Handling very large files with openpyxl python

问题

I have a spreadsheet with 11,000 rows and 10 columns. I am trying to copy each row with selected columns, add additional information per line and output to a txt.

Unfortunately, I am having really bad performance issues, files start to slug after 100 rows and kill my processor. Is there a way to speed this up or use better methodology? I am already using read_only=True and data_only=True

Most memory intensive part is iterating through each cell :

for i in range(probeStart, lastRow+1):
    dataRow =""
    for j in range (1,col+2):
        dataRow = dataRow + str(sheet.cell(row=i, column=j).value)  + "\t"

    sigP = db.get(str(sheet.cell(row= i, column=1).value), "notfound") #my additional information 
    a = str(sheet.cell(row = i, column = max_column-1).value) +"\t" 
    b  = str(sheet.cell(row = i, column = max_column).value) + "\t"
    string1  = dataRow + a + b + sigP + "\n"
    w.write(string1)

回答1:

Question: Is there a way to speed this up or use better methodology?

Try the following to see if this improve performance:

Note: Didn't know the Values of col and max_column!
My Example uses 4 Columns and skips Column C.

Data:
['A1', 'B1', 'C1', 'D1'],
['A2', 'B2', 'C2', 'D2']

---

from openpyxl.utils import range_boundaries
min_col, min_row, max_col, max_row = range_boundaries('A1:D2')

for row_cells in ws.iter_rows(min_col=min_col, min_row=min_row,
                              max_col=max_col, max_row=max_row):

    # Slice Column Values up to B
    data = [cell.value for cell in row_cells[:2]]

    # Extend List with sliced Column Values from D up to End
    data.extend([cell.value for cell in row_cells[3:]])

    # Append db.get(Column A.value)
    data.append(db.get(row_cells[0].value, "notfound"))

    # Join all List Values delimited with \t
    print('{}'.format('\t'.join(data)))

    # Write to CSV
    #w.write(data)

Output:
A1 B1 D1 notfound
A2 B2 D2 notfound

Tested with Python: 3.4.2 - openpyxl: 2.4.1

来源：https://stackoverflow.com/questions/45456300/handling-very-large-files-with-openpyxl-python