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web_crawling_to_blob.py
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"""
Author: You Sen Wang (Ethan)
Started Date: 05/12/2020
Email: [email protected]
"""
import requests
import bs4
from bs4 import BeautifulSoup as bs
import datetime
import csv
import random, time
start_page = 1
num_of_pages = 1
keyword104 = 'SAP'
head = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36',
'Accept-Language':'zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4'}
my_params = {'ro':'0', # 限定全職的工作，如果不限定則輸入0
'keyword':keyword104, # 想要查詢的關鍵字
#'area':'6001001000', # 限定在台北的工作
#'isnew':'90', # 只要最近三個月有更新的過的職缺
#'jobsource' : '2018indexpoc'
'mode':'s',
'kwop': '7',
'order':'15'}
positions_out=f'104人力銀行_{keyword104}_positions.csv'
url = 'https://www.104.com.tw/jobs/search/?'
companies_out = f'104人力銀行_{keyword104}_companies.csv'
company_url = 'https://www.104.com.tw/cust/list/index?'
companies_columns=[
'公司名稱',
'地址',
'產業類別',
'資本額',
'員工人數',
'網址',
'抓取時間'
]
def get_company_data(company):
comp_dat = company.find_all('span')
company_data = {
'公司名稱' : company.h1.a.text,
'地址' : comp_dat[0].text,
'產業類別' : comp_dat[1].text,
'資本額' : comp_dat[2].text.strip('資本額：'),
'員工人數' : comp_dat[3].text.strip('員工人數：'),
'網址' : company.h1.a.get('href'),
'抓取時間' : str(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S"))
}
return company_data
jobs_columns=[
'職缺內容',
'公司名稱',
'地址',
'薪資',
'網址',
'抓取時間'
]
def get_job_data(job):
job_fetch_time = str(datetime.datetime.now().strftime("%Y/%m/%d %H:%M:%S"))
job_name=job.find('a',class_="js-job-link").text
job_company=job.get('data-cust-name')
job_loc=job.find('ul', class_='job-list-intro').find('li').text
job_pay=job.find('span',class_='b-tag--default').text #薪資
urls = job.find_all('a')
job_url = 'https:' + urls[0].get("href")
#company104_url = 'https:' + urls[1].get("href")
job_data = {
'職缺內容':job_name,
'公司名稱':job_company,
'地址':job_loc,
'薪資':job_pay,
'網址':job_url,
'抓取時間': job_fetch_time
}
return job_data
from azure.storage.blob import (
BlobServiceClient, ContainerClient, BlobClient
)
import json
config_source = "./infofab-sapforsales-config.json"
with open(config_source, encoding='utf-8') as f:
config = json.load(f)
storage_name = config['storage_name']
storage_key = config['storage_key1']
storage_string = config['storage_connection_string1']
storage_url = f"https://{storage_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
account_url=storage_url,
credential=storage_key
)
account_info = blob_service_client.get_account_information()
container_name = config['container_name']
container_client = ContainerClient(
account_url=storage_url,
container_name=container_name,
credential=storage_key
)
print(dir(container_client))
#print(dir(blob_client))
blob_list = container_client.list_blobs()
for blob in blob_list:
print("\t" + blob.name)
blob_client_positions = BlobClient(
account_url=storage_url,
container_name=storage_name,
blob_name="104人力銀行_SAP_positions.csv",
credential=storage_key
)
blob_client_companies = BlobClient(
account_url=storage_url,
container_name=storage_name,
blob_name="104人力銀行_SAP_companies.csv",
credential=storage_key
)
from io import BytesIO, StringIO
def convert_string_bytes_buffer(string_buffer: StringIO) -> BytesIO:
string_buffer.seek(0)
bytes_buffer = BytesIO()
while True:
line = string_buffer.readline()
if not line:
break
bytes_buffer.write(line.encode('utf-8'))
bytes_buffer.seek(0)
return bytes_buffer
def save_to_blob(blob_name, col_name, all_data):
print(col_name)
with StringIO() as string_buffer:
dictWriter = csv.DictWriter(string_buffer, fieldnames=col_name)
dictWriter.writeheader()
for dat in all_data:
try:
print(dat)
dictWriter.writerow(dat)
except UnicodeEncodeError:
print(dat)
dictWriter.writerow({k:v.encode("utf-8") for k,v in dat.items()})
pass
blob_client = BlobClient(
account_url=storage_url,
container_name=storage_name,
blob_name=blob_name,
credential=storage_key
)
with convert_string_bytes_buffer(string_buffer) as bytes_buffer:
blob_client.create_append_blob(bytes_buffer)
bytes_buffer.close()
string_buffer.close()
print(f"New rows/data are written in {blob_name}.")
# print(dir(blob_client_companies))
# import os.path
# def save_to_csv(file_name, col_name, all_data):
# write_headers = True
# if os.path.isfile(file_name):
# write_headers = False
# #try:
# with open(file_name,'a+', newline='') as csvFile: #定義CSV的寫入檔,並且每次寫入完會換下一行
# dictWriter = csv.DictWriter(csvFile, fieldnames=col_name) #定義寫入器
# if write_headers:
# dictWriter.writeheader()
# print(f"write headers to {file_name}.")
# for dat in all_data:
# try:
# dictWriter.writerow(dat)
# except UnicodeEncodeError:
# print(dat)
# dictWriter.writerow({k:v.encode("utf-8") for k,v in dat.items()})
# pass
# csvFile.close()
# print(f"New rows/data are written in {file_name}.")
all_job_data = []
all_comp_data = []
for page in range(start_page, num_of_pages+1):
print(("*" * 20) + f"page: {page}" + ("*" * 20))
my_params['page'] = str(page)
res = requests.get(url, my_params, headers=head)
soup = bs(res.text, 'html.parser')
jobs = soup.find_all('article',class_='js-job-item')
for job in jobs:
print("-" *100)
job_data = get_job_data(job)
company_name = job_data['公司名稱']
# Check to see if we already search the company before.
if any(job_dat['公司名稱'] == company_name for job_dat in all_job_data):
print(f"{company_name} already exists, skip.")
continue
all_job_data.append(job_data)
print(f"{positions_out} will append: {job_data['職缺內容']}")
company_param = {
"keyword": str(company_name),
'mode':'s'
}
company_req = requests.get(company_url, company_param, headers=head)
comp_soup = bs(company_req.text, 'html.parser')
#print(comp_soup.body.prettify())
companies = comp_soup.body.find_all('article', class_='items')
for company in companies:
if company_name == company.h1.a.text:
company_data = get_company_data(company)
all_comp_data.append(company_data)
print(f"{companies_out} will append: {company_data['公司名稱']}")
time.sleep(random.randint(1,3))
# save_to_csv(fn, jobs_columns, all_job_data)
# save_to_csv(companies_out, companies_columns, all_comp_data)
save_to_blob(positions_out, jobs_columns, all_job_data)
save_to_blob(companies_out, companies_columns, all_comp_data)
print(f"num_jobs: {len(all_job_data)}")
print(f"num_companies: {len(all_comp_data)}")