[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:def encode_label(df, col, freq_limit=0):\n    df[col][pd.isna(df[col])] = \'nan\'\n    tmp = df[col].value_counts()\n    cols = tmp.index.values\n    freq = tmp.values\n    num_cols = (freq>=freq_limit).sum()\n    print("col: {}, num_cat: {}, num_reduced: {}".format(col, len(cols), num_cols))\n\n    col_new = \'{}_le\'.format(col)\n    df_new = pd.DataFrame(np.ones(len(df), np.int16)*(num_cols-1), columns=[col_new])\n    for i, item in enumerate(cols[:num_cols]):\n        df_new[col_new][df[col]==item] = i\n\n    return df_new\n\ndef get_df_le(df, col_index, col_cat):\n    df_new = df[[col_index]]\n    for col in col_cat:\n        df_tmp = encode_label(df, col)\n        df_new = pd.concat([df_new, df_tmp], axis=1)\n    return df_new\n\ndf_traintest7[\'id\'] = np.arange(len(df_traintest7))\ndf_le = get_df_le(df_traintest7, \'id\', [\'Country_Region\', \'Province_State\'])\ndf_traintest8 = pd.merge(df_traintest7, df_le, on=\'id\', how=\'left\')\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]