我想并行化numpy或pandas操作。为此，我一直在研究pydata的blaze。我的理解是并行化是它的主要卖点。
不幸的是，我找不到在多个内核上运行的操作。火焰中的并行处理是否可用或当前仅是既定目标？难道我做错了什么？我正在使用blaze v0.6.5。
我希望并行化的一个函数的示例:(对pytables列的重复数据删除太大，无法容纳在内存中)
import pandas as pd
import blaze as bz
def f1():
counter = 0
groups = pd.DataFrame(columns=['name'])
t = bz.TableSymbol('t', '{name: string}')
e = bz.distinct(t)
for chunk in store.select('my_names', columns=['name'],
chunksize=1e5):
counter += 1
print('processing chunk %d' % counter)
groups = pd.concat([groups, chunk])
groups = bz.compute(e, groups)
编辑1
遵循Phillip的示例，我遇到了问题:
In [1]: from blaze import Data, compute
In [2]: d = Data('test.bcolz')
In [3]: d.head(5)
Out[3]: <repr(<blaze.expr.collections.Head at 0x7b5e300>) failed: NotImplementedError: Don't know how to compute:
expr: _1.head(5).head(11)
data: {_1: ctable((8769257,), [('index', '<i8'), ('date', 'S10'), ('accessDate', 'S26')])
nbytes: 367.97 MB; cbytes: 35.65 MB; ratio: 10.32
cparams := cparams(clevel=5, shuffle=True, cname='blosclz')
rootdir := 'test.bcolz'
[(0L, '2014-12-12', '2014-12-14T17:39:19.716000')
(1L, '2014-12-11', '2014-12-14T17:39:19.716000')
(2L, '2014-12-10', '2014-12-14T17:39:19.716000') ...,
(1767L, '2009-11-11', '2014-12-15T13:32:39.906000')
(1768L, '2009-11-10', '2014-12-15T13:32:39.906000')
(1769L, '2009-11-09', '2014-12-15T13:32:39.906000')]}>
我的环境:
C:\Anaconda>conda list blaze
# packages in environment at C:\Anaconda:
#
blaze 0.6.8 np19py27_69
但是请注意，大火似乎报告了一个错误的版本:
In [5]: import blaze
In [6]: blaze.__version__
Out[6]: '0.6.7'
对于其他数据源，火焰似乎可以解决问题:
In [6]: d = Data([1,2,2,2,3,4,4,4,5,6])
In [7]: d.head(5)
Out[7]:
_2
0 1
1 2
2 2
3 2
4 3
In [16]: list(compute(d._2.distinct()))
Out[16]: [1, 2, 3, 4, 5, 6]
最佳答案
注意:下面的示例需要最新版本的blaze，您可以通过
conda install -c blaze blaze
您还需要最新版本的新生
into 项目。您需要从into安装master，您可以使用pip install git+git://github.com/ContinuumIO/into.git
您无法使用任意后端进行“无缝”并行化，但是
bcolz 后端以一种不错的方式支持并行化。这是NYC Taxi trip/fare dataset的示例注意:我已将行程和票价数据集合并为一个数据集。数据集中有173,179,759行
In [28]: from blaze import Data, compute
In [29]: ls -d *.bcolz
all.bcolz/ fare.bcolz/ trip.bcolz/
In [30]: d = Data('all.bcolz')
In [31]: d.head(5)
Out[31]:
medallion hack_license \
0 89D227B655E5C82AECF13C3F540D4CF4 BA96DE419E711691B9445D6A6307C170
1 0BD7C8F5BA12B88E0B67BED28BEA73D8 9FD8F69F0804BDB5549F40E9DA1BE472
2 0BD7C8F5BA12B88E0B67BED28BEA73D8 9FD8F69F0804BDB5549F40E9DA1BE472
3 DFD2202EE08F7A8DC9A57B02ACB81FE2 51EE87E3205C985EF8431D850C786310
4 DFD2202EE08F7A8DC9A57B02ACB81FE2 51EE87E3205C985EF8431D850C786310
vendor_id rate_code store_and_fwd_flag pickup_datetime \
0 CMT 1 N 2013-01-01 15:11:48
1 CMT 1 N 2013-01-06 00:18:35
2 CMT 1 N 2013-01-05 18:49:41
3 CMT 1 N 2013-01-07 23:54:15
4 CMT 1 N 2013-01-07 23:25:03
dropoff_datetime passenger_count trip_time_in_secs trip_distance \
0 2013-01-01 15:18:10 4 382 1.0
1 2013-01-06 00:22:54 1 259 1.5
2 2013-01-05 18:54:23 1 282 1.1
3 2013-01-07 23:58:20 2 244 0.7
4 2013-01-07 23:34:24 1 560 2.1
... pickup_latitude dropoff_longitude dropoff_latitude \
0 ... 40.757977 -73.989838 40.751171
1 ... 40.731781 -73.994499 40.750660
2 ... 40.737770 -74.009834 40.726002
3 ... 40.759945 -73.984734 40.759388
4 ... 40.748528 -74.002586 40.747868
tolls_amount tip_amount total_amount mta_tax fare_amount payment_type \
0 0 0 7.0 0.5 6.5 CSH
1 0 0 7.0 0.5 6.0 CSH
2 0 0 7.0 0.5 5.5 CSH
3 0 0 6.0 0.5 5.0 CSH
4 0 0 10.5 0.5 9.5 CSH
surcharge
0 0.0
1 0.5
2 1.0
3 0.5
4 0.5
[5 rows x 21 columns]
为了添加基于进程的并行性，我们从
Pool stdlib模块中引入multiprocessing类，并将Pool实例的map方法作为关键字参数传递给compute:In [32]: from multiprocessing import Pool
In [33]: p = Pool()
In [34]: %timeit -n 1 -r 1 values = compute(trip.medallion.distinct())
1 loops, best of 1: 1min per loop
In [35]: %timeit -n 1 -r 1 values = compute(trip.medallion.distinct(), map=p.map)
1 loops, best of 1: 16.2 s per loop
因此，额外的一行代码的速度大约提高了3倍。请注意，这是一个字符串列，与其他类型相比，它们往往效率很低。在一个整数列上计算出的
distinct表达式在大约1秒(vs 3秒)内完成，并具有多个内核(因此，运行时间的改善大致相同):In [38]: %timeit -n 1 -r 1 values = compute(trip.passenger_count.distinct())
1 loops, best of 1: 3.33 s per loop
In [39]: %timeit -n 1 -r 1 values = compute(trip.passenger_count.distinct(), map=p.map)
1 loops, best of 1: 1.01 s per loop
关于python - pydata大火: does it allow parallel processing or not?，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/27505764/