How to Parse Column Values into Multiple Rows in a DataFrame Using Pandas

Learn how to effectively parse column values into multiple rows in a DataFrame using Pandas, while removing duplicates.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to parse column values into multiple rows in dataframe?

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Parse Column Values into Multiple Rows in a DataFrame Using Pandas

Working with data in pandas can sometimes present challenges, especially when dealing with complex structures within a DataFrame. One common issue that arises is the need to split a single column's delimited values into multiple rows. This is particularly relevant when your DataFrame contains a column where values are separated by a delimiter, such as a vertical bar (|).

The Problem

Imagine you have a DataFrame that looks like this:

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

Here, the id column contains values delimited by a vertical bar (|). You want to transform this DataFrame into the following format:

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

Additionally, after the splitting operation, it's possible that duplicate rows could be introduced. Hence, we also want to make sure that these duplicates are removed.

The Solution

Instead of writing complicated loops to parse and transform the DataFrame, you can accomplish this task with just a few straightforward pandas methods. Here’s how to do it step by step:

Step 1: Split the Column

Step 2: Explode the DataFrame

Next, utilize the explode method, which expands the list of values into separate rows, repeating the values from the other columns as necessary.

Step 3: Remove Duplicates

Finally, leverage the drop_duplicates method to ensure that any potentially duplicated rows are removed from the resulting DataFrame.

Putting It All Together

Here’s the complete code to handle this transformation in one efficient line:

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

Explanation of the Code:

.explode('id'): This takes the newly created lists in the id column and expands them so that each element becomes a new row.

.drop_duplicates(): This ensures that any duplicate rows introduced during the splitting process are removed.

.reset_index(drop=True): This resets the index of the DataFrame and ensures that it’s clean and sequential.

Conclusion

Feel free to apply this approach to your own DataFrames, and enjoy the efficiency and power of pandas!