Reference: list of verbs¶
Overview¶
Whereas the Unix toolkit is made of the separate executables cat, tail, cut,
sort, etc., Miller has subcommands, or verbs, invoked as follows:
mlr tac *.dat
mlr cut --complement -f os_version *.dat
mlr sort -f hostname,uptime *.dat
These fall into categories as follows:
Analogs of their Unix-toolkit namesakes, discussed below as well as in Unix-toolkit context: cat, cut, grep, head, join, sort, tac, tail, top, uniq.
awk-like functionality: filter, put, sec2gmt, sec2gmtdate, step, tee.Statistically oriented: bar, bootstrap, decimate, histogram, least-frequent, most-frequent, sample, shuffle, stats1, stats2.
Particularly oriented toward Record-heterogeneity, although all Miller commands can handle heterogeneous records: group-by, group-like, having-fields.
These draw from other sources (see also How original is Miller?): count-distinct is SQL-ish, and rename can be done by
sed(which does it faster: see Performance. Verbs: check, count-distinct, label, merge-fields, nest, nothing, regularize, rename, reorder, reshape, seqgen.
altkv¶
Map list of values to alternating key/value pairs.
mlr altkv -h
Usage: mlr altkv [options]
Given fields with values of the form a,b,c,d,e,f emits a=b,c=d,e=f pairs.
Options:
-h|--help Show this message.
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.
--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.
-c {character} Fill character: default '*'.
-x {character} Out-of-bounds character: default '#'.
-b {character} Blank character: default '.'.
Nominally the fill, out-of-bounds, and blank characters will be strings of length 1.
However you can make them all longer if you so desire.
-h|--help Show this message.
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 [0.20460330576630303]**********..............................[0.7586799647899636] [0.13418874328430463]********************************........[0.8636244699032729]
eks pan 2 [0.20460330576630303]***************************************#[0.7586799647899636] [0.13418874328430463]*********************...................[0.8636244699032729]
wye wye 3 [0.20460330576630303]#.......................................[0.7586799647899636] [0.13418874328430463]***********.............................[0.8636244699032729]
eks wye 4 [0.20460330576630303]************............................[0.7586799647899636] [0.13418874328430463]#.......................................[0.8636244699032729]
wye pan 5 [0.20460330576630303]**************************..............[0.7586799647899636] [0.13418874328430463]***************************************#[0.8636244699032729]
bootstrap¶
mlr bootstrap --help
Usage: mlr bootstrap [options]
Emits an n-sample, with replacement, of the input records.
See also mlr sample and mlr shuffle.
Options:
-n Number of samples to output. Defaults to number of input records.
Must be non-negative.
-h|--help Show this message.
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.
-N {name} Prepend field {name} to each record with record-counter starting at 1.
-g {a,b,c} Optional group-by-field names for counters, e.g. a,b,c
-h|--help Show this message.
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 [options]
Consumes records without printing any output.
Useful for doing a well-formatted check on input data.
Options:
-h|--help Show this message.
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/or
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.
-h|--help Show this message.
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} Optional group-by-field names for counts, e.g. a,b,c
-n {n} Show only the number of distinct values. Not interesting without -g.
-o {name} Field name for output-count. Default "count".
-h|--help Show this message.
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 {a,b,c} Group-by-field names for counts, e.g. a,b,c
-o {name} Field name for output-counts. Defaults to "count".
-h|--help Show this message.
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.
Options:
-f {a,b,c} Comma-separated field names for cut, e.g. a,b,c.
-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.
-h|--help Show this message.
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]
Passes through one of every n records, optionally by category.
Options:
-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, e.g. a,b,c.
-n {n} Decimation factor (default 10).
-h|--help Show this message.
fill-down¶
mlr fill-down --help
Usage: mlr fill-down [options]
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.
Options:
--all Operate on all fields in the input.
-a|--only-if-absent 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.
-f Field names for fill-down.
-h|--help Show this message.
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 put [options] {DSL expression}
Options:
-f {file name} File containing a DSL expression. If the filename is a directory,
all *.mlr files in that directory are loaded.
