Скачать или смотреть How to Efficiently Add a Condition Column to a Pandas DataFrame Based on User Input

Discover how to enhance your Pandas DataFrame with a `Condition` column based on user input, efficiently collecting inputs for each unique sample value to streamline your processes.
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This video is based on the question https://stackoverflow.com/q/63058520/ asked by the user 'SMadden' ( https://stackoverflow.com/u/8313653/ ) and on the answer https://stackoverflow.com/a/63058645/ provided by the user 'Andrej Kesely' ( https://stackoverflow.com/u/10035985/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Efficiently Add a Condition Column to a Pandas DataFrame Based on User Input

If you've ever worked with Pandas DataFrames, you know that user input can sometimes make your workflows tedious—especially when you have to input information for each instance of a value in the DataFrame. In this post, we'll tackle a common frustration faced by users: how to add a new column based on user input without having to prompt for each row.

We'll explore a sample implementation where we need to create a Condition column based on the unique values present in the Sample column of your DataFrame. Let's dive into the problem and its solution.

The Problem

You have a DataFrame that contains some sample data:

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Current Approach

You have a function, get_choice, that prompts the user to input conditions for each sample:

[[See Video to Reveal this Text or Code Snippet]]

While this works, it can become cumbersome for larger DataFrames with multiple samples, as you are prompted for each row instead of just once per unique sample.

The Solution

To avoid the repetitive input prompts, we can modify the function so it only asks for input once per unique sample. Here's a step-by-step breakdown of the improved approach:

Step 1: Identify Unique Samples

First, we'll identify all unique samples present in the Sample column of the DataFrame.

Step 2: Gather Inputs

We'll then use a loop to collect user input for each unique sample value, storing the inputs in a dictionary.

Step 3: Map Conditions Back to the DataFrame

Finally, we will map these conditions back to the original DataFrame based on the sample values.

Here’s the adapted code that implements the above steps:

[[See Video to Reveal this Text or Code Snippet]]

Example Output

When the function runs, it will ask for input once per unique sample:

[[See Video to Reveal this Text or Code Snippet]]

And the DataFrame output will look like this:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By collecting input only once for each unique sample, we streamline the process of populating the Condition column in a Pandas DataFrame. This method not only simplifies your user interface but also saves time, especially when working with larger datasets.

Now that you have this efficient way of adding conditions based on user input, go ahead and enhance your DataFrame manipulation tasks!