class_docstr: A flow to help you build your favorite movie playlist.
  
  The flow performs the following steps:
  1) Ingests a CSV file containing metadata about movies.
  2) Loads two of the columns from the CSV into python lists.
  3) In parallel branches:
     - A) Filters movies by the genre parameter.
     - B) Choose a random movie from a different genre.
  4) Displays the top entries from the playlist.
docstr: Parse the CSV file and load the values into a dictionary of lists.
code: def start(self):
      """
      Parse the CSV file and load the values into a dictionary of lists.
  
      """
      # For this example, we only need the movie title and the genres.
      columns = ["movie_title", "genres"]
  
      # Create a simple data frame as a dictionary of lists.
      self.dataframe = dict((column, list()) for column in columns)
  
      # Parse the CSV header.
      lines = self.movie_data.split("\n")
      header = lines[0].split(",")
      idx = {column: header.index(column) for column in columns}
  
      # Populate our dataframe from the lines of the CSV file.
      for line in lines[1:]:
          if not line:
              continue
  
          fields = line.rsplit(",", 4)
          for column in columns:
              self.dataframe[column].append(fields[idx[column]])
  
      # Compute genre-specific movies and a bonus movie in parallel.
      self.next(self.bonus_movie, self.genre_movies)
  
file_path: metaflow/metaflow/tutorials/01-playlist/playlist.py
imports: from metaflow import FlowSpec, step, IncludeFile, Parameter
label_desc: Use image node_id 1 for calling the PlayListFlow obj's underlying member method code with example usage: obj.start() without return types