-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
Thus mlr put -s foo=97 '$column += @foo' is like
mlr put 'begin {@foo = 97} $column += @foo'.
The value part is subject to type-inferencing.
May be specified more than once, e.g. -s name1=value1 -s name2=value2.
Note: the value may be an environment variable, e.g. -s sequence=$SEQUENCE
-x (default false) Prints records for which {expression} evaluates to false, not true,
i.e. invert the sense of the filter expression.
-q Does not include the modified record in the output stream.
Useful for when all desired output is in begin and/or end blocks.
-S and -F: There are no-ops in Miller 6 and above, since now type-inferencing is done
by the record-readers before filter/put is executed. Supported as no-op pass-through
flags for backward compatibility.
-h|--help Show this message.
Parser-info options:
-w Print warnings about things like uninitialized variables.
-W Same as -w, but exit the process if there are any warnings.
-p Prints the expressions's AST (abstract syntax tree), which gives full
transparency on the precedence and associativity rules of Miller's grammar,
to stdout.
-d Like -p but uses a parenthesized-expression format for the AST.
-D Like -d but with output all on one line.
-E Echo DSL expression before printing parse-tree
-v Same as -E -p.
-X Exit after parsing but before stream-processing. Useful with -v/-d/-D, if you
only want to look at parser information.
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 "%d".
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 "%f".
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 %!l(int=00000001)lx %!l(float64=0.34679)e %!l(float64=0.726803)e
XeksX XpanX %!l(int=00000002)lx %!l(float64=0.75868)e %!l(float64=0.522151)e
XwyeX XwyeX %!l(int=00000003)lx %!l(float64=0.204603)e %!l(float64=0.338319)e
XeksX XwyeX %!l(int=00000004)lx %!l(float64=0.381399)e %!l(float64=0.134189)e
XwyeX XpanX %!l(int=00000005)lx %!l(float64=0.573289)e %!l(float64=0.863624)e
mlr --opprint format-values -i %08llx -f %.6le -s X%sX -n data/small
a b i x y
XpanX XpanX %!l(float64=1)e %!l(float64=0.34679)e %!l(float64=0.726803)e
XeksX XpanX %!l(float64=2)e %!l(float64=0.75868)e %!l(float64=0.522151)e
XwyeX XwyeX %!l(float64=3)e %!l(float64=0.204603)e %!l(float64=0.338319)e
XeksX XwyeX %!l(float64=4)e %!l(float64=0.381399)e %!l(float64=0.134189)e
XwyeX XpanX %!l(float64=5)e %!l(float64=0.573289)e %!l(float64=0.863624)e
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 0.32638427831629263
female green 192 0.025494622228123754
female blue 337 0.04474837338998805
female purple 468 0.06214314168105165
female yellow 3 0.00039835347231443366
female orange 17 0.002257336343115124
male red 143 0.018988182180321337
male green 227 0.03014207940512548
male blue 2034 0.270083654229186
male purple 12 0.0015934138892577346
male yellow 1192 0.15827911299960165
male orange 448 0.0594874518656221
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 0.7073381294964028
female green 192 0.05525179856115108
female blue 337 0.09697841726618706
female purple 468 0.13467625899280575
female yellow 3 0.0008633093525179857
female orange 17 0.004892086330935252
male red 143 0.035256410256410256
male green 227 0.05596646942800789
male blue 2034 0.5014792899408284
male purple 12 0.0029585798816568047
male yellow 1192 0.2938856015779093
male orange 448 0.11045364891518737
mlr --opprint fraction -f n -g v data/fraction-example.csv
u v n n_fraction
female red 2458 0.9450211457131872
female green 192 0.45823389021479716
female blue 337 0.1421341206242092
female purple 468 0.975
female yellow 3 0.002510460251046025
female orange 17 0.03655913978494624
male red 143 0.05497885428681276
male green 227 0.5417661097852029
male blue 2034 0.8578658793757908
male purple 12 0.025
male yellow 1192 0.9974895397489539
male orange 448 0.9634408602150538
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 32.638427831629265
female green 192 2.5494622228123753
female blue 337 4.474837338998805
female purple 468 6.214314168105165
female yellow 3 0.039835347231443365
female orange 17 0.2257336343115124
male red 143 1.8988182180321338
male green 227 3.014207940512548
male blue 2034 27.0083654229186
male purple 12 0.15934138892577346
male yellow 1192 15.827911299960165
male orange 448 5.94874518656221
