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# pandas.melt

`pandas.``melt`(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)

  “Unpivots” a DataFrame from wide format to long format, optionally leaving id variables set

  Parameters : | frame : DataFrame id_vars : tuple, list, or ndarray value_vars : tuple, list, or ndarray var_name : scalar, if None uses frame.column.name or ‘variable’ value_name : scalar, default ‘value’ col_level : scalar, if columns are a MultiIndex then use this level to melt

  Examples

      >>> import pandas as pd
      >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
      ...                    'B': {0: 1, 1: 3, 2: 5},
      ...                    'C': {0: 2, 1: 4, 2: 6}})

      >>> df
         A  B  C
      0  a  1  2
      1  b  3  4
      2  c  5  6

      >>> melt(df, id_vars=['A'], value_vars=['B'])
         A variable  value
      0  a        B      1
      1  b        B      3
      2  c        B      5

      >>> melt(df, id_vars=['A'], value_vars=['B'],
      ... var_name='myVarname', value_name='myValname')
         A myVarname  myValname
      0  a         B          1
      1  b         B          3
      2  c         B          5

      >>> df.columns = [list('ABC'), list('DEF')]

      >>> melt(df, col_level=0, id_vars=['A'], value_vars=['B'])
         A variable  value
      0  a        B      1
      1  b        B      3
      2  c        B      5

      >>> melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')])
        (A, D) variable_0 variable_1  value
      0      a          B          E      1
      1      b          B          E      3
      2      c          B          E      5

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