def check_na(updated_df):
    """ checks dataframe for NA values and returns a dictionary with columns and their NA counts sorted by counts """
    
    na_cols = list(updated_df.columns)

    # find number of NA values for each column if they exist
    # build dictionary
    na_response = {col: updated_df['{}'.format(col)].isna().value_counts()[1] for col in na_cols \
                   if len(updated_df['{}'.format(col)].isna().value_counts())>1}
    
    if len(na_response) != 0:
        
        # sort dict by greatest NAs count 
        new_nas = dict(sorted(na_response.items(), key = lambda item: item[1]))
        return new_nas
    else: return "No NA values in the dataframe"