30 data visualization tips that can not be ignored

Here, I summarize the of data visualization 30 A little trick , By listing some common mistakes that are easy to ignore , I hope to quickly improve and consolidate your visual production level .

One 、 The charting tips you have to pay attention to

1、 The baseline of a bar chart must start from zero

The principle of a bar chart is to compare the size of values by comparing the length of the bars . When the baseline is changed , The visual effect is distorted .

2、 Use easy to read Fonts

Sometimes , Typesetting can improve the visual effect , Add extra emotion and insight . But data visualization is not included . Stick to simple sans serif fonts ( Usually Excel The default font in the program ). Sans serif fonts are those with no small feet on the edge of the text .

3、 The width of the bar chart is moderate

The interval between bar charts should be 1/2 The width of the column .

4、 Use 2D graphics

Although they look cool , however 3d Shape can distort perception , But it seems to distort the data . Insist on being a 2 Dimension , Make sure the data is accurate , Good ！

5、 Use table number Fonts

Table spacing gives all numbers the same width , So that they line up with each other , Make the comparison easier . Most popular fonts have built-in tables . I'm not sure the font is correct ? Just look at the decimal point ( Or any number ) Whether it's aligned or not .

6、 A sense of unity

A sense of unity makes it easier for us to receive information ： Color , Images , style , source ……

7、 Don't be too keen on pie charts

Show multiple block scale sizes , All blocks （ arc ） The sum of plus is equal to 100%. But it's best to avoid using this chart , Because the naked eye is not sensitive to the size of the area .

I seem to have made this mistake

8、 Use consistent lines in a line chart

The dotted line is a distraction . contrary , Use solid lines and colors , Instead, it's easy to distinguish between each other .

9、 Respect the proportion of the part to the whole

There will be an overlap in the proportion of people's multiple choices , The sum of the percentages of different options is greater than one . To avoid that , You can't make a statistical chart of proportions directly . Compared to presenting values , Some of them focus more on the relationship between the part and the whole .

10、 area 、 Dimensional visualization

For the same kind of figure （ For example, columnar 、 Circles and spiders, etc ） The length of 、 Distinguish by height or area , To clearly express the comparison between the corresponding index values of different indicators . When making visualization graphics of this kind of data , You have to use a mathematical formula to calculate , To express accurate scales and proportions .

11. Use size to visualize values

Size helps to emphasize important information and add contextual cues , The use of size to represent values also works well with maps . If you have multiple data points of the same size in your visualization , They will mix together , It's hard to tell the value .

12、 Use the same details

Added details ( And number ) The more , The longer the brain processes it . Think about what you want to convey with your data , And what's the most effective way .

13、 Use basic graphics

A good rule of thumb is , If you can't understand , Your readers or listeners may also be hard to understand . therefore , Stick to basic graphics : Histogram 、 Bar chart 、 Venntu 、 Scatter and line graphs .

14、 Number of views

Limit the number of views in your visualization to three to four . If you add too many views , Details will be drowned out .

Two 、 About chart color matching , You can refer to 5 Rules

1、 The color is dark and light

The strength and size of the index value can be expressed by the color depth , It is a common method of data visualization design , Users can see at a glance which part of the index data value is more prominent .

2、 Use the same color system

Too much color , It will add an unbearable weight to the data , contrary , Designers should use the same color system , Or analogy color .

3、 Avoid bright colors

Bright colors are like putting all the letters in capitals and trying to emphasize them , Your audience feels like you're trying to sell them . Monotonous colors , On the contrary, it can be used for data visualization , Because they allow your readers to understand your data , Instead of being overwhelmed by data .

4、 Labels are distinguished by different colors

In some cases , In a period of time or a series of values , We may have measured different kinds of objects . for example , Suppose we measure 6 The weight of dogs and cats in the last month . At the end of the experiment , We want to draw the weight of each animal , Distinguish a cat from a dog with blue and red .

5、 Number of colors

Don't use... On a map 6 More than one color ; Keep in mind ~

3、 ... and 、 Standard visual charts must have annotations

1、 Explain the code

Through certain shapes 、 The combination of color and Geometry , Present the data . In order for the reader to read clearly , The graphic designer has to decode these graphs back to the data values .

2、 Axis labels

It may not seem necessary , Or not very helpful , But you can't imagine , If your chart is a bit confusing , Or people who see the data are not very familiar with it , How many times will you be asked x/y What the axis represents . Follow the two previous drawing examples , If you want to set a specific name for the axis .

3、 title

If we're going to present the data to a third party , Another basic but crucial point is the use of headings , It's very similar to the previous axis marking .

4、 Key elements are annotated

Usually , Just using the scale on the left and right sides of the chart itself is not clear . Marking values on a graph is very useful for interpreting graphs .

5、 Important view location

Place the most important view at the top or top left corner . The eyes usually notice this area first .

Four 、 Excellent visualization charts , Abide by 6 Article principle

1、 The data is sorted in order

Data categories are in alphabetical order , Order of size , Or value ranking , Guide the reader to understand the data in a logical and intuitive way .

2、 Comparative data

Comparison is a great way to show differences in data , But if your readers don't see the difference easily , Then your comparison is meaningless . Make sure that all the data is presented to the reader , Choose the most appropriate comparison method .

3、 Do not distort data

Make sure all visualizations are accurate . for example , Bubble size should be expanded by Region , Not the diameter .

4、 Display data

Let the reader see the data , This is the point of Visualization . Make sure that no data is lost or designed . for example , When using standard area maps , You can add transparency , Make sure readers can see all the data .

5、 Delete variables

A lot of times , Too much information can affect the reader's attention , It's a good idea to remove hidden information from Visualization , under these circumstances , I don't think we need to include variable names in the axis .

6、 Avoid data noise

To minimize or remove unimportant things . This includes weakening or removing graphic lines , Change the axis 、 The color of the graphic lines , And drawing spreadsheet rows in light gray . bring “ Data ratio ” Can reach a very high level , It's easier for the audience to understand the data .

5、 ... and 、 Summary

Have you remembered all the above details ？ As the saying goes, practice makes perfect , In the production process of each data visualization, think more , What are the details to pay attention to ？ Whether these details are reasonable , The great God of data visualization is just around the corner .

I have written many visual tools , such as ：Plotly、Pyecharts、Matplotlib、Tableau、Pyg2plot, Which one do you like best ？