Scraping large pdf tables which span across multiple pages

Question

Scraping large pdf tables which span across multiple pages

4.9k Views Asked by Tomas At 20 October 2024 at 05:46

I am trying to scrape PDF tables which span across multiple pages. I tried many things but the best seems to be pdftotext -layout as advised here. The problem is that the resultant text file is not easy to work with, as the table layout differs across pages, so the columns are not aligned. Also note missing values in lines beginning with "Solsonès":

                                                                        TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012

COMARCA          CODI i NOM EMA                    GEN    FEB    MAR         ABR       MAI      JUN      JUL          AGO        SET        OCT        N

Alt Camp         VY   Nulles                        7,5    5,5   10,9         12,3     16,7     21,6     22,3         24,4       20,1        15,9
Alt Camp         DQ   Vila-rodona                   7,9    5,6   11,0         12,0     16,6     21,6     22,0         24,3       19,9        15,8
Alt Empordà      U1   Cabanes                       8,2    6,5   11,7         12,6     17,5     22,0     23,1         24,4       20,4        16,6
Alt Empordà      W1   Castelló d'Empúries           8,1    6,4   11,6         12,9     17,0     21,1     22,0         23,4       20,1        16,4

[...]
                                                                                 TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012

COMARCA          CODI i NOM EMA                             GEN    FEB    MAR         ABR       MAI      JUN      JUL          AGO        SET        OCT

Baix Empordà     DF   la Bisbal d'Empordà                    6,6    5,3   10,9         12,6     17,2     21,9     22,9         24,6       20,3        16
Baix Empordà     UB   la Tallada d'Empordà                   6,1    5,2   10,7         12,3     16,6     21,3     22,2         23,8       19,7        15
Baix Empordà     UC   Monells                                6,1    4,6    9,9         11,4     16,5     21,7     23,0         24,5       19,6        15

[...]

                                                                        TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012

COMARCA         CODI i NOM EMA                      GEN    FEB    MAR         ABR       MAI      JUN      JUL           AGO        SET        OCT
[...]

Solsonès        CA   Clariana de Cardener            4,6    3,3   10,3         10,2     16,7     22,3      d.i.
Solsonès        Z8   el Port del Comte (2.316 m)    -0,9   -6,3   -0,2         -2,0      5,3     10,5     10,9          13,8        7,8         4,2
Solsonès        VO   Lladurs                         3,0    2,6    9,5          9,0     15,3     21,4     21,6          24,3       17,5        13,0
Solsonès        VP   Pinós                           3,0    1,6    8,9          9,2     15,4     21,1     21,3          23,8       17,6        13,3
Solsonès        XT   Solsona                                                                               d.i.         24,3       18,0        13,5
Tarragonès      VQ   Constantí                       7,9   6,0    11,2         13,1     17,1     21,9     22,6          24,6       20,6        16,6
Tarragonès      XE   Tarragona - Complex Educatiu   10,2   7,8    12,3         14,6     18,3     23,0     24,2          26,2       23,0 *      18,4
Tarragonès      DK   Torredembarra                   9,7   7,7    12,3         14,3     17,9     22,8     24,3          26,2       22,7        18,5
Terra Alta      WD   Batea                           6,3   5,0    11,2         12,1     18,3     23,0     23,3          25,5       20,2        15,9
Terra Alta      XP   Gandesa                         6,6   5,2    11,2         12,2     18,1     22,9     23,4          25,6       20,4        16,0

complete file for download - UTF8

So, this output is not very easy to parse. What other approach is available?

It seems that every tool I use is only capable to extract information about layout of the table cells, but it doesn't extract the information of belonging to particular column. This is very much apparent if the cells are empty - the empty cells are not in the output, you only get non-empty "cells" with their layout. Does the PDF itself contain this tabular information? If not, it doesn't make sense to search for tool that will extract it.

Paid solutions are not out of question, as it might in the end be cheaper than invest several working days of my time...

What I have tried:

copy paste - makes problems with missing values (pg 5)
save as text from Acrobat (even worse result than copy-paste)
open in Excel as external data source - will not recognize the table
https://www.pdftoexcelonline.com/ - results in error
http://www.pdftoexcel.org/ as well as their trial of Able2Extract - they messed up some columns. They recognized the columns correctly in the preview but in the excel output they were messed up
http://www.pdftoword.com/ - just takes my email and never sends anything
using python on scraperwiki http://schoolofdata.org/2013/06/18/get-started-with-scraping-extracting-simple-tables-from-pdf-documents/ seems very complicated especially for non-python users and https://scraperwiki.com/ is not free
I have encountered several python libraries like pdftables but they are not easy to use for non-python developer like me (I was not even able to run these things). Is there any easier way to accomplish the task?
I am trying to use tm library in R as recommended here, but I have encountered some problems

EDIT: the Cloud SDK recommended by Ian. I registered but I absolutely don't know where to go from here - how to upload pages, recognize them etc:

enter image description here

Original Q&A

There are 7 best solutions below

**Ian Hopkinson** · Answer 1

In the past I have used pdftohtml which can be used to generate xml, described here. The columns are generally fairly well separated so you could use the positioning to extract columns.

I wrote a large part of pdftables, apologies for the opaqueness! It works OK for some pages of the document you show, for example page 2 gives me the output at the bottom this reply. For other pages it falls over, on page 33, for example. The problem here is that there are two numbers under one column heading and they get stuck together by pdftables. The "COMARCA, CODI i, NOM EMA" columns don't get separated in either case. You can submit issues for pdftables on GitHub, I'm not working on it actively at the moment. It is available by pip install.

If you wanted to go the commercial route then Abbyy FineReader is very good, they produce a cloud SDK which will give you 30 or so pages free. They have example code in multiple languages but their support isn't great.

