pandas.read_csv() 参数 names整理

pandas 官方文档

names : array-like, default None

用于结果的列名列表，如果数据文件中没有列标题行，就需要执行header=None。默认列表中不能出现重复，除非设定参数mangle_dupe_cols=True。

Age	Gender	Education	EducationField	MaritalStatus	Income	OverTime
37	Male	4	Life Sciences	Divorced	5993	No
54	Female	4	Life Sciences	Divorced	10502	No
34	Male	3	Life Sciences	Single	6074	Yes
39	Female	1	Life Sciences	Married	12742	No
28	Male	3	Medical	Divorced	2596	No
24	Female	1	Medical	Married	4162	Yes
29	Male	5	Other	Single	3983	No
36	Male	2	Medical	Married	7596	No
33	Female	4	Medical	Married	2622	No

import pandas as pd  

1.1

data = pd.read_csv('./train.csv',
                   names=['new_0','new_1','new_2','new_3','new_4','new_5','new_6']
                   )

print(data.head(5))

输出结果：

  new_0   new_1      new_2           new_3          new_4   new_5     new_6
0   Age  Gender  Education  EducationField  MaritalStatus  Income  OverTime
1    37    Male          4   Life Sciences       Divorced    5993        No
2    54  Female          4   Life Sciences       Divorced   10502        No
3    34    Male          3   Life Sciences         Single    6074       Yes
4    39  Female          1   Life Sciences        Married   12742        No

1.2

data = pd.read_csv('./train.csv',
                   header=None,
                   names=['new_0','new_1','new_2','new_3','new_4','new_5','new_6']
                   )

print(data.head(5))

输出结果：

  new_0   new_1      new_2           new_3          new_4   new_5     new_6
0   Age  Gender  Education  EducationField  MaritalStatus  Income  OverTime
1    37    Male          4   Life Sciences       Divorced    5993        No
2    54  Female          4   Life Sciences       Divorced   10502        No
3    34    Male          3   Life Sciences         Single    6074       Yes
4    39  Female          1   Life Sciences        Married   12742        No

1.3  header=2,  names=['new_0','new_1','new_2','new_3','new_4','new_5','new_6']

等于header=2，则第2行作为列名，Dataframe 从3行的数据开始，但names定义列名覆盖第2行的列名。

data = pd.read_csv('./train.csv',
                   header=2,
                   names=['new_0','new_1','new_2','new_3','new_4','new_5','new_6']
                   )

print(data.head(5))

输出结果：

   new_0   new_1  new_2          new_3     new_4  new_5 new_6
0     34    Male      3  Life Sciences    Single   6074   Yes
1     39  Female      1  Life Sciences   Married  12742    No
2     28    Male      3        Medical  Divorced   2596    No
3     24  Female      1        Medical   Married   4162   Yes
4     29    Male      5          Other    Single   3983    No