1943
field=b,value=eks,count=2008
field=b,value=hat,count=2050
$ mlr count-distinct -f a,b -o someothername then sort -nr someothername data/medium
a=zee,b=wye,someothername=455
a=pan,b=eks,someothername=429
a=pan,b=pan,someothername=427
a=wye,b=hat,someothername=426
a=hat,b=wye,someothername=423
a=pan,b=hat,someothername=417
a=eks,b=hat,someothername=417
a=eks,b=eks,someothername=413
a=pan,b=zee,someothername=413
a=zee,b=hat,someothername=409
a=eks,b=wye,someothername=407
a=zee,b=zee,someothername=403
a=pan,b=wye,someothername=395
a=wye,b=pan,someothername=392
a=zee,b=eks,someothername=391
a=zee,b=pan,someothername=389
a=hat,b=eks,someothername=389
a=wye,b=eks,someothername=386
a=hat,b=zee,someothername=385
a=wye,b=zee,someothername=385
a=hat,b=hat,someothername=381
a=wye,b=wye,someothername=377
a=eks,b=pan,someothername=371
a=hat,b=pan,someothername=363
a=eks,b=zee,someothername=357
$ mlr count-distinct -n -f a,b data/medium
count=25
count-similar¶
$ mlr count-similar --help
Usage: mlr count-similar [options]
Ingests all records, then emits each record augmented by a count of
the number of other records having the same group-by field values.
Options:
-g {d,e,f} Group-by-field names for counts.
-o {name} Field name for output count. Default "count".
$ mlr --opprint head -n 20 data/medium
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
zee pan 6 0.5271261600918548 0.49322128674835697
eks zee 7 0.6117840605678454 0.1878849191181694
zee wye 8 0.5985540091064224 0.976181385699006
hat wye 9 0.03144187646093577 0.7495507603507059
pan wye 10 0.5026260055412137 0.9526183602969864
pan pan 11 0.7930488423451967 0.6505816637259333
zee pan 12 0.3676141320555616 0.23614420670296965
eks pan 13 0.4915175580479536 0.7709126592971468
eks zee 14 0.5207382318405251 0.34141681118811673
eks pan 15 0.07155556372719507 0.3596137145616235
pan pan 16 0.5736853980681922 0.7554169353781729
zee eks 17 0.29081949506712723 0.054478717073354166
hat zee 18 0.05727869223575699 0.13343527626645157
zee pan 19 0.43144132839222604 0.8442204830496998
eks wye 20 0.38245149780530685 0.4730652428100751
$ mlr --opprint head -n 20 then count-similar -g a data/medium
a b i x y count
pan pan 1 0.3467901443380824 0.7268028627434533 4
pan wye 10 0.5026260055412137 0.9526183602969864 4
pan pan 11 0.7930488423451967 0.6505816637259333 4
pan pan 16 0.5736853980681922 0.7554169353781729 4
eks pan 2 0.7586799647899636 0.5221511083334797 7
eks wye 4 0.38139939387114097 0.13418874328430463 7
eks zee 7 0.6117840605678454 0.1878849191181694 7
eks pan 13 0.4915175580479536 0.7709126592971468 7
eks zee 14 0.5207382318405251 0.34141681118811673 7
eks pan 15 0.07155556372719507 0.3596137145616235 7
eks wye 20 0.38245149780530685 0.4730652428100751 7
wye wye 3 0.20460330576630303 0.33831852551664776 2
wye pan 5 0.5732889198020006 0.8636244699032729 2
zee pan 6 0.5271261600918548 0.49322128674835697 5
zee wye 8 0.5985540091064224 0.976181385699006 5
zee pan 12 0.3676141320555616 0.23614420670296965 5
zee eks 17 0.29081949506712723 0.054478717073354166 5
zee pan 19 0.43144132839222604 0.8442204830496998 5
hat wye 9 0.03144187646093577 0.7495507603507059 2
hat zee 18 0.05727869223575699 0.13343527626645157 2
$ mlr --opprint head -n 20 then count-similar -g a then sort -f a data/medium
a b i x y count
eks pan 2 0.7586799647899636 0.5221511083334797 7
eks wye 4 0.38139939387114097 0.13418874328430463 7
eks zee 7 0.6117840605678454 0.1878849191181694 7
eks pan 13 0.4915175580479536 0.7709126592971468 7
eks zee 14 0.5207382318405251 0.34141681118811673 7
eks pan 15 0.07155556372719507 0.3596137145616235 7
eks wye 20 0.38245149780530685 0.4730652428100751 7
hat wye 9 0.03144187646093577 0.7495507603507059 2
hat zee 18 0.05727869223575699 0.13343527626645157 2
pan pan 1 0.3467901443380824 0.7268028627434533 4
pan wye 10 0.5026260055412137 0.9526183602969864 4
pan pan 11 0.7930488423451967 0.6505816637259333 4
pan pan 16 0.5736853980681922 0.7554169353781729 4
wye wye 3 0.20460330576630303 0.33831852551664776 2
wye pan 5 0.5732889198020006 0.8636244699032729 2
zee pan 6 0.5271261600918548 0.49322128674835697 5
zee wye 8 0.5985540091064224 0.976181385699006 5
zee pan 12 0.3676141320555616 0.23614420670296965 5
zee eks 17 0.29081949506712723 0.054478717073354166 5
zee pan 19 0.43144132839222604 0.8442204830496998 5
cut¶
$ mlr cut --help
Usage: mlr cut [options]
Passes through input records with specified fields included/excluded.
-f {a,b,c} Field names to include for cut.
-o Retain fields in the order specified here in the argument list.
Default is to retain them in the order found in the input data.
-x|--complement Exclude, rather than include, field names specified by -f.
-r Treat field names as regular expressions. "ab", "a.*b" will
match any field name containing the substring "ab" or matching
"a.*b", respectively; anchors of the form "^ab$", "^a.*b$" may
be used. The -o flag is ignored when -r is present.
Examples:
mlr cut -f hostname,status
mlr cut -x -f hostname,status
mlr cut -r -f '^status$,sda[0-9]'
mlr cut -r -f '^status$,"sda[0-9]"'
mlr cut -r -f '^status$,"sda[0-9]"i' (this is case-insensitive)
$ 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 cut -f y,x,i data/small
i x y
1 0.3467901443380824 0.7268028627434533
2 0.7586799647899636 0.5221511083334797
3 0.20460330576630303 0.33831852551664776
4 0.38139939387114097 0.13418874328430463
5 0.5732889198020006 0.8636244699032729
$ echo 'a=1,b=2,c=3' | mlr cut -f b,c,a
a=1,b=2,c=3
$ echo 'a=1,b=2,c=3' | mlr cut -o -f b,c,a
b=2,c=3,a=1
decimate¶
$ mlr decimate --help
Usage: mlr decimate [options]
-n {count} Decimation factor; default 10
-b Decimate by printing first of every n.
-e Decimate by printing last of every n (default).
-g {a,b,c} Optional group-by-field names for decimate counts
Passes through one of every n records, optionally by category.
fill-down¶
$ mlr fill-down --help
Usage: mlr fill-down [options]
-f {a,b,c} Field names for fill-down
-a|--only-if-absent Field names for fill-down
If a given record has a missing value for a given field, fill that from
the corresponding value from a previous record, if any.
By default, a 'missing' field either is absent, or has the empty-string value.
With -a, a field is 'missing' only if it is absent.
$ cat data/fill-down.csv
a,b,c
1,,3
4,5,6
7,,9
$ mlr --csv fill-down -f b data/fill-down.csv
a,b,c
1,,3
4,5,6
7,5,9
$ mlr --csv fill-down -a -f b data/fill-down.csv
a,b,c
1,,3
4,5,6
7,,9
filter¶
$ mlr filter --help
Usage: mlr filter [options] {expression}
Prints records for which {expression} evaluates to true.
If there are multiple semicolon-delimited expressions, all of them are
evaluated and the last one is used as the filter criterion.
Conversion options:
-S: Keeps field values as strings with no type inference to int or float.
-F: Keeps field values as strings or floats with no inference to int.
All field values are type-inferred to int/float/string unless this behavior is
suppressed with -S or -F.
Output/formatting options:
--oflatsep {string}: Separator to use when flattening multi-level @-variables
to output records for emit. Default ":".
--jknquoteint: For dump output (JSON-formatted), do not quote map keys if non-string.
--jvquoteall: For dump output (JSON-formatted), quote map values even if non-string.
Any of the output-format command-line flags (see mlr -h). Example: using
mlr --icsv --opprint ... then put --ojson 'tee > "mytap-".$a.".dat", $*' then ...
the input is CSV, the output is pretty-print tabular, but the tee-file output
is written in JSON format.
--no-fflush: for emit, tee, print, and dump, don't call fflush() after every
record.
Expression-specification options:
-f {filename}: the DSL expression is taken from the specified file rather
than from the command line. Outer single quotes wrapping the expression
should not be placed in the file. If -f is specified more than once,
all input files specified using -f are concatenated to produce the expression.
(For example, you can define functions in one file and call them from another.)
-e {expression}: You can use this after -f to add an expression. Example use
case: define functions/subroutines in a file you specify with -f, then call
them with an expression you specify with -e.
(If you mix -e and -f then the expressions are evaluated in the order encountered.
Since the expression pieces are simply concatenated, please be sure to use intervening
semicolons to separate expressions.)
-s name=value: Predefines out-of-stream variable @name to have value "value".
Thus mlr filter put -s foo=97 '$column += @foo' is like
mlr filter put 'begin {@foo = 97} $column += @foo'.
The value part is subject to type-inferencing as specified by -S/-F.
May be specified more than once, e.g. -s name1=value1 -s name2=value2.
Note: the value may be an environment variable, e.g. -s sequence=$SEQUENCE
Tracing options:
-v: Prints the expressions's AST (abstract syntax tree), which gives
full transparency on the precedence and associativity rules of
Miller's grammar, to stdout.
-a: Prints a low-level stack-allocation trace to stdout.
-t: Prints a low-level parser trace to stderr.
-T: Prints a every statement to stderr as it is executed.
Other options:
-x: Prints records for which {expression} evaluates to false.
Please use a dollar sign for field names and double-quotes for string
literals. If field names have special characters such as "." then you might
use braces, e.g. '${field.name}'. Miller built-in variables are
NF NR FNR FILENUM FILENAME M_PI M_E, and ENV["namegoeshere"] to access environment
variables. The environment-variable name may be an expression, e.g. a field
value.
Use # to comment to end of line.
Examples:
mlr filter 'log10($count) > 4.0'
mlr filter 'FNR == 2' (second record in each file)
mlr filter 'urand() < 0.001' (subsampling)
mlr filter '$color != "blue" && $value > 4.2'
mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)'
mlr filter '($name =~ "^sys.*east$") || ($name =~ "^dev.[0-9]+"i)'
mlr filter '$ab = $a+$b; $cd = $c+$d; $ab != $cd'
mlr filter '
NR == 1 ||
#NR == 2 ||
NR == 3
'
Please see https://miller.readthedocs.io/en/latest/reference.html for more information
including function list. Or "mlr -f". Please also see "mlr grep" which is
useful when you don't yet know which field name(s) you're looking for.
Please see in particular:
http://www.johnkerl.org/miller/doc/reference-verbs.html#filter
format-values¶
$ mlr format-values --help
Usage: mlr format-values [options]
Applies format strings to all field values, depending on autodetected type.
* If a field value is detected to be integer, applies integer format.
