33 lines
1.5 KiB
Python
33 lines
1.5 KiB
Python
from io import StringIO
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import polars as pl
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import data
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d = data.load()
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def property_capacities_weekdays(id: int, scrapeDate: str):
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extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
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weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
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df_calendar = pl.DataFrame()
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numWeeks = 0
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for row in extractions.rows(named=True):
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scrapeDate = row['created_at']
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df_calendar = pl.read_json(StringIO(row['calendar']))
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columnTitles = df_calendar.columns
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df_calendar = df_calendar.transpose()
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df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
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df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
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numWeeks = round((df_calendar.get_column("dates").max() - df_calendar.get_column("dates").min()).days / 7, 0)
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df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday_num"))
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df_calendar = df_calendar.with_columns(pl.col("dates").dt.strftime("%A").alias("weekday"))
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df_calendar = df_calendar.drop("dates")
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df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
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df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks / 2 * 100).alias("column_0"))
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df_calendar = df_calendar.sort('weekday_num')
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df_calendar = df_calendar.drop('weekday_num')
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result = {"scraping-date": scrapeDate, "weekdays": df_calendar['weekday'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
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return result |