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 32.638427831629265
female green 192 35.18789005444164
female blue 337 39.66272739344044
female purple 468 45.87704156154561
female yellow 3 45.916876908777056
female orange 17 46.142610543088566
male red 143 48.041428761120706
male green 227 51.05563670163325
male blue 2034 78.06400212455186
male purple 12 78.22334351347763
male yellow 1192 94.0512548134378
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 70.73381294964028
female green 192 76.2589928057554
female blue 337 85.9568345323741
female purple 468 99.42446043165467
female yellow 3 99.51079136690647
female orange 17 100
male red 143 3.5256410256410255
male green 227 9.122287968441814
male blue 2034 59.27021696252466
male purple 12 59.56607495069034
male yellow 1192 88.95463510848126
male orange 448 100
grep¶
mlr grep -h
Usage: mlr grep [options] {regular expression}
Passes through records which match the regular expression.
Options:
-i Use case-insensitive search.
-v Invert: pass through records which do not match the regex.
-h|--help Show this message.
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 [options] {comma-separated field names}
Outputs records in batches having identical values at specified field names.Options:
-h|--help Show this message.
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 [options]
Outputs records in batches having identical field names.Options:
-h|--help Show this message.
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]
Passes through the first n records, optionally by category.
Options:
-g {a,b,c} Optional group-by-field names for head counts, e.g. a,b,c.
-n {n} Head-count to print. Default 10.
-h|--help Show this message.
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
Just a histogram. Input values < lo or > hi are not counted.
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.
-h|--help Show this message.
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*$x' \
then histogram -f x,x2,x3 --lo 0 --hi 1 --nbins 10 \
data/medium
bin_lo bin_hi x_count x2_count x3_count
0 0.1 1072 3231 4661
0.1 0.2 938 1254 1184
0.2 0.3 1037 988 845
0.3 0.4 988 832 676
0.4 0.5 950 774 576
0.5 0.6 1002 692 476
0.6 0.7 1007 591 438
0.7 0.8 1007 560 420
0.8 0.9 986 571 383
0.9 1 1013 507 341
mlr --opprint put '$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 0.1 1072 3231 4661
0.1 0.2 938 1254 1184
0.2 0.3 1037 988 845
0.3 0.4 988 832 676
0.4 0.5 950 774 576
0.5 0.6 1002 692 476
0.6 0.7 1007 591 438
0.7 0.8 1007 560 420
0.8 0.9 986 571 383
0.9 1 1013 507 341
join¶
mlr join --help
Usage: mlr sort {flags}
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.
Options:
-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
-h|--help Show this message.
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
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 [options] {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.
Options:
-h|--help Show this message.
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
var Compute sample variance of specified fields
stddev Compute sample standard deviation 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.
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 [options]
Drops all input records. Useful for testing, or after tee/print/etc. have
produced other output.
Options:
-h|--help Show this message.
put¶
mlr put --help
Usage: mlr put [options] {DSL expression}
Options:
-f {file name} File containing a DSL expression. If the filename is a directory,
all *.mlr files in that directory are loaded.
-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
Thus mlr put -s foo=97 '$column += @foo' is like
mlr put 'begin {@foo = 97} $column += @foo'.
The value part is subject to type-inferencing.
May be specified more than once, e.g. -s name1=value1 -s name2=value2.
Note: the value may be an environment variable, e.g. -s sequence=$SEQUENCE
-x (default false) Prints records for which {expression} evaluates to false, not true,
i.e. invert the sense of the filter expression.
-q Does not include the modified record in the output stream.
Useful for when all desired output is in begin and/or end blocks.
-S and -F: There are no-ops in Miller 6 and above, since now type-inferencing is done
by the record-readers before filter/put is executed. Supported as no-op pass-through
flags for backward compatibility.
