Write a Python program to reshape a given dataframe in different ways

We can reshape a dataframe using melt(),stack(),unstack() and pivot() function.

Solution 1

• Define a dataframe.

• Apply melt() function to convert wide dataframe column as rows. It is defined below,

df.melt()

Example

Let’s see the below code to get a better understanding −

import pandas as pd df = pd.DataFrame({'Id':[1,2,3],'Age':[13,14,13],'Mark':[80,90,85]}) print("Dataframe is:\n",df) print(df.melt())

Output

Dataframe is:  Id Age Mark 0 1 13   80 1 2 14   90 2 3 13   85 variable value 0    Id    1 1    Id    2 2    Id    3 3   Age    13 4   Age    14 5   Age    13 6   Mark   80 7   Mark   90 8   Mark   85

Solution 2

• Define a dataframe.

• Apply stack() function to increase the level of the index in a dataframe. It is defined below,

df.stack().to_frame()

• If you want to revert back the changes, you can use unstack().

df.unstack().to_frame()

Example

Let’s see the below implementation to get a better understanding −

import pandas as pd df = pd.DataFrame({'Id':[1,2,3],'Age':[13,14,13],'Mark':[80,90,85]}) print("Dataframe is:\n",df) print(df.stack().to_frame()) print(df.unstack().to_frame())

Output

Dataframe is:    Id   Age Mark 0   1    13  80 1   2    14  90 2   3    13  85           0 0   Id    1    Age    13    Mark   80 1   Id     2    Age    14   Mark    90 2   Id    3    Age    13    Mark   85           0 Id   0    1      1    2      2    3 Age  0    13      1    14      2    13 Mark 0    80      1    90      2    85

Solution 3

• Define a dataframe

• Apply pivot() function to reshape a dataframe based on Id column,

df.pivot(columns='Id')

Example

Let’s see the below implementation to get a better understanding −

import pandas as pd df = pd.DataFrame({'Id':[1,2,3],'Age':[13,14,13],'Mark':[80,90,85]}) print("Dataframe is:\n",df) print(df.pivot(columns='Id'))

Output

Dataframe is:  Id Age Mark 0 1 13   80 1 2 14   90 2 3 13   85       Age          Mark Id    1    2    3    1    2    3 0   13.0  NaN  NaN  80.0 NaN  NaN 1   NaN  14.0  NaN  NaN  90.0 NaN 2   NaN  NaN   13.0 NaN  NaN  85.0