极客教程如何转换Pandas数据框架中某一列的索引
数据框架中的每一行（即level=0）都有一个索引值，即从0到n-1的索引位置的值，有很多方法可以将这些索引值转换成pandas数据框架中的一个列。首先，让我们创建一个Pandas数据框架。在这里，我们将创建一个关于学生在某一科目中的分数的Pandas数据框架，其中有卷号、姓名、分数、年级和科目等列。
示例:
# importing the pandas library as pd
import pandas as pd
# Creating the dataframe Ab
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250',
'9917102203', '9917102204',
'9917102231'],
'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA',
'PRAKHAR','ASHISH'],
'Score': [99, 98, 50, 45,97],
'Grade': ['A+', 'A+', 'C+', 'C','A'],
'Subject': ['Operating Systems', 'Operating Systems',
'Operating Systems', 'Operating Systems',
'Operating Systems']})
# Printing the dataframe
AB
输出:
创建的数据框架
方法1：创建一个新的索引列。
这里我们将学习在现有的数据框架中创建一个新的列作为索引，并将每一行的索引值（level=0）添加到该列。
# importing the pandas library as pd
import pandas as pd
# Creating the dataframe Ab
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250',
'9917102203', '9917102204',
'9917102231'],
'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA',
'PRAKHAR','ASHISH'],
'Score': [99, 98, 50, 45,97],
'Grade': ['A+', 'A+', 'C+', 'C','A'],
'Subject': ['Operating Systems', 'Operating Systems',
'Operating Systems', 'Operating Systems',
'Operating Systems']})
# Adding a new index column to existing
# data frame and passing index value
AB['index'] = AB.index
# Printing the dataframe
AB
输出:
在这里，我们在 “AB “数据框架中添加了一个新的列 “index”，使用数据框架中每一行的索引值作为参数值，并将索引转换为列。
方法2：使用reset_index()方法和to_string()方法。
在这里，我们将使用reset_index()方法将索引转换为列，同时使用inplace参数来持续反映变化，我们将使用to_string()方法来隐藏默认显示的索引值，无论何时打印数据框架。
#CREATING A DATAFRAME FOR STUDENTS PORTFOLIO
# importing the pandas library as pd
import pandas as pd
# Creating the dataframe Ab
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250', '9917102203', '9917102204','9917102231'],
'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA', 'PRAKHAR','ASHISH'],
'Score': [99, 98, 50, 45,97],
'Grade': ['A+', 'A+', 'C+', 'C','A'],
'Subject': ['Operating Systems', 'Operating Systems', 'Operating Systems', 'Operating Systems','Operating Systems']})
# importing the pandas library as pd
import pandas as pd
# Creating the dataframe Ab
AB = pd.DataFrame({'Roll Number': ['9917102206', '9917102250',
'9917102203', '9917102204',
'9917102231'],
'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA',
'PRAKHAR','ASHISH'],
'Score': [99, 98, 50, 45,97],
'Grade': ['A+', 'A+', 'C+', 'C','A'],
'Subject': ['Operating Systems', 'Operating Systems',
'Operating Systems', 'Operating Systems',
'Operating Systems']})
# USING RESET_INDEX METHOD
# Adding a new index column to AB dataframe
AB.reset_index(level=0, inplace=True)
# HIDING THE DEFAULT INDEX VALUES AND
# PRINTING DATAFRAME
print( AB.to_string(index=False))
输出:
使用reset_index方法
在这里，我们对给定的数据框架应用了reset_index方法，并将其作为参数传递给to_string方法，使默认的索引值（等于0）。
方法3：使用multi_index
在这里，我们将学习如何从一个具有多索引的数据框架中创建列。
例子1：对于多索引到列
# importing the pandas library as pd
import pandas as pd
# ADDING MULTI INDEX TO DATA FRAME
new_index = pd.MultiIndex.from_tuples([('E4','ECE'),
('E5','ECE'),
('E6','ECE'),
('E7','ECE'),
('E8','ECE')],
names=['BATCH','BRANCH'])
# Creating the dataframe AB
data =({'Roll Number': ['9917102206', '9917102250',
'9917102203', '9917102204',
'9917102231'],
'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA',
'PRAKHAR','ASHISH'],
'Score': [99, 98, 50, 45,97],
'Grade': ['A+', 'A+', 'C+', 'C','A'],
'Subject': ['Operating Systems', 'Operating Systems',
'Operating Systems', 'Operating Systems',
'Operating Systems']})
# COMBING DATA FRAME AND MULTI INDEX
# VALUES AND FORMING DATA FRAME
AB = pd.DataFrame(data, columns = ['Roll Number','Name','Score','Grade','Subject'],
index=new_index)
# MAKING MULTI INDEX NOW A PART OF COLUMN
# OF DATAFRAME
AB.reset_index(inplace=True)
AB
输出:
示例2：将多指标中的特定列作为数据框架的一个列。
# importing the pandas library as pd
import pandas as pd
# ADDING MULTI INDEX TO DATA FRAME
new_index = pd.MultiIndex.from_tuples([('E4','ECE'),
('E5','ECE'),
('E6','ECE'),
('E7','ECE'),
('E8','ECE')],
names=['BATCH','BRANCH'])
# Creating the dataframe AB
data =({'Roll Number': ['9917102206', '9917102250',
'9917102203', '9917102204',
'9917102231'],
'Name': ['TANYA', 'PREETIKA', 'KUSHAGRA',
'PRAKHAR','ASHISH'],
'Score': [99, 98, 50, 45,97],
'Grade': ['A+', 'A+', 'C+', 'C','A'],
'Subject': ['Operating Systems', 'Operating Systems',
'Operating Systems', 'Operating Systems',
'Operating Systems']})
# COMBING DATA FRAME AND MULTI INDEX
# VALUES AND FORMING DATA FRAME
AB = pd.DataFrame(data, columns = ['Roll Number','Name','Score','Grade','Subject'],
index=new_index)
# MAKING SPECIFIC COLUMN OF MULTI INDEX
# NOW A PART OF COLUMN OF DATAFRAME
AB.reset_index(inplace=True,level=['BATCH'])
# BATCH INDEX IS NOW A COLUMN OF DATAFRAME
AB
输出:
