我有一个600 000 x / y点的数据框,带有日期时间信息,沿着另一个字段'status',带有额外的描述性信息
我的目标是,每条记录:
特定缓冲液在t-8小时内且<1小时内。 100米
目前我将数据放在pandas数据框中。
我可以循环遍历行,并为每条记录子集感兴趣的日期,然后计算距离并进一步限制选择。然而,如此多的记录仍然会很慢。
我可以看到我可以用x,y,日期作为纪元时间创建一个三维kdtree。但是,我不确定在合并日期和地理距离时如何正确限制距离。
import numpy.random as npr
import numpy
import pandas as pd
from pandas import DataFrame, date_range
from datetime import datetime, timedelta
np.random.seed(111)
def CreateDataSet(Number=1):
Output = []
for i in range(Number):
# Create a date range with hour frequency
date = date_range(start='10/1/2012', end='10/31/2012', freq='H')
# Create long lat data
laty = npr.normal(4815862, 5000,size=len(date))
longx = npr.normal(687993, 5000,size=len(date))
# status of interest
status = [0,1]
# Make a random list of statuses
random_status = [status[npr.randint(low=0,high=len(status))] for i in range(len(date))]
# user pool
user = ['sally','derik','james','bob','ryan','chris']
# Make a random list of users
random_user = [user[npr.randint(low=0,high=len(user))] for i in range(len(date))]
Output.extend(zip(random_user, random_status, date, longx, laty))
return pd.DataFrame(Output, columns = ['user', 'status', 'date', 'long', 'lat'])
#Create data
data = CreateDataSet(3)
len(data)
#some time deltas
before = timedelta(hours = 8)
after = timedelta(minutes = 1)
def work(df):
output = []
#loop through data index's
for i in range(0, len(df)):
l = []
#first we will filter out the data by date to have a smaller list to compute distances for
#create a mask to query all dates between range for date i
date_mask = (df['date'] >= df['date'].iloc[i]-before) & (df['date'] <= df['date'].iloc[i]+after)
#create a mask to query all users who are not user i (themselves)
user_mask = df['user']!=df['user'].iloc[i]
#apply masks
dists_to_check = df[date_mask & user_mask]
#for point i, create coordinate to calculate distances from
a = np.array((df['long'].iloc[i], df['lat'].iloc[i]))
#create array of distances to check on the masked data
b = np.array((dists_to_check['long'].values, dists_to_check['lat'].values))
#for j in the date queried data
for j in range(1, len(dists_to_check)):
#compute the ueclidean distance between point a and each point of b (the date masked data)
x = np.linalg.norm(a-np.array((b[0][j], b[1][j])))
#if the distance is within our range of interest append the index to a list
if x <=100:
l.append(j)
else:
pass
try:
#use the list of desired index's 'l' to query a final subset of the data
data = dists_to_check.iloc[l]
#summarize the column of interest then append to output list
output.append(data['status'].sum())
except IndexError, e:
output.append(0)
#print "There were no data to add"
return pd.DataFrame(output)
start = datetime.now()
out = work(data)
print datetime.now() - start
有没有办法以矢量化方式执行此查询?或者我应该追逐另一种技术。
&LT; 3
答案 0 :(得分:0)
这至少在某种程度上解决了我的问题。由于循环可以独立地在数据的不同部分上运行,因此并行化在这里是有意义的。
from IPython.parallel import Client
cli = Client()
cli.ids
cli = Client()
dview=cli[:]
with dview.sync_imports():
import numpy as np
import os
from datetime import timedelta
import pandas as pd
#We also need to add the time deltas and output list into the function as
#local variables as well as add the Ipython.parallel decorator
@dview.parallel(block=True)
def work(df):
before = timedelta(hours = 8)
after = timedelta(minutes = 1)
output = []
我仍然对任何人都非常感兴趣,建议在功能体内进行小幅度的改进。