[{'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:f, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, figsize=(15,25))\nfig1 = ax1.bar(range(2), [perc_m[0],perc_w[0]], color=\'orange\')\nfig2 = ax1.bar(range(2), [perc_m[1],perc_w[1]], bottom=[perc_m[0],perc_w[0]])\nax1.set_xlabel("Sex")\nax1.set_ylabel("Proportion")\nax1.set_title("Sex-Income Relationship")\nplt.sca(ax1)\nplt.xticks([0, 1], [\'male\', \'female\'])\nplt.legend([fig1, fig2], [\'<=50K\',\'>50K\'], loc=\'lower right\')\nax2.bar(ages.age, ages[\'perc_<=50K\'], color=\'orange\')\nax2.bar(ages.age, ages[\'perc_>50K\'] , bottom=ages[\'perc_<=50K\'])\nax2.set_ylabel("Proportion")\nax2.set_xlabel("Age")\nax2.set_title("Age-Income Relationship")\nplt.sca(ax2)\nplt.legend([fig1, fig2], [\'<=50K\',\'>50K\'], loc=\'lower right\')\nax3.bar(educ.education, educ[\'perc_<=50K\'], color=\'orange\')\nax3.bar(educ.education, educ[\'perc_>50K\'] , bottom=educ[\'perc_<=50K\'])\nax3.set_ylabel("Proportion")\nax3.set_xlabel("Education Level")\nax3.set_title("Education-Income Relationship")\nplt.sca(ax3)\nplt.legend([fig1, fig2], [\'<=50K\',\'>50K\'], loc=\'lower right\')\nax4.bar(range(7), marital[\'perc_<=50K\'], color=\'orange\')\nax4.bar(range(7), marital[\'perc_>50K\'] , bottom=marital[\'perc_<=50K\'])\nax4.set_ylabel("Proportion")\nax4.set_xlabel("Marital Status")\nax4.set_title("Marital Status-Income Relationship")\nplt.sca(ax4)\nplt.xticks([0, 1, 2, 3, 4, 5,6 ], list(data.marital_status.unique()))\nplt.legend([fig1, fig2], [\'<=50K\',\'>50K\'], loc=\'lower right\')\nax5.bar(hpw.hours_per_week, hpw[\'perc_<=50K\'], color=\'orange\')\nax5.bar(hpw.hours_per_week, hpw[\'perc_>50K\'] , bottom=hpw[\'perc_<=50K\'])\nax5.set_ylabel("Proportion")\nax5.set_xlabel("Hours per Week")\nax5.set_title("Hours per Week-Income Relationship")\nplt.sca(ax5)\nplt.legend([fig1, fig2], [\'<=50K\',\'>50K\'], loc=\'lower right\')'}, {'role': 'assistant', 'content': 'data_exploration'}]