Pandas 教程-Pandas DataFrame.transpose()

整理：python架构师

transpose()函数有助于转置DataFrame的索引和列。它通过将行写为列，反之亦然，沿着主对角线反映DataFrame。

语法

DataFrame.transpose(*args, **kwargs)

参数

copy：如果其值为True，则正在复制基础数据。否则，默认情况下，如果可能，不进行复制。

*args, **kwargs：都是附加关键字，不会影响，但具有接受numpy的兼容性。

返回值

它返回转置后的DataFrame。

示例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)

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                           Weight      Name     Age2010-10-04 06:15:00           27      William   222010-10-04 07:15:00           44       John         72010-10-04 08:15:00           38       Smith     192010-10-04 09:15:00           10       Parker    242010-10-04 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

示例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)

          A       B      C       DRow_1     8.0     4.0    17     15.0Row_2     2.0     3.0    42     18.0Row_3     7.0     NaN    35     NaNRow_4     NaN     9.0    18     11.0Row_5     6.0     2.0    24     12.0     Row1    Row2    Row3    Row4    Row5A    8.0     2.0     7.0     NaN     6.0B    4.0     3.0     NaN     9.0     2.0C    17.0    42.0    35.0    18.0    24.0D    15.0    18.0    NaN     11.0    12.0

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