Partitioning

The easiest way to partition documents in unstructured is to use the partition function. If you call the partition function, unstructured will use libmagic to automatically determine the file type and invoke the appropriate partition function. In cases where libmagic is not available, filetype detection will fall back to using the file extension.

The following table shows the document types the unstructured library currently supports. partition will recognize each of these document types and route the document to the appropriate partitioning function. If you already know your document type, you can use the partitioning function listed in the table directly.

As shown in the examples below, the partition function accepts both filenames and file-like objects as input. partition also has some optional kwargs. For example, if you set include_page_breaks=True, the output will include PageBreak elements if the filetype supports it. Additionally you can bypass the filetype detection logic with the optional content_type argument which may be specified with either the filename or file-like object, file. You can find a full listing of optional kwargs in the documentation below.

from unstructured.partition.auto import partition


filename = os.path.join(EXAMPLE_DOCS_DIRECTORY, "layout-parser-paper-fast.pdf")
elements = partition(filename=filename, content_type="application/pdf")
print("\n\n".join([str(el) for el in elements][:10]))

from unstructured.partition.auto import partition


filename = os.path.join(EXAMPLE_DOCS_DIRECTORY, "layout-parser-paper-fast.pdf")
with open(filename, "rb") as f:
  elements = partition(file=f, include_page_breaks=True)
print("\n\n".join([str(el) for el in elements][5:15]))

The unstructured library also includes partitioning functions targeted at specific document types. The partition function uses these document-specific partitioning functions under the hood. There are a few reasons you may want to use a document-specific partitioning function instead of partition:

• If you already know the document type, filetype detection is unnecessary. Using the document-specific function directly, or passing in the content_type will make your program run faster.

• Fewer dependencies. You don’t need to install libmagic for filetype detection if you’re only using document-specific functions.

• Additional features. The API for partition is the least common denominator for all document types. Certain document-specific function include extra features that you may want to take advantage of. For example, partition_html allows you to pass in a URL so you don’t have to store the .html file locally. See the documentation below learn about the options available in each partitioning function.

Below we see an example of how to partition a document directly with the URL using the partition_html function.

from unstructured.partition.html import partition_html

url = "https://www.cnn.com/2023/01/30/sport/empire-state-building-green-philadelphia-eagles-spt-intl/index.html"
elements = partition_html(url=url)
print("\n\n".join([str(el) for el in elements]))

​

partition

The partition function is the simplest way to partition a document in unstructured. If you call the partition function, unstructured will attempt to detect the file type and route it to the appropriate partitioning function. All partitioning functions called within partition are called using the default kwargs. Use the document-type specific functions if you need to apply non-default settings. partition currently supports .docx, .doc, .odt, .pptx, .ppt, .xlsx, .csv, .tsv, .eml, .msg, .rtf, .epub, .html, .xml, .pdf, .png, .jpg, .heic, and .txt files. If you set the include_page_breaks kwarg to True, the output will include page breaks. This is only supported for .pptx, .html, .pdf, .png, .heic, and .jpg. The strategy kwarg controls the strategy for partitioning documents. Generally available strategies are “fast” for faster processing and “hi_res” for more accurate processing.

import docx

from unstructured.partition.auto import partition

document = docx.Document()
document.add_paragraph("Important Analysis", style="Heading 1")
document.add_paragraph("Here is my first thought.", style="Body Text")
document.add_paragraph("Here is my second thought.", style="Normal")
document.save("mydoc.docx")

elements = partition(filename="mydoc.docx")

with open("mydoc.docx", "rb") as f:
    elements = partition(file=f)

from unstructured.partition.auto import partition

elements = partition(filename="example-docs/pdf/layout-parser-paper-fast.pdf")

The partition function also accepts a url kwarg for remotely hosted documents. If you want to force partition to treat the document as a particular MIME type, use the content_type kwarg in conjunction with url. Otherwise, partition will use the information from the Content-Type header in the HTTP response. The ssl_verify kwarg controls whether or not SSL verification is enabled for the HTTP request. By default it is on. Use ssl_verify=False to disable SSL verification in the request.

from unstructured.partition.auto import partition

url = "https://raw.githubusercontent.com/Unstructured-IO/unstructured/main/LICENSE.md"
elements = partition(url=url)
elements = partition(url=url, content_type="text/markdown")

For more information about the partition function, you can check the source code here.

