Python 加速统计（scipy）计算的尝试失败

我有一个30000行的数据框，我正在尝试计算以下内容：

def calc_exp(x, y, z):
    return stats.lognorm(scale = x, s=y).expect(lb=0, ub=z)

原则上，使用for循环速度太慢，但事实证明，例如使用apply：

   df['expect'] = df.apply(lambda row: calc_exp(row['sev_est'], row['sev_obs'],
                                                                row['_exp_up_b']), axis=1)

但是对于这么大的数据帧，运行大约需要30分钟

我也试着使用swifter：但不幸的是它只是超时了，所以我不知道它是否真的有效

当我尝试这个：

   df['expect'] = df.swifter.apply(lambda row: calc_exp(row['sev_est'], row['sev_obs'],
                                                                row['_exp_up_b']), axis=1)

它告诉我：

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

及

你有什么建议或资源可以帮助我加快我的代码

谢谢你检查我的问题

以下是数据示例：

sample_data = [[47752.06433069426, 1.0357302794658065, 1002500.0],
 [755000.4713730852, 1.2872612468359987, 1002500.0],
 [47752.06433069426, 1.0357302794658065, 1001000.0],
 [57777.829574251584, 1.0312698002906302, 505000.0],
 [69703.2113095638, 1.0299427372756402, 2010000.0],
 [45136.11776248943, 1.0376444095922805, 1001000.0],
 [59132.70813622853, 1.0309407576584755, 1005000.0],
 [43453.5613190105, 1.0390872317110278, 1001000.0],
 [46135.3578194443, 1.0368683857223946, 1001000.0],
 [152082.89966620493, 1.113359803894905, 1002500.0],
 [750446.5937632995, 1.2874160597603732, 2002500.0],
 [53647.95567675417, 1.0326342806970585, 1005000.0],
 [45632.05708701799, 1.0372520411746433, 1001000.0],
 [46581.28690183377, 1.0365403645539104, 1001000.0],
 [54020.70347965895, 1.0324868172323447, 1005000.0],
 [44245.90842544985, 1.0383857082697099, 1002500.0],
 [51162.12793486834, 1.0337556400084722, 3025000.0],
 [107107.86948225822, 1.0383712653241755, 3025000.0],
 [722119.4508688038, 1.2884596680530922, 2025000.0],
 [699903.7852476649, 1.2893820587789995, 2010000.0],
 [48950.10419174958, 1.034974068892171, 505000.0],
 [51738.02683120212, 1.033473643110517, 1002500.0],
 [42901.866305524214, 1.0395999387162294, 1002500.0],
 [45136.11776248943, 1.0376444095922805, 1001000.0],
 [52614.717261016325, 1.0330705313839323, 1002500.0],
 [713225.3474413318, 1.2888174754031487, 1005000.0],
 [57238.01599926183, 1.0314162657284809, 5010000.0],
 [58322.385654926955, 1.0311310363507877, 2005000.0],
 [54045.05749769092, 1.0324773610543825, 2002500.0],
 [42604.59804488991, 1.039884727964432, 1010000.0],
 [92437.93072760757, 1.0336932004002017, 1015000.0],
 [88559.61192571945, 1.0326806977341927, 1002500.0],
 [602239.5164807418, 1.2947510218453246, 2010000.0],
 [54815.71051455691, 1.0321892223847444, 1001000.0],
 [75898.55658703072, 1.0303477696462269, 1005000.0],
 [47915.02339498793, 1.0356232022658125, 252500.0],
 [44569.2886493234, 1.0381108112654438, 502500.0],
 [56189.65757026716, 1.0317269545642969, 1010000.0],
 [48074.68313484722, 1.0355196014148147, 2002500.0],
 [44310.501871381675, 1.0383302791240467, 5015000.0],
 [45861.05802340964, 1.0370756881518186, 1002500.0],
 [131930.89613964988, 1.0481212391831674, 1035000.0],
 [49212.10735631669, 1.0348180280571426, 2002500.0],
 [57489.35708029983, 1.0313469517526663, 1010000.0],
 [45341.39170748647, 1.0374802534677299, 252500.0],
 [50560.21189377367, 1.0340654819168598, 502500.0],
 [43881.69750318484, 1.0387031703640284, 252500.0],
 [45265.59784677777, 1.0375405752126938, 1002500.0],
 [47415.20348405406, 1.035955953776894, 1002500.0],
 [45861.05802340964, 1.0370756881518186, 1005000.0],
 [55740.86462284447, 1.0318709081324582, 1005000.0],
 [45220.34890828568, 1.037576748920849, 502500.0],
 [48535.991864874784, 1.035227436919951, 1001000.0],
 [127047.67786919193, 1.0460887972573536, 2100000.0],
 [46492.46532127476, 1.0366048236011984, 2005000.0],
 [541310.5576520629, 1.2994907072560145, 1002500.0],
 [799382.0622031174, 1.2859240295491776, 3010000.0],
 [44719.592998634034, 1.037985238438699, 1001000.0],
 [51162.12793486834, 1.0337556400084722, 1001000.0],
 [49927.12596481778, 1.0344085721774596, 1005000.0],
 [49663.66474485245, 1.0345566887409923, 5010000.0],
 [47752.06433069426, 1.0357302794658065, 1001000.0],
 [46877.59739500191, 1.0363284400408517, 1001000.0],
 [49255.78753338048, 1.0347923305430622, 1002500.0],
 [49822.50196088387, 1.034467010700811, 1002500.0],
 [58404.390374565475, 1.031110907311516, 1002500.0],
 [65355.04630839962, 1.030054220108931, 2010000.0],
 [43958.656721146, 1.0386353874831535, 2002500.0],
 [57489.35708029983, 1.0313469517526663, 1010000.0],
 [51162.12793486834, 1.0337556400084722, 1001000.0],
 [48902.75800018717, 1.0350026167891064, 1002500.0],
 [45064.43172099421, 1.0377023255444393, 1001000.0],
 [57489.35708029983, 1.0313469517526663, 1005000.0]]

df = pd.DataFrame(sample, columns=['sev_est', 'sev_obs', '_exp_up_b'])

---

一种选择是使用多处理：

import pandas as pd
from scipy import stats
from time import time
from multiprocessing import Pool

def calc_exp(x, y, z):
    return stats.lognorm(scale=x, s=y).expect(lb=0, ub=z)

if __name__ == '__main__':
    # I have used the full sample_data but I copy here the first 3 rows only for readability
    sample_data = [[47752.06433069426, 1.0357302794658065, 1002500.0],
                   [755000.4713730852, 1.2872612468359987, 1002500.0],
                   [47752.06433069426, 1.0357302794658065, 1001000.0]]

    df = pd.DataFrame(sample_data, columns=['sev_est', 'sev_obs', '_exp_up_b'])
    df = pd.concat([df] * 10)  # artificially increasing the size of the data frame

    start = time()
    expect = df.apply(lambda row: calc_exp(row['sev_est'], row['sev_obs'], row['_exp_up_b']), axis=1)
    end = time()
    print(f'Single-threaded: {end - start} sec.')

    pool = Pool(processes=5)

    start = time()
    data = zip(*[df[col] for col in df])
    result = pool.starmap(calc_exp, data)
    pool.close()
    pool.join()
    end = time()
    print(f'Multi-threaded: {end - start} sec.')

输出：

Single-threaded: 37.720906019210815 sec.
Multi-threaded: 9.930521726608276 sec.

Single-threaded: 37.720906019210815 sec.
Multi-threaded: 9.930521726608276 sec.