.. PLEASE DO NOT EDIT DIRECTLY. EDIT THE .rst.in FILE PLEASE. FAQ ========= 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 How do I suppress numeric conversion? ---------------------------------------------------------------- **TL;DR use put -S**. Within ``mlr put`` and ``mlr filter``, the default behavior for scanning input records is to parse them as integer, if possible, then as float, if possible, else leave them as string: :: $ cat data/scan-example-1.tbl value 1 2.0 3x hello :: $ mlr --pprint put '$copy = $value; $type = typeof($value)' data/scan-example-1.tbl value copy type 1 1 int 2.0 2.000000 float 3x 3x string hello hello string The numeric-conversion rule is simple: * Try to scan as integer (``"1"`` should be int); * If that doesn't succeed, try to scan as float (``"1.0"`` should be float); * If that doesn't succeed, leave the value as a string (``"1x"`` is string). This is a sensible default: you should be able to put ``'$z = $x + $y'`` without having to write ``'$z = int($x) + float($y)'``. Also note that default output format for floating-point numbers created by ``put`` (and other verbs such as ``stats1``) is six decimal places; you can override this using ``mlr --ofmt``. Also note that Miller uses your system's C library functions whenever possible: e.g. ``sscanf`` for converting strings to integer or floating-point. But now suppose you have data like these: :: $ cat data/scan-example-2.tbl value 0001 0002 0005 0005WA 0006 0007 0007WA 0008 0009 0010 :: $ mlr --pprint put '$copy = $value; $type = typeof($value)' data/scan-example-2.tbl value copy type 0001 1 int 0002 2 int 0005 5 int 0005WA 0005WA string 0006 6 int 0007 7 int 0007WA 0007WA string 0008 8.000000 float 0009 9.000000 float 0010 8 int The same conversion rules as above are being used. Namely: * By default field values are inferred to int, else float, else string; * leading zeroes indicate octal for integers (``sscanf`` semantics); * since ``0008`` doesn't scan as integer (leading 0 requests octal but 8 isn't a valid octal digit), the float scan is tried next and it succeeds; * default floating-point output format is 6 decimal places (override with ``mlr --ofmt``). Taken individually the rules make sense; taken collectively they produce a mishmash of types here. The solution is to **use the -S flag** for ``mlr put`` and/or ``mlr filter``. Then all field values are left as string. You can type-coerce on demand using syntax like ``'$z = int($x) + float($y)'``. (See also :doc:`reference-dsl`; see also https://github.com/johnkerl/miller/issues/150.) :: $ mlr --pprint put -S '$copy = $value; $type = typeof($value)' data/scan-example-2.tbl value copy type 0001 0001 string 0002 0002 string 0005 0005 string 0005WA 0005WA string 0006 0006 string 0007 0007 string 0007WA 0007WA string 0008 0008 string 0009 0009 string 0010 0010 string How do I examine then-chaining? ---------------------------------------------------------------- Then-chaining found in Miller is intended to function the same as Unix pipes, but with less keystroking. You can print your data one pipeline step at a time, to see what intermediate output at one step becomes the input to the next step. First, look at the input data: :: $ cat data/then-example.csv Status,Payment_Type,Amount paid,cash,10.00 pending,debit,20.00 paid,cash,50.00 pending,credit,40.00 paid,debit,30.00 Next, run the first step of your command, omitting anything from the first ``then`` onward: :: $ mlr --icsv --opprint count-distinct -f Status,Payment_Type data/then-example.csv Status Payment_Type count paid cash 2 pending debit 1 pending credit 1 paid debit 1 After that, run it with the next ``then`` step included: :: $ mlr --icsv --opprint count-distinct -f Status,Payment_Type then sort -nr count data/then-example.csv Status Payment_Type count paid cash 2 pending debit 1 pending credit 1 paid debit 1 Now if you use ``then`` to include another verb after that, the columns ``Status``, ``Payment_Type``, and ``count`` will be the input to that verb. Note, by the way, that you'll get the same results using pipes: :: $ mlr --csv count-distinct -f Status,Payment_Type data/then-example.csv | mlr --icsv --opprint sort -nr count Status Payment_Type count paid cash 2 pending debit 1 pending credit 1 paid debit 1 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. :: $ 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 How can I filter by date? ---------------------------------------------------------------- Given input like :: $ cat dates.csv date,event 2018-02-03,initialization 2018-03-07,discovery 2018-02-03,allocation we can use ``strptime`` to parse the date field into seconds-since-epoch and then do numeric comparisons. Simply match your input dataset's date-formatting to the :ref:`reference-dsl-strptime` format-string. For example: :: $ mlr --csv filter 'strptime($date, "%Y-%m-%d") > strptime("2018-03-03", "%Y-%m-%d")' dates.csv date,event 2018-03-07,discovery Caveat: localtime-handling