* Else, if a field value is detected to be float, applies float format.
* Else, applies string format.
Note: this is a low-keystroke way to apply formatting to many fields. To get
finer control, please see the fmtnum function within the mlr put DSL.
Note: this verb lets you apply arbitrary format strings, which can produce
undefined behavior and/or program crashes. See your system's "man printf".
Options:
-i {integer format} Defaults to "%lld".
Examples: "%06lld", "%08llx".
Note that Miller integers are long long so you must use
formats which apply to long long, e.g. with ll in them.
Undefined behavior results otherwise.
-f {float format} Defaults to "%lf".
Examples: "%8.3lf", "%.6le".
Note that Miller floats are double-precision so you must
use formats which apply to double, e.g. with l[efg] in them.
Undefined behavior results otherwise.
-s {string format} Defaults to "%s".
Examples: "_%s", "%08s".
Note that you must use formats which apply to string, e.g.
with s in them. Undefined behavior results otherwise.
-n Coerce field values autodetected as int to float, and then
apply the float format.
$ mlr --opprint format-values data/small
a b i x y
pan pan 1 0.346790 0.726803
eks pan 2 0.758680 0.522151
wye wye 3 0.204603 0.338319
eks wye 4 0.381399 0.134189
wye pan 5 0.573289 0.863624
$ mlr --opprint format-values -n data/small
a b i x y
pan pan 1.000000 0.346790 0.726803
eks pan 2.000000 0.758680 0.522151
wye wye 3.000000 0.204603 0.338319
eks wye 4.000000 0.381399 0.134189
wye pan 5.000000 0.573289 0.863624
$ mlr --opprint format-values -i %08llx -f %.6le -s X%sX data/small
a b i x y
XpanX XpanX 00000001 3.467901e-01 7.268029e-01
XeksX XpanX 00000002 7.586800e-01 5.221511e-01
XwyeX XwyeX 00000003 2.046033e-01 3.383185e-01
XeksX XwyeX 00000004 3.813994e-01 1.341887e-01
XwyeX XpanX 00000005 5.732889e-01 8.636245e-01
$ mlr --opprint format-values -i %08llx -f %.6le -s X%sX -n data/small
a b i x y
XpanX XpanX 1.000000e+00 3.467901e-01 7.268029e-01
XeksX XpanX 2.000000e+00 7.586800e-01 5.221511e-01
XwyeX XwyeX 3.000000e+00 2.046033e-01 3.383185e-01
XeksX XwyeX 4.000000e+00 3.813994e-01 1.341887e-01
XwyeX XpanX 5.000000e+00 5.732889e-01 8.636245e-01
fraction¶
$ mlr fraction --help
Usage: mlr fraction [options]
For each record's value in specified fields, computes the ratio of that
value to the sum of values in that field over all input records.
E.g. with input records x=1 x=2 x=3 and x=4, emits output records
x=1,x_fraction=0.1 x=2,x_fraction=0.2 x=3,x_fraction=0.3 and x=4,x_fraction=0.4
Note: this is internally a two-pass algorithm: on the first pass it retains
input records and accumulates sums; on the second pass it computes quotients
and emits output records. This means it produces no output until all input is read.
Options:
-f {a,b,c} Field name(s) for fraction calculation
-g {d,e,f} Optional group-by-field name(s) for fraction counts
-p Produce percents [0..100], not fractions [0..1]. Output field names
end with "_percent" rather than "_fraction"
-c Produce cumulative distributions, i.e. running sums: each output
value folds in the sum of the previous for the specified group
E.g. with input records x=1 x=2 x=3 and x=4, emits output records
x=1,x_cumulative_fraction=0.1 x=2,x_cumulative_fraction=0.3
x=3,x_cumulative_fraction=0.6 and x=4,x_cumulative_fraction=1.0
For example, suppose you have the following CSV file:
u=female,v=red,n=2458
u=female,v=green,n=192
u=female,v=blue,n=337
u=female,v=purple,n=468
u=female,v=yellow,n=3
u=female,v=orange,n=17
u=male,v=red,n=143
u=male,v=green,n=227
u=male,v=blue,n=2034
u=male,v=purple,n=12
u=male,v=yellow,n=1192
u=male,v=orange,n=448
Then we can see what each record’s n
contributes to the total n
:
$ mlr --opprint fraction -f n data/fraction-example.csv
u v n n_fraction
female red 2458 0.326384
female green 192 0.025495
female blue 337 0.044748
female purple 468 0.062143
female yellow 3 0.000398
female orange 17 0.002257
male red 143 0.018988
male green 227 0.030142
male blue 2034 0.270084
male purple 12 0.001593
male yellow 1192 0.158279
male orange 448 0.059487
Using -g
we can split those out by gender, or by color:
$ mlr --opprint fraction -f n -g u data/fraction-example.csv
u v n n_fraction
female red 2458 0.707338
female green 192 0.055252
female blue 337 0.096978
female purple 468 0.134676
female yellow 3 0.000863
female orange 17 0.004892
male red 143 0.035256
male green 227 0.055966
male blue 2034 0.501479
male purple 12 0.002959
male yellow 1192 0.293886
male orange 448 0.110454
$ mlr --opprint fraction -f n -g v data/fraction-example.csv
u v n n_fraction
female red 2458 0.945021
female green 192 0.458234
female blue 337 0.142134
female purple 468 0.975000
female yellow 3 0.002510
female orange 17 0.036559
male red 143 0.054979
male green 227 0.541766
male blue 2034 0.857866
male purple 12 0.025000
male yellow 1192 0.997490
male orange 448 0.963441
We can see, for example, that 70.9% of females have red (on the left) while 94.5% of reds are for females.
To convert fractions to percents, you may use -p
:
$ mlr --opprint fraction -f n -p data/fraction-example.csv
u v n n_percent
female red 2458 32.638428
female green 192 2.549462
female blue 337 4.474837
female purple 468 6.214314
female yellow 3 0.039835
female orange 17 0.225734
male red 143 1.898818
male green 227 3.014208
male blue 2034 27.008365
male purple 12 0.159341
male yellow 1192 15.827911
male orange 448 5.948745
Another often-used idiom is to convert from a point distribution to a cumulative distribution, also known as “running sums”. Here, you can use -c
:
$ mlr --opprint fraction -f n -p -c data/fraction-example.csv
u v n n_cumulative_percent
female red 2458 32.638428
female green 192 35.187890
female blue 337 39.662727
female purple 468 45.877042
female yellow 3 45.916877
female orange 17 46.142611
male red 143 48.041429
male green 227 51.055637
male blue 2034 78.064002
male purple 12 78.223344
male yellow 1192 94.051255
male orange 448 100
$ mlr --opprint fraction -f n -g u -p -c data/fraction-example.csv
u v n n_cumulative_percent
female red 2458 70.733813
female green 192 76.258993
female blue 337 85.956835
female purple 468 99.424460
female yellow 3 99.510791
female orange 17 100
male red 143 3.525641
male green 227 9.122288
male blue 2034 59.270217
male purple 12 59.566075
male yellow 1192 88.954635
male orange 448 100
grep¶
$ mlr grep -h
Usage: mlr grep [options] {regular expression}
Passes through records which match {regex}.
Options:
-i Use case-insensitive search.
-v Invert: pass through records which do not match the regex.
Note that "mlr filter" is more powerful, but requires you to know field names.
By contrast, "mlr grep" allows you to regex-match the entire record. It does
this by formatting each record in memory as DKVP, using command-line-specified
ORS/OFS/OPS, and matching the resulting line against the regex specified
here. In particular, the regex is not applied to the input stream: if you
have CSV with header line "x,y,z" and data line "1,2,3" then the regex will
be matched, not against either of these lines, but against the DKVP line
"x=1,y=2,z=3". Furthermore, not all the options to system grep are supported,
and this command is intended to be merely a keystroke-saver. To get all the
features of system grep, you can do
"mlr --odkvp ... | grep ... | mlr --idkvp ..."
group-by¶
$ mlr group-by --help
Usage: mlr group-by {comma-separated field names}
Outputs records in batches having identical values at specified field names.
This is similar to sort
but with less work. Namely, Miller’s sort has three steps: read through the data and append linked lists of records, one for each unique combination of the key-field values; after all records are read, sort the key-field values; then print each record-list. The group-by operation simply omits the middle sort. An example should make this more clear.
$ mlr --opprint group-by a data/small
a b i x y
pan pan 1 0.3467901443380824 0.7268028627434533
eks pan 2 0.7586799647899636 0.5221511083334797
eks wye 4 0.38139939387114097 0.13418874328430463
wye wye 3 0.20460330576630303 0.33831852551664776
wye pan 5 0.5732889198020006 0.8636244699032729
$ mlr --opprint sort -f a data/small
a b i x y
eks pan 2 0.7586799647899636 0.5221511083334797
eks wye 4 0.38139939387114097 0.13418874328430463
pan pan 1 0.3467901443380824 0.7268028627434533
wye wye 3 0.20460330576630303 0.33831852551664776
wye pan 5 0.5732889198020006 0.8636244699032729
In this example, since the sort is on field a
, the first step is to group together all records having the same value for field a
; the second step is to sort the distinct a
-field values pan
, eks
, and wye
into eks
, pan
, and wye
; the third step is to print out the record-list for a=eks
, then the record-list for a=pan
, then the record-list for a=wye
. The group-by operation omits the middle sort and just puts like records together, for those times when a sort isn’t desired. In particular, the ordering of group-by fields for group-by is the order in which they were encountered in the data stream, which in some cases may be more interesting to you.
group-like¶
$ mlr group-like --help
Usage: mlr group-like
Outputs records in batches having identical field names.
This groups together records having the same schema (i.e. same ordered list of field names) which is useful for making sense of time-ordered output as described in Record-heterogeneity – in particular, in preparation for CSV or pretty-print output.
$ mlr cat data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
record_count=100,resource=/path/to/file
resource=/path/to/second/file,loadsec=0.32,ok=true
record_count=150,resource=/path/to/second/file
resource=/some/other/path,loadsec=0.97,ok=false
$ mlr --opprint group-like data/het.dkvp
resource loadsec ok
/path/to/file 0.45 true
/path/to/second/file 0.32 true
/some/other/path 0.97 false
record_count resource
100 /path/to/file
150 /path/to/second/file
having-fields¶
$ mlr having-fields --help
Usage: mlr having-fields [options]
Conditionally passes through records depending on each record's field names.
Options:
--at-least {comma-separated names}
--which-are {comma-separated names}
--at-most {comma-separated names}
--all-matching {regular expression}
--any-matching {regular expression}
--none-matching {regular expression}
Examples:
mlr having-fields --which-are amount,status,owner
mlr having-fields --any-matching 'sda[0-9]'
mlr having-fields --any-matching '"sda[0-9]"'
mlr having-fields --any-matching '"sda[0-9]"i' (this is case-insensitive)
Similar to group-like, this retains records with specified schema.
$ mlr cat data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
record_count=100,resource=/path/to/file
resource=/path/to/second/file,loadsec=0.32,ok=true
record_count=150,resource=/path/to/second/file
resource=/some/other/path,loadsec=0.97,ok=false
$ mlr having-fields --at-least resource data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
record_count=100,resource=/path/to/file
resource=/path/to/second/file,loadsec=0.32,ok=true
record_count=150,resource=/path/to/second/file
resource=/some/other/path,loadsec=0.97,ok=false
$ mlr having-fields --which-are resource,ok,loadsec data/het.dkvp
resource=/path/to/file,loadsec=0.45,ok=true
resource=/path/to/second/file,loadsec=0.32,ok=true
resource=/some/other/path,loadsec=0.97,ok=false
head¶
$ mlr head --help
Usage: mlr head [options]
-n {count} Head count to print; default 10
-g {a,b,c} Optional group-by-field names for head counts
Passes through the first n records, optionally by category.