​

partition_csv

The partition_csv function pre-processes CSV files. The output is a single Table element. The text_as_html attribute in the element metadata will contain an HTML representation of the table.

Examples:

from unstructured.partition.csv import partition_csv

elements = partition_csv(filename="example-docs/stanley-cups.csv")
print(elements[0].metadata.text_as_html)

For more information about the partition_csv function, you can check the source code here.

​

partition_doc

The partition_doc partitioning function pre-processes Microsoft Word documents saved in the .doc format. This partition function uses a combination of the styling information in the document and the structure of the text to determine the type of a text element. The partition_doc can take a filename or file-like object as input. partition_doc uses libreoffice to convert the file to .docx and then calls partition_docx. Ensure you have libreoffice installed before using partition_doc.

Examples:

from unstructured.partition.doc import partition_doc

elements = partition_doc(filename="example-docs/fake.doc")

For more information about the partition_doc function, you can check the source code here.

​

partition_docx

The partition_docx partitioning function pre-processes Microsoft Word documents saved in the .docx format. This partition function uses a combination of the styling information in the document and the structure of the text to determine the type of a text element. The partition_docx can take a filename or file-like object as input, as shown in the two examples below.

Examples:

import docx

from unstructured.partition.docx import partition_docx

document = docx.Document()
document.add_paragraph("Important Analysis", style="Heading 1")
document.add_paragraph("Here is my first thought.", style="Body Text")
document.add_paragraph("Here is my second thought.", style="Normal")
document.save("mydoc.docx")

elements = partition_docx(filename="mydoc.docx")

with open("mydoc.docx", "rb") as f:
    elements = partition_docx(file=f)

In Word documents, headers and footers are specified per section. In the output, the Header elements will appear at the beginning of a section and Footer elements will appear at the end. MSFT Word headers and footers have a header_footer_type metadata field indicating where the header or footer applies. Valid values are "primary", "first_page" and "even_page".

partition_docx will include page numbers in the document metadata when page breaks are present in the document. The function will detect user inserted page breaks and page breaks inserted by the Word document renderer. Some (but not all) Word document renderers insert page breaks when you save the document. If your Word document renderer does not do that, you may not see page numbers in the output even if you see them visually when you open the document. If that is the case, you can try saving the document with a different renderer.

For more information about the partition_docx function, you can check the source code here.

​

partition_email

The partition_email function partitions .eml documents and works with exports from email clients such as Microsoft Outlook and Gmail. The partition_email takes a filename, file-like object, or raw text as input and produces a list of document Element objects as output. Also content_source can be set to text/html (default) or text/plain to process the html or plain text version of the email, respectively. In order for partition_email to return the header information as elements, include_headers must be set to True. Header information is captured in element metadata regardless of the include_headers setting.

Examples:

from unstructured.partition.email import partition_email

elements = partition_email(filename="example-docs/eml/fake-email.eml")

with open("example-docs/eml/fake-email.eml", "r") as f:
    elements = partition_email(file=f)

with open("example-docs/eml/fake-email.eml", "r") as f:
    text = f.read()
elements = partition_email(text=text)

with open("example-docs/eml/fake-email.eml", "r") as f:
    text = f.read()
elements = partition_email(text=text, content_source="text/plain")

with open("example-docs/eml/fake-email.eml", "r") as f:
    text = f.read()
elements = partition_email(text=text, include_headers=True)

partition_email includes a max_partition parameter that indicates the maximum character length for a document element. This parameter only applies if "text/plain" is selected as the content_source. The default value is 1500, which roughly corresponds to the average character length for a paragraph. You can disable max_partition by setting it to None.

You can optionally partition e-mail attachments by setting process_attachments=True. The following is an example of what the workflow looks like:

from unstructured.partition.email import partition_email

filename = "example-docs/eml/fake-email-attachment.eml"
elements = partition_email(filename=filename, process_attachments=True)

If the content of an email is PGP encrypted, partition_email will return an empty list of elements and emit a warning indicated the email is encrypted.

For more information about the partition_email function, you can check the source code here.

​

partition_epub

The partition_epub function processes e-books in EPUB3 format. The function first converts the document to HTML using pandocs and then calls partition_html. You’ll need pandocs installed on your system to use partition_epub.

Examples:

from unstructured.partition.epub import partition_epub

elements = partition_epub(filename="example-docs/winter-sports.epub")

For more information about the partition_epub function, you can check the source code here.

