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.

## Code

```
# 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()
print(result)
# Copyright PHD
```

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

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## Explanation

- 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.

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.

## Conclusion

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!