[{'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:# Load countries data file\nworld_population = pd.read_csv("/kaggle/input/population-by-country-2020/population_by_country_2020.csv")\n\n# Select desired columns and rename some of them\nworld_population = world_population[[\'Country (or dependency)\', \'Population (2020)\', \'Density (P/Km)\', \'Land Area (Km)\', \'Med. Age\', \'Urban Pop %\']]\nworld_population.columns = [\'Country (or dependency)\', \'Population (2020)\', \'Density\', \'Land Area\', \'Med Age\', \'Urban Pop\']\n\n# Replace United States by US\nworld_population.loc[world_population[\'Country (or dependency)\']==\'United States\', \'Country (or dependency)\'] = \'US\'\n\n# Remove the % character from Urban Pop values\nworld_population[\'Urban Pop\'] = world_population[\'Urban Pop\'].str.rstrip(\'%\')\n\n# Replace Urban Pop and Med Age "N.A" by their respective modes, then transform to int\nworld_population.loc[world_population[\'Urban Pop\']==\'N.A.\', \'Urban Pop\'] = int(world_population.loc[world_population[\'Urban Pop\']!=\'N.A.\', \'Urban Pop\'].mode()[0])\nworld_population[\'Urban Pop\'] = world_population[\'Urban Pop\'].astype(\'int16\')\nworld_population.loc[world_population[\'Med Age\']==\'N.A.\', \'Med Age\'] = int(world_population.loc[world_population[\'Med Age\']!=\'N.A.\', \'Med Age\'].mode()[0])\nworld_population[\'Med Age\'] = world_population[\'Med Age\'].astype(\'int16\')\n\nprint("Cleaned country details dataset")\ndisplay(world_population)\n\n# Now join the dataset to our previous DataFrame and clean missings (not match in left join)- label encode cities\nprint("Joined dataset")\nall_data = all_data.merge(world_population, left_on=\'Country_Region\', right_on=\'Country (or dependency)\', how=\'left\')\nall_data[[\'Population (2020)\', \'Density\', \'Land Area\', \'Med Age\', \'Urban Pop\']] = all_data[[\'Population (2020)\', \'Density\', \'Land Area\', \'Med Age\', \'Urban Pop\']].fillna(0)\ndisplay(all_data)\n\nprint("Encoded dataset")\n# Label encode countries and provinces. Save dictionary for exploration purposes\nall_data.drop(\'Country (or dependency)\', inplace=True, axis=1)\nall_data[\'Country_Region\'] = le.fit_transform(all_data[\'Country_Region\'])\nnumber_c = all_data[\'Country_Region\']\ncountries = le.inverse_transform(all_data[\'Country_Region\'])\ncountry_dict = dict(zip(countries, number_c)) \nall_data[\'Province_State\'] = le.fit_transform(all_data[\'Province_State\'])\nnumber_p = all_data[\'Province_State\']\nprovince = le.inverse_transform(all_data[\'Province_State\'])\nprovince_dict = dict(zip(province, number_p)) \ndisplay(all_data)\''}, {'role': 'assistant', 'content': 'load_data'}]