Visualizing common drawings （ Four ） Histogram

One . Introduction to histogram

Basic histogram , Use vertical or horizontal columns to show numerical comparisons between categories . One axis represents the classification dimension to be compared , The other axis represents the corresponding value .

A histogram is different from a histogram , The histogram can't show the continuous change trend of data in an interval . The histogram describes the classified data , The answer is in each category 【 How many? ？】 This problem . It should be noted that , When the histogram shows a lot of categories, it will lead to display problems such as cascade of category names .

Two . The composition of the histogram

The bar chart includes at least the following parts ：

• Horizontal axis category
• Number of longitudinal axes / The number
• graphics
• legend

3、 ... and . Use scenarios

Data used ： A category field , A numeric field .

The main function ： Compare the numerical size of classified data .

Number of data ： No more than 30 Data .

Applicable scenario ：

• It is suitable for classification data comparison .

Not applicable to the scene ：

• When there are too many data categories .
• When you want to show data trends .

Four . Realization

stay matplotlib Use in bar Function to draw a histogram , For a detailed introduction of the function and the annotation of the histogram, see the links below .

notes ：

• bar Function details
• Solution to the problem of cylinder annotation

Take the following table as an example to draw the histogram

The complete program code is as follows ：

import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']  #  Settings support Chinese 
plt.rcParams['axes.unicode_minus'] = False  #  Set up - Number 

plt.style.use('ggplot')
genre = ["Sports", "Strategy", "Action", "Shooter", "Other"]
sold = [27500, 11500, 6000, 3500, 1500]

patches = plt.bar(range(len(sold)), sold, width=0.5, color=['red', 'green', 'blue', "cyan", "olive"])
plt.bar_label(patches)
plt.xticks(range(len(sold)), genre, fontsize=15)
plt.xlabel(" Types of games ", fontsize=20)
plt.ylabel(" sales ( individual )", fontsize=20)
plt.title("2021 Comparison of annual game sales ", fontsize=25, fontweight="bold")
plt.grid()
plt.legend(patches, genre, fontsize=15)
plt.show()

The results are as follows ：

5、 ... and . Reference resources

1. Introduction to stacking diagram
2. Broken line diagram
3. Area map