用宏技术处理Word表格数据

在Microsfot Word2003环境下,利用宏的技术,处理表格数据。通过宏的技术,制作实现数据统计操作的快捷键,通过对快捷键的操作实现对表格数据的处理;利用宏的技术制作出对应操作的工具栏按钮,通过对这些按钮的点击操作处理多表格中数据。这些操作方便快捷,实现了简化操作和提高工效的目的。     

用宏技术处理Word表格数据

在Microsfot Word 2003环境下,利用宏的技术,处理表格数据.通过宏的技术,制作实现数据统计操作的快捷键,通过对快捷键的操作实现对表格数据的处理;利用宏的技术制作出对应操作的工具栏按钮,通过对这些按钮的点击操作处理多表格中数据.这些操作方便快捷,实现了简化操作和提高工效的目的.     

用宏技术处理Word表格数据

在不脱离MicrosoftWord2003环境下，利用宏的技术，处理表格数据。通过宏的技术，制作实现数据统计操作的快捷键，通过对快捷键的操作实现对表格数据的处理；利用宏的技术制作出对应操作的工具栏按钮，通过对这些按钮的点击操作处理多表格中数据。这些操作方便快捷，实现了简化操作和提高工效的目的。     

用Word200窗体功能快速输入表格数据

在Word2003中输入表格数据时，常有重复内容要输入，且常因某个输入对象搞不清而要查资料，这样不仅耽搁时间．而且劳动强度大。利用窗体功能就可比较好地解决这一问题。     

5秒钟将Word表格数据转移到Excel

在编辑文档的时候，有时为了方便，我们会经常将很多表格数据直接插入到Word当中，但是大量的表格数据留存在Word文档中，日后的操作和整理都会变得十分麻烦。这时，我们有可能会想到将原有的Word中的所有表格数据全部转移到Excel中，     

Word表格数据也计算

我们在使用Word编辑文稿时，有时需要在Word中插入表格或公式来对数据统计、分析和对文字内容进行补充说明，平时我们常用的也就是数据“求和”和“平均数”，在Excel中来完成数据的统计、计算大家都会做。而在Word中怎样才能完成数据的统计、计算呢？     

用Word97处理复杂的表格斜线

一般人认为在Word 97中无法绘制此类表格,其实我们稍稍动一下脑筋,充分发挥Word 97的强大编辑功能,也就不难找出解决问题的途径.笔者拟就了一种方法,以供参考.Word 97的单元格中只允许存在对角线,而对非对角斜线则不允许其存在.我们可以采用“化整为零”的办法:先将一个单元格拆分为两个单元格,然后在其中的一个单元格中画上对角线,再在图文框中画另外一条非对角斜线,并叠加到单元格上,这样就能巧妙实现图1中的表格斜线了.     

将Word表格数据转换为Excel表格

有时候我们需要将Word中的表格数据转换到Excel工作表中,那么,你是不是通过复制粘贴的方法来进行的呢？这种方法当然可以,但你会发现它会把文字格式一起复制过去,这给我们在Excel工作表中的编辑带来了不便。这里种介绍两种方法,将Word中整个表格中的数据转换到Excel工作表中。     

Python读取Word表格数据及批量处理的方法

Python目前最新版本为3.9.1,由荷兰人Guido van Rossum于1989年发明,如今在编程和行业应用中几乎无人不知,在大数据和人工智能方面的应用和优势日渐明显.属于面向对象、任务的直译式计算机程序设计语言.主要优势表现3个方面:(1)语言生态.在编程过程和环境中可以使用多种外部编程资源,到目前为止Pyt...     

数据自动化输出到Word表格方法研究

随着信息社会办公自动化的飞速发展,越来越多的数据需要输出到Word表格文档中,本文首先讨论了数据输出到Word表格的基本方法,然后介绍了实现的一般步骤,最后给具体实现过程中关键步骤的代码以供参考。     

数据自动化输出到Word表格方法研究

随着信息社会办公自动化的飞速发展,越来越多的数据需要输出到Word表格文档中,本文首先讨论了数据输出到Word表格的基本方法,然后介绍了实现的一般步骤,最后给具体实现过程中关键步骤的代码以供参考。     

Excel数据处理在教学工作中的应用

很多教师在日常办公中对Excel强大数据处理功能不够了解,仍然用比较传统的方法来处理数据,使得工作效率大打折扣。本文通过几个典型实例来展现EXCEL处理表格数据的强大功能,合理利用EXCEL确实能提高我们的工作效率。     

The supermac macro processor in pascal

SUPERMAC-Pascal is an implementation of the general-purpose macro processor SUPERMAC. It allows the user to write macros using Pascal procedures and to compile these procedures using his normal Pascal compiler. This paper describes how the macro processor was adapted to fit Pascal, and how the apparently intractable problems of performing string processing in Pascal were tackled.     

Runtime data center temperature prediction using Grammatical Evolution techniques

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Grammatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2 °C and 0.5 °C in CPU and server inlet temperature respectively.     

A pattern matching macro processor

A general-purpose pattern matching macro processor is described. Macro patterns can be defined using regular expressions. Macro calls are treated by balancing pattern matching at the token unit level, allowing options, alternatives and repetition. Thus, text in a language with a nested structure can be dealt with. In a macro body, Algol-style macro-time operations are allowed, which improves writing and reading. Our macro processor can also be used as a tool for language conversion since it incorporates a feature to declare language-dependent constructs such as comments, string notations and parentheses pairs. Although our macro processor is not biased towards any particular language, it has successfully converted an Algol 68-style text into a Fortran text. Problems of language conversion using macros are briefly discussed based upon the experience obtained through this macro processor.     

Building and querying e-catalog networks using P2P and data summarisation techniques

One of the critical issues in Web-based e-commerce has been how to efficiently and effectively integrate and query heterogeneous, diverse e-catalogs. We propose an integration framework for building and querying catalogs. Our approach is based on a hybrid of peer-to-peer data sharing paradigm and Web-services architecture. Peers in our system serve as domain-specific data integration mediators. Links between peers are established based on the similarity of the domain they represent. The relationships are used for routing queries among peers. As the number of catalogs involved grow larger, the need for filtering irrelevant data sources will become increasingly high. We apply a summarisation technique to summarise the content of catalogs. The summaries are used to pre-selecting data sources that are relevant to a user query. We use terms e-catalog and catalog interchangeably.     

Statistical techniques using NURE airborne geophysical data and NURE geochemical data

Some standard techniques in multivariate analysis are used to describe the relationships among remotely sensed observations (Landsat and airborne geophysical data) and between these variables and hydrogeochemical and stream sediment analyses. Gray-level pictures of such factors make the analytic results more accessible and easier to interpret.     

Automatic abstraction in reinforcement learning using data mining techniques

In this paper, we used data mining techniques for the automatic discovering of useful temporal abstraction in reinforcement learning. This idea was motivated by the ability of data mining algorithms in automatic discovering of structures and patterns, when applied to large data sets. The state transitions and action trajectories of the learning agent are stored as the data sets for data mining techniques. The proposed state clustering algorithms partition the state space to different regions. Policies for reaching different parts of the space are separately learned and added to the model in a form of options (macro-actions). The main idea of the proposed action sequence mining is to search for patterns that occur frequently within an agent’s accumulated experience. The mined action sequences are also added to the model in a form of options. Our experiments with different data sets indicate a significant speedup of the Q-learning algorithm using the options discovered by the state clustering and action sequence mining algorithms.