import re
from nltk.corpus import stopwords

def preprocess_text(df):
    """
    Preprocesses the text column in a DataFrame by applying various cleaning operations.

    Args:
        df (pandas.DataFrame): The DataFrame containing the text column to be preprocessed.

    Returns:
        None. The text column in the provided DataFrame is modified in place.
    """
    # Remove URLs, user mentions, non-alphanumeric characters and hashtags from the tweets
    df['text'] = df['text'].apply(lambda x: re.sub(r'http\S+', '', str(x))) # remove URLs
    df['text'] = df['text'].apply(lambda x: re.sub(r'@\S+', '', str(x))) # remove user mentions
    df['text'] = df['text'].apply(lambda x: re.sub(r'[^a-zA-Z0-9\s]', '', str(x))) # remove non-alphanumeric characters
    df['text'] = df['text'].apply(lambda x: re.sub(r'#\S+', '', str(x))) # remove hashtags
    
    # Remove punctuation and convert text to lowercase
    df['text'] = df['text'].apply(lambda x: re.sub('[^\w\s]', '', x))
    df['text'] = df['text'].apply(lambda x: x.lower())
    
    # Remove stop word (such as "a", "an", "the", "is", "of", etc.)
    stop_words = set(stopwords.words('english'))
    df['text'] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop_words]))
    
    # Remove any remaining white space
    df['text'] = df['text'].apply(lambda x: x.strip())
    
    # Remove observations with less than 3 words
    df = df[df['text'].apply(lambda x: len(x.split()) >= 3)]
    
    return df