def map(): global total, search_term search_term = request.form['search'] numbers = norm_vals(job_search(search_term)) total = numbers.pop() return render_template("demand_map.html", job_totals = numbers[0], date_avgs = numbers[1], numbers_list = numbers[2], total = total, query = search_term)
def job_info(job_name): ## Initialize global variables date_list, state_list, expired_list, city_list, lat_list, long_list = [], [], [], [], [], [] query_name = job_search(job_name) ## Get a count of the total number of relevant jobs found job_total = num_pages(query_name) page_total = job_total/25 count = 1 for pages in range(page_total): # #for now, the extract_data will only extract state_list state_list += extract_data(query_name, str(count), str(count * 25), date_list, state_list, expired_list, city_list, lat_list, long_list) count += 25 ## Separate the states by uniqueness and count the number of each state with a job post unique_states = list(set(state_list)) state_counts = {i: state_list.count(i) for i in unique_states} state_info = [unique_states, state_counts] return state_info
def top_cities(): global total, search_term state = request.form['state_name'] if state == 'MI1' or state == 'MI2': cities = fifteen_per_state("MI") state = "MI" else: cities = fifteen_per_state(state) search_string = job_search(search_term) number_jobs = city_numbers(search_string, cities, state) sorted_city_indices = sorted(range(len(number_jobs)), key=lambda k: number_jobs[k]) job_list, city_list = [], [] for indices in sorted_city_indices: job_list.append(number_jobs[indices]) city_list.append(cities[indices]) return render_template("top_cities.html", state = state, total = total, jobnum_list = job_list, list_of_cities = city_list, query = search_term, search_string = search_string)
import urllib2 import simplejson as json from python_testing import job_search, norm_vals from city_practice import city_numbers, fifteen_per_state search = "executive chef" number_jobs = norm_vals(job_search(search)) print number_jobs