#import the necessary packagesimport pandas as pdimport usimport numpy as npfrom multiprocessing import Pool,cpu_count,Queue,Manager # the data in one particular column was number in the form that horrible excel version# of a number where '12000' is '12,000' with that beautiful useless comma in there.# did I mention I excel bothers me?# instead of converting the number right away, we only convert them when we need todef median_maker(column):  return np.median([int(x.replace(',','')) for x in column]) # dictionary_of_dataframes contains a dataframe with information for each title; e.g title is 'Data Scientist'# related_title_score_df is the dataframe of information for the title; columns = ['title','score']### where title is a similar_title and score is how closely the two are related, e.g. 'Data Analyst', 0.871# code_title_df contains columns ['code','title']# oes_data_df is a HUGE dataframe with all of the Bureau of Labor Statistics(BLS) data for a given time period (YAY FREE DATA, BOO BAD CENSUS DATA!) def job_title_location_matcher(title,location):  try:    related_title_score_df = dictionary_of_dataframes[title]    # we limit dataframe1 to only those related_titles that are above    # a previously established threshold    related_title_score_df = related_title_score_df[title_score_df['score']>80]     #we merge the related titles with another table and its codes    codes_relTitles_scores = pd.merge(code_title_df,related_title_score_df)    codes_relTitles_scores = codes_relTitles_scores.drop_duplicates()     # merge the two dataframes by the codes    merged_df = pd.merge(codes_relTitles_scores, oes_data_df)    #limit the BLS data to the state we want    all_merged = merged_df[merged_df['area_title']==str(us.states.lookup(location).name)]     #calculate some summary statistics for the time we want    group_med_emp,group_mean,group_pct10,group_pct25,group_median,group_pct75,group_pct90 = all_merged[['tot_emp','a_mean','a_pct10','a_pct25','a_median','a_pct75','a_pct90']].apply(median_maker)    row = [title,location,group_med_emp,group_mean,group_pct10,group_pct25, group_median, group_pct75, group_pct90]    #convert it all to strings so we can combine them all when writing to file    row_string = [str(x) for x in row]    return row_string  except:    # if it doesnt work for a particular title/state just throw it out, there are enough to make this insignificant    'do nothing'

#runs the function and puts the answers in the queuedef worker(row, q):    ans = job_title_location_matcher(row[0],row[1])    q.put(ans) # this writes to the file while there are still things that could be in the queue# this allows for multiple processes to write to the same file without blocking eachotherdef listener(q):  f = open(filename,'wb')  while 1:    m = q.get()    if m =='kill':        break    f.write(','.join(m) + 'n')    f.flush()  f.close() def main():  #load all your data, then throw out all unnecessary tables/columns  filename = 'skill_TEST_POOL.txt'   #sets up the necessary multiprocessing tasks  manager = Manager()  q = manager.Queue()  pool = Pool(cpu_count() + 2)  watcher = pool.map_async(listener,(q,))   jobs = []  #titles_states is a dataframe of millions of job titles and states they were found in  for i in titles_states.iloc:    job = pool.map_async(worker, (i, q))    jobs.append(job)   for job in jobs:    job.get()  q.put('kill')  pool.close()  pool.join() if __name__ == "__main__":  main()