Simultaneously melt multiple columns in Python Pandas

Question

wondering if pd.melt supports melting multiple columns. I have the below examples trying to have the value_vars as list of lists but i am getting an error:

Val

Answer 1

Use double melt for each catogories and then concat, but because duplicted values add cumcount for unique triples in MultiIndex:

df1 = df.melt(id_vars=['City', 'State'], 
              value_vars=['Mango', 'Orange', 'Watermelon'],
              var_name='Fruit', value_name='Pounds')
df2 = df.melt(id_vars=['City', 'State'], 
              value_vars=['Gin', 'Vodka'], 
              var_name='Drink', value_name='Ounces')

df1 = df1.set_index(['City', 'State', df1.groupby(['City', 'State']).cumcount()])
df2 = df2.set_index(['City', 'State', df2.groupby(['City', 'State']).cumcount()])


df3 = (pd.concat([df1, df2],axis=1)
         .sort_index(level=2)
         .reset_index(level=2, drop=True)
         .reset_index())
print (df3)
      City    State       Fruit  Pounds  Drink  Ounces
0   Austin    Texas       Mango      10    Gin   200.0
1   Hoover  Alabama       Mango      90    Gin    34.0
2  Houston    Texas       Mango       4    Gin    16.0
3   Austin    Texas      Orange       8  Vodka    33.0
4   Hoover  Alabama      Orange      14  Vodka    18.0
5  Houston    Texas      Orange      10  Vodka    20.0
6   Austin    Texas  Watermelon      99    NaN     NaN
7   Hoover  Alabama  Watermelon      43    NaN     NaN
8  Houston    Texas  Watermelon      40    NaN     NaN