etl.py

import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *

def get_files(filepath):
    """
    Please do not instantiate this method. This method is deprecated. Please use the process_data method.
    """
    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))
    return all_files

def process_song_file(cur, filepath):
    """
    Process song file will take the following inputs:
    cur - cursor point for the database connection
    filepath - path of file
    
    and inserts song records into the songs table along with artists records into the artists table
    """
    song_files = get_files(filepath)
    # open song file
    df = pd.DataFrame(pd.read_json(filepath,lines=True))

    # insert song record
    song_data = df[['song_id','title','artist_id','year','duration']].values[0].tolist()
    cur.execute(song_table_insert, song_data)
    
    # insert artist record
    artist_data = df[['artist_id','artist_name','artist_location','artist_latitude','artist_longitude']].values[0].tolist()
    cur.execute(artist_table_insert, artist_data)


def process_log_file(cur, filepath):
    """
    Process log file will take the following inputs:
    cur - cursor point for the database connection
    filepath - path of file
    
    drops missing values, loads the user table, songplay records,by executing a query from tables that was loaded previously.
    """
    
    # open log file
    df = pd.DataFrame(pd.read_json(filepath,lines=True))

    # drop the missing data
    df = df.dropna()

    # filter by NextSong action
    df = df[df.page == 'NextSong']
    
    t = df.copy()

    # convert timestamp column to datetime
    df['ts'] = pd.to_datetime(df['ts'],unit='ms')
    t['ts'] = pd.to_datetime(t['ts'],unit='ms')
    
    # insert time data records
    time_data = [t.ts, t.ts.dt.hour, t.ts.dt.day, t.ts.dt.week, t.ts.dt.month, t.ts.dt.year,t.ts.dt.weekday]
    column_labels = ['start_time','hour','day','week','month','year','weekday']
    time_df = pd.DataFrame.from_dict(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']]
    
    # drop missing values
    user_df = user_df.dropna()

    # 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.ts, row.userId, row.level, songid, artistid, row.sessionId, row.location, row.userAgent)
        cur.execute(songplay_table_insert, songplay_data)


def process_data(cur, conn, filepath, func):
    """
    process data method will take the following inputs:
    cur - cursor point for the database connection
    filepath - path of file
    conn - database connection itself
    func - passing a function as a paramter of another function
    
    scans through the folder, identifies all JSON documents, gets all the files matching the extension,
    executes the function that is passed in the main method
    """
    # 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():
    """
    defining all parameters for database connection and cursor; instantiating the process data function with all necessary parameters being passed
  
    """
    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()