Deleted the fact_table schema py files

Completed Lambda_handler for transform_lambda - and other helper functions.
Testing is still to be done.
Need to implement lambda layer to share helper functions across all lambdas

1 files changed, 0 insertions, 71 deletions

diff --git a/src/fact_purchase_table.py b/src/fact_purchase_table.py
deleted file mode 100644
index f1d8fe1..0000000
--- a/src/fact_purchase_table.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from bs4 import BeautifulSoup
-from src.transform_lambda import read_from_s3_subfolder_to_df, tables
-from src.extract_lambda import extract_bucket
-import json
-import boto3
-import re
-import pandas as pd
-from datetime import datetime as dt
-import requests
-
-
-## dim_staff table is the same across the schemas (no change)
-
-## dim_location from address --> drops 2 columns
-def create_dim_location(dict_of_df):
-    df_loc = dict_of_df['address'].drop(labels=['created_at', 'last_updated'], axis=1).rename(columns={'address_id': 'location_id'}).set_index('location_id')
-    return df_loc
-
-## dim_counterparty from address and counterparty
-def create_dim_counterparty(dict_of_df):
-    df_prefixed_address = dict_of_df['address'].add_prefix('counterparty_legal_', axis=1) 
-    df_cp = pd.merge(dict_of_df['counterparty'], 
-            df_prefixed_address, 
-            left_on="legal_address_id", 
-            right_on="address_id", 
-            how="outer").set_index('counterparty_id')
-    return df_cp
-
-## fact_purchase_order from purchase_order
-def create_fact_purchase_order(dict_of_df):
-    df_po = dict_of_df['purchase_order']
-    df_po.index.name = 'purchase_record_id'
-    df_po['created_date'] = df_po['created_at'].date()
-    df_po['created_time'] = df_po['created_at'].dt.time
-    df_po['last_updated_date'] = df_po['last_updated_at'].date()
-    df_po['last_updated_time'] = df_po['last_updated_at'].dt.time
-    df_po['agreed_delivery_date'] = pd.to_datetime(df_po['agreed_delivery_date'],format="%Y-%m-%d")
-    df_po['agreed_payment_date'] = pd.to_datetime(df_po['agreed_payment_date'],format="%Y-%m-%d")
-    df_po.drop(labels=['created_at','last_updated_at'],axis=1,inplace=True)
-    return df_po
-
-## dim_date from purchase_order
-def create_dim_date(dict_of_df):
-    sr_date = pd.concat([df['created_date'],df['last_updated_date'],df['agreed_delivery_date'],df['agreed_payment_date']]).sort()
-    df_date = pd.DataFrame(sr_date,columns='date_id')
-    df_date['year'] = df_date['date_id'].dt.year
-    df_date['month'] = df_date['date_id'].dt.month
-    df_date['day'] = df_date['date_id'].dt.day
-    df_date['day_of_week'] = df_date['date_id'].dt.dayofweek
-    df_date['day_name'] = df_date['date_id'].dt.day_name
-    df_date['month_name'] = df_date['date_id'].dt.month_name
-    df_date['quarter'] = df_date['date_id'].dt.quarter
-    df_date.set_index('date_id')
-
-def scrape_currency_names():
-    response = requests.get('https://www.xe.com/currency/').content
-    soup = BeautifulSoup(response,'html.parser')
-    currency = [item.text for item in soup.findAll('a', attrs={'class' : "sc-299dec64-6 fZPTSw"})]
-    sr = pd.Series(currency)
-    df_cur = sr.str.split(pat=" - ",expand=True).rename({0:'currency_code',1:'currency_name'},axis=1)
-    return df_cur
-
-def create_dim_currency(dict_of_df,names=scrape_currency_names()):
-    df_cur = dict_of_df['currency'].drop(labels=['created_at', 'last_updated'], axis=1)
-    dim_cur = pd.merge(df_cur,names,left_on='currency_code',right_on='currency_code',how='inner').set_index('currency_id')
-    return dim_cur
-
-
-
-
-