I have used the following code but nothing is getting detected. I have also tried on various other PDF formats but getting the same result.
from tabula import read_pdf,convert_into
df=read_pdf("1415_048.pdf",output_format="dataframe",encoding='utf-8',java_options=None,multiple_tables=True)
Where the PDF looks like this
[] #This is the result I'm getting
tabula-py is based on tabula-java. And it works only with text based PDF.
According to tabula.app website https://tabula.technology/ :
Note: Tabula only works on text-based PDFs, not scanned documents.
Related
I am trying to extract a table from a PDF document (example). It's not a scan/an image, so please focus on non-OCR solutions. OCR table extraction is here.
I tried the route of pdf -> html -> extract table. The pdf that I mentioned above when converted to html produces garbage, maybe because of the font, the document is not in English.
Extracting the pdf using x and y coordinate is not an option as this solution needs to work for future pdf from the url mention above which will have the table but not always in the same position.
The PDF does not contain explicit table data. It only contains lines and character glyphs which we tend to interpret as tables. Thus your task involves putting our human table recognition capabilities into code which is quite a task.
Generally speaking, if you are sure enough future PDFs will be generated by the same software in a very similar manner, it might be worth the time to investigate the file for some easy to follow hints to recognize the contents of individual fields.
Your specific document, though, has an additional shortcoming: It does not contain the required information for direct text extraction! You can try copying & pasting from Adobe Reader and you'll get (at least I do) semi-random characters from the WinAnsi range.
This is due to the fact that all fonts in the document claim that they use WinAnsiEncoding even though the characters referenced this way definitively are not from the WinAnsi character selection.
Thus reliable text extraction from your document without OCR is impossible after all!
(Trying copy&paste from Adobe Reader generally is a good first test whether text extraction is feasible at all; the text extraction methods of the Reader have been developed for many many years and, therefore, have become quite good. If you cannot extract anything sensible with Acrobat Reader, text extraction will be a very difficult task indeed.)
You could use Tabula:
http://tabula.nerdpower.org
It's free and kinda easy to use
One option is to use pdf-table-extract: https://github.com/ashima/pdf-table-extract.
Extracting tables from PDF documents is extremely hard as PDF does not contain a semantic layer.
Camelot
You can try camelot, maybe even in combination with its web interface excalibur:
>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
'accuracy': 99.02,
'whitespace': 12.24,
'order': 1,
'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite
>>> tables[0].df # get a pandas DataFrame!
See also python-camelot
Tabula
tabula can be installed via
pip install tabula-py
But it requires Java, as tabula-py is only a wrapper for the Java project.
It's used like this:
import tabula
# Read pdf into list of DataFrame
dfs = tabula.read_pdf("test.pdf", pages='all')
See also:
Reading a specific table with tabula
tabula
AWS Textract
I haven't tried it recently, but AWS Textract claims:
Amazon Textract can extract tables in a document, and extract cells, merged cells, and column headers within a table.
PdfPlumber
pdfplubmer table extraction methods:
import pdfplumber
pdf = pdfplumber.open("example.pdf")
page = pdf.pages[0]
page.extract_table()
See also
Tabula vs Camelot
I am simply trying to upload a csv file using RST formatting in my Python code.
I am following the documentation: https://sublime-and-sphinx-guide.readthedocs.io/en/latest/tables.html (towards the bottom of the page there are instructions to upload a csv file), but I can't get it to work.
Here is my RST code:
.. csv-table::
:file: _static/JSE/JSE_data_gaps.csv
:widths: 1, 3
:header-rows: 1
When I run the build with the above code, the csv doesn't appear in my html webpage.
I am not sure what numbers I should be inserting for the widths and header-rows above, any ideas? I suspect this may be causing it not to appear in my webpage, as it states in the documentation that the parameters you set must match that which is in the csv.
