Accurately separate the TLD from the registered domain and subdomains of a URL, using the Public Suffix List.

Overview

tldextract

Python Module PyPI version Build Status

tldextract accurately separates the gTLD or ccTLD (generic or country code top-level domain) from the registered domain and subdomains of a URL. For example, say you want just the 'google' part of 'http://www.google.com'.

Everybody gets this wrong. Splitting on the '.' and taking the last 2 elements goes a long way only if you're thinking of simple e.g. .com domains. Think parsing http://forums.bbc.co.uk for example: the naive splitting method above will give you 'co' as the domain and 'uk' as the TLD, instead of 'bbc' and 'co.uk' respectively.

tldextract on the other hand knows what all gTLDs and ccTLDs look like by looking up the currently living ones according to the Public Suffix List (PSL). So, given a URL, it knows its subdomain from its domain, and its domain from its country code.

>>> import tldextract

>>> tldextract.extract('http://forums.news.cnn.com/')
ExtractResult(subdomain='forums.news', domain='cnn', suffix='com')

>>> tldextract.extract('http://forums.bbc.co.uk/') # United Kingdom
ExtractResult(subdomain='forums', domain='bbc', suffix='co.uk')

>>> tldextract.extract('http://www.worldbank.org.kg/') # Kyrgyzstan
ExtractResult(subdomain='www', domain='worldbank', suffix='org.kg')

ExtractResult is a namedtuple, so it's simple to access the parts you want.

>>> ext = tldextract.extract('http://forums.bbc.co.uk')
>>> (ext.subdomain, ext.domain, ext.suffix)
('forums', 'bbc', 'co.uk')
>>> # rejoin subdomain and domain
>>> '.'.join(ext[:2])
'forums.bbc'
>>> # a common alias
>>> ext.registered_domain
'bbc.co.uk'

Note subdomain and suffix are optional. Not all URL-like inputs have a subdomain or a valid suffix.

>>> tldextract.extract('google.com')
ExtractResult(subdomain='', domain='google', suffix='com')

>>> tldextract.extract('google.notavalidsuffix')
ExtractResult(subdomain='google', domain='notavalidsuffix', suffix='')

>>> tldextract.extract('http://127.0.0.1:8080/deployed/')
ExtractResult(subdomain='', domain='127.0.0.1', suffix='')

If you want to rejoin the whole namedtuple, regardless of whether a subdomain or suffix were found:

>>> ext = tldextract.extract('http://127.0.0.1:8080/deployed/')
>>> # this has unwanted dots
>>> '.'.join(ext)
'.127.0.0.1.'
>>> # join each part only if it's truthy
>>> '.'.join(part for part in ext if part)
'127.0.0.1'

By default, this package supports the public ICANN TLDs and their exceptions. You can optionally support the Public Suffix List's private domains as well.

This module started by implementing the chosen answer from this StackOverflow question on getting the "domain name" from a URL. However, the proposed regex solution doesn't address many country codes like com.au, or the exceptions to country codes like the registered domain parliament.uk. The Public Suffix List does, and so does this module.

Installation

Latest release on PyPI:

pip install tldextract

Or the latest dev version:

pip install -e 'git://github.com/john-kurkowski/tldextract.git#egg=tldextract'

Command-line usage, splits the url components by space:

tldextract http://forums.bbc.co.uk
# forums bbc co.uk

Note About Caching

Beware when first running the module, it updates its TLD list with a live HTTP request. This updated TLD set is usually cached indefinitely in ``$HOME/.cache/python-tldextract`. To control the cache's location, set TLDEXTRACT_CACHE environment variable or set the cache_dir path in TLDExtract initialization.

(Arguably runtime bootstrapping like that shouldn't be the default behavior, like for production systems. But I want you to have the latest TLDs, especially when I haven't kept this code up to date.)

