[{'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:perc_m = data.groupby('sex')['income'].value_counts()['Male']/data.groupby('sex')['income'].value_counts()['Male'].sum()\nperc_w = data.groupby('sex')['income'].value_counts()['Female']/data.groupby('sex')['income'].value_counts()['Female'].sum()\nages = pd.DataFrame(data.groupby('age')['income'].value_counts().unstack())\nages = pd.DataFrame({'age':ages.index, '<=50K':ages['<=50K'].values,'>50K':ages['>50K'].values})\nages = ages.fillna(0)\nages['perc_<=50K'] = ages['<=50K']/(ages['<=50K'] + ages['>50K'])\nages['perc_>50K'] = ages['>50K']/(ages['<=50K'] + ages['>50K'])\neduc = pd.DataFrame(data.groupby('education_num')['income'].value_counts().unstack())\neduc = pd.DataFrame({'education':educ.index, '<=50K':educ['<=50K'].values,'>50K':educ['>50K'].values})\neduc = educ.fillna(0)\neduc['perc_<=50K'] = educ['<=50K']/(educ['<=50K'] + educ['>50K'])\neduc['perc_>50K'] = educ['>50K']/(educ['<=50K'] + educ['>50K'])\nmarital = pd.DataFrame(data.groupby('marital_status')['income'].value_counts().unstack())\nmarital = pd.DataFrame({'marital_status':marital.index, '<=50K':marital['<=50K'].values,'>50K':marital['>50K'].values})\nmarital = marital.fillna(0)\nmarital['perc_<=50K'] = marital['<=50K']/(marital['<=50K'] + marital['>50K'])\nmarital['perc_>50K'] = marital['>50K']/(marital['<=50K'] + marital['>50K'])\nhpw = pd.DataFrame(data.groupby('hours_per_week')['income'].value_counts().unstack())\nhpw = pd.DataFrame({'hours_per_week':hpw.index, '<=50K':hpw['<=50K'].values,'>50K':hpw['>50K'].values})\nhpw = hpw.fillna(0)\nhpw['perc_<=50K'] = hpw['<=50K']/(hpw['<=50K'] + hpw['>50K'])\nhpw['perc_>50K'] = hpw['>50K']/(hpw['<=50K'] + hpw['>50K'])"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]