/ 
Data-TableReader-0.021
River stage one • 1 direct dependent • 1 total dependent
/ Data::TableReader

NAME

Data::TableReader - Extract records from "dirty" tabular data sources

VERSION

version 0.021

SYNOPSIS

# Find a row in the Excel file containing the headers
#   "address", "city", "state", "zip" (in any order)
# and then convert each row under that into a hashref of those fields.

my $records= Data::TableReader>new(
    input => 'path/to/file.xlsx',
    fields => [qw( address city state zip )],
  )
  ->iterator->all;

but there are plenty of options to choose from...

my $tr= Data::TableReader->new(
  # path or file handle
  # let it auto-detect the format (but can override that if we need)
  input => 'path/to/file.csv',
  
  # We want these fields to exist in the file (identified by headers)
  fields => [
    { name => 'address', header => qr/street|address/i },
    'city',
    'state',
    # can validate with Type::Tiny classes
    { name => 'zip', header => qr/zip\b|postal/i, type => US_Zipcode },
  ],
  
  # Our data provider is horrible; just ignore any nonsense we encounter
  on_blank_row => 'next',
  on_validation_error => 'next',
  
  # Capture warnings and show to user who uploaded file
  log => \(my @messages)
);

my $records= $tr->iterator->all;
...
$http_response->body( encode_json({ messages => \@messages }) );

DESCRIPTION

This module is designed to take "loose" or "dirty" tabular data sources (such as Excel, CSV, TSV, or HTML) which may have been edited by non-technical humans and extract the data into sanitized records, while also verifying that the data file contains roughly the schema you were expecting. It is primarily intended for making automated imports of data from non-automated or unstable sources, and providing human-readable feedback about the validity of the data file.

ATTRIBUTES

input

This can be a file name, Path::Class instance, file handle, arrayref, or Spreadsheet::ParseExcel::Worksheet object. If you supply a file handle, it must be seekable in order to auto-detect the file format, or you may specify the decoder directly to avoid auto-detection. Arrayrefs are passed to the 'Mock' decoder which just returns the data as-is.

decoder

This is either an instance of Data::TableReader::Decoder, or a class name, or a partial class name to be appended as "Data::TableReader::Decoder::$name" or an arrayref or hashref of arguments to build the decoder.

In an arrayref the first argument can be undef, and in a hashref the CLASS argument can be missing or undef. In those cases it will be detected from the input attribute and any default arguments combined with (and if necessary trumped by) the extra arguments in the arrayref or hashref.

Examples:

'CSV'
# becomes Data::TableReader::Decoder::CSV->new()

[ 'CSV', sep_char => "|" ]
# becomes Data::TableReader::Decoder::CSV->new(sep_char => "|")

{ CLASS => 'CSV', sep_char => "|" }
# becomes Data::TableReader::Decoder::CSV->new({ sep_char => "|" })

fields

An arrayref of Data::TableReader::Field objects which this module should search for within the tables (worksheets etc.) of "input".

If an element of this array is a hashref or string, it will be coerced to an instance of Data::TableReader::Field, with plain strings becoming the name attribute. See "header" in Data::TableReader::Field for how names are automatically converted to the header-matching regex.

There are some convenience accessors for the fields:

field_list

List access for @{ $reader->fields }

field_by_name

Map of { $field->name => $field }. If you have multiple fields of the same name (allowed, but not recommended) the value is the first per the order of field_list.

field_by_addr

Map of { refaddr($field) => $field }.

record_class

Default is the special value 'HASH' for un-blessed hashref records. The special value 'ARRAY' will result in arrayrefs with fields in the same order they were specified in the "fields" specification. Setting it to anything else will return records created with $record_class->new(\%fields);

static_field_order

Boolean, whether the "fields" must be found in columns in the exact order that they were specified. Default is false.

header_row_at

Row number, or range of row numbers where the header must be found. (All row numbers in this module are 1-based, to match end-user expectations.) The default is [1,10] to limit header scanning to the first 10 rows. As a special case, if you are reading a source which lacks headers and you trust the source to deliver the columns in the right order, you can set this to undef if you also set static_field_order => 1.

col_map

This is an arrayref, one element per column of input data, listing which field was detected to come from that column. If you specify this to the constructor, "find_table" will respect any defined element of the array, but still search for matching headers in the undefined columns. After a successful "find_table", col_map is changed to refer to the same hash as ->table_search_results->{found}{col_map}. (If you wanted to re-run the search for the table, you need to both clear_table_search_results and reset col_map to whatever you passed to the constructor.)

