# TextPrettifier
TextPrettifier is a Python library for cleaning text data by removing HTML tags, URLs, numbers, special characters, contractions, and stopwords.
## TextPrettifier Key Features
### 1. Removing Emojis
The `remove_emojis` method removes emojis from the text.
### 2. Removing Internet Words
The `remove_internet_words` method removes internet-specific words from the text.
### 3. Removing HTML Tags
The `remove_html_tags` method removes HTML tags from the text.
### 4. Removing URLs
The `remove_urls` method removes URLs from the text.
### 5. Removing Numbers
The `remove_numbers` method removes numbers from the text.
### 6. Removing Special Characters
The `remove_special_chars` method removes special characters from the text.
### 7. Expanding Contractions
The `remove_contractions` method expands contractions in the text.
### 8. Removing Stopwords
The `remove_stopwords` method removes stopwords from the text.
### Additional Functionality
- If `is_lower` and `is_token` are both `True`, the text is returned in lowercase and as a list of tokens.
- If only `is_lower` is `True`, the text is returned in lowercase.
- If only `is_token` is `True`, the text is returned as a list of tokens.
- If neither `is_lower` nor `is_token` is `True`, the text is returned as is.
## Installation
You can install TextPrettifier using pip:
```bash
pip install text-prettifier
```
```python
from text_prettifier import TextPrettifier
```
### Initialize TextPrettifier
text_prettifier = TextPrettifier()
#### Example: Remove Emojis
```python
html_text = "Hi,Pythonogist! I ❤️ Python."
cleaned_html = text_prettifier.remove_emojis(html_text)
print(cleaned_html)
```
**Output**
Hi,Pythonogist! I Python.
#### Example: Remove HTML tags
```python
html_text = "<p>Hello, <b>world</b>!</p>"
cleaned_html = text_prettifier.remove_html_tags(html_text)
print(cleaned_html)
```
**Output**
Hello,world!
#### Example: Remove URLs
```python
url_text = "Visit our website at https://example.com"
cleaned_urls = text_prettifier.remove_urls(url_text)
print(cleaned_urls)
```
**Output**
Visit our webiste at
#### Example: Remove numbers
```python
number_text = "There are 123 apples"
cleaned_numbers = text_prettifier.remove_numbers(number_text)
print(cleaned_numbers)
```
**Output**
There are apples
#### Example: Remove special characters
```python
special_text = "Hello, @world!"
cleaned_special = text_prettifier.remove_special_chars(special_text)
print(cleaned_special)
```
**Output**
Hello world
#### Example: Remove contractions
```python
contraction_text = "I can't do it"
cleaned_contractions = text_prettifier.remove_contractions(contraction_text)
print(cleaned_contractions)
```
**Output**
I cannot do it
#### Example: Remove stopwords
```python
stopwords_text = "This is a test"
cleaned_stopwords = text_prettifier.remove_stopwords(stopwords_text)
print(cleaned_stopwords)
```
**Output**
This test
#### Example: Apply all cleaning methods
```python
all_text = "<p>Hello, @world!</p> There are 123 apples. I can't do it. This is a test."
all_cleaned = text_prettifier.sigma_cleaner(all_text)
print(all_cleaned)
```
**Output**
Hello world 123 apples cannot test
```If you are interested to tokenized and lower the cleaned text write the code```
```python
all_text = "<p>Hello, @world!</p> There are 123 apples. I can't do it. This is a test."
all_cleaned = text_prettifier.sigma_cleaner(all_text,is_token=True,is_lower=True)
print(all_cleaned)
```
**Output**
['Hello','world', '123','apples', 'cannot','test']
**Note:** I didn't include ```remove_numbers``` in ```sigma_cleaner``` because sometimes numbers carry useful information in term of NLP. If you want to remove number you can apply this method seperately on output of ```sigma_cleaner```.