-h|--help Show this message.
Parser-info options:
-w Print warnings about things like uninitialized variables.
-W Same as -w, but exit the process if there are any warnings.
-p Prints the expressions's AST (abstract syntax tree), which gives full
transparency on the precedence and associativity rules of Miller's grammar,
to stdout.
-d Like -p but uses a parenthesized-expression format for the AST.
-D Like -d but with output all on one line.
-E Echo DSL expression before printing parse-tree
-v Same as -E -p.
-X Exit after parsing but before stream-processing. Useful with -v/-d/-D, if you
only want to look at parser information.
regularize¶
mlr regularize --help
Usage: mlr regularize [options]
Outputs records sorted lexically ascending by keys.Options:
-h|--help Show this message.
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 [options]
Omits fields which are empty on every input row. Non-streaming.
Options:
-h|--help Show this message.
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.
-h|--help Show this message.
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]
Moves specified names to start of record, or end of record.
Options:
-e Put specified field names at record end: default is to put them at record start.
-f {a,b,c} Field names to reorder.
-b {x} Put field names specified with -f before field name specified by {x},
if any. If {x} isn't present in a given record, the specified fields
will not be moved.
-a {x} Put field names specified with -f after field name specified by {x},
if any. If {x} isn't present in a given record, the specified fields
will not be moved.
-h|--help Show this message.
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.
-h|--help Show this message.
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.
See also mlr bootstrap and mlr shuffle.
Options:
-g {a,b,c} Optional: group-by-field names for samples, e.g. a,b,c.
-k {k} Required: number of records to output in total, or by group if using -g.
-h|--help Show this message.
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.
--millis Input numbers are treated as milliseconds since the epoch.
--micros Input numbers are treated as microseconds since the epoch.
--nanos Input numbers are treated as nanoseconds since the epoch.
-h|--help Show this message.
sec2gmtdate¶
mlr sec2gmtdate -h
Usage: ../c/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:
../c/mlr sec2gmtdate time1,time2
is the same as
../c/mlr put '$time1=sec2gmtdate($time1);$time2=sec2gmtdate($time2)'
seqgen¶
mlr seqgen -h
Usage: mlr seqgen [options]
Passes input records directly to output. Most useful for format conversion.
Produces a sequence of counters. Discards the input record stream. Produces
output as specified by the options
Options:
-f {name} (default "i") Field name for counters.
--start {value} (default 1) Inclusive start value.
--step {value} (default 1) Step value.
--stop {value} (default 100) Inclusive stop value.
-h|--help Show this message.
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 [options]
Outputs records randomly permuted. No output records are produced until
all input records are read. See also mlr bootstrap and mlr sample.
Options:
-h|--help Show this message.
skip-trivial-records¶
mlr skip-trivial-records -h
Usage: mlr skip-trivial-records [options]
Passes through all records except those with zero fields,
or those for which all fields have empty value.
Options:
-h|--help Show this message.
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}
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.
Options:
-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
-h|--help Show this message.
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 [options]
Outputs records sorted lexically ascending by keys.
Options:
-r Recursively sort subobjects/submaps, e.g. for JSON input.
-h|--help Show this message.
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: one or more of:
median This is the same as p50
p10 p25.2 p50 p98 p100 etc.
TODO: flags for interpolated percentiles
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
var Compute sample variance of specified fields
stddev Compute sample standard deviation 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
-g {d,e,f} Optional group-by-field names
-i Use interpolated percentiles, like R's type=7; default like type=1.\n");
Not sensical for string-valued fields.\n");
-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).
-h|--help Show this message.
