def get_means_of_booleans(data, boolean_cols):
    """ Put boolean_cols of the data in a uniform format and
        compute the mean per customer.

    Parameters
    ----------
    data: The DataFrame.
    boolean_cols: array-like of column names with boolean values to
        process.

    Returns
    -------
    DataFrame with a row for each customer and columns presenting
    the percentage True for every column in boolean_cols.
    """
    # Some values are given in True/False, some in 1/NaN, etc.
    # Here we unify this to 1 and 0.
    data[boolean_cols] *= 1
    data[boolean_cols] = data[boolean_cols].fillna(0)
    # Calculate the percentage of 1s for each fullVisitorId
    data_bool = data.groupby(['fullVisitorId'])[boolean_cols].mean()
    data_bool = data_bool.add_suffix('_avg')
    return data_bool