Without -g, ceases consuming more input (i.e. is fast) when n
records have been read.
Note that head
is distinct from top – head
shows fields which appear first in the data stream; top
shows fields which are numerically largest (or smallest).
$ mlr --opprint head -n 4 data/medium
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
$ mlr --opprint head -n 1 -g b data/medium
a b i x y
pan pan 1 0.3467901443380824 0.7268028627434533
wye wye 3 0.20460330576630303 0.33831852551664776
eks zee 7 0.6117840605678454 0.1878849191181694
zee eks 17 0.29081949506712723 0.054478717073354166
wye hat 24 0.7286126830627567 0.19441962592638418
histogram¶
$ mlr histogram --help
Usage: mlr histogram [options]
-f {a,b,c} Value-field names for histogram counts
--lo {lo} Histogram low value
--hi {hi} Histogram high value
--nbins {n} Number of histogram bins
--auto Automatically computes limits, ignoring --lo and --hi.
Holds all values in memory before producing any output.
-o {prefix} Prefix for output field name. Default: no prefix.
Just a histogram. Input values < lo or > hi are not counted.
This is just a histogram; there’s not too much to say here. A note about binning, by example: Suppose you use --lo 0.0 --hi 1.0 --nbins 10 -f x
. The input numbers less than 0 or greater than 1 aren’t counted in any bin. Input numbers equal to 1 are counted in the last bin. That is, bin 0 has 0.0 ≤ x < 0.1
, bin 1 has 0.1 ≤ x < 0.2
, etc., but bin 9 has 0.9 ≤ x ≤ 1.0
.
$ mlr --opprint put '$x2=$x**2;$x3=$x2*$x' then histogram -f x,x2,x3 --lo 0 --hi 1 --nbins 10 data/medium
bin_lo bin_hi x_count x2_count x3_count
0.000000 0.100000 1072 3231 4661
0.100000 0.200000 938 1254 1184
0.200000 0.300000 1037 988 845
0.300000 0.400000 988 832 676
0.400000 0.500000 950 774 576
0.500000 0.600000 1002 692 476
0.600000 0.700000 1007 591 438
0.700000 0.800000 1007 560 420
0.800000 0.900000 986 571 383
0.900000 1.000000 1013 507 341
$ mlr --opprint put '$x2=$x**2;$x3=$x2*$x' then histogram -f x,x2,x3 --lo 0 --hi 1 --nbins 10 -o my_ data/medium
my_bin_lo my_bin_hi my_x_count my_x2_count my_x3_count
0.000000 0.100000 1072 3231 4661
0.100000 0.200000 938 1254 1184
0.200000 0.300000 1037 988 845
0.300000 0.400000 988 832 676
0.400000 0.500000 950 774 576
0.500000 0.600000 1002 692 476
0.600000 0.700000 1007 591 438
0.700000 0.800000 1007 560 420
0.800000 0.900000 986 571 383
0.900000 1.000000 1013 507 341
join¶
$ mlr join --help
Usage: mlr join [options]
Joins records from specified left file name with records from all file names
at the end of the Miller argument list.
Functionality is essentially the same as the system "join" command, but for
record streams.
Options:
-f {left file name}
-j {a,b,c} Comma-separated join-field names for output
-l {a,b,c} Comma-separated join-field names for left input file;
defaults to -j values if omitted.
-r {a,b,c} Comma-separated join-field names for right input file(s);
defaults to -j values if omitted.
--lp {text} Additional prefix for non-join output field names from
the left file
--rp {text} Additional prefix for non-join output field names from
the right file(s)
--np Do not emit paired records
--ul Emit unpaired records from the left file
--ur Emit unpaired records from the right file(s)
-s|--sorted-input Require sorted input: records must be sorted
lexically by their join-field names, else not all records will
be paired. The only likely use case for this is with a left
file which is too big to fit into system memory otherwise.
-u Enable unsorted input. (This is the default even without -u.)
In this case, the entire left file will be loaded into memory.
--prepipe {command} As in main input options; see mlr --help for details.
If you wish to use a prepipe command for the main input as well
as here, it must be specified there as well as here.
File-format options default to those for the right file names on the Miller
argument list, but may be overridden for the left file as follows. Please see
the main "mlr --help" for more information on syntax for these arguments.
-i {one of csv,dkvp,nidx,pprint,xtab}
--irs {record-separator character}
--ifs {field-separator character}
--ips {pair-separator character}
--repifs
--repips
Please use "mlr --usage-separator-options" for information on specifying separators.
Please see https://miller.readthedocs.io/en/latest/reference-verbs.html#join for more information
including examples.
Examples:
Join larger table with IDs with smaller ID-to-name lookup table, showing only paired records:
$ mlr --icsvlite --opprint cat data/join-left-example.csv
id name
100 alice
200 bob
300 carol
400 david
500 edgar
$ mlr --icsvlite --opprint cat data/join-right-example.csv
status idcode
present 400
present 100
missing 200
present 100
present 200
missing 100
missing 200
present 300
missing 600
present 400
present 400
present 300
present 100
missing 400
present 200
present 200
present 200
present 200
present 400
present 300
$ mlr --icsvlite --opprint join -u -j id -r idcode -f data/join-left-example.csv data/join-right-example.csv
id name status
400 david present
100 alice present
200 bob missing
100 alice present
200 bob present
100 alice missing
200 bob missing
300 carol present
400 david present
400 david present
300 carol present
100 alice present
400 david missing
200 bob present
200 bob present
200 bob present
200 bob present
400 david present
300 carol present
Same, but with sorting the input first:
$ mlr --icsvlite --opprint sort -f idcode then join -j id -r idcode -f data/join-left-example.csv data/join-right-example.csv
id name status
100 alice present
100 alice present
100 alice missing
100 alice present
200 bob missing
200 bob present
200 bob missing
200 bob present
200 bob present
200 bob present
200 bob present
300 carol present
300 carol present
300 carol present
400 david present
400 david present
400 david present
400 david missing
400 david present
Same, but showing only unpaired records:
$ mlr --icsvlite --opprint join --np --ul --ur -u -j id -r idcode -f data/join-left-example.csv data/join-right-example.csv
status idcode
missing 600
id name
500 edgar
Use prefixing options to disambiguate between otherwise identical non-join field names:
$ mlr --csvlite --opprint cat data/self-join.csv data/self-join.csv
a b c
1 2 3
1 4 5
1 2 3
1 4 5
$ mlr --csvlite --opprint join -j a --lp left_ --rp right_ -f data/self-join.csv data/self-join.csv
a left_b left_c right_b right_c
1 2 3 2 3
1 4 5 2 3
1 2 3 4 5
1 4 5 4 5
Use zero join columns:
$ mlr --csvlite --opprint join -j "" --lp left_ --rp right_ -f data/self-join.csv data/self-join.csv
left_a left_b left_c right_a right_b right_c
1 2 3 1 2 3
1 4 5 1 2 3
1 2 3 1 4 5
1 4 5 1 4 5
label¶
$ mlr label --help
Usage: mlr label {new1,new2,new3,...}
Given n comma-separated names, renames the first n fields of each record to
have the respective name. (Fields past the nth are left with their original
names.) Particularly useful with --inidx or --implicit-csv-header, to give
useful names to otherwise integer-indexed fields.
Examples:
"echo 'a b c d' | mlr --inidx --odkvp cat" gives "1=a,2=b,3=c,4=d"
"echo 'a b c d' | mlr --inidx --odkvp label s,t" gives "s=a,t=b,3=c,4=d"
See also rename.
Example: Files such as /etc/passwd
, /etc/group
, and so on have implicit field names which are found in section-5 manpages. These field names may be made explicit as follows:
% grep -v '^#' /etc/passwd | mlr --nidx --fs : --opprint label name,password,uid,gid,gecos,home_dir,shell | head
name password uid gid gecos home_dir shell
nobody * -2 -2 Unprivileged User /var/empty /usr/bin/false
root * 0 0 System Administrator /var/root /bin/sh
daemon * 1 1 System Services /var/root /usr/bin/false
_uucp * 4 4 Unix to Unix Copy Protocol /var/spool/uucp /usr/sbin/uucico
_taskgated * 13 13 Task Gate Daemon /var/empty /usr/bin/false
_networkd * 24 24 Network Services /var/networkd /usr/bin/false
_installassistant * 25 25 Install Assistant /var/empty /usr/bin/false
_lp * 26 26 Printing Services /var/spool/cups /usr/bin/false
_postfix * 27 27 Postfix Mail Server /var/spool/postfix /usr/bin/false
Likewise, if you have CSV/CSV-lite input data which has somehow been bereft of its header line, you can re-add a header line using --implicit-csv-header
and label
:
$ cat data/headerless.csv
John,23,present
Fred,34,present
Alice,56,missing
Carol,45,present
$ mlr --csv --implicit-csv-header cat data/headerless.csv
1,2,3
John,23,present
Fred,34,present
Alice,56,missing
Carol,45,present
$ mlr --csv --implicit-csv-header label name,age,status data/headerless.csv
name,age,status
John,23,present
Fred,34,present
Alice,56,missing
Carol,45,present
$ mlr --icsv --implicit-csv-header --opprint label name,age,status data/headerless.csv
name age status
John 23 present
Fred 34 present
Alice 56 missing
Carol 45 present
least-frequent¶
$ mlr least-frequent -h
Usage: mlr least-frequent [options]
Shows the least frequently occurring distinct values for specified field names.
The first entry is the statistical anti-mode; the remaining are runners-up.
Options:
-f {one or more comma-separated field names}. Required flag.
-n {count}. Optional flag defaulting to 10.
-b Suppress counts; show only field values.
-o {name} Field name for output count. Default "count".
See also "mlr most-frequent".
$ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape -n 5
shape count
circle 2591
triangle 3372
square 4115
$ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape,color -n 5
shape color count
circle orange 68
triangle orange 107
square orange 128
circle green 287
circle purple 289
$ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape,color -n 5 -o someothername
shape color someothername
circle orange 68
triangle orange 107
square orange 128
circle green 287
circle purple 289
$ mlr --opprint --from data/colored-shapes.dkvp least-frequent -f shape,color -n 5 -b
shape color
circle orange
triangle orange
square orange
circle green
circle purple
See also most-frequent.
merge-fields¶
$ mlr merge-fields --help
Usage: mlr merge-fields [options]
Computes univariate statistics for each input record, accumulated across
specified fields.
Options:
-a {sum,count,...} Names of accumulators. One or more of:
count Count instances of fields
mode Find most-frequently-occurring values for fields; first-found wins tie
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
stddev Compute sample standard deviation of specified fields
var Compute sample variance of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
skewness Compute sample skewness of specified fields
kurtosis Compute sample kurtosis of specified fields
min Compute minimum values of specified fields
max Compute maximum values of specified fields
-f {a,b,c} Value-field names on which to compute statistics. Requires -o.
-r {a,b,c} Regular expressions for value-field names on which to compute
statistics. Requires -o.
-c {a,b,c} Substrings for collapse mode. All fields which have the same names
after removing substrings will be accumulated together. Please see
examples below.
-i Use interpolated percentiles, like R's type=7; default like type=1.
Not sensical for string-valued fields.
