## keyword based text extraction toolkit (textsnipper)
## What is it?
**textsnipper** is an all-in-one versatile and efficient Python package designed for keyword-based text search, manipulation, and data cleansing. Whether you need to **extract contextual information around specific keywords**, **remove unwanted terms from texts and dataframes**, or **precisely locate the positions of keywords within a Pandas DataFrame**, textsnipper is your indispensable toolkit for advanced robust toolkit text analysis and data management.
## Main Features
Here are just a few of the things that textsnipper does well:
- Keyword Positioning: Locate the exact start and end positions of a keyword within a given text, facilitating precise information retrieval.
- Contextual Extraction: Extract left and right texts, characters, words, and sentences surrounding a specified keyword as well as words between .
- Flexible Configuration: Customize the number of left and right characters, words, or sentences to tailor the extraction to your specific requirements.
- Text Between Keywords: Extract the text between two occurrences of the same keyword, offering deeper insights into the context of your data.
- Word Removal: Efficiently remove a list of specified words from texts, enhancing text cleanliness and relevance.
- Dataframe Cleansing: Seamlessly remove unwanted words from text columns in Pandas DataFrames, ensuring data integrity.
- Cell Positioning in DataFrame: Identify the row and column positions of a keyword within a Pandas DataFrame, enabling precise data manipulation.
- Easy Integration: Integrate KeyExplorer into your Python projects effortlessly, enhancing your text processing and data cleansing workflows.
## Installation Procedure
```sh
PyPI
pip install textsnipper==1.0.0
```
## Dependencies:
- [Regex - Adds support to itterating and finding keywords from the text and dataframe](https://docs.python.org/3/library/re.html)
## Functionalities (with parameters description):
#### textsnipper.tkeypos(keyword, text)
- Return all starting and ending position of the keyword from a giuven text
- Output will be in list of tuples
#### textsnipper.extract_sents(keyword, text, format='l')
- This function extract all the sentences from a giuven text that contain the keyword
- By default format is l, that means list of sentences. If we pass p then the outpt format will be paragraph.
#### textsnipper.extract_words(keyword, text, left_w=0, right_w=1)
- This function extract the neighbourhood words of the keyword from a given text.
- In case of left_w = 0, right_w = n it will provide n number of words from the right side of the keyword, n should be an integer
- In case of left_w = m, right_w = 0 it will provide m number of words from the left side of the keyword, m should be an integer
- In case of left_w = m, right_w = n it will provide m number of words from the left side of the keyword, n number of words from the right side of the keyword
#### textsnipper.extract_chars(keyword, text, left_chr=0, right_chr=1)
- This function extract the neighbourhood charecters of the keyword from a given text.
- In case of left_chr = 0, right_chr = n it will provide n number of charecters from the right side of the keyword, n should be an integer
- In case of left_chr = m, right_chr = 0 it will provide m number of charecters from the left side of the keyword, m should be an integer
- In case of left_chr = m, right_chr = n it will provide m number of charecters from the left side of the keyword, n number of charecters from the right side of the keyword
#### textsnipper.left_texts(keyword, text, occurrence='all')
- This function will return the left side of the keyword i.e. from the keyword to beginning of the text based on all occurence of keyword
- If we pass the 1 or 2 in occurence then it will return the left side text of 1st or 2nd occurence of the keyword from a text, Occurene should be 1,2,...,n,'all'
- Provid ethe output in list format if occurence is all
#### textsnipper.right_texts(keyword, text, occurrence='all')
- occurence means the repeation of the keyword in text
- This function will return the right side of the keyword i.e. from the keyword to ending of the text based on all occurence of keyword
- If we pass the 1 in occurence then it will return the right side text of 1st occurence of the keyword from a text, Occurene should be 1,2,...,n,'all'
- Provid ethe output in list format if occurence is all