​

partition_html

The partition_html function partitions an HTML document and returns a list of document Element objects. partition_html can take a filename, file-like object, string, or url as input.

The following three invocations of partition_html() are essentially equivalent:

from unstructured.partition.html import partition_html

elements = partition_html(filename="example-docs/example-10k.html")

with open("example-docs/example-10k.html", "r") as f:
    elements = partition_html(file=f)

with open("example-docs/example-10k.html", "r") as f:
    text = f.read()
elements = partition_html(text=text)

The following illustrates fetching a url and partitioning the response content. The ssl_verify kwarg controls whether or not SSL verification is enabled for the HTTP request. By default it is on. Use ssl_verify=False to disable SSL verification in the request.

from unstructured.partition.html import partition_html

elements = partition_html(url="https://python.org/")

# you can also provide custom headers:

elements = partition_html(url="https://python.org/",
                          headers={"User-Agent": "YourScriptName/1.0 ..."})

# and turn off SSL verification

elements = partition_html(url="https://python.org/", ssl_verify=False)

For more information about the partition_html function, you can check the source code.

​

partition_image

The partition_image function has the same API as partition_pdf. The only difference is that partition_image does not need to convert a PDF to an image prior to processing. The partition_image function supports .png, .heic, and .jpg files.

You can also specify what languages to use for OCR with the languages kwarg. For example, use languages=["eng", "deu"] to use the English and German language packs. See the Tesseract documentation for a full list of languages and install instructions.

Examples:

from unstructured.partition.image import partition_image

# Returns a List[Element] present in the pages of the parsed image document
elements = partition_image("example-docs/img/layout-parser-paper-fast.jpg")

# Applies the English and Swedish language pack for ocr
elements = partition_image("example-docs/img/layout-parser-paper-fast.jpg", languages=["eng", "swe"])

The strategy kwarg controls the method that will be used to process the PDF. The available strategies for images are "auto", "hi_res" and "ocr_only".

The "auto" strategy will choose the partitioning strategy based on document characteristics and the function kwargs. If infer_table_structure is passed, the strategy will be "hi_res" because that is the only strategy that currently extracts tables for PDFs. Otherwise, "auto" will choose ocr_only. "auto" is the default strategy.

The "hi_res" strategy will identify the layout of the document using detectron2_onnx. The advantage of “hi_res” is that it uses the document layout to gain additional information about document elements. We recommend using this strategy if your use case is highly sensitive to correct classifications for document elements. If detectron2_onnx is not available, the "hi_res" strategy will fall back to the "ocr_only" strategy.

The "ocr_only" strategy runs the document through Tesseract for OCR and then runs the raw text through partition_text. Currently, "hi_res" has difficulty ordering elements for documents with multiple columns. If you have a document with multiple columns that does not have extractable text, we recommend using the "ocr_only" strategy.

It is helpful to use "ocr_only" instead of "hi_res" if detectron2_onnx does not detect a text element in the image. To run example below, ensure you have the Korean language pack for Tesseract installed on your system.

from unstructured.partition.image import partition_image

filename = "example-docs/img/english-and-korean.png"
elements = partition_image(filename=filename, languages=["eng", "kor"], strategy="ocr_only")

For more information about the partition_image function, you can check the source code here.

​

partition_md

The partition_md function provides the ability to parse markdown files. The following workflow shows how to use partition_md.

Examples:

from unstructured.partition.md import partition_md

elements = partition_md(filename="README.md")

For more information about the partition_md function, you can check the source code here.

​

partition_msg

The partition_msg functions processes .msg files, which is a filetype specific to email exports from Microsoft Outlook. Email header information (bcc_recipient, cc_recipient, email_message_id, sent_from, sent_to, subject, etc.) is captured in element metadata.

Examples:

from unstructured.partition.msg import partition_msg

elements = partition_msg(filename="example-docs/fake-email.msg")

partition_msg includes a max_partition parameter that indicates the maximum character length for a document element. This parameter only applies if "text/plain" is selected as the content_source. The default value is 1500, which roughly corresponds to the average character length for a paragraph. You can disable max_partition by setting it to None.

You can optionally partition e-mail attachments by setting process_attachments=True. The following is an example of what the workflow looks like:

from unstructured.partition.msg import partition_msg

filename = "example-docs/fake-email-attachment.msg"
elements = partition_msg(filename=filename, process_attachments=True)

If the content of an email is PGP encrypted, partition_msg will return an empty list of elements and emit a warning indicated the email is encrypted.

