python – 将索引拆分为pandas中的单独列
发布时间：2020-12-20 12:02:53 所属栏目：Python 来源：网络整理
导读：我有一个大型数据框,从中我可以获得groupby所需的数据.我需要从新数据帧的索引中获取几个单独的列. 部分原始数据框如下所示： code place vl year week0 111.0002.0056 region1 1 2017 291 112.6500.2285 region2 1 2017 312 112.5600.6325 region2 1 2017 3
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我有一个大型数据框,从中我可以获得groupby所需的数据.我需要从新数据帧的索引中获取几个单独的列.
部分原始数据框如下所示： code place vl year week 0 111.0002.0056 region1 1 2017 29 1 112.6500.2285 region2 1 2017 31 2 112.5600.6325 region2 1 2017 30 3 112.5600.6325 region2 1 2017 30 4 112.5600.8159 region2 1 2017 30 5 111.0002.0056 region2 1 2017 29 6 111.0002.0056 region2 1 2017 30 7 111.0002.0056 region2 1 2017 28 8 112.5600.8159 region3 1 2017 31 9 112.5600.8159 region3 1 2017 28 10 111.0002.0114 region3 1 2017 31 .... 应用groupby后,它看起来像这样(代码：df_test1 = df_test.groupby([‘code’,’year’,’week’,’place’])[‘vl’].sum().unstack(fill_value = 0 ))： place region1 region2 region3 region4 index1
code year week
111.0002.0006 2017 29 0 3 0 0 (111.0002.0006,2017,29)
30 0 7 0 0 (111.0002.0006,30)
111.0002.0018 2017 29 0 0 0 0 (111.0002.0018,29)
111.0002.0029 2017 30 0 0 0 0 (111.0002.0029,30)
111.0002.0055 2017 28 0 33 0 8 (111.0002.0055,28)
29 1 155 2 41 (111.0002.0055,29)
30 0 142 1 39 (111.0002.0055,30)
31 0 31 0 13 (111.0002.0055,31)
111.0002.0056 2017 28 9 36 0 4 (111.0002.0056,28)
29 20 75 2 37 (111.0002.0056,29)
30 17 81 2 33 (111.0002.0056,30)
....
我将索引保存在单独的列index1中(代码：df_test1 [‘index1’] = df_test1.index) 结果应如下所示： region1 region2 region3 region4 code year week 0 3 0 0 111.0002.0006 2017 29 0 7 0 0 111.0002.0006 2017 30 0 0 0 0 111.0002.0018 2017 29 0 0 0 0 111.0002.0029 2017 30 0 33 0 8 111.0002.0055 2017 28 1 155 2 41 111.0002.0055 2017 29 0 142 1 39 111.0002.0055 2017 30 0 31 0 13 111.0002.0055 2017 31 .... 我会很感激任何建议！ 解决方法
你添加
reset_index而不是df_test1 [‘index1’] = df_test1.index,对于clean df add
rename_axis – 它删除列名称：
df_test1 = df_test.groupby(['code','year','week','place'])['vl'].sum()
.unstack(fill_value=0)
.reset_index()
.rename_axis(None,axis=1)
print (df_test1)
code year week region1 region2 region3
0 111.0002.0056 2017 28 0 1 0
1 111.0002.0056 2017 29 1 1 0
2 111.0002.0056 2017 30 0 1 0
3 111.0002.0114 2017 31 0 0 1
4 112.5600.6325 2017 30 0 2 0
5 112.5600.8159 2017 28 0 0 1
6 112.5600.8159 2017 30 0 1 0
7 112.5600.8159 2017 31 0 0 1
8 112.6500.2285 2017 31 0 1 0
如有必要,最后更改列的排序： #all cols are columns in df_test1 cols = ['code','week'] df_test1 = df_test1[[x for x in df_test1.columns if x not in cols] + cols] print (df_test1) region1 region2 region3 code year week 0 0 1 0 111.0002.0056 2017 28 1 1 1 0 111.0002.0056 2017 29 2 0 1 0 111.0002.0056 2017 30 3 0 0 1 111.0002.0114 2017 31 4 0 2 0 112.5600.6325 2017 30 5 0 0 1 112.5600.8159 2017 28 6 0 1 0 112.5600.8159 2017 30 7 0 0 1 112.5600.8159 2017 31 8 0 1 0 112.6500.2285 2017 31 （编辑：李大同） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |