Modifies columns in place if the specified predicate is true.
    Args:
        df (pandas.DataFrame): data passed in through the pipe.
        predicate: a function applied to columns that returns a boolean value
        fun: a function that will be applied to columns where predicate returns True

    Example:
        diamonds >> mutate_if(lambda col: min(col) < 1 and mean(col) < 4, lambda row: 2 * row) >> head(3)
           carat      cut color clarity  depth  table  price     x     y     z
        0   0.46    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  4.86
        1   0.42  Premium     E     SI1   59.8   61.0    326  3.89  3.84  4.62
        2   0.46     Good     E     VS1   56.9   65.0    327  4.05  4.07  4.62
        (columns 'carat' and 'z', both having a min < 1 and mean < 4, are doubled, while the
        other rows remain as they were)

def mutate_if(df, predicate, fun):
    """
    Modifies columns in place if the specified predicate is true.
    Args:
        df (pandas.DataFrame): data passed in through the pipe.
        predicate: a function applied to columns that returns a boolean value
        fun: a function that will be applied to columns where predicate returns True

    Example:
        diamonds >> mutate_if(lambda col: min(col) < 1 and mean(col) < 4, lambda row: 2 * row) >> head(3)
           carat      cut color clarity  depth  table  price     x     y     z
        0   0.46    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  4.86
        1   0.42  Premium     E     SI1   59.8   61.0    326  3.89  3.84  4.62
        2   0.46     Good     E     VS1   56.9   65.0    327  4.05  4.07  4.62
        (columns 'carat' and 'z', both having a min < 1 and mean < 4, are doubled, while the
        other rows remain as they were)
    """
    cols = list()
    for col in df:
        try:
            if predicate(df[col]):
                cols.append(col)
        except:
            pass
    df[cols] = df[cols].apply(fun)
    return df