     14 columns, 39 rows
                                      0    1    2    3    4    5    6    7    8    9   10   11   12   13
    -----------------------------------------------------------------------------------------------------
  0 |             COMARCACODI i NOM EMA| GEN| FEB| MAR| ABR| MAI| JUN| JUL| AGO| SET| OCT| NOV| DES| ANY|
  1 |                  VYNullesAlt Camp| 7,5| 5,5|10,9|12,3|16,7|21,6|22,3|24,4|20,1|15,9|11,0| 8,5|14,8|
  2 |             DQVila-rodonaAlt Camp| 7,9| 5,6|11,0|12,0|16,6|21,6|22,0|24,3|19,9|15,8|11,0| 8,6|14,7|
  3 |              Alt EmpordÃ U1Cabanes| 8,2| 6,5|11,7|12,6|17,5|22,0|23,1|24,4|20,4|16,6|11,8| 8,3|15,3|
  4 |  Alt EmpordÃ W1CastellÃ³ d'EmpÃºries| 8,1| 6,4|11,6|12,9|17,0|21,1|22,0|23,4|20,1|16,4|12,1| 8,5|15,0|
  5 |              Alt EmpordÃ VZEspolla| 9,0| 6,7|12,4|12,7|17,8|22,0|23,3|24,8|20,9|16,7|12,0| 8,9|15,6|
  6 |              D6PortbouAlt EmpordÃ | 9,6| 5,5|12,7|12,5|17,4|21,5|22,9|24,4|19,8|17,0|12,3|10,1|15,5|
  7 |                D4RosesAlt EmpordÃ | 9,3| 7,2|13,0|13,6|18,2|22,6|23,9|25,7|21,3|17,5|13,2| 9,9|16,3|
  8 |   Alt EmpordÃ U2Sant Pere Pescador| 7,8| 6,3|11,5|12,9|16,8|21,2|22,2|23,6|20,2|16,5|12,3| 8,5|15,0|
  9 |  Alt EmpordÃ W2Torroella de FluviÃ | 7,4| 6,0|11,2|12,6|16,4|21,2|22,3|23,7|19,9|16,1|11,7| 8,0|14,7|
 10 |             Alt EmpordÃ W3VentallÃ³| 7,3| 6,2|11,4|12,8|16,9|21,8|22,8|24,3|20,4|16,5|12,0| 8,1|15,1|
 11 |            Alt PenedÃ¨sWPCanaletes| 7,0| 5,2|11,3|11,9|16,7|21,5|22,0|24,2|19,7|15,6|10,7| 8,1|14,5|
 12 |            Alt PenedÃ¨sDIFont-rubÃ| 8,1| 6,2|12,0|11,9|16,9|21,8|22,0|24,4|20,0|15,9|11,4| 8,9|15,0|
 13 |           Alt PenedÃ¨sW4la Granada| 7,0| 5,5|11,2|12,6|17,2|21,9|22,4|24,3|20,0|16,0|11,1| 8,3|14,8|
 14 |   Alt PenedÃ¨sU3Sant MartÃ Sarroca| 6,4| 5,1|10,9|12,4|17,0|21,8|22,3|24,3|19,9|15,7|10,8| 8,0|14,6|
 15 | Alt PenedÃ¨sWYSant SadurnÃ d'Anoia| 6,4| 5,1|11,0|12,8|17,6|22,6|23,2|25,0|20,5|16,2|10,9| 7,8|15,0|
 16 |       CDla Seu d'UrgellAlt Urgell| 3,6| 2,5| 8,5| 8,4|14,6|20,3|21,0|23,4|16,9|12,2| 7,0| 3,2|11,8|
 17 |                W5OlianaAlt Urgell| 2,0| 2,7| 9,8|10,2|16,8|23,0|22,9|25,6|19,1|13,9| 8,6| 3,1|13,2|
 18 |               Alt UrgellCJOrganyÃ | 2,6| 3,5| 9,8| 9,9|16,1|22,0|22,6|25,3|18,8|13,5| 8,2| 2,9|13,0|
 19 |     Alta RibagorÃ§aZ2BoÃ (2.535 m)|-2,4|-7,5|-1,3|-3,4| 3,8| 8,6| 9,4|12,0| 6,3| 2,7|-1,1|-3,2| 2,0|
 20 |  Alta RibagorÃ§aCTel Pont de Suert| 0,5| 1,6| 6,9| 7,9|14,1|18,0|19,1|20,4|15,7|10,7| 6,1| 1,3|10,2|
 21 |   CEels Hostalets de PierolaAnoia| 7,3| 5,5|11,7|12,1|17,4|22,4|22,9|25,2|20,3|16,2|11,1| 8,3|15,1|
 22 |                 XBla LlacunaAnoia| 5,4| 3,3| 9,3|10,3|15,6|20,8|20,9|23,3|18,0|14,1| 9,1| 6,9|13,1|
 23 |               AnoiaXAla Panadella| 3,6| 1,7| 9,2| 8,7|14,9|20,5|20,4|23,2|17,2|13,3| 7,9| 5,1|12,2|
 24 |                      H1Ã’denaAnoia| 5,1| 3,3| 9,4|11,5|16,3|21,7|22,5|24,6|19,4|15,2| 9,3| 6,0|13,7|
 25 |                      WWArtÃ©sBages| 3,5| 2,8| 9,2|11,2|16,6|22,4|23,2|25,1|19,3|15,0| 9,1| 4,3|13,5|
 26 |        U4Castellnou de BagesBages| 4,8| 3,8|10,5|10,9|16,3|22,0|22,5|25,0|19,3|15,0| 9,6| 5,9|13,9|
 27 |        R1el Pont de VilomaraBages| 3,8| 3,1| 9,9|12,3|17,4|22,9|23,5|25,4|20,0|15,7| 9,7| 5,0|14,1|
 28 |    BagesWNMontserrat - Sant Dimes| 6,2| 3,3| 9,7| 8,6|14,8|19,5|19,5|22,4|16,9|13,5| 9,0| 7,1|12,6|
 29 | CLSant Salvador de GuardiolaBages| 3,3| 2,8| 9,1|11,5|16,4|22,0|22,4|24,6|19,2|14,9| 9,1| 4,8|13,4|
 30 |   U5Prades - los HortalsBaix Camp| 2,8| 0,0| 6,4| 7,4|13,0|18,4|18,0|21,3|15,0|11,3| 6,5| 4,1|10,4|
 31 |                W6RiudomsBaix Camp| 9,7| 7,1|12,0|13,4|17,6|22,4|23,1|25,2|21,2|17,1|12,3|10,1|16,0|
 32 |     U6Vinyols i els ArcsBaix Camp|10,2| 7,6|12,0|13,8|17,6|22,5|24,0|25,9|22,3|18,2|13,2|11,1|16,6|
 33 |                Baix EbreU7Aldover|10,0| 8,5|13,2|14,8|19,7|24,6|25,2|27,1|22,7|18,3|12,9|11,1|17,4|
 34 |             DBel PerellÃ³Baix Ebre| 8,7| 7,0|12,0|13,3|17,9|22,6|23,3|25,3|21,4|17,2|11,9|10,3|15,9|
 35 |                U9l'AldeaBaix Ebre| 9,9| 8,1|12,5|14,3|18,5|23,3|24,1|26,0|22,1|17,9|13,1|10,7|16,8|
 36 |       UAl'Ametlla de MarBaix Ebre| 9,6| 7,8|12,3|13,8|18,0|22,9|23,9|25,8|22,0|17,6|12,5|10,6|16,4|
 37 |          Baix EbreX5PN dels Ports| 3,4|-0,2| 6,5| 6,8|13,4|18,7|17,8|21,2|15,2|11,3| 6,1| 4,9|10,5|
 38 |       Baix EmpordÃ DOCastell d'Aro| 6,7| 5,1|10,6|12,0|16,2|20,9|21,8|23,8|20,1|16,3|12,2| 8,1|14,5|
    -----------------------------------------------------------------------------------------------------

The unicode problems are down to my dev environment (Spyder).

**hmatt1** · Answer 2

Ok I took a shot at this and I think it will help, although I'm not sure what you want your final output to look like. I'm happy to work more on this so let me know if there are parts you need help with.

I started by downloading a PDF to Text application from CNET.

After installing, I checked these settings:

PDF to text conversion

The important part here is we're using the physical layout option.

This gave us output that looks like this:

Taules de Dades de la Xarxa d’Estacions
    Meteorològiques Automàtiques
            2                                                                                                   Anuari de dades meteorològiques 2012 / Servei Meteorològic de Catalunya
            2                                                           TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012

COMARCA          CODI i NOM EMA                    GEN    FEB    MAR         ABR       MAI      JUN      JUL          AGO        SET        OCT        NOV         DES         ANY

Alt Camp         VY   Nulles                        7,5    5,5   10,9         12,3     16,7     21,6     22,3         24,4       20,1        15,9       11,0        8,5         14,8
Alt Camp         DQ   Vila-rodona                   7,9    5,6   11,0         12,0     16,6     21,6     22,0         24,3       19,9        15,8       11,0        8,6         14,7
Alt Empordà      U1   Cabanes                       8,2    6,5   11,7         12,6     17,5     22,0     23,1         24,4       20,4        16,6       11,8        8,3         15,3
Alt Empordà      W1   Castelló d'Empúries           8,1    6,4   11,6         12,9     17,0     21,1     22,0         23,4       20,1        16,4       12,1        8,5         15,0
Alt Empordà      VZ   Espolla                       9,0    6,7   12,4         12,7     17,8     22,0     23,3         24,8       20,9        16,7       12,0        8,9         15,6

[......]

