Скачать или смотреть How to Assign Random Values from a List to a New Column in Pandas Without Repeating the Same Value

Discover how to effectively assign random peer reviewers in Pandas, ensuring that no one reviews their own job, with clear steps and sample code.
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How to Assign Random Values from a List to a New Column in Pandas Without Repeating the Same Value

When working with datasets in Python's Pandas library, you may encounter situations where you need to assign random values from a list to a new column while ensuring that certain conditions are met. In this guide, we’ll tackle the problem of assigning peer reviewers to jobs where the reviewer cannot be the same as the user who performed the job.

Imagine you have a dataset with job IDs and user IDs, and you want to add a reviewer_id column that assigns a random reviewer from the list of user IDs, but excludes the user who performed that job. Below, we’ll walk through the steps to achieve this using effective Pandas operations and random selection methods.

Understanding the Dataset

Let's first consider the sample dataset you may have:

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In this dataset, we have two columns: job_id and user_id. Our goal is to create a new column called reviewer_id that randomly assigns users from the user_id list without allowing users to review their own jobs.

Solution Approach

Step 1: Get Unique Users

First, we need to create a list of unique user IDs. This will be used to randomly select reviewers.

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Step 2: Initialize the Reviewer Column

Next, we will initialize the reviewer_id column with the current user_id values. This is a placeholder to ensure we have something in the column which we will later adjust.

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Step 3: Assign Reviewers Randomly

Now, we need a loop to ensure the reviewers are not the same as the users who performed the job:

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Here’s what happens in this code:

We define a lambda function self_review to check if any reviewer_id matches the user_id.

We use a while loop to continue generating random reviewers until we ensure no users are reviewing their own jobs.

The np.random.choice function selects random reviewers based on the unique users list.

Step 4: Optimize (Optional)

If you need a more efficient solution, especially with a larger dataset, consider using Python 3.8 or later to leverage the walrus operator (:=). This operator can help in reducing the number of checks required within the loop:

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In this optimized version:

We check for self-reviews while simultaneously creating a list of reviewers for only those entries that need reassignment, which can enhance performance.

Conclusion

By following these steps, you can successfully assign peer reviewers in a way that adheres to your specified conditions. This method is efficient and utilizes the power of Pandas alongside NumPy for random selection. Happy coding!