136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
import os
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import glob
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import psycopg2
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import pandas as pd
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from sql_queries import *
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def process_song_file(cur, filepath):
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"""
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Description: This function can be used to read the file in the filepath (data/song_data)
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to get the song records and used to populate the song and artist dim tables.
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Arguments:
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cur: cursor reference.
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filepath: complete file path for the file to load.
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Returns:
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None
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"""
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# open song file
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df = pd.read_json(filepath, typ='series')
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# insert song record
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song_data = df[['song_id','title','artist_id','year','duration']].values
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cur.execute(song_table_insert, song_data)
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# insert artist record
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artist_data = df[['artist_id', 'artist_name', 'artist_location', 'artist_latitude', 'artist_longitude']]
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cur.execute(artist_table_insert, artist_data)
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def process_log_file(cur, filepath):
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"""
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Description: This function can be used to read the file in the filepath (data/log_data)
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to get the log records and used to populate the time, user dim tables.
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Also build the songplay fact table.
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Arguments:
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cur: cursor reference.
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filepath: complete file path for the file to load.
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Returns:
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None
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"""
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# open log file
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df = pd.read_json(filepath, lines=True)
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# filter by NextSong action
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df = df[df['page'].str.contains('NextSong')]
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# convert timestamp column to datetime
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t = pd.to_datetime(df['ts'], unit='ms')
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# insert time data records
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time_data = (t, t.dt.hour, t.dt.day, t.dt.week, t.dt.month, t.dt.year, t.dt.weekday)
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column_labels = ('timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday')
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time_df = pd.DataFrame(dict(zip(column_labels,time_data)))
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for i, row in time_df.iterrows():
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cur.execute(time_table_insert, list(row))
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# load user table
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user_df = df[['userId', 'firstName', 'lastName', 'gender', 'level']]
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# insert user records
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for i, row in user_df.iterrows():
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cur.execute(user_table_insert, row)
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# insert songplay records
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for index, row in df.iterrows():
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# get songid and artistid from song and artist tables
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cur.execute(song_select, (row.song, row.artist, row.length))
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results = cur.fetchone()
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if results:
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songid, artistid = results
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else:
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songid, artistid = None, None
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# insert songplay record
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# ref.: https://stackoverflow.com/questions/35312981/using-pandas-to-datetime-with-timestamps
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start_time = pd.to_datetime(row.ts, unit='ms').strftime('%Y-%m-%d %I:%M:%S')
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songplay_data = (index, start_time, row.userId, row.level, str(songid), str(artistid), row.sessionId, row.location, row.userAgent)
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cur.execute(songplay_table_insert, songplay_data)
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def process_data(cur, conn, filepath, func):
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"""
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Process function to load data from songs and event log files into Postgres database.
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Arguments:
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cur: cursor reference.
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conn: connection credential for database access.
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filepath: complete file path for the file to load.
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func: function to call
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Returns:
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None
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"""
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# get all files matching extension from directory
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all_files = []
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for root, dirs, files in os.walk(filepath):
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files = glob.glob(os.path.join(root,'*.json'))
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for f in files :
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all_files.append(os.path.abspath(f))
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# get total number of files found
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num_files = len(all_files)
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print('{} files found in {}'.format(num_files, filepath))
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# iterate over files and process
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for i, datafile in enumerate(all_files, 1):
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func(cur, datafile)
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conn.commit()
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print('{}/{} files processed.'.format(i, num_files))
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def main():
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"""
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Main function for loading songs and log data into Postgres database
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"""
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conn = psycopg2.connect("host=127.0.0.1 dbname=sparkifydb user=student password=student")
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cur = conn.cursor()
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process_data(cur, conn, filepath='data/song_data', func=process_song_file)
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process_data(cur, conn, filepath='data/log_data', func=process_log_file)
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conn.close()
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if __name__ == "__main__":
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main() |