NumPy学习笔记（3）--排序与索引
1.排序： .sort
# 方法一：
import numpy as np
a = np.array([[4,3,5,],[1,2,1]])
print (a)
b = np.sort(a, axis=1) # 对a按每行中元素从小到大排序
print (b)
# 输出 [[4 3 5]
[1 2 1]]
[[3 4 5]
[1 1 2]]
# 方法二：
import numpy as np
a = np.array([[4,3,5,],[1,2,1]])
print (a)
a.sort(axis=1)
print (a)
# 输出 [[4 3 5]
[1 2 1]]
[[3 4 5]
[1 1 2]]
# 方法三：
import numpy as np
a = np.array([4, 3, 1, 2])
b = np.argsort(a) # 求a从小到大排序的坐标
print (b)
print (a[b]) # 按求出来的坐标顺序排序
# 输出 [2 3 1 0]
[1 2 3 4]2.按行或按列找到最大值的索引： .argmax
import numpy as np
data = np.sin(np.arange(20)).reshape(5, 4)
print (data)
ind = data.argmax(axis=0) # 按列得到每一列中最大元素的索引，axis=1为按行
print (ind)
data_max = data[ind, range(data.shape[1])] # 将最大值取出来
print (data_max)
# 输出 [[ 0. 0.84147098 0.90929743 0.14112001]
[-0.7568025 -0.95892427 -0.2794155 0.6569866 ]
[ 0.98935825 0.41211849 -0.54402111 -0.99999021]
[-0.53657292 0.42016704 0.99060736 0.65028784]
[-0.28790332 -0.96139749 -0.75098725 0.14987721]]
[2 0 3 1]
[ 0.98935825 0.84147098 0.99060736 0.6569866 ]
print data.max(axis=0) #也可以直接取最大值
# 输出 [ 0.98935825 0.84147098 0.99060736 0.6569866 ]3.多重复制： .tile
import numpy as np
a = np.array([5, 10, 15])
print(a)
print('---')
b = np.tile(a, (4, 1)) # 参数(4, 1)为按行复制4倍，按列复制1倍
print(b)
# 输出 [ 5 10 15]
---
[[ 5 10 15]
[ 5 10 15]
[ 5 10 15]
[ 5 10 15]]
c = np.tile(a, (2, 3)) # 参数(2, 3)为按行复制2倍，按列复制3倍
print(c)
# 输出 [[ 5 10 15 5 10 15 5 10 15]
[ 5 10 15 5 10 15 5 10 15]]