5 Best Ways to Perform Prefix Matching in Python Using PyTrie Module

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πŸ’‘ Problem Formulation: Prefix matching is a common challenge when dealing with strings in data structures, where the task is to find all items with keys that start with a given prefix. For instance, given the input prefix “py”, desired output might include “python”, “pyramid”, and “pythagoras” assuming all of these are keys in our dataset.

Method 1: Using the pytrie.StringTrie Constructor

This method involves initializing a StringTrie object that stores the keys and values. The StringTrie offers the prefixed_by() method which takes a prefix as the argument and returns an iterator over all keys starting with this prefix. It’s highly efficient for finding keys with a common starting substring in a large set of strings.

Here’s an example:

import pytrie
trie = pytrie.StringTrie()

# Adding some entries
trie['python'] = 'a high-level programming language'
trie['pyramid'] = 'a monumental structure'
trie['pythagoras'] = 'a Greek mathematician'

# Matching a prefix


['pyramid', 'pythagoras', 'python']

The code snippet shows how to create a StringTrie and retrieve all keys that match a given prefix. It allows rapid querying which becomes particularly valuable with large datasets.

Method 2: Extending on iteritems()

In scenarios where you might need both keys and their associated values, the iteritems() method can be invaluable. It works similarly to dict.items() but allows an optional prefix argument to restrict the items returned to those with keys starting with the specified prefix.

Here’s an example:

for key, value in trie.iteritems('py'):
    print(f"{key}: {value}")


python: a high-level programming language
pyramid: a monumental structure
pythagoras: a Greek mathematician

The example iterates over the entries in the trie that match the ‘py’ prefix, printing out both the key and its associated value.

Method 3: Using itervalues() for Value Retrieval

When only the values of the matched keys are needed, itervalues() provides an optimized way of retrieval. This function yields values in the trie that correspond to keys with the specified prefix, thus saving memory and time if keys are not needed.

Here’s an example:

for value in trie.itervalues('py'):


a high-level programming language
a monumental structure
a Greek mathematician

This snippet demonstrates value retrieval for all keys starting with ‘py’, effectively omitting the keys themselves in the output.

Method 4: Finding Shortest Prefix with shortest_prefix()

The shortest_prefix() method allows for returning the shortest prefix match for a given string. This can be useful in finding the least amount of characters that uniquely identify a string in the dataset.

Here’s an example:



('python', 'a high-level programming language')

This snippet finds and returns the shortest prefix of ‘pythonista’ that exists in the trie, along with its value.

Bonus One-Liner Method 5: Quick Prefix Matching with Dictionary Comprehension

For simple cases where we may not need the full breadth of PyTrie’s functionality, a Python dictionary comprehension can be used for quick prefix matching.

Here’s an example:

matching_keys = {key: trie[key] for key in trie if key.startswith('py')}


{'python': 'a high-level programming language', 'pyramid': 'a monumental structure', 'pythagoras': 'a Greek mathematician'}

By using the built-in startswith() method of strings within a dictionary comprehension, this snippet quickly retrieves all key-value pairs with the desired prefix.


Each method for prefix matching offers unique strengths:

  • Method 1: Using pytrie.StringTrie. Highly efficient for lots of data. May be more heavyweight than needed for small datasets.
  • Method 2: Extending on iteritems(). Ideal for retrieving both keys and values. Less efficient than itervalues() when keys are not needed.
  • Method 3: Using itervalues(). Memory and time efficient when only values are desired. Cannot retrieve keys if needed later.
  • Method 4: Finding Shortest Prefix with shortest_prefix(). Great for finding unique identifiers. Not suitable for returning all matches.
  • Bonus Method 5: Quick Prefix Matching with dictionary comprehension. Simple and concise, but not as performance-optimized as PyTrie for large datasets.