[{'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:sex = df_train[\'Sex\'].values\nred_female = []\nred_male = []\nblue_female = []\nblue_male = []\nfor i in range(len(survived)):\nif survived[i] == 1:\nif sex[i] == \'female\':\nblue_female.append(1)\nelse:\nblue_male.append(1)\nelse:\nif sex[i] == \'female\':\nred_female.append(0)\nelse:\nred_male.append(0)\nn_groups = 2\nrm = len(red_male)\nbm = len(blue_male)\nrf = len(red_female)\nbf = len(blue_female)\nmeans_red = (rm, rf)\nmeans_blue = (bm, bf)\nfig, ax = plt.subplots()\nax.margins(0.04, 0.00)\nindex = np.arange(n_groups)\nbar_width = 0.35\nopacity = .8\nerror_config = {\'ecolor\': \'0.3\'}\nrects1 = plt.bar(index, means_red, bar_width,\nalpha=opacity,\ncolor=\'r\',\nlabel=\'Died\')\nrects2 = plt.bar(index + bar_width, means_blue, bar_width,\nalpha=opacity,\ncolor=\'b\',\nlabel=\'Survived\')\nplt.xlabel(\'Gender\')\nplt.ylabel(\'Count\')\nplt.title(\'Death Count by Gender\')\nplt.xticks(index + bar_width, (\'Male\', \'Female\'))\nplt.legend()\nplt.tight_layout()\nplt.show()\nprint("Values", \'\\n\', "Male:", rm, bm, \'\\n\', \'Female:\', rf, bf, \'\\n\')\nprint("Probability of survival", \'\\n\', \'Male:\', \'%.2f\' % float(bm/(rm + bm)), \'\\n\', \'Female:\', \'%.2f\' % float(bf/(rf+bf)))'}, {'role': 'assistant', 'content': 'data_exploration'}]