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etl.py
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etl.py
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"""
This file implements the
ETL process.
"""
import os
import glob
import psycopg2
import pandas as pd
from user_agents import parse
from sql_queries import *
def process_song_file(cur, filepath):
"""
Process Song files.
Load Data into dataframe.
Clean, transform and insert into
song and artist tables.
Parameters:
cur (psycopg2.extension.cursor): Allows Python code to execute
PostgreSQL command in a database session
filepath (str): Path to file
"""
# open song file
df = pd.read_json(filepath, lines=True)
# check data quality
df.fillna(0, inplace=True)
# insert song record
song_data = (df['song_id'].values[0], df['title'].values[0],
df['artist_id'].values[0], int(df['year'].values[0]),
float(df['duration'].values[0]))
cur.execute(song_table_insert, song_data)
# insert artist record
artist_data = (df['artist_id'].values[0], df['artist_name'].values[0],
str(df['artist_location'].values[0]),
float(df['artist_latitude'].values[0]),
float(df['artist_longitude'].values[0]))
cur.execute(artist_table_insert, artist_data)
def process_log_file(cur, filepath):
"""
Process Log files.
Load Data into dataframe.
Clean, transform and insert into
time, user, songplay and useragent tables.
Parameters:
cur (psycopg2.extension.cursor): Allows Python code to execute
PostgreSQL command in a database session
filepath (str): Path to file
"""
# open log file
df = pd.read_json(filepath, lines=True)
# check data quality
df.fillna(0, inplace=True)
# filter by NextSong action
df = df[df.page == 'NextSong']
# convert timestamp column to datetime
t = pd.to_datetime(df.ts, unit='ms')
# insert time data records
time_data = (df.ts, t.dt.hour, t.dt.day,
t.dt.week, t.dt.month,
t.dt.year, t.dt.dayofweek)
column_labels = ('start_time', 'hour', 'day',
'week', 'mounth', 'year',
'weekday')
date_dict = {}
for i in range(0, len(column_labels)):
date_dict[column_labels[i]] = time_data[i].astype('int')
time_df = pd.DataFrame.from_dict(date_dict)
time_df = time_df.drop_duplicates(keep='first')
for i, row in time_df.iterrows():
cur.execute(time_table_insert, list(row))
# load user table
users_table = {'userId': df.userId.values,
'first_name': df.firstName.values,
'last_name': df.lastName.values,
'gender': df.gender.values,
'level': df.level.values}
user_df = pd.DataFrame(data=users_table)
user_df = user_df.drop_duplicates(keep='first')
# 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)
# insert useragent record
ua = df.userAgent
# get unique records from current file
ua = list(set(ua))
# parse user_agent data
for row in ua:
user_agent = parse(row)
if user_agent.os.version_string:
os_version = user_agent.os.version_string
else:
os_version = None
if user_agent.os.family == 'Linux':
os_family = 'Other Linux'
else:
os_family = user_agent.os.family
user_agent_data = (row, user_agent.browser.family,
user_agent.browser.version_string,
os_family, os_version, user_agent.is_mobile)
cur.execute(useragent_table_insert, user_agent_data)
def process_data(cur, conn, filepath, func):
"""
Read all files.
Read files inside folders and call functions.
Parameters:
cur (psycopg2.extension.cursor): Allows Python code to execute
PostgreSQL command in a database session
conn(psycopg2.extensions.connection): Handles the connection
to a PostgreSQL database instance
filepath(str): Path to file
func(function): Call the appropriate function.
"""
# 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"
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()