Area map

One . Introduction to area map

Area map is also called area map . It is formed on the basis of line chart , It fills the area between the line and the coordinate axis of the independent variable with color or texture , Such a filled area is called area , Color filling can better highlight the trend information .

It should be noted that the color should have a certain degree of transparency , Transparency can help users observe the overlapping relationship between different sequences , The transparency of different sequences can be reduced .

Like a line chart , Area maps are also used to emphasize the extent to which quantities change over time , It can also be used to arouse people's awareness of General trend Attention . They are most often used to show trends and relationships , Instead of conveying specific values .

Area maps are usually divided into two categories ：

• General area map ： All data starts from the same zero axis .
• Stacking area map ： The starting point of each data set is different , All based on the previous data set . Used to display the trend line of the proportion of each value over time or category , Stacked area maps are particularly useful in showing the changes of the total component of big data .
• Percentage stacked area diagram ： Used to display the trend line of the percentage of each value over time or category . The proportional trend line of each series can be emphasized .

Two . Composition of area map

The general area map consists of the following four parts ：

• The horizontal axis ： Time .
• The vertical axis ： Represents the value .
• Line ： Indicates the trend relationship between data .
• area ： The filled area between the line and the axis .

3、 ... and . Use scenarios

Suitable data ： Data of two consecutive fields .

The main function ： Observe the change trend of the data .

Applicable scenarios ：

1. Show the values that change in the time dimension .

Scenarios that are not applicable ：

1. Numerical comparison between different classifications is not applicable .

Four . Realization

stay matplotlib Drawing an area map in requires two functions ,plot and fill_between .plot Used to draw polylines ,fill_between Used for area filling .fill_between The method is introduced as follows ：

fill_between(x, y1, y2=0, where=None, interpolate=False, step=None, *,data=None, **kwargs)

Parameters 1：x： length N Array of , Execute the of the curve X Shaft span .

Parameters 2：y1： length N Array or scalar , Specify the first curve .

Parameters 3：y2： length N Array or scalar , Specify the second curve .（ The default value is 0）

Parameters 4：where： The length is N An array of Boolean types , Specify the location of the fill .

Parameters 5：interpolate： Boolean type , Unknown .

Parameters 6：step： Qualified string ： Specifies whether the fill uses a step function , Optional ：

• pre：y Values from each x The position continues to the left .
• post：y Values from each x The position continues to the right .
• mid： The step occurs at x In the middle of the position .

Parameters 7：**kwargs： The accepted keyword parameter is passed to the associated PolyCollection object .

Return value ：PolyCollection object .

notes ：

• plot Methods,

Implement complete code ：

import matplotlib.pyplot as plt

import numpy as np

plt.rcParams['font.sans-serif'] = ['SimHei']  #  Settings support Chinese 
plt.rcParams['axes.unicode_minus'] = False  #  Set up - Number 
plt.style.use('seaborn-notebook')

#  Data set used 
year = [1950, 1960, 1970, 1980, 1990, 2000, 2010, 2018]
asia_population = [1394, 1686, 2120, 2625, 3202, 3714, 4169, 4560]
y_label = np.arange(0, 5000, 500)
avg = np.average(asia_population)

plt.plot(asia_population, lw=3, color='k', label="Asia's population")
plt.hlines(avg, -5000, 5000, ls='--', label=f"average:{
      avg}", color='red')
plt.fill_between(range(len(year)), asia_population, alpha=0.3)

plt.title(" Demographic changes in Asia (1950-2018)", fontsize=25, fontweight='bold')
plt.xlabel(" year ", fontsize=15)
plt.ylabel(" Population （ One million ）", fontsize=15)

plt.xticks(range(len(year)), labels=year, fontsize=10)

plt.xlim([-0.5, len(year)-0.5])
plt.grid()
plt.legend(fontsize=15)
plt.show()

The effect is as follows ：

5、 ... and . Reference resources

1. Introduction to stacking diagram
2. Broken line diagram