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ConsultancyProject_2_ETL
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base: stoffelmauro:2508b34ceb4743b35daf3a77054a0098102de947
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stoffelmauro:main
stoffelmauro:refactor-to-mono
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compare: stoffelmauro:65b63d1326f2e7be270034c200b9ad80e8976c23
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stoffelmauro:main
stoffelmauro:refactor-to-mono

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2508b34ceb ... 65b63d1326

Author SHA1 Message Date
mmaurostoffel 65b63d1326 Sortierung für etl_property_capacities_monthly eingefügt 2025-01-09 15:59:16 +01:00
mmaurostoffel a6cbe3bc29 update des etl_capacities_weekdays.py 2025-01-09 15:07:38 +01:00
2 changed files with 16 additions and 9 deletions
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9
etl/src/data/etl_property_capacities_monthly.py
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@ -17,8 +17,11 @@ def property_capacities_monthly(id: int, scrapeDate: str):
df_calendar = df_calendar.transpose()
df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%b") + " " + (pl.col("dates").dt.strftime("%Y"))))
df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%b") + " " + (pl.col("dates").dt.strftime("%Y"))).alias('date_short'))
df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%Y") + " " + (pl.col("dates").dt.strftime("%m"))).alias('dates'))
df_calendar = df_calendar.group_by("dates").agg(pl.col("column_0").sum())
result = {"scraping-date": scrapeDate, "months": df_calendar['dates'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
df_calendar = df_calendar.group_by(['dates', 'date_short']).agg(pl.col("column_0").sum())
df_calendar = df_calendar.sort('dates')
df_calendar = df_calendar.drop('dates')
result = {"scraping-date": scrapeDate, "months": df_calendar['date_short'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
return result

16
etl/src/data/etl_property_capacities_weekdays.py
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@ -8,6 +8,7 @@ d = data.load()
def property_capacities_weekdays(id: int, scrapeDate: str):
extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
df_calendar = pl.DataFrame()
numWeeks = 0
@ -19,12 +20,15 @@ def property_capacities_weekdays(id: int, scrapeDate: str):
df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
numWeeks = round((df_calendar.get_column("dates").max() - df_calendar.get_column("dates").min()).days / 7, 0)
df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday"))
df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday_num"))
df_calendar = df_calendar.with_columns(pl.col("dates").dt.strftime("%A").alias("weekday"))
df_calendar = df_calendar.drop("dates")
df_calendar = df_calendar.group_by("weekday").agg(pl.col("column_0").sum())
df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks).alias("weekday"))
df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks).alias("column_0"))
df_calendar = df_calendar.sort('weekday_num')
df_calendar = df_calendar.drop('weekday_num')
print(df_calendar)
result = {"scraping-date": scrapeDate, "weekday": df_calendar['weekday'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
return result
return result