Pandas DataFrame.transpose
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.
# 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) |
Output :
Now we will use DataFrame.transpose()
function to find the transpose of the given dataframe.
# return the transpose result = df.transpose() # Print the result print (result) |
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.
# 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) |
Output :
Now we will use DataFrame.transpose()
function to find the transpose of the given dataframe.
# return the transpose result = df.transpose() # Print the result print (result) |
Output :
As we can see in the output, the DataFrame.transpose()
function has successfully returned the transpose of the given dataframe.
Last Updated on August 30, 2021 by admin