             3                                                                                                           Anuari de dades meteorològiques 2012 / Servei Meteorològic de Catalunya
             2                                                                   TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012

COMARCA          CODI i NOM EMA                             GEN    FEB    MAR         ABR       MAI      JUN      JUL          AGO        SET        OCT        NOV         DES         ANY

Baix Empordà     DF   la Bisbal d'Empordà                    6,6    5,3   10,9         12,6     17,2     21,9     22,9         24,6       20,3        16,6       11,9        7,6         14,9
Baix Empordà     UB   la Tallada d'Empordà                   6,1    5,2   10,7         12,3     16,6     21,3     22,2         23,8       19,7        15,8       11,7        7,6         14,4
Baix Empordà     UC   Monells                                6,1    4,6    9,9         11,4     16,5     21,7     23,0         24,5       19,6        15,7       11,7        7,2         14,3
Baix Empordà     UD   Serra de Daró                          6,3    5,3   10,6         12,3     16,8     21,6     22,7         24,3       20,3        16,6       12,2        7,7         14,8

[......]

             4                                                                                                              Anuari de dades meteorològiques 2012 / Servei Meteorològic de Catalunya
             2                                                                      TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012

COMARCA           CODI i NOM EMA                               GEN    FEB    MAR         ABR       MAI      JUN      JUL          AGO        SET        OCT        NOV         DES         ANY

Maresme           UQ   Dosrius - PN Montnegre Corredor          7,2    4,6   10,8         10,7     15,8     20,4     20,8         23,4       18,6        15,1       10,7        7,8         13,9
Maresme           WT   Malgrat de Mar                           7,4    5,4   11,0         13,0     16,7     21,5     22,8         24,6       20,9        17,2       12,9        8,8         15,2
Maresme           DD   Vilassar de Mar                         10,1    7,5   12,6         13,9     17,9     22,4     23,7         25,7       22,1        18,4       13,8       10,8         16,6
Montsià           US   Alcanar                                 10,0    7,6   11,8         14,2     17,9     22,7     24,0         25,8       22,0        18,2       13,7       10,7         16,6
Montsià           UU   Amposta                                  9,6    7,5   12,1         14,3     18,3     22,8     23,5         25,3       21,6        18,0       13,1       10,8         16,4

[......]

You can see the columns line up much better, but we also have headers and page numbers. Also the COMARCA and i NOM EMA columns were variying length. We want to normalize this to fixed width columns.

I wrote a Perl program to do normalize it, and it also combines tables with the same title, and only prints the headers at the top. It creates an output folder with all the files with the title as the file name.

Here's the code:

#!/bin/perl

use strict;
use warnings;
use open qw(:std :utf8);
use utf8;

my $comarca;
my $nom;
my $print_headers;
my $title = "";
my $fh;

while(<>) {

    if (    !/Xarxa d’Estacions/
        and !/Meteorològiques Automàtiques/
        and !/Servei/
        and !/^\s*\d+\s*$/
        and !/^\s*$/ ) {

        chomp($_);


        if ( /^\s*2/ ) { #title
            s/^\s*2\s*//;
            if ( $title ne $_ ) {
                $title = $_;
                $print_headers = 1;
            }

        } elsif ( /COMARCA/ ) { #column headers

            my ($first_col, $second_col, @the_rest) = split(/(CODI +i NOM EMA *)/, $_);


            $comarca = length $first_col;
            $nom = length $second_col;

            if ( $print_headers ) {
                my $str = sprintf "%-50s %-50s %s\n", $first_col, $second_col, join("", @the_rest);
                write_string($str);
                $print_headers = 0;
            }

        } else { #data

            my ($one, $two, $three) = unpack("A${comarca}A${nom}A*", $_);
            my $str = sprintf "%-50s %-50s $three\n", $one, $two;
            write_string($str);
        }

    }
}

sub write_string {

    my $string = shift;
    my $file_name = $title;
    $file_name =~ s/[\/\\]//g;

    open ($fh, '>>', ".\/output_folder\/${file_name}.txt") or die "Couldn't open: $!";
    print $fh $string;
    close ($fh);
}

There are still a few imperfections in the output (you'll see these when you run this), but I wanted to get some feedback on what output would work best for you. There is definitely more we can do to improve the code! The output directory tree looks like this:

Matt@MattPC ~/perl/pdftotext
$ find .
.
./convert.pl
./EMAtaules2012.txt
./output.txt
./output_folder
./output_folder/AMPLITUD TÈRMICA MITJANA MENSUAL ( ºC ) - 2012?.txt
./output_folder/AMPLITUD TÈRMICA MÀXIMA MENSUAL ( ºC ) - 2012?.txt
./output_folder/DIRECCIÓ DOMINANT DEL VENT - 2012?.txt
./output_folder/GRUIX MÀXIM MENSUAL DE NEU AL TERRA ( cm ) - 2012?.txt
./output_folder/HUMITAT RELATIVA MITJANA MENSUAL ( % ) - 2012?.txt
./output_folder/MITJANA MENSUAL DE LA HUMITAT RELATIVA MÀXIMA DIÀRIA ( % ) - 2012?.txt
./output_folder/MITJANA MENSUAL DE LA HUMITAT RELATIVA MÍNIMA DIÀRIA ( % ) - 2012?.txt
[......]

Where a file might look like this:

COMARCA                                            CODI i NOM EMA                                     GEN    FEB    MAR         ABR       MAI      JUN      JUL          AGO        SET        OCT        NOV         DES         ANY
Alt Camp                                           VY   Nulles                                         7,5    5,5   10,9         12,3     16,7     21,6     22,3         24,4       20,1        15,9       11,0        8,5         14,8
Alt Camp                                           DQ   Vila-rodona                                    7,9    5,6   11,0         12,0     16,6     21,6     22,0         24,3       19,9        15,8       11,0        8,6         14,7
Alt Empordà                                        U1   Cabanes                                        8,2    6,5   11,7         12,6     17,5     22,0     23,1         24,4       20,4        16,6       11,8        8,3         15,3
Alt Empordà                                        W1   Castelló d'Empúries                            8,1    6,4   11,6         12,9     17,0     21,1     22,0         23,4       20,1        16,4       12,1        8,5         15,0
Alt Empordà                                        VZ   Espolla                                        9,0    6,7   12,4         12,7     17,8     22,0     23,3         24,8       20,9        16,7       12,0        8,9         15,6
Alt Empordà                                        D6   Portbou                                        9,6    5,5   12,7         12,5     17,4     21,5     22,9         24,4       19,8        17,0       12,3       10,1         15,5
[......]

Headers are only at the top and all the columns line up. This one is TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012.

I've been thinking of uploading more of the output to a file hosting site, but I don't know which would be a good one, suggestions?

Hope this helps you Tomas!

