Handling ValueError When Reading Excel Files with Pandas and OpenPyXL

Resolving XML Parsing Errors in Excel Files with Pandas and OpenPyXL

Using Python with Selenium and Pandas to handle XML structure issues in Excel files

import os
import pandas as pd
import time
from glob import glob
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
# Set up download options for Chrome
options = webdriver.ChromeOptions()
download_dir = os.path.abspath("./ETF/test")
options.add_experimental_option("prefs", {"download.default_directory": download_dir})
driver_path = "./webdriver/chromedriver.exe"
driver_service = Service(driver_path)
driver = webdriver.Chrome(service=driver_service, options=options)
# Automate download of Excel file with Selenium
driver.get('https://www.amundietf.de/de/professionell')
driver.maximize_window()
WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, "//button[normalize-space()='Professioneller Anleger']"))).click()
WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.ID, "confirmDisclaimer"))).click()
WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.ID, "CookiesDisclaimerRibbonV1-AllOn"))).click()
time.sleep(2)
file_path = os.path.join(download_dir, "test.xlsx")
# Rename file
file_glob = glob(os.path.join(download_dir, "Fondszusammensetzung_Amundi*"))
if file_glob:
    os.rename(file_glob[0], file_path)
else:
    print("File not found for renaming")
driver.quit()
# Read and process the file
try:
    df = pd.read_excel(file_path, engine='openpyxl', skiprows=18, skipfooter=4, header=1, usecols="B:H")
    df.to_csv('./ETF/test/test.csv', sep=';', encoding='latin-1', decimal=',')
except ValueError as e:
    print(f"Error reading Excel file: {e}")
    # Alternative method with openpyxl direct read (backup approach)
    import openpyxl
    workbook = openpyxl.load_workbook(file_path)
    sheet = workbook.active
    data = sheet.values
    print("Data loaded using backup approach")

Alternative Solution: Using a Compatibility Mode to Avoid XML Errors

This approach minimizes dependencies on XML by saving a secondary Excel format if initial parsing fails.

import pandas as pd
import openpyxl
def safe_load_excel(file_path):
    try:
        # First attempt using pandas' read_excel with openpyxl
        df = pd.read_excel(file_path, engine='openpyxl')
    except ValueError:
        print("Switching to secondary method due to XML issues")
        workbook = openpyxl.load_workbook(file_path)
        sheet = workbook.active
        data = sheet.values
        headers = next(data)
        df = pd.DataFrame(data, columns=headers)
    return df
# Usage example
file_path = './ETF/test/test.xlsx'
df = safe_load_excel(file_path)
df.to_csv('./ETF/test/test_fixed.csv', sep=';', encoding='latin-1', decimal=',')

Test Script for Environment Compatibility

Unit tests to ensure file reading compatibility in different environments

import unittest
import os
from your_module import safe_load_excel
class TestExcelFileLoad(unittest.TestCase):
    def test_file_exists(self):
        self.assertTrue(os.path.exists('./ETF/test/test.xlsx'), "Excel file should exist")
    def test_load_excel(self):
        df = safe_load_excel('./ETF/test/test.xlsx')
        self.assertIsNotNone(df, "DataFrame should not be None after loading")
        self.assertGreater(len(df), 0, "DataFrame should contain data")
if __name__ == '__main__':
    unittest.main()

Common Questions on Processing Excel Files in Python

1. Why does reading an Excel file in Pandas cause a ValueError?
2. This error usually arises when the Excel file contains invalid XML or non-standard formatting. Try using the engine="openpyxl" parameter in pd.read_excel or OpenPyXL’s load_workbook for a more flexible approach.
3. How can I automate downloading an Excel file in Python?
4. You can use Selenium to automate the download by opening the website, navigating to the download button, and setting Chrome options to control file handling.
5. What does the glob module do in Python?
6. glob helps locate files in a directory using pattern matching. This is useful for finding files with unpredictable names, especially when automating file downloads.
7. How can I rename files after downloading with Selenium?
8. Once a file is downloaded, use os.rename to change its name. This is essential in automations to ensure the file has a consistent name before processing.
9. How do I handle cookies and pop-ups with Selenium?
10. Use Selenium’s WebDriverWait and ExpectedConditions to wait for pop-ups or disclaimers to load, and then interact with them using element locators like By.ID or By.XPATH.
11. What’s the difference between pd.read_excel and openpyxl.load_workbook?
12. pd.read_excel is a high-level function that reads data into a DataFrame but may encounter XML issues. openpyxl.load_workbook provides a lower-level interface to control sheet-level data extraction directly.
13. Is there a way to validate if my file loads correctly?
14. Use unittest to check if the file exists and loads properly. Set up simple tests to verify that data loads as expected, especially when deploying to multiple systems.
15. How do I process only part of an Excel file?
16. Use the parameters skiprows and usecols in pd.read_excel to focus on specific rows and columns. This is helpful for loading only the essential data.
17. Can I export the processed DataFrame to a CSV file?
18. Yes, after loading and processing data, use df.to_csv to save the DataFrame as a CSV. You can specify settings like sep=";" and encoding for compatibility.
19. What’s the best way to handle XML issues in Excel files?
20. Try reading the file with openpyxl directly, which offers a more robust way to handle XML errors. If errors persist, consider saving a copy of the file as .csv and processing it from there.
21. How can I deal with dynamic element loading on a webpage in Selenium?
22. Using WebDriverWait in Selenium allows you to wait for elements to load before interacting with them. This ensures the script doesn’t break due to timing issues on the page.

Ensuring Smooth Data Processing with Automation and Error Handling

Incorporating automation with Selenium and careful error handling allows you to create a reliable and repeatable process for downloading and processing Excel files. Using Pandas alongside OpenPyXL with backup methods helps bypass XML issues, making it possible to import, edit, and export data even with potential formatting inconsistencies. 🖥️

By following these techniques, you save time and reduce the chances of manual errors. These strategies make your data handling smoother, minimizing interruptions, especially when dealing with files from third-party sources. This way, you can focus on analysis instead of troubleshooting. 📊