如何摆脱“未命名:0"从 CSV 文件读入的 Pandas DataFrame 中的列? [英] How to get rid of "Unnamed: 0" column in a pandas DataFrame read in from CSV file?

问题描述

我有一种情况，有时当我从 df 读取 csv 时，我会得到一个名为 unnamed:0 的不需要的类似索引的列.

I have a situation wherein sometimes when I read a csv from df I get an unwanted index-like column named unnamed:0.

file.csv

,A,B,C
0,1,2,3
1,4,5,6
2,7,8,9

CSV 是这样读取的:

The CSV is read with this:

pd.read_csv('file.csv')

   Unnamed: 0  A  B  C
0           0  1  2  3
1           1  4  5  6
2           2  7  8  9

这很烦人！有没有人知道如何摆脱这种情况?

This is very annoying! Does anyone have an idea on how to get rid of this?

推荐答案

是索引列，通过 pd.to_csv(..., index=False) 不写出未命名的索引列首先，请参阅 to_csv() 文档.

It's the index column, pass pd.to_csv(..., index=False) to not write out an unnamed index column in the first place, see the to_csv() docs.

示例:

In [37]:
df = pd.DataFrame(np.random.randn(5,3), columns=list('abc'))
pd.read_csv(io.StringIO(df.to_csv()))

Out[37]:
   Unnamed: 0         a         b         c
0           0  0.109066 -1.112704 -0.545209
1           1  0.447114  1.525341  0.317252
2           2  0.507495  0.137863  0.886283
3           3  1.452867  1.888363  1.168101
4           4  0.901371 -0.704805  0.088335

比较:

In [38]:
pd.read_csv(io.StringIO(df.to_csv(index=False)))

Out[38]:
          a         b         c
0  0.109066 -1.112704 -0.545209
1  0.447114  1.525341  0.317252
2  0.507495  0.137863  0.886283
3  1.452867  1.888363  1.168101
4  0.901371 -0.704805  0.088335

您也可以选择通过传递index_col=0来告诉read_csv第一列是索引列:

You could also optionally tell read_csv that the first column is the index column by passing index_col=0:

In [40]:
pd.read_csv(io.StringIO(df.to_csv()), index_col=0)

Out[40]:
          a         b         c
0  0.109066 -1.112704 -0.545209
1  0.447114  1.525341  0.317252
2  0.507495  0.137863  0.886283
3  1.452867  1.888363  1.168101
4  0.901371 -0.704805  0.088335

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