What is the Fastest Way to Remove Punctuation from a Pandas DataFrame?

Fast Punctuation Removal with Pandas

Punctuation removal is a common text cleaning task. While pandas str.replace is a widely used method, it may not be sufficiently performant for large datasets.

Alternatives to str.replace:

• regex.sub: Uses the re module to perform regex-based substitution. This option offers improved performance over str.replace.
• str.translate: Utilizes the C-implemented str.translate function, resulting in significant speed improvements.

Benchmarks:

• str.translate exhibits the best performance, followed by regex.sub and then str.replace.
• The gap in performance widens with increasing dataset size.

Considerations:

• regex.sub and str.translate cannot handle NaN values in the DataFrame.
• str.translate requires special handling when the data contains characters that may be excluded by the default punctuation exclusion.

Code:

import pandas as pd
import re

# Regex.sub
df['text'] = [re.compile(r'[^\w\s]+').sub('', x) for x in df['text'].tolist()]

# str.translate
punct = '!"#$%&\'()*+,-./:;<=>?@[\]^_`{|}~'
transtab = str.maketrans(dict.fromkeys(punct, ''))
df['text'] = '|'.join(df['text'].tolist()).translate(transtab).split('|')

The above is the detailed content of What is the Fastest Way to Remove Punctuation from a Pandas DataFrame?. For more information, please follow other related articles on the PHP Chinese website!