Скачать или смотреть Mastering Pandas: How to Merge Multiple Rows Based on Two Columns

Learn how to efficiently merge multiple rows in a Pandas DataFrame based on two columns with this detailed step-by-step guide.
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Merging Multiple Rows Based on Two Columns in Pandas

When working with Pandas, data often comes in row formats that require specific modifications for analysis. One common task is merging rows based on certain conditions involving multiple columns. In this post, we will explore how to tackle the problem of merging rows where the end time of one row matches the start time of another row based on two columns.

Understanding the Problem

Consider a DataFrame with the following structure:

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In this scenario, our objective is to merge the rows so that if the End time of one row is equal to the Start time of the subsequent row, they should be combined into a single interval. The desired output would be:

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The Solution Approach

To solve this problem efficiently, we can follow a systematic approach using the groupby function in Pandas. Here’s a step-by-step breakdown of how this can be achieved:

Step 1: Sort the DataFrame

First, we need to ensure that our DataFrame is sorted by the Start time. This step is crucial as it prepares the data for merging.

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Step 2: Use GroupBy and Apply

Next, we will utilize the groupby function along with apply to group based on unique combinations of Name1 and Name2. Within each group, we'll look for intervals that can be merged.

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Step 3: Explanation of the Logic

Cumulative Maximum End: The expression g.End.cummax().shift() computes the cumulative maximum of the End values shifted by one position. This is used to compare the end of one interval to the start of the next interval.

Cumulative Sum for Grouping: The cumsum().agg() is creating a group number for each interval in the DataFrame as we parse through sorted data. When the cumulative maximum of End is less than the Start of the next row, it indicates the start of a new group.

Aggregation of Start and End: Finally, we aggregate the data using the min and max functions to determine the start and end points of the merged intervals.

Conclusion

Using the above method, you can efficiently merge rows in a Pandas DataFrame based on conditions across multiple columns. This approach minimizes the need for explicit loops and leverages the powerful group operations in Pandas.

Whether you're cleaning up time-series data or preparing datasets for analysis, mastering techniques like this one ensures your data is both accurate and well-structured. Happy coding!