C C++ CMake CSS Cypress date-fns Django Django REST Framework ESLint Git GNU Make HTML htmx HTTP JavaScript MariaDB NumPy pandas PHP PostgreSQL Python PyTorch Qt SQLite Tailwind CSS

String Suffix Removal in Pandas Series: Exploring pandas.Series.str.removesuffix


Functionality

  • If the suffix isn't present in a particular string, the original string remains unchanged.
  • It identifies and removes a specified suffix (characters at the end) from each string within the Series.
  • This function targets a Series containing string data.

Breakdown

  • suffix (str)
    This argument represents the characters you want to remove from the end of the strings.
  • str.removesuffix
    This is an attribute attached to the string accessor (str) of a Series. It provides methods for string manipulation on each element of the Series.
  • pandas.Series
    This refers to a one-dimensional labeled array within the pandas library, commonly used to store and manipulate textual data.

Example

import pandas as pd

data = pd.Series(['apple.txt', 'banana.jpg', 'kiwi.pdf', 'orange'])

# Remove the '.txt' suffix
modified_data = data.str.removesuffix('.txt')

print(modified_data)

This code will output:

0    apple
1  banana
2     kiwi
3  orange
dtype: object

As you can see, the '.txt' suffix is removed only from the first element ("apple.txt"), while other strings remain unchanged.

  • For more complex suffix removal with regular expressions, consider using .str.replace with appropriate regular expression patterns.
  • .str.removesuffix is similar to .str.rstrip() but specifically targets suffixes, whereas rstrip removes any trailing characters (including whitespaces).

Removing Suffix with Different Lengths

import pandas as pd

data = pd.Series(['file_name.csv', 'image_data.png', 'important_document.docx'])

# Remove suffixes of different lengths
modified_data = data.str.removesuffix(pat=r'\.\w+$')  # Uses regular expression

print(modified_data)

This code uses a regular expression (r'\.\w+$') to match any dot (.) followed by one or more word characters (\w+) at the end of the string. This ensures removal of suffixes like ".csv", ".png", and ".docx".

Handling Missing Suffixes

import pandas as pd

data = pd.Series(['file_name', 'image', 'data.xlsx'])

# Suffix removal with default behavior for missing suffixes
modified_data = data.str.removesuffix('.xlsx')

print(modified_data)

In this example, only "data.xlsx" has the specified suffix. The str.removesuffix function will leave "file_name" and "image" unchanged (their original values are returned).

Conditional Removal based on Suffix Length

import pandas as pd

data = pd.Series(['report.docx', 'data.txt', 'presentation.pptx'])

def remove_suffix_if_3_chars(text):
  # Custom function to remove suffix only if 3 characters long
  if len(text.split('.')[-1]) == 3:
    return text.rsplit('.', 1)[0]  # Split and remove last part
  else:
    return text

modified_data = data.apply(remove_suffix_if_3_chars)  # Apply custom function

print(modified_data)

This code defines a custom function remove_suffix_if_3_chars that checks if the suffix length is 3. It removes the suffix only if the condition is met. This approach allows for more control over suffix removal criteria.


String Slicing

This method uses basic Python string slicing to achieve suffix removal. It's efficient for simple cases but can become cumbersome for complex patterns.

import pandas as pd

data = pd.Series(['file_name.txt', 'data.csv', 'report'])

# Remove suffix using slicing (works for fixed length suffixes)
modified_data = data.str[:-4]  # Remove last 4 characters (assuming suffix is '.txt' or '.csv')

print(modified_data)

str.rstrip with Optional Argument

This approach utilizes the str.rstrip function with an optional argument to specify the characters to remove from the right side.

import pandas as pd

data = pd.Series(['file_name.txt', 'data.csv', 'report'])

# Remove suffix using rstrip with optional argument (works for specific characters)
modified_data = data.str.rstrip('.txtcsv')  # Remove '.txt' or '.csv' suffixes

print(modified_data)

Regular Expressions with str.replace

For more intricate suffix removal patterns, regular expressions offer greater flexibility. You can use str.replace with a regular expression to target specific suffix patterns.

import pandas as pd

data = pd.Series(['file_name.docx', 'data.xlsx', 'report.pdf'])

# Remove suffix using regex with str.replace (works for complex patterns)
modified_data = data.str.replace(r'\.\w+$', '')  # Remove any dot followed by word characters

print(modified_data)
  • pandas.Series.str.removesuffix offers a concise and pandas-specific way to remove suffixes, making it a good default option for many scenarios.
  • If you need more control over the suffix pattern or want to remove suffixes of varying lengths, regular expressions with str.replace are a better choice.
  • For simple, fixed-length suffixes, string slicing or str.rstrip might be sufficient.