NAME
    App::td - Manipulate table data

VERSION
    This document describes version 0.110 of App::td (from Perl distribution
    App-td), released on 2023-02-10.

FUNCTIONS
  td
    Usage:

     td(%args) -> [$status_code, $reason, $payload, \%result_meta]

    Manipulate table data.

    What is td?

    *td* receives table data from standard input and performs an action on
    it. It has functionality similar to some Unix commands like *head*,
    *tail*, *wc*, *cut*, *sort* except that it operates on table
    rows/columns instead of lines/characters. This is convenient to use with
    CLI scripts that output table data.

    What is table data?

    A *table data* is JSON-encoded data in the form of either: "hos" (hash
    of scalars, which is viewed as a two-column table where the columns are
    "key" and "value"), "aos" (array of scalars, which is viewed as a
    1-column array where the column is "elem"), "aoaos" (array of arrays of
    scalars), or "aohos" (array of hashes of scalars).

    The input can also be an *enveloped* table data, where the envelope is
    an array: "[status, message, content, meta]" and "content" is the actual
    table data. This kind of data is produced by "Perinci::CmdLine"-based
    scripts and can contain more detailed table specification in the "meta"
    hash, which "td" can parse.

    What scripts/modules output table data?

    CLI scripts that are written using Perinci::CmdLine framework output
    enveloped table data. There are at least hundreds of such scripts on
    CPAN. Some examples include: lcpan (from App::lcpan), pmlist (from
    App::PMUtils), and bencher (from Bencher).

    "TableData::*" modules contain table data. They can easily be output to
    CLI using the tabledata utility (from App::TableDataUtils).

    CSV output from any module/script can be easily converted to table data
    using the csv2td utility:

     % csv2td YOUR.csv | td ...
     % program-that-outputs-csv | csv2td - | td ...

    Table data can also be converted from several other formats e.g. JSON,
    YAML, XLS/XLSX/ODS.

    What scripts/modules accept table data?

    This *td* script, for one, accept table data.

    If a module/script expects CSV, you can feed table data and convert it
    to CSV using td2csv utility.

    Several other formats can also be converted to table data, e.g. JSON,
    YAML, XLS/XLSX/ODS.

    Using td

    First you might want to use the "info" action to see if the input is a
    table data:

     % osnames -l --json | td info

    If input is not valid JSON, a JSON parse error will be displayed. If
    input is valid JSON but not a table data, another error will be
    displayed. Otherwise, information about the table will be displayed
    (form, number of columns, column names, number of rows, and so on).

    Next, you can use these actions:

     # List available actions
     % td actions
 
     # Convert table data (which might be hash, aos, or aohos) to aoaos form
     % list-files -l --json | td as-aoaos
 
     # Convert table data (which might be hash, aos, or aoaos) to aohos form
     % list-files -l --json | td as-aohos
 
     # Display table data on the browser using datatables (to allow interactive sorting and filtering)
     % osnames -l | td cat --format html+datatables
 
     # Convert table data to CSV
     % list-files -l --json | td as-csv
 
     # Calculate arithmetic average of numeric columns
     % list-files -l --json | td avg
 
     # Append a row at the end containing arithmetic average of number columns
     % list-files -l --json | td avg-row
 
     # Count number of columns
     % osnames -l --json | td colcount
 
     # Append a single-column row at the end containing number of columns
     % osnames -l --json | td colcount-row
 
     # Return the column names only
     % lcpan related-mods Perinci::CmdLine | td colnames
 
     # append a row containing column names
     % lcpan related-mods Perinci::CmdLine | td colnames-row
 
     # Only show first 5 rows
     % osnames -l --json | td head -n5
 
     # Show all but the last 5 rows
     % osnames -l --json | td head -n -5
 
     # Check if input is table data and show information about the table
     % osnames -l --json | td info
 
     # Count number of rows
     % osnames -l --json | td rowcount
     % osnames -l --json | td wc            ;# shorter alias
 
     # Append a single-column row containing row count
     % osnames -l --json | td rowcount-row
     % osnames -l --json | td wc-row        ;# shorter alias
 
     # Add a row number column (1, 2, 3, ...)
     % list-files -l --json | td rownum-col
 
     # Select some columns
     % osnames -l --json | td select value description
 
     # Select all columns but some
     % osnames -l --json | td select '*' -E value -E description
 
     # Return the rows in a random order
     % osnames -l --json | td shuf
 
     # Pick 5 random rows from input
     % osnames -l --json | td shuf -n5
     % osnames -l --json | td pick -n5  ;# synonym for 'shuf'
 
     # Sort by column(s) (add "-" prefix to for descending order)
     % osnames -l --json | td sort value tags
     % osnames -l --json | td sort -- -value
 
     # Return sum of all numeric columns
     % list-files -l --json | td sum
 
     # Append a sum row
     % list-files -l --json | td sum-row
 
     # Only show last 5 rows
     % osnames -l --json | td tail -n5
 
     # Show rows from the row 5 onwards
     % osnames -l --json | td tail -n +5
 
     # Remove adjacent duplicate rows:
     % command ... | td uniq
     % command ... | td uniq -i ;# case-insensitive
     % command ... | td uniq --repeated ;# only shows the duplicate rows
     % command ... | td uniq -i C1 -i C2 ;# only use columns C1 & C2 to check uniqueness
     % command ... | td uniq -E C5 -E C6 ;# use all columns but C5 & C6 to check uniqueness
 