### Contact Information
Feel free to reach out to me on social media:
[](https://github.com/mrqadeer)
[](https://www.linkedin.com/in/qadeer-ahmad-3499a4205/)
[](https://twitter.com/mr_sin_of_me)
[](https://web.facebook.com/mrqadeerofficial/)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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"description": "\n# TextPrettifier\n\nTextPrettifier is a Python library for cleaning text data by removing HTML tags, URLs, numbers, special characters, contractions, and stopwords.\n## TextPrettifier Key Features\n\n### 1. Removing Emojis\nThe `remove_emojis` method removes emojis from the text.\n\n### 2. Removing Internet Words\nThe `remove_internet_words` method removes internet-specific words from the text.\n\n### 3. Removing HTML Tags\nThe `remove_html_tags` method removes HTML tags from the text.\n\n### 4. Removing URLs\nThe `remove_urls` method removes URLs from the text.\n\n### 5. Removing Numbers\nThe `remove_numbers` method removes numbers from the text.\n\n### 6. Removing Special Characters\nThe `remove_special_chars` method removes special characters from the text.\n\n### 7. Expanding Contractions\nThe `remove_contractions` method expands contractions in the text.\n\n### 8. Removing Stopwords\nThe `remove_stopwords` method removes stopwords from the text.\n\n### Additional Functionality\n- If `is_lower` and `is_token` are both `True`, the text is returned in lowercase and as a list of tokens.\n- If only `is_lower` is `True`, the text is returned in lowercase.\n- If only `is_token` is `True`, the text is returned as a list of tokens.\n- If neither `is_lower` nor `is_token` is `True`, the text is returned as is.\n\n\n## Installation\n\nYou can install TextPrettifier using pip:\n\n```bash\npip install text-prettifier\n```\n```python\nfrom text_prettifier import TextPrettifier\n```\n### Initialize TextPrettifier\ntext_prettifier = TextPrettifier()\n\n#### Example: Remove Emojis\n```python\nhtml_text = \"Hi,Pythonogist! I \u2764\ufe0f Python.\"\ncleaned_html = text_prettifier.remove_emojis(html_text)\nprint(cleaned_html)\n```\n**Output**\nHi,Pythonogist! I Python.\n#### Example: Remove HTML tags\n```python\nhtml_text = \"<p>Hello, <b>world</b>!</p>\"\ncleaned_html = text_prettifier.remove_html_tags(html_text)\nprint(cleaned_html)\n```\n**Output**\nHello,world!\n#### Example: Remove URLs\n```python\nurl_text = \"Visit our website at https://example.com\"\ncleaned_urls = text_prettifier.remove_urls(url_text)\nprint(cleaned_urls)\n```\n**Output**\nVisit our webiste at\n#### Example: Remove numbers\n```python\nnumber_text = \"There are 123 apples\"\ncleaned_numbers = text_prettifier.remove_numbers(number_text)\nprint(cleaned_numbers)\n```\n**Output**\nThere are apples\n#### Example: Remove special characters\n```python\nspecial_text = \"Hello, @world!\"\ncleaned_special = text_prettifier.remove_special_chars(special_text)\nprint(cleaned_special)\n```\n**Output**\nHello world\n#### Example: Remove contractions\n```python\ncontraction_text = \"I can't do it\"\ncleaned_contractions = text_prettifier.remove_contractions(contraction_text)\nprint(cleaned_contractions)\n```\n**Output**\nI cannot do it\n#### Example: Remove stopwords\n```python\nstopwords_text = \"This is a test\"\ncleaned_stopwords = text_prettifier.remove_stopwords(stopwords_text)\nprint(cleaned_stopwords)\n```\n**Output**\nThis test\n#### Example: Apply all cleaning methods\n```python\nall_text = \"<p>Hello, @world!</p> There are 123 apples. I can't do it. This is a test.\"\nall_cleaned = text_prettifier.sigma_cleaner(all_text)\nprint(all_cleaned)\n\n```\n**Output**\nHello world 123 apples cannot test\n\n\n```If you are interested to tokenized and lower the cleaned text write the code```\n```python\nall_text = \"<p>Hello, @world!</p> There are 123 apples. I can't do it. This is a test.\"\nall_cleaned = text_prettifier.sigma_cleaner(all_text,is_token=True,is_lower=True)\nprint(all_cleaned)\n\n```\n**Output**\n['Hello','world', '123','apples', 'cannot','test']\n\n**Note:** I didn't include ```remove_numbers``` in ```sigma_cleaner``` because sometimes numbers carry useful information in term of NLP. If you want to remove number you can apply this method seperately on output of ```sigma_cleaner```.\n\n\n### Contact Information\n\nFeel free to reach out to me on social media:\n\n[](https://github.com/mrqadeer)\n[](https://www.linkedin.com/in/qadeer-ahmad-3499a4205/)\n[](https://twitter.com/mr_sin_of_me)\n[](https://web.facebook.com/mrqadeerofficial/)\n\n\n\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n",
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