[TODO: more]
Example: mlr stats1 -a min,p10,p50,p90,max -f value -g size,shape
mlr stats1
Example: mlr stats1 -a count,mode -f size
mlr stats1
Example: mlr stats1 -a count,mode -f size -g shape
mlr stats1
Example: mlr stats1 -a count,mode --fr '^[a-h].*$' -gr '^k.*$'
mlr stats1
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 4986.019681679581
x_min 0.00004509679127584487
x_p10 0.09332217805283527
x_p50 0.5011592202840128
x_mean 0.49860196816795804
x_p90 0.900794437962015
x_max 0.999952670371898
y_count 10000
y_sum 5062.057444929905
y_min 0.00008818962627266114
y_p10 0.10213207378968225
y_p50 0.5060212582772865
y_mean 0.5062057444929905
y_p90 0.9053657573378745
y_max 0.9999648102177897
mlr --opprint stats1 -a mean -f x,y -g b then sort -f b data/medium
b x_mean y_mean
eks 0.5063609846272304 0.510292657158104
hat 0.4878988625336502 0.5131176341556505
pan 0.4973036405471583 0.49959885012092725
wye 0.4975928392133964 0.5045964890907357
zee 0.5042419022900586 0.5029967546798116
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 0.5010187906650703 0.9890464545334569 0.5206303554834582 0.9870337429747029 1.9740705797093183 1.8958436298977264
red 0.48503770531462564 0.9900536015797581 0.49258608624814926 0.9944442307252868 2.0411889441410493 2.0188232239761583
purple 0.501319018852234 0.9888929892441335 0.5045708384576747 0.9882869130316426 1.9725822321846005 1.9586683131600438
green 0.5020151016389706 0.9907635833945612 0.5053591509128329 0.9901745564521951 1.9735732653458684 1.9593482272234264
blue 0.525225660059 0.9926547550299167 0.48516993577967726 0.993872833141726 1.8899586035427312 2.0485045750919286
orange 0.4835478569328253 0.9936350141409035 0.48091255603363914 0.9891023960550895 2.0548845370623567 2.0567198415711636
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-ols Linear regression using ordinary least squares
linreg-pca Linear regression using principal component analysis
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 0.000042574820827444476
x_y_corr 0.0005042001844467462
y_y_cov 0.08461122467974003
y_y_corr 1
x2_xy_cov 0.04188382281779374
x2_xy_corr 0.630174342037994
x2_y2_cov -0.00030953725962542085
x2_y2_corr -0.0034249088761121966
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 0.01702551273681908 0.5004028922897639 2081 0.00028691820445814767 1 0 2081 1 0.8781320866715662 0.11908230147563566 2081 0.41749827377311266
eks 0.0407804923685586 0.48140207967651016 1965 0.0016461239223448587 1 0 1965 1 0.8978728611690183 0.10734054433612333 1965 0.45563223864254526
wye -0.03915349075204814 0.5255096523974456 1966 0.0015051268704373607 1 0 1966 1 0.8538317334220835 0.1267454301662969 1966 0.38991721818599295
zee 0.0027812364960399147 0.5043070448033061 2047 0.000007751652858786137 1 0 2047 1 0.8524439912011013 0.12401684308018937 2047 0.39356598090006495
hat -0.018620577041095078 0.5179005397264935 1941 0.0003520036646055585 1 0 1941 1 0.8412305086345014 0.13557328318623216 1941 0.3687944261732265
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 -32.75691673397728
upsec_count_pca_b 1213.7227296044375
upsec_count_pca_n 24
upsec_count_pca_quality 0.9999839351341062
donesec 37.052410624028525
color red
upsec_count_pca_m -37.367646434187435
upsec_count_pca_b 3810.1334002923936
upsec_count_pca_n 30
upsec_count_pca_quality 0.9999894618183773
donesec 101.9634299688333
color blue
upsec_count_pca_m -29.2312120633493
upsec_count_pca_b 2698.9328203182517
upsec_count_pca_n 25
upsec_count_pca_quality 0.9999590846136102
donesec 92.33051350964094
color purple
upsec_count_pca_m -39.03009744795354
upsec_count_pca_b 979.9883413064914
upsec_count_pca_n 21
upsec_count_pca_quality 0.9999908956206317
donesec 25.10852919630297
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.
As of Miller 6 this happens automatically, but the flag is accepted
as a no-op for backward compatibility with Miller 5 and below.
-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.
-h|--help Show this message.
