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409 lines
15 KiB
ReStructuredText
409 lines
15 KiB
ReStructuredText
FAQ
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=========
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No output at all
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----------------------------------------------------------------
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Try ``od -xcv`` and/or ``cat -e`` on your file to check for non-printable characters.
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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.
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Fields not selected
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----------------------------------------------------------------
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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 ...}``.
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Also try ``od -xcv`` and/or ``cat -e`` on your file to check for non-printable characters.
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Diagnosing delimiter specifications
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----------------------------------------------------------------
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::
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POKI_INCLUDE_ESCAPED(data/delimiter-examples.txt)HERE
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How do I suppress numeric conversion?
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----------------------------------------------------------------
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**TL;DR use put -S**.
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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:
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POKI_RUN_COMMAND{{cat data/scan-example-1.tbl}}HERE
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POKI_RUN_COMMAND{{mlr --pprint put '$copy = $value; $type = typeof($value)' data/scan-example-1.tbl}}HERE
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The numeric-conversion rule is simple:
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* Try to scan as integer (``"1"`` should be int);
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* If that doesn't succeed, try to scan as float (``"1.0"`` should be float);
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* If that doesn't succeed, leave the value as a string (``"1x"`` is string).
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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.
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But now suppose you have data like these:
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POKI_RUN_COMMAND{{cat data/scan-example-2.tbl}}HERE
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POKI_RUN_COMMAND{{mlr --pprint put '$copy = $value; $type = typeof($value)' data/scan-example-2.tbl}}HERE
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The same conversion rules as above are being used. Namely:
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* By default field values are inferred to int, else float, else string;
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* leading zeroes indicate octal for integers (``sscanf`` semantics);
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* 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;
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* default floating-point output format is 6 decimal places (override with ``mlr --ofmt``).
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Taken individually the rules make sense; taken collectively they produce a mishmash of types here.
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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($y)'``. (See also :doc:`reference-dsl`; see also https://github.com/johnkerl/miller/issues/150.)
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POKI_RUN_COMMAND{{mlr --pprint put -S '$copy = $value; $type = typeof($value)' data/scan-example-2.tbl}}HERE
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How do I examine then-chaining?
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----------------------------------------------------------------
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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.
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First, look at the input data:
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POKI_RUN_COMMAND{{cat data/then-example.csv}}HERE
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Next, run the first step of your command, omitting anything from the first ``then`` onward:
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POKI_RUN_COMMAND{{mlr --icsv --opprint count-distinct -f Status,Payment_Type data/then-example.csv}}HERE
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After that, run it with the next ``then`` step included:
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POKI_RUN_COMMAND{{mlr --icsv --opprint count-distinct -f Status,Payment_Type then sort -nr count data/then-example.csv}}HERE
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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.
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Note, by the way, that you'll get the same results using pipes:
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POKI_RUN_COMMAND{{mlr --csv count-distinct -f Status,Payment_Type data/then-example.csv | mlr --icsv --opprint sort -nr count}}HERE
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I assigned $9 and it's not 9th
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----------------------------------------------------------------
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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.
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POKI_RUN_COMMAND{{echo x,y,z | mlr --dkvp cat}}HERE
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POKI_RUN_COMMAND{{echo x,y,z | mlr --dkvp put '$6="a";$4="b";$55="cde"'}}HERE
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POKI_RUN_COMMAND{{echo x,y,z | mlr --nidx cat}}HERE
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POKI_RUN_COMMAND{{echo x,y,z | mlr --csv --implicit-csv-header cat}}HERE
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POKI_RUN_COMMAND{{echo x,y,z | mlr --dkvp rename 2,999}}HERE
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POKI_RUN_COMMAND{{echo x,y,z | mlr --dkvp rename 2,newname}}HERE
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POKI_RUN_COMMAND{{echo x,y,z | mlr --csv --implicit-csv-header reorder -f 3,1,2}}HERE
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How can I filter by date?
