如何循环分组数据帧

Question

我对python非常陌生，但我会尝试使用像pandas这样的分析工具来解决现实世界中的问题

我已经从csv导入了数据，但这里有一个数据的小复制：

df2 = pd.DataFrame({'SKU': [22335, 22335, 22335, 22335, 33442, 33442, 33442, 33442],
                'Date': ['2019-12-31', '2020-01-07', '2020-01-14', '2020-01-21', '2019-12-31', '2020-01-07', '2020-01-14', '2020-01-21'],
                'Urgent': [10,8,4,20,50,45,65,32],
                'Delivered': [4,7,12,10, 35,75,23,42]})

有两个项目SKU编号，22335和33442，一周开始日期，每周设备紧急请求和每周设备交付数量。此时，我已了解如何在整个数据集上计算for循环，对于每一行，该循环引用前一行的计算值：

# Create new numeric column 'Result'
df['Result'] = np.nan

# Assign initial value for the first row of 'Result' (Should be first row in each SKU group)
df.loc[0, 'Result'] = df.loc[0, 'Delivered'] + df.loc[1, 'Delivered'] - df.loc[0, 'Urgent']

# Loop through each row except for last row to calculate
for i in range(1, len(df)-1):
    df.loc[i,'Result'] = max(df.loc[i-1, 'Result'], 0) + df.loc[i+1, 'Delivered'] - df.loc[i, 'Urgent']

print(df)

但是，我的下一步是仅对每个单独的SKU执行上述操作（分别为22335和33442）。我尝试使用groupby按日期按SKU对每一行进行排名，但我不知道如何在循环中引用这一点：

# Convert Date datatype
df['Date'] = pd.to_datetime(df['Date'])

# Use groupby to create ranking by SKU and Date
df['SKURank'] = df.groupby('SKU')['Date'].rank(ascending = True).astype('int64')

我曾尝试定义一个函数，该函数可以在每次迭代中引用自己的输出，然后使用.apply样式的循环调用该函数，但没有成功，但说实话，我完全不明白这一点

我还尝试遵守拆分、应用、组合原则，按SKU对数据进行分组，应用循环，然后将所有行重新组合在一起，但我真的不知道从哪里开始

以下是我的主要问题：

对于每个单独的SKU组，我应该使用什么样的循环来执行与上述代码相同的任务（为组中的第一行返回初始值，然后循环通过后续的每一行）？

如果推荐的循环形式（无论性能如何，我还没有那么快）要求我事先定义一个函数，我如何创建一个函数，在每个SKU的第一行之后的每一行引用它自己的输出？

更新：

哦，上帝。亲爱的上帝，我创造了什么。。。是。。。真恶心

是的，我创建了一个带有嵌套if语句的巨型for循环。是的，这太可怕了。不，它并没有做我需要的一切，比如在数据帧的最后一行执行循环。如果下面的任何部分有意义，并且您可以为我指出如何使其真正起作用的方向，我将非常感谢您的建议

import pandas as pd
import numpy as np

# Create dataframe for two SKUs, a weekly process date, urgent requested quantity, and delivered quantity
df = pd.DataFrame({'SKU': [22335, 22335, 22335, 22335, 33442, 33442, 33442, 33442],
                    'Date': ['2019-12-31', '2020-01-07', '2020-01-14', '2020-01-21', '2019-12-31', '2020-01-07', '2020-01-14', '2020-01-21'],
                    'Urgent': [10,8,4,20,50,45,65,32],
                    'Delivered': [4,7,12,10, 35,75,23,42]})

# Create new numeric column 'Result'
df['Result'] = np.nan

# Convert Date datatype and create 3 necessary columns
df['Date'] = pd.to_datetime(df['Date'])
df['Result'] = np.nan
df['WeeklyMiss'] = np.nan
df['Logic'] = ''

# Create list of unique SKUs in dataframe
skulst = df.SKU.unique()

print(skulst)


# Set initial indeces value 
skunum = 0
i = 0

# While loop with nested for loop to iterate over the dataframe
while skunum <= len(skulst):
    for i in range(0, len(df)-1):
        # Calculate first SKU row
        if i == 0 and df.loc[i, 'SKU'] == skulst[skunum]: 
            df.loc[i, 'Result'] = max(df.loc[i, 'Delivered'] + df.loc[i+1, 'Delivered'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'WeeklyMiss'] = min(df.loc[i, 'Delivered'] + df.loc[i+1, 'Delivered'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'Logic'] = 'First Row'
        # Calculate next SKU rows
        elif i > 0 and df.loc[i, 'SKU'] == skulst[skunum] and df.loc[i+1,'SKU'] == skulst[skunum]:
            df.loc[i, 'Result'] = max(df.loc[i+1, 'Delivered'] + min(df.loc[i-1, 'Result'], df.loc[i, 'Delivered']) - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'WeeklyMiss'] = min(df.loc[i-1, 'Result'] + df.loc[i+1, 'Delivered'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'Logic'] = 'Next SKU Row'
        # Calculate last SKU row
        elif i > 0 and df.loc[i, 'SKU'] == skulst[skunum] and (df.loc[i+1,'SKU'] != skulst[skunum] or i == len(df)):
            df.loc[i, 'Result'] = max(df.loc[i-1, 'Result'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'WeeklyMiss'] = min(df.loc[i-1, 'Result'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'Logic'] = 'Last SKU Row'
        
            
        # Calculate first SKU row and switch to next SKU 
        elif i > 0 and i < len(df) and df.loc[i, 'SKU'] != skulst[skunum] and df.loc[i-1,'SKU'] == skulst[skunum] :
            df.loc[i, 'Result'] = max(df.loc[i, 'Delivered'] + df.loc[i+1, 'Delivered'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'WeeklyMiss'] = min(df.loc[i, 'Delivered'] + df.loc[i+1, 'Delivered'] - df.loc[i, 'Urgent'], 0)
            df.loc[i, 'Logic'] = 'First SKU Row'
            if skunum + 1 <= len(skulst):
                skunum += 1
            else:
                df.loc[i, 'Result'] = max(df.loc[i-1, 'Result'] - df.loc[i, 'Urgent'], 0)
                df.loc[i, 'WeeklyMiss'] = min(df.loc[i-1, 'Result'] - df.loc[i, 'Urgent'], 0)
                df.loc[i, 'Logic'] = 'Last SKU Row'
                continue
    else:
        print(df)
        break

Answer 1

请参阅pandas文档中的Group By: split-apply-combine指南，了解如何迭代组