In this article, we will learn how to Add Group-Level Summary Statistic as a New Column in DataFrame Pandas. This can be done by using the concept of Statistic mean, mode, etc. This requires the following steps :
- Select a dataframe
- Form a statistical data from a column or a group of columns
- Store data as a series
- Add the series in dataframe as a column.
Here, we take a dataframe, The Dataframe consists of student id, name, marks and grades. Let’s create the dataframe
Python3
# importing packages import pandas as pd # dictionary of data dct = {'ID': {0: 23, 1: 43, 2: 12, 3: 13, 4: 67, 5: 89, 6: 90, 7: 56, 8: 34}, 'Name': {0: 'Ram', 1: 'Deep', 2: 'Yash', 3: 'Aman', 4: 'Arjun', 5: 'Aditya', 6: 'Divya', 7: 'Chalsea', 8: 'Akash'}, 'Marks': {0: 89, 1: 97, 2: 45, 3: 78, 4: 56, 5: 76, 6: 100, 7: 87, 8: 81}, 'Grade': {0: 'B', 1: 'A', 2: 'F', 3: 'C', 4: 'E', 5: 'C', 6: 'A', 7: 'B', 8: 'B'} } # create dataframe df = pd.DataFrame(dct) # view dataframe df |
Output:
Now, we will find the group level statistics summary using the above approach.
Python3
# make a series new_column = df.groupby('Grade').Marks.transform('mean') # view new series print(new_column) # add column in dataframe df["Marks Mean"] = df.groupby('Grade').Marks.transform('mean') # view modified dataframe print(df) |
Output:
