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A Python API for How Long to Beat

Project description

HowLongToBeat Python API

Python Test Released Published Version CodeQL

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A simple Python API to read data from howlongtobeat.com.

It is inspired by ckatzorke - howlongtobeat JS API.

Content

Usage

Installation

Installing the package downloading the last release

pip install howlongtobeatpy

Installing the package from the source code

Download the repo, enter the folder with 'setup.py' and run the command

pip install .

Usage in code

Start including it in your file

from howlongtobeatpy import HowLongToBeat

Now call search()

The API main functions are:

results = HowLongToBeat().search("Awesome Game")

or, if you prefer using async:

results = await HowLongToBeat().async_search("Awesome Game")

The return of that function is a list of possible games, or None in case you passed an invalid "game name" as parameter or if there was an error in the request.

If the list is not None you should choose the best entry checking the Similarity value with the original name, example:

results_list = await HowLongToBeat().async_search("Awesome Game")
if results_list is not None and len(results_list) > 0:
    best_element = max(results_list, key=lambda element: element.similarity)

Once done, "best_element" will contain the best game found in the research. Every entry in the list (if not None in case of errors) is an object of type: HowLongToBeatEntry.

Alternative search (by ID)

If you prefer, you can get a game by ID, this can be useful if you already have the game's howlongtobeat-id (the ID is the number in the URL, for example in https://howlongtobeat.com/game/7231 the ID is 7231).

To avoid a new parser, the search by ID use a first request to get the game title, and then use the standard search with that title, filtering the results and returning the unique game with that ID.

Remember that it could be a bit slower, but you avoid searching the game in the array by similarity.

Here's the example:

result = HowLongToBeat().search_from_id(123456)

or, if you prefer using async:

result = await HowLongToBeat().async_search_from_id(123456)

This call will return an unique HowLongToBeatEntry or None in case of errors.

DLC search

An enum has been added to have a filter in the search:

SearchModifiers.NONE # default
SearchModifiers.ISOLATE_DLC
SearchModifiers.HIDE_DLC

This optional parameter allow you to specify in the search if you want the default search (with DLCs), to HIDE DLCs and only show games, or to ISOLATE DLCs (show only DLCs).

Results auto-filter

To ignore games with a very different name, the standard search automatically filter results with a game name that has a similarity with the given name > than 0.4, not adding the others to the result list. If you want all the results, or you want to change this value, you can put a parameter in the constructor:

results = HowLongToBeat(0.0).search("Awesome Game")

putting 0.0 (or just 0) will return all the found games, otherwise you can write another (float) number between 0...1 to set a new filter, such as 0.7.

Also remember that by default the similarity check is case-sensitive between the name given and the name found, if you want to ignore the case you can use:

results = HowLongToBeat(0.0).search("Awesome Game", similarity_case_sensitive=False)

Remember that, when searching by ID, the similarity value and the case-sensitive bool are ignored.

Reading an entry

An entry is made of few values, you can check them in the Entry class file. It also include the full JSON of values (already converted to Python dict) received from HLTB.

Issues, Questions & Discussions

If you found a bug report it as soon as you can creating an issue, the code may not be perfect.

If you need any new feature, or want to discuss the current implementation/features, consider opening a discussion or even propose a change with a Pull Request.

Authors

  • ScrappyCocco - Thank you for using my API

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Project details


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howlongtobeatpy-1.0.2.tar.gz (8.3 kB view hashes)

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