# SWAHILI CLEANER
## _Best Swahili Processing Library_
Swahili Cleaner is the swahili version Text Processing library for Natural Language Processing
- ✨Magic ✨
## Features
- It can clean text by removing emojs
- Remove Unicode characters
- Cleaning the urls
- Cleaning the html elements
- remove parentheses
- remove numbers and keep text/alphabet only
- set in lowercase
- Removing stop words both Swahili and English
- Cleaning the whitespaces
- Remove non-alphanumeric characters
- Remove non-alphabetic characters
- Remove short words
## Installation
```sh
!pip install eganetswahilicleaner
```
## How to use
```sh
from eganetswahilicleaner.clean import clean_text
train['text']=train['text'].apply(clean_text)
test['text']=test['text'].apply(clean_text)
```
_Where train['text'] this is a column in a pandas dataframe_
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"description": "# SWAHILI CLEANER\n## _Best Swahili Processing Library_\n\n\n\nSwahili Cleaner is the swahili version Text Processing library for Natural Language Processing\n\n- \u2728Magic \u2728\n\n## Features\n\n- It can clean text by removing emojs\n- Remove Unicode characters\n- Cleaning the urls\n- Cleaning the html elements\n- remove parentheses\n- remove numbers and keep text/alphabet only\n- set in lowercase\n- Removing stop words both Swahili and English\n- Cleaning the whitespaces\n- Remove non-alphanumeric characters\n- Remove non-alphabetic characters\n- Remove short words\n\n\n\n\n## Installation\n\n\n\n```sh\n!pip install eganetswahilicleaner\n```\n\n\n## How to use\n\n```sh\nfrom eganetswahilicleaner.clean import clean_text\ntrain['text']=train['text'].apply(clean_text)\ntest['text']=test['text'].apply(clean_text)\n\n```\n_Where train['text'] this is a column in a pandas dataframe_\n",
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