pandas求行最大值及其索引的实现
在平时训练完模型后，需要对模型预测的值做进一步的数据操作，例如在对模型得到类别的概率值按行取最大值，并将最大值所在的列单独放一列。数据格式如下：
array
array([[ 0.47288769,0.23982215,0.2261405 ,0.06114962],
[ 0.67969596,0.11435176,0.17647322,0.02947907],
[ 0.00621393,0.01652142,0.31117165,0.66609299],
[ 0.24093366,0.23636758,0.30113828,0.22156043],
[ 0.44093642,0.2245989 ,0.24515967,0.08930501],
[ 0.05540339,0.10013942,0.30361843,0.54083872],
[ 0.11221886,0.75674808,0.09237131,0.03866173],
[ 0.24885316,0.28243011,0.28312165,0.18559511],
[ 0.01205211,0.03740638,0.271065,0.67947656]], dtype=float32)想在想实现的功能是在上述DataFrame后面增加两列：一列是最大值，一列是最大值所在的行索引。
首先先来了解一下argmax函数。
argmax(a, axis=None)
# a 表示DataFrame
# axis 表示指定的轴，默认是None，表示把array平铺，等于1表示按行，等于0表示按列。对于DataFrame来说，求解过程如下：
代码如下：
#导入库
import pandas as pd
import numpy as np
#将array转化为DataFrame
arr=pd.DataFrame(array,columns=["one","two","three","four"])
#分别求行最大值及最大值所在索引
arr['max_value']=arr.max(axis=1)
arr['max_index']=np.argmax(array,axis=1)
#得出如下结果：
arr
Out:
one two three fourmax_indexmax_value
00.4728880.2398220.2261400.061150 0 0.472888
10.6796960.1143520.1764730.029479 0 0.679696
20.0062140.0165210.3111720.666093 3 3.000000
30.2409340.2363680.3011380.221560 2 2.000000
40.4409360.2245990.2451600.089305 0 0.440936
50.0554030.1001390.3036180.540839 3 3.000000
60.1122190.7567480.0923710.038662 1 1.000000
70.2488530.2824300.2831220.185595 2 2.000000
80.0120520.0374060.2710650.679477 3 3.000000假如现在要找出行第二大的值及其索引时，该怎么操作呢：
解决思路：可以将行的最大值置为0，然后在寻找每行的最大值及其索引。
具体代码实现过程如下：
#将最大值置为0
array=0
array
array([[ 0. ,0.23982215,0.2261405 ,0.06114962],
[ 0. ,0.11435176,0.17647322,0.02947907],
[ 0.00621393,0.01652142,0.31117165,0. ],
[ 0.24093366,0.23636758,0. ,0.22156043],
[ 0. ,0.2245989 ,0.24515967,0.08930501],
[ 0.05540339,0.10013942,0.30361843,0. ],
[ 0.11221886,0. ,0.09237131,0.03866173],
[ 0.24885316,0.28243011,0. ,0.18559511],
[ 0.01205211,0.03740638,0.271065,0. ]], dtype=float32)
#取出第二大值及其索引
arr['second_value']=array.max(axis=1)
arr['second_index']=np.argmax(array,axis=1)
arr
Out:
one two three fourmax_valuemax_indexsecond_value\
00.4728880.2398220.2261400.061150 0.472888 0 0.239822
10.6796960.1143520.1764730.029479 0.679696 0 0.176473
20.0062140.0165210.3111720.666093 0.666093 3 0.311172
30.2409340.2363680.3011380.221560 0.301138 2 0.240934
40.4409360.2245990.2451600.089305 0.440936 0 0.245160
50.0554030.1001390.3036180.540839 0.540839 3 0.303618
60.1122190.7567480.0923710.038662 0.756748 1 0.112219
70.2488530.2824300.2831220.185595 0.283122 2 0.282430
80.0120520.0374060.2710650.679477 0.679477 3 0.271065
second_index
0 1
1 2
2 2
3 0
4 2
5 2
6 0
7 1
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