Example Analysis of multiple attributes of data Visualization

Editor to share with you the many attributes of data visualization example analysis, I believe that most people do not know much about it, so share this article for your reference, I hope you will learn a lot after reading this article, let's go to know it!

1. Single data and multiple attributes

1.1. Bar chart (or bar chart)

The following picture shows the grades of "Xiao Wang" in various subjects.

1.2. Line chart (or scatter chart)

The following picture shows the grades of "Xiao Wang" in various subjects.

1.3. Pie chart (or ring chart)

The following picture shows the proportion of Xiao Wang's grades in each subject to his "total grade".

1.4. Radar chart

The following picture shows the grades of "Xiao Wang" in various subjects. This kind of picture can directly represent the ability of "Xiao Wang".

two。 Multiple data and multiple attributes

2.1.1. Juxtaposed bar chart (or bar chart)

The following picture is divided into groups according to "subjects". You can directly see the comparison of the scores of students in each subject. Of course, you can be grouped by "student name".

2.1.2 stacked bar chart (or bar chart)

As shown in the picture below, it not only shows each student's scores in each subject (color marking), but also shows their total scores.

2.1.3 percentage stacked bar chart (or bar chart)

The figure below shows the percentage of each subject in the total score of each student, as well as the comparison between different students.

2.2. Line chart (or scatter chart)

The following picture is grouped according to the "name", you can directly see the comparison of the scores of the students of each subject. Of course, it can be grouped by "discipline".

2.3. Radar chart

The following picture is grouped according to the "name", you can directly see the comparison of the scores of the students of each subject. Of course, it can be grouped by "discipline".

The above is all the contents of the article "sample Analysis of multiple attributes of data Visualization". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!