I have a dataframe like this:
cluster org time
1 a 8
1 a 6
2 h 34
1 c 23
2 d 74
3 w 6
I would like to calculate the average of time per org per cluster.
Expected result:
cluster mean(time)
1 15 #=((8 + 6) / 2 + 23) / 2
2 54 #=(74 + 34) / 2
3 6
I do not know how to do it in Pandas, can anybody help?
If you want to first take mean on the combination of ['cluster', 'org'] and then take mean on cluster groups, you can use:
In [59]: (df.groupby(['cluster', 'org'], as_index=False).mean()
.groupby('cluster')['time'].mean())
Out[59]:
cluster
1 15
2 54
3 6
Name: time, dtype: int64
If you want the mean of cluster groups only, then you can use:
In [58]: df.groupby(['cluster']).mean()
Out[58]:
time
cluster
1 12.333333
2 54.000000
3 6.000000
You can also use groupby on ['cluster', 'org'] and then use mean():
In [57]: df.groupby(['cluster', 'org']).mean()
Out[57]:
time
cluster org
1 a 438886
c 23
2 d 9874
h 34
3 w 6
I would simply do this, which literally follows what your desired logic was:
df.groupby(['org']).mean().groupby(['cluster']).mean()
Another possible solution is to reshape the dataframe using pivot_table() then take mean(). Note that it's necessary to pass aggfunc='mean' (this averages time by cluster and org).
df.pivot_table(index='org', columns='cluster', values='time', aggfunc='mean').mean()
Another possibility is to use level parameter of mean() after the first groupby() to aggregate:
df.groupby(['cluster', 'org']).mean().mean(level='cluster')