For backward compatibility, if you did not specify this attribute to the constructor and try accessing it before calling "find_table", it automatically calls "find_table" for you (and die if it fails).

has_col_map

Check whether col_map has been defined, to avoid lazy-building it.

table_search_results

This is the output of the most recent "find_table" operation.

{
  candidates => [
    { dataset_idx => $n,
      row => $n,
      col_map => [ $field_or_undef, $field_or_undef, ... ],
      missing_required => \@fields,
      ambiguous_columns => { $col_idx => \@fields, ... },
      ambiguous_fields => { $field_name => { $col_idx => $field }, ... },
      unmatched => \@col_idx,
      messages => [],
    },
    ...
  ],
  found => $ref_to_candidate, # undef if find_table failed
}

The fields describing problems (missing_required, ambiguous_columns, ambiguous_fields, and unmatched) are not present unless they contain data. All @fields are refs to the actual Field objects. col_map has one element per element of the header row. If missing_required is populated, the analysis of ambiguity may be incomplete, because missing required columns abort the search for the header. All "_idx" values are 0-based, but the errors in messages use 1-based descriptions.

has_table_search_results, clear_table_search_results

Predicate and clearer for lazy-built table_search_results.

on_partial_match

on_partial_match    => 'next'    # keep searching for a better line of headers
on_partial_match    => 'last'    # return failure from ->find_table
on_partial_match    => sub {
  my ($reader, $candidate, $header_row)= @_;
  return $action; # one of the above values
}

During "find_table", if a row is found that matches at least one header, but fails to match all the requirements (required columns, unknown or ambiguous columns if those are configured as an error) you can either keep searching for a better header row, or stop here. The default is 'next', to keep searching, but this may result in a lot of noise. The 'last' setting allows you to stop after a likely header row.

If you supply a coderef, you receive the "candidate" info described in "table_search_results".

on_ambiguous_columns

on_ambiguous_columns => 'warn'    # warn, and omit from the match
on_ambiguous_columns => 'error'   # fail the header match for this row

During "find_table", when matching a field's header pattern vs. the columns of a row, if the pattern could match more than one cell it is an error. You might want to handle it in various ways:

'warn'

If a Field matches multiple columns (and isn't an array field) omit the field from the col_map entirely. If a column matches multiple fields, leave the col_map blank for this column. Both generate warnings, but the header match can still proceeed to a successful result.

'error' (default)

Any ambiguities (field matching multiple columns, multiple fields matching a column) cause the match of the header on this row to fail. Further attempts at finding the header depend on the "on_partial_headers" setting.

on_unknown_columns

on_unknown_columns => 'warn'  # warn, and then accept these headers
on_unknown_columns => 'error' # fail the header match for this row
on_unknown_columns => sub {
  my ($reader, $col_headers)= @_;
  ...;
  return $opt; # one of the above values
}

This determines handling for columns that aren't associated with any field. The "required" columns must all be found before it considers this setting, but once it has found everything it needs to make this a candidate, you might or might not care about the leftover columns.

'warn' (default)

You don't care if there are extra columns, just log warnings about them and proceed extracting from this table.

'error'

Extra columns mean that you didn't find the table you wanted. Log the near-miss, and keep searching additional rows or additional tables, according to "on_partial_headers".

sub {}

You can add your own logic to handle this. Inspect the headers however you like, and then return one of the above values.

on_blank_rows

on_blank_rows => 'next' # warn, and then skip the row(s)
on_blank_rows => 'last' # warn, and stop iterating the table
on_blank_rows => 'die'  # fatal error
on_blank_rows => 'use'  # actually try to return the blank rows as records
on_blank_rows => sub {
  my ($reader, $first_blank_rownum, $last_blank_rownum)= @_;
  ...;
  return $opt; # one of the above values
}

This determines what happens when you've found the table, are extracting records, and encounter a series of blank rows (defined as a row with no printable characters in any field) followed by non-blank rows. If you use the callback, it suppresses the default warning, since you can generate your own.

