> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.

# 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.

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `extract_email_address`

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

```python theme={null}
from unstructured.cleaners.extract import extract_email_address

text = """Me me@email.com and You <You@email.com>
    ([ba23::58b5:2236:45g2:88h2]) (10.0.2.01)"""

# Returns "['me@email.com', 'you@email.com']"
extract_email_address(text)

```

For more information about the `extract_email_address` function, you can check the [source code here](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `extract_ip_address`

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

```python theme={null}
from unstructured.cleaners.extract import extract_ip_address

text = """Me me@email.com and You <You@email.com>
  ([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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `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.

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `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.

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `extract_ordered_bullets`

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

Examples:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `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:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `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:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `extract_us_phone_number`

Extracts a phone number from a section of text.

Examples:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/extract.py).

## `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:

```python theme={null}
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)

```

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/core.py).

## `remove_punctuation`

Removes ASCII and unicode punctuation from a string.

Examples:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/core.py).

## `replace_unicode_quotes`

Replaces unicode quote characters such as `\x91` in strings.

Examples:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/core.py).

## `translate_text`

The `translate_text` cleaning function translates text between languages. `translate_text` uses the [Helsinki NLP MT models](https://huggingface.co/Helsinki-NLP) 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:

```python theme={null}
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](https://github.com/Unstructured-IO/unstructured/blob/main/unstructured/cleaners/translate.py).