-o {name} Output field basename for -f/-r.
-k Keep the input fields which contributed to the output statistics;
the default is to omit them.
-F Computes integerable things (e.g. count) in floating point.
String-valued data make sense unless arithmetic on them is required,
e.g. for sum, mean, interpolated percentiles, etc. In case of mixed data,
numbers are less than strings.
Example input data: "a_in_x=1,a_out_x=2,b_in_y=4,b_out_x=8".
Example: mlr merge-fields -a sum,count -f a_in_x,a_out_x -o foo
produces "b_in_y=4,b_out_x=8,foo_sum=3,foo_count=2" since "a_in_x,a_out_x" are
summed over.
Example: mlr merge-fields -a sum,count -r in_,out_ -o bar
produces "bar_sum=15,bar_count=4" since all four fields are summed over.
Example: mlr merge-fields -a sum,count -c in_,out_
produces "a_x_sum=3,a_x_count=2,b_y_sum=4,b_y_count=1,b_x_sum=8,b_x_count=1"
since "a_in_x" and "a_out_x" both collapse to "a_x", "b_in_y" collapses to
"b_y", and "b_out_x" collapses to "b_x".
This is like mlr stats1
but all accumulation is done across fields within each given record: horizontal rather than vertical statistics, if you will.
Examples:
$ mlr --csvlite --opprint cat data/inout.csv
a_in a_out b_in b_out
436 490 446 195
526 320 963 780
220 888 705 831
$ mlr --csvlite --opprint merge-fields -a min,max,sum -c _in,_out data/inout.csv
a_min a_max a_sum b_min b_max b_sum
436 490 926 195 446 641
320 526 846 780 963 1743
220 888 1108 705 831 1536
$ mlr --csvlite --opprint merge-fields -k -a sum -c _in,_out data/inout.csv
a_in a_out b_in b_out a_sum b_sum
436 490 446 195 926 641
526 320 963 780 846 1743
220 888 705 831 1108 1536
most-frequent¶
$ mlr most-frequent -h
Usage: mlr most-frequent [options]
Shows the most frequently occurring distinct values for specified field names.
The first entry is the statistical mode; the remaining are runners-up.
Options:
-f {one or more comma-separated field names}. Required flag.
-n {count}. Optional flag defaulting to 10.
-b Suppress counts; show only field values.
-o {name} Field name for output count. Default "count".
See also "mlr least-frequent".
$ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape -n 5
shape count
square 4115
triangle 3372
circle 2591
$ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape,color -n 5
shape color count
square red 1874
triangle red 1560
circle red 1207
square yellow 589
square blue 589
$ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape,color -n 5 -o someothername
shape color someothername
square red 1874
triangle red 1560
circle red 1207
square yellow 589
square blue 589
$ mlr --opprint --from data/colored-shapes.dkvp most-frequent -f shape,color -n 5 -b
shape color
square red
triangle red
circle red
square yellow
square blue
See also least-frequent.
nest¶
$ mlr nest -h
Usage: mlr nest [options]
Explodes specified field values into separate fields/records, or reverses this.
Options:
--explode,--implode One is required.
--values,--pairs One is required.
--across-records,--across-fields One is required.
-f {field name} Required.
--nested-fs {string} Defaults to ";". Field separator for nested values.
--nested-ps {string} Defaults to ":". Pair separator for nested key-value pairs.
--evar {string} Shorthand for --explode --values ---across-records --nested-fs {string}
--ivar {string} Shorthand for --implode --values ---across-records --nested-fs {string}
Please use "mlr --usage-separator-options" for information on specifying separators.
Examples:
mlr nest --explode --values --across-records -f x
with input record "x=a;b;c,y=d" produces output records
"x=a,y=d"
"x=b,y=d"
"x=c,y=d"
Use --implode to do the reverse.
mlr nest --explode --values --across-fields -f x
with input record "x=a;b;c,y=d" produces output records
"x_1=a,x_2=b,x_3=c,y=d"
Use --implode to do the reverse.
mlr nest --explode --pairs --across-records -f x
with input record "x=a:1;b:2;c:3,y=d" produces output records
"a=1,y=d"
"b=2,y=d"
"c=3,y=d"
mlr nest --explode --pairs --across-fields -f x
with input record "x=a:1;b:2;c:3,y=d" produces output records
"a=1,b=2,c=3,y=d"
Notes:
* With --pairs, --implode doesn't make sense since the original field name has
been lost.
* The combination "--implode --values --across-records" is non-streaming:
no output records are produced until all input records have been read. In
particular, this means it won't work in tail -f contexts. But all other flag
combinations result in streaming (tail -f friendly) data processing.
* It's up to you to ensure that the nested-fs is distinct from your data's IFS:
e.g. by default the former is semicolon and the latter is comma.
See also mlr reshape.
nothing¶
$ mlr nothing -h
Usage: mlr nothing
Drops all input records. Useful for testing, or after tee/print/etc. have
produced other output.
put¶
$ mlr put --help
Usage: mlr put [options] {expression}
Adds/updates specified field(s). Expressions are semicolon-separated and must
either be assignments, or evaluate to boolean. Booleans with following
statements in curly braces control whether those statements are executed;
booleans without following curly braces do nothing except side effects (e.g.
regex-captures into \1, \2, etc.).
Conversion options:
-S: Keeps field values as strings with no type inference to int or float.
-F: Keeps field values as strings or floats with no inference to int.
All field values are type-inferred to int/float/string unless this behavior is
suppressed with -S or -F.
Output/formatting options:
--oflatsep {string}: Separator to use when flattening multi-level @-variables
to output records for emit. Default ":".
--jknquoteint: For dump output (JSON-formatted), do not quote map keys if non-string.
--jvquoteall: For dump output (JSON-formatted), quote map values even if non-string.
Any of the output-format command-line flags (see mlr -h). Example: using
mlr --icsv --opprint ... then put --ojson 'tee > "mytap-".$a.".dat", $*' then ...
the input is CSV, the output is pretty-print tabular, but the tee-file output
is written in JSON format.
--no-fflush: for emit, tee, print, and dump, don't call fflush() after every
record.
Expression-specification options:
-f {filename}: the DSL expression is taken from the specified file rather
than from the command line. Outer single quotes wrapping the expression
should not be placed in the file. If -f is specified more than once,
all input files specified using -f are concatenated to produce the expression.
(For example, you can define functions in one file and call them from another.)
-e {expression}: You can use this after -f to add an expression. Example use
case: define functions/subroutines in a file you specify with -f, then call
them with an expression you specify with -e.
(If you mix -e and -f then the expressions are evaluated in the order encountered.
Since the expression pieces are simply concatenated, please be sure to use intervening
semicolons to separate expressions.)
-s name=value: Predefines out-of-stream variable @name to have value "value".
Thus mlr put put -s foo=97 '$column += @foo' is like
mlr put put 'begin {@foo = 97} $column += @foo'.
The value part is subject to type-inferencing as specified by -S/-F.
May be specified more than once, e.g. -s name1=value1 -s name2=value2.
Note: the value may be an environment variable, e.g. -s sequence=$SEQUENCE
Tracing options:
-v: Prints the expressions's AST (abstract syntax tree), which gives
full transparency on the precedence and associativity rules of
Miller's grammar, to stdout.
-a: Prints a low-level stack-allocation trace to stdout.
-t: Prints a low-level parser trace to stderr.
-T: Prints a every statement to stderr as it is executed.
Other options:
-q: Does not include the modified record in the output stream. Useful for when
all desired output is in begin and/or end blocks.
Please use a dollar sign for field names and double-quotes for string
literals. If field names have special characters such as "." then you might
use braces, e.g. '${field.name}'. Miller built-in variables are
NF NR FNR FILENUM FILENAME M_PI M_E, and ENV["namegoeshere"] to access environment
variables. The environment-variable name may be an expression, e.g. a field
value.
Use # to comment to end of line.
Examples:
mlr put '$y = log10($x); $z = sqrt($y)'
mlr put '$x>0.0 { $y=log10($x); $z=sqrt($y) }' # does {...} only if $x > 0.0
mlr put '$x>0.0; $y=log10($x); $z=sqrt($y)' # does all three statements
mlr put '$a =~ "([a-z]+)_([0-9]+); $b = "left_\1"; $c = "right_\2"'
mlr put '$a =~ "([a-z]+)_([0-9]+) { $b = "left_\1"; $c = "right_\2" }'
mlr put '$filename = FILENAME'
mlr put '$colored_shape = $color . "_" . $shape'
mlr put '$y = cos($theta); $z = atan2($y, $x)'
mlr put '$name = sub($name, "http.*com"i, "")'
mlr put -q '@sum += $x; end {emit @sum}'
mlr put -q '@sum[$a] += $x; end {emit @sum, "a"}'
mlr put -q '@sum[$a][$b] += $x; end {emit @sum, "a", "b"}'
mlr put -q '@min=min(@min,$x);@max=max(@max,$x); end{emitf @min, @max}'
mlr put -q 'is_null(@xmax) || $x > @xmax {@xmax=$x; @recmax=$*}; end {emit @recmax}'
mlr put '
$x = 1;
#$y = 2;
$z = 3
'
Please see also 'mlr -k' for examples using redirected output.
Please see https://miller.readthedocs.io/en/latest/reference.html for more information
including function list. Or "mlr -f".
Please see in particular:
http://www.johnkerl.org/miller/doc/reference-verbs.html#put
regularize¶
$ mlr regularize --help
Usage: mlr regularize
For records seen earlier in the data stream with same field names in
a different order, outputs them with field names in the previously
encountered order.
Example: input records a=1,c=2,b=3, then e=4,d=5, then c=7,a=6,b=8
output as a=1,c=2,b=3, then e=4,d=5, then a=6,c=7,b=8
This exists since hash-map software in various languages and tools encountered in the wild does not always print similar rows with fields in the same order: mlr regularize
helps clean that up.
See also reorder.
remove-empty-columns¶
$ mlr remove-empty-columns --help
Usage: mlr remove-empty-columns
Omits fields which are empty on every input row. Non-streaming.
$ cat data/remove-empty-columns.csv
a,b,c,d,e
1,,3,,5
2,,4,,5
3,,5,,7
$ mlr --csv remove-empty-columns data/remove-empty-columns.csv
a,c,e
1,3,5
2,4,5
3,5,7
Since this verb needs to read all records to see if any of them has a non-empty value for a given field name, it is non-streaming: it will ingest all records before writing any.
rename¶
$ mlr rename --help
Usage: mlr rename [options] {old1,new1,old2,new2,...}
Renames specified fields.
Options:
-r Treat old field names as regular expressions. "ab", "a.*b"
will match any field name containing the substring "ab" or
matching "a.*b", respectively; anchors of the form "^ab$",
"^a.*b$" may be used. New field names may be plain strings,
or may contain capture groups of the form "\1" through
"\9". Wrapping the regex in double quotes is optional, but
is required if you wish to follow it with 'i' to indicate
case-insensitivity.
-g Do global replacement within each field name rather than
first-match replacement.