EDIT: Example of missing entries from AMPLITUD TÈRMICA MÀXIMA MENSUAL ( ºC ) - 2012:

Solsonès                                           VP   Pinós                          1              3,1   26   16,9   13   16,7   15   16,6   17   19,2   11   19,6   24   20,4    17      19,1   01   17,5   16   16,5   06   13,1   08   13,9   24   20,4    17/07
Solsonès                                           XT   Solsona                                                                                                              22,2    25      22,2   09   20,1   16   18,6   06   15,3   07   18,2   23   22,2    09/08
Tarragonès                                         VQ   Constantí                      1              6,4   19   21,9   23   19,7   11   12,9   07   17,4   23   17,2   21   15,1    18      14,2   18   18,0   15   15,1   02   14,9   07   16,0   10   21,9    23/02

Update

Updated scripts for processing the input file:

#!/bin/perl

use strict;
use warnings;
use open qw(:std :utf8);
use utf8;
use charnames ':full';

my @column_lengths;
my $print_headers;
my $title = "";
my $fh;

while(<>) {

    if (    !/Xarxa d’Estacions/
        and !/Meteorològiques Automàtiques/
        and !/Servei/
        and !/^\s*\d+\s*$/
        and !/^\s*$/ ) {

        s/[\r\n]+//g;
        s/ +\d+$//;
        if ( /^\s*2/ ) { #title
            s/^\s*2\s*//;
            if ( $title ne $_ ) {
                $title = $_;
                $print_headers = 1;
            }

        } elsif ( /COMARCA/ ) { #column headers

            my $comarca = (split(/(COMARCA *)/, $_))[1];
            my $codi = (split(/(CODI *)/, $_))[1];
            my $inomema = (split(/(i NOM EMA *)  /, $_))[1];

            my $the_rest = (split(/(i NOM EMA *)  /, $_))[2];

            my @rest = split(/( \w+ *)/, $the_rest);

            undef @column_lengths;

            push @column_lengths, length $comarca;
            push @column_lengths, length $codi;
            push @column_lengths, length $inomema;

            for (@rest) {
                if ( $_ ) {
                    push @column_lengths, length $_;
                }
            }

            $column_lengths[-1] = "*";

            if ( $print_headers ) {
                $print_headers = 0;
                write_string(join(";", unpack( "A" . join("A", @column_lengths), $_)) . "\n");
            }

        } else { #data

            write_string(join(";", unpack( "A" . join("A", @column_lengths), $_)) . "\n");

        }

    }
}

sub write_string {

    my $string = shift;
    my $file_name = $title;
    $file_name =~ s/[º]//g;
    $file_name =~ s/[^\w ]//g;
    $file_name =~ s/ +/ /g;
    $file_name =~ s/È/E/g;
    $file_name =~ s/À/A/g;
    $file_name =~ s/Ó/O/g;
    $file_name =~ s/Í/I/g;
    $file_name =~ s/Ç/C/g;

    open ($fh, '>>', ".\/output_folder\/${file_name}.txt") or die "Couldn't open: $!";
    print $fh $string;
    close ($fh);
}

This one combines lines with the d.i. on the next line.

#!/bin/perl -i

use strict;
use warnings;

my $last = <>;

while(<>) {

    my @current_array = split(";", $_);

    if ( /^;+[ \t]+.d\.i\./ ) {

        my @last_array = split(";", $last);
        my @combined_array;

        #print "matches\n";

        for my $element (@current_array) {

            if ( $element =~ /d\.i\./ ) {
                push @combined_array, $element;
                shift @last_array;
            } else {
                push @combined_array, $last_array[0];
                shift @last_array;
            }

        }
        undef @current_array;
        @current_array = @combined_array;
    }
    $last = join ";", @current_array;
    print $last;

}

The output is in csv format with semicolon delimiters.

**Glenn** · Answer 3

Although the layout differs across pages when using pdftotext, note that the column headings on individual pages (COMARCA, CODI, etc) seem to line up with the data on that page.

Also, there are many different types of data in your pdf - wind direction, wind strength, humidity, precipitation, etc. So not only does the layout differ across pages for the same data, but the layout differs because there are different data sets as well.

And just for completeness - the missing data for "Solsonès" (as one example) exists in the original PDF. It seems like pdftotext did a reasonable job - the missing data is whitespace, just like in the original PDF.

As a result, it may make sense to stay with pdftotext and treat the pages (which are separated by form feeds) as columnar data and parse using struct as documented here:

How to efficiently parse fixed width files?

One way to make this work would be to detect the form feed, look for the next line starting with "COMARCA", and use the spacing in that line to set up the columns for struct.

**canary_in_the_data_mine** · Answer 4

If you're wary of diving too deeply into Python or other code-based solutions, a completely different approach for a quick and dirty solution for a small number of pdfs is to outsource the task to MechanicalTurk.

Having multiple users per column allows you to double-check the submitted answers, and you can also publish the resulting .csv table and pay a large amount (say, $5) for every error that a worker can find. Often ends up being way cheaper than your or others' time programming a solution.

**A5C1D2H2I1M1N2O1R2T1** · Answer 5

Here is an R solution, but it is not without its flaws.

Part 1: Setup steps

# Read the lines of your file into R
x <- readLines("EMAtaules2012.txt")

# Make sure it shows up as UTF-8 to get proper accents and so on
Encoding(x) <- "UTF-8"

# Identify the lines where the data starts
Start <- grep("COMARCA", x)

# Grab the names of each table
ListNames <- gsub("\\s+", " ", x[Start-2])

# Figure out the number of rows of data per page
Runs <- rle(diff(cumsum(x != "")))
Nrows <- Runs$lengths[Runs$lengths > 4]+1

# Make our life easier by making this column name
#  a single string
x <- gsub("i NOM EMA", "i_NOM_EMA", x)

# Since these are fixed width files, we need to figure
#  out the widths of each column. This is the sum of
#  the number of characters in the header row plus
#  the number of spaces between each column name
Spaces <- gregexpr(x[Start], pattern="\\s+")
Spaces <- lapply(Spaces, function(x) c(attr(x, "match.length"), 0))
Chars <- lapply(strsplit(x[Start], "\\s+"), nchar)
Widths <- lapply(seq_along(Spaces), 
                 function(x) rowSums(cbind(Spaces[[x]], 
                                           Chars[[x]])))

Part 2: Using `read.fwf` to get the data in

# Now, you can use `read.fwf` to read your data files in
temp <- lapply(seq_along(Start), function(fwf) {
  A <- read.fwf(textConnection(x), 
                widths = c(Widths[[fwf]]), 
                header = FALSE, 
                skip = Start[fwf]+1, 
                n = Nrows[fwf]-2, 
                blank.lines.skip = TRUE,
                strip.white = TRUE,
                stringsAsFactors = FALSE)
  # Add in the column names
  names(A) <- scan(what = "character", 
                   file = textConnection(x[Start[fwf]]), 
                   quiet = TRUE)
  A
})

# Assign the table names
names(temp) <- ListNames

# Some more cleanup. The original tables span multiple pages
#  in the PDF, but we can `rbind` them together in R
Tables <- unique(ListNames)
final <- lapply(seq_along(Tables), function(final) {
  A <- do.call(rbind, temp[names(temp) %in% Tables[final]])
  rownames(A) <- NULL
  A
})
# Add the names back in
names(final) <- Tables

Part 3: Did it work?

# View the first few rows and columns of the first three tables
lapply(final[1:3], function(y) head(y[1:5], 3))
# $` TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012`
#       COMARCA CODI           i_NOM_EMA GEN FEB
# 1    Alt Camp   DQ         Vila-rodona 7,9 5,6
# 2 Alt Empordà   U1             Cabanes 8,2 6,5
# 3 Alt Empordà   W1 Castelló d'Empúries 8,1 6,4
# 
# $` TEMPERATURA MÀXIMA MITJANA MENSUAL ( ºC ) - 2012`
#       COMARCA CODI           i_NOM_EMA  GEN  FEB
# 1    Alt Camp   DQ         Vila-rodona 13,1 11,7
# 2 Alt Empordà   U1             Cabanes 15,1 12,4
# 3 Alt Empordà   W1 Castelló d'Empúries 14,4 11,7
# 
# $` TEMPERATURA MÍNIMA MITJANA MENSUAL ( ºC ) - 2012`
#       COMARCA CODI           i_NOM_EMA GEN FEB
# 1    Alt Camp   DQ         Vila-rodona 3,8 0,5
# 2 Alt Empordà   U1             Cabanes 2,4 0,9
# 3 Alt Empordà   W1 Castelló d'Empúries 2,1 0,5

# Some tables, like those on page 76 (for the table "DIRECCIÓ DOMINANT DEL VENT"), had more columns than others. 
# Did our script take care of that?
names(final$` DIRECCIÓ DOMINANT DEL VENT`)
#  [1] "COMARCA"   "CODI"      "i_NOM_EMA" "vent"      "GEN"       "FEB"      
#  [7] "MAR"       "ABR"       "MAI"       "JUN"       "JUL"       "AGO"      
# [13] "SET"       "OCT"       "NOV"       "DES"       "ANY"

It sort of worked. But, your input file is not perfect, and that means that there will still be a lot of cleaning up to to. For instance, some columns in the PDF seem to have multiple values. Not sure how you would be able to do any analysis on those.

