Salvador Dali - 1 year ago 94
Python Question

# Average of multiple dataframes with the same columns and indices

I have a few dataframes. Each of them has the same columns and the same indices. For each index I want to average the values in each column (if these would be matrices, I would just sum them up and divide by the number of matrices).

Here is the example.

``````v1 = pd.DataFrame([['ind1', 1, 2, 3], ['ind2', 4, 5, 6]], columns=['id', 'c1', 'c2', 'c3']).set_index('id')
v2 = pd.DataFrame([['ind1', 2, 3, 4], ['ind2', 6, 1, 2]], columns=['id', 'c1', 'c2', 'c3']).set_index('id')
v3 = pd.DataFrame([['ind1', 1, 2, 1], ['ind2', 1, 1, 3]], columns=['id', 'c1', 'c2', 'c3']).set_index('id')
``````

In real situation indices and columns can be in different order.

For this situation the result will be

(the value for ind1, c1 is
`(1 + 1 + 2) / 3`
, for ind2, c2 is
`(1 + 5 + 1) / 3`
and so on).

Currently I do this with loops:

``````dfs = [v1, v2, v3]
cols= ['c1', 'c2', 'c3']

data = []
for ind, _ in dfs[0].iterrows():
vals = [sum(df.loc[ind][col] for df in dfs) / float(len(dfs)) for col in cols]
data.append([ind] + vals)

pd.DataFrame(data, columns=['id'] + cols).set_index('id')
``````

, but this is clearly inefficient for big dataframes with a lot of columns. So how can I achieve this without loops?

You can use `groupby.mean` on the `index` level after concatenating the dataframes:
``````pd.concat([v1, v2, v3]).groupby(level=0).mean()