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This article mainly introduces "what is the data analysis method based on Python". In the daily operation, I believe that many people have doubts about what the data analysis method based on Python is. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubts about "what is the data analysis method based on Python?" Next, please follow the editor to study!

With the arrival of big data and the era of artificial intelligence, network and information technology began to infiltrate all aspects of human daily life, and the amount of data generated also showed an exponential growth trend. at the same time, the magnitude of the existing data has far exceeded the scope that human can handle at present. In this context, data analysis has become a new research in the field of data science.

The subject. In the choice of program language for data analysis, because of the advantages of Python language in data analysis and processing, a large number of practitioners in the field of data science use Python.

To carry out research work related to data science.

1. The concept of data analysis

Data analysis refers to the process of using appropriate analysis methods to analyze a large number of collected data, extract useful information and form conclusions, study and summarize the data in detail. With the rapid development of information technology, the ability of enterprises to produce, collect, store and process data is greatly improved, and the amount of data is also increasing day by day. Extract these complicated data through data analysis methods, so as to study the development law of the data and predict the trend, and then help the management of enterprises to make decisions.

2. The process of data analysis

Data analysis is a process and method to solve problems. The main steps are requirements analysis, data acquisition, data preprocessing, analysis modeling, model evaluation and optimization, and deployment.

1) demand analysis

Requirement analysis in data analysis is not only the first step in data analysis, but also a very important step, which determines the subsequent analysis method and direction. The main content is to put forward the overall analysis direction and content of data analysis requirements according to the needs of business, production and finance departments, combined with the existing data, and finally agree with the demand side.

2) data acquisition

Data acquisition is the basis of data analysis, which refers to the extraction and collection of data according to the results of demand analysis. There are two main ways of data acquisition: Web crawler acquisition and local acquisition. Web crawler acquisition refers to writing crawler programs through Python to legally obtain all kinds of text, voice, pictures, videos and other information in the Internet; local acquisition refers to obtaining historical and real-time data of production, marketing, finance and other systems stored in the local database through computer tools.

3) data preprocessing

Data preprocessing refers to the process of data merging, data cleaning, data standardization and data transformation, which is directly used in analysis and modeling. Among them, data merging can merge multiple interrelated tables into one; data cleaning can remove duplicate, missing, abnormal and inconsistent data; data standardization can remove dimensional differences between features; data exchange can meet the data requirements of post-analysis and modeling through discretization, dumb variable processing and other technologies. In the process of data analysis, the various processes of data preprocessing cross each other, and there is no fixed sequence.

4) Analytical modeling

Analytical modeling refers to the process of discovering valuable information in data through comparative analysis, grouping analysis, cross analysis, regression analysis and other analysis methods, as well as clustering model, classification model, association rules, intelligent recommendation and other models and algorithms.

5) Model evaluation and optimization.

Model evaluation refers to the process of using different indicators to evaluate the performance of one or more models according to the types of models. The optimization of the model refers to the process that the performance of the model has met the requirements after the evaluation of the model, but in the application process of the actual production environment, it is found that the performance of the model is not ideal, and then reconstruct and optimize the model.

6) deployment

Deployment is the process of applying the results and conclusions of data analysis to the actual production system. According to the different requirements, the deployment phase can be a data analysis report containing specific corrective measures of the current situation, or a solution to deploy the model in the entire production system. In most projects, the data analyst provides a data analysis report or a set of solutions, which are actually implemented and deployed on the demand side.

3. Python is a powerful data analysis tool.

Python has a rich and powerful library, which is often called glue language, which can easily connect various modules made in other languages. It is a more easy to learn and more rigorous programming language. It is often used in data analysis, machine learning, matrix operation, scientific data visualization, digital image processing, web crawlers, Web applications, etc. R language is often used in statistical analysis, machine learning, scientific data visualization, etc. MATLAB is used in matrix operation, numerical analysis, scientific data visualization, machine learning, symbol operation, digital image processing and signal processing. It can be seen that the above three languages can be used for data analysis.

4. The advantages of Python in data analysis.

Python is a widely used computer language, which has incomparable advantages in the field of data science. Python is becoming the mainstream language in the field of data science. Python data analysis has the following advantages:

1 "the grammar is simple and concise. For beginners, Python is easier to use than other programming languages.

2 "there are many powerful libraries. Combined with the strong strength in programming, you can use only Python to build data-centric applications.

3 "is not only suitable for research and prototype construction, but also for the construction of production system. Researchers and engineers use the same programming tool, which can bring significant organizational benefits and reduce the operating costs of the enterprise.

4 "Python programs can be easily" glued "to components in other languages in many ways. For example, Python's C language API can help Python programs call C programs flexibly, which means that users can add functions to Python programs as needed, or use Python in other environment systems.

5 "Python is a hybrid, with a rich set of tools that puts it between the system's scripting language and the system language. Python not only has the characteristics of simplicity and ease of use of all scripting languages, but also provides advanced software engineering tools that compiler languages have.

5. Introduction of common class libraries for Python data analysis.

Python has libraries with complete functions and unified interface, such as IPython, Num Py, Sci Py, pandas, Matplot lib, scikit-learn and Spyder, which can provide great convenience for data analysis. Among them, Num Py mainly has the following characteristics:

1) ndarray with fast and efficient multi-dimensional array objects

2) functions that perform element-level calculations on arrays and perform mathematical operations on arrays directly

3) it has the functions of linear algebraic operation, Fourier transform and random number generation.

4) can integrate C, C++ and Fortran code into Python

5) it can be used as a container for transferring data between algorithms.

At this point, the study of "what is the method of data analysis based on Python" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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