Examples:
mlr rename old_name,new_name'
mlr rename old_name_1,new_name_1,old_name_2,new_name_2'
mlr rename -r 'Date_[0-9]+,Date,' Rename all such fields to be "Date"
mlr rename -r '"Date_[0-9]+",Date' Same
mlr rename -r 'Date_([0-9]+).*,\1' Rename all such fields to be of the form 20151015
mlr rename -r '"name"i,Name' Rename "name", "Name", "NAME", etc. to "Name"
$ 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 rename i,INDEX,b,COLUMN2 data/small
a COLUMN2 INDEX 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
As discussed in Performance, sed
is significantly faster than Miller at doing this. However, Miller is format-aware, so it knows to do renames only within specified field keys and not any others, nor in field values which may happen to contain the same pattern. Example:
$ sed 's/y/COLUMN5/g' data/small
a=pan,b=pan,i=1,x=0.3467901443380824,COLUMN5=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,COLUMN5=0.5221511083334797
a=wCOLUMN5e,b=wCOLUMN5e,i=3,x=0.20460330576630303,COLUMN5=0.33831852551664776
a=eks,b=wCOLUMN5e,i=4,x=0.38139939387114097,COLUMN5=0.13418874328430463
a=wCOLUMN5e,b=pan,i=5,x=0.5732889198020006,COLUMN5=0.8636244699032729
$ mlr rename y,COLUMN5 data/small
a=pan,b=pan,i=1,x=0.3467901443380824,COLUMN5=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,COLUMN5=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,COLUMN5=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,COLUMN5=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,COLUMN5=0.8636244699032729
See also label.
reorder¶
$ mlr reorder --help
Usage: mlr reorder [options]
-f {a,b,c} Field names to reorder.
-e Put specified field names at record end: default is to put
them at record start.
Examples:
mlr reorder -f a,b sends input record "d=4,b=2,a=1,c=3" to "a=1,b=2,d=4,c=3".
mlr reorder -e -f a,b sends input record "d=4,b=2,a=1,c=3" to "d=4,c=3,a=1,b=2".
This pivots specified field names to the start or end of the record – for example when you have highly multi-column data and you want to bring a field or two to the front of line where you can give a quick visual scan.
$ 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 reorder -f i,b data/small
i b a x y
1 pan pan 0.3467901443380824 0.7268028627434533
2 pan eks 0.7586799647899636 0.5221511083334797
3 wye wye 0.20460330576630303 0.33831852551664776
4 wye eks 0.38139939387114097 0.13418874328430463
5 pan wye 0.5732889198020006 0.8636244699032729
$ mlr --opprint reorder -e -f i,b data/small
a x y i b
pan 0.3467901443380824 0.7268028627434533 1 pan
eks 0.7586799647899636 0.5221511083334797 2 pan
wye 0.20460330576630303 0.33831852551664776 3 wye
eks 0.38139939387114097 0.13418874328430463 4 wye
wye 0.5732889198020006 0.8636244699032729 5 pan
repeat¶
$ mlr repeat --help
Usage: mlr repeat [options]
Copies input records to output records multiple times.
Options must be exactly one of the following:
-n {repeat count} Repeat each input record this many times.
-f {field name} Same, but take the repeat count from the specified
field name of each input record.
Example:
echo x=0 | mlr repeat -n 4 then put '$x=urand()'
produces:
x=0.488189
x=0.484973
x=0.704983
x=0.147311
Example:
echo a=1,b=2,c=3 | mlr repeat -f b
produces:
a=1,b=2,c=3
a=1,b=2,c=3
Example:
echo a=1,b=2,c=3 | mlr repeat -f c
produces:
a=1,b=2,c=3
a=1,b=2,c=3
a=1,b=2,c=3
This is useful in at least two ways: one, as a data-generator as in the
above example using urand()
; two, for reconstructing individual
samples from data which has been count-aggregated:
$ cat data/repeat-example.dat
color=blue,count=5
color=red,count=4
color=green,count=3
$ mlr repeat -f count then cut -x -f count data/repeat-example.dat
color=blue
color=blue
color=blue
color=blue
color=blue
color=red
color=red
color=red
color=red
color=green
color=green
color=green
After expansion with repeat
, such data can then be sent on to
stats1 -a mode
, or (if the data are numeric) to stats1 -a
p10,p50,p90
, etc.
reshape¶
$ mlr reshape --help
Usage: mlr reshape [options]
Wide-to-long options:
-i {input field names} -o {key-field name,value-field name}
-r {input field regexes} -o {key-field name,value-field name}
These pivot/reshape the input data such that the input fields are removed
and separate records are emitted for each key/value pair.
Note: this works with tail -f and produces output records for each input
record seen.
Long-to-wide options:
-s {key-field name,value-field name}
These pivot/reshape the input data to undo the wide-to-long operation.
Note: this does not work with tail -f; it produces output records only after
all input records have been read.
Examples:
Input file "wide.txt":
time X Y
2009-01-01 0.65473572 2.4520609
2009-01-02 -0.89248112 0.2154713
2009-01-03 0.98012375 1.3179287
mlr --pprint reshape -i X,Y -o item,value wide.txt
time item value
2009-01-01 X 0.65473572
2009-01-01 Y 2.4520609
2009-01-02 X -0.89248112
2009-01-02 Y 0.2154713
2009-01-03 X 0.98012375
2009-01-03 Y 1.3179287
mlr --pprint reshape -r '[A-Z]' -o item,value wide.txt
time item value
2009-01-01 X 0.65473572
2009-01-01 Y 2.4520609
2009-01-02 X -0.89248112
2009-01-02 Y 0.2154713
2009-01-03 X 0.98012375
2009-01-03 Y 1.3179287
Input file "long.txt":
time item value
2009-01-01 X 0.65473572
2009-01-01 Y 2.4520609
2009-01-02 X -0.89248112
2009-01-02 Y 0.2154713
2009-01-03 X 0.98012375
2009-01-03 Y 1.3179287
mlr --pprint reshape -s item,value long.txt
time X Y
2009-01-01 0.65473572 2.4520609
2009-01-02 -0.89248112 0.2154713
2009-01-03 0.98012375 1.3179287
See also mlr nest.
sample¶
$ mlr sample --help
Usage: mlr sample [options]
Reservoir sampling (subsampling without replacement), optionally by category.
-k {count} Required: number of records to output, total, or by group if using -g.
-g {a,b,c} Optional: group-by-field names for samples.
See also mlr bootstrap and mlr shuffle.
This is reservoir-sampling: select k items from n with
uniform probability and no repeats in the sample. (If n is less than
k, then of course only n samples are produced.) With -g
{field names}
, produce a k-sample for each distinct value of the
specified field names.
$ mlr --opprint sample -k 4 data/colored-shapes.dkvp
color shape flag i u v w x
purple triangle 0 90122 0.9986871176198068 0.3037738877233719 0.5154934457238382 5.365962021016529
red circle 0 3139 0.04835898233323954 -0.03964684310055758 0.5263660881848111 5.3758779366493625
orange triangle 0 67847 0.36746306902109926 0.5161574810505635 0.5176199566173642 3.1748088656576567
yellow square 1 33576 0.3098376725521097 0.8525628505287842 0.49774122460981685 4.494754378604669
$ mlr --opprint sample -k 4 data/colored-shapes.dkvp
color shape flag i u v w x
blue square 1 16783 0.09974385090654347 0.7243899920872646 0.5353718443278438 4.431057737383438
orange square 1 93291 0.5944176543007182 0.17744449786454086 0.49262281749172077 3.1548117990710653
yellow square 1 54436 0.5268161165014636 0.8785588662666121 0.5058773791931063 7.019185838783636
yellow square 1 55491 0.0025440267883102274 0.05474106287787284 0.5102729153751984 3.526301273728043
$ mlr --opprint sample -k 2 -g color data/colored-shapes.dkvp
color shape flag i u v w x
yellow triangle 1 11 0.6321695890307647 0.9887207810889004 0.4364983936735774 5.7981881667050565
yellow square 1 917 0.8547010348386344 0.7356782810796262 0.4531511689924275 5.774541777078352
red circle 1 4000 0.05490416175132373 0.07392337815122155 0.49416101516594396 5.355725080701707
red square 0 87506 0.6357719216821314 0.6970867759393995 0.4940826462055272 6.351579417310387
purple triangle 0 14898 0.7800986870203719 0.23998073813992293 0.5014775988383656 3.141006771777843
purple triangle 0 151 0.032614487569017414 0.7346633365041219 0.7812143304483805 2.6831992610568047
green triangle 1 126 0.1513010528347546 0.40346767294704544 0.051213231883952326 5.955109300797182
green circle 0 17635 0.029856606049114442 0.4724542934246524 0.49529606749929744 5.239153910272168
blue circle 1 1020 0.414263129226617 0.8304946402876182 0.13151094520189244 4.397873687920433
blue triangle 0 220 0.441773289968473 0.44597731903759075 0.6329360666849821 4.3064608776550894
orange square 0 1885 0.8079311983747106 0.8685956833908394 0.3116410800256374 4.390864584500387
orange triangle 0 1533 0.32904497195507487 0.23168161807490417 0.8722623057355134 5.164071635714438
$ mlr --opprint sample -k 2 -g color then sort -f color data/colored-shapes.dkvp
color shape flag i u v w x
blue circle 0 215 0.7803586969333292 0.33146680638888126 0.04289047852629113 5.725365736377487
blue circle 1 3616 0.8548431579124808 0.4989623130006362 0.3339426415875795 3.696785877560498
green square 0 356 0.7674272008085286 0.341578843118008 0.4570224877870851 4.830320062215299
green square 0 152 0.6684429446914862 0.016056003736548696 0.4656148241291592 5.434588759225423
orange triangle 0 587 0.5175826237797857 0.08989091493635304 0.9011709461770973 4.265854207755811
orange triangle 0 1533 0.32904497195507487 0.23168161807490417 0.8722623057355134 5.164071635714438
purple triangle 0 14192 0.5196327866973567 0.7860928603468063 0.4964368415453642 4.899167143824484
purple triangle 0 65 0.6842806710360729 0.5823723856331258 0.8014053396013747 5.805148213865135
red square 1 2431 0.38378504852300466 0.11445015005595527 0.49355539228753786 5.146756570128739
red triangle 0 57097 0.43763430414406546 0.3355450325004481 0.5322349637512487 4.144267240289442
yellow triangle 1 11 0.6321695890307647 0.9887207810889004 0.4364983936735774 5.7981881667050565
yellow square 1 158 0.41527900739142165 0.7118027080775757 0.4200799665161291 5.33279067554884
Note that no output is produced until all inputs are in. Another way to do
sampling, which works in the streaming case, is mlr filter 'urand() &
0.001'
where you tune the 0.001 to meet your needs.
sec2gmt¶
$ mlr sec2gmt -h
Usage: mlr sec2gmt [options] {comma-separated list of field names}
Replaces a numeric field representing seconds since the epoch with the
corresponding GMT timestamp; leaves non-numbers as-is. This is nothing
more than a keystroke-saver for the sec2gmt function:
mlr sec2gmt time1,time2
is the same as
mlr put '$time1=sec2gmt($time1);$time2=sec2gmt($time2)'
Options:
-1 through -9: format the seconds using 1..9 decimal places, respectively.
sec2gmtdate¶
$ mlr sec2gmtdate -h
Usage: mlr sec2gmtdate {comma-separated list of field names}
Replaces a numeric field representing seconds since the epoch with the
corresponding GMT year-month-day timestamp; leaves non-numbers as-is.
This is nothing more than a keystroke-saver for the sec2gmtdate function:
mlr sec2gmtdate time1,time2
is the same as
mlr put '$time1=sec2gmtdate($time1);$time2=sec2gmtdate($time2)'
seqgen¶
$ mlr seqgen -h
Usage: mlr seqgen [options]
Produces a sequence of counters. Discards the input record stream. Produces
output as specified by the following options:
-f {name} Field name for counters; default "i".
