我目前正面临一个问题,我需要将下面图片中显示的所有数据都只包含在一行中。
因此,使用Python和Openpyxl,我尝试编写一个解析行的解析脚本,只在值为非null或不相同的情况下复制到新工作簿中。
我超出了范围错误,代码不能保留我想要的数据。我已经花了好几个小时,所以我想我会问这里,看看我能不能解开。
我已经阅读了一些关于Openpyxl的文档以及关于在python中创建列表的文档,在youtube上尝试了几个视频,但它们都没有完全符合我的目标。
import openpyxl
from openpyxl import Workbook
path = "sample.xlsx"
wb = openpyxl.load_workbook(path)
ws = wb.active
path2 = "output.xlsx"
wb2 = Workbook()
ws2 = wb2.active
listab = []
rows = ws.max_row
columns = ws.max_column
for i in range (1, rows+1):
listab.append([])
cellValue = " "
prevCell = " "
for c in range (1, rows+1):
for r in range(1, columns+1):
cellValue = ws.cell(row=r, column=c).value
if cellValue == prevCell:
listab[r-1].append(prevCell)
elif cellValue == "NULL":
listab[r-1].append(prevCell)
elif cellValue != prevCell:
listab[r-1].append(cellValue)
prevCell = cellValue
for r in range(1, rows+1):
for c in range (1, columns+1):
j = ws2.cell(row = r, column=c)
j.value = listab[r-1][c-1]
print(listab)
wb2.save("output.xlsx")
应该有一行包含以下信息:
ods_service_id | SERVICE_NAME | service_plan_name | CPU | RAM | NIC |驱动器|
我个人会和pandas
一起去。
import pandas as pd
#Loading into pandas
df_data = pd.read_excel('sample.xlsx')
df_data.fillna("NO DATA",inplace=True) ## Replaced nan values with "NO DATA"
unique_ids = df_data.ods_service_ids.unique()
#Storing pd into a list
records_list = df_data.to_dict('records')
keys_to_check = ['service_name', 'service_plan_name', 'CPU','RAM','NIC','DRIVE']
processed = {}
#Go through unique ids
for key in unique_ids:
processed[key] = {}
#Get related records
matching_records = [y for y in records_list if y['ods_service_ids'] == key]
#Loop through records
for record in matching_records:
#For each key to check, save in dict if non null
processed[key]['ods_service_ids'] = key
for detail_key in keys_to_check:
if record[detail_key] != "NO DATA" :
processed[key][detail_key] = record[detail_key]
##Note : doesn't handle duplicate values for different keys so far
#Records are put back in list
output_data = [processed[x] for x in processed.keys()]
# -> to Pandas
df = pd.DataFrame(output_data)[['ods_service_ids','service_name', 'service_plan_name', 'CPU','RAM','NIC','DRIVE']]
#Export to Excel
df.to_excel("output.xlsx",sheet_name='Sheet_name_1', index=False)
以上应该可以工作,但我不确定你想如何保存相同ID的重复记录。你想把它们存储为DRIVE_0
,DRIVE_1
,DRIVE_2
吗?
df可以以不同的方式导出。在#export to Excel
下面用以下内容替换:
df.to_excel("output.xlsx",sheet_name='Sheet_name_1')
没有输入数据,很难看到任何流量。用假数据更正了上面的代码
说实话,我认为你已经设法让数据结构感到困惑,并提出了比你需要的更复杂的东西。
一种适合的方法是为每个服务使用Python字典,逐行更新。
wb = load_workbook("sample.xlsx")
ws = wb.active
objs = {}
headers = next(ws.iter_rows(min_row=1, max_row=1, values_only=True))
for row in ws.iter_rows(min_row=2, values_only=True):
if row[0] not in objs:
obj = {key:value for key, value in zip(headers, row)}
objs[obj['ods_service_id']] = obj
else:# update dict with non-None values
extra = {key:value for key, value in zip(headers[3:], row[3:]) if value != "NULL"}
obj.update(extra)
# write to new workbook
wb2 = Workbook()
ws2 = wb2.active
ws2.append(headers)
for row in objs.values(): # do they need sorting?
ws2.append([obj[key] for key in headers])
请注意如何在不使用计数器的情况下执
我建议使用pandas库,然后你可以轻松地进行任何类型的转换。
import pandas as pd
exceldata = pd.read_excel('tmp.xlsx', index_col=0)
print(exceldata)
您可以轻松删除null/na value
或者您可以替换它并将其导出为excel格式。
参考帮助: