This function is used to convert a categorical variable into an indicator DataFrame, which is essentially a truth table of possible values of the categorical variable. An example of this is the following command:

In [408]: melted=melt(USIndexDataDF[:2], id_vars=['TradingDate'], var_name='Index Name', value_name='Index Value')  

melted

Out[408]: TradingDate   Index Name  Index Value
    0     2014/01/30   Nasdaq     4123.13
    1     2014/01/31   Nasdaq     4103.88
    2     2014/01/30   S&P 500     1794.19
    3     2014/01/31   S&P 500     1782.59
    4     2014/01/30   Russell 2000   1139.36
    5     2014/01/31   Russell 2000   1130.88
    6     2014/01/30   DJIA        15848.61
    7     2014/01/31   DJIA        15698.85
    
    In [413]: pd.get_dummies(melted['Index Name'])

    Out[413]:         DJIA  Nasdaq  Russell 2000  S&P 500
            0   0   1      0            0
            1   0   1      0            0
            2   0   0      0            1
            3   0   0      0            1
            4   0   0      1            0
            5   0   0      1            0
            6   1   0      0            0
            7   1   0      0            0
  

The source of the preceding data is http://vincentarelbundock.github.io/Rdatasets/csv/datasets/PlantGrowth.csv.