How to reset index after Groupby pandas?

Python’s groupby() function is versatile.  It is used to split the data into groups based on some criteria like mean, median, value_counts, etc. In order to reset the index after groupby() we will use the reset_index() function.

Below are various examples which depict how to reset index after groupby() in pandas:

Example 1 

Python3

# import required modules
import numpy as np
import pandas as pd

# creating dataframe
df = pd.DataFrame({'Subject': ['Physics',
                               'Chemistry',
                               'Maths'],
                   'Marks': [4, 8, 5]})

# grouping the data on the basis of
# subject and mean of marks.
df_grouped = df.groupby(['Subject']).mean()

# display dataset
df_grouped

Output:

Resetting the index after grouping data, using reset_index(), it is a function provided by python to add indexes to the data.

Python3

# reset index
df_grouped.reset_index()

Output:

Example 2: 

Creating Dataframe.

Python3

# import required modules
import pandas as pd
import numpy as np

# creating dataframe
df2 = pd.DataFrame({'Student': [1, 2, 3, 4, 1, 3, 2, 4, 1, 2, 4, 3],
                    'Amount': [
                   10, 20, 30, 40, 20, 60, 40, 80, 30, 60, 120, 90]})

# grouping the data
df2_group = df2.groupby(['Student'])

# grouped on the basis of students and
# with the value of count of amount
df2_grouped = df2_group['Amount'].value_counts()

# display dataset
print(df2_grouped)

Output:

Resetting the index. This will give you an error.

Python3

# this will generate an error.
df2_grouped.reset_index()

Output:

Naming the reset_index() will group and reset the index.

Python3

# resetting index on the basis of count
df2_grouped.reset_index(name = 'count')

Output:

Example 3

Here, is another example to depict how to reset dataframe after using groupby().

Python3

# import required modules
import numpy as np
import pandas as pd

# creating dataframe
df = pd.DataFrame({'Subject': ['B',
                               'C',
                               'A','D','C','B','A'],
                   'Marks': [4, 8, 5,9,8,1,0]})

# grouping the data on the basis of
# subject and mean of marks.
df_grouped = df.groupby(['Subject']).mean()

# display dataset
df_grouped

# reset index
df_grouped.reset_index()

Output: