[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:def load_data_frame(file_path, column_separator, nan_text):\nif file_path != '' and column_separator != '':\nif nan_text != '':\ndf = pd.read_csv(file_path, sep=column_separator, na_values=nan_text, keep_default_na=False, dtype='unicode')\nelse:\ndf = pd.read_csv(file_path, sep=column_separator, dtype='unicode')\nreturn df\nelse:\nraise Exception('Mandatory parameters missing')\ndef filter_unwated_rows(df, columns, values):\nif df is not None and len(columns) > 0 and len(values) > 0:\nif len(columns) == len(values):\nfiltered_df = df\nfilter_pairs = zip(columns, values)\nfor filter_pair in filter_pairs:\nfiltered_df = filtered_df[~df[filter_pair[0]].isin(filter_pair[1])]\nreturn filtered_df\nelse:\nraise Exception('Parameters mismatch')\nelse:\nraise Exception('Mandatory parameters missing')\ndef filter_unique_groups(df, groups):\nif df is not None and len(groups) > 0:\nfiltered_df = df\nfor group in groups:\nfiltered_df = filtered_df.drop_duplicates(subset=group)\nreturn filtered_df\nelse:\nraise Exception('Mandatory parameters missing')\ndef filter_count_treshold(df, columns, operators, tresholds):\nif df is not None and len(columns) > 0 and len(tresholds) > 0 and len(operators) > 0:\nif len(columns) == len(tresholds) == len(operators):\nfiltered_df = df\nfilter_pairs = zip(columns, operators, tresholds)\nfor filter_pair in filter_pairs:\nfiltered_df = filtered_df[filter_pair[1](df[filter_pair[0]].map(df[[filter_pair[0]]].stack().value_counts()), filter_pair[2])]\nreturn filtered_df\nelse:\nraise Exception('Parameters mismatch')\nelse:\nraise Exception('Mandatory parameters missing')"}, {'role': 'assistant', 'content': "['load_data']"}]