In this guide, I'll show you how to count special characters in a column using Pandas. Whether you want to count special characters row-wise or in the entire column or a single column, these methods will help.

(1) Count special characters in each row

df['column_name'].str.count(r'[^a-zA-Z0-9\s]')

(2) Count total special characters in the entire column

df['column_name'].str.count(r'[^a-zA-Z0-9\s]').sum()

(3) Count occurrences of a specific special character (e.g., @)

df['column_name'].str.count(r'@').sum()

(4) Count the chars by difference of the lengths

df['text'].str.len() - df['text'].str.replace(r'.', '').str.len()

1: Example DataFrame

Let's create a sample DataFrame with text values:

import pandas as pd

data = {
    'text': ['Hello@World!', 'Python#Pandas$', 'Data&Science*', 'Special_Chars%']
}

df = pd.DataFrame(data)

Output:

text
0 Hello@World!
1 Python#Pandas$
2 Data&Science*
3 Special_Chars%

2: Count Special Characters in Each Row

To count special characters in each row, use .str.count() with a regex pattern:

df['special_char_count'] = df['text'].str.count(r'[^a-zA-Z0-9\s]')

Output:

text special_char_count
0 Hello@World! 2
1 Python#Pandas$ 2
2 Data&Science* 2
3 Special_Chars% 1

3: Count Total Special Characters in Column

To count the total number of special characters across all rows:

total_special_chars = df['text'].str.count(r'[^a-zA-Z0-9\s]').sum()

Output:

8

4: Count Occurrences of a Specific Special Character (e.g., @)

If you need to count how many times a specific character (like @) appears in the column:

at_count = df['text'].str.count(r'@').sum()
at_count = df['text'].str.count(r'@')

Output:

1

and

0    1
1    0
2    0
3    0

5: Count Special Characters Across Multiple Columns

If you want to check special characters in multiple text columns:

cols = ['text']  # Add more columns if needed
df['special_char_count'] = df[cols].apply(lambda x: x.str.count(r'[^a-zA-Z0-9\s]')).sum(axis=1)

6: Count number of dots in column

As an alternative solution we can remove the characters from the column and get the difference from the original length:

df['text'].str.len() - df['text'].str.replace(r'.', '').str.len()

Conclusion

This guide covered multiple ways to count special characters in Pandas, including:

  • Counting special characters in each row
  • Summing special characters across the entire column
  • Finding occurrences of specific special characters

These methods are useful for text processing, data cleaning, and validation tasks.

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