The default is 'next'.

on_validation_error

on_validation_error => 'next'  # warn, and then skip the record
on_validation_error => 'use'   # warn, and then use the record anyway
on_validation_error => 'die'   # fatal error
on_validation_error => sub {
  my ($tablereader, $failures, $record, $data_iterator)= @_;
  # $record is the assembled hashref (unblessed) or arrayref of fields
  # $data_iterator is the Decoder's row iterator, useful for context
  for (@$failures) {
    my ($field, $value_ref, $message, $path)= @$_;
    ...
    # $field is a Data::TableReader::Field
    # $$value_ref is the string that failed validation
    # $message is the error returned from the validation function
    # $path is the element (and maybe sub-element) of $record
    #   i.e.  $value_ref= \$record->{$path[0]}[$path[1]]
    # You may modify $$value_ref or $record to alter the output
  }
  # Clear the failures array to suppress warnings, if you actually corrected
  # the validation problems.
  @$failures= ();
  # return one of the above constants to tell the iterator what to do next
  return $opt;
}

This determines what happens when you've found the table, are extracting records, and one row fails its validation. In addition to deciding an option, the callback gives you a chance to alter the record before 'use'ing it.

The default is 'die'.

log

If undefined (the default) all log messages above 'info' will be emitted with warn "$message\n". If set to an object, it should support an API of:

trace,  is_trace
debug,  is_debug
info,   is_info
warn,   is_warn
error,  is_error

such as Log::Any and may other perl logging modules use. You can also set it to a coderef such as:

my @messages;
sub { my ($level, $message)= @_;
  push @messages, [ $level, $message ]
    if grep { $level eq $_ } qw( info warn error );
};

for a simple way to capture the messages without involving a logging module. And for extra convenience, you can set it to an arrayref which will receive any message that would otherwise have gone to 'warn' or 'error'.

METHODS

detect_input_format

my ($class, @args)= $tr->detect_input_format( $filename, $head_of_file );

This is used internally to detect the format of a file, but you can call it manually if you like. The first argument (optional) is a file name, and the second argument (also optional) is the first few hundred bytes of the file. Missing arguments will be pulled from "input" if possible. The return value is the best guess of module name and constructor arguments that should be used to parse the file. However, this doesn't guarantee such module actually exists or is installed; it might just echo the file extension back to you.

find_table

if ($tr->find_table) { ... }

Search through the input for the beginning of the records, identified by a header row matching the various constraints defined in "fields". If "header_row_at" is undef, then this does nothing and assumes success.

Returns a boolean of whether it succeeded. This method does not croak on failure like "iterator" does, on the assumption that you want to handle them gracefully. All diagnostics about the search are logged via "log", but also reported in "table_search_results".

field_map

Build a hashref of { $field_name => $col_idx_or_arrayref } for the current "col_map". If the field is defined as an array field, the value will be an arrayref (even if only found in one column). Otherwise, the value is a simple scalar of the column index.

iterator

my $iter= $tr->iterator;
while (my $rec= $iter->()) { ... }

Create an iterator. If the table has not been located, then find it and croak if it can't be found. Depending on the decoder and input filehandle, you might only be able to have one instance of the iterator at a time.

The iterator derives from Data::TableReader::Iterator but also has a method "all" which returns all records in an arrayref.

my $records= $tr->iterator->all;

THANKS

Portions of this software were funded by Ellis, Partners in Management Solutions and Candela Corporation.

AUTHOR

Michael Conrad <mike@nrdvana.net>

CONTRIBUTOR

Christian Walde <walde.christian@gmail.com>

COPYRIGHT AND LICENSE

This software is copyright (c) 2024 by Michael Conrad.

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