SteamPI: a simple API for Steam
This repository contains Python code to download some data through Steam API.
Installation
The code is packaged for PyPI, so that the installation consists in running:
pip install steampiUsage
Download app details of a Steam game, given its appID
import steampi.api
app_id = '440'
(app_details, is_success, status_code) = steampi.api.load_app_details(app_id)Retrieve the release date of a Steam game, given its appID
import steampi.calendar
app_id = '440'
release_date = steampi.calendar.get_release_date_as_datetime(app_id)Retrieve the release year of a Steam game, given its appID
import steampi.calendar
app_id = '440'
release_year = steampi.calendar.get_release_year(app_id)Find the most similar game names to an input text
Using the Levenshtein distance
The Levenshtein distance is an edit distance, which is useful to fix typos for instance.
import steampi.text_distances
import steamspypi
steamspy_database = steamspypi.load()
input_text = 'Crash Bandicoot'
sorted_app_ids, text_distances = steampi.text_distances.find_most_similar_game_names(input_text,
steamspy_database)
num_games_to_print = 5
for i in range(num_games_to_print):
similar_game_name = steamspy_database[sorted_app_ids[i]]
print(similar_game_name)Using the longest contiguous matching subsequence
The code snippet below makes use of the longest contiguous matching subsequence. This leads to different results compared to Levenshtein distance, which you might find more suitable for your needs.
However:
- the code is slower than with Levenshtein distance: for instance, the run-time is 140% longer for the unit test,
- the text distances are bound to [0,1], so they do not have the same value range as for Levenshtein distance,
- the text distances do not have the same meaning as for Levenshtein distance, which was the minimal number of edits,
- the results do not contain all the text distances, but only these with less than 0.4 distance (i.e. 0.6 similarity).
Junk characters can be specified with junk_str.
import steampi.text_distances
import steamspypi
steamspy_database = steamspypi.load()
num_games_to_print = 5
junk_str=''
input_text = 'Crash Bandicoot'
sorted_app_ids, text_distances = steampi.text_distances.find_most_similar_game_names(input_text,
steamspy_database,
use_levenshtein_distance=False,
n=num_games_to_print,
junk_str=junk_str,
)
for i in range(len(sorted_app_ids)):
similar_game_name = steamspy_database[sorted_app_ids[i]]
print(similar_game_name)References
- Levenshtein module for the Levenshtein distance,
- Difflib module for the longest contiguous matching subsequence.
