Pandas Mean and Merge Two DataFrames

I have two dataframes that I need to get the means for plus merge based on their original column names. An example is this:

 df = pd.DataFrame({
'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0],
'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4],
'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5],
'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2]
 })

df2 = pd.DataFrame({
'sepal_length': [0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2],
'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4],
'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5],
'petal_width': [1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5]
})

I get the means like this:

df_one=df.mean(axis=0).to_frame('Mean_One')
df_two=df2.mean(axis=0).to_frame('Mean_Two')

The question is how to merge these two datagrams (df_one and df_two) since there is no column name for the original petal info (e.g., sepal_length, sepal_width, etc.). If there were I could do this

 pd.merge(df_one, df_two, on='?')

Thanks for any help on this.

If I'm understanding correctly you're trying to join the two averaged dataframes to have a column of average measurements for each of the dataframes.

If that's the case then you can join using their indexes:

pd.merge(df_one, df_two, left_index=True, right_index=True)

Output:

                Mean_One  Mean_Two
sepal_length	4.9125	  0.2375
sepal_width	    3.3875	  3.3875
petal_length	1.4500	  1.4500
petal_width	    0.2375	  1.4500