这篇文章将为大家详细讲解有关如何使用pandas读取csv文件的指定列，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。
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之所以想实现读取前面的几列是因为我手头的一个csv文件恰好有后面几列没有可用数据，但是却一直存在着。原来的数据如下：
GreydeMac-mini:chapter06 greyzhang$ cat data.csv
1,name_01,coment_01,,,,
2,name_02,coment_02,,,,
3,name_03,coment_03,,,,
4,name_04,coment_04,,,,
5,name_05,coment_05,,,,
6,name_06,coment_06,,,,
7,name_07,coment_07,,,,
8,name_08,coment_08,,,,
9,name_09,coment_09,,,,
10,name_10,coment_10,,,,
11,name_11,coment_11,,,,
12,name_12,coment_12,,,,
13,name_13,coment_13,,,,
14,name_14,coment_14,,,,
15,name_15,coment_15,,,,
16,name_16,coment_16,,,,
17,name_17,coment_17,,,,
18,name_18,coment_18,,,,
19,name_19,coment_19,,,,
20,name_20,coment_20,,,,
21,name_21,coment_21,,,,
如果使用pandas读取出全部的数据，打印的时候会出现以下结果：
In [41]: data = pd.read_csv('data.csv')
In [42]: data
Out[42]:
1 name_01 coment_01 Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 6
0 2 name_02 coment_02 NaN NaN NaN NaN
1 3 name_03 coment_03 NaN NaN NaN NaN
2 4 name_04 coment_04 NaN NaN NaN NaN
3 5 name_05 coment_05 NaN NaN NaN NaN
4 6 name_06 coment_06 NaN NaN NaN NaN
5 7 name_07 coment_07 NaN NaN NaN NaN
6 8 name_08 coment_08 NaN NaN NaN NaN
7 9 name_09 coment_09 NaN NaN NaN NaN
8 10 name_10 coment_10 NaN NaN NaN NaN
9 11 name_11 coment_11 NaN NaN NaN NaN
10 12 name_12 coment_12 NaN NaN NaN NaN
11 13 name_13 coment_13 NaN NaN NaN NaN
12 14 name_14 coment_14 NaN NaN NaN NaN
13 15 name_15 coment_15 NaN NaN NaN NaN
14 16 name_16 coment_16 NaN NaN NaN NaN
15 17 name_17 coment_17 NaN NaN NaN NaN
16 18 name_18 coment_18 NaN NaN NaN NaN
17 19 name_19 coment_19 NaN NaN NaN NaN
18 20 name_20 coment_20 NaN NaN NaN NaN
19 21 name_21 coment_21 NaN NaN NaN NaN
所说在学习的过程中这并不会给我带来什么障碍，但是在命令行终端界面呆久了总喜欢稍微清爽一点的风格。使用read_csv的参数usecols能够在一定程度上减少这种混乱感。
In [45]: data = pd.read_csv('data.csv',usecols=[0,1,2,3])
In [46]: data
Out[46]:
1 name_01 coment_01 Unnamed: 3
0 2 name_02 coment_02 NaN
1 3 name_03 coment_03 NaN
2 4 name_04 coment_04 NaN
3 5 name_05 coment_05 NaN
4 6 name_06 coment_06 NaN
5 7 name_07 coment_07 NaN
6 8 name_08 coment_08 NaN
7 9 name_09 coment_09 NaN
8 10 name_10 coment_10 NaN
9 11 name_11 coment_11 NaN
10 12 name_12 coment_12 NaN
11 13 name_13 coment_13 NaN
12 14 name_14 coment_14 NaN
13 15 name_15 coment_15 NaN
14 16 name_16 coment_16 NaN
15 17 name_17 coment_17 NaN
16 18 name_18 coment_18 NaN
17 19 name_19 coment_19 NaN
18 20 name_20 coment_20 NaN
19 21 name_21 coment_21 NaN
为了能够看到数据的“边界”，读取的时候显示了第一列无效的数据。正常的使用中，或许我们是想连上面结果中最后一列的信息也去掉的，那只需要在参数重去掉最后一列的列号。
In [47]: data = pd.read_csv('data.csv',usecols=[0,1,2])
In [48]: data
Out[48]:
1 name_01 coment_01
0 2 name_02 coment_02
1 3 name_03 coment_03
2 4 name_04 coment_04
3 5 name_05 coment_05
4 6 name_06 coment_06
5 7 name_07 coment_07
6 8 name_08 coment_08
7 9 name_09 coment_09
8 10 name_10 coment_10
9 11 name_11 coment_11
10 12 name_12 coment_12
11 13 name_13 coment_13
12 14 name_14 coment_14
13 15 name_15 coment_15
14 16 name_16 coment_16
15 17 name_17 coment_17
16 18 name_18 coment_18
17 19 name_19 coment_19
18 20 name_20 coment_20
19 21 name_21 coment_21
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