Python中处理大量记录的时空查询

Question

我有一个包含60万个x/y坐标点的数据表，这些点还有日期和时间的信息，另外还有一个叫做“状态”的字段，里面有额外的描述信息。

我的目标是，对于每一条记录：

• 在一定的时间和空间范围内，统计“状态”这一列的值。

这个特定的范围是时间上在t之前8小时以内，并且距离小于100米。

目前我把数据放在一个pandas数据框里。

我可以遍历每一行数据，对于每一条记录，筛选出感兴趣的日期，然后计算距离，再进一步限制选择。不过这样做对于这么多记录来说，速度还是很慢。

• 这样运行需要4.4个小时。

我发现可以创建一个三维的kdtree，使用x、y和日期（以时间戳的形式）来表示。不过，我不太确定如何在结合日期和地理距离时，正确限制距离。

这里有一些可以让你们测试的代码：

导入

import numpy.random as npr
import numpy
import pandas as pd
from pandas import DataFrame, date_range
from datetime import datetime, timedelta

创建数据

np.random.seed(111)

生成测试数据的函数

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)

加速的函数

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)

运行代码并计时

start = datetime.now()
out = work(data)
print datetime.now() - start

有没有办法以向量化的方式来完成这个查询？还是说我应该尝试其他的技术。

<3

Answer 1

这里有一些方法可以部分解决我的问题。因为这个循环可以独立地处理数据的不同部分，所以在这里使用并行处理是有意义的。

使用 Ipython...

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 = []

最终时间 1:17:54.910206，大约是原来时间的四分之一

我仍然很希望有人能在这个函数的内部提出一些小的速度提升建议。