Python: Merging/joining two dataframes

Question

I'm trying to merge/join two dataframes, each with three keys (Age, Gender and Signed_In). Both dataframes have the same parent and were created by groupby, but have unique value columns.

It seems like the merge/join should be painless given the unique combined keys are shared across both dataframes. Thinking there must be some simple error with my attempt at 'merge' and 'join' but can't for the life of me resolve it.

times = pd.read_csv('nytimes.csv')

# Produces times_mean table consisting of two value columns, avg_impressions and avg_clicks
times_mean = times.groupby(['Age','Gender','Signed_In']).mean()
times_mean.columns = ['avg_impressions', 'avg_clicks']

# Produces times_max table consisting of two value columns, max_impressions and max_clicks
times_max = times.groupby(['Age','Gender','Signed_In']).max()
times_max.columns = ['max_impressions', 'max_clicks']

# Following intended to produce combined table with four value columns
times_join = times_mean.join(times_max, on = ['Age', 'Gender', 'Signed_In'])
times_join2 = pd.merge(times_mean, times_max, on=['Age', 'Gender', 'Signed_In'])

Answer 1

You don't need to the on kwarg when joining on equivalently structured MultiIndex

Here's an example demonstrating this:

import numpy as np
import pandas

a = np.random.normal(size=10)
b = a + 10
index = pandas.MultiIndex.from_product([['A', 'B'], list('abcde')])

df_a = pandas.DataFrame(a, index=index, columns=['colA'])
df_b = pandas.DataFrame(b, index=index, columns=['colB'])

df_a.join(df_b)

Which gives me:

    colA       colB
A a -1.525376   8.474624
  b  0.778333  10.778333
  c  1.153172  11.153172
  d  0.966560  10.966560
  e  0.089765  10.089765
B a  0.717717  10.717717
  b  0.305545  10.305545
  c  0.123548  10.123548
  d -1.018660   8.981340
  e -0.635103   9.364897