Introducing Python’s Parse: The Ultimate Alternative to Regular Expressions

Use best practices and real-world examples to demonstrate the powerful text parser library

Introducing Python’s Parse: The Ultimate Alternative to Regular Expressions
The parse library is very simple to use. Photo by Amanda Jones on Unsplash

This article introduces a Python library called parse for quickly and conveniently parsing and extracting data from text, serving as a great alternative to Python regular expressions.

And which covers the best practices with the parse library and a real-world example of parsing nginx log text.


I have a colleague named Wang. One day, he came to me with a worried expression, saying he encountered a complex problem: his boss wanted him to analyze the server logs from the past month and provide statistics on visitor traffic.

I told him it was simple. Just use regular expressions. For example, to analyze nginx logs, use the following regular expression, and it’s elementary.

content: - - [04/Jan/2019:16:06:38 +0800] "GET HTTP/1.1" 200 273932
regular expression:
(?<ip>\d+\.\d+\.\d+\.\d+)( - - \[)(?<datetime>[\s\S]+)(?<t1>\][\s"]+)(?<request>[A-Z]+) (?<url>[\S]*) (?<protocol>[\S]+)["] (?<code>\d+) (?<sendbytes>\d+)

But Wang was still worried, saying that learning regular expressions is too tricky. Although there are many ready-made examples online to learn from, he needs help with parsing uncommon text formats.

Moreover, even if he could solve the problem this time, what if his boss asked for changes in the parsing rules when he submitted the analysis? Wouldn’t he need to fumble around for a long time again?

Is there a simpler and more convenient method?

I thought about it and said, of course, there is. Let’s introduce our protagonist today: the Python parse library.

Installation & Setup

As described on the parse GitHub page, it uses Python’s format() syntax to parse text, essentially serving as a reverse operation of Python f-strings.

Before starting to use parse, let’s see how to install the library.

Direct installation with pip:

python -m pip install parse

Installation with conda can be more troublesome, as parse is not in the default conda channel and needs to be installed through conda-forge:

conda install -c conda-forge parse

After installation, you can use from parse import * in your code to use the library’s methods directly.

Features & Usage

The parse API is similar to Python Regular Expressions, mainly consisting of the parse, search, and findall methods. Basic usage can be learned from the parse documentation.

Pattern format

The parse format is very similar to the Python format syntax. You can capture matched text using {} or {field_name}.

For example, in the following text, if I want to get the profile URL and username, I can write it like this:

Hello everyone, my Medium profile url is,
and my username is @qtalen.

parse pattern:
Hello everyone, my Medium profile url is {profile},
and my username is {username}.

Or you want to extract multiple phone numbers. Still, the phone numbers have different formats of country codes in front, and the phone numbers are of a fixed length of 11 digits. You can write it like this:

compiler = Parser("{country_code}{phone:11.11},")
content = "0085212345678901, +85212345678902, (852)12345678903,"

results = compiler.findall(content)

for result in results:

Or if you need to process a piece of text in an HTML tag, but the text is preceded and followed by an indefinite length of whitespace, you can write it like this:

<div>           Hello World               </div>


In the code above, {:11} refers to the width, which means to capture at least 11 characters, equivalent to the regular expression (.{11,})?. {:.11} refers to the precision, which means to capture at most 11 characters, equivalent to the regular expression (.{,11})?. So when combined, it means (.{11, 11})?. The result is:

Capture fixed-width characters.
Capture fixed-width characters. Image by Author

The most powerful feature of parse is its handling of time text, which can be directly parsed into Python datetime objects. For example, if we want to parse the time in an HTTP log:

[04/Jan/2019:16:06:38 +0800]


Retrieving results

There are two ways to retrieve the results:

  1. For capturing methods that use {} without a field name, you can directly use result.fixed to get the result as a tuple.
  2. For capturing methods that use {field_name}, you can use result.named to get the result as a dictionary.

Custom Type Conversions

Although using {field_name} is already quite simple, the source code reveals that {field_name} is internally converted to (?P<field_name>.+?). So, parse still uses regular expressions for matching. .+? represents one or more random characters in non-greedy mode.

The transformation process of parse format to regular expressions.
The transformation process of parse format to regular expressions. Image by Author

However, often we hope to match more precisely. For example, the text “my email is”, “my email is {email}” can capture the email. Sometimes we may get dirty data, for example, “my email is xxxx@xxxx”, and we don’t want to grab it.

