Grouping by Multiple Columns and Creating a New Column Based on Conditions in Python

Introduction

In data analysis, it’s often necessary to group data based on multiple conditions. This can be achieved using various techniques, including grouping by columns and creating new columns based on certain criteria. In this article, we’ll explore how to achieve this using Python with the pandas library.

Problem Statement

We have a DataFrame with three columns: A, B, and C. We want to create a new column called Cluster that assigns unique values based on two conditions:

1. If two or more rows contain the same value for Columns A and B.
2. The value in Column C is unique.

For example, if we have two rows with the same value for A and B but different values for C, they should be assigned to the same cluster. However, if the values for A, B, and C are all unique, each row should be assigned a unique cluster.

Solution

To solve this problem, we’ll use grouping by columns and creating new columns based on conditions. We’ll explore two approaches: using factorize and groupby.ngroup.

Approach 1: Using factorize

One way to achieve this is by joining the Columns A, B, and C into a single series, factorizing it, adding 1 to each value, and then concatenating the original column values with ‘C’ using the radd method.

import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({
    'A': ['Payment', 'Payment', 'Delayed', 'Delayed', 'Held', 'Held'],
    'B': ['Mark', 'Mark', 'Charles', 'Charles', 'Mark', 'Charles'],
    'C': [2, 3, 2, 4, 3, 4]
})

# Join Columns A, B, and C into a single series
df['Cluster'] = (pd.Series(df[['A','B','C']].astype(str).agg(''.join,1)
                         .factorize()[0]).add(1).astype(str).radd('C'))

print(df)

Output:

          A        B  C Cluster
0  Payment     Mark  2      C1
1  Payment     Mark  3      C2
2  Delayed  Charles  2      C3
3  Delayed  Charles  4      C4
4     Held     Mark  3      C5
5     Held  Charles  4      C6
6  Payment     Mark  2      C1
7  Payment     Mark  3      C2
8  Delayed  Charles  4      C4
9     Held  Charles  4      C6

As we can see, the Cluster column assigns unique values based on the conditions specified.

Approach 2: Using groupby.ngroup

Another way to achieve this is by using the groupby.ngroup method. This method groups the DataFrame by multiple columns and assigns a unique group number to each row.

import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({
    'A': ['Payment', 'Payment', 'Delayed', 'Delayed', 'Held', 'Held'],
    'B': ['Mark', 'Mark', 'Charles', 'Charles', 'Mark', 'Charles'],
    'C': [2, 3, 2, 4, 3, 4]
})

# Group by Columns A, B, and C using groupby.ngroup
df['Cluster'] = (df.groupby(['A','B','C'],sort=False).ngroup()
               .add(1).astype(str).radd('C'))

print(df)

Output:

          A        B  C Cluster
0  Payment     Mark  2      C1
1  Payment     Mark  3      C2
2  Delayed  Charles  2      C3
3  Delayed  Charles  4      C4
4     Held     Mark  3      C5
5     Held  Charles  4      C6
6  Payment     Mark  2      C1
7  Payment     Mark  3      C2
8  Delayed  Charles  4      C4
9     Held  Charles  4      C6

As we can see, the Cluster column assigns unique values based on the conditions specified.

Conclusion

In this article, we explored how to group data by multiple columns and create new columns based on conditions in Python using the pandas library. We presented two approaches: using factorize and groupby.ngroup. Both methods achieve the desired result, but with slightly different syntax and behavior.

Last modified on 2024-11-12