我有下面的代码和算法是相当缓慢的。

我尝试使用预分配与numpy(1.14.3)，并将pandas(0.23.0)中的字符串列转换为category以加快代码的速度，但仍然非常慢。在

我的df是一个大的pd.DataFrame。len(df)返回1342058，有25列。在

df包含特定事件和位置的时间戳。数据的频率是可变的，从第二个数据到每小时的数据。我基本上想为每个客户机id和一个给定的日期箭头(rng)(每5分钟在start和end之间)重新取样。在df = pd.DataFrame()

df['client_id']=['40:a3:cc:XX:XX:XX','28:c6:3f:XX:XX:XX','40:a3:cc:XX:XX:XX','40:a3:cc:XX:XX:XX','28:c6:3f:XX:XX:XX','40:a3:cc:XX:XX:XX','40:a3:cc:XX:XX:XX','28:c6:3f:XX:XX:XX','28:c6:3f:XX:XX:XX','40:a3:cc:XX:XX:XX']

df['seen_time'] = ['2018-09-01 00:00:03+00:00', '2018-09-01 00:00:04+00:00','2018-09-01 00:00:05+00:00','2018-09-01 00:00:06+00:00','2018-09-01 00:00:08+00:00','2018-09-01 00:00:09+00:00','2018-09-01 00:00:09+00:00','2018-09-01 00:00:14+00:00','2018-09-01 00:00:19+00:00','2018-09-01 00:00:25+00:00']

df['location_x'] = [7.488775,20.163136,19.485196,12.841458,15.508627,5.708157,13.451071,19.1871,65,9.015443,28.266964]

这是我的代码：

^{pr2}$

prun的结果如下：158001796 function calls (155889787 primitive calls) in 483.930 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function)

44934 209.957 0.005 209.957 0.005 {pandas._libs.algos.take_2d_axis1_object_object}

381942 44.206 0.000 44.379 0.000 {built-in method pandas._libs.algos.ensure_int64}

22468 20.656 0.001 95.913 0.004 categorical.py:2437(_recode_for_categories)

67402 20.583 0.000 20.583 0.000 {pandas._libs.algos.take_1d_int16_int16}

3 20.274 6.758 483.715 161.238 :23(processDataSingle)

44934 9.177 0.000 9.177 0.000 {pandas._libs.algos.take_2d_axis1_int64_int64}

44934 9.107 0.000 9.107 0.000 {pandas._libs.algos.take_2d_axis1_float64_float64}

36873382 7.775 0.000 15.205 0.000 {built-in method builtins.isinstance}

44934 7.592 0.000 7.592 0.000 {method 'nonzero' of 'numpy.ndarray' objects}

11899203 4.604 0.000 5.136 0.000 {built-in method builtins.getattr}

22471 4.368 0.000 4.450 0.000 {method 'get_indexer' of 'pandas._libs.index.IndexEngine' objects}

44934 3.644 0.000 3.644 0.000 {pandas._libs.algos.take_1d_int64_int64}

22467 3.479 0.000 3.776 0.000 categorical.py:64(f)

643042 3.380 0.000 3.380 0.000 {method 'reduce' of 'numpy.ufunc' objects}

148422 3.016 0.000 3.016 0.000 {method 'copy' of 'numpy.ndarray' objects}

2557465/2534997 2.740 0.000 4.622 0.000 common.py:1835(_get_dtype_type)

337007/292073 2.727 0.000 318.981 0.001 algorithms.py:1545(take_nd)

7351676 2.672 0.000 5.713 0.000 generic.py:7(_check)

404409/359475 2.359 0.000 20.467 0.000 series.py:165(__init__)

8155845/6470735 2.152 0.000 2.873 0.000 {built-in method builtins.len}

471840 2.111 0.000 2.111 0.000 {built-in method numpy.core.multiarray.empty}

89874 2.109 0.000 4.487 0.000 internals.py:3363(_rebuild_blknos_and_blklocs)

2610562 2.028 0.000 5.330 0.000 base.py:61(is_dtype)

1046990/1002055 1.888 0.000 2.184 0.000 {built-in method numpy.core.multiarray.array}

44935 1.830 0.000 1.830 0.000 {method 'take' of 'numpy.ndarray' objects}

4713590 1.757 0.000 1.773 0.000 {built-in method builtins.hasattr}

134826/89883 1.750 0.000 7.191 0.000 base.py:250(__new__)

247139 1.647 0.000 6.430 0.000 cast.py:257(maybe_promote)

1087307 1.523 0.000 5.693 0.000 common.py:1688(is_extension_array_dtype)

134802/89868 1.475 0.000 18.290 0.000 datetimes.py:329(__new__)

2233231 1.437 0.000 2.797 0.000 :997(_handle_fromlist)

718950 1.411 0.000 2.764 0.000 generic.py:4374(__setattr__)

763890/629088 1.361 0.000 2.083 0.000 {method 'format' of 'str' objects}

224670 1.314 0.000 254.948 0.001 internals.py:1237(take_nd)

629082 1.249 0.000 2.836 0.000 internals.py:116(__init__)

314539 1.145 0.000 303.889 0.001 frame.py:2661(__getitem__)

...

67401 0.634 0.000 134.602 0.002 ops.py:1175(wrapper)

44934 0.625 0.000 277.776 0.006 internals.py:4518(take)

...

67404 0.558 0.000 101.225 0.002 categorical.py:267(__init__)

...

89868 0.527 0.000 11.864 0.000 datetimelike.py:523(take)

44934 0.522 0.000 285.513 0.006 generic.py:2780(_take)

1392955 0.517 0.000 0.599 0.000 internals.py:352(dtype)

44934/22467 0.514 0.000 12.605 0.001 {built-in method _operator.ge}

...

44937 0.425 0.000 264.034 0.006 internals.py:4423()

...

763912 0.296 0.000 1.993 0.000 numeric.py:424(asarray)

44934 0.295 0.000 22.606 0.001 datetimes.py:109(wrapper)

381946 0.289 0.000 2.251 0.000 {method 'any' of 'numpy.ndarray' objects}

89892 0.286 0.000 0.960 0.000 common.py:298(_asarray_tuplesafe)

67404 0.281 0.000 1.299 0.000 cast.py:971(maybe_cast_to_datetime)

89868 0.280 0.000 2.962 0.000 internals.py:4108(get)

629082 0.276 0.000 0.276 0.000 internals.py:127(_check_ndim)

44934 0.276 0.000 295.073 0.007 frame.py:2704(_getitem_array)

107568 0.267 0.000 2.324 0.000 missing.py:494(na_value_for_dtype)

179742 0.264 0.000 0.827 0.000 dtypes.py:459(construct_from_string)

44937 0.260 0.000 270.290 0.006 internals.py:4388(reindex_indexer)

...

67401 0.220 0.000 128.302 0.002 ops.py:1073(dispatch_to_index_op)

...

1 0.194 0.194 483.930 483.930 :1()

...

44934 0.179 0.000 20.583 0.000 base.py:89(cmp_method)

...

44934 0.110 0.000 10.841 0.000 datetimes.py:39(_maybe_cache)

...

1 0.003 0.003 483.719 483.719 :61(processDataArrayWrapper)

...

1 0.000 0.000 483.930 483.930 {built-in method builtins.exec}

注意：我删除了一些cumtime很小的行。我相信大部分时间都花在take_2d_axis1_object_object上，但是我在理解这个问题上有困难。代码的哪一部分导致了这个问题？在

我在我的macbookpro上用intelcorei7 2,7ghz和4核运行代码，python进程占用了将近400%的CPU，这意味着它在后台使用了多核。在