[{'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 impute(data):\naverage_age   = data['custAge'].mean()\nstd_age      = data['custAge'].std()\ncount_nan_age = data['custAge'].isnull().sum()\nrand_1 = np.random.randint(average_age - std_age, average_age + std_age, size = count_nan_age)\ndata['custAge'][np.isnan(data['custAge'])] = rand_1\ndata['custAge'] = data['custAge'].astype(int)\ndata['schooling'].fillna('unknown',inplace=True)\ndata['day_of_week'].fillna('unknown',inplace=True)\nreturn data\nX = test\nX = impute(X)\nX_num = X[X.columns.drop(cat[:-1])]\nX_cat = X[cat[:-1]]\nX_scaled = DataFrame(StandardScaler().fit_transform(X_num), columns=X_num.columns)\nX_cat = pd.get_dummies(X_cat)\nX_clean = pd.concat([X_cat,X_scaled],axis=1)\npca = PCA()\ndata_scaled_pca = DataFrame(pca.fit_transform(X_clean), columns=X_clean.columns)\nv = pca.explained_variance_ratio_\nvc = v.cumsum()\ndata_scaled_pca = data_scaled_pca.ix[:, :27]"}, {'role': 'assistant', 'content': 'data_preprocessing'}]