For more information about the partition_msg function, you can check the source code here.

​

partition_multiple_via_api

partition_multiple_via_api is similar to partition_via_api, but allows you to partition multiple documents in a single REST API call. The result has the type List[List[Element]], for example:

[
  [NarrativeText("Narrative!"), Title("Title!")],
  [NarrativeText("Narrative!"), Title("Title!")]
]

Examples:

from unstructured.partition.api import partition_multiple_via_api

filenames = ["example-docs/eml/fake-email.eml", "example-docs/fake.docx"]

documents = partition_multiple_via_api(filenames=filenames)

from contextlib import ExitStack

from unstructured.partition.api import partition_multiple_via_api

filenames = ["example-docs/eml/fake-email.eml", "example-docs/fake.docx"]
files = [open(filename, "rb") for filename in filenames]

with ExitStack() as stack:
    files = [stack.enter_context(open(filename, "rb")) for filename in filenames]
    documents = partition_multiple_via_api(files=files, metadata_filenames=filenames)

For more information about the partition_multiple_via_api function, you can check the source code here.

​

partition_odt

The partition_odt partitioning function pre-processes Open Office documents saved in the .odt format. The function first converts the document to .docx using pandoc and then processes it using partition_docx.

Examples:

from unstructured.partition.odt import partition_odt

elements = partition_odt(filename="example-docs/fake.odt")

For more information about the partition_odt function, you can check the source code here.

​

partition_org

The partition_org function processes Org Mode (.org) documents. The function first converts the document to HTML using pandoc and then calls partition_html. You’ll need pandoc installed on your system to use partition_org.

Examples:

from unstructured.partition.org import partition_org

elements = partition_org(filename="example-docs/README.org")

For more information about the partition_org function, you can check the source code here.

​

partition_pdf

The partition_pdf function segments a PDF document by using a document image analysis model. If you set url=None, the document image analysis model will execute locally. You need to install unstructured[local-inference] if you’d like to run inference locally. If you set the URL, partition_pdf will make a call to a remote inference server. partition_pdf also includes a token function that allows you to pass in an authentication token for a remote API call.

You can also specify what languages to use for OCR with the languages kwarg. For example, use languages=["eng", "deu"] to use the English and German language packs. See the Tesseract documentation for a full list of languages and install instructions. OCR is only applied if the text is not already available in the PDF document.

Examples:

from unstructured.partition.pdf import partition_pdf

# Returns a List[Element] present in the pages of the parsed pdf document
elements = partition_pdf("example-docs/pdf/layout-parser-paper-fast.pdf")

# Applies the English and Swedish language pack for ocr. OCR is only applied
# if the text is not available in the PDF.
elements = partition_pdf("example-docs/pdf/layout-parser-paper-fast.pdf", languages=["eng", "swe"])

The strategy kwarg controls the method that will be used to process the PDF. The available strategies for PDFs are "auto", "hi_res", "ocr_only", and "fast".

• The "auto" strategy will choose the partitioning strategy based on document characteristics and the function kwargs. If infer_table_structure is passed, the strategy will be "hi_res" because that is the only strategy that currently extracts tables for PDFs. Otherwise, "auto" will choose "fast" if the PDF text is extractable and "ocr_only" otherwise. "auto" is the default strategy.

• The "hi_res" strategy will identify the layout of the document using detectron2_onnx. The advantage of “hi_res” is that it uses the document layout to gain additional information about document elements. We recommend using this strategy if your use case is highly sensitive to correct classifications for document elements. If detectron2_onnx is not available, the "hi_res" strategy will fall back to the "ocr_only" strategy.

• The "ocr_only" strategy runs the document through Tesseract for OCR and then runs the raw text through partition_text. Currently, "hi_res" has difficulty ordering elements for documents with multiple columns. If you have a document with multiple columns that does not have extractable text, we recommend using the "ocr_only" strategy. "ocr_only" falls back to "fast" if Tesseract is not available and the document has extractable text.

• The "fast" strategy will extract the text using pdfminer and process the raw text with partition_text. If the PDF text is not extractable, partition_pdf will fall back to "ocr_only". We recommend using the "fast" strategy in most cases where the PDF has extractable text.

