Скачать или смотреть How to Generate a sequence of Previous and Next Rows in Pandas DataFrame

Learn how to create a new column in your Pandas DataFrame that includes a `sequence` of previous, current, and next rows using simple steps and code examples.
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Understanding the Problem: Building a Sequence in Pandas

If you're working with data in Python using the Pandas library, you may encounter situations where you need to analyze a row along with its surrounding data. Perhaps you have a DataFrame with columns of interest, and you want to create a new column that contains a sequence of values from both previous and next rows.

For example, imagine we have the following DataFrame:

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

We want to add a new column that captures two rows before the current one, the current row itself, and two rows after the current one. The expected output would look like this:

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

The Challenge

The difficulty arises from the need to handle the boundaries of the DataFrame correctly. Specifically:

The first two and last two rows have fewer preceding or following rows, which should be represented by (nan, nan) tuples in the sequence.

Using the .shift() method inside df.apply() doesn't work as intended since .apply() treats each element as an atomic value, not as part of the DataFrame.

The Solution: Creating the Sequence Column

To solve this problem, we can generate the sequences efficiently using list comprehension and the zip function in the following way:

Create a List of Tuples: First, we'll create a list of tuples from the x and y columns of the DataFrame.

Add Padding: We'll prepend and append (nan, nan) tuples to ensure that we have room for the sequences.

Construct the Sequence: Use list comprehension to build the sequence of five elements for each row.

Here is the step-by-step code to implement this solution:

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

Output

After executing this code, the DataFrame df will be updated with a new column seq, showing the desired sequences for each row:

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

Conclusion

This method is efficient and straightforward, giving you a powerful way to analyze surrounding data within a Pandas DataFrame. By encapsulating rows into sequences, you enhance your ability to carry out further analyses or machine learning tasks based on the temporal flow of the data. Happy coding!