Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision...Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.展开更多
准确预测电容器组在一段时间内的电容,分析其变化趋势,对并联电容器组的安全使用、延长寿命有很重要的意义。本文提出一种基于Microsoft时序算法,采用电能质量监测数据的10 k V侧并联电容器组电容预测模型,并在此基础上设计了并联电容...准确预测电容器组在一段时间内的电容,分析其变化趋势,对并联电容器组的安全使用、延长寿命有很重要的意义。本文提出一种基于Microsoft时序算法,采用电能质量监测数据的10 k V侧并联电容器组电容预测模型,并在此基础上设计了并联电容组电容趋势分析及预测预警系统。通过该系统,用户不仅能够监测电容器组的运行状态,为电容器组故障分析提供可靠依据,而且可以预测电容器组电容值的变化趋势,并实现其寿命预警。实际运行效果验证了该模型的可行性与准确性。展开更多
文摘Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.
文摘准确预测电容器组在一段时间内的电容,分析其变化趋势,对并联电容器组的安全使用、延长寿命有很重要的意义。本文提出一种基于Microsoft时序算法,采用电能质量监测数据的10 k V侧并联电容器组电容预测模型,并在此基础上设计了并联电容组电容趋势分析及预测预警系统。通过该系统,用户不仅能够监测电容器组的运行状态,为电容器组故障分析提供可靠依据,而且可以预测电容器组电容值的变化趋势,并实现其寿命预警。实际运行效果验证了该模型的可行性与准确性。