Hopefully, the comments in the above code help get you started on figuring out how to go about scraping the data in a better way.

Update: Extracting just the data

Continuing after "Part 1" above, here's a solution that relies on (gasp) Excel. The basic idea is that Excel actually does a pretty decent job of detecting where the column breaks are if you import text as Fixed Width.

So, we use R to break up the text into separate pages, one file per page, only the data (not the column names or the row names, which are mostly the same across all datasets).

With that, here's the last R step:

# Output just the data
temp <- lapply(seq_along(Widths), function(y) {
  DEL <- sum(Widths[[y]][1:3])-2
  A <- substring(x[(Start[y]+1):(sum(Start[y], Nrows[y]))], DEL)
  writeLines(A, paste("temp_", y, ".txt", collapse = ""))
  A
})

Let's open file "temp_9.txt", which is one that has the missing columns:

enter image description here

^^ Make sure "Fixed Width" is selected -- It should be by default since the file has no delimiters.

enter image description here

^^ Excel shows you a preview of where it is going to make the columns.

enter image description here

^^ I've highlighted the "problem rows" for you to see how it worked out.

**IRTFM** · Answer 6

Efforts to construct an Index for this (presumably the variation in formats relates to the different sub-reports. These all seem to be for Catalunya:

heads <- grep("                                                                .+2012", txt)
notheads <- grep("                                                                .+Anuari de", txt)
 headtxt <-  unique(trim(txt[1:length(txt) %in% heads & !1:length(txt) %in% notheads]))

 [1] "TEMPERATURA MITJANA MENSUAL ( ºC ) - 2012"                            
 [2] "TEMPERATURA MÀXIMA MITJANA MENSUAL ( ºC ) - 2012"                     
 [3] "TEMPERATURA MÍNIMA MITJANA MENSUAL ( ºC ) - 2012"                     
 [4] "TEMPERATURA MÀXIMA ABSOLUTA MENSUAL ( ºC ) - 2012"                    
 [5] "TEMPERATURA MÍNIMA ABSOLUTA MENSUAL ( ºC ) - 2012"                    
 [6] "AMPLITUD TÈRMICA MITJANA MENSUAL ( ºC ) - 2012"                       
 [7] "AMPLITUD TÈRMICA MÀXIMA MENSUAL ( ºC ) - 2012"                        
 [8] "NOMBRE DE DIES DE GLAÇADA ( TN ≤ 0 ºC ) - 2012"                       
 [9] "PRECIPITACIÓ MENSUAL ( mm ) - 2012"                                   
[10] "PRECIPITACIÓ MENSUAL MÀXIMA EN 24 HORES ( mm ) - 2012"                
[11] "PRECIPITACIÓ MENSUAL MÀXIMA EN 1 HORA ( mm ) - 2012"                  
[12] "PRECIPITACIÓ MENSUAL MÀXIMA EN 30 MINUTS ( mm ) - 2012"               
[13] "PRECIPITACIÓ MENSUAL MÀXIMA EN UN 1 MINUT ( mm ) - 2012"              
[14] "NOMBRE DE DIES DE PRECIPITACIÓ (PPT ≥ 0,1 mm) - 2012"                 
[15] "NOMBRE DE DIES DE PRECIPITACIÓ (PPT > 0,2 mm) - 2012"                 
[16] "VELOCITAT MITJANA DEL VENT MENSUAL ( m/s ) - 2012"                    
[17] "DIRECCIÓ DOMINANT DEL VENT - 2012"                                    
[18] "MITJANA MENSUAL DE LA RATXA MÀXIMA DIÀRIA DEL VENT ( m/s ) - 2012"    
[19] "RATXA MÀXIMA ABSOLUTA DEL VENT MENSUAL ( m/s ) - 2012"                
[20] "HUMITAT RELATIVA MITJANA MENSUAL ( % ) - 2012"                        
[21] "MITJANA MENSUAL DE LA HUMITAT RELATIVA MÀXIMA DIÀRIA ( % ) - 2012"    
[22] "MITJANA MENSUAL DE LA HUMITAT RELATIVA MÍNIMA DIÀRIA ( % ) - 2012"    
[23] "MITJANA MENSUAL DE LA IRRADIACIÓ SOLAR GLOBAL DIÀRIA ( MJ/m2 ) - 2012"
[24] "PRESSIÓ ATMOSFÈRICA MITJANA MENSUAL, A NIVELL DE L'EMA ( hPa ) - 2012"
[25] "PRESSIÓ ATMOSFÈRICA MÀXIMA ABSOLUTA MENSUAL ( hPa ) - 2012"           
[26] "PRESSIÓ ATMOSFÈRICA MÍNIMA ABSOLUTA MENSUAL ( hPa ) - 2012"           
[27] "GRUIX MÀXIM MENSUAL DE NEU AL TERRA ( cm ) - 2012"

The parens and dashes interfere with grepping. So trying to get into a form where those values can be use to identify page header locations by grep(val, txt) succeeds by removing the "\\(.+$" matches with a single exception (which I decided to fix "by hand":

 headtxt[14:15]
#[14] "NOMBRE DE DIES DE PRECIPITACIÓ (PPT ≥ 0,1 mm) - 2012"                 
#[15] "NOMBRE DE DIES DE PRECIPITACIÓ (PPT > 0,2 mm) - 2012"  

headtxt <- gsub("\\(.+$", "", headtxt)

pagedivs <- lapply(headtxt, grep, txt)
# Seemed reasonable that the first 5 (of 10) should be the first section
pagedivs[[14]] <- pagedivs[[14]][1:5]
pagedivs[[15]] <- pagedivs[[15]][6:10]

So looking for a marker to end pages it looks like 4 empty lines is reliable

> length(notheads)
[1] 113
> rl.lens <- rle( nchar(txt) )
> table(rl.lens$lengths[rl.lens$values==0])
#  1   4 
#226 113

Removed all the "Ã" because they were creating non-fixed width columns:

txt <- gsub("Ã", "", txt)
write(txt, "txt_noAs.txt)

Interestingly, my text editor now shows "à"'s where the "Ã"'s used to appear. At this point one can loop over the pages within page type starting at pagedivs+4 to the location of 4 empty rows and use read.fwf from the 'utils' package. What remains to support this is a layout definition, which you say you already have a handle on, but which could be also inferred using pkg:gsubfn's strapply or a regex solution.

Looking for an approach to develop a regex solution:

> numfields <- gregexpr("[-[:digit:].]+ ", txt)
> table( sapply( numfields,  length))

   1    2    3    5    6    7    8   11   12   13   14   15 
1201  193    8    1   13   15    2    4 1162  869  308   32 
  16   17   19   20   21   23   24   25   26   27   28   30 
   1    3    1    1    1    7   10  688  481  168   13    1

So clearly the pages fall into two classes: those where the number of numeric columns is 12-14 and those where they number 23-28. I would have expected this to be a bit different, but I guess the "ANY" columns threw off my expectations.

**Kurt Pfeifle** · Answer 7

It's very clear that the original Excel spreadsheet was composed of different sheets which used different column widths.

