Pandas provides a convenient way to handle data and its transformation. Let’s see how can we convert a column to row name/index in Pandas.
Create a dataframe first with dict of lists.
# imporing pandas as pd import pandas as pd # Creating a dict of lists data = {'Name':["Akash", "Geeku", "Pankaj", "Sumitra","Ramlal"], 'Branch':["B.Tech", "MBA", "BCA", "B.Tech", "BCA"], 'Score':["80","90","60", "30", "50"], 'Result': ["Pass","Pass","Pass","Fail","Fail"]} # creating a dataframe df = pd.DataFrame(data) df |
Output:
Method #1: Using set_index() method.
# importing pandas as pd import pandas as pd # Creating a dict of lists data = {'Name':["Akash", "Geeku", "Pankaj", "Sumitra","Ramlal"], 'Branch':["B.Tech", "MBA", "BCA", "B.Tech", "BCA"], 'Score':["80","90","60", "30", "50"], 'Result': ["Pass","Pass","Pass","Fail","Fail"]} # Creating a dataframe df = pd.DataFrame(data) # Using set_index() method on 'Name' column df = df.set_index('Name') df |
Output:
Now, set index name as None.
# set the index to 'None' via its name property df.index.names = [None] df |
Output:
Method #2: Using pivot() method.
In order to convert a column to row name/index in dataframe, Pandas has a built-in function Pivot.
Now, let’s say we want Result to be the rows/index, and columns be name in our dataframe, to achieve this pandas has provided a method called Pivot. Let us see how it works,
# importing pandas as pd import pandas as pd # Creating a dict of lists data = {'name':["Akash", "Geeku", "Pankaj", "Sumitra", "Ramlal"], 'Branch':["B.Tech", "MBA", "BCA", "B.Tech", "BCA"], 'Score':["80", "90", "60", "30", "50"], 'Result': ["Pass", "Pass", "Pass", "Fail", "Fail"]} df = pd.DataFrame(data) # pivoting the dataframe df.pivot(index ='Result', columns ='name') df |
Output:
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