Is there a way to use regular expressions for more accurate matching?

That’s when the with_pattern decorator comes in handy.

For example, for capturing email addresses, we can write it like this:

def email(text: str) -> str:
    return text

compiler = Parser("my email address is {email:Email}", dict(Email=email))

legal_result = compiler.parse("my email address is")  # legal email
illegal_result = compiler.parse("my email address is xx@xx")     # illegal email

Using the with_pattern decorator, we can define a custom field type, in this case, Emailwhich will match the email address in the text. We can also use this approach to match other complicated patterns.

A Real-world Example: Parsing Nginx Log

After understanding the basic usage of parse, let’s return to the troubles of Wang mentioned at the beginning of the article. Let’s see how to parse logs if we have server log files for the past month.

Note: We chose NASA’s HTTP log dataset for this experiment, which is free to use.

The text fragment to be parsed looks like this:

What is the text fragment look like.
What is the text fragment look like. Screenshot by Author

First, we need to preprocess the parse expression. This way, when parsing large files, we don’t have to compile the regular expression for each line of text, thus improving performance.

from parse import Parser, with_pattern
import pandas as pd

FILE_NAME = "../../data/access_log_Jul95_min"
compiler = Parser('{source} - - [{timestamp:th}] "{method} {path} {version}" {status_code} {length}\n')

Next, the parse_line method is the core of this example. It uses the preprocessed expression to parse the text, returning the corresponding match if there is one and an empty dictionary if not.

def process_line(text: str) -> dict:
    parse_result = compiler.parse(text)
    return parse_result.named if parse_result else {}

Then, we use the read_file method to process the text line by line using a generator, which can minimize memory usage. However, due to the disk’s 4k capability limitations, this method may not guarantee performance.

def read_file(name: str) -> list[dict]:
    result = []
    with open(name, 'r') as f:
        for line in f:
            obj: dict = process_line(line)

    return result

Since we need to perform statistics on the log files, we must use the from_records method to construct a DataFrame from the matched results.

def build_dataframe(records: list[dict]) -> pd.DataFrame:
    result: pd.DataFrame = pd.DataFrame.from_records(records, index='timestamp')
    return result

Finally, in the main method, we put all the methods together and try to count the different status_code occurrences:

def main():
    records: list[dict] = read_file(FILE_NAME)
    dataframe = build_dataframe(records)
Wang’s troubles have been easily solved.
Wang’s troubles have been easily solved. Image by Author

That’s it. Wang’s troubles have been easily solved.

Best Practices with parse Library

Although the parse library is so simple that I only have a little to write about in the article. There are still some best practices to follow, just like regular expressions.

Readability and maintainability

To efficiently capture text and maintain expressions, it is recommended to always use {field_name} instead of {}. This way, you can directly use result.named to obtain key-value results.

Using Parser(pattern) to preprocess the expression is recommended, rather than parse(pattern, text).

On the one hand, this can improve performance. On the other hand, when using Custom Type Conversions, you can keep the pattern and extra_type together, making it easier to maintain.

Optimizing performance for large datasets

If you look at the source code, you can see that {} and {field_name} use the regular expressions (.+?) and (?P<field_name>.+?) for capture, respectively. Both expressions use the non-greedy mode. So when you use with_pattern to write your own expressions, also try to use non-greedy mode.

At the same time, when writing with_pattern, if you use () for capture grouping, please use regex_group_count to specify the specific groups like this: @with_pattern(r’((\d+))’, regex_group_count=2) .

Finally, if a group is not needed in with_pattern, use (?:x) instead. @with_pattern(r’(?:<input.?>)(.?)(?:</input>)’, regex_group_count=1) means you want to capture the content between input tags. The input tags will not be captured.


In this article, I changed my usual way of writing lengthy papers. By solving a colleague’s problem, I briefly introduced the use of the parse library. I hope you like this style.

This article does not cover the detailed usage methods on the official website. Still, it introduces some best practices and performance optimization solutions based on my experience.

At the same time, I explained in detail the use of the parse library to parse nginx logs with a practical example.

As the new series title suggests, besides improving code execution speed and performance, using various tools to improve work efficiency is also a performance enhancement.

This article helps data scientists simplify text parsing and spend time on more critical tasks. If you have any thoughts on this article, feel free to leave a comment and discuss.

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