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----------------------------------------------------------------
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Given input like
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POKI_RUN_COMMAND{{cat dates.csv}}HERE
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we can use ``strptime`` to parse the date field into seconds-since-epoch and then do numeric comparisons. Simply match your input dataset's date-formatting to the :ref:`reference-dsl-strptime` format-string. For example:
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POKI_RUN_COMMAND{{mlr --csv filter 'strptime($date, "%Y-%m-%d") > strptime("2018-03-03", "%Y-%m-%d")' dates.csv}}HERE
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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!
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How can I handle commas-as-data in various formats?
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----------------------------------------------------------------
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:doc:`CSV <file-formats>` handles this well and by design:
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POKI_RUN_COMMAND{{cat commas.csv}}HERE
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Likewise :ref:`file-formats-json`:
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POKI_RUN_COMMAND{{mlr --icsv --ojson cat commas.csv}}HERE
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For Miller's :ref:`vertical-tabular format <file-formats-xtab>` there is no escaping for carriage returns, but commas work fine:
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POKI_RUN_COMMAND{{mlr --icsv --oxtab cat commas.csv}}HERE
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But for :ref:`Key-value_pairs <file-formats-dkvp>` and :ref:`index-numbered <file-formats-nidx>`, 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:
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POKI_RUN_COMMAND{{mlr --icsv --odkvp cat commas.csv}}HERE
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One solution is to use a different delimiter, such as a pipe character:
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POKI_RUN_COMMAND{{mlr --icsv --odkvp --ofs pipe cat commas.csv}}HERE
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To be extra-sure to avoid data/delimiter clashes, you can also use control
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characters as delimiters -- here, control-A:
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POKI_RUN_COMMAND{{mlr --icsv --odkvp --ofs '\001' cat commas.csv | cat -v}}HERE
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How can I handle field names with special symbols in them?
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----------------------------------------------------------------
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Simply surround the field names with curly braces:
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POKI_RUN_COMMAND{{echo 'x.a=3,y:b=4,z/c=5' | mlr put '${product.all} = ${x.a} * ${y:b} * ${z/c}'}}HERE
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How to escape '?' in regexes?
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----------------------------------------------------------------
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One way is to use square brackets; an alternative is to use simple string-substitution rather than a regular expression.
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POKI_RUN_COMMAND{{cat data/question.dat}}HERE
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POKI_RUN_COMMAND{{mlr --oxtab put '$c = gsub($a, "[?]"," ...")' data/question.dat}}HERE
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POKI_RUN_COMMAND{{mlr --oxtab put '$c = ssub($a, "?"," ...")' data/question.dat}}HERE
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The ``ssub`` function exists precisely for this reason: so you don't have to escape anything.
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How can I put single-quotes into strings?
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----------------------------------------------------------------
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This is a little tricky due to the shell's handling of quotes. For simplicity, let's first put an update script into a file:
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POKI_INCLUDE_ESCAPED(data/single-quote-example.mlr)HERE
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POKI_RUN_COMMAND{{echo a=bcd | mlr put -f data/single-quote-example.mlr}}HERE
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So, it's simple: Miller's DSL uses double quotes for strings, and you can put single quotes (or backslash-escaped double-quotes) inside strings, no problem.
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Without putting the update expression in a file, it's messier:
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POKI_RUN_COMMAND{{echo a=bcd | mlr put '$a="It'\''s OK, I said, '\''for now'\''."'}}HERE
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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:
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* ``$a="It``
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* ``\'``
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* ``s OK, I said,``
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* ``\'``
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* ``for now``
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* ``\'``
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* ``.``
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Why doesn't mlr cut put fields in the order I want?
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----------------------------------------------------------------
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Example: columns ``x,i,a`` were requested but they appear here in the order ``a,i,x``:
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POKI_RUN_COMMAND{{cat data/small}}HERE
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POKI_RUN_COMMAND{{mlr cut -f x,i,a data/small}}HERE
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The issue is that Miller's ``cut``, by default, outputs cut fields in the order they appear in the input data. This design decision was made intentionally to parallel the Unix/Linux system ``cut`` command, which has the same semantics.
