1. pandarallel (pip install )
对于一个带有Pandas DataFrame df的简单用例和一个应用func的函数，只需用parallel_apply替换经典的apply。
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from pandarallel import pandarallel
# Initialization
pandarallel.initialize()
# Standard pandas apply
df.apply(func)
# Parallel apply
df.parallel_apply(func)
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注意，如果不想并行化计算，仍然可以使用经典的apply方法。
另外可以通过在initialize函数中传递progress_bar=True来显示每个工作CPU的一个进度条。
2. joblib (pip install )
https://pypi.python.org/pypi/joblib
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# Embarrassingly parallel helper: to make it easy to write readable parallel code and debug it quickly
from math import sqrt
from joblib import Parallel, delayed
def test():
start = time.time()
result1 = Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10000))
end = time.time()
print(end-start)
result2 = Parallel(n_jobs=8)(delayed(sqrt)(i**2) for i in range(10000))
end2 = time.time()
print(end2-end)
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-------输出结果----------
0.4434356689453125
0.6346755027770996
3. multiprocessing
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import multiprocessing as mp
with mp.Pool(mp.cpu_count()) as pool:
df['newcol'] = pool.map(f, df['col'])
multiprocessing.cpu_count()
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返回系统的CPU数量。
该数量不同于当前进程可以使用的CPU数量。可用的CPU数量可以由 len(os.sched_getaffinity(0)) 方法获得。
可能引发 NotImplementedError 。
4. 几种方法性能比较
（1）代码
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import sys
import time
import pandas as pd
import multiprocessing as mp
from joblib import Parallel, delayed
from pandarallel import pandarallel
from tqdm import tqdm, tqdm_notebook
def get_url_len(url):
url_list = url.split(".")
time.sleep(0.01) # 休眠0.01秒
return len(url_list)
def test1(data):
"""
不进行任何优化
"""
start = time.time()
data['len'] = data['url'].apply(get_url_len)
end = time.time()
cost_time = end - start
res = sum(data['len'])
print("res:{}, cost time:{}".format(res, cost_time))
def test_mp(data):
"""
采用mp优化
"""
start = time.time()
with mp.Pool(mp.cpu_count()) as pool:
data['len'] = pool.map(get_url_len, data['url'])
end = time.time()
cost_time = end - start
res = sum(data['len'])
print("test_mp \t res:{}, cost time:{}".format(res, cost_time))
def test_pandarallel(data):
"""
采用pandarallel优化
"""
start = time.time()
pandarallel.initialize()
data['len'] = data['url'].parallel_apply(get_url_len)
end = time.time()
cost_time = end - start
res = sum(data['len'])
print("test_pandarallel \t res:{}, cost time:{}".format(res, cost_time))
def test_delayed(data):
"""
采用delayed优化
"""
def key_func(subset):
subset["len"] = subset["url"].apply(get_url_len)
return subset
start = time.time()
data_grouped = data.groupby(data.index)
# data_grouped 是一个可迭代的对象，那么就可以使用 tqdm 来可视化进度条
results = Parallel(n_jobs=8)(delayed(key_func)(group) for name, group in tqdm(data_grouped))
data = pd.concat(results)
end = time.time()
cost_time = end - start
res = sum(data['len'])
print("test_delayed \t res:{}, cost time:{}".format(res, cost_time))
if __name__ == '__main__':
columns = ['title', 'url', 'pub_old', 'pub_new']
temp = pd.read_csv("./input.csv", names=columns, nrows=10000)
data = temp
"""
for i in range(99):
data = data.append(temp)
"""
print(len(data))
"""
test1(data)
test_mp(data)
test_pandarallel(data)
"""
test_delayed(data)
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(2) 结果输出
1k
res:4338, cost time:0.0018074512481689453
test_mp res:4338, cost time:0.2626469135284424
test_pandarallel res:4338, cost time:0.3467681407928467
1w
res:42936, cost time:0.008773326873779297
test_mp res:42936, cost time:0.26111721992492676
test_pandarallel res:42936, cost time:0.33237743377685547
10w
res:426742, cost time:0.07944369316101074
test_mp res:426742, cost time:0.294996976852417
test_pandarallel res:426742, cost time:0.39208269119262695
100w
res:4267420, cost time:0.8074917793273926
test_mp res:4267420, cost time:0.9741342067718506
test_pandarallel res:4267420, cost time:0.6779992580413818
1000w
res:42674200, cost time:8.027287006378174
test_mp res:42674200, cost time:7.751036882400513
test_pandarallel res:42674200, cost time:4.404983282089233
在get_url_len函数里加个sleep语句（模拟复杂逻辑），数据量为1k，运行结果如下：
1k
res:4338, cost time:10.054503679275513
test_mp res:4338, cost time:0.35697126388549805
test_pandarallel res:4338, cost time:0.43415403366088867
test_delayed res:4338, cost time:2.294757843017578
5. 小结
（1）如果数据量比较少，并行处理比单次执行效率更慢；
（2）如果apply的函数逻辑简单，并行处理比单次执行效率更慢。
6. 问题及解决方法
（1）ImportError: This platform lacks a functioning sem_open implementation, therefore, the required synchronization primitives needed will not function, see issue 3770.
https://www.jianshu.com/p/0be1b4b27bde
（2）Linux查看物理CPU个数、核数、逻辑CPU个数
https://lover.blog.csdn.net/article/details/113951192
(3) 进度条的使用
http://www.zzvips.com/article/190442.html
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原文链接：https://blog.csdn.net/jingyi130705008/article/details/113949730