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 modulesimport numpy as npimport pandas as pd # creating dataframedf = 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 datasetdf_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 indexdf_grouped.reset_index() |
Output:
Example 2:
Creating Dataframe.
Python3
# import required modulesimport pandas as pdimport numpy as np # creating dataframedf2 = 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 datadf2_group = df2.groupby(['Student']) # grouped on the basis of students and# with the value of count of amountdf2_grouped = df2_group['Amount'].value_counts() # display datasetprint(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 countdf2_grouped.reset_index(name = 'count') |
Output:
Example 3
Here, is another example to depict how to reset dataframe after using groupby().
Python3
# import required modulesimport numpy as npimport pandas as pd # creating dataframedf = 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 datasetdf_grouped # reset indexdf_grouped.reset_index() |
Output:
