Calculating the Sum of “sub-ID” values for each ID

What will you learn?

In this engaging tutorial, you will master the art of calculating the sum of “sub-ID” values for each unique ID using Python. Dive into the world of data aggregation and manipulation with Pandas.

Introduction to Problem and Solution

Delve into the realm of data analysis as we tackle the challenge of aggregating and summing up “sub-ID” values corresponding to distinct IDs. By leveraging Python’s Pandas library, we will unravel an efficient solution to this problem, paving the way for seamless data processing.


# Importing pandas library
import pandas as pd

# Sample data (replace this with your own dataset)
data = {'ID': [1, 1, 2, 2, 3],
        'sub-ID': [10, 20, 30, 40, 50]}

df = pd.DataFrame(data)

# Calculating the sum of "sub-ID" values for each ID
result = df.groupby('ID')['sub-ID'].sum()


# Copyright PHD

Note: Replace the sample data with your dataset for accurate results.



  • Import the pandas library to handle data efficiently.
  • Create a DataFrame df with ‘ID’ and ‘sub-ID’ columns.
  • Utilize groupby() along with sum() to aggregate and sum ‘sub-ID’ values by unique IDs.
  • Display the calculated sums.
    How does groupby() work in Pandas?

    The groupby() function in Pandas divides data into groups based on specified criteria for individualized operations.

    Can I apply multiple aggregations after grouping data?

    Yes! You can apply various aggregation functions like sum(), count(), mean(), etc., post grouping in Pandas.

    Is sorting necessary before using groupby()?

    Sorting isn’t obligatory but may enhance performance when employing groupby().

    How do I reset index after grouping data?

    After using groupby(), employ .reset_index() method to reset index if needed.

    Can column names be customized after aggregation?

    Certainly! You can assign custom names during column aggregation within a groupby operation in Pandas.

    What happens with missing values in datasets?

    Pandas automatically excludes missing values (NaNs) when performing aggregations like sum() during a groupby operation.


    In conclusion, you have acquired proficiency in calculating the sum of “sub-ID” values for each unique ID using Python’s robust Pandas library. Enhance your data analysis skills and efficiency!

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