Extracting

extract_datetimetz

Extracts the date, time, and timezone in the Received field(s) from an .eml file. extract_datetimetz takes in a string and returns a datetime.datetime object from the input string.

from unstructured.cleaners.extract import extract_datetimetz

text = """from ABC.DEF.local ([ba23::58b5:2236:45g2:88h2]) by
  \n ABC.DEF.local2 ([ba23::58b5:2236:45g2:88h2%25]) with mapi id\
  n 32.88.5467.123; Fri, 26 Mar 2021 11:04:09 +1200"""

# Returns datetime.datetime(2021, 3, 26, 11, 4, 9, tzinfo=datetime.timezone(datetime.timedelta(seconds=43200)))
extract_datetimetz(text)

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

extract_email_address

Extracts email addresses from a string input and returns a list of all the email addresses in the input string.

from unstructured.cleaners.extract import extract_email_address

text = """Me [e-mail] and You <[e-mail]>
    ([ba23::58b5:2236:45g2:88h2]) (10.0.2.01)"""

# Returns "['[e-mail]', '[e-mail]']"
extract_email_address(text)

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

extract_ip_address

Extracts IPv4 and IPv6 IP addresses in the input string and returns a list of all IP address in input string.

from unstructured.cleaners.extract import extract_ip_address

text = """Me [e-mail] and You <[e-mail]>
  ([ba23::58b5:2236:45g2:88h2]) (10.0.2.01)"""

# Returns "['ba23::58b5:2236:45g2:88h2', '10.0.2.01']"
extract_ip_address(text)

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

extract_ip_address_name

Extracts the names of each IP address in the Received field(s) from an .eml file. extract_ip_address_name takes in a string and returns a list of all IP addresses in the input string.

from unstructured.cleaners.extract import extract_ip_address_name

text = """from ABC.DEF.local ([ba23::58b5:2236:45g2:88h2]) by
  \n ABC.DEF.local2 ([ba23::58b5:2236:45g2:88h2%25]) with mapi id\
  n 32.88.5467.123; Fri, 26 Mar 2021 11:04:09 +1200"""

# Returns "['ABC.DEF.local', 'ABC.DEF.local2']"
extract_ip_address_name(text)

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

extract_mapi_id

Extracts the mapi id in the Received field(s) from an .eml file. extract_mapi_id takes in a string and returns a list of a string containing the mapi id in the input string.

from unstructured.cleaners.extract import extract_mapi_id

text = """from ABC.DEF.local ([ba23::58b5:2236:45g2:88h2]) by
  \n ABC.DEF.local2 ([ba23::58b5:2236:45g2:88h2%25]) with mapi id\
  n 32.88.5467.123; Fri, 26 Mar 2021 11:04:09 +1200"""

# Returns "['32.88.5467.123']"
extract_mapi_id(text)

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

extract_ordered_bullets

Extracts alphanumeric bullets from the beginning of text up to three “sub-section” levels.

Examples:

from unstructured.cleaners.extract import extract_ordered_bullets

# Returns ("1", "1", None)
extract_ordered_bullets("1.1 This is a very important point")

# Returns ("a", "1", None)
extract_ordered_bullets("a.1 This is a very important point")

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

extract_text_after

Extracts text that occurs after the specified pattern.

Options:

• If index is set, extract after the (index + 1)th occurrence of the pattern. The default is 0.

• Strips trailing whitespace if strip is set to True. The default is True.

Examples:

from unstructured.cleaners.extract import extract_text_after

text = "SPEAKER 1: Look at me, I'm flying!"

# Returns "Look at me, I'm flying!"
extract_text_after(text, r"SPEAKER \d{1}:")

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

extract_text_before

Extracts text that occurs before the specified pattern.

Options:

• If index is set, extract before the (index + 1)th occurrence of the pattern. The default is 0.

• Strips leading whitespace if strip is set to True. The default is True.

Examples:

from unstructured.cleaners.extract import extract_text_before

text = "Here I am! STOP Look at me! STOP I'm flying! STOP"

# Returns "Here I am!"
extract_text_before(text, r"STOP")

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

extract_us_phone_number

Extracts a phone number from a section of text.

Examples:

from unstructured.cleaners.extract import extract_us_phone_number

# Returns "215-867-5309"
extract_us_phone_number("Phone number: 215-867-5309")

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

group_broken_paragraphs

Groups together paragraphs that are broken up with line breaks for visual or formatting purposes. This is common in .txt files. By default, group_broken_paragraphs groups together lines split by \n. You can change that behavior with the line_split kwarg. The function considers \n\n to be a paragraph break by default. You can change that behavior with the paragraph_split kwarg.

Examples:

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."""

group_broken_paragraphs(text)

import re
from unstructured.cleaners.core import group_broken_paragraphs

para_split_re = re.compile(r"(\s*\n\s*){3}")

text = """The big brown fox

was walking down the lane.


At the end of the lane, the

fox met a bear."""

group_broken_paragraphs(text, paragraph_split=para_split_re)

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

remove_punctuation

Removes ASCII and unicode punctuation from a string.

Examples:

from unstructured.cleaners.core import remove_punctuation

# Returns "A lovely quote"
remove_punctuation("“A lovely quote!”")

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

replace_unicode_quotes

Replaces unicode quote characters such as \x91 in strings.

Examples:

from unstructured.cleaners.core import replace_unicode_quotes

# Returns "“A lovely quote!”"
replace_unicode_characters("\x93A lovely quote!\x94")

# Returns ""‘A lovely quote!’"
replace_unicode_characters("\x91A lovely quote!\x92")

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

translate_text

The translate_text cleaning function translates text between languages. translate_text uses the Helsinki NLP MT models from transformers for machine translation. Works for Russian, Chinese, Arabic, and many other languages.

Parameters:

• text: the input string to translate.

• source_lang: the two letter language code for the source language of the text. If source_lang is not specified, the language will be detected using langdetect.

• target_lang: the two letter language code for the target language for translation. Defaults to "en".

Examples:

from unstructured.cleaners.translate import translate_text

# Output is "I'm a Berliner!"
translate_text("Ich bin ein Berliner!")

# Output is "I can also translate Russian!"
translate_text("Я тоже можно переводать русский язык!", "ru", "en")

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