Issue 3 fast fertig

#3

Der Issue ist soweit bereit, es gibt aber noch das Problem, dass das ScrapeDate nicht als Datum sondern asl Integer interpretiert wird im database.py. Deshalb ist es im Moment als konstante implementiert

etl/src/api/main.py

@@ -1,6 +1,7 @@
 import data
 import polars as pl
 from data import etl_property_capacities as etl_pc
+from data import etl_property_capacities_monthly as etl_pcm
 from data import etl_region_capacities as etl_rc
 from data import etl_region_capacities_comparison as etl_rcc
 from fastapi import FastAPI, Response
@@ -42,6 +43,11 @@ def property_capacities_data(id: int):
 	capacities = etl_pc.property_capacities(id)
 	return capacities
 
+@app.get("/property/{id}/capacities/monthly/{scrapeDate}")
+def property_capacities_data(id: int, scrapeDate: str):
+	capacities = etl_pcm.property_capacities_monthly(id, scrapeDate)
+	return capacities
+
 @app.get("/property/{id}/base")
 def property_base_data(id: int):
 	return d.property_base_data(id).pl().to_dicts()

etl/src/data/database.py

@@ -334,6 +334,23 @@ class Database:
 				created_at
 		""")
 
+	def extractions_propId_scrapeDate(self, property_id: int, scrape_date: str):
+		return self.connection.sql(f"""
+			SELECT
+				JSON_EXTRACT(body, '$.content.days') as calendar,
+				created_at 
+			FROM 
+				consultancy_d.extractions
+			WHERE
+				type == 'calendar' AND
+				property_id = {property_id} AND
+				calendar NOT NULL AND
+				created_at >= '2024-04-16'
+			ORDER BY
+				created_at
+			LIMIT 1
+		""")
+
 	# Anzahl der extrahierten properties pro Exktraktionsvorgang
 	def properties_per_extraction(self, property_id):
 		return self.connection.sql("""

etl/src/data/etl_property_capacities_monthly.py

@@ -0,0 +1,24 @@
+from io import StringIO
+
+import polars as pl
+
+import data
+
+d = data.load()
+
+def property_capacities_monthly(id: int, scrapeDate: str):
+	extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
+	df_calendar = pl.DataFrame()
+
+	for row in extractions.rows(named=True):
+		scrapeDate = row['created_at']
+		df_calendar = pl.read_json(StringIO(row['calendar']))
+		columnTitles = df_calendar.columns
+		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.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()}
+	return result