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This article focuses on "from database to visual performance analysis python", interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Next, let the editor take you to learn "from database to visual performance analysis python"!

First, why to do big data analysis

The main purpose of big data's analysis is to assist the company in decision-making and improve work efficiency.

For enterprises, it is undoubtedly very important to follow the times and follow the trend. Nokia, an once-popular mobile phone maker, was abandoned by the times because it refused to use Android and continued to use its own Symbian system. Later, Android was reused, and even so, it will not be able to return to the top of the company.

There are so many such cases that enterprises should learn from them.

Second, the process of big data's analysis:

Clarify the purpose and framework of the analysis

Data collection and data processing

Data analysis

Data presentation

Write a report

III. Tool selection

There are many kinds of big data analysis tools.

Part 1: data acquisition tool

Part 2: open source data tool

Part 3: data Visualization

Part 4: open source database

Big data analysts, often use the function of data analysis + data visualization, so I will talk about it from these two aspects.

1 、 excel

Excel can be said to be a very basic big data analysis tool, many people use excel to get started with big data analysis. Although excel is a basic big data analysis tool, in fact, excel has a very complete big data analysis function, which is very suitable for big data to analyze new people.

Advantages:

The newcomer is friendly and easy to use.

Easy to use, no need to master one more operating language

Simple operation and low learning cost

Disadvantages:

Sharing: excel is analyzed by big data in the form of a spreadsheet, which is not easy to share data. People who often need to share data must be devastated.

Data sources: when there are multiple data sources required, it is very troublesome to integrate data sources

Visualization: excel can perform basic visualization operations. But excel is static, and it is troublesome to operate when the data changes.

2 、 tableau

Tableau belongs to the leader of big data analysis tools abroad, and it is a more professional big data analysis tool.

Advantages:

Highly professional and suitable for people with high demand

Data cleaning / adjustment, checking / filtering data, joining / merging data all have a data processing strategy of a comparative system.

Property settings are very flexible.

Disadvantages:

In the aspect of learning, the operation is difficult and the learning cost is high.

Local adaptability: foreign developers, poor ability to solve data problems of domestic enterprises

Data source: you can connect to international mainstream databases, but not to some domestic databases

3 、 finebi

Finebi is a mainstream BI big data analysis tool, accounting for 16.3% of the domestic business intelligence market, with good big data analysis and data visualization capabilities.

Advantages:

The newlyweds have a good degree of friendliness, and can carry out self-service big data analysis.

Support multiple data source connections to adapt to the domestic data market

Easy to operate, you can edit the data analysis report by dragging and dropping

To adapt to the domestic environment, there is a set of solutions to the problems of domestic enterprises.

Shortcoming

The performance is not very stable. The biggest problem with BI now is that it can not be tested in the real environment, and so is Ali's. BI still has a long way to go.

Compared with tableau, it is relatively low-key and does not get the recognition it deserves.

(Mini Program has been added here. Please check it on Jinri Toutiao client)

4 、 python

Python is a cross-platform high-level programming language, which can be used in many fields, especially in the field of big data analysis. And python has many tools such as umpy, matplotlib, scikit-learn, pandas and ipython, which has a lot of advantages in big data's analysis.

Advantages:

Strong programming ability. In addition to data analysis, you can also crawl, write games, etc.

It is easy to learn. Compared with java, python is still very easy to learn.

Disadvantages:

You can't drag and drop data for analysis, which is difficult for people who have no programming foundation to understand.

When dealing with big data, the efficiency is not high

5 、 finereport

While making reports, finereport also has a very good visualization function, which can make a large visualization screen of the company.

Data visualization can better show the good big data, finereport built-in a variety of visual templates, but also can do dynamic reports!

Advantages:

Written by pure Java, it integrates data display (report) and data entry (form) functions.

Can directly connect to databases and support multiple databases

Support multiple functions such as report management, report rights assignment, etc.

Disadvantages:

When the amount of data is large, it is not easy to search the data. However, the directory management search function has been added to the updated August version of finereport.

At this point, I believe you have a deeper understanding of "from database to visual performance analysis python". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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