Why Do Two Hashtag Counting Methods in a Twitter DataFrame Yield Different Results?

I am analyzing a pandas DataFrame created from the cyberbullying dataset that contains two columns: 'tweet_text' and 'cyberbullying_type'.

I want to count the total number of hashtags used in each 'cyberbullying_type' category. To do this, I tried two different methods which I believe both count duplicates, but surprisingly, they produce different totals.

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First Method:

PYTHONCopied
import re

# Define the pattern for valid hashtags
hashtag_pattern = r'#[A-Za-z0-9]+'

# Function to count the total number of hashtags in a dataframe
def count_total_hashtags(dataframe):
    return dataframe['tweet_text'].str.findall(hashtag_pattern).apply(len).sum()

for category in df['cyberbullying_type'].unique():
    count = count_total_hashtags(df[df['cyberbullying_type'] == category])
    print(f"Number of hashtags in all tweets for the '{category}' category: {count}")

Output: 'not_cyberbullying': 3265, 'gender': 2691, 'religion': 1798, 'other_cyberbullying': 1625, 'age': 728, 'ethnicity': 1112

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Second Method:

This manual approach counts hashtags per tweet for each category:

PYTHONCopied
def count_hashtags_by_category(dataframe):
    hashtag_counts = {}
    for category in dataframe['cyberbullying_type'].unique():
        # Filter tweets by category
        category_tweets = dataframe[dataframe['cyberbullying_type'] == category]
        
        # Count hashtags in each tweet
        hashtag_counts[category] = category_tweets['tweet_text'].apply(
            lambda text: sum(1 for word in text.split() if word.startswith('#') and word[1:].isalnum())
        ).sum()
    
    return hashtag_counts

# Count hashtags for each category
hashtags_per_category = count_hashtags_by_category(df)
print(hashtags_per_category)

The output here is: {'not_cyberbullying': 3018, 'gender': 2416, 'religion': 1511, 'other_cyberbullying': 1465, 'age': 679, 'ethnicity': 956}

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Why do these two methods produce different hashtag counts?