I'm learning web scraping and found a fun challenge scraping a Javascript handlebars table from this page: Samsung Knox Devices

I eventually got the output I wanted, but I think it feels "hacky", so I'd appreciate any refinements to make it more elegant.

Desired output is a dataframe/csv table with columns = Device, Model_Nums, OS/Platform, Knox Version. Don't need anything else on the page, and I will split/expand and melt the Model Nums separately.

import pandas as pd

# Libraries for this task: 
from bs4 import BeautifulSoup
from selenium import webdriver

# Because the target table is built using Javascript handlebars, we have to use Selenium and a webdriver
driver = webdriver.Edge("MY_PATH") # REPLACE WITH >YOUR< PATH!

# Point the driver at the target webpage:
driver.get('https://www.samsungknox.com/en/knox-platform/supported-devices')

# Get the page content
html = driver.page_source
# Typically I'd do something like: soup = BeautifulSoup(html, "lxml")
# Link below suggested the following, which works; I don't know if it matters
sp = BeautifulSoup(html, "html.parser")

# The 'table here is really a bunch of nested divs 
tables = soup.find_all("div", class_='table-row')
# https://www.angularfix.com/2021/09/how-to-extract-text-from-inside-div-tag.html
rows = []
for t in tables:
    row = t.text
    rows.append(row)

# These are the table-row div classes within each table-row from the output at the previous step that I want:    
    # div 
    # div 
    # div 
    # div 

# Define div class names:
targets = ["supported-devices pivot-fixed", "model", "operating-system", "knox-version"]

# Create an empty list and loop through each target div class; append to list
data = []
for t in targets:
    hold = sp.find_all("div", class_=t)
    for h in hold:
        row = h.text
        data.append({'column': t, 'value': row}) 

df = pd.DataFrame(data)

# This feels like a hack, but I got stuck and it works, so \shrug/
# Create Series from filtered df based on 'column' value (corresponding to the the four "targets" above)
name = pd.Series(df['value'][df['column']=='supported-devices pivot-fixed']).reset_index(drop=True)
model = pd.Series(df['value'][df['column']=='model']).reset_index(drop=True)
os = pd.Series(df['value'][df['column']=='operating-system']).reset_index(drop=True)
knox = pd.Series(df['value'][df['column']=='knox-version']).reset_index(drop=True)
# Concatenate Series into df
df2 = pd.concat([df_name, df_model, df_os, df_knox], axis=1)

# Make the first row the column names:
new_header = df2.iloc[0] #grab the first row for the header
sam_knox_table = df2[1:] #take the data less the header row
sam_knox_table.columns = new_header #set the header row as the df header

# Bob's your uncle
sam_knox_table.to_csv('sam_knox.csv', index=False)

CodePudding user response：

To scrape the texts from the DEVICE and MODEL CODE column you need to create a list of the desired texts using list comprehension inducing WebDriverWait for the visibility_of_all_elements_located() and then write it into a DataFrame using Pandas and you can use the following locator strategies:

• Code Block:

  driver.get("https://www.samsungknox.com/en/knox-platform/supported-devices")
  devices = [my_elem.text for my_elem in WebDriverWait(driver, 20).until(EC.visibility_of_all_elements_located((By.CSS_SELECTOR, "div.table-row:not(.table-header) > div.supported-devices")))]
  models = [my_elem.text for my_elem in WebDriverWait(driver, 20).until(EC.visibility_of_all_elements_located((By.CSS_SELECTOR, "div.table-row:not(.table-header) > div.model")))]
  df = pd.DataFrame(data=list(zip(devices, models)), columns=['DEVICE', 'MODEL CODE'])
  print(df)
  driver.quit()
  

• Console Output:

                 DEVICE                                      MODEL CODE
  0       Galaxy A42 5G          SM-A426N, SM-A426U, SM-A4260, SM-A426B
  1          Galaxy A52                              SM-A525F, SM-A525M
  2       Galaxy A52 5G                                        SM-A5260
  3       Galaxy A52 5G            SM-A526U, SC-53B, SM-A526W, SM-A526B
  4      Galaxy A52s 5G                              SM-A528B, SM-A528N
  ..                ...                                             ...
  371        Gear Sport                                         SM-R600
  372   Gear S3 Classic                                        SM-R775V
  373  Gear S3 Frontier                                        SM-R765V
  374           Gear S2           SM-R720, SM-R730A, SM-R730S, SM-R730V
  375   Gear S2 Classic  SM-R732, SM-R735, SM-R735A, SM-R735V, SM-R735S
  
  [376 rows x 2 columns]