在Pandas DataFrame对象中重新定义索引

本文介绍了在Pandas DataFrame对象中重新定义索引的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在尝试重新索引熊猫DataFrame对象，

I am trying to re-index a pandas DataFrame object, like so,

From: a b c 0 1 2 3 1 10 11 12 2 20 21 22 To : b c 1 2 3 10 11 12 20 21 22

我正在如下所示进行操作，并且得到了错误的答案.有关如何执行此操作的任何线索?

I am going about this as shown below and am getting the wrong answer. Any clues on how to do this?

>>> col = ['a','b','c'] >>> data = DataFrame([[1,2,3],[10,11,12],[20,21,22]],columns=col) >>> data a b c 0 1 2 3 1 10 11 12 2 20 21 22 >>> idx2 = data.a.values >>> idx2 array([ 1, 10, 20], dtype=int64) >>> data2 = DataFrame(data,index=idx2,columns=col[1:]) >>> data2 b c 1 11 12 10 NaN NaN 20 NaN NaN

知道为什么会这样吗?

推荐答案

为什么不简单地使用 set_index 方法?

Why don't you simply use set_index method?

In : col = ['a','b','c'] In : data = DataFrame([[1,2,3],[10,11,12],[20,21,22]],columns=col) In : data Out: a b c 0 1 2 3 1 10 11 12 2 20 21 22 In : data2 = data.set_index('a') In : data2 Out: b c a 1 2 3 10 11 12 20 21 22