dataframes combined into one file

1 files changed, 238 insertions, 0 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
new file mode 100644
index 0000000..9ce3be0
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+++ b/src/dataframes.py
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+import pandas as pd
+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
+from datetime import datetime as dt
+import requests
+
+#Table names:
+# fact_sales_order
+# fact_purchase_orders
+# fact_payment
+# dim_transaction
+# dim_staff
+# dim_payment_type
+# dim_location
+# dim_design
+# dim_date
+# dim_currency
+# dim_counterparty
+
+def create_dim_transaction(dict_of_df):
+    pass
+
+def create_fact_sales_order(dict_of_df):
+    df_sales = dict_of_df["sales_order"]
+    df_sales.index.name = "sales_record_id"
+    df_sales["created_date"] = pd.to_datetime(df_sales["created_at"]).dt.date
+    df_sales["created_time"] = pd.to_datetime(df_sales["created_at"]).dt.time
+    df_sales["last_updated_date"] = pd.to_datetime(df_sales["last_updated"]).dt.date
+    df_sales["last_updated_time"] = pd.to_datetime(df_sales["last_updated"]).dt.time
+    pd.merge(dict_of_df["staff"], df_sales["sales_staff_id"], on="staff_id", how="left")
+    # df_sales.rename(columns={"staff_id": "sales_staff_id"})
+    fact_sales_order = df_sales.loc[:,[
+        "sales_record_id",
+        "sales_order_id",
+        "created_date",
+        "created_time",
+        "last_updated_date",
+        "last_updated_time",
+        "sales_staff_id",
+        "counterparty_id",
+        "units_sold",
+        "unit_price",
+        "currency_id",
+        "design_id",
+        "agreed_payment_date",
+        "agreed_delivery_date",
+        "agreed_delivery_location_id"
+    ]]
+    return fact_sales_order
+
+## fact_purchase_order from purchase_order
+def create_fact_purchase_orders(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
+
+
+def create_fact_payment(dict_of_df):
+    df_payment = dict_of_df["payment"]
+    df_payment.index.name = "payment_record_id"
+    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"]).dt.date
+    df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
+    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
+    df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
+    fact_payment = df_payment.loc[:,[
+        "payment_record_id",
+        "payment_id",
+        "created_date",
+        "created_time",
+        "last_updated_date",
+        "last_updated_time",
+        "transaction_id",
+        "counterparty_id",
+        "payment_amount",
+        "currency_id",
+        "payment_type_id",
+        "paid",
+        "payment_date"
+    ]]
+    return fact_payment
+
+## 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
+
+
+## dim_date from purchase_order
+def create_dim_date(dict_of_df):
+    sr_date = pd.concat([dict_of_df['created_date'],dict_of_df['last_updated_date'],dict_of_df['agreed_delivery_date'],dict_of_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
+
+
+
+
+
+
+
+def create_dim_payment_type(dict_of_df):
+    df_payment_type = dict_of_df["payment_type"]
+    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
+    return dim_payment_type
+
+def create_fact_payment(dict_of_df):
+    df_payment = dict_of_df["payment"]
+    df_payment.index.name = "payment_record_id"
+    df_payment["created_date"] = pd.to_datetime(df_payment["created_at"]).dt.date
+    df_payment["created_time"] = pd.to_datetime(df_payment["created_at"]).dt.time
+    df_payment["last_updated_date"] = pd.to_datetime(df_payment["last_updated"]).dt.date
+    df_payment["last_updated_time"] = pd.to_datetime(df_payment["last_updated"]).dt.time
+    fact_payment = df_payment.loc[:,[
+        "payment_record_id",
+        "payment_id",
+        "created_date",
+        "created_time",
+        "last_updated_date",
+        "last_updated_time",
+        "transaction_id",
+        "counterparty_id",
+        "payment_amount",
+        "currency_id",
+        "payment_type_id",
+        "paid",
+        "payment_date"
+    ]]
+    return fact_payment
+
+def create_dim_design(dict_of_df):
+    df_design = dict_of_df["design"]
+    dim_design = df_design.loc[:, ["design_id", "design_name", "file_name", "file_location"]]
+    return dim_design
+
+def create_dim_staff(dict_of_df):
+    staff_department = pd.merge(dict_of_df["staff"], dict_of_df["department"], on='department_id', how="left")
+    dim_staff = staff_department.loc[:, ['staff_id', 'first_name', 'last_name', 'department_name', 'location', 'email_address']]
+    return dim_staff
+
+def create_dim_currency(dict_of_df):
+    df_currency = dict_of_df["currency"]
+    dim_currency = df_currency.loc[:, ["currency_id", "currency_code"]]
+    mappings = {
+        "GBP": "Pound",
+        "USD": "US Dollar",
+        "EUR": "Euro"
+    }
+    dim_currency["currency_name"] = dim_currency["currency_code"].map(mappings)
+    return dim_currency
+
+
+def create_dim_date(dict_of_df):
+    df_sales = dict_of_df["sales"]
+    df_sales = df_sales.loc[:, ["agreed_delivery_date"]]
+    df_sales["agreed_delivery_date"] = pd.to_datetime["agreed_delivery_date"]
+    df_sales["year"] = df_sales["agreed_delivery_date"].dt.year
+    df_sales["month"] = df_sales["agreed_delivery_date"].dt.month
+    df_sales["day"] = df_sales["agreed_delivery_date"].dt.day
+    df_sales["day_of_week"] = df_sales["agreed_delivery_date"].dt.dayofweek
+    df_sales["day_name"] = df_sales["agreed_delivery_date"].dt.day_name()
+    df_sales["month_name"] = df_sales["agreed_delivery_date"].dt.month_name()
+    df_sales["quarter"] = df_sales["agreed_delivery_date"].dt.quarter()
+    dim_date = ["date_id", "year", "month", "day", "day_of_week", "day_name", "month_name", "quarter"]   #series.dt.quarter()
+    return dim_date
+
+
+# TO DO:                                    
+# complete dim_date from merged fact table
+# merge dataframes into one dataframe
+# remove duplicates
+# test dim_date and fact_sales_order
+
+def create_sales_star_schema(dict_of_df):
+    dim_design = create_dim_design(dict_of_df)
+    dim_staff = create_dim_staff(dict_of_df)
+    dim_currency = create_dim_currency(dict_of_df)
+    dim_date = create_dim_date(dict_of_df)
+    
+    fact_sales_order = create_fact_sales_order(dict_of_df)
+    
+    fact_sales_order = fact_sales_order.merge(dim_design, on='design_id', how='left')
+    fact_sales_order = fact_sales_order.merge(dim_staff, left_on='sales_staff_id', right_on='staff_id', how='left')
+    fact_sales_order = fact_sales_order.merge(dim_currency, on='currency_id', how='left')
+    fact_sales_order = fact_sales_order.merge(dim_date, left_on='agreed_delivery_date', right_on='date_id', how='left')
+    
+    return fact_sales_order
+
+
+
+def create_dim_payment_type(dict_of_df):
+    df_payment_type = dict_of_df["payment_type"]
+    dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]]
+    return dim_payment_type
+
+
+
+
+