diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/dataframes.py | 182 |
1 files changed, 97 insertions, 85 deletions
diff --git a/src/dataframes.py b/src/dataframes.py index 4b32b36..43facd6 100644 --- a/src/dataframes.py +++ b/src/dataframes.py @@ -20,8 +20,11 @@ import requests def create_fact_sales_order(dict_of_df): df_sales = dict_of_df["sales_order"] df_sales.index.name = "sales_record_id" -<<<<<<< HEAD - df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date + + +<< << << < HEAD + 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 ) @@ -30,27 +33,29 @@ def create_fact_sales_order(dict_of_df): ) df_sales["last_updated_time"] = ( df_sales["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time -======= - df_sales["created_date"] = pd.to_datetime(df_sales["created_at"], format="%Y-%m-%d") - df_sales["created_time"] = pd.to_datetime(df_sales["created_at"], format="%H-%M-%S") - df_sales["last_updated_date"] = pd.to_datetime( +== == == = + df_sales["created_date"]=pd.to_datetime( + df_sales["created_at"], format="%Y-%m-%d") + df_sales["created_time"]=pd.to_datetime( + df_sales["created_at"], format="%H-%M-%S") + df_sales["last_updated_date"]=pd.to_datetime( df_sales["last_updated"], format="%Y-%m-%d" ) - df_sales["last_updated_time"] = pd.to_datetime( + df_sales["last_updated_time"]=pd.to_datetime( df_sales["last_updated"], format="%H-%M-%S" ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) ) - df_sales["agreed_delivery_date"] = pd.to_datetime( + 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"]=pd.to_datetime( df_sales["agreed_payment_date"], format="%Y-%m-%d" ) -<<<<<<< HEAD - df_sales = df_sales.drop(labels=["created_at", "last_updated"], axis=1) -======= +<< << << < HEAD + df_sales=df_sales.drop(labels=["created_at", "last_updated"], axis=1) +== == == = df_sales.drop(labels=["created_at", "last_updated"], axis=1, inplace=True) ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) df_sales.reset_index(inplace=True) return df_sales @@ -59,37 +64,40 @@ def create_fact_sales_order(dict_of_df): def create_fact_purchase_orders(dict_of_df): - df_po = dict_of_df["purchase_order"] - df_po.index.name = "purchase_record_id" -<<<<<<< HEAD - df_po["created_date"] = df_po["created_at"].astype("datetime64[ns]").dt.date - df_po["created_time"] = ( + df_po=dict_of_df["purchase_order"] + df_po.index.name="purchase_record_id" +<< << << < HEAD + 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_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["created_date"] = pd.to_datetime(df_po["created_at"], format="%Y-%m-%d") - df_po["created_time"] = pd.to_datetime(df_po["created_at"], format="%H-%M-%S") - df_po["last_updated_date"] = pd.to_datetime( +== == == = + df_po["created_date"]=pd.to_datetime( + df_po["created_at"], format="%Y-%m-%d") + df_po["created_time"]=pd.to_datetime( + df_po["created_at"], format="%H-%M-%S") + df_po["last_updated_date"]=pd.to_datetime( df_po["last_updated"], format="%Y-%m-%d" ) - df_po["last_updated_time"] = pd.to_datetime( + df_po["last_updated_time"]=pd.to_datetime( df_po["last_updated"], format="%H-%M-%S" ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) ) - df_po["agreed_delivery_date"] = pd.to_datetime( + 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"]=pd.to_datetime( df_po["agreed_payment_date"], format="%Y-%m-%d" ) -<<<<<<< HEAD - df_po = df_po.drop(labels=["created_at", "last_updated"], axis=1) -======= +<< << << < HEAD + df_po=df_po.drop(labels=["created_at", "last_updated"], axis=1) +== == == = df_po.drop(labels=["created_at", "last_updated"], axis=1, inplace=True) ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) df_po.reset_index(inplace=True) return df_po @@ -98,42 +106,44 @@ def create_fact_purchase_orders(dict_of_df): def create_fact_payment(dict_of_df): - df_payment = dict_of_df["payment"] - df_payment.index.name = "payment_record_id" -<<<<<<< HEAD - df_payment["created_date"] = ( + df_payment=dict_of_df["payment"] + df_payment.index.name="payment_record_id" +<< << << < HEAD + df_payment["created_date"]=( df_payment["created_at"].astype("datetime64[ns]").dt.date ) - df_payment["created_time"] = ( + 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_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["created_date"] = pd.to_datetime( + df_payment["last_updated_time"]=( + df_payment["last_updated"].astype( + "datetime64[ns]").dt.floor("s").dt.time +== == == = + df_payment["created_date"]=pd.to_datetime( df_payment["created_at"], format="%Y-%m-%d" ) - df_payment["created_time"] = pd.to_datetime( + df_payment["created_time"]=pd.to_datetime( df_payment["created_at"], format="%H-%M-%S" ) - df_payment["last_updated_date"] = pd.to_datetime( + df_payment["last_updated_date"]=pd.to_datetime( df_payment["last_updated"], format="%Y-%m-%d" ) - df_payment["last_updated_time"] = pd.to_datetime( + df_payment["last_updated_time"]=pd.to_datetime( df_payment["last_updated"], format="%H-%M-%S" ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) ) - df_payment["payment_date"] = pd.to_datetime( + df_payment["payment_date"]=pd.to_datetime( df_payment["payment_date"], format="%Y-%m-%d" ) -<<<<<<< HEAD - df_payment = df_payment.drop(labels=["created_at", "last_updated"], axis=1) -======= - df_payment.drop(labels=["created_at", "last_updated"], axis=1, inplace=True) ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +<< << << < HEAD + df_payment=df_payment.drop(labels=["created_at", "last_updated"], axis=1) +== == == = + df_payment.drop( + labels=["created_at", "last_updated"], axis=1, inplace=True) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) df_payment.reset_index(inplace=True) return df_payment @@ -142,7 +152,7 @@ def create_fact_payment(dict_of_df): def create_dim_transaction(dict_of_df): - df_transaction = dict_of_df["transaction"].drop( + df_transaction=dict_of_df["transaction"].drop( labels=["created_at", "last_updated"], axis=1 ) return df_transaction @@ -152,7 +162,7 @@ def create_dim_transaction(dict_of_df): def create_dim_location(dict_of_df): - df_loc = ( + df_loc=( dict_of_df["address"] .drop(labels=["created_at", "last_updated"], axis=1) .rename(columns={"address_id": "location_id"}) @@ -161,10 +171,10 @@ def create_dim_location(dict_of_df): def create_dim_counterparty(dict_of_df): - df_prefixed_address = dict_of_df["address"].drop(labels=["created_at", "last_updated"], axis=1).add_prefix( + df_prefixed_address=dict_of_df["address"].drop(labels=["created_at", "last_updated"], axis=1).add_prefix( "counterparty_legal_", axis=1 ) - df_cp = pd.merge( + df_cp=pd.merge( dict_of_df["counterparty"], df_prefixed_address, left_on="legal_address_id", @@ -181,32 +191,32 @@ def create_dim_counterparty(dict_of_df): def create_dim_date(dict_of_df): - fact_dfs = [ + 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 = [] + list_of_date_columns=[] for df in fact_dfs: - date_col_names = [ -<<<<<<< HEAD + date_col_names=[ +<< << << < HEAD col_name for col_name in list(df.columns) if "_date" in col_name -======= +== == == = col_name for col_name in list(df.columns) if "date" in col_name ->>>>>>> 5db3f61 (style: format code with Autopep8, Black and Ruff Formatter) +>> >>>> > 5db3f61(style: format code with Autopep8, Black and Ruff Formatter) ] 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"]) + 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 + 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 @@ -214,13 +224,13 @@ def create_dim_date(dict_of_df): def scrape_currency_names(): - response = requests.get("https://www.xe.com/currency/").content - soup = BeautifulSoup(response, "html.parser") - currency = [ + 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( + sr=pd.Series(currency) + df_cur=sr.str.split(pat=" - ", expand=True).rename( {0: "currency_code", 1: "currency_name"}, axis=1 ) return df_cur @@ -230,8 +240,9 @@ def scrape_currency_names(): 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=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 @@ -241,8 +252,9 @@ def create_dim_currency(dict_of_df, names=scrape_currency_names()): 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"]] + 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 @@ -250,8 +262,8 @@ def create_dim_payment_type(dict_of_df): def create_dim_design(dict_of_df): - df_design = dict_of_df["design"] - dim_design = df_design.loc[ + df_design=dict_of_df["design"] + dim_design=df_design.loc[ :, ["design_id", "design_name", "file_name", "file_location"] ] return dim_design @@ -261,10 +273,10 @@ def create_dim_design(dict_of_df): def create_dim_staff(dict_of_df): - staff_department = pd.merge( + staff_department=pd.merge( dict_of_df["staff"], dict_of_df["department"], on="department_id", how="left" ) - dim_staff = staff_department.loc[ + dim_staff=staff_department.loc[ :, [ "staff_id", |