How to use Python Pandas to find the total number of count for more than one special characters present in each word in a given series?

When working with text data in Pandas, you might need to count words containing multiple special characters. This tutorial shows how to find the total count of words that have more than one special character in a given Series.

Input Data

Let's start with a sample series containing words with special characters ?

import pandas as pd

data = pd.Series(["fruits!!", "*cakes*", "$nuts", "#drinks"])
print(data)

0    fruits!!
1     *cakes*
2       $nuts
3     #drinks
dtype: object

From this series, we want to count words with more than one special character. Words like "fruits!!" and "*cakes*" have multiple special characters, so the expected result is 2.

Method 1: Using String Module and Loop

This approach uses Python's string.punctuation to identify special characters and loops through each word ?

import pandas as pd
import string

data = pd.Series(["fruits!!", "*cakes*", "$nuts", "#drinks"])
special_chars = list(string.punctuation)
total_count = 0

for word in data:
    chars_count = 0
    for char in word:
        if char in special_chars:
            chars_count += 1
    
    if chars_count > 1:
        total_count += 1

print("Words with more than one special character:", total_count)

Words with more than one special character: 2

Method 2: Using Regular Expression with Lambda

A more concise approach using regular expressions and lambda functions ?

import pandas as pd
import re

words = ["fruits!!", "*cakes*", "$nuts", "#drinks"]
filtered_data = pd.Series(list(filter(lambda x: len(re.findall(r"\W", x)) > 1, words)))

print("Filtered series:")
print(filtered_data)
print("\nCount:", len(filtered_data))

Filtered series:
0    fruits!!
1     *cakes*
dtype: object

Count: 2

Method 3: Using Pandas str.count()

The most Pandas-native approach using vectorized string operations ?

import pandas as pd

data = pd.Series(["fruits!!", "*cakes*", "$nuts", "#drinks"])

# Count special characters using regex pattern
special_char_counts = data.str.count(r'[^\w\s]')
print("Special character counts per word:")
print(special_char_counts)

# Count words with more than one special character
words_with_multiple = (special_char_counts > 1).sum()
print("\nWords with more than one special character:", words_with_multiple)

Special character counts per word:
0    2
1    2
2    1
3    1
dtype: int64

Words with more than one special character: 2

Comparison

Conclusion

Use Pandas str.count() with regex patterns for the most efficient solution. The loop-based approach helps understand the logic, while regex with lambda provides a concise alternative for functional programming styles.