NumPy实现数据的聚合，计算最大值，最小值和其他值

发布时间：2020-12-14 04:50:03 所属栏目：百科来源：网络整理

导读：1.数组值的求和首先构造一个具有100个值的数组，然后我们利用两个不同的方法进行求和： l=np.random.random( 100 ) l的数据如下: larray([ 0.63330856 , 0.55254815 , 0.681117 , 0.0392779 , 0.55515459 , 0.65577685 , 0.93779694 , 0.38145863 , 0.15571

1.数组值的求和

首先构造一个具有100个值的数组，然后我们利用两个不同的方法进行求和：

>>> l=np.random.random(100)

l的数据如下:

>>> l
array([0.63330856,0.55254815,0.681117,0.0392779,0.55515459,0.65577685,0.93779694,0.38145863,0.15571406,0.58656667,0.05014379,0.22707423,0.2206218,0.99183227,0.067189,0.85587266,0.38610259,0.58482566,0.21639326,0.66505995,0.47360391,0.553394,0.6861513,0.36460573,0.25960476,0.80718606,0.61228608,0.47824396,0.98466131,0.13550462,0.2296882,0.41334125,0.0028512,0.00706611,0.66774287,0.26150011,0.98494222,0.16255418,0.55893817,0.63001863,0.0151125,0.13388626,0.3116983,0.70979666,0.36033375,0.70286921,0.08094839,0.38973694,0.07205708,0.23503885,0.56665754,0.72277441,0.00386346,0.86161187,0.09270819,0.36279124,0.14414812,0.83186456,0.759372,0.26563921,0.5059324,0.35014357,0.55575501,0.5613696,0.00100515,0.40608559,0.89754344,0.13651899,0.334764,0.77378823,0.69603667,0.65702436,0.98306105,0.93510312,0.71863035,0.14813637,0.92719219,0.3230562,0.36282925,0.26928228,0.70444039,0.03080534,0.21334398,0.14623021,0.85840572,0.51886698,0.40347232,0.84893857,0.17807356,0.02207469,0.05365235,0.47315195,0.48036338,0.54677648,0.73090216,0.20840042,0.0531166,0.59713323,0.76020517,0.50951197])

利用np里面的sum函数明显求和会更快，但是直接利用python当中的函数则会比较慢，这也是有科学依据的，但是我们只要记住即可，感兴趣的同学可以利用%timeit?来求出两个不同函数进行计算的时间：
计算结果如下：

>>> sum(l)
45.22175110164667
>>> np.sum(l)
45.221751101646674

?2.求解最大最小值

>>> np.min(l)
0.0010051507515725921
>>> np.max(l)
0.9918322686313938

3.多维度聚合

import numpy as np
arr = np.array([[1,2,3],[4,5,6]])
result = np.sum(arr)
print(result)

（编辑：李大同）

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