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How to carry out excel and python data analysis and comparison, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain for you in detail, people with this need can come to learn, I hope you can gain something.

I often hear the question, "Why should I learn a programming language like python or even use it over excel in financial analysis?"

In the financial sector, python has become a hot analysis tool, which has almost become a consensus. In the face of excel and python, who is more suitable for data analysis has indeed been discussed.

Excel VS python

Excel does not need to make any more introductions. It is a necessary form tool for the office. With regard to python, here is a brief account of its background:

Python is an open source high-level programming language with strong community support and development team. This means that python has a rich library of third-party tools in all areas and is constantly updated and improved.

On the other hand, as a glue language, python has the excellent characteristics of simplicity, easy to read, smooth, easy to maintain, modular, and easy to integrate with other programming languages and software.

Why is python more suitable for data analysis? There are several reasons:

1. The analysis process can be reproduced.

Using python for data analysis, the analysis code can be saved as a script, which helps to continuously optimize the version of the code and make the improvement process clearer.

Without changing the data source, the results of the code output are consistent, and different analysis results will not appear with the change of time and personnel, and the reproducibility is strong.

2. Higher efficiency and scalability

It is true that excel is used by most people for data analysis, but excel drives people crazy when faced with large data sets and complex operations.

Python is faster in Icano, complex computing, data pipelines and automatic processing, and the efficiency of dealing with big data is much higher than that of excel. In terms of performance optimization, python also has more room for operation.

3. Machine learning

One of the most important reasons for using python for data analysis is machine learning. Python has rich and powerful machine learning and deep learning libraries.

Not only in finance, but also in various fields, there is a growing need for machine learning. Practitioners can easily create machine learning models and dig deep into the value of the data using python.

For example, natural language processing can be used to analyze the text emotion of media network comments, so as to judge customer needs and market rules.

4. Integration

As mentioned earlier, python is a glue language that can be integrated with many programming languages and applications.

For example, python can connect to various databases and extract, write and change database data by writing sql statements.

Python can also connect to application API and write automated scripts to manipulate applications, such as excel. Python has many libraries to connect to, which is very efficient.

In the process of data exploration, python provides data analysis tools such as pandas to help you explore and analyze more clearly and quickly, and there are a large number of visualization libraries for visualization.

You can easily read and store xlsx, csv and other data format files, making data operation flexible and efficient.

As a rapidly developing data analysis tool, python is one of the skills that financial practitioners have to master in the future. Its grammar is very approachable for beginners and can be easily mastered by taking time.

Python and excel complement each other, their advantages and disadvantages complement each other, and both have excellent data analysis capabilities. If you are already using excel, why not consider python?

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