text: import os
  import glob
  import psycopg2
  from psycopg2.extras import execute_batch
  import pandas as pd
  from sql_queries import *
  
  
  def process_song_file(cur, filepath):
      """Parse and insert relevant song data from song files into the database.
  
      Arguments:
      cur -- database cursor used to insert data
      filepath -- path to JSON file containing song data
      """
      # open song file
      df = pd.read_json(filepath, lines=True)
  
      # efficiently insert song records in batchs by minimizing server round-trips
      song_data = [tuple(x) for x in df[[
          "song_id",
          "title",
          "artist_id",
          "year",
          "duration"]].values]
      execute_batch(cur, song_table_insert, song_data)
  
      # efficiently insert artist records in batchs by minimizing server round-trips
      artist_data = [tuple(x) for x in df[[
          "artist_id",
          "artist_name",
          "artist_location",
          "artist_latitude",
          "artist_longitude"]].values]
      execute_batch(cur, artist_table_insert, artist_data)
  
  
  def process_log_file(cur, filepath):
      """Parse and insert relevant service usage data from log files into the database.
  
      Arguments:
      cur -- database cursor used to insert data
      filepath -- path to JSON file containing log data
      """
      # open log file
      df = pd.read_json(filepath, lines=True)
  
      # filter by NextSong action
      df = df.loc[df.page.isin(["NextSong"])]
  
      # convert timestamp column to datetime
      df = df.assign(ts=pd.to_datetime(df.ts, unit="ms", utc=True))
  
      # insert time data records
      time_df = pd.DataFrame(
          {
              "timestamp": df.ts,
              "hour": df.ts.dt.hour,
              "day": df.ts.dt.day,
              "week_of_year": df.ts.dt.week,
              "month": df.ts.dt.month,
              "year": df.ts.dt.year,
              "weekday": df.ts.dt.weekday,
          })
  
      for i, row in time_df.iterrows():
          cur.execute(time_table_insert, list(row))
  
      # load user table
      user_df = df[[
          "userId",
          "firstName",
          "lastName",
          "gender",
          "level"]].drop_duplicates(subset="userId", keep="last")
  
      # insert user records
      for i, row in user_df.iterrows():
          cur.execute(user_table_insert, row)
  
      # insert songplay records
      for index, row in df.iterrows():
  
          # get songid and artistid from song and artist tables
          cur.execute(song_select, (row.song, row.artist, row.length))
          results = cur.fetchone()
  
          if results:
              songid, artistid = results
          else:
              songid, artistid = None, None
  
          # insert songplay record
          songplay_data = (
              row.userId,
              songid,
              artistid,
              row.sessionId,
              row.ts,
              row.level,
              row.location,
              row.userAgent,
          )
          cur.execute(songplay_table_insert, songplay_data)
  
  
  def process_data(cur, conn, filepath, func):
      """Resolve files under filepath root, parse and insert elements into the database.
  
      Arguments:
      cur -- database cursor used to insert data
      conn -- database network connection
      filepath -- path to JSON data files
      func -- function used to extract data elements
      """
      # get all files matching extension from directory
      all_files = []
      for root, dirs, files in os.walk(filepath):
          files = glob.glob(os.path.join(root, '*.json'))
          for f in files:
              all_files.append(os.path.abspath(f))
  
      # get total number of files found
      num_files = len(all_files)
      print('{} files found in {}'.format(num_files, filepath))
  
      # iterate over files and process
      for i, datafile in enumerate(all_files, 1):
          func(cur, datafile)
          conn.commit()
          print('{}/{} files processed.'.format(i, num_files))
  
  
  def main(debug=False):
      """Opens database connection and inserts items parsed from data files into database.
  
      Arguments:
      debug -- True the database is cleared of data by dropping and re-creating all tables
               False data from files is inserted into existing tables
      """
      # connect to database
      conn = psycopg2.connect(
          "host=127.0.0.1 dbname=sparkifydb user=student password=student")
      cur = conn.cursor()
  
      if debug:
          # drop and re-create all tables
          for sql_cmd in drop_table_queries + create_table_queries:
              cur.execute(sql_cmd)
  
      # pipeline: read in files > insert into postgres tables
      process_data(cur, conn, filepath='data/song_data', func=process_song_file)
      process_data(cur, conn, filepath='data/log_data', func=process_log_file)
  
      conn.close()
  
  
  if __name__ == "__main__":
      main(debug=False)
  