Python循环lambda函数

我试着在谷歌上寻找答案，但我不确定我的措辞是否正确，因为我还是一个初学者，这是我的第一个项目

我有一个代码，可以对excel文档和它的工作表进行排序，并通过分隔的工作表按过道创建新文档。这对我来说很好，尽管有很多重复的代码行。但我希望能够有更多的灵活性（添加或删除它整理出来的过道的数量）

这是我所拥有的东西的一个片段，但它持续了23个通道。我想把它分类

import pandas as pd
import openpyxl
from openpyxl import load_workbook
import xlsxwriter

saveFolder = '/home/Work/Splitting/Aisles/'
aisles = pd.ExcelFile('Split_All.xlsx')
disco = pd.read_excel(aisles, 'Disco')
closed = pd.read_excel(aisles, 'Closed')
oi = pd.read_excel(aisles, 'OI+')
dropship = pd.read_excel(aisles, 'Dropship')
trueouts = pd.read_excel(aisles, 'True Outs')
largemiss = pd.read_excel(aisles, 'Large Missing')
twomiss = pd.read_excel(aisles, 'Two Missing')
onemiss = pd.read_excel(aisles, 'One Missing')

writer=pd.ExcelWriter(saveFolder+'Aisle 01.xlsx', engine='xlsxwriter')
disco2 = disco[disco['Loc'].map(lambda x: x.startswith('01'))]
closed2 = closed[closed['Loc'].map(lambda x: x.startswith('01'))]
oi2 = oi[oi['Loc'].map(lambda x: x.startswith('01'))]
dropship2 = dropship[dropship['Loc'].map(lambda x: x.startswith('01'))]
trueouts2 = trueouts[trueouts['Loc'].map(lambda x: x.startswith('01'))]
largemiss2 = largemiss[largemiss['Loc'].map(lambda x: x.startswith('01'))]
twomiss2 = twomiss[twomiss['Loc'].map(lambda x: x.startswith('01'))]
onemiss2 = onemiss[onemiss['Loc'].map(lambda x: x.startswith('01'))]
disco2.to_excel(writer, sheet_name='Disco', index=False)
closed2.to_excel(writer, sheet_name='Closed', index=False)
oi2.to_excel(writer, sheet_name='OI+', index=False)
dropship2.to_excel(writer, sheet_name='Dropship', index=False)
trueouts2.to_excel(writer, sheet_name='True Outs', index=False)
largemiss2.to_excel(writer, sheet_name='Large Missing', index=False)
twomiss2.to_excel(writer, sheet_name='Missing Two', index=False)
onemiss2.to_excel(writer, sheet_name='Missing One', index=False)
writer.save()

writer=pd.ExcelWriter(saveFolder+'Aisle 03.xlsx', engine='xlsxwriter')
disco4 = disco[disco['Loc'].map(lambda x: x.startswith('03'))]
closed4 = closed[closed['Loc'].map(lambda x: x.startswith('03'))]
oi4 = oi[oi['Loc'].map(lambda x: x.startswith('03'))]
dropship4 = dropship[dropship['Loc'].map(lambda x: x.startswith('03'))]
trueouts4 = trueouts[trueouts['Loc'].map(lambda x: x.startswith('03'))]
largemiss4 = largemiss[largemiss['Loc'].map(lambda x: x.startswith('03'))]
twomiss4 = twomiss[twomiss['Loc'].map(lambda x: x.startswith('03'))]
onemiss4 = onemiss[onemiss['Loc'].map(lambda x: x.startswith('03'))]
disco4.to_excel(writer, sheet_name='Disco', index=False)
closed4.to_excel(writer, sheet_name='Closed', index=False)
oi4.to_excel(writer, sheet_name='OI+', index=False)
dropship4.to_excel(writer, sheet_name='Dropship', index=False)
trueouts4.to_excel(writer, sheet_name='True Outs', index=False)
largemiss4.to_excel(writer, sheet_name='Large Missing', index=False)
twomiss4.to_excel(writer, sheet_name='Missing Two', index=False)
onemiss4.to_excel(writer, sheet_name='Missing One', index=False)
writer.save()

