import pandas as pd
data = pd.read_csv("person.csv")
print(data)
输出结果：
ID Name Age City Salary 0 1 Jack 28 Beijing 22000 1 2 Lida 32 Shanghai 19000 2 3 John 43 Shenzhen 12000 3 4 Helen 38 Hengshui 3500通过列索引（标签）读取多列数据。
import pandas as pd
#设置"Name"为行索引
data = pd.read_csv("person.csv", index_col ="Name")
# 通过列标签选取多列数据
a = data[["City","Salary"]]
print(a)
输出结果：
City Salary
Name
Jack Beijing 22000
Lida Shanghai 19000
John Shenzhen 12000
Helen Hengshui 3500
再看一组简单的示例：
import pandas as pd
info =pd.read_csv("person.csv", index_col ="Name")
#获取单列数据，或者以列表的形式传入["Salary"]
a =info["Salary"]
print(a)
输出结果：
Salary
Name
Jack 22000
Lida 19000
John 12000
Helen 3500
info = pd.DataFrame({'Name': ['Parker', 'Terry', 'Smith', 'William'], 'Year': [2011, 2009, 2014, 2010],
'Leaves': [10, 15, 9, 4]})
#设置Name为行索引
print(info.set_index('Name'))
输出结果：
Year Leaves
Name
Parker 2011 10
Terry 2009 15
Smith 2014 9
William 2010 4
import pandas as pd
import numpy as np
info = pd.DataFrame([('William', 'C'),
('Smith', 'Java'),
('Parker', 'Python'),
('Phill', np.nan)],
index=[1, 2, 3, 4],
columns=('name', 'Language'))
print(info)
print(info.reset_index())
输出结果：
重置前：
name Language
1 William C
2 Smith Java
3 Parker Python
4 Phill NaN
重置后：
index name Language
0 1 William C
1 2 Smith Java
2 3 Parker Python
3 4 Phill NaN
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