Update daily_energy_pipeline.py

daily_energy_pipeline.py

@@ -1,49 +1,49 @@
-import datetime
-import pandas as pd
-import os
-from entsoe import EntsoePandasClient
-from pymongo.mongo_client import MongoClient
-from pymongo.server_api import ServerApi
-
-entsoe_api_key = os.getenv("ENSTO_API")
-mongo_password= os.getenv("mango_password")
-client = EntsoePandasClient(api_key=entsoe_api_key)
-country_code = "SE_3"
-energy_load_data = pd.DataFrame()
-
-start_date = pd.Timestamp(
-    datetime.datetime.now() + datetime.timedelta(days=-2), tz="Europe/Berlin"
-)
-end_date = pd.Timestamp(
-    datetime.datetime.now() + datetime.timedelta(days=1), tz="Europe/Berlin"
-)
-
-
-
-load = client.query_load(country_code, start=start_date, end=end_date)
-if load is not None and not load.empty:
-            # Resample hourly data to daily averages
-            daily_load = load.resample("D").mean()
-            daily_load = daily_load.reset_index()
-            daily_load.columns = ["date", "load"]
-
-            # Append to the main DataFrame
-            energy_load_data = pd.concat([energy_load_data, daily_load], axis=0)
-energy_load_data['country_code']=country_code
-
-
-energy_load_data['date'] = pd.to_datetime(energy_load_data['date'], format='%Y-%m-%d')
-energy_load_data['date'] = pd.to_datetime(energy_load_data['date']).dt.date
-energy_load_data['date'] = pd.to_datetime(energy_load_data['date'])
-energy_load_data = energy_load_data.iloc[[-1]]
-
-
-uri = "mongodb+srv://pgmjo:"+mongo_password+"@cluster0.noq3s.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
-# Create a new client and connect to the server
-client = MongoClient(uri)
-db = client["daily_energy_load"]  # Replace 'mydatabase' with your database name
-collection = db["S3"]  # Replace 'mycollection' with your collection name
-data_dict = energy_load_data.to_dict("records")
-
-# Insérer dans la collection MongoDB
+import datetime
+import pandas as pd
+import os
+from entsoe import EntsoePandasClient
+from pymongo.mongo_client import MongoClient
+from pymongo.server_api import ServerApi
+
+entsoe_api_key = "22cb6d0f-5368-4495-95b0-3856c4bb6f7b"
+mongo_password= "aIdg0yUMUaZHyVN7"
+client = EntsoePandasClient(api_key=entsoe_api_key)
+country_code = "SE_3"
+energy_load_data = pd.DataFrame()
+
+start_date = pd.Timestamp(
+    datetime.datetime.now() + datetime.timedelta(days=-2), tz="Europe/Berlin"
+)
+end_date = pd.Timestamp(
+    datetime.datetime.now() + datetime.timedelta(days=1), tz="Europe/Berlin"
+)
+
+
+
+load = client.query_load(country_code, start=start_date, end=end_date)
+if load is not None and not load.empty:
+            # Resample hourly data to daily averages
+            daily_load = load.resample("D").mean()
+            daily_load = daily_load.reset_index()
+            daily_load.columns = ["date", "load"]
+
+            # Append to the main DataFrame
+            energy_load_data = pd.concat([energy_load_data, daily_load], axis=0)
+energy_load_data['country_code']=country_code
+
+
+energy_load_data['date'] = pd.to_datetime(energy_load_data['date'], format='%Y-%m-%d')
+energy_load_data['date'] = pd.to_datetime(energy_load_data['date']).dt.date
+energy_load_data['date'] = pd.to_datetime(energy_load_data['date'])
+energy_load_data = energy_load_data.iloc[[-1]]
+
+
+uri = "mongodb+srv://pgmjo:"+mongo_password+"@cluster0.noq3s.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
+# Create a new client and connect to the server
+client = MongoClient(uri)
+db = client["daily_energy_load"]  # Replace 'mydatabase' with your database name
+collection = db["S3"]  # Replace 'mycollection' with your collection name
+data_dict = energy_load_data.to_dict("records")
+
+# Insérer dans la collection MongoDB
 result = collection.insert_many(data_dict)