从分割索引获得的分组值
我需要找到两列（p_1_logreg，p_2_logreg）的最大值，其中比较应该只限制为14行。从分割索引获得的分组值
My csv file
我试图切开我的索引：
int1_str1_str2_int2_str3_int4 最大应该在哪里INT1，STR1，STR2 INT2和STR3固定行之间只可以发现，和int4会发生变化（从索引0到索引13，依此类推）。
我试图一次修复每个元素并使用groupby，但是我无法遍历int4值。
以下是查找列p_1_label的最大值的代码，但结果不是我正在寻找的。
max_1_row=raw_prob.loc[raw_prob.groupby(raw_prob['id'].str.split('_').str[1])['p_1_'+label].idxmax()] max_1_row=max_1_row.loc[raw_prob.groupby(raw_prob['id'].str.split('_').str[3])['p_1_'+label].idxmax()]
max_1_row=max_1_row.loc[raw_prob.groupby(raw_prob['id'].str.split('_').str[5])['p_1_'+label].idxmax()]
任何想法？
回答：
我想你需要DataFrameGroupBy.idxmax替换最后_空字符串，然后通过loc选择：
df = pd.read_csv('myProb.csv', index_col=[0]) idx = df.drop('id', 1).groupby(df['id'].str.replace('_\d+$', '')).idxmax()
print (idx.head(15))
p_0_logreg p_1_logreg p_2_logreg
id
6_PanaCleanerJune_sub_12_ICA 2 9 6
6_PanaCleanerJune_sub_13_ICA 17 19 23
6_PanaCleanerJune_sub_14_ICA 34 37 33
6_PanaCleanerJune_sub_15_ICA 52 51 43
6_PanaCleanerJune_sub_17_ICA 66 67 69
6_PanaCleanerJune_sub_18_ICA 82 79 76
6_PanaCleanerJune_sub_19_ICA 89 87 90
6_PanaCleanerJune_sub_20_ICA 98 103 104
6_PanaCleanerJune_sub_21_ICA 114 117 112
6_PanaCleanerJune_sub_22_ICA 129 133 127
6_PanaCleanerJune_sub_23_ICA 145 146 143
6_PanaCleanerJune_sub_24_ICA 155 166 161
6_PanaCleanerJune_sub_25_ICA 176 173 174
6_PanaCleanerJune_sub_26_ICA 186 191 189
6_PanaCleanerJune_sub_27_ICA 202 203 209
df1 = df.loc[idx['p_1_logreg']] print (df1.head(15))
id p_0_logreg p_1_logreg p_2_logreg
9 6_PanaCleanerJune_sub_12_ICA_10 0.013452 0.985195 0.001353
19 6_PanaCleanerJune_sub_13_ICA_6 0.051184 0.948816 0.000000
37 6_PanaCleanerJune_sub_14_ICA_10 0.013758 0.979351 0.006890
51 6_PanaCleanerJune_sub_15_ICA_10 0.076056 0.923944 0.000000
67 6_PanaCleanerJune_sub_17_ICA_12 0.051060 0.947660 0.001280
79 6_PanaCleanerJune_sub_18_ICA_10 0.051184 0.948816 0.000000
87 6_PanaCleanerJune_sub_19_ICA_4 0.078162 0.917751 0.004087
103 6_PanaCleanerJune_sub_20_ICA_6 0.076400 0.921263 0.002337
117 6_PanaCleanerJune_sub_21_ICA_6 0.155002 0.791753 0.053245
133 6_PanaCleanerJune_sub_22_ICA_8 0.000000 0.998623 0.001377
146 6_PanaCleanerJune_sub_23_ICA_7 0.017549 0.973995 0.008457
166 6_PanaCleanerJune_sub_24_ICA_13 0.025215 0.974785 0.000000
173 6_PanaCleanerJune_sub_25_ICA_6 0.025656 0.960220 0.014124
191 6_PanaCleanerJune_sub_26_ICA_10 0.098872 0.895526 0.005602
203 6_PanaCleanerJune_sub_27_ICA_8 0.066493 0.932470 0.001037
df2 = df.loc[idx['p_2_logreg']] print (df2.head(15))
id p_0_logreg p_1_logreg p_2_logreg
6 6_PanaCleanerJune_sub_12_ICA_7 0.000000 0.000351 0.999649
23 6_PanaCleanerJune_sub_13_ICA_10 0.000000 0.000351 0.999649
33 6_PanaCleanerJune_sub_14_ICA_6 0.080748 0.000352 0.918900
43 6_PanaCleanerJune_sub_15_ICA_2 0.017643 0.000360 0.981996
69 6_PanaCleanerJune_sub_17_ICA_14 0.882449 0.000290 0.117261
76 6_PanaCleanerJune_sub_18_ICA_7 0.010929 0.000360 0.988711
90 6_PanaCleanerJune_sub_19_ICA_7 0.010929 0.000351 0.988720
104 6_PanaCleanerJune_sub_20_ICA_7 0.006714 0.000360 0.992925
112 6_PanaCleanerJune_sub_21_ICA_1 0.869393 0.000339 0.130269
127 6_PanaCleanerJune_sub_22_ICA_2 0.000000 0.000351 0.999649
143 6_PanaCleanerJune_sub_23_ICA_4 0.017218 0.000360 0.982421
161 6_PanaCleanerJune_sub_24_ICA_8 0.369685 0.000712 0.629603
174 6_PanaCleanerJune_sub_25_ICA_7 0.307056 0.000496 0.692448
189 6_PanaCleanerJune_sub_26_ICA_8 0.850195 0.000368 0.149437
209 6_PanaCleanerJune_sub_27_ICA_14 0.000000 0.000351 0.999649
详细信息：
print (df['id'].str.replace('_\d+$', '').head(15)) 0 6_PanaCleanerJune_sub_12_ICA
1 6_PanaCleanerJune_sub_12_ICA
2 6_PanaCleanerJune_sub_12_ICA
3 6_PanaCleanerJune_sub_12_ICA
4 6_PanaCleanerJune_sub_12_ICA
5 6_PanaCleanerJune_sub_12_ICA
6 6_PanaCleanerJune_sub_12_ICA
7 6_PanaCleanerJune_sub_12_ICA
8 6_PanaCleanerJune_sub_12_ICA
9 6_PanaCleanerJune_sub_12_ICA
10 6_PanaCleanerJune_sub_12_ICA
11 6_PanaCleanerJune_sub_12_ICA
12 6_PanaCleanerJune_sub_12_ICA
13 6_PanaCleanerJune_sub_12_ICA
14 6_PanaCleanerJune_sub_13_ICA
Name: id, dtype: object
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