Camelot can be installed like so:Ĭamelot does have some additional dependencies, including GhostScript, which are listed here. nvert_into_by_batch('/path/to/files', output_format = 'json', pages = 'all')Ĭamelot is another possibility for scraping tables from PDFs. We can perform the same operation, except drop the files out to JSON instead, like below. nvert_into_by_batch('/path/to/files', output_format = 'csv', pages = 'all') Tabula-py can also scrape all of the PDFs in a directory in just one line of code, and drop the tables from each into CSV files. nvert_into(file, 'iris_all.csv', all = True) # output all the tables in the PDF to a CSV # output just the first table in the PDF to a CSV If we add the parameter all = True, we can write all of the PDF’s tables to the CSV. The first line below will find the first table in the PDF and output it to a CSV. You can also use tabula-py to convert a PDF file directly into a CSV. To search for all the tables in a file you have to specify the parameters page = “all” and multiple_tables = True. The result stored into tables is a list of data frames which correspond to all the tables found in the PDF file. Tables = tabula.read_pdf(file, pages = 'all', multiple_tables = True) Below we use it scrape all the tables from a paper on classification regarding the Iris dataset ( available here). Once installed, tabula-py is straightforward to use. If you have issues with installation, check this. Tabula-py is a very nice package that allows you to both scrape PDFs, as well as convert PDFs directly into CSV files.

Note, this options will only work for PDFs that are typed – not scanned-in images. To learn more about scraping tables and other data from PDFs with R, click here.

This post will go through a few ways of scraping tables from PDFs with Python.

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