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Pandas Groupby Multiple Fields for Time-Based Differences
In the realm of data analysis, comparing changes over time is a crucial task. Pandas, a versatile Python library, offers robust capabilities for handling such operations. When dealing with data organized by multiple categorical fields and time, the groupby.diff() method proves invaluable.
Consider a DataFrame like the one provided, where each site has varying scores across countries and dates. The goal is to compute the 1/3/5-day differential in scores for each site/country combination.
Problem Resolution
To achieve this, we utilize the following steps:
<code class="python">df = df.sort_values(by=['site', 'country', 'date']) df['diff'] = df.groupby(['site', 'country'])['score'].diff().fillna(0)</code>
Output:
The result is a DataFrame that showcases the computed score differences:
date | site | country | score | diff |
---|---|---|---|---|
2018-01-01 | fb | es | 100 | 0.0 |
2018-01-02 | fb | gb | 100 | 0.0 |
2018-01-01 | fb | us | 50 | 0.0 |
2018-01-02 | fb | us | 55 | 5.0 |
2018-01-03 | fb | us | 100 | 45.0 |
2018-01-01 | ch | 50 | 0.0 | |
2018-01-02 | ch | 10 | -40.0 | |
2018-01-01 | us | 100 | 0.0 | |
2018-01-02 | us | 70 | -30.0 | |
2018-01-03 | us | 60 | -10.0 |
Advanced Sorting
In cases where an arbitrary order is required, such as prioritizing "google" over "fb," a categorical column can be created and assigned as the sorting parameter. This ensures that the specified order is maintained.
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