# iterate over extracted tables and export as excel individuallyįor i, table in enumerate(tables, start=1):
The below code is an example of iterating over all extracted tables and saving them as Excel spreadsheets: # save them in a folder You can also pass a URL to this method and it'll automatically download the PDF before extracting tables. We set pages to "all" to extract tables in all the PDF pages, the tabula.read_pdf() method returns a list of pandas DataFrames, each DataFrame corresponds to a table. Tables = tabula.read_pdf("", pages="all") We simply use read_pdf() method to extract tables within PDF files (again, get the example PDF here): # read PDF file Open up a new Python file and import tabula: import tabula
Read also: How to Split PDF Files in Python. If you can click and drag to select text in your table in a PDF viewer, then it is a text-based PDF, so this will work on papers, books, documents, and much more! It is worth noting that Camelot only works with text-based PDFs and not scanned documents. Or you can export to other formats such as JSON and Excel too. You can also export the tables to HTML format: # export to HTML
By setting compress parameter equals to True, this will create a ZIP file that contains all the tables in CSV format. Tables.export("foo.csv", f="csv", compress=True)į parameter indicates the file format, in this case, "csv". Or if you want to export all tables in one go: # or export all in a zip
That's precise, let's export the table to a CSV file: # export individually as CSVĬSV isn't the only option, you can also use to_excel(), to_html(), to_json() and to_sqlite() methods, here is an example exporting to Excel spreadsheet: # export individually as Excel (.xlsx extension) Sure enough, it contains only one table, printing this table as a Pandas DataFrame: # print the first table as Pandas DataFrameĠ Cycle \nName KI \n(1/km) Distance \n(mi) Percent Fuel Savingsġ Improved \nSpeed Decreased \nAccel Eliminate \nStops Decreased \nIdle Print("Total tables extracted:", tables.n) Read_pdf() function extracts all tables in a PDF file, let's print number of tables extracted: # number of tables extracted Just a random table, let's extract it in Python: # extract all the tables in the PDF file I have a PDF file in the current directory called "foo.pdf" (get it here) which is a normal PDF page that contains one table shown in the following image: Now that you have installed all requirements for this tutorial, open up a new Python file and follow along: import camelot Note that you need to make sure that you have Tkinter and ghostscript (which are the required dependencies for camelot) installed properly in your computer.
Related: How to Extract Images from PDF in Python.įirst, you need to install the required dependencies for camelot library to work properly, and then you can install the libraries using the command line: pip3 install camelot-py tabula-py In this tutorial, you will learn how you can extract tables in PDF using both camelot and tabula-py libraries in Python. It enables you to convert a PDF file into a CSV, TSV, JSON, or even a pandas DataFrame. Whereas Tabula-py is a simple Python wrapper of tabula-java, which can read tables in a PDF. Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.ĭo you want to export tables from PDF files with Python programming language? You're in the right place.Ĭamelot is a Python library and a command-line tool that makes it easy for anyone to extract data tables trapped inside PDF files, check their official documentation and Github repository.