我需要匹配两个非常大的Numpy数组（一个有20000行，另一个有大约100000行），并尝试构建一个脚本以高效地完成它。对数组进行简单循环非常慢，有人能提出更好的方法吗？这是我想做的：数组
感谢您，Aina。
datesSecondDict和数组pwfs2Dates包含日期时间值，我需要从数组pwfs2Dates（较小的数组）中取出每个日期时间值，并查看是否存在类似于数组datesSecondDict中的日期时间值（加减5分钟）。如果有一个或多个，我将使用数组valsSecondDict（只是与datesSecondDict相应的数字值的数组）中的值之一填充一个新数组（大小与数组pwfs2Dates相同）。这是@unutbu和@joaquin提出的解决方案，对我有用（谢谢你们！）：import time
import datetime as dt
import numpy as np
def combineArs(dict1, dict2):
"""Combine data from 2 dictionaries into a list.
dict1 contains primary data (e.g. seeing parameter).
The function compares each timestamp in dict1 to dict2
to see if there is a matching timestamp record(s)
in dict2 (plus/minus 5 minutes).
==If yes: a list called data gets appended with the
corresponding parameter value from dict2.
(Note that if there are more than 1 record matching,
the first occuring value gets appended to the list).
==If no: a list called data gets appended with 0."""
# Specify the keys to use
pwfs2Key = 'pwfs2:dc:seeing'
dimmKey = 'ws:seeFwhm'
# Create an iterator for primary dict
datesPrimDictIter = iter(dict1[pwfs2Key]['datetimes'])
# Take the first timestamp value in primary dict
nextDatePrimDict = next(datesPrimDictIter)
# Split the second dictionary into lists
datesSecondDict = dict2[dimmKey]['datetime']
valsSecondDict = dict2[dimmKey]['values']
# Define time window
fiveMins = dt.timedelta(minutes = 5)
data = []
#st = time.time()
for i, nextDateSecondDict in enumerate(datesSecondDict):
try:
while nextDatePrimDict < nextDateSecondDict - fiveMins:
# If there is no match: append zero and move on
data.append(0)
nextDatePrimDict = next(datesPrimDictIter)
while nextDatePrimDict < nextDateSecondDict + fiveMins:
# If there is a match: append the value of second dict
data.append(valsSecondDict[i])
nextDatePrimDict = next(datesPrimDictIter)
except StopIteration:
break
data = np.array(data)
#st = time.time() - st
return data
感谢您，Aina。