Python中如何将for循环的输出结果全部追加到列表中？

你遇到的问题核心是列表初始化位置错误：你把diffList = []写在了for循环内部，每次循环迭代时都会重新创建一个空列表，之前计算的diff值会被直接丢弃，最后自然只保留了最后一次循环的结果。

解决方案 #

把diffList = []移到for循环外面，让列表只初始化一次，之后每次循环都往同一个列表里追加diff值。同时还可以优化一些重复定义的代码，提升运行效率：

# Question 1
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.interpolate import CubicSpline
from numpy import log

# 提前定义sim函数，避免循环内重复定义
def sim(variables, t, params):
    # Prey population level
    x = variables[0]
    # Predator population level
    y = variables[1]

    alpha = params[0]
    beta = params[1]
    delta = params[2]
    gamma = params[3]

    dxdt = alpha * x - beta * x * y
    dydt = delta * x * y - gamma * y

    return [dxdt, dydt]

y0 = [10,1] # [Prey, Predator] units in hundreds
pd = [100,200,300,400,500,600,700,800,900,1000, 1100, 1200,
     1300,1400,1500,1600,1700,1800,1900,2000]

# 列表初始化移到循环外，只创建一次
diffList = []

# 模型参数提前定义，无需每次循环重复赋值
alpha = 0.1
beta = 0.02
gamma = 0.4
delta = 0.02
params = [alpha, beta, delta, gamma]

for x in pd:
    t = np.linspace(0.,50.,num=x) 

    y = odeint(sim, y0, t, args=(params,))

    lv_prey = y[:,0] # 单独保存猎物种群数据
    lv_pred = y[:,1] # 单独保存捕食者种群数据

    prey_array = np.array(lv_prey) 
    pred_array = np.array(lv_pred)

    # 拟合三次样条
    num_pts = 10
    tsp = np.array([t[i] for i in range(0, len(t), len(t)//num_pts)])

    # 猎物样条拟合
    prey_sp = np.array([lv_prey[i] for i in range(0, len(t), len(t)//num_pts)])
    Curve1 = CubicSpline(tsp, prey_sp, bc_type = 'natural', extrapolate = False)

    # 捕食者样条拟合
    pred_sp = np.array([lv_pred[i] for i in range(0, len(t), len(t)//num_pts)])
    Curve2 = CubicSpline(tsp, pred_sp, bc_type='natural', extrapolate=False)

    prey_extrap_dots=Curve1(tsp)
    prey_extrap_line = Curve1(np.array([tt for tt in t if tt<=tsp[-1]]))
    pred_extrap_dots=Curve2(tsp)
    pred_extrap_line=Curve2(np.array([tt for tt in t if tt<=tsp[-1]]))

    # 计算Lotka-Volterra模型的不变量差值
    Cspline = alpha*np.log(pred_extrap_line) - beta*(pred_extrap_line) - delta*(prey_extrap_line) + gamma*np.log(prey_extrap_line)
    diff = np.max(Cspline) - np.min(Cspline)
    diffList.append(diff)
    print("The difference is {}".format(diff))

# 现在diffList包含所有循环计算的diff值
print("All calculated differences:", diffList)

额外优化说明 #

除了修复列表初始化问题，还做了两处实用优化：

• 将sim函数移到循环外，避免每次循环重复定义同一个函数，减少不必要的性能开销
• 把固定不变的模型参数（alpha、beta等）移到循环外定义，避免重复赋值

内容的提问来源于stack exchange，提问作者s_o_c_account