【Numpy基础3 arg运算】

版权声明:2018/4/10重启blog;转载请注明出处 https://blog.csdn.net/zhaiqiming2010/article/details/86539750
import numpy as np 
In [2]:

x = np.random.normal(0, 1 ,1000000)
In [3]:

x
Out[3]:

array([-0.24158872, -0.07802815,  1.32448525, ..., -1.3227526 ,
       -0.86054656, -0.32346336])
In [4]:

np.min(x)
Out[4]:

-5.032246781466751
In [7]:

np.argmin(x)  # 返回最小值的索引
Out[7]:

997609
In [8]:

x[997609]
Out[8]:

-5.032246781466751
In [10]:

np.argmax(x)
Out[10]:

769253
In [11]:

x[769253]
Out[11]:

4.559205373798893
In [12]:

np.max(x)
Out[12]:

4.559205373798893
排序相关
In [13]:

x= np.arange(16)
x
Out[13]:

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])
In [19]:

np.random.shuffle(x) # 乱序操作
x
Out[19]:

array([ 4,  3,  6,  2, 13,  1,  5, 15, 11, 14, 12,  9,  0, 10,  8,  7])
In [20]:

np.sort(x) # 并没有对x进行修改,而是返回了一个新的array
Out[20]:

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])
In [21]:

x
Out[21]:

array([ 4,  3,  6,  2, 13,  1,  5, 15, 11, 14, 12,  9,  0, 10,  8,  7])
In [22]:

x.sort()
In [23]:

x
Out[23]:

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])
In [26]:

# 二维的
X= np.random.randint(10, size=(4, 4))
X
Out[26]:

array([[5, 0, 4, 5],
       [9, 8, 6, 4],
       [7, 0, 5, 0],
       [3, 4, 0, 9]])
In [27]:

np.sort(X)
Out[27]:

array([[0, 4, 5, 5],
       [4, 6, 8, 9],
       [0, 0, 5, 7],
       [0, 3, 4, 9]])
In [28]:

x
Out[28]:

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])
In [30]:

np.random.shuffle(x)
x
Out[30]:

array([ 6,  3, 11, 12,  8, 13,  2,  9, 10,  7, 14,  1,  4, 15,  5,  0])
In [31]:

np.argsort(x)  # 返回排序后的索引
Out[31]:

array([15, 11,  6,  1, 12, 14,  0,  9,  4,  7,  8,  2,  3,  5, 10, 13])
In [32]:

np.partition(x, 3)
Out[32]:

array([ 1,  2,  0,  3,  4,  5,  6, 13, 10,  7, 14,  8, 12, 15, 11,  9])
In [33]:

np.argpartition(x, 3)
Out[33]:

array([11,  6, 15,  1, 12, 14,  0,  5,  8,  9, 10,  4,  3, 13,  2,  7])
In [34]:

X
Out[34]:

array([[5, 0, 4, 5],
       [9, 8, 6, 4],
       [7, 0, 5, 0],
       [3, 4, 0, 9]])
In [41]:

np.argsort(X,axis=1)
Out[41]:

array([[1, 2, 0, 3],
       [3, 2, 1, 0],
       [1, 3, 2, 0],
       [2, 0, 1, 3]])
In [42]:

np.argsort(X,axis=0)
Out[42]:

array([[3, 0, 3, 2],
       [0, 2, 0, 1],
       [2, 3, 2, 0],
       [1, 1, 1, 3]])
In [ ]:

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