Pandas DataFrame.transpose()

The transpose() function helps to transpose the index and columns of the dataframe. It reflects DataFrame over its main diagonal by writing the rows as columns and vice-versa.

Syntax

Parameters

copy: If its value is True, then the underlying data is being copied. Otherwise, by default, no copy is made, if possible.

*args, **kwargs: Both are additional keywords that do not affect, but has an acceptance that provide compatibility with a numpy.

Returns

It returns the transposed DataFrame.

Example1

# 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)

Output

                           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

Example2

# 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)	

Output

          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

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