Examples of using Exploratory data analysis in English and their translations into Chinese
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Exploratory data analysis(EDA) is an exciting task.
探索性的数据分析（EDA）是一项令人兴奋的任务。Now, it's your turn to conduct your own exploratory data analysis.
现在，轮到你来进行自己的探索性数据分析了。This is an exploratory data analysis with no labelled data available.
Note that data exploration is also called exploratory data analysis.
注意到，数据探索也称为探索性数据分析，.Exploratory data analysis(EDA) the very first step in a data project.
探索性数据分析（EDA）是数据项目的第一步。Start by learn about what exploratory data analysis(EDA) is and why it is important.
我们将先学习什么是探索性数据分析(EDA)以及它为何如此重要。Exploratory data analysis focuses on discovering new features of the data. .
探索性数据分析，侧重于在数据中，发现新的特征。More on this in A Gentle Introduction to Exploratory Data Analysis.
更多相关内容，请参阅AGentleIntroductiontoExploratoryDataAnalysis。Exploratory data analysis(EDA) is the first step of any data science project.
探索性数据分析（EDA）是数据项目的第一步。It involves learning how to perform exploratory data analysis and running sklearn regressors and classifiers.
它涉及学习如何执行ExploratoryDataAnalysis和运行Sklearn回归和分类器。Exploratory data analysis(EDA) is the first step in the data analysis process.
探索性数据分析（EDA）是数据分析过程的第一步。In the early stages of a project,you will often be doing an Exploratory Data Analysis(EDA) to gain some insights into your data. .
在项目的早期阶段，你通常会进行探索性数据分析(EDA)以获取对数据的一些洞察。Exploratory data analysis(EDA) is the first part of your data analysis process.
探索性数据分析（EDA）是数据分析过程的第一部分。It is a 2 Dimensional graphical library that produces clear andconcise graphs that are essential for Exploratory Data Analysis(EDA).
通过该二维图形库，用户可以生成各种清晰明了的图形，这对于探索性数据分析(EDA)是至关重要的。This is called exploratory data analysis, and typically focuses on correlations among variables.
这称为探索性数据分析，通常关注变量之间的相关性。In the early stages of a project,you will often be doing an Exploratory Data Analysis(EDA) to gain some insights into your data. .
在项目的早期阶段，你通常会进行探索性数据分析（EDA），以获得对数据的一些见解。IoGAS is a leading exploratory data analysis software application developed specifically for the resources industry.
IoGAS是专为资源行业开发的领先的探索性数据分析软件应用程序。The last section covers two popular Python packages for data analysis, Numpy and Pandas,and includes an exploratory data analysis.
最后一节涵盖了数据分析，numpy的和熊猫两种流行的Python包，以及包括探索性数据分析。What you can do is to use different exploratory data analysis and visualization techniques to have a better understanding of your data set.
您可以使用不同的探索性数据分析和可视化技术来更好地理解您的数据集。In statistical applications,data analysis can be divided into descriptive statistics, exploratory data analysis(EDA), and confirmatory data analysis(CDA).
在统计应用中，数据分析可分为描述性统计，探索性数据分析（EDA）和验证性数据分析（CDA）。Exploratory data analysis(EDA) is an approach analyzing data sets to summarize their main characteristics, often with visual methods.
探索性数据分析（EDA）是分析数据集以总结其主要特征的方法，通常使用可视化的方法。GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local spatial autocorrelation.
GeoDa有强的力量来执行空间分析，多元探索性数据解析，以及全球及地面的空间数据。Exploratory data analysis(EDA) is a technique that analyze data to recapitulate their major features, frequently with visual approaches.
探索性数据分析（EDA）是分析数据集以总结其主要特征的方法，通常使用可视化的方法。GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local spatial autocorrelation.
GeoDa具有强大的功力来执行空间分析，多元探索性数据解析，以及满世界和地点的空间数据。Introduction Exploratory Data Analysis(EDA) helps us to uncover the underlying structure of data and its dynamics through which we can maximize the insights.
探索性数据分析（EDA）帮助我们认识底层的数据基结构及其动力学，以此来最大限度发掘出数据的可能性。Today, organizations can choose from a rapidly growing range of tools and technologies like streaming analytics,graph analytics, and exploratory data analysis in HPC environments.
今天，企业可以选择快速增长的工具和技术，如流分析，图形分析和在HPC环境中的探索性数据分析。Exploratory data analysis, data summarization, and data visualizations can be used to help frame your predictive modeling problem and better understand the data. .
探索性的数据分析，数据汇总和数据可视化可用于帮助构建预测性建模问题并更好地理解数据。In statistical applications,some people divide data analysis into descriptive statistics, exploratory data analysis(EDA), and confirmatory data analysis(CDA).
在统计应用中，数据分析可分为描述性统计，探索性数据分析（EDA）和验证性数据分析（CDA）。
