[{'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:# Function to calculate missing values by column (By DSA)\ndef missing_values_table(df):\n    # Total missing values\n    mis_val = df.isnull().sum()\n\n    # Percentage of missing values\n    mis_val_percent = 100 * df.isnull().sum() / len(df)\n\n    # Make a table with the results\n    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n\n    # Rename the columns\n    mis_val_table_ren_columns = mis_val_table.rename(\n    columns = {0 : \'Missing Values\', 1 : \'% of Total Values\'})\n\n    # Sort the table by percentage of missing descending\n    mis_val_table_ren_columns = mis_val_table_ren_columns[\n        mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n    \'% of Total Values\', ascending=False).round(1)\n\n    # Print some summary information\n    print ("Your selected dataframe has " + str(df.shape[1]) + " columns.\\\n"      \n        "There are " + str(mis_val_table_ren_columns.shape[0]) +\n          " columns that have missing values.")\n\n    # Return the dataframe with missing information\n    return mis_val_table_ren_columns\n\nmissing_values_table(df_full)\''}, {'role': 'assistant', 'content': "['data_exploration']"}]