PyMuPDF - How to extract tables

def extract_text_from_pdf(pdf_path):

"""Read table content only of all pages in the document.

Chatbots typically have limitations on the amount of data that can

can be passed in (number of tokens).

We therefore only extract information on the PDF's pages that are

contained in tables.

As we even know that the PDF actually contains ONE logical table

that has been segmented for reporting purposes, our approach

is the following:

* The cell contents of each table row are joined into one string

separated by ";".

* If table segment on the first page also has an external header row,

join the column names separated by ";". Also ignore any subsequent

table row that equals the header string. This deals with table

header repeat situations.

"""

# open document

doc = pymupdf.open(pdf_path)

text = "" # we will return this string

row_count = 0 # counts table rows

header = "" # overall table header: output this only once!

# iterate over the pages

for page in doc:

# only read the table rows on each page, ignore other content

tables = page.find_tables() # a "TableFinder" object

for table in tables:

# on first page extract external column names if present

if page.number == 0 and table.header.external:

# build the overall table header string

# technical note: incomplete / complex tables may have

# "None" in some header cells. Just use empty string then.

header = (

";".join(

[

name if name is not None else ""

for name in table.header.names

]

)

+ "\n"

)

text += header

row_count += 1 # increase row counter

# output the table body

for row in table.extract(): # iterate over the table rows

# again replace any "None" in cells by an empty string

row_text = (

";".join([cell if cell is not None else "" for cell in row]) + "\n"

)

if row_text != header: # omit duplicates of header row

text += row_text

row_count += 1 # increase row counter

doc.close() # close document

print(f"Loaded {row_count} table rows from file '{doc.name}'.\n")

return text

 references:

https://python.plainenglish.io/why-pymupdf4llm-is-the-best-tool-for-extracting-data-from-pdfs-even-if-you-didnt-know-you-needed-7bff75313691