So the PDF tables also use different column widths. If you look at the PDF, you can see the following groups of page ranges, which have identical column widths each. Each group also describes different things, as can be seen from the change headlines for each group's starting page (I can identify these differences even without being able to understand Spanish):

pages 2-6 (5 pages)
pages 7-11 (5 pages)
pages 12-16 (5 pages)
pages 17-21 (5 pages)
pages 22-26 (5 pages)
pages 27-31 (5 pages)
pages 32-36 (5 pages)
pages 37-41 (5 pages)
pages 42-46 (5 pages)
pages 47-51 (5 pages)
pages 52-56 (5 pages)
pages 57+58 (2 pages)
pages 59-62 (4 pages)
pages 63-67 (5 pages)
pages 68-72 (5 pages)
pages 73-76 (4 pages)
pages 77-80 (4 pages)
pages 81-84 (4 pages)
pages 85-88 (5 pages)
pages 89-93 (5 pages)
pages 84-98 (5 pages)
pages 99-103 (5 pages)
pages 104-107 (4 pages)
pages 108+109 (2 pages)
pages 110+111 (2 pages)
pages 112+113 (2 pages)
finally, page 114 (1 page only)

So, you could to let pdftotext extract the table data by these page groups. If the results will be not be perfectly aligned columns within each page range, you would have to extract the tables page-by-page. These should be easy enough to import into Excel as "fixed-width" table data.

To show you an example (created with Poppler's version of pdftotext):

pdftotext \ -layout \ -enc UTF-8 \ -f 22 -l 26 \ -nopgbrk \ -x 20 -y 82 \ -W 810 -H 450 \ EMAtaules2012.pdf \ -

-f 22 -l 26:
This tells the tool to extract page 22 as the first in the range, and page 26 as the last one.
-nopgbrk:
Tells the tool to not insert page breaks.
-x 20 -y 82:
Sets the upper left corner (in pixels) of the area where to extract the table data from. Note, I used such values here which also exclude the column headers, not just the page headers and the table names.
-W 810 -H 450: Sets the width and height (in pixels) of the area to use for table data extraction.

_{Note, if you use XPDF's version of pdftotext (as available at www.foolabs.com/xpdf/download.html) the command line options for -x, -y, -W and -H are not supported. But if you use -table instead of -layout with the XPDF-pdftotext, then the result should be similar (you will however still have to remove the page and column headers manually).}

Above command gives you this output (I show only the output for the first two pages with the width jump at exactly page border, 2 lines after the Baix Ebre entries):

