I have some pretty strange data i'm working with, as can be seen in the image. Now I can't seem to find any source data for the numbers these graphs are presenting.
Furthermore if I search for the source it only points to an empty cell for each graph.
Ideally I want to be able to retrieve the highlighted labels in each case using python, and it seems finding the source is the only way to do this, so if you know of a python module that can do that i'd be happy to use it. Otherwise if you can help me find the source data that would be even perfecter :P
So far i've tried the XLDR module for python as well as manually showing all hidden cells, but neither work.
Here's a link to the file: Here
EDIT I ended up just converting the xlsx to a pdf using cloudconvert.com API
Then using pdftotext to convert the data to a .txt which just analyses everything including the numbers on the edge of the chart which can then be searched using an algorithm.
If a hopeless internet wanderer comes upon this thread with the same problem, you can PM me for more details :P
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I have been trying to pull out financial statements embedded in annual reports in pdf and export them in excel/CSV format using python But I am encountering some problems:
1. A specific Financial statement can be on any page in the report. If I were to process hundreds of pdfs, I would have to specify page numbers which takes alot of time. Is there any way through which the scraper knows where the exact statement is?
2. Some reports span over multiple pages and the end result after scraping a pdf isnt what I want
3. Different annual reports have different financial statement formats. Is there any way to process them and change them to a specific standard format?
I would also appreciate if anyone have done something like this and can share examples.
Ps I am working with python and used tabula and Camelot
I had a similar case where the problem was to extract specific form information from pdfs (name, date of birth and so on). I used the tesseract open source software with pytesseract to perform OCR on the files . Since I did not need the whole pdfs, but specific information from them, I designed an algorithm to find the information: In my case I used simple heuristics (specific fields, specific line number and some other domain specific stuff), but you can also use a machine-learning approach and train a classifier which can find the needed text-parts. You could use domain-specific heuristics as well, because I am sure that a financial statement has special vocabulary or some text markers which indicate its beginning/its end.
I hope I could at least give you some ideas how to approach the problem
P.S.: With tesseract you can also process multipage pdfs. To 3) - Machine learning approach would need some samples to learn a good generalization of how a financial statement may look like.
Searched quite a bit but as I couldn't find a solution for this kind of problem, hence posting a clear question on the same. Most answers cover image/text extraction which are comparatively easier.
I've a requirement of extracting tables and graphs as text (csv) and images respectively from PDFs.
Can anyone help me with an efficient python 3.6 code to solve the same?
Till now I could achieve extracting jpgs using startmark = b"\xff\xd8" and endmark = b"\xff\xd9", but not all tables and graphs in a PDF are plain jpgs, hence my code fails badly in achieving that.
Example, I want to extract table from page 11 and graphs from page 12 as image or something which is feasible from the below given link. How to go about it?
https://hartmannazurecdn.azureedge.net/media/2369/annual-report-2017.pdf
For extracting tables you can use camelot
Here is an article about it.
For images I've found this question and answer Extract images from PDF without resampling, in python?
Try using PyMuPdf(https://github.com/pymupdf/PyMuPDF/tree/1.18.3) for amalgamation of texts, bars, lines and axis. It has so many extra utilities.
I have many graphs in Excel that I would like to convert to Python but am struggling with how to do so using Matplotlib. Is there a package or method that would essentially convert/translate all the formatting and data series selection into python?
Once I could see a few examples of the correct code I think I could start doing this directly in python but I do not have much experience manually creating graph code (I use Excel insert graphs mostly) so am looking for a bridge.
I am using QGIS and there is a plugin Qgis2threejs that can accept collada files along with the current map layer and produce three.js output. I am not familiar with collada, but I want to use pycollada to build a set of 3d lines that I can import into the plugin.
I am having a lot of trouble understanding how to do this with pycollada, I don't see many tutorials or examples on the internet, the ones I see are mostly for cubes.
I basically want to know how to build the simplest python script that will create lines if I know the x,y,z coordinates of each point and then write them to the file.
Does anyone know of a tutorial that does this, or something similar.
This lineset test in the repo should give you a good example.
You should also read the Creating A Collada Object section of the docs.
I have lot of PDF, DOC[X], TIFF and others files (scans from a shared folder). Each file converted into pack of text files: one text file per page.
Each pack of files could contain multiple documents (for example thee contracts). Document kind could be not only contract.
During the processing the pack of the files I don't know what kind of the documents current pack contains and it's possible that one pack contains multiple document kinds (contracts, invoices, etc).
I'm looking for some possible approaches to solve this programmatically.
I'm tried to search something like that but without any success.
UPD: I tried to create binary classificator with scikit-learn and now looking for another solution.
This at its basis, being they are "scans" sounds more like something that could be approached with computer vision, however this is currently far far above my current level of programming.
E.g. projects like SimpleCV may be a good starting point,
http://www.simplecv.org/
Or possibly you could get away with OCR reading the "scans" and working based on the contents. pytesseract seems popular for this type of task,
https://pypi.org/project/pytesseract/
However that still lacks defining how you would tell your program that this part of the image means that this is 3 separate contracts, Is there anything about these files in particular that make this clear, e.g. "1 of 3" on the pages,, a logo or otherwise? that will be the main part that determines how complex a problem you are trying to solve.
Best solution was to create binary classifier (SGDClassifier) and train it on classes first-page and not-first-page. Each item from the dataset was trimmed to 100 tokens (words)