什么是探索性数据分析(EDA)？ (What is Exploratory Data Analysis(EDA)?)

If we want to explain EDA in simple terms, it means trying to understand the given data much better, so that we can make some sense out of it.

如果我们想用简单的术语来解释EDA，则意味着试图更好地理解给定的数据探索性数据分析，以便我们可以从中获得一些意义。

We can find a more formal definition in Wikipedia.

我们可以在Wikipedia中找到更正式的定义。

In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

在统计学中， 探索性数据分析是一种分析数据集以总结其主要特征的方法，通常使用视觉方法。 可以使用统计模型，也可以不使用统计模型，但是EDA主要用于查看数据可以在形式建模或假设检验任务之外告诉我们的内容。

EDA in Python uses data visualization to draw meaningful patterns and insights. It also involves the preparation of data sets for analysis by removing irregularities in the data.

Python中的EDA使用数据可视化来绘制有意义的模式和见解。 它还涉及通过消除数据中的不规则性来准备用于分析的数据集。

Based on the results of EDA, companies also make business decisions, which can have repercussions later.

根据EDA的结果探索性数据分析，公司还制定业务决策，这可能会在以后产生影响。

In this article we’ll see about the following topics:

在本文中，我们将看到以下主题：

Data Sourcing 资料采购 Data Cleaning 数据清理 Univariate analysis 单变量分析 Bivariate analysis 双变量分析 Multivariate analysis 多元分析 1.数据采购 (1. Data Sourcing)

Data Sourcing is the process of finding and loading the data into our system. Broadly there are two ways in which we can find data.

数据采购是查找数据并将其加载到我们的系统中的过程。 大致而言，我们可以通过两种方式查找数据。

Private Data 私人数据 Public Data 公开资料

Private Data

私人数据

As the name suggests, private data is given by private organizations. There are some security and privacy concerns attached to it. This type of data is used for mainly organizations internal analysis.

顾名思义，私有数据是由私有组织提供的。 附加了一些安全和隐私问题。 这类数据主要用于组织内部分析。

Public Data

公开资料

This type of Data is available to everyone. We can find this in government websites and public organizations etc. Anyone can access this data, we do not need any special permissions or approval.

此类数据适用于所有人。 我们可以在政府网站和公共组织等中找到此文件。任何人都可以访问此数据，我们不需要任何特殊权限或批准。

We can get public data on the following sites.

我们可以在以下站点上获取公共数据。

The very first step of EDA is Data Sourcing, we have seen how we can access data and load into our system. Now, the next step is how to clean the data.

EDA的第一步是数据采购，我们已经看到了如何访问数据并将其加载到系统中。 现在，下一步是如何清除数据。

2.数据清理 (2. Data Cleaning)

After completing the Data Sourcing, the next step in the process of EDA is Data Cleaning. It is very important to get rid of the irregularities and clean the data after sourcing it into our system.

完成数据采购后，EDA过程的下一步是数据清理 。 将数据来源到我们的系统中之后，消除

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