To extract images and elements as image blocks from a PDF, it is mandatory to set strategy="hi_res" when setting extract_images_in_pdf=True. With this configuration, detected images are saved in a specified directory or encoded within the file. However, keep in mind that extract_images_in_pdf is being phased out in favor of extract_image_block_types. This option allows you to specify types of images or elements, like “Image” or “Table”. If some extracted images have content clipped, you can adjust the padding by specifying two environment variables “EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD” and “EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD” (for example, EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD = 20, EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD = 10). For integrating these images directly into web applications or APIs, extract_image_block_to_payload can be used to convert them into base64 format, including details about the image type, currently it’s always image/jpeg. Lastly, the extract_image_block_output_dir can be used to specify the filesystem path for saving the extracted images when not embedding them in payloads.

Examples:

from unstructured.partition.pdf import partition_pdf

partition_pdf(
    filename="path/to/your/pdf_file.pdf",                  # mandatory
    strategy="hi_res",                                     # mandatory to use ``hi_res`` strategy
    extract_images_in_pdf=True,                            # mandatory to set as ``True``
    extract_image_block_types=["Image", "Table"],          # optional
    extract_image_block_to_payload=False,                  # optional
    extract_image_block_output_dir="path/to/save/images",  # optional - only works when ``extract_image_block_to_payload=False``
    )

If a PDF is copy protected, partition_pdf can process the document with the "hi_res" strategy (which will treat it like an image), but cannot process the document with the "fast" strategy. If the user chooses "fast" on a copy protected PDF, partition_pdf will fall back to the "hi_res" strategy. If detectron2_onnx is not installed, partition_pdf will fail for copy protected PDFs because the document will not be processable by any of the available methods.

Examples:

from unstructured.partition.pdf import partition_pdf

# This will process without issue
elements = partition_pdf("example-docs/pdf/copy-protected.pdf", strategy="hi_res")

# This will output a warning and fall back to hi_res
elements = partition_pdf("example-docs/pdf/copy-protected.pdf", strategy="fast")

partition_pdf includes a max_partition parameter that indicates the maximum character length for a document element. This parameter only applies if the "ocr_only" strategy is used for partitioning. The default value is 1500, which roughly corresponds to the average character length for a paragraph. You can disable max_partition by setting it to None.

For more information about the partition_pdf function, you can check the source code here.

​

partition_ppt

The partition_ppt partitioning function pre-processes Microsoft PowerPoint documents saved in the .ppt format. This partition function uses a combination of the styling information in the document and the structure of the text to determine the type of a text element. The partition_ppt can take a filename or file-like object. partition_ppt uses libreoffice to convert the file to .pptx and then calls partition_pptx. Ensure you have libreoffice installed before using partition_ppt.

Examples:

from unstructured.partition.ppt import partition_ppt

elements = partition_ppt(filename="example-docs/fake-power-point.ppt")

For more information about the partition_ppt function, you can check the source code here.

​

partition_pptx

The partition_pptx partitioning function pre-processes Microsoft PowerPoint documents saved in the .pptx format. This partition function uses a combination of the styling information in the document and the structure of the text to determine the type of a text element. The partition_pptx can take a filename or file-like object as input, as shown in the two examples below.

Examples:

from unstructured.partition.pptx import partition_pptx

elements = partition_pptx(filename="example-docs/fake-power-point.pptx")

with open("example-docs/fake-power-point.pptx", "rb") as f:
    elements = partition_pptx(file=f)

For more information about the partition_pptx function, you can check the source code here.

​

partition_rst

The partition_rst function processes ReStructured Text (.rst) documents. The function first converts the document to HTML using pandoc and then calls partition_html. You’ll need pandoc installed on your system to use partition_rst.

Examples:

from unstructured.partition.rst import partition_rst

elements = partition_rst(filename="example-docs/README.rst")

For more information about the partition_rst function, you can check the source code here.

​

partition_rtf

The partition_rtf function processes rich text files. The function first converts the document to HTML using pandocs and then calls partition_html. You’ll need pandocs installed on your system to use partition_rtf.

Examples:

from unstructured.partition.rtf import partition_rtf

elements = partition_rtf(filename="example-docs/fake-doc.rtf")

For more information about the partition_rtf function, you can check the source code here.

​

partition_text

The partition_text function partitions text files. The partition_text takes a filename, file-like object, and raw text as input and produces Element objects as output.

