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This article mainly introduces the "Python visualization chart can show which data relations", in the daily operation, I believe that many people in the Python visualization chart can show which data relations have doubts, the editor consulted all kinds of information, sorted out a simple and easy-to-use method of operation, hope to answer the "Python visualization chart can show which data relations" of the doubt is helpful! Next, please follow the editor to study!

1. Basic column chart

The basic bar chart is often used to compare the size of numerical values, and it is widely used, such as Kobe Bryant's scores in different seasons, the number of App downloads in different games, the size of comprehensive search users in different periods, and so on. Figure 2-33 shows the sales of different kinds of fruits.

▲ figure 2-33 basic bar chart

It should be noted that too many classifications are not suitable for using vertical bar charts, as shown in figure 2-34.

▲ figure 2-34 Vertical column chart

At this point, you need to use a horizontal bar chart.

▲ figure 2-35 horizontal column chart

two。 Grouped bar chart

Grouped histogram, also known as aggregate histogram. When users need to display different groups under each category on the same axis, they need to use the grouping bar chart.

Similar to a column chart, the height of the column is used to map and compare the data values. The columns in each group are distinguished by different colors or different transparent ways of the same color, and the intervals between the groups need to be maintained.

Group bar charts are often used to compare data between different groups, all of which contain the same classification of data. For example, it shows the changes of urban and rural population since the reform and opening up, and the comparison of downloads of leisure, intelligence and fighting App of different game companies. Figure 2-36 compares the sales of different fruits between 2015 and 2017.

▲ figure 2-36 grouped bar chart

3. Stacked bar chart

Unlike grouped histograms that display classifications side by side, stacked histograms divide each column to show the size of each data of the same type. It can vividly show the data of each small category contained in a large category, as well as the proportion of each small category, showing the relationship between a single project and the whole. We divide stacked bar charts into two types:

1) General stacked histogram: the values on each column represent different data sizes, and the sum of the data on each layer represents the height of the whole column. It is very suitable for comparing the total amount of data in each packet.

2) stacked bar chart of the percentage: each layer of the column represents the percentage of the category data to the total data of the group.

One disadvantage of stacked bar charts is that it is difficult to distinguish and compare data when there are too many stacks on columns, and it is difficult to compare data of the same dimension under different classifications because they are not aligned to the same baseline.

▲ figure 2-37 stacked bar chart

4. Bidirectional bar chart

A bi-directional bar chart, also known as a positive and negative bar chart, uses positive and reverse columns to display numerical comparisons between categories. Among them, the classification axis represents the classification dimension that needs to be compared, and the continuous axis represents the corresponding value, which is divided into two cases, one is that the forward scale value is completely symmetrical with the reverse scale value, and the other is the reverse symmetry between the forward scale value and the reverse scale value. that is, opposite numbers to each other.

Figure 2-38 shows the import and export volume of different fruits from 2015 to 2017.

▲ figure 2-38 two-way bar chart

5. Waterfall diagram

Waterfall Waterfall Plot is an original type of chart created by McKinsey Consulting, which is called waterfall chart because it is similar to waterfall flow. This kind of chart uses the combination of absolute value and relative value, and is suitable for expressing the quantitative relationship between several specific values. Figure 2-39 shows the time span of sprint champions over the years, which shows that the physical limit of human beings is getting higher and higher.

At this point, the study of "which data relationships can be shown by Python visual charts" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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