NumPy学习笔记（3）--排序与索引

来源：互联网发布：感性和理性知乎编辑：程序博客网时间：2024/06/02 02:41

1.排序： .sort

# 方法一：import numpy as npa = 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 npa = 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 npa = 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 npdata = 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 npa = 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]]

1 0