Python利用多进程将大量数据放入有限内存的教程

#import the necessary packages
import pandas as pd
import us
import numpy as np
from 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 to
def 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 queue
def 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 eachother
def 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()