Dynamically changing Xpath

[AgileAVS]

Your code what can i say could be a lot better.
There are many loops,but a many are unnecessary and code still repeating to much in the loops🥨
When code start to get over 200 lines or a lot less,it's about time to think some structure like eg function.

Can giver you some tips and using Pandas is for me an better an easier option than using xlsxwriter.
In Pandas you get similar look that Excel when finish then is just to use df.to_excel().

from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import time
from io import StringIO
import pandas as pd

#--| Setup
options = Options()
options.add_argument("--headless")
browser = webdriver.Chrome(executable_path=r'C:\cmder\bin\chromedriver.exe', options=options)
#--| Parse or automation
url = "https://www.macrotrends.net/stocks/charts/BAC/bank-of-america/financial-ratios"
browser.get(url)
time.sleep(3)

# Make a loop for 10 rows
lst = []
for number in range(1,11):
    row = browser.find_elements_by_css_selector(f'#row{number}jqxgrid')
    lst.append(row[0].text.replace('\n', ','))

# Make DataFrame
df = pd.read_csv(StringIO('\n'.join(lst)), sep=",")
print(df)
# Write to excel
df.to_excel("output.xlsx", index=False)

Output:
   Long-term Debt / Capital   0.4907   0.4763  ...   0.4598   0.4489   0.4803
0         Debt/Equity Ratio   1.9196   1.9389  ...   1.9395    1.781   1.9757
1              Gross Margin        -        -  ...        -        -        -
2          Operating Margin        -        -  ...        -        -        -
3               EBIT Margin        -        -  ...        -        -        -
4             EBITDA Margin        -        -  ...        -        -        -
5     Pre-Tax Profit Margin   25.597  37.3674  ...  34.8895  31.2356  27.8019
6         Net Profit Margin  22.1984  29.6598  ...  19.8471  20.1488   18.078
7            Asset Turnover   0.0263    0.036  ...   0.0367   0.0366   0.0372
8  Inventory Turnover Ratio        -        -  ...        -        -        -

Thank you so much for the suggestion. I am still a newbie to Python, so definitely will lean more about functions and Panda now.
However, the main problem still remains for the code you wrote as well. The code does only get data upto 7 columns or upto the year 2015 but I need the data from 2020 to 2005. Could you suggest on how do I extract more data covering the entire table? Anyways thank you so very much for the help.