Extracting Table Data from PDFs Using Python
PDF is one of the most common document formats in daily work, but its "tables" are a different thing from tables in Excel: a PDF file itself does not store structured table data. A table on a page is merely a visual effect presented by text and lines according to their positional relationships. Therefore, reading PDF tables programmatically is essentially about letting a library analyze the page…
PDF files often contain tables, but their structure differs from Excel or other spreadsheet programs. Unlike Excel, PDF tables are visual effects made up of text and lines based on their positions. To access this data programmatically, you must analyze the page layout and identify the row and column structure. This article uses the Spire.PDF library for Python to demonstrate how to read PDF tables cell by cell on a page-by-page basis.
The process consists of three main steps: loading the document, extracting tables page by page, and iterating through rows and columns to read the cells.
The first step is environment setup. Install the Spire.PDF library using pip, either the standard version or the free version with a page limit. Once the library is installed, you can load a PDF document using the PdfDocument object and create a PdfTableExtractor object. The extractor analyzes the layout page by page, detecting table structures.
Next, extract tables from each page using the ExtractTable() method. This method returns a list of tables found on the specified page. A page may contain multiple tables, so iterate through the list to process each table. Within each table, use the GetRowCount() and GetColumnCount() methods to determine the number of rows and columns, respectively.
The GetText() method reads the text within a specific cell, with line breaks replaced by spaces and leading/trailing whitespace removed for cleaner output. Finally, join the cells of each row with a tab character, making it easy to copy the data into spreadsheet software or save it as CSV.
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