Ruqia lib

This library used for Arabic NLP to process, prepare and clean the Arabic text

مكتبة مخصصة لخدمة معالجة اللغة العربية وتشمل عدد من الوظائف لتنظيف النصوص وغيرها

Install

pip install ruqia

Use

from ruqiya import ruqiya

Example: Apply a Function to Pandas Single Column

from ruqiya.ruqiya import clean_text

# Often df['text'] be Object not String, so we need to apply str 
df['text']=df['text'].apply(str)
# Now apply our function
df['cleaned_text']=df['text'].apply(clean_text)
# Show the result
df['cleaned_text']

All Functions

Clean the text

clean_text function includes all these functions:

 1. remove_emails  
 2. remove_URLs  
 3. remove_mentions   
 4. hashtags_to_words     
 5. remove_punctuations  
 6. normalize_arabic   
 7. remove_diacritics   
 8. remove_repeating_char   
 9. remove_stop_words   
 10. remove_emojis

In other words, clean_text includes all functions except remove_hashtags

text_cleaned1=ruqiya.clean_text(text)
print(text_cleaned1)

Remove repeating character

remove_repeating_char function

text_cleaned2=ruqiya.remove_repeating_char(text)
print(text_cleaned2)

Remove punctuations

remove_punctuations function

text_cleaned3=ruqiya.remove_punctuations(text)
print(text_cleaned3)

Normalize Arabic

normalize_arabic function

text_cleaned4=ruqiya.normalize_arabic(text)
print(text_cleaned4)

Remove diacritics

remove_diacritics function

text_cleaned5=ruqiya.remove_diacritics(text)
print(text_cleaned5)

Remove stop words

remove_stop_words function

text_cleaned6=ruqiya.remove_stop_words(text)
print(text_cleaned6)

Remove emojis

remove_emojis function

text_cleaned7=ruqiya.remove_emojis(text)
print(text_cleaned7)

Remove mentions

remove_mentions function

text_cleaned8=ruqiya.remove_mentions(text)
print(text_cleaned8)

Convert any hashtags to words

hashtags_to_words function

text_cleaned9=ruqiya.hashtags_to_words(text)
print(text_cleaned9)

Remove hashtags

remove_hashtags function

text_cleaned10=ruqiya.remove_hashtags(text)
print(text_cleaned10)

Remove emails

remove_emails function

text_cleaned11=ruqiya.remove_emails(text)
print(text_cleaned11)

Remove URLs

remove_URLs function

text_cleaned12=ruqiya.remove_URLs(text)
print(text_cleaned12)

Example

from ruqiya import ruqiya

text="""
!!أهلًا وسهلًا بك 👋 في الإصدارِ الأولِ من مكتبة رقيا
هل هذه هي المرة الأولى التي تستخدم فيها المكتبة😀؟!!
معلومات التواصل 
ايميل
example@email.com
الموقع
https://pypi.org/project/ruqia/
تويتر
@Ru0Sa
وسم
#معالجة_العربية
"""

print('===========clean_text===========')
text_cleaned1=ruqiya.clean_text(text)
print(text_cleaned1)
print('===========remove_repeating_char===========')
text_cleaned2=ruqiya.remove_repeating_char(text)
print(text_cleaned2)
print('===========remove_punctuations===========')
text_cleaned3=ruqiya.remove_punctuations(text)
print(text_cleaned3)
print('===========normalize_arabic===========')
text_cleaned4=ruqiya.normalize_arabic(text)
print(text_cleaned4)
print('===========remove_diacritics===========')
text_cleaned5=ruqiya.remove_diacritics(text)
print(text_cleaned5)
print('===========remove_stop_words===========')
text_cleaned6=ruqiya.remove_stop_words(text)
print(text_cleaned6)
print('===========remove_emojis===========')
text_cleaned7=ruqiya.remove_emojis(text)
print(text_cleaned7)
print('===========remove_mentions===========')
text_cleaned8=ruqiya.remove_mentions(text)
print(text_cleaned8)
print('===========hashtags_to_words===========')
text_cleaned9=ruqiya.hashtags_to_words(text)
print(text_cleaned9)
print('===========remove_hashtags===========')
text_cleaned10=ruqiya.remove_hashtags(text)
print(text_cleaned10)
print('===========remove_emails===========')
text_cleaned11=ruqiya.remove_emails(text)
print(text_cleaned11)
print('===========remove_URLs===========')
text_cleaned12=ruqiya.remove_URLs(text)
print(text_cleaned12)

Example 2: Apply a Function to Pandas DataFrame (Single Column)

from ruqiya.ruqiya import clean_text
import pandas as pd

data="https://raw.githubusercontent.com/Ruqyai/data4test/main/test_with_lables.csv"
df=pd.read_csv(data)
df['text']=df['poem_text']

#--------------------
# Often df['text'] be Object not String, so we need to apply str 
df['text']=df['text'].apply(str)
# Now apply our function
df['cleaned_text']=df['text'].apply(clean_text)
#--------------------

# Show the result
df['cleaned_text']