I've a huge .xml with thousands of customers which includes a few tags relevant for different purposes.
I would like to build a logic where I can decide which tags should be received and where to place them in the pdf.
Which language is best to use in this matter?
*Note: I'm a beginner in programing
*
Thanks in advance.
I've not started yet
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Can any one help me on how to convert pdf file to xml file using python code? My pdf contains:
Unstructured data
It has images
Mathematical equations
Chemical Equations
Table Data
Logo's tag's etc.
I tried using PDFMiner, but my pdf data was not converted into .xml/json file format. Are there any libraries other than PDFMiner? PyPDF2, Tabula-py, PDFQuery, comelot, PyMuPDF, pdf to dox, pandas- these other libraries/utilities all not suitable for my requirement.
Please advise me on any other options. Thank you.
The first thing I would recommend you trying is GROBID (see here for the full documentation). You can play with an online demo here to see if fits your needs (select TEI -> Process Fulltext Document, and upload a PDF). You can also check out this from the Allen Institute (it is based on GROBID and has a handy function for converting TEI.XML to JSON).
The other package which--obviously--does a good job is the Adobe PDF Extract API (see here). It's of course a paid service but when you register for an account you get 1.000 document transactions for free. It's easy to implement in Python, well documented, and a good way for experimenting and getting a feel for the difficulties of reliable data extraction from PDF.
I worked with both options to extract text, figures, tables etc. from scientific papers. Both yielded good results. The main problem with out-of-the-box solutions is that, when you work with complex formats (or badly formatted docs), erroneously identified document elements are quite common (for example a footnote or a header gets merged with the main text). Both options are based on machine learning models and, at least for GROBID, it is possible to retrain these models for your specific task (I haven't tried this so far, so I don't know how worthwhile it is).
However, if your target PDFs are all of the same (simple) format (or if you can control their format) you should be fine with either option.
I work in Python and have to generate spreadsheets frequently to share my data with programming-naive colleagues. I embed large blocks of text explaining the contents of the spreadsheet and how it was generated into the first page of these spreadsheets routinely. I don't like relying on an associated document to explain definitions, criteria, algorithms, and reliability when I send my results out into the world.
It's really awkward to edit and store the long strings that make up these blocks of text. I'd love to store them in dedicated files that I can work with using a tool INTENDED to edit large blocks of text. I'm wondering how other people deal with this kind of situation. JSON files? YAML? Some obvious built-in functionality in Python I don't know about?
This is obviously a very open-ended question. I'm sure there a lot of different approaches and solutions out there. It's a difficult thing to search for online as there are a lot of obfuscating factors when you search for things like 'python large strings' or 'python text files'. I'm hoping to hear about a number of different approaches.
I am currently developing a proprietary PDF parser that can read multiple types of documents with various types of data. Before starting, I was thinking about if reading PowerPoint slides was possible. My employer uses presentation guidelines that requires imagery and background designs - is it possible to build a parser that can read the data from these PowerPoint PDFs without the slide decor getting in the way?
So the workflow would basically be this:
At the end of a project, the project report is delivered in the form of a presentation.
The presentation would be converted to PDF.
The PDF would be submitted to my application.
The application would read the slides and create a data-focused report for quick review.
The goal of the application is to cut down on the amount of reading that needs to be done by a significant amount as some of these presentation reports can be many pages long with not enough time in the day.
Parsing PDFs into structured data is always tricky, as the format is geared towards precise printing, rather than ease of editing or data extraction.
Basically, a PDF contains information like "there's a label with such text at such (x,y) position on a certain page", or things like that.
Basically, you will very likely need some heuristics in order to turn that into structured data.
It will basically be a form of scraping.
Search on your favorite search engine for PDF scraping, or something like that, and it would be a good start.
Also, you may want to look at those similar posts:
PDF Data and Table Scraping to Excel
How to extract table as text from the PDF using Python?
A PowerPoint PDF isn't a type of PDF.
