如何用python拟合指数函数

import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

def exponential_func(x, a, b, c):
    return a * np.exp(b * x) + c

x_data = np.array([1, 2, 3, 4, 5])  # 自变量数据
y_data = np.array([2.3, 4.5, 7.2, 11.1, 17.2])  # 因变量数据

params, params_covariance = curve_fit(exponential_func, x_data, y_data)

x_range = np.linspace(1, 5, 100)  # 生成拟合曲线的自变量范围
y_range = exponential_func(x_range, params[0], params[1], params[2])  # 计算拟合曲线的因变量值

plt.scatter(x_data, y_data, label='Data')  # 绘制原始数据点
plt.plot(x_range, y_range, label='Fitted curve', color='r')  # 绘制拟合曲线
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()