#### textsnipper.between_fixed_keyword(keyword, text)
- Provide the part of the text between two same keyword
- Output will come in list format
#### textsnipper.between_distinct_keywords(keyword_start, keyword_end, text, keyword_start_occurence=1, keyword_end_occurence=1)
- keyword_start_occurence indicates the the repeatition of the starting keyword in given string
- keyword_end_occurence indicates the the repeatition of the starting keyword in given string
- Provide the part of the text between two distinct keyword
- Output will come in list format
- For getting all snap texts in list format pass keyword_start_occurence = 0 and keyword_end_occurence = 0
#### textsnipper.text_keyword_remover(remover_list, text, replaced_by)
- This function remove the keyword from the text
- Non alphanumeric charecters need to be write in regex format
### textsnipper.dkeypos(keyword, dataframe)
- Return all cells position of the keyword from a giuven dataframe
- Output will be in list of tuples
### textsnipper.dataframe_keyword_remover(remover_list, dataframe, replaced_by)
- This function remove the keyword from the dataframe
- Non alphanumeric charecters need to be write in regex format
## Contributing to pandas
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata)
## Change Log
0.0.1 (03/01/2024)
------------------
- First Release
Raw data
{
"_id": null,
"home_page": "",
"name": "textsnipper",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "textmining,NLP,document intelligence",
"author": "Soumyajit Basak",
"author_email": "soumyabasak96@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/3a/ed/f7ea4b5f84476409c6df45f6ca25c1f72155dc3afca2ce5bb19fb3f1e497/textsnipper-1.0.0.tar.gz",
"platform": null,
"description": "## keyword based text extraction toolkit (textsnipper)\r\n\r\n## What is it?\r\n\r\n**textsnipper** is an all-in-one versatile and efficient Python package designed for keyword-based text search, manipulation, and data cleansing. Whether you need to **extract contextual information around specific keywords**, **remove unwanted terms from texts and dataframes**, or **precisely locate the positions of keywords within a Pandas DataFrame**, textsnipper is your indispensable toolkit for advanced robust toolkit text analysis and data management.\r\n\r\n\r\n## Main Features\r\nHere are just a few of the things that textsnipper does well:\r\n\r\n - Keyword Positioning: Locate the exact start and end positions of a keyword within a given text, facilitating precise information retrieval.\r\n - Contextual Extraction: Extract left and right texts, characters, words, and sentences surrounding a specified keyword as well as words between .\r\n - Flexible Configuration: Customize the number of left and right characters, words, or sentences to tailor the extraction to your specific requirements.\r\n - Text Between Keywords: Extract the text between two occurrences of the same keyword, offering deeper insights into the context of your data.\r\n - Word Removal: Efficiently remove a list of specified words from texts, enhancing text cleanliness and relevance.\r\n - Dataframe Cleansing: Seamlessly remove unwanted words from text columns in Pandas DataFrames, ensuring data integrity.\r\n - Cell Positioning in DataFrame: Identify the row and column positions of a keyword within a Pandas DataFrame, enabling precise data manipulation.\r\n - Easy Integration: Integrate KeyExplorer into your Python projects effortlessly, enhancing your text processing and data cleansing workflows.\r\n\r\n\r\n## Installation Procedure\r\n```sh\r\nPyPI\r\npip install textsnipper==1.0.0\r\n```\r\n\r\n## Dependencies:\r\n- [Regex - Adds support to itterating and finding keywords from the text and dataframe](https://docs.python.org/3/library/re.html)\r\n\r\n\r\n## Functionalities (with parameters description):\r\n\r\n#### textsnipper.tkeypos(keyword, text)\r\n\t- Return all starting and ending position of the keyword from a giuven text\r\n\t- Output will be in list of tuples\r\n\r\n#### textsnipper.extract_sents(keyword, text, format='l') \r\n\t- This function extract all the sentences from a giuven text that contain the keyword\r\n\t- By default format is l, that means list of sentences. If we pass p then the outpt format will be paragraph.\r\n \r\n#### textsnipper.extract_words(keyword, text, left_w=0, right_w=1)\r\n\t- This function extract the neighbourhood words of the keyword from a given text.