Calculating Cumulative Sum of Previous Month’s FTE_YTD In this section, we will explore how to update the FTE_YTD calculation to be a cumulative sum of previous month’s values based on CALENDAR_MONTH and CALENDAR_DATE. Current Calculation The current calculation is as follows: SELECT count(*) as Workdays_Month, SAFE_DIVIDE(AMOUNT, SAFE_MULTIPLY((count(*) OVER (PARTITION BY extract(year from date_trunc(CALENDAR_DATE, month)) ORDER BY CALENDAR_DATE)), 7.35)) as FTE_MONTH, count(*) OVER (PARTITION BY extract(year from date_trunc(CALENDAR_DATE, month)) ORDER BY CALENDAR_DATE) as Workdays_YTD, SAFE_DIVIDE(AMOUNT, SAFE_MULTIPLY((count(*) OVER (PARTITION BY extract(year from date_trunc(CALENDAR_DATE, month)) ORDER BY CALENDAR_DATE)), 7.

Understanding the Issue with if Statements and Factors in R Introduction In this article, we will delve into a common issue that arises when using if statements within a loop to manipulate factors in R. The problem typically manifests itself as an error where a missing value where TRUE/FALSE needed is encountered. This can be particularly frustrating when trying to modify specific rows of a data frame based on certain conditions.

Using Environment-Dependent Source Specifications in DBT ===================================================== As a data engineer, managing source specifications across different environments is crucial for maintaining data lineage and consistency. DBT (Data Build Tool) provides an efficient way to manage these sources using environment-dependent configurations. In this article, we will explore how to use environment-dependent source specifications in DBT. Introduction to DBT Sources DBT’s source function allows you to reference external databases as if they were part of your schema.

Iterating Over Query Results in PostgreSQL In this article, we will explore how to iterate over the results of a query in PostgreSQL. We will discuss the different approaches available and provide examples to illustrate each concept. Introduction PostgreSQL is a powerful and flexible relational database management system that supports a wide range of SQL features, including iteration over query results. In this article, we will focus on iterating over the results of a query using various techniques.

Working with Large Excel Files: Understanding the Issue and Finding a Solution When working with large Excel files, it’s not uncommon to encounter issues related to memory usage or permission errors. In this article, we’ll delve into the problem you’re experiencing with copying cells from one Excel file to another and provide a solution that involves reading the files in chunks. Understanding the Problem The code snippet you provided uses the openpyxl library to load two Excel files and copy data from one sheet to another.

Loading the DeepSeek-V3 Model from a Local Repository As a professional technical blogger, I’ll guide you through the process of loading the DeepSeek-V3 model inference using the Hugging-Face Transformer library. In this article, we’ll delve into the details of working with local repositories and provide a step-by-step approach to achieve this. Introduction The DeepSeek-V3 model is a popular choice for natural language processing tasks, particularly in the realm of conversational AI.

Understanding Timestamps in SQLite As a developer, working with databases is an essential part of any project. When it comes to storing timestamps in SQLite, there are several ways to do so. In this article, we’ll delve into the different methods of saving timestamp values in SQLite and explore their implications. Introduction to Timestamps A timestamp is a value that represents the date and time when something happened or was stored.

Here’s the SQL query that calculates the begin and end values based on the seat_change and ref. WITH distinct_refs AS ( SELECT DISTINCT ref FROM test_table ), months AS ( SELECT d.ref, to_char(date_trunc('month', dateadd(month, seq4() - 1, '2023-11-01')), 'yyyy-mm') as month FROM distinct_refs d CROSS JOIN table(generator(rowcount => 15)) -- 15 months from 2023-11 to 2025-01 ), changes AS ( SELECT ref, date_trunc('month', start_date) as month, sum(seat) as seat_change FROM test_table GROUP BY ref, date_trunc('month', start_date) ), monthly_seats AS ( SELECT m.

Creating Grid of Scatter Plots with Seaborn in Python ===================================================== In this article, we will explore how to create a grid of scatter plots using the popular data visualization library Seaborn in Python. We will provide an example code and explanation for creating a 3x3 grid of scatter plots. Introduction Seaborn is a powerful data visualization library built on top of matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

Understanding the iOS App Sandbox and Cache Directory Behavior When it comes to developing apps for Apple devices, including iPhones and iPads, developers need to be aware of the app sandbox model. This concept is central to understanding how the operating system handles various aspects of an app’s data and storage. What is the App Sandbox? The app sandbox is a security feature introduced by Apple to protect user data and ensure that apps do not access sensitive information without explicit permission.