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Full Python code for web scraping Kanview utilizing Selenium, Beautiful Soup, and pandas
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| // source : https://date-fns.org/ | |
| import { addYears, formatWithOptions, toUpper } from 'date-fns/fp' | |
| import { eo } from 'date-fns/locale' | |
| const addFiveYears = addYears(5) | |
| const dateToString = formatWithOptions({ locale: eo }, 'D MMMM YYYY') | |
| const dates = [ | |
| new Date(2017, 0, 1), | |
| new Date(2017, 1, 11), | |
| new Date(2017, 6, 2) | |
| ] | |
| const toUpper = arg => String(arg).toUpperCase() | |
| const formattedDates = dates.map(addFiveYears).map(dateToString).map(toUpper) | |
| //=> ['1 JANUARO 2022', '11 FEBRUARO 2022', '2 JULIO 2022'] |
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| from selenium import webdriver | |
| from selenium.webdriver.common.keys import Keys | |
| from bs4 import BeautifulSoup | |
| import re | |
| import pandas as pd | |
| from tabulate import tabulate | |
| import os | |
| #launch url | |
| url = "http://kanview.ks.gov/PayRates/PayRates_Agency.aspx" | |
| # create a new Firefox session | |
| driver = webdriver.Firefox() | |
| driver.implicitly_wait(30) | |
| driver.get(url) | |
| #After opening the url above, Selenium clicks the specific agency link | |
| python_button = driver.find_element_by_id('MainContent_uxLevel1_Agencies_uxAgencyBtn_33') #FHSU | |
| python_button.click() #click fhsu link | |
| #Selenium hands the page source to Beautiful Soup | |
| soup_level1=BeautifulSoup(driver.page_source, 'lxml') | |
| datalist = [] #empty list | |
| x = 0 #counter | |
| #Beautiful Soup finds all Job Title links on the agency page and the loop begins | |
| for link in soup_level1.find_all('a', id=re.compile("^MainContent_uxLevel2_JobTitles_uxJobTitleBtn_")): | |
| #Selenium visits each Job Title page | |
| python_button = driver.find_element_by_id('MainContent_uxLevel2_JobTitles_uxJobTitleBtn_' + str(x)) | |
| python_button.click() #click link | |
| #Selenium hands of the source of the specific job page to Beautiful Soup | |
| soup_level2=BeautifulSoup(driver.page_source, 'lxml') | |
| #Beautiful Soup grabs the HTML table on the page | |
| table = soup_level2.find_all('table')[0] | |
| #Giving the HTML table to pandas to put in a dataframe object | |
| df = pd.read_html(str(table),header=0) | |
| #Store the dataframe in a list | |
| datalist.append(df[0]) | |
| #Ask Selenium to click the back button | |
| driver.execute_script("window.history.go(-1)") | |
| #increment the counter variable before starting the loop over | |
| x += 1 | |
| #end loop block | |
| #loop has completed | |
| #end the Selenium browser session | |
| driver.quit() | |
| #combine all pandas dataframes in the list into one big dataframe | |
| result = pd.concat([pd.DataFrame(datalist[i]) for i in range(len(datalist))],ignore_index=True) | |
| #convert the pandas dataframe to JSON | |
| json_records = result.to_json(orient='records') | |
| #pretty print to CLI with tabulate | |
| #converts to an ascii table | |
| print(tabulate(result, headers=["Employee Name","Job Title","Overtime Pay","Total Gross Pay"],tablefmt='psql')) | |
| #get current working directory | |
| path = os.getcwd() | |
| #open, write, and close the file | |
| f = open(path + "\\fhsu_payroll_data.json","w") #FHSU | |
| f.write(json_records) | |
| f.close() |
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