First commit

Data+Modeling/Project+Data+Modeling+with+Postgres/etl.py

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+import os
+import glob
+import psycopg2
+import pandas as pd
+from sql_queries import *
+
+
+def process_song_file(cur, filepath):
+    """
+    Description: This function can be used to read the file in the filepath (data/song_data)
+    to get the song records and used to populate the song and artist dim tables.
+
+    Arguments:
+        cur: cursor reference. 
+        filepath: complete file path for the file to load. 
+
+    Returns:
+        None
+    """
+        
+    # open song file
+    df = pd.read_json(filepath, typ='series')
+
+    # insert song record
+    song_data = df[['song_id','title','artist_id','year','duration']].values
+    cur.execute(song_table_insert, song_data)
+    
+    # insert artist record
+    artist_data = df[['artist_id', 'artist_name', 'artist_location', 'artist_latitude', 'artist_longitude']]
+    cur.execute(artist_table_insert, artist_data)
+
+
+def process_log_file(cur, filepath):
+    """
+    Description: This function can be used to read the file in the filepath (data/log_data)
+    to get the log records and used to populate the time, user dim tables.
+    Also build the songplay fact table.
+
+    Arguments:
+        cur: cursor reference. 
+        filepath: complete file path for the file to load. 
+
+    Returns:
+        None
+    """
+    
+    # open log file
+    df = pd.read_json(filepath, lines=True)
+
+    # filter by NextSong action
+    df = df[df['page'].str.contains('NextSong')]
+
+    # convert timestamp column to datetime
+    t = pd.to_datetime(df['ts'], unit='ms')
+    
+    # insert time data records
+    time_data = (t, t.dt.hour, t.dt.day, t.dt.week, t.dt.month, t.dt.year, t.dt.weekday)
+    column_labels = ('timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday')
+    time_df = pd.DataFrame(dict(zip(column_labels,time_data)))
+
+    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']]
+
+    # 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
+        # ref.: https://stackoverflow.com/questions/35312981/using-pandas-to-datetime-with-timestamps
+        start_time = pd.to_datetime(row.ts, unit='ms').strftime('%Y-%m-%d %I:%M:%S')
+        songplay_data = (index, start_time, row.userId, row.level, str(songid), str(artistid), row.sessionId, row.location, row.userAgent)
+        cur.execute(songplay_table_insert, songplay_data)
+
+
+def process_data(cur, conn, filepath, func):
+    """
+    Process function to load data from songs and event log files into Postgres database.
+
+    Arguments:
+        cur: cursor reference.
+        conn: connection credential for database access.
+        filepath: complete file path for the file to load.
+        func: function to call
+
+    Returns:
+        None
+    """
+    
+    # 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():
+    """
+    Main function for loading songs and log data into Postgres database
+    """
+    conn = psycopg2.connect("host=127.0.0.1 dbname=sparkifydb user=student password=student")
+    cur = conn.cursor()
+
+    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()