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The solution is to use the ``-o`` option:
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POKI_RUN_COMMAND{{mlr cut -o -f x,i,a data/small}}HERE
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NR is not consecutive after then-chaining
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Given this input data:
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POKI_RUN_COMMAND{{cat data/small}}HERE
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why don't I see ``NR=1`` and ``NR=2`` here??
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POKI_RUN_COMMAND{{mlr filter '$x > 0.5' then put '$NR = NR' data/small}}HERE
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The reason is that ``NR`` is computed for the original input records and isn't dynamically updated. By contrast, ``NF`` is dynamically updated: it's the number of fields in the current record, and if you add/remove a field, the value of ``NF`` will change:
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POKI_RUN_COMMAND{{echo x=1,y=2,z=3 | mlr put '$nf1 = NF; $u = 4; $nf2 = NF; unset $x,$y,$z; $nf3 = NF'}}HERE
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``NR``, by contrast (and ``FNR`` as well), retains the value from the original input stream, and records may be dropped by a ``filter`` within a ``then``-chain. To recover consecutive record numbers, you can use out-of-stream variables as follows:
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POKI_INCLUDE_AND_RUN_ESCAPED(data/dynamic-nr.sh)HERE
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Or, simply use ``mlr cat -n``:
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POKI_RUN_COMMAND{{mlr filter '$x > 0.5' then cat -n data/small}}HERE
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Why am I not seeing all possible joins occur?
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**This section describes behavior before Miller 5.1.0. As of 5.1.0, -u is the default.**
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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:
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POKI_RUN_COMMAND{{mlr --icsvlite --opprint cat data/join-u-left.csv}}HERE
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POKI_RUN_COMMAND{{mlr --icsvlite --opprint cat data/join-u-right.csv}}HERE
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POKI_RUN_COMMAND{{mlr --icsvlite --opprint join -s -j ipaddr -f data/join-u-left.csv data/join-u-right.csv}}HERE
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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.
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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:
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POKI_RUN_COMMAND{{mlr --icsvlite --opprint join -u -j ipaddr -f data/join-u-left.csv data/join-u-right.csv}}HERE
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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.
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How to rectangularize after joins with unpaired?
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Suppose you have the following two data files:
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POKI_INCLUDE_ESCAPED(data/color-codes.csv)HERE
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POKI_INCLUDE_ESCAPED(data/color-names.csv)HERE
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Joining on color the results are as expected:
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POKI_RUN_COMMAND{{mlr --csv join -j id -f data/color-codes.csv data/color-names.csv}}HERE
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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:
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POKI_RUN_COMMAND{{mlr --csv join --ul -j id -f data/color-codes.csv data/color-names.csv}}HERE
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To fix this, we can use **unsparsify**:
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POKI_RUN_COMMAND{{mlr --csv join --ul -j id -f data/color-codes.csv then unsparsify --fill-with "" data/color-names.csv}}HERE
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Thanks to @aborruso for the tip!
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What about XML or JSON file formats?
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----------------------------------------------------------------
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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**.
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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.
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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:
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# DKVP
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x=1,y=2
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z=3
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# XML
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<table>
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<record>
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<field>
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<key> x </key> <value> 1 </value>
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</field>
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<field>
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<key> y </key> <value> 2 </value>
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</field>
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</record>
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<record>
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<field>
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<key> z </key> <value> 3 </value>
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</field>
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</record>
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</table>
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# JSON
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[{"x":1,"y":2},{"z":3}]
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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.
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Miller does support tabular data represented in JSON: please see :doc:`file-formats`. See also `jq <https://stedolan.github.io/jq/>`_ for a truly powerful, JSON-specific tool.
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For XML, my suggestion is to use a tool like `ff-extractor <http://ff-extractor.sourceforge.net>`_ to do format conversion.
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