I have a question for you, I'm working on a new jenkins instance and as a result of the job I get a csv file with errors if there were any during the test. I would like to generate an HTML report based on this csv file, which would be more convenient to use than opening excel and loading the csv file to see the errors. I came across a plugin like HTML Publisher, unfortunately I don't know if it supports generating HTML reports based on csv files. Alternatively, you could do something like this with a python script and show the resulting html file in artifats. Do you have any ideas ??
I'm currently experimenting with tabula-py, but all documentation samples I tried when extracting pdf data resulted in the following error: returned non-zero exit status 1.
So I'm just curious if there is other ways to convert data in tables on a pdf to a csv file using python.
The answer for tabula-py is already available on StackOverflow & other resources.. to try using Camelot:
pip install camelot-py[cv]
import camelot
tables = camelot.read_pdf('X.pdf')
tables.export('X.csv', f='csv', compress=True) # you can also save it different file formats
Refer this link for more.
If you are looking to export tables from PDF to CSV files using Python the best way it to use libraries like Taluba and Camelot.
First we'll need to extract tables from individual pages and then libraries like pandas to export them into CSVs or other required formats.
However, if the documents are non-electronic, we'll have to use OCR or ML techniques to extract tables.
Here's a blog post which has a few examples: https://nanonets.com/blog/pdf-table-to-csv/#pdf-table-extraction-to-csv-with-python
I am trying to extract a table from a PDF document (example). It's not a scan/an image, so please focus on non-OCR solutions. OCR table extraction is here.
I tried the route of pdf -> html -> extract table. The pdf that I mentioned above when converted to html produces garbage, maybe because of the font, the document is not in English.
Extracting the pdf using x and y coordinate is not an option as this solution needs to work for future pdf from the url mention above which will have the table but not always in the same position.
The PDF does not contain explicit table data. It only contains lines and character glyphs which we tend to interpret as tables. Thus your task involves putting our human table recognition capabilities into code which is quite a task.
Generally speaking, if you are sure enough future PDFs will be generated by the same software in a very similar manner, it might be worth the time to investigate the file for some easy to follow hints to recognize the contents of individual fields.
Your specific document, though, has an additional shortcoming: It does not contain the required information for direct text extraction! You can try copying & pasting from Adobe Reader and you'll get (at least I do) semi-random characters from the WinAnsi range.
This is due to the fact that all fonts in the document claim that they use WinAnsiEncoding even though the characters referenced this way definitively are not from the WinAnsi character selection.
Thus reliable text extraction from your document without OCR is impossible after all!
(Trying copy&paste from Adobe Reader generally is a good first test whether text extraction is feasible at all; the text extraction methods of the Reader have been developed for many many years and, therefore, have become quite good. If you cannot extract anything sensible with Acrobat Reader, text extraction will be a very difficult task indeed.)
You could use Tabula:
http://tabula.nerdpower.org
It's free and kinda easy to use
One option is to use pdf-table-extract: https://github.com/ashima/pdf-table-extract.
Extracting tables from PDF documents is extremely hard as PDF does not contain a semantic layer.
Camelot
You can try camelot, maybe even in combination with its web interface excalibur:
>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
'accuracy': 99.02,
'whitespace': 12.24,
'order': 1,
'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite
>>> tables[0].df # get a pandas DataFrame!
See also python-camelot
Tabula
tabula can be installed via
pip install tabula-py
But it requires Java, as tabula-py is only a wrapper for the Java project.
It's used like this:
import tabula
# Read pdf into list of DataFrame
dfs = tabula.read_pdf("test.pdf", pages='all')
See also:
Reading a specific table with tabula
tabula
AWS Textract
I haven't tried it recently, but AWS Textract claims:
Amazon Textract can extract tables in a document, and extract cells, merged cells, and column headers within a table.
PdfPlumber
pdfplubmer table extraction methods:
import pdfplumber
pdf = pdfplumber.open("example.pdf")
page = pdf.pages[0]
page.extract_table()
See also
Tabula vs Camelot