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 0.3467901443380824 1
eks pan 2 0.7586799647899636 0.5221511083334797 0.3467901443380824 0.41188982045188116 1.105470109128046 2
wye wye 3 0.20460330576630303 0.33831852551664776 0.7586799647899636 -0.5540766590236605 1.3100734148943491 3
eks wye 4 0.38139939387114097 0.13418874328430463 0.20460330576630303 0.17679608810483793 1.6914728087654902 4
wye pan 5 0.5732889198020006 0.8636244699032729 0.38139939387114097 0.19188952593085962 2.264761728567491 5
zee pan 6 0.5271261600918548 0.49322128674835697 0.5732889198020006 -0.04616275971014583 2.7918878886593457 6
eks zee 7 0.6117840605678454 0.1878849191181694 0.5271261600918548 0.08465790047599064 3.403671949227191 7
zee wye 8 0.5985540091064224 0.976181385699006 0.6117840605678454 -0.013230051461422976 4.0022259583336135 8
hat wye 9 0.03144187646093577 0.7495507603507059 0.5985540091064224 -0.5671121326454867 4.033667834794549 9
pan wye 10 0.5026260055412137 0.9526183602969864 0.03144187646093577 0.47118412908027796 4.536293840335763 10
pan pan 11 0.7930488423451967 0.6505816637259333 0.5026260055412137 0.29042283680398295 5.32934268268096 11
zee pan 12 0.3676141320555616 0.23614420670296965 0.7930488423451967 -0.4254347102896351 5.696956814736522 12
eks pan 13 0.4915175580479536 0.7709126592971468 0.3676141320555616 0.12390342599239201 6.1884743727844755 13
eks zee 14 0.5207382318405251 0.34141681118811673 0.4915175580479536 0.02922067379257154 6.709212604625001 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 0.3467901443380824 1
eks pan 2 0.7586799647899636 0.5221511083334797 - 0 0.7586799647899636 1
wye wye 3 0.20460330576630303 0.33831852551664776 - 0 0.20460330576630303 1
eks wye 4 0.38139939387114097 0.13418874328430463 0.7586799647899636 -0.3772805709188226 1.1400793586611044 2
wye pan 5 0.5732889198020006 0.8636244699032729 0.20460330576630303 0.36868561403569755 0.7778922255683036 2
zee pan 6 0.5271261600918548 0.49322128674835697 - 0 0.5271261600918548 1
eks zee 7 0.6117840605678454 0.1878849191181694 0.38139939387114097 0.23038466669670443 1.75186341922895 3
zee wye 8 0.5985540091064224 0.976181385699006 0.5271261600918548 0.07142784901456767 1.1256801691982772 2
hat wye 9 0.03144187646093577 0.7495507603507059 - 0 0.03144187646093577 1
pan wye 10 0.5026260055412137 0.9526183602969864 0.3467901443380824 0.1558358612031313 0.8494161498792961 2
pan pan 11 0.7930488423451967 0.6505816637259333 0.5026260055412137 0.29042283680398295 1.6424649922244927 3
zee pan 12 0.3676141320555616 0.23614420670296965 0.5985540091064224 -0.23093987705086083 1.4932943012538389 3
eks pan 13 0.4915175580479536 0.7709126592971468 0.6117840605678454 -0.1202665025198918 2.2433809772769036 4
eks zee 14 0.5207382318405251 0.34141681118811673 0.4915175580479536 0.02922067379257154 2.7641192091174287 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 0.3467901443380824 0.3467901443380824
eks pan 2 0.7586799647899636 0.5221511083334797 0.3879791263832706 0.7174909827447755
wye wye 3 0.20460330576630303 0.33831852551664776 0.36964154432157387 0.25589207346415027
eks wye 4 0.38139939387114097 0.13418874328430463 0.37081732927653055 0.3688486618304419
wye pan 5 0.5732889198020006 0.8636244699032729 0.3910644883290776 0.5528448940048447
zee pan 6 0.5271261600918548 0.49322128674835697 0.4046706555053553 0.5296980334831537
eks zee 7 0.6117840605678454 0.1878849191181694 0.4253819960116043 0.6035754578593763
zee wye 8 0.5985540091064224 0.976181385699006 0.44269919732108615 0.5990561539817179
hat wye 9 0.03144187646093577 0.7495507603507059 0.40157346523507115 0.08820330421301396
pan wye 10 0.5026260055412137 0.9526183602969864 0.41167871926568544 0.46118373540839375
