按位置选择 pandas 列 [英] Selecting pandas column by location

问题描述

我只是想通过一个整数来访问命名的 Pandas 列.

您可以使用df.ix[3]按位置选择一行.

但是如何按整数选择列?

我的数据框:

df=pandas.DataFrame({'a':np.random.rand(5), 'b':np.random.rand(5)})

解决方案

想到的两种方法:

<预><代码>>>>dfA B C D0 0.424634 1.716633 0.282734 2.0869441 -1.325816 2.056277 2.583704 -0.7764032 1.457809 -0.407279 -1.560583 -1.3162463 -0.757134 -1.321025 1.325853 -2.5133734 1.366180 -1.265185 -2.184617 0.881514>>>df.iloc[:, 2]0 0.2827341 2.5837042 -1.5605833 1.3258534 -2.184617名称:C>>>df[df.columns[2]]0 0.2827341 2.5837042 -1.5605833 1.3258534 -2.184617名称:C

<小时>

编辑:原始答案建议使用 df.ix[:,2] 但此功能现已弃用.用户应该切换到 df.iloc[:,2].

I'm simply trying to access named pandas columns by an integer.

You can select a row by location using df.ix[3].

But how to select a column by integer?

My dataframe:

df=pandas.DataFrame({'a':np.random.rand(5), 'b':np.random.rand(5)})

解决方案

Two approaches that come to mind:

>>> df
          A         B         C         D
0  0.424634  1.716633  0.282734  2.086944
1 -1.325816  2.056277  2.583704 -0.776403
2  1.457809 -0.407279 -1.560583 -1.316246
3 -0.757134 -1.321025  1.325853 -2.513373
4  1.366180 -1.265185 -2.184617  0.881514
>>> df.iloc[:, 2]
0    0.282734
1    2.583704
2   -1.560583
3    1.325853
4   -2.184617
Name: C
>>> df[df.columns[2]]
0    0.282734
1    2.583704
2   -1.560583
3    1.325853
4   -2.184617
Name: C

---

Edit: The original answer suggested the use of df.ix[:,2] but this function is now deprecated. Users should switch to df.iloc[:,2].

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