What is python data analysis used for?

Data analysis refers to the process of analyzing a large amount of collected data using appropriate statistical analysis methods, extracting useful information and forming conclusions, and then conducting detailed research and summary of the data. This process is also a supporting process of the quality management system. In practical terms, data analysis helps people make judgments so that appropriate actions can be taken.

The mathematical foundation of data analysis was established in the early 20th century, but it was not until the advent of computers that practical operations became possible and data analysis was promoted. Data analysis is a combination of mathematics and computer science.

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In the field of statistics, some people divide data analysis into descriptive statistical analysis, Exploratory data analysis and confirmatory data analysis; among them, exploratory data analysis focuses on discovering new features in the data, while confirmatory data analysis focuses on the confirmation or falsification of existing hypotheses.

Exploratory data analysis refers to a method of analyzing data in order to form a worthy test of hypotheses. It is a supplement to traditional statistical hypothesis testing methods. This method was named by the famous American statistician John Tukey.

Qualitative data analysis, also known as "qualitative data analysis", "qualitative research" or "qualitative research data analysis", refers to the analysis of non-numeric data such as words, photos, observations (or Analysis of data)

It is recommended that you use python for data analysis for the following four reasons:

Python language is very simple and easy to learn, and is suitable for beginners as an introductory language

Python's syntax is simple, the code is highly readable, and it is easy to get started, which is conducive to beginners' learning. For example, if we are processing data and want to digitize the user's gender data, that is, turn it into a numerical form that can be calculated by the computer, we can directly use a one-line list derivation to complete it, which is very concise,

Python has powerful general programming capabilities

Unlike R or matlab, python is not only powerful in data analysis, but also in many fields such as crawlers, web, automated operation and maintenance, and even games. Has a wide range of applications. This makes it possible for the company to use one technology to complete all services, which is conducive to business integration between various technology groups. For example, we use python's crawler framework scrapy to crawl data, then hand it over to pandas for data processing, and finally use python's web framework django to display it to users. This series of tasks can all be completed in python, which can greatly improve the company's technical efficiency. .

Python has a huge and active scientific computing community

Python has very mature libraries and libraries in data analysis and interaction, exploratory computing and data visualization. An active community makes python an important solution for data processing tasks. In terms of scientific computing, python has a series of excellent libraries and tools such as numpy, pandas, matplotlib, scikit-learn, ipython, etc. In particular, pandas can be said to have unparalleled advantages in processing medium-sized data and is becoming a popular choice for data processing in various industries. The preferred library for tasks.

Python is the universal language in the era of artificial intelligence

In today's hot era of artificial intelligence, python has become the most popular programming language. Thanks to python's simplicity, rich libraries and community, most deep learning frameworks give priority to supporting python language programming. For example, tensorflow, the hottest deep learning framework today, although it is written in C, has the best support for python language.

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