converted weekly new store monitoring

monitoring_weekly/__init__.py

@@ -20,8 +20,9 @@ def main(mytimer: func.TimerRequest) -> None:
     #run_script('weekly_sama_reporting')
     #run_script('weekly_autolinker_100_links_audit')
     #run_script('weekly_insights_brand_monitoring')
-    run_script('weekly_new_brand_combos')
-
+    #run_script('weekly_new_brand_combos')
+    #run_script('weekly_new_store_monitoring')
+
 
 def run_script(script_name):
     script_path = os.path.join(os.path.dirname(__file__), f'{script_name}.py')

monitoring_weekly/weekly_new_brand_combos.py

@@ -5,14 +5,12 @@
 import time
 
 from sqlalchemy import create_engine
+from sparkpost import SparkPost
 import azure.functions as func
 import pandas as pd
 import sqlalchemy.dialects.sqlite
 
 
-from sparkpost import SparkPost
-
-
 
 def main(mytimer: func.TimerRequest) -> None:
     utc_timestamp = datetime.datetime.utcnow().replace(
@@ -144,7 +142,7 @@ def main(mytimer: func.TimerRequest) -> None:
     """
     try:
         sp.transmissions.send(
-        recipients=['mlaferla@headset.io'],
+        recipients=['mfelipe@headset.io', 'rmontenigro@headset.io'],
         html=msgHtml,
         from_email='analytics-monitor@headset.io',
         subject="New Brand Combos",

monitoring_weekly/weekly_new_store_monitoring.py

@@ -0,0 +1,166 @@
+import datetime
+import logging
+import os
+import string
+import time
+
+from sparkpost import SparkPost
+from sqlalchemy import create_engine
+import azure.functions as func
+import pandas as pd
+import sqlalchemy.dialects.sqlite
+
+
+
+def main(mytimer: func.TimerRequest) -> None:
+    utc_timestamp = datetime.datetime.utcnow().replace(
+        tzinfo=datetime.timezone.utc).isoformat()
+
+    if mytimer.past_due:
+        logging.info('The timer is past due!')
+
+    logging.info('Python timer trigger function ran at %s', utc_timestamp)
+
+
+    logging.info("Weekly Monday New Store Monitoring")
+
+sp = SparkPost(os.environ["SPARKPOST_KEY"])
+
+dir_path = os.getcwd()
+
+
+engine = create_engine(
+    'snowflake://{user}:{password}@{account}/'.format(
+        user=os.environ["SNOWFLAKE_USER"],
+        password=os.environ["SNOWFLAKE_PASSWORD"],
+        account=os.environ["SNOWFLAKE_ACCOUNT"],
+    )
+)
+
+
+usedb_string = "use FIVETRAN;"
+
+
+sql_string = """
+with chain as (
+        select company_id, count(distinct store_id) as num_stores_in_chain
+        from base.stores
+        where ACTIVE = 1
+        group by 1
+    )
+    , new_stores as (
+        select state
+            , company_id
+            , store_id
+        from base.stores s
+        where ACTIVE = 1
+        and date_created between current_date() - 7 and current_date() -1
+    )
+    , state_sales_T30days as (
+        select ps.state
+            , sum(base_price - discount) as state_sales
+        from staging.product_sales ps
+        where transaction_date_local between current_date - 30 and current_date -1
+        group by 1
+    )
+    , store_sales_T30days as (
+        select ps.state
+            , ps.COMPANY_ID
+            , ps.store_id
+            , sum(base_price-discount) as store_sales
+            , count(distinct case when ps.customer_key is not null
+                        and gender in ('M', 'F')
+                        and dob is not null
+                    then receipt_id
+                    else 0
+                end) as demographics_trxns
+            , count(distinct receipt_id) as num_trxns
+            , count(distinct case when is_delivery = 1 then receipt_id else null end) as delivery_trxns
+            , count(distinct transaction_date_local::date) as num_days
+        from staging.PRODUCT_SALES ps
+        join new_stores ns on ps.STORE_ID = ns.STORE_ID
+        join base.customers c on ps.CUSTOMER_KEY = c.CUSTOMER_KEY
+        where transaction_date_local between current_date - 30 and current_date -1
+        group by 1, 2, 3
+        )
+    , avg_store_sales as (
+        select
+            store.state
+            , store.COMPANY_ID
+            , store_id
+            , case when c.num_stores_in_chain > 1 then 'Y' else 'N' end as is_chain
+            , c.num_stores_in_chain
+            , store_sales / num_days as avg_daily_store_sales
+            , (state_sales/30) as avg_daily_state_sales
+            , avg_daily_store_sales / avg_daily_state_sales as avg_daily_store_mkt_share
+            , demographics_trxns / num_trxns as perc_demo_trxns
+            , delivery_trxns / num_trxns as perc_delivery
+        from store_sales_T30days store
+        join state_sales_T30days state on store.state = state.state
+        left join chain c on c.COMPANY_ID = store.COMPANY_ID
+    )
+    select * from avg_store_sales
+    where avg_daily_store_mkt_share > 0.05
+    or perc_demo_trxns > 0.25
+    or perc_delivery > 0.1
+    or num_stores_in_chain > 1
+    order by 6 desc
+"""
+
+try:
+    #create connection to DB and tell to use Fivetran
+    connection = engine.connect()
+    connection.execute(usedb_string) 
+
+    print("executing query")
+    sql_df = pd.read_sql_query(sql_string, connection)
+
+finally:
+    connection.close()
+    engine.dispose()
+
+
+
+filename = dir_path+r'\new_store_monitoring_results.csv'
+sql_df.to_csv(filename, index = False)
+
+
+# # print(data_to_email)    
+if len(sql_df.count()) > 0:
+
+    msgHtml = """
+        <html>
+    <body><p>Hello, friend.</p>
+    <p>See attached for your data:</p>
+    <p>Regards,</p>
+    <p>Me</p>
+    </body></html>
+    """
+    try:
+        sp.transmissions.send(
+        recipients=['mlaferla@headset.io'],
+        html=msgHtml,
+        from_email='analytics-monitor@headset.io',
+        subject="New Store Monitoring",
+        attachments=[
+            {
+                "name": filename.split('\\')[-1],
+                "type": "text/csv",
+                "filename": filename
+            }
+        ]
+        )
+    except SparkPostAPIException as err:
+        # http response status code
+        print(err.status)
+        # python requests library response object
+        # http://docs.python-requests.org/en/master/api/#requests.Response
+        print(err.response.json())
+        # list of formatted errors
+        print(err.errors)
+
+
+# Finally, remove files
+os.remove(filename)       
+
+print("finished")