Shuffle Rows in Pandas DataFrame While Keeping Duplicates Together

Understanding Data Manipulation in Pandas: Shuffle Rows While Keeping Duplicates Together

Pandas is a powerful library for data manipulation and analysis in Python. It provides efficient data structures and operations to handle structured data, including tabular data like tables and spreadsheets. In this article, we’ll explore how to shuffle rows in a pandas DataFrame while keeping duplicates together.

Background: Understanding Data Structures and Grouping

Before diving into the solution, it’s essential to understand some fundamental concepts:

DataFrames: A two-dimensional table of data with rows and columns. Each column represents a variable, and each row represents an observation.
Grouping: Pandas provides various grouping methods, such as groupby(), which allows us to split the DataFrame into groups based on specific values.

Problem Statement

Given a DataFrame with duplicate rows, we want to shuffle all rows while keeping duplicates together without deleting any of them. The input DataFrame should be in the following format:

    A   B  C   D   E   F
0  35  1  2  35  25  65
1  40  5  7  47  57  67
2  20  1  8  74  58  63
3  35  1  2  37  28  69
4  40  5  7  49  58  69
5  20  1  8  74  58  63
6  35  1  2  47  29  79
7  40  5  7  55  77  87
8  20  1  8  74  58  63

We want to shuffle this DataFrame such that all duplicate rows are together, like so:

    A   B  C   D   E   F
0  35  1  2  35  25  65
3  35  1  2  37  28  69
6  35  1  2  47  29  79
4  40  5  7  49  58  69
1  40  5  7  47  57  67
7  40  5  7  55  77  87
2  20  1  8  74  58  63
5  20  1  8  74  58  63
8  20  1  8  74  58  63

Solution

We can solve this problem using the groupby() method in pandas, which groups rows by specific values and returns a GroupBy object.

Step 1: Grouping Rows

First, we need to group our DataFrame by columns A, B, and C. This will create groups containing all duplicate rows.

import pandas as pd

# Sample data
data = {
    'A': [35, 40, 20, 35, 40, 20, 35, 40, 20],
    'B': [1, 5, 1, 1, 5, 1, 1, 5, 1],
    'C': [2, 7, 8, 2, 7, 8, 2, 7, 8],
    'D': [35, 47, 74, 37, 49, 74, 47, 55, 74],
    'E': [25, 57, 58, 28, 58, 58, 29, 77, 58],
    'F': [65, 67, 63, 69, 69, 63, 79, 87, 63]
}

df = pd.DataFrame(data)

# Grouping rows
grouped = df.groupby(['A', 'B', 'C'], sort=False)

Step 2: Concatenating Groups

Next, we use the concat() method to concatenate all groups together. This will create a new DataFrame with duplicate rows sorted alphabetically by column A, B, and C.

# Concatenating groups
df_shuf = pd.concat(group[1] for group in grouped)

Step 3: Printing the Result

Finally, we print the resulting DataFrame to see the shuffled rows with duplicates together.

print(df_shuf.to_string(index=False))

Explanation and Advice

Here’s a breakdown of each step:

groupby(['A', 'B', 'C']): Groups rows by columns A, B, and C.
sort=False: Prevents sorting the groups alphabetically by default. Instead, the groups are sorted based on the specified values in columns A, B, and C.

Using groupby() with sort=False is more efficient than using other methods, as it avoids unnecessary sorting operations.

Alternative Solutions

While this approach achieves the desired result, there might be alternative solutions depending on your specific use case. For example:

Using df.duplicated() to find duplicate rows and then removing or reordering them manually.
Applying a custom function to each group of duplicate rows.

However, these approaches may not provide the same level of efficiency or elegance as using groupby() with sort=False.

Conclusion

In this article, we’ve explored how to shuffle rows in a pandas DataFrame while keeping duplicates together. By leveraging the power of grouping and concatenation, we can efficiently manipulate our data and achieve the desired output.

Last modified on 2023-10-24