Shapes of data

No output at all

Try od -xcv and/or cat -e on your file to check for non-printable characters.

If you’re using Miller version less than 5.0.0 (try mlr --version on your system to find out), when the line-ending-autodetect feature was introduced, please see http://johnkerl.org/miller-releases/miller-4.5.0/doc/index.html.

Fields not selected

Check the field-separators of the data, e.g. with the command-line head program. Example: for CSV, Miller’s default record separator is comma; if your data is tab-delimited, e.g. aTABbTABc, then Miller won’t find three fields named a, b, and c but rather just one named aTABbTABc. Solution in this case: mlr --fs tab {remaining arguments ...}.

Also try od -xcv and/or cat -e on your file to check for non-printable characters.

Diagnosing delimiter specifications

Use the file command to see if there are CR/LF terminators (in this case, # there are not):

 file data/colours.csv
 data/colours.csv: UTF-8 Unicode text

Look at the file to find names of fields

 cat data/colours.csv
 KEY;DE;EN;ES;FI;FR;IT;NL;PL;RO;TR
 masterdata_colourcode_1;Weiß;White;Blanco;Valkoinen;Blanc;Bianco;Wit;Biały;Alb;Beyaz
 masterdata_colourcode_2;Schwarz;Black;Negro;Musta;Noir;Nero;Zwart;Czarny;Negru;Siyah

Extract a few fields:

 mlr --csv cut -f KEY,PL,RO data/colours.csv
 (only blank lines appear)

Use XTAB output format to get a sharper picture of where records/fields are being split:

 mlr --icsv --oxtab cat data/colours.csv
 KEY;DE;EN;ES;FI;FR;IT;NL;PL;RO;TR masterdata_colourcode_1;Weiß;White;Blanco;Valkoinen;Blanc;Bianco;Wit;Biały;Alb;Beyaz

 KEY;DE;EN;ES;FI;FR;IT;NL;PL;RO;TR masterdata_colourcode_2;Schwarz;Black;Negro;Musta;Noir;Nero;Zwart;Czarny;Negru;Siyah

Using XTAB output format makes it clearer that KEY;DE;...;RO;TR is being treated as a single field name in the CSV header, and likewise each subsequent line is being treated as a single field value. This is because the default field separator is a comma but we have semicolons here. Use XTAB again with different field separator (--fs semicolon):

 mlr --icsv --ifs semicolon --oxtab cat data/colours.csv
 KEY masterdata_colourcode_1
 DE  Weiß
 EN  White
 ES  Blanco
 FI  Valkoinen
 FR  Blanc
 IT  Bianco
 NL  Wit
 PL  Biały
 RO  Alb
 TR  Beyaz

 KEY masterdata_colourcode_2
 DE  Schwarz
 EN  Black
 ES  Negro
 FI  Musta
 FR  Noir
 IT  Nero
 NL  Zwart
 PL  Czarny
 RO  Negru
 TR  Siyah

Using the new field-separator, retry the cut:

 mlr --csv --fs semicolon cut -f KEY,PL,RO data/colours.csv
 KEY;PL;RO
 masterdata_colourcode_1;Biały;Alb
 masterdata_colourcode_2;Czarny;Negru

I assigned $9 and it’s not 9th

Miller records are ordered lists of key-value pairs. For NIDX format, DKVP format when keys are missing, or CSV/CSV-lite format with --implicit-csv-header, Miller will sequentially assign keys of the form 1, 2, etc. But these are not integer array indices: they’re just field names taken from the initial field ordering in the input data, when it is originally read from the input file(s).

 echo x,y,z | mlr --dkvp cat
 1=x,2=y,3=z

 echo x,y,z | mlr --dkvp put '$6="a";$4="b";$55="cde"'
 1=x,2=y,3=z,6=a,4=b,55=cde

 echo x,y,z | mlr --nidx cat
 x,y,z

 echo x,y,z | mlr --csv --implicit-csv-header cat
 1,2,3
 x,y,z

 echo x,y,z | mlr --dkvp rename 2,999
 1=x,999=y,3=z

 echo x,y,z | mlr --dkvp rename 2,newname
 1=x,newname=y,3=z

 echo x,y,z | mlr --csv --implicit-csv-header reorder -f 3,1,2
 3,1,2
 z,x,y

Why doesn’t mlr cut put fields in the order I want?

Example: columns x,i,a were requested but they appear here in the order a,i,x:

 cat data/small
 a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
 a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
 a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
 a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
 a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

 mlr cut -f x,i,a data/small
 a=pan,i=1,x=0.3467901443380824
 a=eks,i=2,x=0.7586799647899636
 a=wye,i=3,x=0.20460330576630303
 a=eks,i=4,x=0.38139939387114097
 a=wye,i=5,x=0.5732889198020006

The issue is that Miller’s cut, by default, outputs cut fields in the order they appear in the input data. This design decision was made intentionally to parallel the Unix/Linux system cut command, which has the same semantics.

