Pandas DataFrame.transpose()使用示例

transpose()函数有助于转置数据帧的索引和列。通过将行写为列, 反之亦然, DataFrame在其主要对角线上反映了DataFrame。

句法

DataFrame.transpose(*args, **kwargs)

参数

复制：如果其值为True, 则将复制基础数据。否则, 默认情况下, 如果可能, 不进行任何复制。

* args, ** kwargs：两者都是不影响但具有接受能力的附加关键字, 它们与numpy兼容。

Return

它返回转置的DataFrame。

例1

# importing pandas as pd 
import pandas as pd   
# Creating the DataFrame 
info = pd.DataFrame({'Weight':[27, 44, 38, 10, 67], 'Name':['William', 'John', 'Smith', 'Parker', 'Jones'], 'Age':[22, 17, 19, 24, 27]})   
# Create the index 
index_ = pd.date_range('2010-10-04 06:15', periods = 5, freq ='H')  
# Set the index
info.index = index_  
# Print the DataFrame 
print(info) 
# return the transpose 
result = info.transpose()   
# Print the result 
print(result)

输出

Weight      Name     Age
2010-10-04 06:15:00           27      William   22
2010-10-04 07:15:00           44       John         7
2010-10-04 08:15:00           38       Smith     19
2010-10-04 09:15:00           10       Parker    24
2010-10-04 10:15:00           67       Jones      27
       2010-10-04 06:15:00 2010-10-04 07:15:00 2010-10-04 08:15:00  \
Weight                  27               44                  38   
Name               William         John               Smith   
Age                     22                   7                   19   

       2010-10-04 09:15:00 2010-10-04 10:15:00  
Weight                  10               67  
Name                Parker      Jones  
Age                     24                  27

例2

# importing pandas as pd 
import pandas as pd 
# Creating the DataFrame 
info = pd.DataFrame({"A":[8, 2, 7, None, 6], "B":[4, 3, None, 9, 2], "C":[17, 42, 35, 18, 24], "D":[15, 18, None, 11, 12]})  
# Create the index 
index_ = ['Row1', 'Row2', 'Row3', 'Row4', 'Row5'] 
# Set the index 
info.index = index_   
# Print the DataFrame 
print(info) 
# return the transpose 
result = info.transpose()   
# Print the result 
print(result)

输出

A       B      C       D
Row_1     8.0     4.0    17     15.0
Row_2     2.0     3.0    42     18.0
Row_3     7.0     NaN    35     NaN
Row_4     NaN     9.0    18     11.0
Row_5     6.0     2.0    24     12.0
     Row1    Row2    Row3    Row4    Row5
A    8.0     2.0     7.0     NaN     6.0
B    4.0     3.0     NaN     9.0     2.0
C    17.0    42.0    35.0    18.0    24.0
D    15.0    18.0    NaN     11.0    12.0