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Discover how to effectively utilize `if else` conditions in Pandas DataFrames using multiple columns as arguments to create new columns easily.
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How to Use if else Conditions in Pandas with Multiple Columns

Pandas is an incredibly powerful tool for data manipulation and analysis in Python. One common task you may encounter when working with DataFrames is the need to create new columns based on the conditions of existing ones. Specifically, the if else condition can be especially useful when you want to derive values from multiple columns. In this guide, we will dive into how to implement if else conditions in Pandas using multiple columns as arguments.

The Challenge

Let's start with a simple example. Suppose you have a DataFrame like this:

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

In this case, we want to create a new column called out. The value in this new column should match the qty column if the code is 'sp'. Otherwise, it should be set to 0. Your expected output after performing this operation should look like this:

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

The Solution

To achieve this, you need to use the apply() method in combination with a lambda function to set the conditional logic. Importantly, you must include the axis=1 parameter to apply the function across columns for each row.

Step-by-Step Implementation

Using the apply() Method: The apply() function allows you to apply a function along an axis of the DataFrame. Setting axis=1 means you are applying the function row-wise.

Defining the Lambda Function: The lambda function will check the value of the code column and return the corresponding qty or 0.

Creating the New Column: Store the result of the operation in the new column out.

Here’s how you do it:

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

Explanation of the Code

lambda x: This is a small anonymous function we define. x represents each row of the DataFrame.

x['qty'] if x['code'] == 'sp': This checks if the code is 'sp', and if true, returns the value from the qty column.

else 0: If the condition is not met, it returns 0.

axis=1: This parameter ensures that the function operates over the rows rather than the columns.

Final Result

After executing the code snippet above, your DataFrame will be updated as expected, with the new out column accurately reflecting the conditional values:

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

This will output:

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

Conclusion

Utilizing if else conditions in Pandas with multiple columns is straightforward once you understand how the apply() function works. This method allows for easily extending your DataFrame with new columns based on existing data, making your data analysis tasks much more convenient.

Feel free to experiment with different conditions or apply this logic to more complex scenarios in your DataFrames. Happy coding!