moved extract_l & dataframes into own directory in src

1 files changed, 0 insertions, 228 deletions

diff --git a/src/dataframes.py b/src/dataframes.py
deleted file mode 100644
index f122368..0000000
--- a/src/dataframes.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import pandas as pd
-from bs4 import BeautifulSoup
-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
-
-
-# no test, same as fact_payment
-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"] = df_sales["created_at"].astype("datetime64[ns]").dt.date
-    df_sales["created_time"] = (
-        df_sales["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time
-    )
-    df_sales["last_updated_date"] = (
-        df_sales["last_updated"].astype("datetime64[ns]").dt.date
-    )
-    df_sales["last_updated_time"] = (
-        df_sales["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time
-    )
-    df_sales["agreed_delivery_date"] = pd.to_datetime(
-        df_sales["agreed_delivery_date"], format="%Y-%m-%d"
-    )
-    df_sales["agreed_payment_date"] = pd.to_datetime(
-        df_sales["agreed_payment_date"], format="%Y-%m-%d"
-    )
-    df_sales = df_sales.drop(labels=["created_at", "last_updated"], axis=1)
-    df_sales.reset_index(inplace=True)
-    return df_sales
-
-
-# no test, same as fact_payment
-
-
-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"].astype("datetime64[ns]").dt.date
-    df_po["created_time"] = (
-        df_po["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time
-    )
-    df_po["last_updated_date"] = df_po["last_updated"].astype("datetime64[ns]").dt.date
-    df_po["last_updated_time"] = (
-        df_po["last_updated"].astype("datetime64[ns]").dt.floor("s").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 = df_po.drop(labels=["created_at", "last_updated"], axis=1)
-    df_po.reset_index(inplace=True)
-    return df_po
-
-
-# test passed
-
-
-def create_fact_payment(dict_of_df):
-    df_payment = dict_of_df["payment"]
-    df_payment.index.name = "payment_record_id"
-    df_payment["created_date"] = (
-        df_payment["created_at"].astype("datetime64[ns]").dt.date
-    )
-    df_payment["created_time"] = (
-        df_payment["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time
-    )
-    df_payment["last_updated_date"] = (
-        df_payment["last_updated"].astype("datetime64[ns]").dt.date
-    )
-    df_payment["last_updated_time"] = (
-        df_payment["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time
-    )
-    df_payment["payment_date"] = pd.to_datetime(
-        df_payment["payment_date"], format="%Y-%m-%d"
-    )
-    df_payment = df_payment.drop(labels=["created_at", "last_updated"], axis=1)
-    df_payment.reset_index(inplace=True)
-    return df_payment
-
-
-# test passed
-
-
-def create_dim_transaction(dict_of_df):
-    df_transaction = dict_of_df["transaction"].drop(
-        labels=["created_at", "last_updated"], axis=1
-    )
-    return df_transaction
-
-
-# test passed
-
-
-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"})
-    )
-    return df_loc
-
-
-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="counterparty_legal_address_id",
-        how="outer",
-    )
-    df_cp.drop(
-        columns=["legal_address_id", "counterparty_legal_address_id"], inplace=True
-    )
-    return df_cp
-
-
-# test passed
-
-
-def create_dim_date(dict_of_df):
-    fact_dfs = [
-        create_fact_payment(dict_of_df),
-        create_fact_purchase_orders(dict_of_df),
-        create_fact_sales_order(dict_of_df),
-    ]
-    list_of_date_columns = []
-    for df in fact_dfs:
-        date_col_names = [
-            col_name for col_name in list(df.columns) if "_date" in col_name
-        ]
-        for col in date_col_names:
-            list_of_date_columns.append(df[col])
-    sr_date = pd.array(pd.concat(list_of_date_columns), dtype="datetime64[ns]")
-    df_date = pd.DataFrame(data=sr_date, columns=["date_id"])
-    df_date.drop_duplicates(inplace=True)
-    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
-    return df_date
-
-
-# tests passed
-
-
-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
-
-
-# tests passed
-
-
-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"
-    )
-    return dim_cur
-
-
-# tests passed
-
-
-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
-
-
-# tests passed
-
-
-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
-
-
-# tests passed
-
-
-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