How to Split String on Multiple Delimiters in Python


Among the plenty of string operations, splitting a string is a significant one, offering the capability to divide a large, composite text into smaller, manageable components. Typically, we use a single delimiter like a comma, space, or a special character for this purpose. But what if you need to split a string based on multiple delimiters?

Imagine a situation where you're dealing with text data punctuated with various separators, or you're parsing a complex file with inconsistent delimiters. This is where Python's ability to split strings on multiple delimiters truly shines.

In this article, we'll give you a comprehensive overview of the different techniques of multi-delimiter string splitting in Python. We'll explore core Python methods, regular expressions, and even external libraries like Pandas to achieve this.

The str.split() Method can Split Strings on Only One Delimiter

The str.split() method is Python's built-in approach to dividing a string into a list of substrings. By default, str.split() uses whitespace (spaces, tabs, and newlines) as the delimiter. However, you can specify any character or sequence of characters as the delimiter:

text = "Python is a powerful language"
words = text.split()

Running this code will result in:

['Python', 'is', 'a', 'powerful', 'language']

In this case, we've split the string into words using the default delimiter - whitespace. But what if we want to use a different delimiter? We can pass it as an argument to split():

text = "Python,is,a,powerful,language"
words = text.split(',')

Which will give us:

['Python', 'is', 'a', 'powerful', 'language']

While str.split() is highly useful for splitting strings with a single delimiter, it falls short when we need to split a string on multiple delimiters. For example, if we have a string with words separated by commas, semicolons, and/or spaces, str.split() cannot handle all these delimiters simultaneously.

Advice: Reading our guide "Python: Split String into List with split()" will help you gain a deeper understanding of the split() method in Python.

In the upcoming sections, we will explore more sophisticated techniques for splitting strings based on multiple delimiters in Python.

Using Regular Expressions - the re.split() Method

To tackle the issue of splitting a string on multiple delimiters, Python provides us with the re (Regular Expressions) module. Specifically, the re.split() function is an effective tool that allows us to split a string using multiple delimiters.

Regular expressions (or regex) are sequences of characters that define a search pattern. These are highly versatile, making them excellent for complex text processing tasks.

Consider the following string:

text = "Python;is,a powerful:language"

If you want to extract words from it, you must consider multiple delimiters. Let's take a look at how we can use re.split() to split a string based on multiple delimiters:

import re

text = "Python;is,a powerful:language"
words = re.split(';|,| ', text)

This will give us:

['Python', 'is', 'a', 'powerful', 'language']

We used the re.split() method to split the string at every occurrence of a semicolon (;), comma (,), or space ( ). The | symbol is used in regular expressions to mean "or", so ;|,| can be read as "semicolon or comma or space".

This function demonstrates far greater versatility and power than str.split(), allowing us to easily split a string on multiple delimiters.

Advice: You can find more about Python regular expressions in our "Introduction to Regular Expressions in Python".

In the next section, we'll take a look at another Pythonic way to split strings using multiple delimiters, leveraging the translate() and maketrans() methods.

Using translate() and maketrans() Methods

Python's str class provides two powerful methods for character mapping and replacement: maketrans() and translate(). When used in combination, they offer an efficient way to replace multiple delimiters with a single common one, allowing us to use str.split() effectively.

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The maketrans() method returns a translation table that can be used with the translate() method to replace specific characters. So, let's take a look at how to utilize those two methods to fit our needs.

First of all, we need to create a translation table that maps semicolons (;) and colons (:) to commas (,):

text = "Python;is,a powerful:language"

# Create a translation table mapping ';' and ':' to ','
table = text.maketrans(";:", ",,")

Then we use the translate() method to apply this table to our text. This replaces all semicolons and colons with commas:

# Apply the translation table
text = text.translate(table)

Finally, we can use str.split(',') to split the text into words and print extracted words:

# Now we can split on the comma
words = text.split(',')

This will result in:

['Python', 'is', 'a powerful', 'language']

Note: This approach is particularly useful when you want to standardize the delimiters in a string before splitting it.

In the next section, we'll explore how to utilize an external library, Pandas, for splitting strings on multiple delimiters.

Leveraging the Pandas Library

Pandas, a powerful data manipulation library in Python, can also be used for splitting strings on multiple delimiters. Its str.split() function is capable of handling regex, making it another effective tool for this task.

While the built-in string methods are efficient for smaller data, when you're working with large datasets (like a DataFrame), using Pandas for string splitting can be a better choice. The syntax is also quite intuitive.

Here's how you can use Pandas to split a string on multiple delimiters:

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'Text': ['Python;is,a powerful:language']})

# Use the str.split() function with a regex pattern
df = df['Text'].str.split(';|,|:', expand=True)


This will give us:

0   1    2         3         4
0  Python  is  a  powerful  language

We first created a DataFrame with our text. We then used the str.split() function, passing in a regex pattern similar to what we used with re.split(). The expand=True argument makes the function return a DataFrame where each split string is a separate column.

Note: Although this method returns a DataFrame instead of a list, it can be highly useful when you're already working within the Pandas ecosystem.

Performance Comparison

When choosing a method to split strings on multiple delimiters, performance can be an important factor, especially when working with large datasets. Let's examine the performance of the methods we've discussed.

The built-in str.split() method is quite efficient for smaller data sets and a single delimiter, but its performance suffers when used with multiple delimiters and large datasets due to the necessary extra processing.

The re.split() method is versatile and relatively efficient, as it can handle multiple delimiters well. However, its performance might also degrade when dealing with huge amounts of data, because regular expressions can be computationally intensive.

Using translate() and maketrans() can be an efficient way to handle multiple delimiters, especially when you want to standardize the delimiters before splitting. However, it involves an extra step, which can affect performance with large datasets.

Finally, while the Pandas library offers a very efficient and flexible method to split strings on multiple delimiters, it might be overkill for simple, small tasks. The overhead of creating a DataFrame can affect performance when working with smaller data, but it excels in handling large datasets.

In conclusion, the best method to use depends on your specific use case. For small datasets and tasks, Python's built-in methods might be more suitable, while for larger, more complex data manipulation tasks, Pandas could be the way to go.


String splitting, especially on multiple delimiters, is a common yet crucial operation in Python. It serves as the backbone in many text processing, data cleaning, and parsing tasks. As we've seen, Python provides a range of techniques for this task, each with its own strengths and weaknesses. From the built-in str.split(), to the versatile Regular Expressions, the character mapping translate() and maketrans() methods, and even the external Pandas library, Python offers solutions suitable for any complexity and size of data.

It's important to understand the different methods available and choose the one that best suits your specific requirements. Whether it's simplicity, versatility, or performance, Python's tools for string splitting can cater to various needs.

We hope this article helps you become more proficient in handling and manipulating strings in Python.

Last Updated: May 30th, 2023
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