实训1代码：

import numpy as np import matplotlib.pyplot as plt

读取数据

data = np.loadtxt('population.txt', dtype=int)

创建画布和子图

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

绘制散点图

ax1.scatter(data[:,5], data[:,0], s=5) ax1.set_xlabel('Year') ax1.set_ylabel('Total Population') ax1.set_title('Total Population vs Year')

绘制折线图

ax2.plot(data[:,5], data[:,1], label='Male') ax2.plot(data[:,5], data[:,2], label='Female') ax2.set_xlabel('Year') ax2.set_ylabel('Population') ax2.set_title('Male vs Female Population') ax2.legend()

保存和显示图像

plt.savefig('population_relationship.png') plt.show()

实训2代码：

import numpy as np import matplotlib.pyplot as plt

读取数据

data = np.loadtxt('population.txt', dtype=int)

创建画布和子图

fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))

绘制直方图

ax1.hist(data[:,1], bins=20, alpha=0.5, label='Male') ax1.hist(data[:,2], bins=20, alpha=0.5, label='Female') ax1.set_xlabel('Population') ax1.set_ylabel('Frequency') ax1.set_title('Population Distribution by Gender') ax1.legend()

ax2.hist(data[:,3], bins=20, alpha=0.5, label='Urban') ax2.hist(data[:,4], bins=20, alpha=0.5, label='Rural') ax2.set_xlabel('Population') ax2.set_ylabel('Frequency') ax2.set_title('Population Distribution by Area') ax2.legend()

绘制饼图

total = np.sum(data[:,1:5]) male = np.sum(data[:,1]) female = np.sum(data[:,2]) urban = np.sum(data[:,3]) rural = np.sum(data[:,4])

ax3.pie([male/total, female/total], labels=['Male', 'Female'], autopct='%1.1f%%') ax3.set_title('Gender Ratio')

保存和显示图像

plt.savefig('population_distribution.png') plt.show()

绘制箱线图

fig, ax = plt.subplots() ax.boxplot(data[:,1:5], labels=['Total', 'Male', 'Female', 'Urban', 'Rural']) ax.set_ylabel('Population') ax.set_title('Population Distribution by Feature')

保存和显示图像

plt.savefig('population_boxplot.png') plt.show()

分析

从直方图和饼图可以看出，我国人口结构呈现老龄化趋势，女性人口比例高于男性人口比例，城镇人口比例逐渐增加。

从箱线图可以看出，年末总人口和男性人口增长速率逐渐变缓，女性人口增长速率逐渐加快，城镇人口和乡村人口增长速率差异不大，但城镇人口的波动范围更大