06.numpy聚合运算

>>> import numpy as np
>>> L = np.random.random(100)
>>> L
array([0.82846513, 0.19136857, 0.27040895, 0.56103442, 0.90238039,
       0.85178834, 0.41808196, 0.39347627, 0.01622051, 0.29921337,
       0.35377822, 0.89350267, 0.78613657, 0.77138693, 0.42005486,
       0.77602514, 0.46430814, 0.18177017, 0.8840256 , 0.71879227,
       0.6718813 , 0.25656363, 0.43080182, 0.01645358, 0.23499383,
       0.51117131, 0.29200924, 0.50189351, 0.49827313, 0.10377152,
       0.44644312, 0.96918917, 0.73847112, 0.71955061, 0.89304339,
       0.96267468, 0.19705023, 0.71458996, 0.16192394, 0.86625477,
       0.62382025, 0.95945512, 0.52414204, 0.03643288, 0.72687158,
       0.00390984, 0.050294  , 0.99199232, 0.2122575 , 0.94737066,
       0.45154055, 0.99879467, 0.64750149, 0.70224071, 0.42958177,
>>> sum(L)
52.03087325680787
>>> np.sum(L)
52.030873256807865

big_array = np.random.rand(1000000)

>>> np.min(big_array)
4.459899819675428e-06

>>> big_array.max()
0.9999999038835905

>>> X = np.arange(16).reshape(4,4)
>>> X
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15]])

>>> np.sum(X)
120

>>> np.sum(X,axis=0)
array([24, 28, 32, 36])

>>> np.sum(X,axis=1)
array([ 6, 22, 38, 54])

>>> np.prod(X)
0

>>> np.prod(X + 1)
2004189184

>>> np.mean(X)
7.5

>>> np.median(X)
7.5

>>> V = np.array([1,1,2,2,10])
>>> np.mean(V)
3.2

>>> np.median(V)
2.0

>>> np.percentile(big_array,q=50)
0.499739362948878
>>> for percent in [0,25,50,75,100]:
...     print(np.percentile(big_array,q=percent))
...
4.459899819675428e-06
0.24975691457362903
0.499739362948878
0.7498092671305248
0.9999999038835905

>>> X = np.random.normal(0,1,size=1000000)
>>> np.mean(X)
0.00026937497963613595

>>> np.std(X)
0.9996291605602685

>>> np.min(X)
-5.333919783687649

>>> np.argmin(X)
661675

>>> np.argmax(X)
774515

>>> X[91952]
-0.5633231945005146

>>> np.max(X)
4.53612178954408

>>> x = np.arange(16)
>>> x
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])

>>> np.random.shuffle(x)
>>> x
array([ 2,  7,  8,  4, 14, 15,  6, 11, 13,  1, 12,  0,  9, 10,  3,  5])

>>> np.sort(x)
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])

>>> x.sort()
>>> x
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])

>>> x = np.random.randint(10, size=(4,4))
>>> x
array([[7, 0, 0, 7],
       [0, 3, 5, 7],
       [9, 7, 3, 9],
       [4, 0, 9, 2]])

>>> np.sort(x)
array([[0, 0, 7, 7],
       [0, 3, 5, 7],
       [3, 7, 9, 9],
       [0, 2, 4, 9]])

>>> np.sort(x,axis=0)
array([[0, 0, 0, 2],
       [4, 0, 3, 7],
       [7, 3, 5, 7],
       [9, 7, 9, 9]])

>>> np.partition(X,3)
array([-5.33391978, -5.13221775, -4.86828137, ...,  0.16378629,
        1.09224809,  1.00502282])