如何使用公式作为参考，使用 Python 和 Pandas（或类似的东西）从另一个 Excel 选项卡中获取数据

Question

First, I'm not that sure if pandas is the right approach to this, it may be better done with VBA or another lib like openpyxl.

I have a excel sheet which has two different tabs (tab1 has a name and a value, which is a formula like: ='tab2'!H10 , for instance, tab2 has said value (or sum of values) and other bunch of information).

I want to get information from the value column on tab1, which may have reference for more than one cell on the second tab ='tab2'!H10 + 'tab2'!H12 + 'tab2'!H20 on row = Name1 . Extract those ROWS (row 10, 12 and 20 on this example) and fetch information from 3 columns on tab2, for those rows.

Then, I want to "join" (not sure if a join) the name on tab1 with those 3 columns from tab2 on said lines. Something like this as the end result:

| Name 1 (from tab 1 - line) | Column 1 (from tab2) | Column 2 | Column 3 | from row 10

| Name 1 (from tab 1 - line) | Column 1 (from tab2) | Column 2 | Column 3 | from row 12

| Name 1 (from tab 1 - line) | Column 1 (from tab2) | Column 2 | Column 3 | from row 20

Code that I'm trying and it's not currently working, error ValueError: cannot join with no overlapping index names

import numpy as np
import pandas as pd
from IPython.display import display
from openpyxl import Workbook
from openpyxl import load_workbook

wbx = load_workbook(filename= 'test.xlsx')

sheet_names = wbx.sheetnames

name1 = sheet_names[0]
sheet_ranges1 = wbx[name1]

df1 = pd.DataFrame(sheet_ranges1.values)

name2 = sheet_names[1]
sheet_ranges2 = wbx[name2]

df2 = pd.DataFrame(sheet_ranges2.values)

pd.set_option("display.max_rows", None, "display.max_columns", None)

c1 = df1.iloc[:,[1]]
c2 = df1.iloc[:,24]
print(c1.dtypes)

res = c2.str.extractall(r"!H(?P<line>\d+)?")
res2 = c1.merge(pd.DataFrame(res), how='left', left_index=True, right_index=True)

Answer 1

hope it helps:

import pandas as pd
df1 = pd.read_excel(r'.\foldername\filename.xlsx', sheet_name='sheet1')
df2 = pd.read_excel(r'.\foldername\filename.xlsx', sheet_name='sheet2')
df3 = pd.read_excel(r'.\foldername\filename.xlsx', sheet_name='sheet3')

# drop columns as needed that are not to include in merged result, or to avoid duplicate column that will be col_x and col_y
df1 = df1.drop(columns=['col2', 'col3'], index=False)

# join table
dfx = df1.merge(df2, how="inner", left_on="col1", right_on="col2)
merged = dfx.merge(df3, how="left", left_on="col7", right_on="col3)
print(merged.head())

you can do as well in VBA

Sub JoinTables()

 Dim connection As ADODB.Connection
 Set connection = New ADODB.Connection

 With connection
     .Provider = "Microsoft.Jet.OLEDB.4.0"
     .ConnectionString = "Data Source=" & ThisWorkbook.FullName & ";" & "Extended Properties=Excel 8.0;"
     .Open
 End With

 Dim recordset As ADODB.Recordset
 Set recordset = New ADODB.Recordset

 recordset.Open "SELECT * FROM [Sheet1$] INNER JOIN [Sheet2$] ON [Sheet1$].[type] = " & "[Sheet2$].[type]", connection

 With Worksheets("Sheet3")
     .Cells(2, 1).CopyFromRecordset recordset
 End With

 recordset.Close
 connection.Close

 End Sub