Examples:

from unstructured.partition.text import partition_text

elements = partition_text(filename="example-docs/fake-text.txt")

with open("example-docs/fake-text.txt", "r") as f:
  elements = partition_text(file=f)

with open("example-docs/fake-text.txt", "r") as f:
  text = f.read()
elements = partition_text(text=text)

If the text has extra line breaks for formatting purposes, you can group together the broken text using the paragraph_grouper kwarg. The paragraph_grouper kwarg is a function that accepts a string and returns another string.

Examples:

from unstructured.partition.text import partition_text
from unstructured.cleaners.core import group_broken_paragraphs


text = """The big brown fox
was walking down the lane.

At the end of the lane, the
fox met a bear."""

partition_text(text=text, paragraph_grouper=group_broken_paragraphs)

partition_text includes a max_partition parameter that indicates the maximum character length for a document element. The default value is 1500, which roughly corresponds to the average character length for a paragraph. You can disable max_partition by setting it to None.

For more information about the partition_text function, you can check the source code here.

​

partition_tsv

The partition_tsv function pre-processes TSV files. The output is a single Table element. The text_as_html attribute in the element metadata will contain an HTML representation of the table.

Examples:

from unstructured.partition.tsv import partition_tsv

elements = partition_tsv(filename="example-docs/stanley-cups.tsv")
print(elements[0].metadata.text_as_html)

For more information about the partition_tsv function, you can check the source code here.

​

partition_via_api

partition_via_api allows users to partition documents using the hosted Unstructured API. The API partitions documents using the automatic partition function through the Unstructured SDK Client. This is helpful if you’re hosting the API yourself or running it locally through a container. You can pass in your API key using the api_key kwarg. You can use the content_type kwarg to pass in the MIME type for the file. If you do not explicitly pass it, the MIME type will be inferred.

from unstructured.partition.api import partition_via_api

filename = "example-docs/eml/fake-email.eml"

elements = partition_via_api(filename=filename, api_key="MY_API_KEY", content_type="message/rfc822")

with open(filename, "rb") as f:
  elements = partition_via_api(file=f, metadata_filename=filename, api_key="MY_API_KEY")

You can pass additional settings such as strategy, languages and encoding to the API through optional kwargs. These options get added to the request body when the API is called. See the API documentation for a full list of settings supported by the API.

from unstructured.partition.api import partition_via_api

filename = "example-docs/pdf/DA-1p.pdf"

elements = partition_via_api(
  filename=filename, api_key=api_key, strategy="auto"
)

If you are using the Unstructured Serverless API, you can use the api_url kwarg to point the partition_via_api function at your Unstructured Serverless API URL.

import os

from unstructured.partition.api import partition_via_api

filename = "example-docs/eml/fake-email.eml"

elements = partition_via_api(
  filename=filename,
  api_key=os.getenv("UNSTRUCTURED_API_KEY"),
  api_url=os.getenv("UNSTRUCTURED_API_URL")
)

If you are self-hosting or running the API locally, you can use the api_url kwarg to point the partition_via_api function at your self-hosted or local API. See here for documentation on how to run the API as a container locally.

import os

from unstructured.partition.api import partition_via_api

filename = "example-docs/eml/fake-email.eml"

elements = partition_via_api(
  filename=filename,
  api_key=os.getenv("UNSTRUCTURED_API_KEY"),
  api_url=os.getenv("UNSTRUCTURED_API_URL")
)

For more information about the partition_via_api function, you can check the source code here.

​

partition_xlsx

The partition_xlsx function pre-processes Microsoft Excel documents. Each sheet in the Excel file will be stored as a Table object. The plain text of the sheet will be the text attribute of the Table. The text_as_html attribute in the element metadata will contain an HTML representation of the table.

Examples:

from unstructured.partition.xlsx import partition_xlsx

elements = partition_xlsx(filename="example-docs/stanley-cups.xlsx")
print(elements[0].metadata.text_as_html)

For more information about the partition_xlsx function, you can check the source code here.

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partition_xml

The partition_xml function processes XML documents. If xml_keep_tags=False, the function only returns the text attributes from the tags. You can use xml_path in conjunction with xml_keep_tags=False to restrict the text extraction to specific tags. If xml_keep_tags=True, the function returns tag information in addition to tag text. xml_keep_tags is False be default.

from unstructured.partition.xml import partition_xml

elements = partition_xml(filename="example-docs/factbook.xml", xml_keep_tags=True)

elements = partition_xml(filename="example-docs/factbook.xml", xml_keep_tags=False)

For more information about the partition_xml function, you can check the source code here.