in timezones with DST is still a work in progress; see https://github.com/johnkerl/miller/issues/170. See also https://github.com/johnkerl/miller/issues/208 -- thanks @aborruso! How can I handle commas-as-data in various formats? ---------------------------------------------------------------- :doc:`CSV ` handles this well and by design: :: $ cat commas.csv Name,Role "Xiao, Lin",administrator "Khavari, Darius",tester Likewise :ref:`file-formats-json`: :: $ mlr --icsv --ojson cat commas.csv { "Name": "Xiao, Lin", "Role": "administrator" } { "Name": "Khavari, Darius", "Role": "tester" } For Miller's :ref:`vertical-tabular format ` there is no escaping for carriage returns, but commas work fine: :: $ mlr --icsv --oxtab cat commas.csv Name Xiao, Lin Role administrator Name Khavari, Darius Role tester But for :ref:`Key-value_pairs ` and :ref:`index-numbered `, commas are the default field separator. And -- as of Miller 5.4.0 anyway -- there is no CSV-style double-quote-handling like there is for CSV. So commas within the data look like delimiters: :: $ mlr --icsv --odkvp cat commas.csv Name=Xiao, Lin,Role=administrator Name=Khavari, Darius,Role=tester One solution is to use a different delimiter, such as a pipe character: :: $ mlr --icsv --odkvp --ofs pipe cat commas.csv Name=Xiao, Lin|Role=administrator Name=Khavari, Darius|Role=tester To be extra-sure to avoid data/delimiter clashes, you can also use control characters as delimiters -- here, control-A: :: $ mlr --icsv --odkvp --ofs '\001' cat commas.csv | cat -v Name=Xiao, Lin^ARole=administrator Name=Khavari, Darius^ARole=tester How can I handle field names with special symbols in them? ---------------------------------------------------------------- Simply surround the field names with curly braces: :: $ echo 'x.a=3,y:b=4,z/c=5' | mlr put '${product.all} = ${x.a} * ${y:b} * ${z/c}' x.a=3,y:b=4,z/c=5,product.all=60 How to escape '?' in regexes? ---------------------------------------------------------------- One way is to use square brackets; an alternative is to use simple string-substitution rather than a regular expression. :: $ cat data/question.dat a=is it?,b=it is! $ mlr --oxtab put '$c = gsub($a, "[?]"," ...")' data/question.dat a is it? b it is! c is it ... $ mlr --oxtab put '$c = ssub($a, "?"," ...")' data/question.dat a is it? b it is! c is it ... The ``ssub`` function exists precisely for this reason: so you don't have to escape anything. How can I put single-quotes into strings? ---------------------------------------------------------------- This is a little tricky due to the shell's handling of quotes. For simplicity, let's first put an update script into a file: :: $a = "It's OK, I said, then 'for now'." :: $ echo a=bcd | mlr put -f data/single-quote-example.mlr a=It's OK, I said, then 'for now'. So, it's simple: Miller's DSL uses double quotes for strings, and you can put single quotes (or backslash-escaped double-quotes) inside strings, no problem. Without putting the update expression in a file, it's messier: :: $ echo a=bcd | mlr put '$a="It'\''s OK, I said, '\''for now'\''."' a=It's OK, I said, 'for now'. The idea is that the outermost single-quotes are to protect the ``put`` expression from the shell, and the double quotes within them are for Miller. To get a single quote in the middle there, you need to actually put it *outside* the single-quoting for the shell. The pieces are the following, all concatenated together: * ``$a="It`` * ``\'`` * ``s OK, I said,`` * ``\'`` * ``for now`` * ``\'`` * ``.`` 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 NR is not consecutive after then-chaining ---------------------------------------------------------------- Given this input data: :: $ 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 why don't I see ``NR=1`` and ``NR=2`` here?? :: $ mlr filter '$x > 0.5' then put '$NR = NR' data/small a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,NR=2 a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,NR=5 The reason is that ``NR`` is computed for the original input records and isn't dynamically updated. By contrast, ``NF`` is dynamically updated: it's the number of fields in the current record, and if you add/remove a field, the value of ``NF`` will change: :: $ echo x=1,y=2,z=3 | mlr put '$nf1 = NF; $u = 4; $nf2 = NF; unset $x,$y,$z; $nf3 = NF' nf1=3,u=4,nf2=5,nf3=3 ``NR``, by contrast (and ``FNR`` as well), retains the value from the original input stream, and records may be dropped by a ``filter`` within a ``then``-chain. To recover consecutive record numbers, you can use out-of-stream variables as follows: :: $ mlr --opprint --from data/small put ' begin{ @nr1 = 0 } @nr1 += 1; $nr1 = @nr1 ' \ then filter '$x>0.5' \ then put ' begin{ @nr2 = 0 } @nr2 += 1; $nr2 = @nr2 ' a b i x y nr1 nr2 eks pan 2 0.7586799647899636 0.5221511083334797 2 1 wye pan 5 0.5732889198020006 0.8636244699032729 5 2 Or, simply use ``mlr cat -n``: :: $ mlr filter '$x > 0.5' then cat -n data/small n=1,a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797 n=2,a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729 Why am I not seeing all possible joins occur? ---------------------------------------------------------------- **This section describes behavior before Miller 5.1.0. As of 5.1.0, -u is the default.