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This article introduces the relevant knowledge of "how to achieve python data visualization". In the operation of actual cases, many people will encounter such a dilemma, 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!

Introduction to Python data Visualization

As a famous data analyst, it is essential to master visualization skills. In most cases, superiors are more concerned about the results presented. When the visual results are presented in front of you, you can intuitively experience the beauty of data. In terms of content expression, pictures are far better than words, which can not only reflect the authenticity of data, but also give people a lot of room for imagination.

We often hear that Tableau and PowerBI are commercial visualization tools, which are powerful in visual and flexible analysis, and are mainly aimed at professional data analysts. At the same time, it is highly used in work situations, so mastery is very helpful for promotion and job hunting, and then DataScience will also launch relevant training.

Python is the preferred language for data analysis, if our learning goal is data mining engineers, or algorithm engineers, then the most important thing is to understand and master Python data visualization. School students and researchers can also use Python for visualization. In addition, when we use Python to interact with the database, it is more convenient to analyze and observe the data directly in Python after obtaining the data.

Python includes many visualization libraries, such as Matplotlib, Seaborn, Bokeh, Plotly, Pyecharts, Mapbox and Geoplotlib. Among them, the frequency of use, the most need to master is Matplotlib and Seaborn. Matplotlib is the visual basic library of Python, and its drawing style is similar to MATLAB, so it is called Matplotlib. To learn the visualization of Python data, you will learn from Matplotlib and then learn other Python visualization libraries.

Seaborn is a "Matplotlib-based" visualization library at the level of visualization, which is encapsulated at a more advanced level for Matplotlib, making it easier to draw.

The content of this course includes Python installation, language fundamentals, drawing basics, and ten common visualization attempts to draw using Matplotlib and Seaborn libraries, such as line charts, histograms, box charts, etc., and learn how to apply scenarios in different situations.

Installation and environment building

There are two main versions of Python: 2.7.x and 3.x. Some old projects use packages based on version 2.7. If so, you can only use version 2.7. For now, we only need to use the new 3.x version. For basic students, it is recommended to use Anaconda to install the Python environment.

Anaconda installation

Open the downloaded installation package and click "Next" on the next page: "I Agree"--

Next page: Install For: Just Me if there is only one user All User if the computer has more than one user choose All User, I choose All User here, and continue to click "Next"

Next page: select the destination folder: if the C disk has plenty of space, you can choose the default address; click "Next"

Next page: advanced options: the first is to add environment variables, and the second is to use Python2.7 by default; check both and click "Next"

After waiting for the installation to complete, click "FInish" to complete the installation.

Start Jupyter Notebook

Jupyter Notebook is an open source Web application that allows users to create and share documents that contain code, equations, visualization, and text. Using Jupyter Notebook allows us to edit, run and debug code on a web page, which is very convenient to use.

After Aanconda is installed, find the menu directory, find the Anaconda Navigtor icon, double-click to open it, and the following interface appears:

Select Jupyter Notebook and click "Launch". Start Jupyter Notebook, and the web browser will open File, the file interface.

We can create a file on the desktop, named data Visualization, to save the code file.

In Jupyter Notebook, select the path: Desktop/ data Visualization /, click New in the upper right corner, and create a new Python3 file:

After the file is built, we edit the code in the text box and click the button "run" to debug and produce the results.

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