本文原创链接:https:////www.cnblogs.com/zhanling/p/12192978.html 1 import numpy as np import xarray as xr import cartopy.crs as ccrs import cartopy.feature as cfeat from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER import matplotlib.
#xiaodeng#python3#基于SQL和PYTHON的数据库数据查询语句import pymysql #1.基本用法cur.execute("select * from biao") #2.查询某表中的特定数据,如某制定id和名字的数据cur.execute("select * from biao where id="XXXX" and name="xxx" ") #3.统计函数select count(1) from
起因 Python处理一下数据,大概有六七个G,然后再存到另外一个文件中,单线程跑起来发现太慢了,数据总量大概是千万行的级别,然后每秒钟只能处理不到20行--遂想怎么提高一下速度 尝试1-multiprocessing 代码如下: from multiprocessing.dummy import Pool as ThreadPool pool = ThreadPool(20) pool.map(func_name, args) pool.close() pool.join() 这里参考了这篇文