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This article mainly introduces "what is Python's process of data analysis". In daily operation, I believe many people have doubts about what Python's process of data analysis is. Xiaobian consulted all kinds of materials and sorted out simple and easy operation methods. I hope to help you answer the doubts of "Python's process of data analysis"! Next, please follow the small series to learn together!

Why Python for Data Analysis?

Python is a dynamic, object-oriented scripting language and a simple, easy-to-understand programming language. Python is easy to get started, code is readable, a good Python code, reading like reading a foreign language article. This feature of Python is called pseudocode, and it allows you to focus only on what kind of work you are doing, rather than obsessing about Python syntax.

Python is open source and has a number of excellent libraries that can be used for data analysis and other areas. More importantly, Python has good compatibility with the open source big data platform Hadoop. Therefore, learning Python is a very cost-effective thing for data analysts interested in developing into big data analysis positions.

Python's many advantages make it one of the popular programming languages, and many companies at home and abroad are already using Python, such as YouTube, Google, Alibaba Cloud, etc.

programming Foundation

To learn how to use Python for data analysis, I suggest that the first step is to understand some Python programming basics, know Python data structure, what is vector, list, array, dictionary, etc.; understand Python's various functions and modules. The following chart summarizes the knowledge points to be mastered at this stage:

1. data acquisition

Generally, companies with data analyst job requirements will have their own databases, and data analysts can obtain the desired data in the database through SQL query statements. Python already has interface packages for connecting to SQL Server, MySQL, Orcale and other mainstream databases, such as pymsql, pymysql, cx_Oracle, etc.

There are two main ways to obtain external data, one is to obtain public data on some domestic websites; the other is to automatically crawl data by writing crawler code. If we want to use Python crawlers to retrieve data, we can use the following Python tools:

Requests-Mainly used to issue requests when crawling data.

BeautifulSoup-Used to read XML and HTML type data when crawling data, parse it into objects and then process it.

Scapy-A packet that handles interactive data and decodes most network protocol packets

2. data storage

For projects with small data volume, Excel can be used for storage and processing, but for projects with more than 10,000 data volume, it is more efficient and convenient to use database for storage and management.

3. data preprocessing

Data preprocessing is also known as data cleansing. In most cases, the data we get are inconsistent in format, with outliers, missing values and other problems, and the methods of data preprocessing steps for different projects are also different. I think 80% of the work in data analysis is processing data. If Python is chosen as the data cleansing tool, we can use Numpy and Pandas:

Numpy -Used for scientific calculations in Python. It is well suited for operations related to linear algebra, Fourier transforms and random numbers. It can handle multidimensional data well and is compatible with various databases.

Pandas -Pandas is an extension of Numpy and provides a set of functions to handle data structures and operations such as time series.

4. modeling and analysis

At this stage, we must first understand the structure of the data and select the model according to the project requirements.

Common data mining models are:

From the above figure, we can also know that Python can support our data analysis work well throughout the data analysis process, whether it is data extraction, data preprocessing, data modeling and analysis, or data visualization.

At this point, the study of "What is Python's process of data analysis" is over, hoping to solve everyone's doubts. Theory and practice can better match to help everyone learn, go and try it! If you want to continue learning more relevant knowledge, please continue to pay attention to the website, Xiaobian will continue to strive to bring more practical articles for everyone!

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