油井数据清洗系统研究

Yao Feng, Li Zhao
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引用次数: 0

摘要

在信息时代,随着互联网技术的不断发展,信息数据占据了当代社会的各个领域。大数据时代的发展使得这些数据越来越突出。在用户从这些海量数据中读取所需信息的同时,数据质量也成为了用户关注的问题。在数据分析前对大量数据进行预处理,如一些重复值、缺失值处理不准确等异常数据,并通过数据清洗系统对数据进行过滤,提高数据的规范化程度,从而提高数据的分析效率,减少一些不必要的开支,节省时间和精力。本文的数据清洗系统是基于flash框架实现的。以Python为主要的数据清洗语言,对一些不同源数据的一些结构性问题、重复问题和缺失问题进行技术清洗和标准集成。通过对异常数据的处理,大大提高了数据质量和数据分析效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Oil Well Data Cleaning System
Abstract In the information age, with the continuous development of Internet technology, information data occupies every field of contemporary society. The development of the big data age makes these data more and more prominent. While users read the information they need from these massive data, data quality has also become a concern of users. A large number of data are preprocessed before data analysis, such as some duplicate values, missing values deal with inaccurate and other abnormal data, and filter the data through the data cleaning system to improve the standardization of the data, so as to improve the analysis efficiency of the data, reduce some unnecessary expenses, and save time and effort. The data cleaning system in this paper is implemented based on flash framework. Taking Python as the main language for data cleaning, technical cleaning and standard integration are carried out for some structural problems, duplication problems and missing problems of some different source data. Through the processing of abnormal data, the data quality and data analysis efficiency are greatly improved.
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