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 [ ]: