Mastering Conditional Multiplication in DataFrames Using np.multiply and pd.mul

Learn how to conditionally multiply DataFrame values with elements from NumPy arrays using Python's pandas and NumPy libraries.
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Have you ever encountered a scenario in data manipulation where you needed to conditionally multiply elements from two different DataFrames by elements of NumPy arrays? This straightforward task can become complex when conditions dictate which multiplier to use for each column. In this guide, we'll learn how to tackle this problem effectively using Python's pandas and NumPy libraries.

The Problem

Consider the following example with two DataFrames, df1 and df2, along with two NumPy arrays, mul1 and mul2:

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

The task is to conditionally multiply columns in df1 based on the values in df2. Here are the criteria:

For column A, multiply by mul1[0] if df2['A'] > 3, otherwise use mul2[0].

For column B, use mul1[1] if df2['B'] > 12, otherwise use mul2[1].

For column C, apply mul1[2] if df2['C'] > 15, otherwise use mul2[2].

The expected output after applying these conditions is:

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

The Solution

Step-by-Step Implementation

Here’s how we can implement this logic:

Column A Modification:

Check if df2['A'] > 3. If True, multiply df1['A'] by mul1[0], else multiply by mul2[0].

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

Column B Modification:

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

Column C Modification:

Follow the same logic for column C, checking if df2['C'] > 15.

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

Final Code

Combining all steps, here's the complete code snippet:

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

Conclusion

Feel free to experiment with different conditions and arrays in your own DataFrame manipulations. Happy coding!