I encountered this issue when I tried to clean/fix the columns of my pandas DataFrame after I pivoted with more than one values.

Let me quickly demonstrate what I mean by that.

Let’s create a pandas DataFrame, where each user might have multiple rows depending on the devices they use. And we have two metrics, time_spent and revenue

df = pd.DataFrame({'first_name': ['Alice', 'Alice', 'Bill', 'Bill', 'Steve',
                   'device': ['ios', 'web', 'ios', 'web', 'ios', 'web'],
                   'time_spent': [90,20,65,20,80,30],
                   'revenue': [220,55,160,70,120,80]})
  first_name device time_spent revenue
0 Alice ios 90 220
1 Alice web 20 55
2 Bill ios 65 160
3 Bill web 20 70
4 Steve ios 80 120
5 Steve web 30 80

Here’s how I summarise this table and get a row per each user with Pandas’ pivot function

pivot_df = df.pivot(index='first_name', columns='device', values=['time_spent','revenue'])
  time_spent   revenue  
device ios web ios web
Alice 90 20 220 55
Bill 65 20 160 70
Steve 80 30 120 80

So we get a DataFrame with MultiIndex. And it’s quite challenging to deal with especially if you are going to merge this table with some other tables.

MultiIndex([('time_spent', 'ios'),
            ('time_spent', 'web'),
            (   'revenue', 'ios'),
            (   'revenue', 'web')],
           names=[None, 'device'])

So to handle this issue, you need to remove the MultiIndex in your pandas DataFrame.

The easiest way to do is just overwriting the columns by passing a new list of strings with

df.columns = ['t_ios','t_web','r_ios','r_web']

But obviously it’s a super manual way and it’s not scalable and it’s error prone (if you happen to change the metric names or devices etc.)

But we can achieve this by using zip and droplevel functions in python!

Let’s see how does droplevel function work in our case:

>> Index(['ios', 'web', 'ios', 'web'], dtype='object', name='device')

>> Index(['time_spent', 'time_spent', 'revenue', 'revenue'], dtype='object')

So if we merge these two lists element wise and build a string array, we can easily assign it to our DataFrame’s columns.

# initializing lists   
metrics = pivot_df.columns.droplevel(1)
devices = pivot_df.columns.droplevel(0)
# using list comprehension + zip() 
# interlist element concatenation 
new_columns = [metric + '_' + device for metric, device in zip(metrics, devices)]
>> ['time_spent_ios', 'time_spent_web', 'revenue_ios', 'revenue_web']

We then assign our new variable to the DataFrame’s columns pivot_df.columns = new_columns and here’s what we get 👌

  time_spent_ios time_spent_web revenue_ios revenue_web
Alice 90 20 220 55
Bill 65 20 160 70
Steve 80 30 120 80