** For example, the right file here has nine records, and the left file should add in the ``hostname`` column -- so the join output should also have 9 records: :: $ mlr --icsvlite --opprint cat data/join-u-left.csv hostname ipaddr nadir.east.our.org 10.3.1.18 zenith.west.our.org 10.3.1.27 apoapsis.east.our.org 10.4.5.94 :: $ mlr --icsvlite --opprint cat data/join-u-right.csv ipaddr timestamp bytes 10.3.1.27 1448762579 4568 10.3.1.18 1448762578 8729 10.4.5.94 1448762579 17445 10.3.1.27 1448762589 12 10.3.1.18 1448762588 44558 10.4.5.94 1448762589 8899 10.3.1.27 1448762599 0 10.3.1.18 1448762598 73425 10.4.5.94 1448762599 12200 :: $ mlr --icsvlite --opprint join -s -j ipaddr -f data/join-u-left.csv data/join-u-right.csv ipaddr hostname timestamp bytes 10.3.1.27 zenith.west.our.org 1448762579 4568 10.4.5.94 apoapsis.east.our.org 1448762579 17445 10.4.5.94 apoapsis.east.our.org 1448762589 8899 10.4.5.94 apoapsis.east.our.org 1448762599 12200 The issue is that Miller's ``join``, by default (before 5.1.0), took input sorted (lexically ascending) by the sort keys on both the left and right files. This design decision was made intentionally to parallel the Unix/Linux system ``join`` command, which has the same semantics. The benefit of this default is that the joiner program can stream through the left and right files, needing to load neither entirely into memory. The drawback, of course, is that is requires sorted input. The solution (besides pre-sorting the input files on the join keys) is to simply use **mlr join -u** (which is now the default). This loads the left file entirely into memory (while the right file is still streamed one line at a time) and does all possible joins without requiring sorted input: :: $ mlr --icsvlite --opprint join -u -j ipaddr -f data/join-u-left.csv data/join-u-right.csv ipaddr hostname timestamp bytes 10.3.1.27 zenith.west.our.org 1448762579 4568 10.3.1.18 nadir.east.our.org 1448762578 8729 10.4.5.94 apoapsis.east.our.org 1448762579 17445 10.3.1.27 zenith.west.our.org 1448762589 12 10.3.1.18 nadir.east.our.org 1448762588 44558 10.4.5.94 apoapsis.east.our.org 1448762589 8899 10.3.1.27 zenith.west.our.org 1448762599 0 10.3.1.18 nadir.east.our.org 1448762598 73425 10.4.5.94 apoapsis.east.our.org 1448762599 12200 General advice is to make sure the left-file is relatively small, e.g. containing name-to-number mappings, while saving large amounts of data for the right file. How to rectangularize after joins with unpaired? ---------------------------------------------------------------- Suppose you have the following two data files: :: id,code 3,0000ff 2,00ff00 4,ff0000 :: id,color 4,red 2,green Joining on color the results are as expected: :: $ mlr --csv join -j id -f data/color-codes.csv data/color-names.csv id,code,color 4,ff0000,red 2,00ff00,green However, if we ask for left-unpaireds, since there's no ``color`` column, we get a row not having the same column names as the other: :: $ mlr --csv join --ul -j id -f data/color-codes.csv data/color-names.csv id,code,color 4,ff0000,red 2,00ff00,green id,code 3,0000ff To fix this, we can use **unsparsify**: :: $ mlr --csv join --ul -j id -f data/color-codes.csv then unsparsify --fill-with "" data/color-names.csv id,code,color 4,ff0000,red 2,00ff00,green 3,0000ff, Thanks to @aborruso for the tip! What about XML or JSON file formats? ---------------------------------------------------------------- Miller handles **tabular data**, which is a list of records each having fields which are key-value pairs. Miller also doesn't require that each record have the same field names (see also :doc:`record-heterogeneity`). Regardless, tabular data is a **non-recursive data structure**. XML, JSON, etc. are, by contrast, all **recursive** or **nested** data structures. For example, in JSON you can represent a hash map whose values are lists of lists. Now, you can put tabular data into these formats -- since list-of-key-value-pairs is one of the things representable in XML or JSON. Example: :: # DKVP x=1,y=2 z=3 :: # XML x 1 y 2 z 3
:: # JSON [{"x":1,"y":2},{"z":3}] However, a tool like Miller which handles non-recursive data is never going to be able to handle full XML/JSON semantics -- only a small subset. If tabular data represented in XML/JSON/etc are sufficiently well-structured, it may be easy to grep/sed out the data into a simpler text form -- this is a general text-processing problem. Miller does support tabular data represented in JSON: please see :doc:`file-formats`. See also `jq `_ for a truly powerful, JSON-specific tool. For XML, my suggestion is to use a tool like `ff-extractor `_ to do format conversion.