1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
|
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
|
