How to Calculate Average Values Within Multiple Groups in Pandas?

Group By Multiple Columns and Calculate Average

In Pandas, you can perform multi-level grouping and aggregation to calculate complex statistics. One common task is to calculate the average of a column within groups defined by multiple other columns.

Consider the following DataFrame:

cluster  org      time
1      a       8
1      a       6
2      h       34
1      c       23
2      d       74
3      w       6 

To calculate the average of time per org within each cluster, you can group the DataFrame by both cluster and org:

df.groupby(['cluster', 'org'], as_index=False).mean()

This will produce a DataFrame grouped by cluster and org, with the average of time calculated for each group:

  cluster org     time
0       1   a  12.333333
1       1   c   23.0
2       2   h   34.0
3       2   d   74.0
4       3   w    6.0

If you only want the mean of time within each cluster, you can group only by cluster:

df.groupby('cluster').mean()

This will produce a DataFrame with the average of time calculated for each cluster:

  cluster  time
0       1   12.333333
1       2   54.0
2       3    6.0

Alternatively, you can use the groupby method on the multi-column combination ['cluster', 'org'] and then calculate the mean of time:

df.groupby(['cluster', 'org']).mean()['time']

This will produce a Series with the average of time calculated for each combination of cluster and org.

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