python实现根据已知csv文件中的总人数,自然增长率,男女比例等数据建立模型预测未来人口结构变化
时间: 2023-07-15 10:11:14 浏览: 91
人口预测模型
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首先,我们需要导入所需的库,如下所示:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
```
然后,我们需要读取CSV文件并进行数据预处理,如下所示:
```python
data = pd.read_csv('population.csv')
# 提取数据
year = data['year'].values
population = data['population'].values
birth_rate = data['birth_rate'].values
death_rate = data['death_rate'].values
gender_ratio = data['gender_ratio'].values
# 计算出每年的出生人数、死亡人数、男女人数
births_per_year = population * birth_rate
deaths_per_year = population * death_rate
male_population = population * gender_ratio / (1 + gender_ratio)
female_population = population - male_population
```
接下来,我们需要定义一个自然增长率的函数,如下所示:
```python
def natural_growth_rate(t, a, b, c):
return a * np.exp(-b * t) + c
```
然后,我们需要使用Scipy库中的curve_fit函数拟合自然增长率函数,如下所示:
```python
popt, pcov = curve_fit(natural_growth_rate, year, population)
# 计算未来10年的预测结果
years_future = 10
predict_year = np.array(range(year[0], year[-1] + years_future))
predict_population = natural_growth_rate(predict_year, *popt)
```
最后,我们可以使用Matplotlib库绘制结果图表,如下所示:
```python
plt.plot(year, population, 'o', label='Original data')
plt.plot(predict_year, predict_population, 'r-', label='Fitted curve')
plt.legend()
plt.show()
```
完整代码如下所示:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
data = pd.read_csv('population.csv')
# 提取数据
year = data['year'].values
population = data['population'].values
birth_rate = data['birth_rate'].values
death_rate = data['death_rate'].values
gender_ratio = data['gender_ratio'].values
# 计算出每年的出生人数、死亡人数、男女人数
births_per_year = population * birth_rate
deaths_per_year = population * death_rate
male_population = population * gender_ratio / (1 + gender_ratio)
female_population = population - male_population
def natural_growth_rate(t, a, b, c):
return a * np.exp(-b * t) + c
popt, pcov = curve_fit(natural_growth_rate, year, population)
# 计算未来10年的预测结果
years_future = 10
predict_year = np.array(range(year[0], year[-1] + years_future))
predict_population = natural_growth_rate(predict_year, *popt)
plt.plot(year, population, 'o', label='Original data')
plt.plot(predict_year, predict_population, 'r-', label='Fitted curve')
plt.legend()
plt.show()
```
运行代码后,将会生成一个图表,显示出历史人口数量和预测未来10年的人口数量。
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