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This article introduces the relevant knowledge of "how to analyze data with Python". Many people will encounter such a dilemma in the operation of actual cases, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Why choose Python for data analysis?

Python is not only a dynamic, object-oriented scripting language, but also a simple, easy-to-understand programming language. Python is easy to start, the code is readable, a good Python code, read like reading a foreign language article. Python, a feature called "pseudocode", allows you to focus only on what tasks are done, rather than obsessing about the syntax of Python.

In addition, Python is open source, it has a lot of excellent libraries, can be used in data analysis and other areas. More importantly, Python has good compatibility with Hadoop, the most popular open source big data platform. Therefore, learning Python is a very cost-saving thing for data analysts who are interested in developing an analytical position for big data.

Many advantages of Python make it one of the most popular programming languages, and many companies at home and abroad have already used Python, such as YouTube,Google, Aliyun and so on.

two。 Programming basis

To learn how to use Python for data analysis, CDA data analysts suggest that the first step is to understand some of the programming basics of Python, know the data structure of Python, what are vectors, lists, arrays, dictionaries, etc., and understand the various functions and modules of Python. The following figure collates the knowledge points to be mastered at this stage:

3. Data analysis process

Python is a sharp tool for data analysis. After mastering the programming foundation of Python, you can gradually enter the wonderful world of data analysis. CDA data analysts believe that a complete data analysis project can be roughly divided into the following five processes:

1) data acquisition

Generally speaking, companies with job requirements for data analysts have their own database, and data analysts can obtain the desired data in the database through SQL query statements. Python already has interface packages to connect to mainstream databases such as sql server, mysql, orcale, such as pymssql, pymysql, cx_Oracle, and so on.

There are two main ways to obtain external data, one is to obtain the data published on some domestic websites, such as the National Bureau of Statistics, and the other is to crawl data automatically by writing crawler code. If you want to use the Python crawler to get the data, we can use the following Python tools:

Requests- is mainly used to issue request operations when crawling data.

BeautifulSoup- is used to read data of XML and HTML types when crawling data, parse it into objects and then process it.

Scapy- A packet that handles interactive data and can decode packets from most network protocols.

2) data storage

For projects with small amount of data, excel can be used for storage and processing, but for projects with more than 10,000 data, using database for storage and management will be more efficient and convenient.

3) data preprocessing

Data preprocessing is also called data cleaning. In most cases, the format of the data we get is inconsistent, and there are some problems such as abnormal values, missing values and so on, and the methods of data preprocessing steps for different projects are also different. CDA data analysts believe that 80% of the work of data analysis is processing data. If we choose Python as the data cleaning tool, we can use two tool libraries, Numpy and Pandas:

Numpy-for scientific calculations in Python. It is very suitable for operations related to linear algebra, Fourier transform and random numbers. It can handle multi-dimensional data well and is compatible with all kinds of databases.

Pandas-Pandas is extended based on Numpy and can provide a series of functions to deal with data structures and operations, such as time series.

4) Modeling and analysis

At this stage, we should first make clear the structure of the data and select the model according to the project requirements.

Common data mining models are:

At this stage, Python also has a good tool library to support our modeling work:

Scikit-learn- is suitable for the machine learning algorithm library implemented by Python. Scikit-learn can realize common machine learning algorithms such as data preprocessing, classification, regression, dimensionality reduction, model selection and so on.

Tensorflow- is suitable for projects with deep learning and low data processing requirements. Such projects tend to have a large amount of data and ultimately require higher accuracy.

5) Visual analysis

The last step of data analysis is to write a data analysis report, which is also a process of data visualization. In terms of data visualization, Python's current mainstream visual chemical industry has:

Matplotlib- is mainly used for two-dimensional drawing, it allows users to easily graphic data, and provides a variety of output formats.

Seaborn- is a module based on matplotlib, which specializes in statistical visualization and can be seamlessly linked to Pandas.

According to this process, the knowledge points involved in each stage can be subdivided as follows:

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

This is the end of the content of "how to analyze data with Python". Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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