# extract callable that falls back to the included TLD snapshot, no live HTTP fetching
no_fetch_extract = tldextract.TLDExtract(suffix_list_urls=None)
no_fetch_extract('http://www.google.com')

# extract callable that reads/writes the updated TLD set to a different path
custom_cache_extract = tldextract.TLDExtract(cache_dir='/path/to/your/cache/')
custom_cache_extract('http://www.google.com')

# extract callable that doesn't use caching
no_cache_extract = tldextract.TLDExtract(cache_dir=False)
no_cache_extract('http://www.google.com')

If you want to stay fresh with the TLD definitions--though they don't change often--delete the cache file occasionally, or run

tldextract --update

or:

env TLDEXTRACT_CACHE="~/tldextract.cache" tldextract --update

It is also recommended to delete the file after upgrading this lib.

Advanced Usage

Public vs. Private Domains

The PSL maintains a concept of "private" domains.

PRIVATE domains are amendments submitted by the domain holder, as an expression of how they operate their domain security policy. … While some applications, such as browsers when considering cookie-setting, treat all entries the same, other applications may wish to treat ICANN domains and PRIVATE domains differently.

By default, tldextract treats public and private domains the same.

>>> extract = tldextract.TLDExtract()
>>> extract('waiterrant.blogspot.com')
ExtractResult(subdomain='waiterrant', domain='blogspot', suffix='com')

The following overrides this.

>>> extract = tldextract.TLDExtract()
>>> extract('waiterrant.blogspot.com', include_psl_private_domains=True)
ExtractResult(subdomain='', domain='waiterrant', suffix='blogspot.com')

or to change the default for all extract calls,

>>> extract = tldextract.TLDExtract( include_psl_private_domains=True)
>>> extract('waiterrant.blogspot.com')
ExtractResult(subdomain='', domain='waiterrant', suffix='blogspot.com')

The thinking behind the default is, it's the more common case when people mentally parse a URL. It doesn't assume familiarity with the PSL nor that the PSL makes such a distinction. Note this may run counter to the default parsing behavior of other, PSL-based libraries.

Specifying your own URL or file for the Suffix List data

You can specify your own input data in place of the default Mozilla Public Suffix List:

extract = tldextract.TLDExtract(
    suffix_list_urls=["http://foo.bar.baz"],
    # Recommended: Specify your own cache file, to minimize ambiguities about where
    # tldextract is getting its data, or cached data, from.
    cache_dir='/path/to/your/cache/',
    fallback_to_snapshot=False)

The above snippet will fetch from the URL you specified, upon first need to download the suffix list (i.e. if the cached version doesn't exist).

If you want to use input data from your local filesystem, just use the file:// protocol:

extract = tldextract.TLDExtract(
    suffix_list_urls=["file://absolute/path/to/your/local/suffix/list/file"],
    cache_dir='/path/to/your/cache/',
    fallback_to_snapshot=False)

Use an absolute path when specifying the suffix_list_urls keyword argument. os.path is your friend.

FAQ

Can you add suffix ____? Can you make an exception for domain ____?

This project doesn't contain an actual list of public suffixes. That comes from the Public Suffix List (PSL). Submit amendments there.

(In the meantime, you can tell tldextract about your exception by either forking the PSL and using your fork in the suffix_list_urls param, or adding your suffix piecemeal with the extra_suffixes param.)

If I pass an invalid URL, I still get a result, no error. What gives?

To keep tldextract light in LoC & overhead, and because there are plenty of URL validators out there, this library is very lenient with input. If valid URLs are important to you, validate them before calling tldextract.

This lenient stance lowers the learning curve of using the library, at the cost of desensitizing users to the nuances of URLs. Who knows how much. But in the future, I would consider an overhaul. For example, users could opt into validation, either receiving exceptions or error metadata on results.

Contribute

Setting up

  1. git clone this repository.
  2. Change into the new directory.
  3. pip install tox

Running the Test Suite

Run all tests against all supported Python versions:

tox --parallel

Run all tests against a specific Python environment configuration:

tox -l
tox -e py37
Owner
John Kurkowski
UX Engineering Consultant
John Kurkowski
An ETL framework + Monitoring UI/API (experimental project for learning purposes)

Fastlane An ETL framework for building pipelines, and Flask based web API/UI for monitoring pipelines. Project structure fastlane |- fastlane: (ETL fr

Dan Katz 2 Jan 06, 2022
Reading streams of Twitter data, save them to Kafka, then process with Kafka Stream API and Spark Streaming

Using Streaming Twitter Data with Kafka and Spark Reading streams of Twitter data, publishing them to Kafka topic, process message using Kafka Stream

Rustam Zokirov 1 Dec 06, 2021
Bearsql allows you to query pandas dataframe with sql syntax.

