Understanding pandas.DataFrame.join: Resolving Index Issues in DataFrame Concatenation

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The Problem

Let’s consider a scenario illustrated by the following DataFrame named inverters:

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

With the code you've used to attempt to normalize the voltage and join DataFrames, you’ve constructed a new DataFrame _, carrying normalized voltage values:

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

After joining the normalized values to the original inverters, you print the result:

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

If you discover that the result contains more rows than expected, it’s likely due to duplicate indices in your original DataFrame, which leads to a Cartesian product of matching rows during the join operation.

Understanding the Cause

Non-Unique Index Problem

When you perform a join in pandas, if the indices in either DataFrame are not unique, pandas will return a result that includes every combination of rows with the same index from both DataFrames. This results in an inflated number of rows that can confuse users who expect a simple merge.

Example

Solution: Using concat Instead of join

Here’s how you would do it:

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

Alternatively, if you want to directly assign the normalized voltage to your original DataFrame, you can achieve it using the transform method on a grouped DataFrame:

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

Results

Using either of these methods will yield a DataFrame where the number of rows remains consistent and matches your expectations after adding the normalized values:

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

Conclusion