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![]() ![]() When querying large datasets, consider utilizing batch processing to conserve memory: # Batch processing for large datasetsįor weekly_sale in session.query(Sale).filter(Sale.timestamp >= last_week).yield_per(1000): This demonstrates injecting a raw SQL query into the SQLAlchemy session. Weekly_sales = session.execute(raw_query).fetchall() WHERE timestamp >= datetime('now', '-7 days') In such cases, using raw SQL or hybrid properties can be useful: # Raw SQL query example ![]() Sometimes the ORM might not provide the exact functionality you need, or complex queries might be slow. This query gets all the sales records from the last month, considering the actual month change and not approximately 30 days before. filter(extract('month', Sale.timestamp) = last_month_number) filter(extract('year', Sale.timestamp) = current_year) Last_month_number = (datetime.utcnow() - timedelta(days=30)).month SQLAlchemy also gives you the ability to use SQL functions directly, like the EXTRACT function, for more complex queries like this: current_year = datetime.utcnow().year Last_day = datetime.utcnow().replace(tzinfo=utc) - timedelta(days=1)īy setting the ‘replace(tzinfo=utc)’, you are making sure that the comparison takes place in the UTC timezone. Here’s how you can make your queries timezone-aware: from sqlalchemy import extract It’s important to consider timezones when performing time-based queries. Adjust the days for the month query based on the specific requirements (e.g., 28, 30, 31). In these queries, the timedelta changes to capture a larger span of time. Monthly_sales = session.query(Sale).filter(Sale.timestamp >= last_month).all() Last_month = datetime.utcnow() - timedelta(days=30) # Approximation Weekly_sales = session.query(Sale).filter(Sale.timestamp >= last_week).all() Last_week = datetime.utcnow() - timedelta(weeks=1) To select records from the last week or month, you adjust the timedelta accordingly: # Last week This simple query filters records where the timestamp is greater than or equal to one day ago from now. Recent_sales = session.query(Sale).filter(Sale.timestamp >= last_day).all() Last_day = datetime.utcnow() - timedelta(days=1) Here’s a basic example: from sqlalchemy import func Selecting records from the last day involves understanding datetime operations. With this setup, you can insert some records with timestamps for demonstration. _init_() got multiple values for argument 'schema'Ĭonsider a simple model representing a ‘Sale’ as follows: from sqlalchemy import create_engine, Column, Integer, DateTimeįrom import declarative_baseĮngine = create_engine('sqlite:///sales.db') 'int' object has no attribute '_sa_instance_state' This table will have the data of 2 employees including the time that he enters the office and the exit time (start time and end time).AttributeError: Can't Set Attribute in SQLite For this purpose, we will create a table named WorkTime. In this example, we will calculate the total minutes worked by 2 customers. How to get the number of minutes worked by day of some employees using SQL subtract dates To calculate the work order ID that took longer, in the WHERE clause we are comparing our WorkOrder time with the longest one in the table using the MAX function. We will use the StartDate and EndDate columns with the DATEDIFF function to calculate the number of days spent. You have the Word Order IDs to check them. ![]() The orders took longer took 32 days to be completed. (SELECT MAX(DATEDIFF(day,StartDate,EndDate)) FROM. We will now work with the Adventureworks WorkOrder table.ĭATEDIFF(day,StartDate,EndDate) totalTime ![]() How to get the order id of the order which took more days using SQL subtract dates We got the date of the maximum average rate value and the minimum and then calculate the difference in months. As you can see, it took 31 months (2 years and 7 months) to have the maximum and minimum average price for the EUR to USD rate price. ![]()
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