Alt Camp         VY   Nulles                        -1,4   19    -4,9   12     1,1   07     4,0   07    4,8   01   11,2   13   12,0   02   12,7   31    8,3   27     0,7   29     0,1   30    -1,7   01    -4,9   12/02
Alt Camp         DQ   Vila-rodona                   -0,5   30    -4,5   03     1,3   07     3,4   17    5,5   02   13,0   14   12,8   02   14,6   31    8,9   27     2,6   28     0,2   30     0,6   12    -4,5   03/02
Alt Empordà      U1   Cabanes                       -3,0   15    -6,0   09    -0,3   02     2,9   25    3,6   01   12,2   11   10,5   24   12,6   27    6,6   27     2,8   30     2,0   30    -4,3   12    -6,0   09/02
Alt Empordà      W1   Castelló d'Empúries           -2,7   15    -6,2   09     0,3   02     3,2   07    6,0   01   12,1   16   11,1   24   13,3   27    7,5   27     0,7   30     2,2   23    -3,7   12    -6,2   09/02
Alt Empordà      VZ   Espolla                       -1,8   15    -6,8   09     1,5   19     2,9   07    5,7   01   12,2   12   10,3   24   13,7   07    7,6   20     2,5   30     2,5   07    -4,8   12    -6,8   09/02
Alt Empordà      D6   Portbou                        1,7   29    -4,5   04     4,8   06     3,3   16    9,4   01   12,6   11   13,3   01   15,3   06   12,4   26     4,7   28     4,0   30     1,4   12    -4,5   04/02
Alt Empordà      D4   Roses                         -1,6   15    -4,2   09     2,9   16     4,6   07    7,0   01   13,5   12   13,5   24   15,7   27    8,7   27     2,1   30     3,5   23    -2,5   12    -4,2   09/02
Alt Empordà      U2   Sant Pere Pescador            -3,5   15    -6,1   09    -0,2   02     2,6   07    5,8   01   10,3   12    9,6   24   12,7   27    8,0   27    -0,2   30     1,9   23    -3,5   12    -6,1   09/02
Alt Empordà      W2   Torroella de Fluvià           -4,0   15    -6,7   09    -1,3   02     1,6   07    3,4   02    9,5   12    9,5   24   12,6   27    6,4   27    -0,6   30     0,9   30    -4,2   12    -6,7   09/02
Alt Empordà      W3   Ventalló                      -5,0   15    -6,8   09    -0,7   02     1,9   07    4,3   01   10,2   12   10,6   24   12,5   27    6,9   27    -0,7   30    -0,8   30    -5,2   12    -6,8   09/02
Alt Penedès      WP   Canaletes                     -1,0   14    -5,3   12     1,6   07     3,1   17    5,7   03   11,2   13   12,1   02   13,7   31    9,0   27     1,8   29    -0,8   30    -0,6   02    -5,3   12/02
Alt Penedès      DI   Font-rubí                     -1,1   29    -4,9   12     2,0   08     4,4   17    6,9   01   11,6   09   11,8   02   15,1   31   10,0   26     0,3   29    -0,3   30    -0,3   02    -4,9   12/02
Alt Penedès      W4   la Granada                    -0,9   31    -5,4   13     1,0   07     3,7   17    5,9   01   11,1   13   12,1   02   13,5   31    9,0   26     1,7   29    -0,9   30    -0,3   02    -5,4   13/02
Alt Penedès      U3   Sant Martí Sarroca            -4,1   14    -7,2   13    -0,3   08     3,0   07    4,6   03   11,2   12   11,4   02   13,2   31    8,2   26    -0,6   29    -1,1   30    -4,3   02    -7,2   13/02
Alt Penedès      WY   Sant Sadurní d'Anoia          -2,7   31    -5,7   13    -0,3   08     2,4   07    4,7   01   10,7   12   12,0   02   13,8   31    8,0   27     1,6   30    -2,2   30    -2,8   02    -5,7   13/02
Alt Urgell       CD   la Seu d'Urgell               -6,9   15   -10,7   12    -4,6   06    -1,5   17    2,1   01    6,3   12    7,5   02    7,2   31    3,1   27    -3,0   29    -4,0   30    -8,4   12   -10,7   12/02
Alt Urgell       W5   Oliana                        -6,6   31   -12,0   12    -4,3   08    -1,1   14    1,4   01    7,8   12    9,6   02   11,2   26    7,4   26    -3,1   29    -4,5   30    -6,8   10   -12,0   12/02
Alt Urgell       CJ   Organyà                       -8,2   14    -8,8   05    -2,4   19    -0,9   20    1,1   01    6,6   12    9,9   02   10,4   31    5,6   27    -2,2   30    -1,7   30    -7,8   12    -8,8   05/02
Alta Ribagorça   Z2   Boí (2.535 m)                -14,3   29   -23,0   03   -13,6   06   -11,5   16   -7,2   01   -1,8   12    0,7   01   -2,0   31   -3,5   26   -14,2   28   -12,9   29   -11,5   06   -23,0   03/02
Alta Ribagorça   CT   el Pont de Suert             -10,3   15   -11,8   21    -6,4   07    -3,4   17   -0,1   01    3,5   12    5,4   15    5,2   31    1,5   27    -4,9   29    -6,7   30    -9,6   12   -11,8   21/02
Anoia            CE   els Hostalets de Pierola      -2,0   14    -5,1   13     1,3   07     3,4   17    5,8   01   12,4   12   12,2   02   13,1   31   10,0   27     1,2   29    -0,2   30    -1,9   02    -5,1   13/02
Anoia            XB   la Llacuna                    -6,2   14    -8,2   12    -2,8   07     1,1   17    2,4   03    6,4   13    9,8   24   10,2   31    5,0   27    -1,5   29    -3,2   30    -3,9   01    -8,2   12/02
Anoia            XA   la Panadella                  -3,9   30   -10,1   03    -2,2   06    -1,4   17    4,2   01    8,3   12    8,5   02    9,5   31    7,5   27    -1,2   28    -2,0   30    -4,4   02   -10,1   03/02
Anoia            H1   Òdena                         -5,6   14    -8,7   13    -4,2   07     0,3   17    2,3   01    7,9   13   10,4   02   12,2   31    5,0   27    -0,7   30    -3,3   30    -4,8   02    -8,7   13/02
Bages            WW   Artés                         -5,9   14   -10,3   11    -4,9   06    -2,1   17    2,2   01    9,0   12   10,4   24   10,6   31    5,0   27    -2,6   29    -5,0   30    -5,6   02   -10,3   11/02
Bages            U4   Castellnou de Bages           -5,5   14    -7,5   03    -1,7   06     1,3   17    3,8   01    9,6   12   11,3   02   11,6   31    6,7   27    -0,3   29    -2,9   30    -3,8   02    -7,5   03/02
Bages            R1   el Pont de Vilomara           -5,3   14    -9,6   13    -3,0   07    -0,6   17    2,9   01    9,6   13   11,3   02   12,3   31    6,0   27    -1,2   29    -3,4   30    -5,0   02    -9,6   13/02
Bages            WN   Montserrat - Sant Dimes       -0,3   29    -7,4   12     0,4   19     1,8   17    5,3   21    9,5   12    9,5   02   11,5   31    8,6   26     2,4   29    -0,1   30    -1,0   06    -7,4   12/02
Bages            CL   Sant Salvador de Guardiola    -6,3   30   -10,1   13    -4,2   07     0,3   17    1,6   01    7,8   13    9,9   24    9,9   31    4,7   27    -1,5   30    -5,0   30    -6,4   02   -10,1   13/02
Baix Camp        U5   Prades - los Hortals          -6,6   30   -12,9   12    -5,8   09    -2,7   17    0,7   01    6,8   09    4,9   02    7,8   31    3,8   02    -3,1   29    -5,0   30    -6,6   01   -12,9   12/02
Baix Camp        W6   Riudoms                        0,0   13    -3,2   03     2,7   01     4,9   07    6,3   01   13,9   13   14,8   02   16,1   31   10,7   26     4,1   28     3,7   30     1,6   10    -3,2   03/02
Baix Camp        U6   Vinyols i els Arcs            -1,1   15    -2,1   03     1,9   15     4,7   07    6,9   01   15,6   02   15,1   01   17,3   31   11,7   26     6,4   28     4,6   30     2,4   10    -2,1   03/02
Baix Ebre        U7   Aldover                        0,4   31    -2,0   03     3,7   01     4,0   07    6,6   01   13,4   09   14,8   02   17,1   31   12,2   27     4,5   30     3,7   30     1,0   10    -2,0   03/02
Baix Ebre        DB   el Perelló                    -0,2   15    -2,8   03     3,2   07     6,0   17    7,4   01   15,5   09   15,3   02   16,9   31   12,0   29     5,0   30     3,5   30     1,7   01    -2,8   03/02
Baix Ebre        U9   l'Aldea                       -1,3   13    -1,2   04     3,5   01     5,2   07    7,1   01   14,3   09   15,5   01   18,2   31   11,4   27     6,0   30     5,6   30     0,6   10    -1,3   13/01
Baix Ebre        UA   l'Ametlla de Mar               1,1   15    -2,2   03     4,5   23     5,0   07    6,6   01   14,9   09   15,2   01   17,1   31   11,7   27     4,8   30     4,1   30     2,4   12    -2,2   03/02
Baix Ebre        X5   PN dels Ports                 -4,5   30   -11,3   04    -4,0   07    -2,8   17    0,2   01    5,8   09    7,4   01    8,0   31    4,8   27    -2,6   29    -4,6   30    -5,8   01   -11,3   04/02
Baix Empordà     DO   Castell d'Aro                 -1,7   15    -7,4   05    -0,4   06     2,2   17    4,9   01   11,2   12   12,1   24   13,6   31    9,1   27    -0,7   29    -1,5   30    -3,0   12    -7,4   05/02
Baix Empordà     DF   la Bisbal d'Empordà                    -3,2   15    -6,8   12    -2,4   06    0,5   17    4,6   01   11,1   12   10,3   24   11,6   31    7,7   27    -1,0   29    -2,2   30    -4,2   12    -6,8   12/02
Baix Empordà     UB   la Tallada d'Empordà                   -4,1   15    -7,1   12    -2,0   06    1,8   17    4,8   01   11,9   12   10,8   24   12,4   31    7,2   27    -0,5   30    -2,2   30    -5,1   12    -7,1   12/02
Baix Empordà     UC   Monells                                -3,7   15    -8,0   13    -3,2   06   -1,2   17    2,7   01   10,5   13   10,5   24    8,8   31    6,2   27    -2,1   29    -2,5   30    -4,8   12    -8,0   13/02