pan pan 11 0.7930488423451967 0.6505816637259333 0.44981573157363663 0.7598623316515164
zee pan 12 0.3676141320555616 0.23614420670296965 0.4415955716218291 0.4068389520151571
eks pan 13 0.4915175580479536 0.7709126592971468 0.4465877702644416 0.48304969744467396
eks zee 14 0.5207382318405251 0.34141681118811673 0.4540028164220499 0.51696937840094
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 0.3467901443380824 0.3467901443380824
eks pan 2 0.7586799647899636 0.5221511083334797 0.3879791263832706 0.7174909827447755
wye wye 3 0.20460330576630303 0.33831852551664776 0.36964154432157387 0.25589207346415027
eks wye 4 0.38139939387114097 0.13418874328430463 0.37081732927653055 0.3688486618304419
wye pan 5 0.5732889198020006 0.8636244699032729 0.3910644883290776 0.5528448940048447
zee pan 6 0.5271261600918548 0.49322128674835697 0.4046706555053553 0.5296980334831537
eks zee 7 0.6117840605678454 0.1878849191181694 0.4253819960116043 0.6035754578593763
zee wye 8 0.5985540091064224 0.976181385699006 0.44269919732108615 0.5990561539817179
hat wye 9 0.03144187646093577 0.7495507603507059 0.40157346523507115 0.08820330421301396
pan wye 10 0.5026260055412137 0.9526183602969864 0.41167871926568544 0.46118373540839375
pan pan 11 0.7930488423451967 0.6505816637259333 0.44981573157363663 0.7598623316515164
zee pan 12 0.3676141320555616 0.23614420670296965 0.4415955716218291 0.4068389520151571
eks pan 13 0.4915175580479536 0.7709126592971468 0.4465877702644416 0.48304969744467396
eks zee 14 0.5207382318405251 0.34141681118811673 0.4540028164220499 0.51696937840094
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 [options]
Prints records in reverse order from the order in which they were encountered.
Options:
-h|--help Show this message.
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]
Passes through the last n records, optionally by category.
Options:
-g {a,b,c} Optional group-by-field names for head counts, e.g. a,b,c.
-n {n} Head-count to print. Default 10.
-h|--help Show this message.
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}
Options:
-a Append to existing file, if any, rather than overwriting.
-p Treat filename as a pipe-to command.
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.
-h|--help Show this message.
template¶
mlr template --help
Usage: mlr template [options]
Places input-record fields in the order specified by list of column names.
If the input record is missing a specified field, it will be filled with the fill-with.
If the input record possesses an unspecified field, it will be discarded.
Options:
-f {a,b,c} Comma-separated field names for template, e.g. a,b,c.
-t {filename} CSV file whose header line will be used for template.
--fill-with {filler string} What to fill absent fields with. Defaults to the empty string.
-h|--help Show this message.
Example:
* Specified fields are a,b,c.
* Input record is c=3,a=1,f=6.
* Output record is a=1,b=,c=3.
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 0.999952670371898
2 0.9998228522652893
3 0.99973332327313
4 0.9995625801977208
mlr --opprint top -n 4 -f x -o someothername data/medium
someothername x_top
1 0.999952670371898
2 0.9998228522652893
3 0.99973332327313
4 0.9995625801977208
mlr --opprint top -n 2 -f x -g a then sort -f a data/medium
a top_idx x_top
eks 1 0.9988110946859143
eks 2 0.9985342548358704
hat 1 0.999952670371898
hat 2 0.99973332327313
pan 1 0.9994029107062516
pan 2 0.9990440068491747
wye 1 0.9998228522652893
wye 2 0.9992635865771493
zee 1 0.9994904324789629
zee 2 0.9994378171787394
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. 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.
-h|--help Show this message.
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 -