How to Calculate Average Values with Multiple Groupings in Pandas?

Group-by Aggregation with Multiple Groupings and Average

In Pandas, performing aggregations on data grouped by multiple levels is a common operation. 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 

A common task is to calculate the average of a given column, such as "time," per group defined by multiple variables, such as "cluster" and "org."

Solution 1: Mean on Cluster Groupings Only

To compute the mean of "time" grouped by "cluster" only, you can use the following code:

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

Result:

              time
cluster
1        12.333333
2        54.000000
3         6.000000

Solution 2: Mean on a Combination of Groupings

If you want to calculate the mean of "time" for each combination of "cluster" and "org," you can use:

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

Result:

               time
cluster org
1       a    438886
        c        23
2       d      9874
        h        34
3       w         6

Solution 3: Nested Mean on Groupings

To perform a nested mean, first averaging on the combination of "cluster" and "org" and then averaging on "cluster" groups, use:

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

Result:

cluster  mean(time)
1          15 #=((8 + 6) / 2 + 23) / 2
2          54 #=(74 + 34) / 2
3           6

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