Baix Empordà     UD   Serra de Daró                          -3,2   15    -6,8   12    -1,7   06    0,9   17    4,6   01   11,7   12   10,1   24   11,5   31    7,3   27     0,5   30    -1,7   30    -3,8   12    -6,8   12/02
Baix Empordà     UE   Torroella de Montgrí                   -1,8   15    -5,6   12    -1,1   02    2,5   07    5,5   01   12,6   12   11,8   24   14,3   27    8,4   27     1,0   30    -0,5   30    -3,4   12    -5,6   12/02
Baix Llobregat   UF   Begues - PN del Garraf                  0,1   29    -5,8   04     2,5   06    3,1   17    6,4   21   11,8   12   12,3   01   14,2   31   10,1   26     1,8   28     0,1   30    -0,4   02    -5,8   04/02
Baix Llobregat   XL   el Prat de Llobregat                    0,6   30    -4,6   05     2,1   06    5,5   07    8,5   01   12,4   12   14,8   02   16,8   31    9,9   26     3,3   29     1,9   30     0,9   09    -4,6   05/02
Baix Llobregat   D3   Vallirana                               0,6   29    -3,1   03     4,1   07    5,4   17    6,7   01   12,9   12   13,9   02   15,9   31   11,3   27     4,7   29     1,9   30     0,4   01    -3,1   03/02
Baix Llobregat   UG   Viladecans                              1,2   30    -4,1   05     3,8   08    6,2   11    8,4   01   15,0   16   15,4   02   17,5   31   12,1   26     4,2   29     2,2   30     1,1   02    -4,1   05/02
Baix Penedès     WZ   Cunit                                  -1,9   30    -4,7   13     3,1   10    2,2   17    7,1   01   13,0   12   13,5   02   14,4   31   11,3   26     1,8   29     1,4   30    -1,6   02    -4,7   13/02
Baix Penedès     UH   el Montmell                            -0,7   29    -4,7   03     1,9   07    3,9   17    5,4   01   11,4   12   10,0   01   13,8   31    9,8   27     1,5   29     0,4   30     0,4   02    -4,7   03/02
Baix Penedès     D9   el Vendrell                            -1,4   30    -4,2   12     1,2   10    5,3   07    6,4   02   12,9   12   13,2   02   17,7   08   10,7   26     4,3   29     1,1   30     0,1   11    -4,2   12/02
Baix Penedès     WO   la Bisbal del Penedès                  -5,4   14    -5,9   13    -1,3   10    4,5   02    3,8   01   11,6   15   12,9   24   14,6   08    7,0   27     0,9   30    -2,1   30    -2,9   01    -5,9   13/02
Barcelonès       WU   Badalona - Museu                        2,2   14    -0,8   04     4,9   07    6,7   17    9,9   01   16,7   12   15,9   02   17,2   31   14,2   27     5,6   29     2,9   30     2,4   02    -0,8   04/02
Barcelonès       X4   Barcelona - el Raval                    5,5   30     0,6   04     7,9   09    9,1   17   11,6   01   17,6   12   16,6   01   19,4   30   16,3   29     7,6   29     5,6   30     4,5   02     0,6   04/02
Barcelonès       D5   Barcelona - Observatori Fabra           1,0   30    -4,7   03     4,5   07    4,5   17    7,7   21   12,7   12   13,4   02   15,2   31   12,4   27     3,2   28     1,9   30     0,5   02    -4,7   03/02
Barcelonès       X8   Barcelona - Zona Universitària          1,9   14    -1,8   04     4,8   06    6,1   17    7,6   01   14,5   12   14,6   01   16,8   31   13,3   27     5,4   29     2,3   30     2,1   02    -1,8   04/02
Barcelonès       X2   Barcelona - Zoo                         3,1   13    -2,3   05     5,1   10    8,5   07   10,1   01   15,9   12   16,6   02   18,0   31   14,8   02     6,8   29     4,3   30     2,2   02    -2,3   05/02
Berguedà         UI   Gisclareny                             -5,1   16   -12,5   04    -4,1   05   -2,7   17   -0,6   01    5,7   13    7,4   02    6,1   31    3,2   26    -2,8   29    -5,1   30    -5,6   12   -12,5   04/02
Berguedà         WV   Guardiola de Berguedà                  -7,4   14   -11,7   12    -5,8   07   -2,9   14    0,6   02    5,7   12    6,3   02    6,3   31    0,9   27    -4,4   30    -5,7   30    -8,4   01   -11,7   12/02
Berguedà         CR   la Quar                                -3,5   29   -11,5   12    -1,8   07   -2,3   17    1,2   01    5,7   12   10,0   15    8,9   31    5,0   27    -1,9   29    -2,7   30    -4,7   01   -11,5   12/02
Berguedà         WM   Santuari de Queralt                    -2,4   29    -9,1   04    -0,8   06   -0,2   11    2,9   01    6,2   12    9,2   02    9,7   31    7,2   26    -1,0   28    -1,3   30    -2,8   12    -9,1   04/02
Cerdanya         Z9   Cadí Nord (2.143 m) - Prat d'Aguiló   -11,5   30   -19,6   03   -10,4   06   -9,0   17   -4,5   01    1,8   12    2,9   01    0,9   31   -1,0   26   -10,5   28   -11,4   30    -9,2   02   -19,6   03/02
Cerdanya         DP   Das                                   -12,9   14   -16,6   12    -9,7   10   -5,5   14   -2,2   14    0,6   12    2,3   02    3,6   27   -2,8   27    -6,9   30    -8,3   30   -13,5   12   -16,6   12/02
Cerdanya         Z3   Malniu (2.230 m)                      -12,2   29   -20,6   03   -10,7   06   -9,6   16   -5,4   01    0,4   12    2,9   01   -0,2   31   -0,4   27   -12,1   28   -11,3   30    -9,1   02   -20,6   03/02
Conca de B.      W8   Blancafort                             -3,1   19    -8,2   11    -2,8   07    1,9   17    2,9   01   10,7   13   11,8   02   12,5   31    6,2   27    -0,3   30    -1,2   30    -3,1   11    -8,2   11/02
Conca de B.      CW   l'Espluga de Francolí                  -2,0   16    -5,9   04    -0,9   07    2,5   17    2,8   01   11,5   04   10,4   02   13,2   31    6,5   27    -0,3   30    -1,0   30    -3,2   12    -5,9   04/02
Conca de B.      UJ   Santa Coloma de Queralt                -3,4   14    -8,9   03    -1,1   07   -0,4   17    3,4   01    8,3   13    9,2   02   10,7   31    6,7   27    -0,3   28    -1,6   30    -3,4   02    -8,9   03/02
Garraf           UK   Sant Pere de Ribes - PN del Garraf     -0,3   29    -3,8   04     2,8   06    4,2   17    7,1   01   12,9   12   12,4   02   13,2   31   12,0   27     2,6   29     0,3   30     0,2   02    -3,8   04/02
Garrigues        UL   Castelldans                            -4,9   26    -7,0   06    -1,9   10    1,7   07    3,2   01   11,5   15   12,8   03   13,6   31    5,8   27    -0,5   30    -1,5   30    -5,1   12    -7,0   06/02
Garrigues        UM   la Granadella                          -3,4   11    -7,6   03    -2,5   10    0,6   17    2,7   01   10,9   13   10,8   02   11,5   31    6,2   02     1,1   29    -0,9   30    -3,4   12    -7,6   03/02
Garrotxa         W9   la Vall d'en Bas                       -6,3   14   -10,9   13    -5,8   07   -2,2   17    1,7   01    8,8   12    6,7   24    8,5   31    4,3   27    -4,3   29    -5,0   30    -6,6   09   -10,9   13/02
Garrotxa         DC   Olot                                   -4,9   15    -9,9   12    -3,6   07   -1,8   17    2,6   01    9,0   12    9,9   24    9,6   31    5,5   27    -3,3   29    -3,9   30    -5,9   12    -9,9   12/02
Gironès          UN   Cassà de la Selva                      -4,2   15   -10,7   05    -3,0   06    0,5   17    1,9   01    8,8   12   11,0   24   10,5   31    6,7   27    -3,2   29    -4,4   30    -5,3   12   -10,7   05/02
Gironès          UO   Fornells de la Selva                   -5,8   15   -10,4   13    -4,9   07   -1,5   17    2,2   01    9,3   12    9,2   24   10,3   31    6,1   27    -3,5   29    -4,3   30    -6,3   12   -10,4   13/02
Gironès          XJ   Girona                                 -5,1   15    -9,6   13    -4,0   07   -1,6   17    3,1   01   10,2   12    9,7   24   10,4   31    5,7   27    -3,1   29    -3,8   30    -5,7   12    -9,6   13/02
Gironès          WF   Vilablareix                            -5,2   15    -9,9   13    -4,3   07   -1,7   17    3,0   02    9,0   12    9,7   24   11,7   31    5,7   27    -2,8   29    -2,8   30    -4,6   12    -9,9   13/02
Maresme          UP   Cabrils                                 1,6   30    -2,6   11     3,2   07    6,7   17    8,5   01   13,9   12   15,1   02   15,9   31   13,3   26     3,7   28     3,0   30     2,6   12    -2,6   11/02

If you know how to properly operate a text editor, it is very easy and fast to fix this text output, so it will smoothly get imported by Excel...

Scraping large pdf tables which span across multiple pages

There are 7 best solutions below

Update

Part 1: Setup steps

Part 2: Using `read.fwf` to get the data in

Part 3: Did it work?

Update: Extracting just the data

Related Questions in R

Related Questions in PERL

Related Questions in MS-ACCESS

Related Questions in PDF-SCRAPING

Trending Questions

Popular # Hahtags

Popular Questions

Scraping large pdf tables which span across multiple pages

There are 7 best solutions below

Update

Part 1: Setup steps

Part 2: Using read.fwf to get the data in

Part 3: Did it work?

Update: Extracting just the data

Related Questions in R

Related Questions in PERL

Related Questions in MS-ACCESS

Related Questions in PDF-SCRAPING

Trending Questions

Popular # Hahtags

Popular Questions

Part 2: Using `read.fwf` to get the data in