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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,71 @@ | ||
| import logging | ||
| from pathlib import Path | ||
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| import pandas as pd | ||
| from pandas import DatetimeIndex | ||
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| logger = logging.getLogger(__name__) | ||
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| def normalize_platform(platform: str): | ||
| normalized = { | ||
| "android os": "Android OS", | ||
| "android [arm 0]": "Android OS", | ||
| "android-tablet os": "Android OS", | ||
| "partner android_tv": "Android TV", | ||
| "partner google cast": "Chromecast", | ||
| "ios": "iOS", | ||
| "partner ios": "iOS", | ||
| "osx": "MacOS", | ||
| "os x": "MacOS", | ||
| "sonos_": "Sonos", | ||
| "partner sonos": "Sonos", | ||
| "webos tv": "WebOS TV", | ||
| "partner webos_tv": "WebOS tv", | ||
| "webplayer": "WebPlayer", | ||
| "web_player": "WebPlayer", | ||
| "partner spotify web_player": "WebPlayer", | ||
| "windows": "Windows", | ||
| "not_applicable": "not_applicable", | ||
| } | ||
| normalized_matches = [value for key, value in normalized.items() if platform.lower().startswith(key.lower())] | ||
| if len(normalized_matches) > 1: | ||
| logger.warning( | ||
| "There are several matches for the `%s` platform: %s (the first one is taken)", | ||
| platform, | ||
| normalized_matches, | ||
| ) | ||
| return normalized_matches[0] | ||
| if len(normalized_matches) < 1: | ||
| logger.warning("There is no match for the `%s` platform", platform) | ||
| return platform | ||
| return normalized_matches[0] | ||
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| def main(enriched_path: Path, metrics_path: Path): | ||
| df_stream = pd.read_csv(enriched_path) | ||
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| df_stream["year"] = DatetimeIndex(df_stream.end_time).year.map(lambda x: f"{x:0>4}") | ||
| df_stream["month"] = (DatetimeIndex(df_stream.end_time).month).map(lambda x: f"{x:0>2}") | ||
| df_stream["month_name"] = DatetimeIndex(df_stream.end_time).month_name() | ||
| df_stream["day"] = DatetimeIndex(df_stream.end_time).day.map(lambda x: f"{x:0>2}") | ||
| df_stream["day_of_week"] = DatetimeIndex(df_stream.end_time).day_of_week.map(lambda x: f"{x:0>2}") | ||
| df_stream["day_name"] = DatetimeIndex(df_stream.end_time).day_name() | ||
| df_stream["hour"] = DatetimeIndex(df_stream.end_time).hour.map(lambda x: f"{x:0>2}") | ||
| df_stream["minute"] = DatetimeIndex(df_stream.end_time).minute.map(lambda x: f"{x:0>2}") | ||
| # ":04" writting is fixed in Python 3.10+ : https://stackoverflow.com/a/36044788 | ||
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| df_stream["min_played"] = df_stream.ms_played / 1000 / 60 | ||
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| df_stream["percentage_played"] = round((df_stream.ms_played / df_stream.track_duration_ms) * 100, 2) | ||
| df_stream["percentage_played"] = df_stream["percentage_played"].clip(0, 100) | ||
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| df_stream["is_new_track"] = ~df_stream["track_uri"].duplicated(keep="first") | ||
| df_stream["is_new_artist"] = ~df_stream["artist_uri"].duplicated(keep="first") | ||
| df_stream["is_new_album"] = ~df_stream["album_uri"].duplicated(keep="first") | ||
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| df_stream["normalized_platform"] = df_stream["platform"].apply(normalize_platform) | ||
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| df_stream["skipped"] = df_stream["skipped"].astype(bool) | ||
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| df_stream.to_csv(metrics_path, mode="w", index=False) |