5 Best Ways to Retrieve Row Values Based on Conditions in Selenium with Python

by
5 Best Ways to Retrieve Row Values Based on Conditions in Selenium with Python

💡 Problem Formulation: When automating web application tests using Selenium with Python, one common task is to extract data from a spreadsheet-like structure, such as an HTML table. The goal is to retrieve all the values from a particular row where a specific condition is met. For instance, we might want to select the entire row of a table where the “Status” column is marked as “Complete”.

Method 1: Using find_elements_by_xpath()

This method involves using the Selenium WebDriver’s find_elements_by_xpath() function. It allows us to traverse the DOM of the web page and identify the specific row that meets our condition. Then, we iterate over the cells in that row to collect the values.

Here’s an example:

from selenium import webdriver

driver = webdriver.Chrome()
driver.get("http://example.com/sheet")
rows = driver.find_elements_by_xpath("//tr[td[.='Complete']]")
for row in rows:
    cells = row.find_elements_by_tag_name('td')
    row_values = [cell.text for cell in cells]
    print(row_values)
driver.quit()

The output will list all row values in rows that have a cell with text ‘Complete’.

This code snippet creates a WebDriver instance for Chrome, navigates to the web page containing the table, and then locates all rows where the ‘Status’ column has ‘Complete’ marked. It stores and prints values from those rows before quitting the browser.

Method 2: Using CSS Selectors

CSS selectors offer a way of selecting elements with more precision. With the find_elements_by_css_selector() function, we can pinpoint rows based on class, id, or other attributes, and then extract the row values accordingly.

Here’s an example:

from selenium import webdriver

driver = webdriver.Chrome()
driver.get("http://example.com/sheet")
rows = driver.find_elements_by_css_selector("tr.complete")
for row in rows:
    cells = row.find_elements_by_tag_name('td')
    row_values = [cell.text for cell in cells]
    print(row_values)
driver.quit()

The output will be identical to Method 1 but uses CSS classes to find the right rows.

This snippet identifies rows with a specific CSS class ‘complete’, then iterates through each cell within the row to fetch and print their content.

Method 3: Using Pandas with Selenium

In some cases, it might be more efficient to export the table into Pandas for more advanced filtering and processing capabilities. We can use Selenium to scrape the data and then use Pandas for analysis and extraction.

Here’s an example:

import pandas as pd
from selenium import webdriver

driver = webdriver.Chrome()
driver.get("http://example.com/sheet")
table_data = driver.find_element_by_id('data_table')
df = pd.read_html(table_data.get_attribute('outerHTML'))[0]
filtered_rows = df[df['Status'] == 'Complete']
print(filtered_rows)
driver.quit()

The output will be a DataFrame that only includes rows where the ‘Status’ column is ‘Complete’.

After obtaining the entire table data as a DataFrame, this code applies a filter to select only the rows satisfying our condition, which allows for more complex operations if needed.

Method 4: Using Regular Expressions with Selenium

If the extraction condition is text-based and follows a consistent pattern, regular expressions can be a powerful tool alongside Selenium to identify the correct row.

Here’s an example:

import re
from selenium import webdriver

driver = webdriver.Chrome()
driver.get("http://example.com/sheet")
rows = driver.find_elements_by_xpath("//tr")
for row in rows:
    if re.search(r"Complete", row.text):
        cells = row.find_elements_by_tag_name('td')
        row_values = [cell.text for cell in cells]
        print(row_values)
driver.quit()

The output will print rows where the text ‘Complete’ is found within the row text.

This snippet navigates through each row in the table and checks if it contains the text ‘Complete’ using a regular expression. If a match is found, it collects and prints the values from that row.

Bonus One-Liner Method 5: Using List Comprehensions with find_elements_by_xpath()

This concise method combines the power of list comprehensions with XPath to get the desired row values in just one line of code.

Here’s an example:

from selenium import webdriver

driver = webdriver.Chrome()
driver.get("http://example.com/sheet")
row_values = [cell.text for cell in driver.find_elements_by_xpath("//tr[td[contains(.,'Complete')]]/td")]
print(row_values)
driver.quit()

This one-liner will output the values of the row(s) containing ‘Complete’.

It’s a compact version of Method 1, directly constructing the list of row values through a list comprehension applied on elements found by an XPath with a condition.

Summary/Discussion

  • Method 1: XPath selection with find_elements_by_xpath(). Strengths: Direct and explicit selection. Weaknesses: Requires knowledge of XPath syntax.
  • Method 2: CSS Selectors. Strengths: Can leverage page styling to select elements, potentially cleaner than XPath. Weaknesses: Depends on consistent styling.
  • Method 3: Pandas with Selenium. Strengths: Powerful data manipulation and analysis capabilities. Weaknesses: Additional dependency on Pandas, greater overhead.
  • Method 4: Regular Expressions with Selenium. Strengths: Flexibility in text pattern matching. Weaknesses: Complexity increases with pattern complexity.
  • Method 5: One-Liner List Comprehension. Strengths: Concise and elegant. Weaknesses: Can be less readable, harder to debug for complex conditions.