     # Remove non-adjacent duplicate rows:
     % command ... | td nauniq
     % command ... | td nauniq -i ;# case-insensitive
     % command ... | td nauniq --repeated ;# only shows the duplicate rows
     % command ... | td nauniq -i C1 -i C2 ;# only use columns C1 & C2 to check uniqueness
     % command ... | td nauniq -E C5 -E C6 ;# use all columns but C5 & C6 to check uniqueness
 
     # Transpose table (make first column of rows as column names in the
     # transposed table)
 
     % osnames -l --json | td transpose
 
     # Transpose table (make columns named 'row1', 'row2', 'row3', ... in the
     # transposed table)
 
     % osnames -l --json | td transpose --no-header-column
 
     # Use Perl code to filter rows. Perl code gets row in $row or $_
     # (scalar/aos/hos) or $rowhash (always a hos) or $rowarray (always aos).
     # There are also $rownum (integer, starts at 0) and $td (table data object).
     # Perl code is eval'ed in the 'main' package with strict/warnings turned
     # off. The example below selects videos that are larger than 480p.
 
     % media-info *.mp4 | td grep 'use List::Util qw(min); min($_->{video_height}, $_->{video_width}) > 480'
 
     # Use Perl code to filter columns. Perl code gets column name in $colname or
     # $_. There's also $colidx (column index, from 1) and $td (table data
     # object). If table data form is 'hash' or 'aos', it will be transformed
     # into 'aoaos'. The example below only select even columns that match
     # /col/i. Note that most of the time, 'td select' is better. But when you
     # have a lot of columns and want to select them programmatically, you have
     # grep-col.
 
     % somecd --json | td grep-col '$colidx % 2 == 0 && /col/i'
 
     # Use Perl code to transform row. Perl code gets row in $row or $_
     # (scalar/hash/array) and is supposed to return the new row. As in 'grep',
     # $rowhash, $rowarray, $rownum, $td are also available as helper. The
     # example below adds a field called 'is_landscape'.
 
     % media-info *.jpg | td map '$_->{is_landscape} = $_->{video_height} < $_->{video_width} ? 1:0; $_'
 
     # Use perl code to sort rows. Perl sorter code gets row in $a & $b or $_[0]
     # & $_[1] (hash/array). Sorter code, like in Perl's standard sort(), is
     # expected to return -1/0/1. The example belows sort videos by height,
     # descendingly then by width, descendingly.
 
     % media-info *.mp4 | td psort '$b->{video_height} <=> $a->{video_height} || $b->{video_width} <=> $b->{video_width}'

    This function is not exported.

    Arguments ('*' denotes required arguments):

    *   action* => *str*

        Action to perform on input table.

    *   argv => *array[str]* (default: [])

        Arguments.

    *   case_insensitive => *true*

        (No description)

    *   detail => *bool*

        (No description)

    *   exclude_columns => *array[str]*

        (No description)

    *   include_columns => *array[str]*

        (No description)

    *   lines => *str*

        (No description)

    *   no_header_column => *true*

        Don't make the first column as column names of the transposed table;
        instead create column named 'row1', 'row2', ...

    *   repeated => *bool*

        Allow/show duplicates.

        For shuf/pick actions, setting this option means sampling with
        replacement which makes a single row can be sampled/picked multiple
        times. The default is to sample without replacement.

        For uniq/nauniq actions, setting this option means instructing to
        return duplicate rows instead of the unique rows.

    *   weight_column => *str*

        Select a column that contains weight.

    Returns an enveloped result (an array).

    First element ($status_code) is an integer containing HTTP-like status
    code (200 means OK, 4xx caller error, 5xx function error). Second
    element ($reason) is a string containing error message, or something
    like "OK" if status is 200. Third element ($payload) is the actual
    result, but usually not present when enveloped result is an error
    response ($status_code is not 2xx). Fourth element (%result_meta) is
    called result metadata and is optional, a hash that contains extra
    information, much like how HTTP response headers provide additional
    metadata.

    Return value: (any)

HOMEPAGE
    Please visit the project's homepage at
    <https://metacpan.org/release/App-td>.

SOURCE
    Source repository is at <https://github.com/perlancar/perl-App-td>.

SEE ALSO
    Rinci::function for a more detailed explanation on enveloped result.

    TableDef for more detailed explanation of table data definition, which
    can be specified in enveloped result's `meta` hash in the `table` key
    (see Perinci::Sub::Property::result::table).

    Data::TableData::Object

    Perinci::CmdLine

AUTHOR
    perlancar <perlancar@cpan.org>

CONTRIBUTOR
    Steven Haryanto <stevenharyanto@gmail.com>

CONTRIBUTING
    To contribute, you can send patches by email/via RT, or send pull
    requests on GitHub.

    Most of the time, you don't need to build the distribution yourself. You
    can simply modify the code, then test via:

     % prove -l

    If you want to build the distribution (e.g. to try to install it locally
    on your system), you can install Dist::Zilla,
    Dist::Zilla::PluginBundle::Author::PERLANCAR,
    Pod::Weaver::PluginBundle::Author::PERLANCAR, and sometimes one or two
    other Dist::Zilla- and/or Pod::Weaver plugins. Any additional steps
    required beyond that are considered a bug and can be reported to me.

COPYRIGHT AND LICENSE
    This software is copyright (c) 2023, 2022, 2021, 2020, 2019, 2017, 2016,
    2015 by perlancar <perlancar@cpan.org>.

    This is free software; you can redistribute it and/or modify it under
    the same terms as the Perl 5 programming language system itself.

BUGS
    Please report any bugs or feature requests on the bugtracker website
    <https://rt.cpan.org/Public/Dist/Display.html?Name=App-td>

    When submitting a bug or request, please include a test-file or a patch
    to an existing test-file that illustrates the bug or desired feature.

