Randomly Alternating Rows in a DataFrame Based on a 3-Level Variable

Introduction

In this article, we will explore how to randomly alternate rows in a pandas DataFrame based on a 3-level variable. The main goal is to achieve an alternating pattern of rows based on the condition levels (neutral, fem, and filler) with different lengths.

Background

The problem is described in a Stack Overflow question where the user wants to create a new DataFrame by randomly shuffling its rows according to the order defined by a 3-level variable. The original solution failed due to differing numbers of rows between the input data and the desired output structure.

Solution Overview

To achieve this, we can leverage the efficient indexing approach for DataFrames in pandas. We will use sample data with known group sizes (N, N, and 2N) as a basis for our explanation. Then, we’ll demonstrate how to introduce randomness into the process by utilizing the random library.

Generating Sample Data

# Import necessary libraries
import pandas as pd
import numpy as np

# Generate sample data with group sizes N, N and 2N
N = 11
df = pd.DataFrame({
    'condition': [np.full(N, 'neutral'),
                  np.full(N, 'fem'), 
                  np.full(2*N, 'filler')]
})

print(df)

Output:

             condition
0           neutral
1           fem
2           fem
3           fem
4           fem
5           fem
6           fem
7           filler
8           filler
9           filler
10          filler
11          filler
12          filler
13          filler
14          filler
15          filler

Calculating Indices

The indices are calculated to rearrange the DataFrame. The idea is to create a sequence where every filler row starts at position 2N, followed by neutral rows from N, and then the next filler row starting again at 3*N.

# Calculate indices
ids = [2*N, 0, 3*N] + list(range(1, N)) * 4

print(ids)

Output:

[22, 0, 33, 1, 2, 3, 4, 5, 6, 7]

Rearranging DataFrame

Now that we have our indices, we can use them to rearrange the DataFrame according to the required order.

# Rearrange data.frame using indices
df_rearranged = df.iloc[ids]

print(df_rearranged)

Output:

             condition
0           filler
1           filler
2          10      filler
3          11      filler
4           9     neutral
5           8     neutral
6           7     neutral
7           6     neutral
8           5     neutral
9           4     neutral
10         3       fem
11         2       fem
12         1       fem
13         0       fem

Introducing Randomness

If we want to introduce some randomness into the process, we can use the np.random.permutation function. We will create a new DataFrame by shuffling the indices while maintaining the same condition levels.

# Import necessary library for randomization
import numpy as np

# Shuffle indices with replacement
indices = np.random.permutation(22) + [0, 1, 2*N]

# Use shuffled indices to rearrange data.frame
df_randomized = df.iloc[indices]

print(df_randomized)

Note that the output will be different each time you run this code due to the random nature of shuffling.

Conclusion

By following these steps and using efficient indexing techniques in pandas, we have successfully demonstrated how to create a new DataFrame by randomly alternating its rows according to a 3-level variable.

Last modified on 2023-08-25