[{'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:frames = [batting, pitching]\noffense_defense_df = pd.concat(frames)\noffense_defense_df.drop([\'IBB\', \'HBP\', \'SH\', \'SF\', \'GIDP\', \'lgID\', \'stint\', \'teamID\', \'playerID\', \'2B\', \'BAOpp\', \'BB\', \'BFP\', \'BK\', \'BatAvg\', \'ERA\', \'G\', \'RBI\', \'SHO\', \'SO\', \'WP\'], axis=1, inplace=True)\noffense_defense_grouped_year = offense_defense_df.groupby(by=\'yearID\')\noffense_defense_avg = offense_defense_grouped_year.mean()\ncorrelation = offense_defense_avg.corr()\nc = correlation[correlation.abs() > .8]\ncmap = sns.diverging_palette(220, 10, as_cmap=True)\nsns.set(style="darkgrid") # one of the many styles to plot using\nf, ax = plt.subplots(figsize=(9, 9))\nsns.heatmap(c, cmap=cmap, annot=True)'}, {'role': 'assistant', 'content': 'data_exploration'}]