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

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

The idea behind bar charts is to compare values by comparing the lengths of the bars. When the baseline is changed, the visual effect is distorted.

2. Use easy-to-read typefaces Sometimes typography can enhance the visual effect, adding extra emotion and insight. Data visualization is not included. Stick to simple sans serif fonts (usually the default font in programs like Excel). Sans serif fonts are those with no small feet at the edges of the text.

3. Bar chart width is moderate

The spacing between bars should be 1/2 column width.

4. Use 2D graphics

While they look cool, 3D shapes can distort perception and therefore distort data. Stick to the 2nd dimension to ensure that the data is accurate.

5. Use tabular numeric fonts

Table spacing gives all the numbers the same width so that they align with each other when arranged, making comparisons easier. Most popular fonts have tables built in. Not sure if the font is correct? Just see if the decimal point (or any number) is aligned.

6. sense of unity

A sense of unity makes it easier for us to receive information: colors, images, styles, sources…

7. Don't get too attached to pie charts.

Shows the proportional size of multiple blocks, the sum of all blocks (arcs) equals 100%. But it is best to avoid using this chart because the naked eye is insensitive to area size.

8. Use coherent lines in line charts

Dotted lines, dotted lines are easy to distract. On the contrary, using solid lines and colors makes it easier to distinguish one from the other.

9. Proportion of respect to total

There is an overlap of proportions in the questions people choose, where the percentages of different options add up to more than one. In order to avoid this situation, the scale cannot be directly plotted as a statistical chart. Rather than presenting numerical values, some graphs emphasize the relationship between parts and the whole.

10. Area, size visualization

Distinguish the length, height or area of the same type of graph (such as column, ring and spider graph) to clearly express the comparison between the index values corresponding to different indicators. When making such data visualizations, mathematical formulas are used to express accurate scales and proportions.

11. Using size to visualize values can help emphasize important information and add contextual cues, and using size to represent values works well with maps. If you have multiple data points of the same size in your visualization, they will be mixed up and it will be difficult to distinguish between values.

12. Use the same details

The more details (and numbers) are added, the longer the brain takes to process them. Think about what you want to convey with your data and what the most effective way is.

13. Working with Basic Graphics

A good rule of thumb is that if you don't understand efficiently, your readers or listeners probably won't understand either. Therefore, stick to basic graphs: histograms, bar charts, Venn charts, scatter charts, and line charts.

14. number of views

Limit the number of views in your visualization to three or four. If you add too many views, the big picture will be overwhelmed with detail.

2. Five guidelines for chart color matching that you can refer to

1. color depth

It is a common method of data visualization design to express the strength and size of the index value through the depth of color. The user can see which part of the index data value is more prominent at a glance.

2. Use the same color scheme

Using too much color will add unbearable weight to the data. Instead, designers should use the same color system, or analog color.

3. Avoid using bright colors

Bright colors are like capitalizing all the letters for emphasis. Your audience feels like you're selling them loud. Monotonic colors work well for data visualization because they allow your readers to understand your data without being overwhelmed by it.

4. Labels are distinguished by different colors

In some cases, we may have measured different kinds of objects over a period of time or a series of values. For example, suppose we measure the weight of dogs and cats for six months. At the end of the experiment, we wanted to plot the weight of each animal, distinguishing cats and dogs in blue and red, respectively.

5. number of colors

Do not use more than 6 colors on a single image.

3. Standard visual charts must have annotations

1. interpretive coding

Data is presented through a combination of shapes, colors, and geometries. To be readable, the chart designer decodes these graphs back into data values.

2. axis labels

This may seem unnecessary or not very helpful, but you can't imagine how many times you'd be asked what the x/y axis represents if your chart was a bit confusing or if the person seeing the data wasn't familiar with it. Follow the previous two drawing examples if you want to set a specific name for an axis.

3. title

Another basic but critical point if we are presenting data to a third party is to use headings, which are very similar to the previous axis markers.

4. Comment on key elements

Often, just using scales on the left and right sides of a chart is not very clear in itself. Labeling values on graphs is useful for interpreting graphs.

5. Important View Location

Place the most important view at the top or upper-left corner. The eye usually notices this area first.

4. Excellent visual charts, 6 principles to follow

1. data ordering

The data categories are sorted alphabetically, by size, or by value to guide the reader through the data in a logical and intuitive way.

2. comparative data

Comparisons are a great way to show differences in data, but if your readers can't see them easily, your comparisons are meaningless. Make sure all data is presented to the reader and choose the most appropriate comparison method.

3. undistorted data

Make sure all visualizations are accurate. For example, bubble plot size should expand by area, not diameter.

4. presentation data

Let the reader see the data, which is the focus of visualization. Ensure that no data is lost or designed. For example, when using standard area plots, transparency can be added to ensure that readers can see all the data.

5. delete a variable

A lot of times too much information can affect the reader's attention, and it's a good idea to remove implicit information from the visualization, in which case I don't think we need to include the names of variables in the axis.

6. Avoid data noise

Minimize or eliminate unimportant things. This includes weakening or removing graphic lines, changing the color of axes, graphic lines, and drawing spreadsheet rows in light gray. This allows the "data ratio" to reach a high level, and the audience will understand the data more easily.

At this point, the study of "What Python Drawing Tips" 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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