Data Quality updated to include Regions and more information

etl/src/data/__init__.py

@@ -2,7 +2,7 @@ import os
 
 from dotenv import load_dotenv
 
-from data import database
+from etl.src.data import database
 
 dirname = os.path.dirname(__file__)
 envfile = os.path.join(dirname, '../.env')

etl/src/data/database.py

@@ -221,6 +221,23 @@ class Database:
 				property_id
 		""")
 
+	def extractions_with_region(self):
+		return self.connection.sql("""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendar, 
+				extractions.property_id, 
+				extractions.created_at,
+				properties.seed_id, 
+				regions.name
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			LEFT JOIN 
+				consultancy_d.seeds ON seeds.id = properties.seed_id 
+			LEFT JOIN 
+				consultancy_d.regions ON regions.id = seeds.region_id 
+			""")
 
 	def extractions_for(self, property_id):
 		return self.connection.sql(f"""

etl/src/mauro/data_quality.py

@@ -1,4 +1,4 @@
-import data
+from etl.src import data
 import json
 import polars as pl
 from datetime import datetime
@@ -9,7 +9,7 @@ import numpy as np
 # Get Data from DB
 inst = data.load()
 
-df = inst.extractions().pl()
+df = inst.extractions_with_region().pl()
 print(df)
 
 counter = 0
@@ -17,7 +17,7 @@ data = []
 for row in df.iter_rows():
     property_id = row[1]
     created_at = row[2].date()
-    dict = {'property_id': property_id, 'created_at': created_at}
+    dict = {'property_id': property_id, 'created_at': created_at, 'name': row[3]}
 
     jsonStr = row[0]
     if jsonStr:
@@ -30,8 +30,10 @@ for row in df.iter_rows():
 dfNew = pl.from_dicts(data)
 dfNew.write_csv('results/data_quality.csv')
 print(dfNew)
-'''
 
+
+
+'''
 dfNew = pl.read_csv('results/data_quality.csv')
 dfNew = dfNew.with_columns(pl.col("created_at").map_elements(lambda x: datetime.strptime(x, "%Y-%m-%d").date()))
 
@@ -42,11 +44,30 @@ prop = dfTemp.get_column('property_id')
 dfTemp = dfTemp.drop('property_id')
 crea = dfTemp.get_column('created_at')
 dfTemp = dfTemp.drop('created_at')
+name = dfTemp.get_column('name')
+dfTemp = dfTemp.drop('name')
 dfTemp = dfTemp.with_columns(sum=pl.sum_horizontal(dfTemp.columns))
 sumCol = dfTemp.get_column('sum')
 
-# Create new DF with only property_id, created_at and sum
+# Create new DF with only property_id, created_at ,Location name and sum
-df = pl.DataFrame([prop, crea, sumCol])
+df = pl.DataFrame([prop, crea, name, sumCol])
+df = df.sort('created_at')
+
+# Create Full Copy
+# 0 = Alles
+# 1 = Heidiland
+# 2 = Davos
+# 3 = Engadin
+# 4 = St. Moritz
+filterList = ['Alle Regionen', 'Heidiland', 'Davos', 'Engadin', 'St. Moritz']
+
+filter = 4
+if filter != 0:
+    df = df.filter(pl.col("name") == filter)
+
+# Remove Location name
+df = df.drop('name')
+
 
 # Get unique property_ids
 propsIDs = df.unique(subset=["property_id"])
@@ -65,13 +86,21 @@ for id in propsIDs:
 matrix = pl.DataFrame(matrix)
 dates = matrix.columns
 matrix = matrix.to_numpy()
+# normalized
+matrix = matrix/1111
+
 
 yRange = range(len(dates))
+xRange = range(len(propsIDs))
 matrix = matrix.T
 plt.imshow(matrix)
 plt.yticks(yRange[::5], dates[::5])
+plt.xticks(xRange[::10], propsIDs[::10])
+plt.title(filterList[filter])
 plt.xlabel("Property ID")
 plt.ylabel("Scrape Date")
+plt.colorbar()
+plt.tight_layout()
 
 # Create DiffMatrix
 diffMatrix = np.zeros((len(matrix)-1, len(matrix[0])))
@@ -82,6 +111,13 @@ for y in range(len(matrix[0])):
 plt.figure()
 plt.imshow(diffMatrix, cmap="Reds")
 plt.yticks(yRange[::5], dates[::5])
+plt.xticks(xRange[::10], propsIDs[::10])
+plt.title(filterList[filter])
 plt.xlabel("Property ID")
 plt.ylabel("Scrape Date")
-plt.show()
+plt.colorbar()
+plt.tight_layout()
+
+plt.show()
+
+

etl/src/mauro/vorbuchungsZeit.py

@@ -1,5 +1,5 @@
-import data
+from etl.src import data
-from data import etl_pipelines as ep
+from etl.src.data import etl_pipelines as ep
 import polars as pl
 from datetime import datetime, timedelta
 import pandas as pd