transforming or reshaping a pandas df table into a vector and renaming columns

Question

After some aggregations and other manipulation, I get a pandas df that looks like this:

However, I am trying to get the same info into a different format, that combines all the info in a single row, with columns names like this:

cols = [amin_qcut0, amax_qcut0, mean_qcut0, amin_qcut1, amax_qcut1, mean_qcut1, amin_qcut2, amax_qcut2, mean_qcut2]

and the values corresponding to the values in the table. Is there a way to do this w/o using df.iterrows() ?

Thanks

Answer 1

Try this, there isn't a need to iterate use dataframe reshaping:

#Create dummy dataframe
df = pd.DataFrame(np.arange(9).reshape(-1,3), columns=['amin','amax','mean'])
df.index.name='qcut'

#Use unstack to reshape dataframe with T from transpose
df_out = df.unstack().to_frame().T.sort_index(level=1, axis=1)
#Flatten multiindex column header created from unstack
df_out.columns = [f'{i}_{j}' for i, j in df_out.columns]
#Show output
df_out

Output:

   amax_0  amin_0  mean_0  amax_1  amin_1  mean_1  amax_2  amin_2  mean_2
0       1       0       2       4       3       5       7       6       8