writer=pd.ExcelWriter(saveFolder+'Aisle 04.xlsx', engine='xlsxwriter')
disco2 = disco[disco['Loc'].map(lambda x: x.startswith('04'))]
closed2 = closed[closed['Loc'].map(lambda x: x.startswith('04'))]
oi2 = oi[oi['Loc'].map(lambda x: x.startswith('04'))]
dropship2 = dropship[dropship['Loc'].map(lambda x: x.startswith('04'))]
trueouts2 = trueouts[trueouts['Loc'].map(lambda x: x.startswith('04'))]
largemiss2 = largemiss[largemiss['Loc'].map(lambda x: x.startswith('04'))]
twomiss2 = twomiss[twomiss['Loc'].map(lambda x: x.startswith('04'))]
onemiss2 = onemiss[onemiss['Loc'].map(lambda x: x.startswith('04'))]
disco2.to_excel(writer, sheet_name='Disco', index=False)
closed2.to_excel(writer, sheet_name='Closed', index=False)
oi2.to_excel(writer, sheet_name='OI+', index=False)
dropship2.to_excel(writer, sheet_name='Dropship', index=False)
trueouts2.to_excel(writer, sheet_name='True Outs', index=False)
largemiss2.to_excel(writer, sheet_name='Large Missing', index=False)
twomiss2.to_excel(writer, sheet_name='Missing Two', index=False)
onemiss2.to_excel(writer, sheet_name='Missing One', index=False)
writer.save()

我相当肯定这可以被清理，并减少它使用的线路数量，但这是我为让它为我工作所做的

我认为答案是循环，但我发现的大多数循环信息只是告诉我如何循环列表或字符串，其中我希望根据用户输入使用不同的通道循环以下内容（用户表示他们有28个通道，所以它循环28次，而不是我有23个）：

---

这段代码中有两个明显的重复模式，因此它们都可以被

for

循环所取代。还可以使用输入数据帧字典（以工作表名称为键）替换用于这些数据帧的不同变量。您似乎可以执行以下操作，但如果没有输入数据，这是我无法测试的：

import os
import pandas as pd

saveFolder = '/home/Work/Splitting/Aisles/'

aisles = pd.ExcelFile('Split_All.xlsx')

sheets = ['Disco', 'Closed', 'OI+', 'Dropship', 'True Outs',
          'Large Missing', 'Two Missing', 'One Missing']

dataframes = {}
for sheet in sheets:
    dataframes[sheet] = pd.read_excel(aisles, sheet)

for num in range(1, 29):

    nn = "{:02d}".format(num)  # e.g. 02
    
    filename = os.path.join(saveFolder, 'Aisle {}.xlsx'.format(nn))

    writer = pd.ExcelWriter(filename, engine='xlsxwriter')

    for sheet in sheets:
        df = dataframes[sheet]
        df2 = df[df['Loc'].map(lambda x: x.startswith(nn))]
        df2.to_excel(writer, sheet_name=sheet, index=False)

    writer.save()

---

lambda

函数可以在

工作表的循环外定义，但它不是很重要。
谢谢您的回答！这很有意义，并且清理了我的代码！看看你的回答，我明白是什么让我从其他数据中困惑，它只是帮助我看到我个人一直在做的事情，而不是其他数据的例子，因为它与我所需要的还不够接近。
import os
import pandas as pd

saveFolder = '/home/Work/Splitting/Aisles/'

aisles = pd.ExcelFile('Split_All.xlsx')

sheets = ['Disco', 'Closed', 'OI+', 'Dropship', 'True Outs',
          'Large Missing', 'Two Missing', 'One Missing']

dataframes = {}
for sheet in sheets:
    dataframes[sheet] = pd.read_excel(aisles, sheet)

for num in range(1, 29):

    nn = "{:02d}".format(num)  # e.g. 02
    
    filename = os.path.join(saveFolder, 'Aisle {}.xlsx'.format(nn))

    writer = pd.ExcelWriter(filename, engine='xlsxwriter')

    for sheet in sheets:
        df = dataframes[sheet]
        df2 = df[df['Loc'].map(lambda x: x.startswith(nn))]
        df2.to_excel(writer, sheet_name=sheet, index=False)

    writer.save()