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Learn how to leverage the power of `np.where` with multiple conditions to efficiently fill columns in your Pandas DataFrame with this step-by-step guide.
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How to Use np.where with Multiple Conditions to Fill Columns in a Pandas DataFrame?

Working with data often involves conditional operations. When using Python with Pandas, an efficient way to achieve conditional assignments in a DataFrame is through np.where from the NumPy library. This guide explores how to use np.where with multiple conditions to fill columns in a Pandas DataFrame.

Introduction to np.where
np.where is a function provided by the NumPy library that can be used to evaluate conditions on arrays, and based on those conditions, elements in a corresponding output array can be assigned specific values.

Basic Syntax
The basic syntax for np.where is:

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Here:

condition: The condition to be checked.

x: Elements to include in the output array if the condition is True.

y: Elements to include in the output array if the condition is False.

Using np.where with Pandas DataFrame
To use np.where within the context of a DataFrame, we often need to involve multiple conditions. Here is an example to illustrate:

Example

Suppose you have a DataFrame with students' grades and you want to assign a pass/fail tag based on the grades.

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This will output:

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Multiple Conditions with np.where
Let’s add complexity by considering another condition, such as if a student scored exactly 95, we want to assign 'Excellent' instead of 'Pass'.

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Result:

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Conclusion
Using np.where with multiple conditions in a Pandas DataFrame provides a powerful way to implement complex logic in concise and readable code. This function enables data scientists and analysts to perform condition-based assignments effectively, enhancing data processing pipelines.

By mastering these techniques, you can handle a wide range of data manipulation tasks with greater efficiency and precision. Whether you're labeling data based on thresholds, classifying groups, or applying rules across various columns, np.where combined with multiple conditions in Pandas serves as an indispensable tool.