code: import sys
  import pandas as pd
  from sqlalchemy import create_engine
  
  def load_data(messages_filepath, categories_filepath):
      """Load disaster messages and categories from csv files.
      
      Arguments:
          messages_filepath {String} -- disaster message file path
          categories_filepath {String} -- disaster categories file path
      
      Returns:
          pandas dataframe -- merged disaster data
      """    
      messages = pd.read_csv(messages_filepath)
      categories = pd.read_csv(categories_filepath)
      df = messages.merge(categories, on='id')
      return df
  
  
  
  def clean_data(df):
      """Preprocess data
      
      Arguments:
          df {pandas dataframe} -- disaster data
      """    
      # create a dataframe of the 36 individual category columns
      categories = df['categories'].str.split(';', expand=True)
  
      # select the first row of the categories dataframe
      row = categories.iloc[0]
  
      # use this row to extract a list of new column names for categories.
      # one way is to apply a lambda function that takes everything 
      # up to the second to last character of each string with slicing
      category_colnames = [val.split('-')[0] for val in row]
      print(category_colnames)
  
      # rename the columns of `categories`
      categories.columns = category_colnames
  
      for column in categories:
          # set each value to be the last character of the string
          categories[column] = categories[column].str[-1]
          # convert column from string to numeric
          categories[column] = categories[column].astype(int)
  
      # drop the original categories column from `df`
      df.drop(['categories'], axis=1, inplace=True)
      # concatenate the original dataframe with the new `categories` dataframe
      df = pd.concat([df, categories], axis=1)
      # drop duplicates
      df.drop_duplicates(inplace=True)
      return df
  
  
  
  
  def save_data(df, database_filename):
      """Store the data in mysql db.
      
      Arguments:
          df {pandas dataframe} -- disaster data
          database_filename {String} -- path to the db
      """    
      engine = create_engine('sqlite:///{}'.format(database_filename))
      df.to_sql('disaster_response', engine, index=False)  
  
  
  def main():
      if len(sys.argv) == 4:
  
          messages_filepath, categories_filepath, database_filepath = sys.argv[1:]
  
          print('Loading data...\n    MESSAGES: {}\n    CATEGORIES: {}'
                .format(messages_filepath, categories_filepath))
          df = load_data(messages_filepath, categories_filepath)
  
          print('Cleaning data...')
          df = clean_data(df)
          
          print('Saving data...\n    DATABASE: {}'.format(database_filepath))
          save_data(df, database_filepath)
          
          print('Cleaned data saved to database!')
      
      else:
          print('Please provide the filepaths of the messages and categories '\
                'datasets as the first and second argument respectively, as '\
                'well as the filepath of the database to save the cleaned data '\
                'to as the third argument. \n\nExample: python process_data.py '\
                'disaster_messages.csv disaster_categories.csv '\
                'DisasterResponse.db')
  
  
  if __name__ == '__main__':
      main()
apis: pandas.read_csv pandas.concat