--start {number} Inclusive start value; default "1".
--stop {number} Inclusive stop value; default "100".
--step {number} Step value; default "1".
Start, stop, and/or step may be floating-point. Output is integer if start,
stop, and step are all integers. Step may be negative. It may not be zero
unless start == stop.
$ mlr seqgen --stop 10
i=1
i=2
i=3
i=4
i=5
i=6
i=7
i=8
i=9
i=10
$ mlr seqgen --start 20 --stop 40 --step 4
i=20
i=24
i=28
i=32
i=36
i=40
$ mlr seqgen --start 40 --stop 20 --step -4
i=40
i=36
i=32
i=28
i=24
i=20
shuffle¶
$ mlr shuffle -h
Usage: mlr shuffle {no options}
Outputs records randomly permuted. No output records are produced until
all input records are read.
See also mlr bootstrap and mlr sample.
skip-trivial-records¶
$ mlr skip-trivial-records -h
Usage: mlr skip-trivial-records [options]
Passes through all records except:
* those with zero fields;
* those for which all fields have empty value.
$ cat data/trivial-records.csv
a,b,c
1,2,3
4,,6
,,
,8,9
$ mlr --csv skip-trivial-records data/trivial-records.csv
a,b,c
1,2,3
4,,6
,8,9
sort¶
$ 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
Example:
$ mlr --opprint sort -f a -nr x data/small
a b i x y
eks pan 2 0.7586799647899636 0.5221511083334797
eks wye 4 0.38139939387114097 0.13418874328430463
pan pan 1 0.3467901443380824 0.7268028627434533
wye pan 5 0.5732889198020006 0.8636244699032729
wye wye 3 0.20460330576630303 0.33831852551664776
Here’s an example filtering log data: suppose multiple threads (labeled here by color) are all logging progress counts to a single log file. The log file is (by nature) chronological, so the progress of various threads is interleaved:
$ head -n 10 data/multicountdown.dat
upsec=0.002,color=green,count=1203
upsec=0.083,color=red,count=3817
upsec=0.188,color=red,count=3801
upsec=0.395,color=blue,count=2697
upsec=0.526,color=purple,count=953
upsec=0.671,color=blue,count=2684
upsec=0.899,color=purple,count=926
upsec=0.912,color=red,count=3798
upsec=1.093,color=blue,count=2662
upsec=1.327,color=purple,count=917
We can group these by thread by sorting on the thread ID (here,
color
). Since Miller’s sort is stable, this means that
timestamps within each thread’s log data are still chronological:
$ head -n 20 data/multicountdown.dat | mlr --opprint sort -f color
upsec color count
0.395 blue 2697
0.671 blue 2684
1.093 blue 2662
2.064 blue 2659
2.2880000000000003 blue 2647
0.002 green 1203
1.407 green 1187
1.448 green 1177
2.313 green 1161
0.526 purple 953
0.899 purple 926
1.327 purple 917
1.703 purple 908
0.083 red 3817
0.188 red 3801
0.912 red 3798
1.416 red 3788
1.587 red 3782
1.601 red 3755
1.832 red 3717
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:
$ mlr sort -n x data/sort-missing.dkvp
x=1
x=2
x=4
a=3
$ mlr sort -nr x data/sort-missing.dkvp
x=4
x=2
x=1
a=3
sort-within-records¶
$ mlr sort-within-records -h
Usage: mlr sort-within-records [no options]
Outputs records sorted lexically ascending by keys.
$ cat data/sort-within-records.json
{
"a": 1,
"b": 2,
"c": 3
}
{
"b": 4,
"a": 5,
"c": 6
}
{
"c": 7,
"b": 8,
"a": 9
}
$ mlr --ijson --opprint cat data/sort-within-records.json
a b c
1 2 3
b a c
4 5 6
c b a
7 8 9
$ mlr --json sort-within-records data/sort-within-records.json
{ "a": 1, "b": 2, "c": 3 }
{ "a": 5, "b": 4, "c": 6 }
{ "a": 9, "b": 8, "c": 7 }
$ mlr --ijson --opprint sort-within-records data/sort-within-records.json
a b c
1 2 3
5 4 6
9 8 7
stats1¶
$ mlr stats1 --help
Usage: mlr stats1 [options]
Computes univariate statistics for one or more given fields, accumulated across
the input record stream.
Options:
-a {sum,count,...} Names of accumulators: p10 p25.2 p50 p98 p100 etc. and/or
one or more of:
count Count instances of fields
mode Find most-frequently-occurring values for fields; first-found wins tie
antimode Find least-frequently-occurring values for fields; first-found wins tie
sum Compute sums of specified fields
mean Compute averages (sample means) of specified fields
stddev Compute sample standard deviation of specified fields
var Compute sample variance of specified fields
meaneb Estimate error bars for averages (assuming no sample autocorrelation)
skewness Compute sample skewness of specified fields
kurtosis Compute sample kurtosis of specified fields
min Compute minimum values of specified fields
max Compute maximum values of specified fields
-f {a,b,c} Value-field names on which to compute statistics
--fr {regex} Regex for value-field names on which to compute statistics
(compute statistics on values in all field names matching regex)
--fx {regex} Inverted regex for value-field names on which to compute statistics
(compute statistics on values in all field names not matching regex)
-g {d,e,f} Optional group-by-field names
--gr {regex} Regex for optional group-by-field names
(group by values in field names matching regex)
--gx {regex} Inverted regex for optional group-by-field names
(group by values in field names not matching regex)
--grfx {regex} Shorthand for --gr {regex} --fx {that same regex}
-i Use interpolated percentiles, like R's type=7; default like type=1.
Not sensical for string-valued fields.
-s Print iterative stats. Useful in tail -f contexts (in which
case please avoid pprint-format output since end of input
stream will never be seen).
-F Computes integerable things (e.g. count) in floating point.
Example: mlr stats1 -a min,p10,p50,p90,max -f value -g size,shape
Example: mlr stats1 -a count,mode -f size
Example: mlr stats1 -a count,mode -f size -g shape
Example: mlr stats1 -a count,mode --fr '^[a-h].*$' -gr '^k.*$'
This computes count and mode statistics on all field names beginning
with a through h, grouped by all field names starting with k.
Notes:
* p50 and median are synonymous.
* min and max output the same results as p0 and p100, respectively, but use
less memory.
* String-valued data make sense unless arithmetic on them is required,
e.g. for sum, mean, interpolated percentiles, etc. In case of mixed data,
numbers are less than strings.
* count and mode allow text input; the rest require numeric input.
In particular, 1 and 1.0 are distinct text for count and mode.
* When there are mode ties, the first-encountered datum wins.
These are simple univariate statistics on one or more number-valued fields
(count
and mode
apply to non-numeric fields as well),
optionally categorized by one or more other fields.
$ mlr --oxtab stats1 -a count,sum,min,p10,p50,mean,p90,max -f x,y data/medium
x_count 10000
x_sum 4986.019682
x_min 0.000045
x_p10 0.093322
x_p50 0.501159
x_mean 0.498602
x_p90 0.900794
x_max 0.999953
y_count 10000
y_sum 5062.057445
y_min 0.000088
y_p10 0.102132
y_p50 0.506021
y_mean 0.506206
y_p90 0.905366
y_max 0.999965
$ mlr --opprint stats1 -a mean -f x,y -g b then sort -f b data/medium
b x_mean y_mean
eks 0.506361 0.510293
hat 0.487899 0.513118
pan 0.497304 0.499599
wye 0.497593 0.504596
zee 0.504242 0.502997
$ mlr --opprint stats1 -a p50,p99 -f u,v -g color then put '$ur=$u_p99/$u_p50;$vr=$v_p99/$v_p50' data/colored-shapes.dkvp
color u_p50 u_p99 v_p50 v_p99 ur vr
yellow 0.501019 0.989046 0.520630 0.987034 1.974069 1.895845
red 0.485038 0.990054 0.492586 0.994444 2.041189 2.018823
purple 0.501319 0.988893 0.504571 0.988287 1.972582 1.958668
green 0.502015 0.990764 0.505359 0.990175 1.973574 1.959350
blue 0.525226 0.992655 0.485170 0.993873 1.889958 2.048505
orange 0.483548 0.993635 0.480913 0.989102 2.054884 2.056717
$ mlr --opprint count-distinct -f shape then sort -nr count data/colored-shapes.dkvp
shape count
square 4115
triangle 3372
circle 2591
$ mlr --opprint stats1 -a mode -f color -g shape data/colored-shapes.dkvp
shape color_mode
triangle red
square red
circle red
stats2¶
$ mlr stats2 --help
Usage: mlr stats2 [options]
Computes bivariate statistics for one or more given field-name pairs,
accumulated across the input record stream.
-a {linreg-ols,corr,...} Names of accumulators: one or more of:
linreg-pca Linear regression using principal component analysis
linreg-ols Linear regression using ordinary least squares
r2 Quality metric for linreg-ols (linreg-pca emits its own)
logireg Logistic regression
corr Sample correlation
cov Sample covariance
covx Sample-covariance matrix
-f {a,b,c,d} Value-field name-pairs on which to compute statistics.
There must be an even number of names.
-g {e,f,g} Optional group-by-field names.
-v Print additional output for linreg-pca.
-s Print iterative stats. Useful in tail -f contexts (in which
case please avoid pprint-format output since end of input
stream will never be seen).
--fit Rather than printing regression parameters, applies them to
the input data to compute new fit fields. All input records are
held in memory until end of input stream. Has effect only for
linreg-ols, linreg-pca, and logireg.
Only one of -s or --fit may be used.
Example: mlr stats2 -a linreg-pca -f x,y
Example: mlr stats2 -a linreg-ols,r2 -f x,y -g size,shape
Example: mlr stats2 -a corr -f x,y
These are simple bivariate statistics on one or more pairs of number-valued fields, optionally categorized by one or more fields.
$ mlr --oxtab put '$x2=$x*$x; $xy=$x*$y; $y2=$y**2' then stats2 -a cov,corr -f x,y,y,y,x2,xy,x2,y2 data/medium
x_y_cov 0.000043
x_y_corr 0.000504
y_y_cov 0.084611
y_y_corr 1.000000
x2_xy_cov 0.041884
x2_xy_corr 0.630174
x2_y2_cov -0.000310
x2_y2_corr -0.003425
$ mlr --opprint put '$x2=$x*$x; $xy=$x*$y; $y2=$y**2' then stats2 -a linreg-ols,r2 -f x,y,y,y,xy,y2 -g a data/medium
a x_y_ols_m x_y_ols_b x_y_ols_n x_y_r2 y_y_ols_m y_y_ols_b y_y_ols_n y_y_r2 xy_y2_ols_m xy_y2_ols_b xy_y2_ols_n xy_y2_r2
pan 0.017026 0.500403 2081 0.000287 1.000000 0.000000 2081 1.000000 0.878132 0.119082 2081 0.417498
eks 0.040780 0.481402 1965 0.001646 1.000000 0.000000 1965 1.000000 0.897873 0.107341 1965 0.455632
wye -0.039153 0.525510 1966 0.001505 1.000000 0.000000 1966 1.000000 0.853832 0.126745 1966 0.389917
zee 0.002781 0.504307 2047 0.000008 1.000000 0.000000 2047 1.000000 0.852444 0.124017 2047 0.393566
hat -0.018621 0.517901 1941 0.000352 1.000000 0.000000 1941 1.000000 0.841230 0.135573 1941 0.368794
Here’s an example simple line-fit. The x
and y
fields of the data/medium
dataset are just independent uniformly
distributed on the unit interval. Here we remove half the data and fit a line to it.
