A recent update of tabula-py
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This article is a repost of Patreon article published last December. I’m planning to bump up the next version of tabula-py within few weeks.
This is my first post on patreon. Apologies for delayed announcement of recent update of tabula-py. I will introduce the key features of updates.
Use Tabula app template
Tabula app has template exporting feature to reuse same bounding box for extraction. tabula-py now load and extract with tabula app’s template.
dfs = tabula.read_pdf_with_template(
'./examples/data.pdf',
'./examples/data.tabula-template.json',
pandas_options={'header': 0})
Support file-like object
Like many python libraries, tabula-py has been able to extract from file-like object.
# With file-like object
pdf\_path = ‘tests/resources/data.pdf’
with open(pdf\_path, ‘rb’) as f:
df = tabula.read_pdf(f)
# With pathlib
from pathlib import Path
pdf_path = 'tests/resources/data.pdf'
df = tabula.read_pdf(Path(pdf_path))
Allow multiple area option
As of tabula-java v1.0.2, tabula can handle multiple area option.
pdf_path = 'tests/resources/MultiColumn.pdf'
# Relative area
df_relative = tabula.read_pdf(
pdf_path, pages=1,
area=[[0, 0, 100, 50], [0, 50, 100, 100]], relative_area=True)
# Absolute area
df_absolute = tabula.read_pdf(
pdf_path, pages=1, area=[[0, 0, 451, 212], [0, 212, 451, 425]])
Tip: Get table position
This is not a new feature, but I think it might be helpful for some PDFs.
Detailed post: https://github.com/chezou/tabula-py/issues/102
read_pdf with JSON contains position info, so you can get the table position as follows:
In [5]: tables = read_pdf("./examples/data.pdf", output_format="json", page=2)
In [9]: top = tables[0]['top']
In [10]: left = tables[0]['left']
In [11]: bottom = tables[0]['height'] + top
In [12]: right = tables[0]['width'] + left
In [13]: top, bottom, left, right
Out[13]: (0.0, 528.8800048828125, 0.0, 564.8800048828125)
If you have any question, ask on Stack Overflow!
Other tabula-py articles
Aki Ariga
Staff Software Engineer
Interested in Machine Learning, ML Ops, and Data driven business. If you like my blog post, I’m glad if you can buy me a tea 😉