Plotly.Express + Pandas multi-index column

📊 Plotly Python
1

I am relatively new to Pandas and Plotly. I will pose my question directly with a MWE of what I want to do:

import pandas
import plotly.express as px

df = pandas.DataFrame(
	{
		'n': [1,1,1,1,2,2,2,3,3,3,4,4],
		'x': [0,0,0,0,1,1,1,2,2,2,3,3],
		'y': [1,2,1,1,2,3,3,3,4,3,4,5],
	}
)

mean_df = df.groupby(by=['n']).agg(['mean','std'])

fig = px.scatter(
	mean_df,
	x = ('x','mean'),
	y = ('y','mean'),
	error_y = ('y','std'),
)
fig.show()

This code is not doing what I want. The mean_df dataframe looks like this:

     x              y          
  mean  std      mean       std
n                              
1    0  0.0  1.250000  0.500000
2    1  0.0  2.666667  0.577350
3    2  0.0  3.333333  0.577350
4    3  0.0  4.500000  0.707107

I want to plot x_mean vs y_mean using plotly.express. I am not sure how to do this when there are sub-columns in the data frame…

After some research I have found that mean_df.columns = [' '.join(col).strip() for col in mean_df.columns.values] converts the previous dataframe into

   x mean  x std    y mean     y std
n                                   
1       0    0.0  1.250000  0.500000
2       1    0.0  2.666667  0.577350
3       2    0.0  3.333333  0.577350
4       3    0.0  4.500000  0.707107

so now I can just do

fig = px.scatter(
	mean_df,
	x = 'x mean',
	y = 'y mean',
	error_y = 'y std',
)

to obtain the desired result. However, despite this does exactly what I want to do, it does not feel like the way to go…

How can I tell plotly.express to use the column ('x','mean') for the x axis and ('y','mean') for the y axis with the original mean_df dataframe?