In the following picture, we have an example of payroll data, which has mixed data structures. For example, HR staff are likely to keep historical payroll data, which might not be created in tabular form. However, many data are only available in an unstructured format. To implement statistical analysis, data visualization and machine learning model, we need the data in tabular form (panel data). Next, we will explore something more interesting - PFD data in an unstructured format. file = 'state_population.pdf' data = tb.read_pdf(file, area = (300, 0, 600, 800), pages = '1') Scrape PDF Data in Unstructured Form tabula-py should be able to detect the rows and columns automatically. If the PDF page only includes the target table, then we don’t even need to specify the area. In practice, you will learn what values to use by trial and error. We just need to input the location of the tabular data in the PDF page by specifying the (top, left, bottom, right) coordinates of the area. Scraping PDF data in structured form is straightforward using tabula-py.
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