我正在编写一个仿真程序，并且试图将每个迭代的结果附加到跟踪所有迭代的数据框中。
尽管一切都可以很好地收集结果，但我找不到每次都将结果附加到新列中的方法。我已经在这个问题上花了很长时间，无法解决这个问题。
我已经构建了一个简化版本，可以最好地解释我的问题：
import simpy
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import pandas as pd
###dataframe for the simulation
df = pd.DataFrame({'Id' : ['1183', '1187']})
df['average_demand'] = [7426,989]
df['lead_time'] = [1.5, 1.5]
df['sale_price'] = [1.98, 2.01]
df['buy_price'] = [0.11, 0.23]
df['beg_inventory'] = [1544,674]
df['margin'] = df['sale_price'] - df['buy_price']
df['holding_cost'] = 0.2/12
df['aggregate_order_placement_cost'] = 1000
df['review_time'] = 0
df['periods'] = 30
#df['cap_ts'] = 1.5
df['min_ts'] = 1
df['low_demand'] = [300, 30]#,3000,350,220,40,42,40,10,25,240]
df['high_demand'] = [1000, 130]#,12000,700,500,100,90,210,135,200,800]
df['low_sd'] = [160,30]#,3400,100,90,10,5,50,26,45,170]
df['high_sd'] = [400,90]#,5500,200,160,60,50,100,78,113,300]
cap_ts = 0
big_df = pd.DataFrame(df)
for i in df.index:
for cap_ts in range(1,12, 1):
def warehouse_run(env, df):
df['inventory'] = df['beg_inventory']
df['balance'] = 0.0
df['quantity_on_order'] = 0
df['count_order_placed'] = 0
df['commands_on_order'] = 0
df['demand'] = 0
df['safety_stock'] = 0
df['stockout_occurence'] = 0
df['inventory_position'] = 0
while True:
interarrival = generate_interarrival()
yield env.timeout(interarrival)
df['balance'] -= df['inventory'] * df['holding_cost'] * interarrival
df['demand'] = generate_demand()
if df['demand'].loc[i] < df['inventory'].loc[i]:
df['balance'] += df['sale_price'] * df['demand']
df['inventory'] -= df['demand']
print('{:.2f} sold {}'.format(env.now, df['demand'].loc[i]))
else:
df['balance'] += df['sale_price'] * df['inventory']
df['inventory'] = 0
df['stockout_occurence'] += 1
print('{:.2f} demand {} but inventory{}'.format(env.now, df['demand'].loc[i], df['inventory'].loc[i]))
print('{:.2f} sold {} ( nb stockout)'.format(env.now, df['stockout_occurence'].loc[i]))
if df['demand'].loc[i] > df['inventory'].loc[i]:
env.process(handle_order(env,
df))
df['count_order_placed'] += 1
print("inventory", df['inventory'].loc[i])
print("number of orders placed", df['count_order_placed'].loc[i])
def handle_order(env, df):
df['quantity_ordered'] = cap_ts *df['average_demand']
df['quantity_on_order'] += df['quantity_ordered']
df['commands_on_order'] += 1
print("{:.2f} placed order for {}".format(env.now, df['quantity_ordered'].loc[i]))
df['balance'] -= df['buy_price'] * df['quantity_ordered'] + df['aggregate_order_placement_cost']
yield env.timeout(df['lead_time'].loc[i], 0)
df['inventory'] += df['quantity_ordered']
df['quantity_on_order'] -= df['quantity_ordered']
df['commands_on_order'] -= 1
print('{:.2f} receive order,{} in inventory'.format(env.now, df['inventory'].loc[i]))
# number of orders per month
def generate_interarrival():
return np.random.exponential(1. / 1)
# quantity of demand per months
def generate_demand():
return np.random.randint(df['low_demand'].loc[i], df['high_demand'].loc[i])
def generate_standard_deviation():
return np.random.randint(df['low_sd'].loc[i], df['high_sd'].loc[i])
obs_time = []
inventory_level = []
demand_level = []
safety_stock_level = []
inventory_position_level = []
def observe(env, df):
while True:
obs_time.append(env.now)
inventory_level.append(df['inventory'].loc[i])
demand_level.append(df['demand'].loc[i])
safety_stock_level.append(df['safety_stock'].loc[i])
inventory_position_level.append(df['inventory_position'].loc[i])
yield env.timeout(0.1)
np.random.seed(0)
env = simpy.Environment()
env.process(warehouse_run(env, df))
env.process(observe(env, df))
# #RUN FOR 12 MONTHS
env.run(until=36.0)
recap = pd.DataFrame(df.loc[i])
recap = recap.transpose()
#big_df.append(recap)
big_df['Iteration {}'.format(i)] = recap
print(recap)
因此在此代码中，问题在于将包含在recap中的结果附加到big_df中。理想情况下，在模拟结束时，big_df应该包含24列，对于模拟的每次迭代，这将是一列结果。对此，我们将不胜感激，谢谢
更新：多亏了wnsfan40，我才能够获得一个可合并每次迭代结果的df，但是big_df会在每次迭代时重置，并且不会连续追加每个新df。
预期的输出看起来像这样：
Id result_columns
0 11198 x
1 11198 x
2 11198 x
3 11198 x
4 11198 x
5 11198 x
6 11198 x
7 11198 x
8 11198 x
9 11198 x
10 11198 x
11 11198 x
12 11187 y
13 11187 y
14 11187 y
15 11187 y
16 11187 y
17 11187 y
18 11187 y
19 11187 y
20 11187 y
21 11187 y
22 11187 y
23 11187 y
result columns是包含有关每一行结果的所有列的快捷方式。
答案 0 :(得分：0)
使用初始化时，将df的列分配为big_df的索引
big_df = pd.DataFrame(index = df.index)
尝试从附加更改为分配列值，例如
big_df['Iteration {}'.format(i)] = recap