I have a large pandas dataframe and would like to perform a thorough text cleaning on it. For this, I have crafted the below code that evaluates if a character is either an emoji, number, Roman number, or a currency symbol, and replaces these with their unidode name from the unicodedata package.

The code uses a double for loop though and I believe there must be far more efficient solutions than that but I haven't managed to figure out yet how I could implement it in a vectorized manner.

My current code is as follows:

from unicodedata import name as unicodename 

def clean_text(text):
    for item in text:
        for char in item: 
            # Simple space
            if char == ' ':
                newtext  = char 
            # Letters
            elif category(char)[0] == 'L':
                newtext  = char
            # Other symbols: emojis
            elif category(char) == 'So':
                newtext  = f" {unicodename(char)} "
            # Decimal numbers 
            elif category(char) == 'Nd':
                newtext  = f" {unicodename(char).replace('DIGIT ', '').lower()} "
            # Letterlike numbers e.g. Roman numerals 
            elif category(char) == 'Nl':
                newtext  = f" {unicodename(char)} "
            # Currency symbols
            elif category(char) == 'Sc':
                newtext  = f" {unicodename(char).replace(' SIGN', '').lower()} "
            # Punctuation, invisibles (separator, control chars), maths symbols...
            else:
                newtext  = " "

At the moment I am using this function on my dataframe with an apply:

df['Texts'] = df['Texts'].apply(lambda x: clean_text(x))

Sample data:

l = [
    "thumbs ups should be replaced: