Python | Pandas DataFrame.transpose

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas.

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

Syntax: DataFrame.transpose(*args, **kwargs)



Parameter :
copy : If True, the underlying data is copied. Otherwise (default), no copy is made if possible.
*args, **kwargs : Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns : The transposed DataFrame

Example #1: Use DataFrame.transpose() function to find the transpose of the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the DataFrame
df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],
                   'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],
                   'Age':[14, 25, 55, 8, 21]})
  
# Create the index
index_ = pd.date_range('2010-10-09 08:45', periods = 5, freq ='H')
  
# Set the index
df.index = index_
  
# Print the DataFrame
print(df)

chevron_right


Output :

Now we will use DataFrame.transpose() function to find the transpose of the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# return the transpose
result = df.transpose()
  
# Print the result
print(result)

chevron_right


Output :

As we can see in the output, the DataFrame.transpose() function has successfully returned the transpose of the given dataframe.
 
Example #2: Use DataFrame.transpose() function to find the transpose of the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the DataFrame
df = pd.DataFrame({"A":[12, 4, 5, None, 1], 
                   "B":[7, 2, 54, 3, None], 
                   "C":[20, 16, 11, 3, 8], 
                   "D":[14, 3, None, 2, 6]}) 
  
# Create the index
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
  
# Set the index
df.index = index_
  
# Print the DataFrame
print(df)

chevron_right


Output :

Now we will use DataFrame.transpose() function to find the transpose of the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# return the transpose
result = df.transpose()
  
# Print the result
print(result)

chevron_right


Output :

As we can see in the output, the DataFrame.transpose() function has successfully returned the transpose of the given dataframe.



My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.