\r\n\t- In case of left_w = 0, right_w = n it will provide n number of words from the right side of the keyword, n should be an integer\r\n\t- In case of left_w = m, right_w = 0 it will provide m number of words from the left side of the keyword, m should be an integer\r\n\t- In case of left_w = m, right_w = n it will provide m number of words from the left side of the keyword, n number of words from the right side of the keyword\r\n \r\n#### textsnipper.extract_chars(keyword, text, left_chr=0, right_chr=1)\r\n - This function extract the neighbourhood charecters of the keyword from a given text.\r\n\t- In case of left_chr = 0, right_chr = n it will provide n number of charecters from the right side of the keyword, n should be an integer\r\n\t- In case of left_chr = m, right_chr = 0 it will provide m number of charecters from the left side of the keyword, m should be an integer\r\n\t- In case of left_chr = m, right_chr = n it will provide m number of charecters from the left side of the keyword, n number of charecters from the right side of the keyword\r\n\r\n#### textsnipper.left_texts(keyword, text, occurrence='all')\r\n\t- This function will return the left side of the keyword i.e. from the keyword to beginning of the text based on all occurence of keyword\r\n\t- If we pass the 1 or 2 in occurence then it will return the left side text of 1st or 2nd occurence of the keyword from a text, Occurene should be 1,2,...,n,'all'\r\n\t- Provid ethe output in list format if occurence is all\r\n\t\r\n#### textsnipper.right_texts(keyword, text, occurrence='all')\r\n\t- occurence means the repeation of the keyword in text\r\n\t- This function will return the right side of the keyword i.e. from the keyword to ending of the text based on all occurence of keyword\r\n\t- If we pass the 1 in occurence then it will return the right side text of 1st occurence of the keyword from a text, Occurene should be 1,2,...,n,'all'\r\n\t- Provid ethe output in list format if occurence is all\r\n\t\r\n#### textsnipper.between_fixed_keyword(keyword, text)\r\n\t- Provide the part of the text between two same keyword\r\n\t- Output will come in list format\r\n\r\n#### textsnipper.between_distinct_keywords(keyword_start, keyword_end, text, keyword_start_occurence=1, keyword_end_occurence=1)\r\n\t- keyword_start_occurence indicates the the repeatition of the starting keyword in given string\r\n\t- keyword_end_occurence indicates the the repeatition of the starting keyword in given string\r\n\t- Provide the part of the text between two distinct keyword\r\n\t- Output will come in list format\r\n\t- For getting all snap texts in list format pass keyword_start_occurence = 0 and keyword_end_occurence = 0\r\n\r\n#### textsnipper.text_keyword_remover(remover_list, text, replaced_by)\r\n\t- This function remove the keyword from the text\r\n\t- Non alphanumeric charecters need to be write in regex format\r\n\r\n### textsnipper.dkeypos(keyword, dataframe)\r\n\t- Return all cells position of the keyword from a giuven dataframe\r\n\t- Output will be in list of tuples\r\n\r\n### textsnipper.dataframe_keyword_remover(remover_list, dataframe, replaced_by)\r\n\t- This function remove the keyword from the dataframe\r\n\t- Non alphanumeric charecters need to be write in regex format\r\n\r\n\r\n## Contributing to pandas\r\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.\r\nFeel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata)\r\n\r\n\r\n## Change Log\r\n0.0.1 (03/01/2024)\r\n------------------\r\n- First Release\r\n\r\n\r\n",
"bugtrack_url": null,
"license": "",
"summary": "Keyword based text extraction Pacakage (textsnipper)",
"version": "1.0.0",
"project_urls": null,
"split_keywords": [
"textmining",
"nlp",
"document intelligence"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "3aedf7ea4b5f84476409c6df45f6ca25c1f72155dc3afca2ce5bb19fb3f1e497",
"md5": "06213fbf6ebc96fd3a9611106eb8e56b",
"sha256": "f20b280a2dc5557069d8488aecd3c6473b8add39b7f8791f0c31ee3ebb38a98e"
},
"downloads": -1,
"filename": "textsnipper-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "06213fbf6ebc96fd3a9611106eb8e56b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 5603,
"upload_time": "2024-01-03T05:00:27",
"upload_time_iso_8601": "2024-01-03T05:00:27.336593Z",
"url": "https://files.pythonhosted.org/packages/3a/ed/f7ea4b5f84476409c6df45f6ca25c1f72155dc3afca2ce5bb19fb3f1e497/textsnipper-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-03 05:00:27",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "textsnipper"
}
JSON