How to Calculate Grouped Differences in Pandas with Multiple Fields?

Grouped Differences in Pandas with Multiple Fields

In this situation, we aim to calculate the differences in scores for different site and country combinations over time.

To achieve this, we begin by sorting the dataframe according to site, country, and date:

<code class="python">df = df.sort_values(by=['site', 'country', 'date'])</code>

Next, we utilize groupby and diff to calculate the differences within each site and country group:

<code class="python">df['diff'] = df.groupby(['site', 'country'])['score'].diff().fillna(0)</code>

This generates differences within each site and country group and fills any missing values with 0.

Finally, we display the results:

<code class="python">print(df)

Output:

         date    site country  score  diff
8  2018-01-01      fb      es    100   0.0
9  2018-01-02      fb      gb    100   0.0
5  2018-01-01      fb      us     50   0.0
6  2018-01-02      fb      us     55   5.0
7  2018-01-03      fb      us    100  45.0
1  2018-01-01  google      ch     50   0.0
4  2018-01-02  google      ch     10 -40.0
0  2018-01-01  google      us    100   0.0
2  2018-01-02  google      us     70 -30.0
3  2018-01-03  google      us     60 -10.0</code>

Please note that sorting by arbitrary order is not directly supported. For such scenarios, consider storing your order in a collection and making your column categorical. That way, sort_values will align with the provided order.

The above is the detailed content of How to Calculate Grouped Differences in Pandas with Multiple Fields?. For more information, please follow other related articles on the PHP Chinese website!