Pandas：動態 append dataframe 列進入運行總 Z6A8064B5DF4794555500553C47DZC 內的循環

Question

我正在編寫一個模擬，我正在嘗試將每個迭代的結果 append 變成一個跟蹤所有迭代的 dataframe。

雖然收集結果一切正常，但我每次都找不到將結果 append 放入新列的方法。 一段時間以來，我一直在努力解決這個問題，但無法解決這個問題。

我已經構建了我正在做的事情的簡化版本，以最好地解釋我的問題：

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，在此代碼中，問題在於將包含在recap中的結果附加到big_df 。 理想情況下，在模擬結束時， big_df應該包含 24 列，這將是模擬每次迭代的一列結果。 對此的任何幫助將不勝感激，謝謝

更新：感謝 wnsfan40，我已經能夠獲得一個連接每次迭代結果的 df，但是big_df在每次迭代時重置，並且不會不斷地 append 每個新的 df。

預計 output 看起來像這樣：

      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是包含每行結果的所有列的快捷方式。

Answer 1

使用初始化時，將 df 的列指定為 big_df 的索引

big_df = pd.DataFrame(index = df.index)

嘗試從 append 更改為分配列值，例如

big_df['Iteration {}'.format(i)] = recap