根据教程实现了读取csv文件前面的几行数据，一下就想到了是不是可以实现前面几列的数据。经过多番尝试总算试出来了一种方法。

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

以上这篇使用pandas读取csv文件的指定列方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。

原始csv文件，第一行为列名

import pandas as pd
# 按位置进行选取
# 获取原始数据
df = pd.read_csv("./test.csv")
# 第1行
print(df.iloc[0])
# 前3行
print(df.iloc[:3])
# 第1列
print(df.iloc[:, 0])
# 前2列
print(df.iloc[:, :2])