Bearsql adds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine

14 Jun 22, 2022
Methylation/modified base calling separated from basecalling.

Remora Methylation/modified base calling separated from basecalling. Remora primarily provides an API to call modified bases for basecaller programs s

Oxford Nanopore Technologies 72 Jan 05, 2023
Very basic but functional Kakuro solver written in Python.

kakuro.py Very basic but functional Kakuro solver written in Python. It uses a reduction to exact set cover and Ali Assaf's elegant implementation of

Louis Abraham 4 Jan 15, 2022
Statistical Analysis 📈 focused on statistical analysis and exploration used on various data sets for personal and professional projects.

Statistical Analysis 📈 This repository focuses on statistical analysis and the exploration used on various data sets for personal and professional pr

Andy Pham 1 Sep 03, 2022
BigDL - Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems

Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems.

Vo Cong Thanh 1 Jan 06, 2022
Data-sets from the survey and analysis

bachelor-thesis "Umfragewerte.xlsx" contains the orginal survey results. "umfrage_alle.csv" contains the survey results but one participant is cancele

1 Jan 26, 2022
Automatic earthquake catalog building workflow: EQTransformer + Siamese EQTransformer + PickNet + REAL + HypoInverse

Automatic regional-scale earthquake catalog building workflow: EQTransformer + Siamese EQTransforme

Xiao Zhuowei 9 Nov 27, 2022
VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

André Rodrigues 2 Feb 14, 2022
fds is a tool for Data Scientists made by DAGsHub to version control data and code at once.

Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc

DAGsHub 359 Dec 22, 2022
This repo is dedicated to the data extraction and manipulation of the World Bank's database called STEP.

Overview Welcome to the Step-X repository. This repo is dedicated to the data extraction and manipulation of the World Bank's database called STEP. Be

Keanu Pang 0 Jan 20, 2022
CSV database for chihuahua (HUAHUA) blockchain transactions

super-fiesta Shamelessly ripped components from https://github.com/hodgerpodger/staketaxcsv - Thanks for doing all the hard work. This code does only

Arlene Macciaveli 1 Jan 07, 2022
First and foremost, we want dbt documentation to retain a DRY principle. Every time we repeat ourselves, we waste our time. Second, we want to understand column level lineage and automate impact analysis.

dbt-osmosis First and foremost, we want dbt documentation to retain a DRY principle. Every time we repeat ourselves, we waste our time. Second, we wan

Alexander Butler 150 Jan 06, 2023
pyETT: Python library for Eleven VR Table Tennis data

pyETT: Python library for Eleven VR Table Tennis data Documentation Documentation for pyETT is located at https://pyett.readthedocs.io/. Installation

Tharsis Souza 5 Nov 19, 2022
signac-flow - manage workflows with signac

signac-flow - manage workflows with signac The signac framework helps users manage and scale file-based workflows, facilitating data reuse, sharing, a

Glotzer Group 44 Oct 14, 2022
Kennedy Institute of Rheumatology University of Oxford Project November 2019

TradingBot6M Kennedy Institute of Rheumatology University of Oxford Project November 2019 Run Change api.txt to binance api key: https://www.binance.c

Kannan SAR 2 Nov 16, 2021
Calculate multilateral price indices in Python (with Pandas and PySpark).

IndexNumCalc Calculate multilateral price indices using the GEKS-T (CCDI), Time Product Dummy (TPD), Time Dummy Hedonic (TDH), Geary-Khamis (GK) metho

Dr. Usman Kayani 3 Apr 27, 2022
Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge S

1 Jan 09, 2022
Stochastic Gradient Trees implementation in Python

Stochastic Gradient Trees - Python Stochastic Gradient Trees1 by Henry Gouk, Bernhard Pfahringer, and Eibe Frank implementation in Python. Based on th

John Koumentis 2 Nov 18, 2022