Скачать или смотреть How to Automatically Populate a New Col in Python Based on Conditions

Learn how to create a new column in a Python DataFrame that lists column names when 'Yes' is encountered. Perfect for data analysis!
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This video is based on the question https://stackoverflow.com/q/67630550/ asked by the user 'Gabriel Choo' ( https://stackoverflow.com/u/15987964/ ) and on the answer https://stackoverflow.com/a/67630865/ provided by the user 'norie' ( https://stackoverflow.com/u/2850026/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Automatically Populate a New Column in Python Based on Conditions

When working with data in Python, particularly with DataFrames using Pandas, you might find yourself needing to derive new information based on existing data. One common scenario is creating a new column that lists the names of other columns if they meet a specific condition, like when the value is "Yes".

In this post, we will explore how to achieve this by utilizing Python's Pandas library. Let's break it down step by step.

Introduction to the Problem

Imagine you have a DataFrame representing survey responses, where each row corresponds to an individual respondent's answers to various questions. Each column contains either "Yes" or "No" based on the response. Your goal is to create a new column named New Col that lists all the questions (column names) for which the respondent answered "Yes".

For example, if a respondent answered "Yes" to questions in columns col1 and col3, the New Col for that row should reflect col1, col3.

Step-by-Step Solution

1. Importing Necessary Libraries

First, make sure you have the Pandas library installed. If you haven't installed it yet, you can do so using pip:

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Then, you can import the library in your Python script:

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2. Creating the DataFrame

Next, we define our DataFrame with the unique identifiers and responses. Here's how you can set up the dataset:

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3. Applying the Condition to Populate New Col

Now comes the core logic. We want to iterate through each row of the DataFrame and check each column (excluding the first one) to see if the value is "Yes". We can achieve this using a list comprehension within the apply function. Here’s how it looks:

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This code does the following:

apply(lambda row: ...): Applies a function along the rows.

[col for col in df.columns[1:] if row[col] == 'Yes']: A list comprehension that collects column names where the corresponding row value is "Yes".

','.join(...): Joins the collected column names into a single string, separated by commas.

4. Viewing the Result

Finally, to see the changes, you can print the DataFrame:

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Output

When you run the code, the output will look like this:

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Conclusion

Creating a new column based on conditions in your DataFrame can be highly beneficial in data analysis tasks. In this example, we illustrated how to dynamically generate a new column that lists the names of fields responding "Yes". This technique can be further customized for various conditions or used in more complex datasets.

Feel free to experiment with this code to suit your specific application. Happy coding!