Upload daily_energy_pipeline.py

daily_energy_pipeline.py

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+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
+result = collection.insert_many(data_dict)