# Prepare input data:
mlr filter '($x<.5 && $y<.5) || ($x>.5 && $y>.5)' data/medium > data/medium-squares
# Do a linear regression and examine coefficients:
mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares
x_y_pca_m=1.014419
x_y_pca_b=0.000308
x_y_pca_quality=0.861354
# Option 1 to apply the regression coefficients and produce a linear fit:
# Set x_y_pca_m and x_y_pca_b as shell variables:
eval $(mlr --ofs newline stats2 -a linreg-pca -f x,y data/medium-squares)
# In addition to x and y, make a new yfit which is the line fit, then plot
# using your favorite tool:
mlr --onidx put '$yfit='$x_y_pca_m'*$x+'$x_y_pca_b then cut -x -f a,b,i data/medium-squares \
| pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
# Option 2 to apply the regression coefficients and produce a linear fit: use --fit option
mlr --onidx stats2 -a linreg-pca --fit -f x,y then cut -f a,b,i data/medium-squares \
| pgr -p -title 'linreg-pca example' -xmin 0 -xmax 1 -ymin 0 -ymax 1
I use pgr for plotting; here’s a screenshot.
(Thanks Drew Kunas for a good conversation about PCA!)
Here’s an example estimating time-to-completion for a set of jobs. Input data comes from a log file, with number of work units left to do in the count
field and accumulated seconds in the upsec
field, labeled by the color
field:
$ head -n 10 data/multicountdown.dat
upsec=0.002,color=green,count=1203
upsec=0.083,color=red,count=3817
upsec=0.188,color=red,count=3801
upsec=0.395,color=blue,count=2697
upsec=0.526,color=purple,count=953
upsec=0.671,color=blue,count=2684
upsec=0.899,color=purple,count=926
upsec=0.912,color=red,count=3798
upsec=1.093,color=blue,count=2662
upsec=1.327,color=purple,count=917
We can do a linear regression on count remaining as a function of time: with c = m*u+b
we want to find the time when the count goes to zero, i.e. u=-b/m
.
$ mlr --oxtab stats2 -a linreg-pca -f upsec,count -g color then put '$donesec = -$upsec_count_pca_b/$upsec_count_pca_m' data/multicountdown.dat
color green
upsec_count_pca_m -32.756917
upsec_count_pca_b 1213.722730
upsec_count_pca_n 24
upsec_count_pca_quality 0.999984
donesec 37.052410
color red
upsec_count_pca_m -37.367646
upsec_count_pca_b 3810.133400
upsec_count_pca_n 30
upsec_count_pca_quality 0.999989
donesec 101.963431
color blue
upsec_count_pca_m -29.231212
upsec_count_pca_b 2698.932820
upsec_count_pca_n 25
upsec_count_pca_quality 0.999959
donesec 92.330514
color purple
upsec_count_pca_m -39.030097
upsec_count_pca_b 979.988341
upsec_count_pca_n 21
upsec_count_pca_quality 0.999991
donesec 25.108529
step¶
$ mlr step --help
Usage: mlr step [options]
Computes values dependent on the previous record, optionally grouped
by category.
Options:
-a {delta,rsum,...} Names of steppers: comma-separated, one or more of:
delta Compute differences in field(s) between successive records
shift Include value(s) in field(s) from previous record, if any
from-first Compute differences in field(s) from first record
ratio Compute ratios in field(s) between successive records
rsum Compute running sums of field(s) between successive records
counter Count instances of field(s) between successive records
ewma Exponentially weighted moving average over successive records
-f {a,b,c} Value-field names on which to compute statistics
-g {d,e,f} Optional group-by-field names
-F Computes integerable things (e.g. counter) in floating point.
-d {x,y,z} Weights for ewma. 1 means current sample gets all weight (no
smoothing), near under under 1 is light smoothing, near over 0 is
heavy smoothing. Multiple weights may be specified, e.g.
"mlr step -a ewma -f sys_load -d 0.01,0.1,0.9". Default if omitted
is "-d 0.5".
-o {a,b,c} Custom suffixes for EWMA output fields. If omitted, these default to
the -d values. If supplied, the number of -o values must be the same
as the number of -d values.
Examples:
mlr step -a rsum -f request_size
mlr step -a delta -f request_size -g hostname
mlr step -a ewma -d 0.1,0.9 -f x,y
mlr step -a ewma -d 0.1,0.9 -o smooth,rough -f x,y
mlr step -a ewma -d 0.1,0.9 -o smooth,rough -f x,y -g group_name
Please see https://miller.readthedocs.io/en/latest/reference-verbs.html#filter or
https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
for more information on EWMA.
Most Miller commands are record-at-a-time, with the exception of stats1
, stats2
, and histogram
which compute aggregate output. The step
command is intermediate: it allows the option of adding fields which are functions of fields from previous records. Rsum is short for running sum.
$ mlr --opprint step -a shift,delta,rsum,counter -f x data/medium | head -15
a b i x y x_shift x_delta x_rsum x_counter
pan pan 1 0.3467901443380824 0.7268028627434533 - 0 0.346790 1
eks pan 2 0.7586799647899636 0.5221511083334797 0.3467901443380824 0.411890 1.105470 2
wye wye 3 0.20460330576630303 0.33831852551664776 0.7586799647899636 -0.554077 1.310073 3
eks wye 4 0.38139939387114097 0.13418874328430463 0.20460330576630303 0.176796 1.691473 4
wye pan 5 0.5732889198020006 0.8636244699032729 0.38139939387114097 0.191890 2.264762 5
zee pan 6 0.5271261600918548 0.49322128674835697 0.5732889198020006 -0.046163 2.791888 6
eks zee 7 0.6117840605678454 0.1878849191181694 0.5271261600918548 0.084658 3.403672 7
zee wye 8 0.5985540091064224 0.976181385699006 0.6117840605678454 -0.013230 4.002226 8
hat wye 9 0.03144187646093577 0.7495507603507059 0.5985540091064224 -0.567112 4.033668 9
pan wye 10 0.5026260055412137 0.9526183602969864 0.03144187646093577 0.471184 4.536294 10
pan pan 11 0.7930488423451967 0.6505816637259333 0.5026260055412137 0.290423 5.329343 11
zee pan 12 0.3676141320555616 0.23614420670296965 0.7930488423451967 -0.425435 5.696957 12
eks pan 13 0.4915175580479536 0.7709126592971468 0.3676141320555616 0.123903 6.188474 13
eks zee 14 0.5207382318405251 0.34141681118811673 0.4915175580479536 0.029221 6.709213 14
$ mlr --opprint step -a shift,delta,rsum,counter -f x -g a data/medium | head -15
a b i x y x_shift x_delta x_rsum x_counter
pan pan 1 0.3467901443380824 0.7268028627434533 - 0 0.346790 1
eks pan 2 0.7586799647899636 0.5221511083334797 - 0 0.758680 1
wye wye 3 0.20460330576630303 0.33831852551664776 - 0 0.204603 1
eks wye 4 0.38139939387114097 0.13418874328430463 0.7586799647899636 -0.377281 1.140079 2
wye pan 5 0.5732889198020006 0.8636244699032729 0.20460330576630303 0.368686 0.777892 2
zee pan 6 0.5271261600918548 0.49322128674835697 - 0 0.527126 1
eks zee 7 0.6117840605678454 0.1878849191181694 0.38139939387114097 0.230385 1.751863 3
zee wye 8 0.5985540091064224 0.976181385699006 0.5271261600918548 0.071428 1.125680 2
hat wye 9 0.03144187646093577 0.7495507603507059 - 0 0.031442 1
pan wye 10 0.5026260055412137 0.9526183602969864 0.3467901443380824 0.155836 0.849416 2
pan pan 11 0.7930488423451967 0.6505816637259333 0.5026260055412137 0.290423 1.642465 3
zee pan 12 0.3676141320555616 0.23614420670296965 0.5985540091064224 -0.230940 1.493294 3
eks pan 13 0.4915175580479536 0.7709126592971468 0.6117840605678454 -0.120267 2.243381 4
eks zee 14 0.5207382318405251 0.34141681118811673 0.4915175580479536 0.029221 2.764119 5
$ mlr --opprint step -a ewma -f x -d 0.1,0.9 data/medium | head -15
a b i x y x_ewma_0.1 x_ewma_0.9
pan pan 1 0.3467901443380824 0.7268028627434533 0.346790 0.346790
eks pan 2 0.7586799647899636 0.5221511083334797 0.387979 0.717491
wye wye 3 0.20460330576630303 0.33831852551664776 0.369642 0.255892
eks wye 4 0.38139939387114097 0.13418874328430463 0.370817 0.368849
wye pan 5 0.5732889198020006 0.8636244699032729 0.391064 0.552845
zee pan 6 0.5271261600918548 0.49322128674835697 0.404671 0.529698
eks zee 7 0.6117840605678454 0.1878849191181694 0.425382 0.603575
zee wye 8 0.5985540091064224 0.976181385699006 0.442699 0.599056
hat wye 9 0.03144187646093577 0.7495507603507059 0.401573 0.088203
pan wye 10 0.5026260055412137 0.9526183602969864 0.411679 0.461184
pan pan 11 0.7930488423451967 0.6505816637259333 0.449816 0.759862
zee pan 12 0.3676141320555616 0.23614420670296965 0.441596 0.406839
eks pan 13 0.4915175580479536 0.7709126592971468 0.446588 0.483050
eks zee 14 0.5207382318405251 0.34141681118811673 0.454003 0.516969
$ mlr --opprint step -a ewma -f x -d 0.1,0.9 -o smooth,rough data/medium | head -15
a b i x y x_ewma_smooth x_ewma_rough
pan pan 1 0.3467901443380824 0.7268028627434533 0.346790 0.346790
eks pan 2 0.7586799647899636 0.5221511083334797 0.387979 0.717491
wye wye 3 0.20460330576630303 0.33831852551664776 0.369642 0.255892
eks wye 4 0.38139939387114097 0.13418874328430463 0.370817 0.368849
wye pan 5 0.5732889198020006 0.8636244699032729 0.391064 0.552845
zee pan 6 0.5271261600918548 0.49322128674835697 0.404671 0.529698
eks zee 7 0.6117840605678454 0.1878849191181694 0.425382 0.603575
zee wye 8 0.5985540091064224 0.976181385699006 0.442699 0.599056
hat wye 9 0.03144187646093577 0.7495507603507059 0.401573 0.088203
pan wye 10 0.5026260055412137 0.9526183602969864 0.411679 0.461184
pan pan 11 0.7930488423451967 0.6505816637259333 0.449816 0.759862
zee pan 12 0.3676141320555616 0.23614420670296965 0.441596 0.406839
eks pan 13 0.4915175580479536 0.7709126592971468 0.446588 0.483050
eks zee 14 0.5207382318405251 0.34141681118811673 0.454003 0.516969
Example deriving uptime-delta from system uptime:
$ each 10 uptime | mlr -p step -a delta -f 11
...
