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split_bc_drop.py
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split_bc_drop.py
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import datetime
import numpy as np
import os
import pandas as pd
import psycopg2
from dotenv import load_dotenv, find_dotenv
from scipy import stats
from v2_functions_and_classes import Clean_and_classify_class
load_dotenv()
############################################################################################################
'''Verify the credentials before running deployment. '''
############################################################################################################
def split_basic_clean_drop():
'''
Pulls the raw data, do a basic cleanning and drop the info in the correspondig table.
'''
try:
# Stablishes connection with our db
connection = psycopg2.connect(user=os.environ.get('aws_db_user'),
password=os.environ.get('aws_db_password'),
host=os.environ.get('aws_db_host'),
port=os.environ.get('aws_db_port'),
database=os.environ.get('aws_db_name'))
# Create the cursor.
cursor = connection.cursor()
cursor.execute('''
SELECT DISTINCT product_name, market_id, source_id, currency_code
FROM raw_table;
''')
rows = cursor.fetchall()
if rows:
for row in rows:
product_name = row[0]
market_id = row[1]
source_id = row[2]
currency_code = row[3]
cursor.execute('''
SELECT market_name, country_code
FROM markets
WHERE market_id = %s;
''',(market_id,))
result = cursor.fetchall()
market_name = result[0][0]
country_code = result[0][1]
cursor.execute('''
SELECT source_name
FROM sources
WHERE id = %s;
''',(source_id,))
source_name = cursor.fetchall()[0][0]
quoted_product_name = "'%s'" % row[0]
quoted_market_id = "'%s'" % row[1]
quoted_currency_code = "'%s'" % row[3]
# For retail
query_retail = '''SELECT date_price, unit_scale, retail_observed_price FROM raw_table WHERE product_name = {} AND market_id = {} AND source_id = {} AND currency_code = {}'''.format(quoted_product_name,quoted_market_id,source_id,quoted_currency_code)
data = pd.read_sql_query(query_retail,connection)
clean_class = Clean_and_classify_class()
unit_scale, data = clean_class.basic_cleanning(data)
if not data.empty:
data_dict = data.to_dict()
for i in range(len(data)):
vector = (product_name, market_id,market_name,country_code,unit_scale,source_id,source_name,currency_code,data_dict['date_price'][i],data_dict['retail_observed_price'][i])
cursor.execute('''
SELECT id
FROM retail_prices
WHERE product_name = %s
AND market_id = %s
AND unit_scale = %s
AND source_id = %s
AND currency_code = %s
AND date_price = %s
AND observed_price =%s
''', (product_name, market_id,unit_scale,source_id,currency_code,data_dict['date_price'][i],data_dict['retail_observed_price'][i]))
result = cursor.fetchall()
if not result:
query_insert_vector = '''
INSERT INTO retail_prices (
product_name,
market_id,
market_name,
country_code,
unit_scale,
source_id,
source_name,
currency_code,
date_price,
observed_price
)
VALUES (
%s,
%s,
%s,
%s,
%s,
%s,
%s,
%s,
%s,
%s
);
'''
cursor.execute(query_insert_vector,vector)
connection.commit()
# For wholesale
query_wholesale = '''SELECT date_price, unit_scale, wholesale_observed_price FROM raw_table WHERE product_name = {} AND market_id = {} AND source_id = {} AND currency_code = {}'''.format(quoted_product_name,quoted_market_id,source_id,quoted_currency_code)
data = pd.read_sql_query(query_wholesale,connection)
clean_class = Clean_and_classify_class()
unit_scale, data = clean_class.basic_cleanning(data)
if not data.empty:
data_dict = data.to_dict()
for i in range(len(data)):
try: # A problem was popping up with the combination df['product']=='Kilombero Rice') & (df['market'] == 'Arusha') & (df['country'] == 'TZA')
vector = (product_name, market_id,market_name,country_code,unit_scale,source_id,source_name,currency_code,data_dict['date_price'][i],data_dict['wholesale_observed_price'][i])
cursor.execute('''
SELECT id
FROM wholesale_prices
WHERE product_name = %s
AND market_id = %s
AND unit_scale = %s
AND source_id = %s
AND currency_code = %s
AND date_price = %s
AND observed_price =%s
''', (product_name, market_id,unit_scale,source_id,currency_code,data_dict['date_price'][i],data_dict['wholesale_observed_price'][i]))
result = cursor.fetchall()
if not result:
query_insert_vector = '''
INSERT INTO wholesale_prices (
product_name,
market_id,
market_name,
country_code,
unit_scale,
source_id,
source_name,
currency_code,
date_price,
observed_price
)
VALUES (
%s,
%s,
%s,
%s,
%s,
%s,
%s,
%s,
%s,
%s
);
'''
cursor.execute(query_insert_vector,vector)
connection.commit()
except:
print('There has been a problem in this combination:')
print(product_name)
print(market_id)
print(market_name)
print(country_code)
print(unit_scale)
print(source_id)
print(source_name)
print(currency_code)
# cursor.close()
# connection.close()
except (Exception, psycopg2.Error) as error:
print('Error pulling possible combinations.')
finally:
if (connection):
cursor.close()
connection.close()
print('Connection closed.')
if __name__ == "__main__":
split_basic_clean_drop()