The solution is to use the -o option:

 mlr cut -o -f x,i,a data/small
 x=0.3467901443380824,i=1,a=pan
 x=0.7586799647899636,i=2,a=eks
 x=0.20460330576630303,i=3,a=wye
 x=0.38139939387114097,i=4,a=eks
 x=0.5732889198020006,i=5,a=wye

Numbering and renumbering records

The awk-like built-in variable NR is incremented for each input record:

 cat data/small
 a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
 a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
 a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
 a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
 a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

 mlr put '$nr = NR' data/small
 a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,nr=1
 a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,nr=2
 a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,nr=3
 a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,nr=4
 a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,nr=5

However, this is the record number within the original input stream – not after any filtering you may have done:

 mlr filter '$a == "wye"' then put '$nr = NR' data/small
 a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,nr=3
 a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,nr=5

There are two good options here. One is to use the cat verb with -n:

 mlr filter '$a == "wye"' then cat -n data/small
 n=1,a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
 n=2,a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

The other is to keep your own counter within the put DSL:

 mlr filter '$a == "wye"' then put 'begin {@n = 1} $n = @n; @n += 1' data/small
 a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,n=1
 a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,n=2

The difference is a matter of taste (although mlr cat -n puts the counter first).

Splitting nested fields

Suppose you have a TSV file like this:

a   b
x   z
s   u:v:w

The simplest option is to use mlr nest:

 mlr --tsv nest --explode --values --across-records -f b --nested-fs : data/nested.tsv
 a   b
 x   z
 s   u
 s   v
 s   w

 mlr --tsv nest --explode --values --across-fields  -f b --nested-fs : data/nested.tsv
 a   b_1
 x   z

 a   b_1     b_2     b_3
 s   u       v       w

While mlr nest is simplest, let’s also take a look at a few ways to do this using the put DSL.

One option to split out the colon-delimited values in the b column is to use splitnv to create an integer-indexed map and loop over it, adding new fields to the current record:

 mlr --from data/nested.tsv --itsv --oxtab put '
   o = splitnv($b, ":");
   for (k,v in o) {
     $["p".k]=v
   }
 '
 a  x
 b  z
 p1 z

 a  s
 b  u:v:w
 p1 u
 p2 v
 p3 w

while another is to loop over the same map from splitnv and use it (with put -q to suppress printing the original record) to produce multiple records:

 mlr --from data/nested.tsv --itsv --oxtab put -q '
   o = splitnv($b, ":");
   for (k,v in o) {
     x = mapsum($*, {"b":v});
     emit x
   }
 '
 a x
 b z

 a s
 b u

 a s
 b v

 a s
 b w

 mlr --from data/nested.tsv --tsv put -q '
   o = splitnv($b, ":");
   for (k,v in o) {
     x = mapsum($*, {"b":v}); emit x
   }
 '
 a   b
 x   z
 s   u
 s   v
 s   w

Options for dealing with duplicate rows

If your data has records appearing multiple times, you can use mlr uniq to show and/or count the unique records.

If you want to look at partial uniqueness – for example, show only the first record for each unique combination of the account_id and account_status fields – you might use mlr head -n 1 -g account_id,account_status. Please also see mlr head.

Rectangularizing data

Suppose you have a method (in whatever language) which is printing things of the form

outer=1
outer=2
outer=3

and then calls another method which prints things of the form

middle=10
middle=11
middle=12
middle=20
middle=21
middle=30
middle=31

and then, perhaps, that second method calls a third method which prints things of the form

inner1=100,inner2=101
inner1=120,inner2=121
inner1=200,inner2=201
inner1=210,inner2=211
inner1=300,inner2=301
inner1=312
inner1=313,inner2=314

with the result that your program’s output is

outer=1
middle=10
inner1=100,inner2=101
middle=11
middle=12
inner1=120,inner2=121
outer=2
middle=20
inner1=200,inner2=201
middle=21
inner1=210,inner2=211
outer=3
middle=30
inner1=300,inner2=301
middle=31
inner1=312
inner1=313,inner2=314

The idea here is that middles starting with a 1 belong to the outer value of 1, and so on. (For example, the outer values might be account IDs, the middle values might be invoice IDs, and the inner values might be invoice line-items.) If you want all the middle and inner lines to have the context of which outers they belong to, you can modify your software to pass all those through your methods. Alternatively, don’t refactor your code just to handle some ad-hoc log-data formatting – instead, use the following to rectangularize the data. The idea is to use an out-of-stream variable to accumulate fields across records. Clear that variable when you see an outer ID; accumulate fields; emit output when you see the inner IDs.

 mlr --from data/rect.txt put -q '
   is_present($outer) {
     unset @r
   }
   for (k, v in $*) {
     @r[k] = v
   }
   is_present($inner1) {
     emit @r
   }'
 outer=1,middle=10,inner1=100,inner2=101
 outer=1,middle=12,inner1=120,inner2=121
 outer=2,middle=20,inner1=200,inner2=201
 outer=2,middle=21,inner1=210,inner2=211
 outer=3,middle=30,inner1=300,inner2=301
 outer=3,middle=31,inner1=312,inner2=301
 outer=3,middle=31,inner1=313,inner2=314