The issue you’re facing seems related to the fact that multiple eahv-iv-2469-000101:decisionRef0 nodes are being processed and appended to a single row in your data frame. This can be resolved by identifying and handling each unique decisionRef0 node separately. Here’s an updated version of your code snippet, including some adjustments to handle the repeated occurrence of eahv-iv-2469-000101:decisionRef0 nodes: ################################################################################################## # Konvertierung von xml zu csv. ################################################################################################## doc <- read_xml(path/my_file) # Namespace bestimmen nmsp <- c(doc = "http://www.

Drawing a Filled Circle with an Outline: Understanding the Problem and Solution When it comes to graphics programming, one of the most basic yet fundamental shapes we encounter is the circle. However, in many cases, we need not just draw a circle but also add an outline around it for better visibility or visual appeal. In this article, we’ll delve into the world of Core Graphics on iOS and explore how to achieve this seemingly simple task.

Understanding the Problem with Null Values in MySQL When working with data, especially in complex formats like JSON, it’s common to encounter null values. These can be frustrating, as they don’t provide any meaningful information and can cause errors in your queries. In this article, we’ll explore how to handle null values in MySQL, specifically when dealing with JSON fields. Background on JSON and MySQL JSON (JavaScript Object Notation) is a lightweight data interchange format that has become widely used for storing and exchanging data.

Calculating Date Differences in SQL Server Overview When working with dates in SQL Server, it’s common to need to calculate the difference between two dates or times. In this article, we’ll explore how to do just that, including calculating date differences in hours and minutes. Introduction to Dates and Times In SQL Server, dates and times are stored as 8-byte integers, which can lead to confusion when trying to perform calculations involving these values.

Inserting/joining Table on Multiple Conditions In this article, we’ll explore a common problem in database design: inserting or joining tables based on multiple conditions. We’ll dive into the technical details of the solution and provide examples to illustrate the concepts. Problem Statement We have a table t with columns version, id, and an additional column we want to insert, say groupId. The goal is to create a new group id for each version based on the ids in that version.

Converting Time Durations in Pandas DataFrames ==================================================================== When working with time-related data in pandas DataFrames, it’s common to encounter columns containing time durations. These can be days, hours, minutes, or even combinations thereof. In this article, we’ll explore how to convert these time durations into a usable format, such as dates. Background: Understanding Time Durations Time durations are typically represented as strings, with each part of the duration separated by spaces or other characters.

Creating a Deadline Based on Criteria Introduction In this article, we’ll explore how to create a deadline based on specific criteria using Python and the pandas library. We’ll cover how to calculate deadlines for dates that fall on weekends or holidays, as well as for dates within specific time ranges. Holidays and Weekends When dealing with deadlines that are relative to specific dates, we need to consider holidays and weekends. A holiday is a day when most businesses are closed, while a weekend is a period of two consecutive days when most businesses are closed.

Group by Dimensions and Gather from Wide to Long with Multiple Metrics Introduction In this article, we will explore how to group data by dimensions and gather values from wide tables into a long format. This problem is commonly encountered in data analysis and business intelligence tasks. The example provided uses Big Query as the database management system. However, the concepts can be applied to other databases, such as SQL Server, Oracle, or MySQL.

Aggregate Functions in Having: Understanding the MySQL Extensions Introduction When working with SQL queries, it’s essential to understand when to use aggregate functions like AVG(), MAX(), or MIN() in the HAVING clause. This tutorial will delve into the world of aggregate functions in having and explain the underlying MySQL extensions that make these concepts possible. The Problem: Aggregate Functions in Having Let’s start with a question from Stack Overflow: “I understand why aggregate functions have to be used in the having part of a query, but do not understand the reasoning why the two queries below return different values.

Understanding Cross Joins and Not-Exists Queries When dealing with database queries, it’s essential to understand the differences between various types of joins and subqueries. In this article, we’ll delve into cross joins, not-exists queries, and explore how to identify them. Introduction to Cross Joins A cross join is a type of join that results in a Cartesian product of two tables. It produces a large number of rows where each row from the first table is combined with every row from the second table.