mirror of
https://github.com/Steffo99/unimore-bda-3.git
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101 lines
2.5 KiB
Python
101 lines
2.5 KiB
Python
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from unimore_bda_3.prelude import *
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import httpx
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import collections
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steam_api = httpx.Client(base_url="https://api.steampowered.com")
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def _load_news(appid: int) -> list[dict]:
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"""
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Load all news items for the given app id, from the most recent to the least recent.
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"""
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count = 100
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enddate = {}
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newsitems = []
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while count == 100:
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request = steam_api.get(
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"/ISteamNews/GetNewsForApp/v0002/",
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params={
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"appid": appid,
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"count": 100,
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"format": "json",
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**enddate,
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}
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)
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request.raise_for_status()
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data = request.json()["appnews"]
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count = len(data["newsitems"])
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newsitems.extend(data["newsitems"])
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enddate = {"enddate": newsitems[-1]["date"]}
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return newsitems
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def _categorize_news(news: list[dict]) -> dict[str, list[dict]]:
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"""
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Group news items by their tags.
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"""
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result = collections.defaultdict(list)
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for item in news:
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tags = item.get("tags", [])
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if tags:
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for tag in set(item.get("tags", [])):
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result[tag].append(item)
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else:
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result["no_tags"].append(item)
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return result
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def _serialize_news(name: str, news: list[dict]) -> pd.Series:
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"""
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Convert a list of news into a `pandas.Series` with the dates as index and 1 as the data.
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"""
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index = pd.to_datetime([datetime.fromtimestamp(item["date"]) for item in news])
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return pd.Series(
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data=[1 for _ in index],
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index=index,
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name=f"""Steam · Count of News tagged {name}""",
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dtype=np.uint8,
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)
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def fetch(appid: int) -> pd.DataFrame:
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"""
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Load announcements related to a certain app id into a `pandas.DataFrame`.
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"""
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raw_news = _load_news(appid=appid)
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categorized_news = _categorize_news(news=raw_news)
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serialized_news = [_serialize_news(name=name, news=news).to_frame() for name, news in categorized_news.items()]
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dataframe = utils.join_frames(*serialized_news).fillna(0)
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dataframe = dataframe.groupby(dataframe.index.date).sum()
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dataframe.index = pd.to_datetime(dataframe.index)
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return dataframe
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def load(fd: t.IO[str]) -> pd.DataFrame:
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"""
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Load announcements related to the app id contained in the given file into a `pandas.DataFrame`.
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"""
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appid = int(fd.read().strip())
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data = fetch(appid=appid)
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return data
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__all__ = (
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"fetch",
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"load",
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)
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