20:08 up 36 days, 10:38, 5 users, load averages: 1.42 1.62 1.73 0.000000
20:08 up 36 days, 10:38, 5 users, load averages: 1.55 1.64 1.74 0.020000
20:08 up 36 days, 10:38, 7 users, load averages: 1.58 1.65 1.74 0.010000
20:08 up 36 days, 10:38, 9 users, load averages: 1.78 1.69 1.76 0.040000
20:08 up 36 days, 10:39, 9 users, load averages: 2.12 1.76 1.78 0.070000
20:08 up 36 days, 10:39, 9 users, load averages: 2.51 1.85 1.81 0.090000
20:08 up 36 days, 10:39, 8 users, load averages: 2.79 1.92 1.83 0.070000
20:08 up 36 days, 10:39, 4 users, load averages: 2.64 1.90 1.83 -0.020000
tac¶
$ mlr tac --help
Usage: mlr tac
Prints records in reverse order from the order in which they were encountered.
Prints the records in the input stream in reverse order. Note: this requires Miller to retain all input records in memory before any output records are produced.
$ mlr --icsv --opprint cat data/a.csv
a b c
1 2 3
4 5 6
$ mlr --icsv --opprint cat data/b.csv
a b c
7 8 9
$ mlr --icsv --opprint tac data/a.csv data/b.csv
a b c
7 8 9
4 5 6
1 2 3
$ mlr --icsv --opprint put '$filename=FILENAME' then tac data/a.csv data/b.csv
a b c filename
7 8 9 data/b.csv
4 5 6 data/a.csv
1 2 3 data/a.csv
tail¶
$ mlr tail --help
Usage: mlr tail [options]
-n {count} Tail count to print; default 10
-g {a,b,c} Optional group-by-field names for tail counts
Passes through the last n records, optionally by category.
Prints the last n records in the input stream, optionally by category.
$ mlr --opprint tail -n 4 data/colored-shapes.dkvp
color shape flag i u v w x
blue square 1 99974 0.6189062525431605 0.2637962404841453 0.5311465405784674 6.210738209085753
blue triangle 0 99976 0.008110504040268474 0.8267274952432482 0.4732962944898885 6.146956761817328
yellow triangle 0 99990 0.3839424618160777 0.55952913620132 0.5113763011485609 4.307973891915119
yellow circle 1 99994 0.764950884927175 0.25284227383991364 0.49969878539567425 5.013809741826425
$ mlr --opprint tail -n 1 -g shape data/colored-shapes.dkvp
color shape flag i u v w x
yellow triangle 0 99990 0.3839424618160777 0.55952913620132 0.5113763011485609 4.307973891915119
blue square 1 99974 0.6189062525431605 0.2637962404841453 0.5311465405784674 6.210738209085753
yellow circle 1 99994 0.764950884927175 0.25284227383991364 0.49969878539567425 5.013809741826425
tee¶
$ mlr tee --help
Usage: mlr tee [options] {filename}
Passes through input records (like mlr cat) but also writes to specified output
file, using output-format flags from the command line (e.g. --ocsv). See also
the "tee" keyword within mlr put, which allows data-dependent filenames.
Options:
-a: append to existing file, if any, rather than overwriting.
--no-fflush: don't call fflush() after every record.
Any of the output-format command-line flags (see mlr -h). Example: using
mlr --icsv --opprint put '...' then tee --ojson ./mytap.dat then stats1 ...
the input is CSV, the output is pretty-print tabular, but the tee-file output
is written in JSON format.
top¶
$ mlr top --help
Usage: mlr top [options]
-f {a,b,c} Value-field names for top counts.
-g {d,e,f} Optional group-by-field names for top counts.
-n {count} How many records to print per category; default 1.
-a Print all fields for top-value records; default is
to print only value and group-by fields. Requires a single
value-field name only.
--min Print top smallest values; default is top largest values.
-F Keep top values as floats even if they look like integers.
-o {name} Field name for output indices. Default "top_idx".
Prints the n records with smallest/largest values at specified fields,
optionally by category.
Note that top
is distinct from head – head
shows fields which appear first in the data stream; top
shows fields which are numerically largest (or smallest).
$ mlr --opprint top -n 4 -f x data/medium
top_idx x_top
1 0.999953
2 0.999823
3 0.999733
4 0.999563
$ mlr --opprint top -n 4 -f x -o someothername data/medium
someothername x_top
1 0.999953
2 0.999823
3 0.999733
4 0.999563
$ mlr --opprint top -n 2 -f x -g a then sort -f a data/medium
a top_idx x_top
eks 1 0.998811
eks 2 0.998534
hat 1 0.999953
hat 2 0.999733
pan 1 0.999403
pan 2 0.999044
wye 1 0.999823
wye 2 0.999264
zee 1 0.999490
zee 2 0.999438
uniq¶
$ mlr uniq --help
Usage: mlr uniq [options]
Prints distinct values for specified field names. With -c, same as
count-distinct. For uniq, -f is a synonym for -g.
Options:
-g {d,e,f} Group-by-field names for uniq counts.
-c Show repeat counts in addition to unique values.
-n Show only the number of distinct values.
-o {name} Field name for output count. Default "count".
-a Output each unique record only once. Incompatible with -g.
With -c, produces unique records, with repeat counts for each.
With -n, produces only one record which is the unique-record count.
With neither -c nor -n, produces unique records.
There are two main ways to use mlr uniq
: the first way is with -g
to specify group-by columns.
$ wc -l data/colored-shapes.dkvp
10078 data/colored-shapes.dkvp
$ mlr uniq -g color,shape data/colored-shapes.dkvp
color=yellow,shape=triangle
color=red,shape=square
color=red,shape=circle
color=purple,shape=triangle
color=yellow,shape=circle
color=purple,shape=square
color=yellow,shape=square
color=red,shape=triangle
color=green,shape=triangle
color=green,shape=square
color=blue,shape=circle
color=blue,shape=triangle
color=purple,shape=circle
color=blue,shape=square
color=green,shape=circle
color=orange,shape=triangle
color=orange,shape=square
color=orange,shape=circle
$ mlr --opprint uniq -g color,shape -c then sort -f color,shape data/colored-shapes.dkvp
color shape count
blue circle 384
blue square 589
blue triangle 497
green circle 287
green square 454
green triangle 368
orange circle 68
orange square 128
orange triangle 107
purple circle 289
purple square 481
purple triangle 372
red circle 1207
red square 1874
red triangle 1560
yellow circle 356
yellow square 589
yellow triangle 468
$ mlr --opprint uniq -g color,shape -c -o someothername then sort -nr someothername data/colored-shapes.dkvp
color shape someothername
red square 1874
red triangle 1560
red circle 1207
yellow square 589
blue square 589
blue triangle 497
purple square 481
yellow triangle 468
green square 454
blue circle 384
purple triangle 372
green triangle 368
yellow circle 356
purple circle 289
green circle 287
orange square 128
orange triangle 107
orange circle 68
$ mlr --opprint uniq -n -g color,shape data/colored-shapes.dkvp
count
18
The second main way to use mlr uniq
is without group-by columns, using -a
instead:
$ cat data/repeats.dkvp
color=red,shape=square,flag=0
color=purple,shape=triangle,flag=0
color=yellow,shape=circle,flag=1
color=red,shape=circle,flag=1
color=red,shape=square,flag=0
color=yellow,shape=circle,flag=1
color=red,shape=square,flag=0
color=red,shape=square,flag=0
color=yellow,shape=circle,flag=1
color=red,shape=circle,flag=1
color=yellow,shape=circle,flag=1
color=yellow,shape=circle,flag=1
color=purple,shape=triangle,flag=0
color=yellow,shape=circle,flag=1
color=yellow,shape=circle,flag=1
color=red,shape=circle,flag=1
color=red,shape=square,flag=0
color=purple,shape=triangle,flag=0
color=yellow,shape=circle,flag=1
color=red,shape=square,flag=0
color=purple,shape=square,flag=0
color=red,shape=square,flag=0
color=red,shape=square,flag=1
color=red,shape=square,flag=0
color=red,shape=square,flag=0
color=purple,shape=triangle,flag=0
color=red,shape=square,flag=0
color=purple,shape=triangle,flag=0
color=red,shape=square,flag=0
color=red,shape=square,flag=0
color=purple,shape=square,flag=0
color=red,shape=square,flag=0
color=red,shape=square,flag=0
color=purple,shape=triangle,flag=0
color=yellow,shape=triangle,flag=1
color=purple,shape=square,flag=0
color=yellow,shape=circle,flag=1
color=purple,shape=triangle,flag=0
color=red,shape=circle,flag=1
color=purple,shape=triangle,flag=0
color=purple,shape=triangle,flag=0
color=red,shape=square,flag=0
color=red,shape=circle,flag=1
color=red,shape=square,flag=1
color=red,shape=square,flag=0
color=red,shape=circle,flag=1
color=purple,shape=square,flag=0
color=purple,shape=square,flag=0
color=red,shape=square,flag=1
color=purple,shape=triangle,flag=0
color=purple,shape=triangle,flag=0
color=purple,shape=square,flag=0
color=yellow,shape=circle,flag=1
color=red,shape=square,flag=0
color=yellow,shape=triangle,flag=1
color=yellow,shape=circle,flag=1
color=purple,shape=square,flag=0
$ wc -l data/repeats.dkvp
57 data/repeats.dkvp
$ mlr --opprint uniq -a data/repeats.dkvp
color shape flag
red square 0
purple triangle 0
yellow circle 1
red circle 1
purple square 0
red square 1
yellow triangle 1
$ mlr --opprint uniq -a -n data/repeats.dkvp
count
7
$ mlr --opprint uniq -a -c data/repeats.dkvp
count color shape flag
17 red square 0
11 purple triangle 0
11 yellow circle 1
6 red circle 1
7 purple square 0
3 red square 1
2 yellow triangle 1
unsparsify¶
$ mlr unsparsify --help
Usage: mlr unsparsify [options]
Prints records with the union of field names over all input records.
For field names absent in a given record but present in others, fills in a
value. Without -f, this verb retains all input before producing any output.
Options:
--fill-with {filler string} What to fill absent fields with. Defaults to
the empty string.
-f {a,b,c} Specify field names to be operated on. Any other fields won't be
modified, and operation will be streaming.
Example: if the input is two records, one being 'a=1,b=2' and the other
being 'b=3,c=4', then the output is the two records 'a=1,b=2,c=' and
'a=,b=3,c=4'.
Examples:
$ cat data/sparse.json
{"a":1,"b":2,"v":3}
{"u":1,"b":2}
{"a":1,"v":2,"x":3}
{"v":1,"w":2}
$ mlr --json unsparsify data/sparse.json
{ "a": 1, "b": 2, "v": 3, "u": "", "x": "", "w": "" }
{ "a": "", "b": 2, "v": "", "u": 1, "x": "", "w": "" }
{ "a": 1, "b": "", "v": 2, "u": "", "x": 3, "w": "" }
{ "a": "", "b": "", "v": 1, "u": "", "x": "", "w": 2 }
$ mlr --ijson --opprint unsparsify data/sparse.json
a b v u x w
1 2 3 - - -
- 2 - 1 - -
1 - 2 - 3 -
- - 1 - - 2
$ mlr --ijson --opprint unsparsify --fill-with missing data/sparse.json
a b v u x w
1 2 3 missing missing missing
missing 2 missing 1 missing missing
1 missing 2 missing 3 missing
missing missing 1 missing missing 2
$ mlr --ijson --opprint unsparsify -f a,b,u data/sparse.json
a b v u
1 2 3 -
u b a
1 2 -
a v x b u
1 2 3 - -
v w a b u
1 2 - - -
$ mlr --ijson --opprint unsparsify -f a,b,u,v,w,x then regularize data/sparse.json
a b v u w x
1 2 3 - - -
- 2 - 1 - -
1 - 2 - - 3
- - 1 - 2 -