There isn't going to be anything natively in the PDF that identifies elements on the page as being 'slide' graphics the originated from a PowerPoint file for example.
You could try building an algorithm that makes decision about content to drop from the created PDF but that would be tricky and seems like the wrong approach to me.
A better approach would be to "Export" the PPT to text first, e.g. in Microsoft PowerPoint Export it to a RTF file so you get all of the text out and use that directly or then convert that to PDF.
To start I just want to state that I'm an Electrical Engineer with basic knowledge of programming.
My requirement is as follows:
I want to create an app where I can load and view PDF files that
contain tables.
These PDF files tables are of irregular shapes and in a different
position on every page. (that's why tools like tabular couldn't help
me)
Each table entry is multiline and of irregular dimensions (I cannot
select a whole row at a time it has to be each element alone. simply
copying the lines to excel won't work either because it will need a
lot of formatting)
So I want to be able to select each table entry individually from the
table (like a selection or cropping box over the required text),
delete new line if there is a new line in the text and just keep spaces.
The generated excel (or access database I do not really mind any)
should be reviewable and saveable (if those are even words XD).
I have a good knowledge of python and a very elementary knowledge of Django and I'm seeking some expert who can tell me what do I really need to learn (and if possible where to learn it) to execute my project.
Is it very much for me to execute and if I can dedicate 10 hours a week, how much would it take me to execute such a project.
Thanks all for your help in advance.
Don't use Python, use Word. Open the pdf, then step through the tables collection to collect the data and put it into excel. See this for an example
Here are the advises i can provide you :
first of all, ask internet for questions :
https://lmddgtfy.net/?q=python%20library%20tabular%20pdf
-> Camelot , which is mentioned multiple time seems to be relevant
For the use of excel sheet, i present you one of the most famous library for manipulating DataFrame: Pandas
You can use small courses on internet which will offer you a quick ability to manage your project easier.
for the application, you can easily find on youtube courses on a library made by someone who will explain you how to do a basic application. It could offer you the entry point you are talking about. Then, You can just wonder what else do you need or simply want for making it better.
for the time needed, it depends on how much time do you need to understand the basics, how much time you spend on having a deeper comprehension. I think in one week, working during your free time with a real interest, it could be working( not perfect, but working, which is a good beginning)
PS: I am not sure if your question is relevant for the aims of stackoverflow. I suggest you to read this file. ( https://stackoverflow.com/help/how-to-ask)
I'd like to tokenise out wikipedia pages of interest with a python library or libraries. I'm most interested in tables and listings. I want to be able to then import this data into Postgres or Neo4j.
For example, here are three data sets that I'd be interested in:
How many points each country awarded one another in the Eurovision Song contest of 2008:
http://en.wikipedia.org/wiki/Eurovision_Song_Contest_2008#Final
List of currencies and the countries in which they circulate (a many-to-many relationship):
http://en.wikipedia.org/wiki/List_of_circulating_currencies
Lists of solar plants around the world: http://en.wikipedia.org/wiki/List_of_solar_thermal_power_stations
The source of each of these is written with wikipedia's brand of markup which is used to render them out. There are many wikipedia-specific tags and syntax used in the raw data form. The HTML might almost be the easier solution as I can just use BeautifulSoup.
Anyone know of a better way of tokenizeing? I feel that I'd reinvent the wheel if I took the final HTML and parsing it with BeautifulSoup. Also, if I could find a way to output these pages in XML, the table data might not be tokenized enough and it would require further processing.
Since Wikipedia is built on MediWiki, there is an api you can exploit. There is also Special:Export that you can use.
Once you have the raw data, then you can run it through mwlib to parse it.
This goes more to semantic web direction, but DBPedia allows querying parts (community conversion effort) of wikipedia data with SPARQL. This makes it theoretically straightforward to extract the needed data, however dealing with RDF triples might be cumbersome.
Furthermore, I don't know if DBPedia yet contains any data that is of interest for you.