title: Simultaneously melt multiple columns in Python Pandas
body: <p>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:</p>
  
  <p><code>ValueError: Location based indexing can only have [labels (MUST BE IN THE INDEX), slices of labels (BOTH endpoints included! Can be slices of integers if the index is integers), listlike of labels, boolean] types</code></p>
  
  <p>Using pandas 0.23.1.</p>
  
  <pre><code>df = pd.DataFrame({'City': ['Houston', 'Austin', 'Hoover'],
                     'State': ['Texas', 'Texas', 'Alabama'],
                     'Name':['Aria', 'Penelope', 'Niko'],
                     'Mango':[4, 10, 90],
                     'Orange': [10, 8, 14], 
                     'Watermelon':[40, 99, 43],
                     'Gin':[16, 200, 34],
                     'Vodka':[20, 33, 18]},
                   columns=['City', 'State', 'Name', 'Mango', 'Orange', 'Watermelon', 'Gin', 'Vodka'])
  </code></pre>
  
  <p>Desired output:</p>
  
  <pre><code>      City    State       Fruit  Pounds  Drink  Ounces
  0  Houston    Texas       Mango       4    Gin    16.0
  1   Austin    Texas       Mango      10    Gin   200.0
  2   Hoover  Alabama       Mango      90    Gin    34.0
  3  Houston    Texas      Orange      10  Vodka    20.0
  4   Austin    Texas      Orange       8  Vodka    33.0
  5   Hoover  Alabama      Orange      14  Vodka    18.0
  6  Houston    Texas  Watermelon      40    nan     NaN
  7   Austin    Texas  Watermelon      99    nan     NaN
  8   Hoover  Alabama  Watermelon      43    nan     NaN
  </code></pre>
  
  <p>I tried that and i get the aforementioned error:</p>
  
  <pre><code>df.melt(id_vars=['City', 'State'], 
          value_vars=[['Mango', 'Orange', 'Watermelon'], ['Gin', 'Vodka']],var_name=['Fruit', 'Drink'], 
          value_name=['Pounds', 'Ounces'])
  </code></pre>