added

etl/month_end_automation_wave_2_no_14.py

@@ -96,9 +96,9 @@ ra = get_df(df, "ra", ["completed rdsap 9.9", "completed rdsap 10"], "RA")
 filtered_dfs.append(ra)
 
 
-# ATT
-att = get_df(df, "att", ["completed"], "ATT")
-filtered_dfs.append(att)
+# # ATT
+# att = get_df(df, "att", ["completed"], "ATT")
+# filtered_dfs.append(att)
 
 # V1 Coordination
 v1 = get_df(df, "v1 coordination status (ioe,mtp)".lower(), [

etl/month_end_automation_wave_accent_housing.py

@@ -0,0 +1,104 @@
+# Wave 3's month end automation
+
+from tqdm import tqdm
+from monday import MondayClient
+from etl.osmosis_complaince_address_to_files import get_all_items, extract_asset_ids
+from pprint import pprint
+import pandas as pd
+import json
+
+monday_key = "eyJhbGciOiJIUzI1NiJ9.eyJ0aWQiOjQ5ODc2ODQxOCwiYWFpIjoxMSwidWlkIjozNjE3ODAzNCwiaWFkIjoiMjAyNS0wNC0xMVQxMToyMzoxNy40NjdaIiwicGVyIjoibWU6d3JpdGUiLCJhY3RpZCI6MTM5OTc4MjMsInJnbiI6InVzZTEifQ.-2Lit4s46ZF6AXuMW9t0TxIaFLkHqD4Yo-PyM9i2XZY"
+monday = MondayClient(monday_key)
+board_ids = [
+    "8829428746", # 2502 Accent Housing
+]
+
+empty = "Rate card info missing"
+
+rate_card_data_2502_accent_housing = {
+    "job_type": [
+        "First half of MTP", "Second half of MTP", "Full MTP"
+    ],
+    "rate": [
+        150, 130, 280
+    ]
+}
+
+rate_card_df = pd.DataFrame(rate_card_data_2502_accent_housing)
+
+
+for board in tqdm(board_ids):
+    print(f"working on board {board}")
+    board_data = monday.boards.fetch_boards_by_id(board)
+    columns = board_data["data"]["boards"][0]["columns"]
+    col_id_map = {col["title"].lower(): col["id"] for col in columns}
+    reversed_col_id_map = {v: k for k, v in col_id_map.items()}
+    
+    
+    items = get_all_items(board, monday)
+
+    all_records = []
+    for row in tqdm(items):
+        data = {}
+        data.update({"address": row['name']})
+        data.update({"client": row['group']['title']})
+        for col in row.get("column_values", []):
+            if col.get("id") in reversed_col_id_map:
+                if col.get("type") == "file":
+                    value = col.get("value")
+                    no_of_files = 0
+
+                    if value:
+                        value = json.loads(col["value"])
+                        no_of_files = len(value.get('files', []))
+                    data.update({reversed_col_id_map[col.get("id")]: no_of_files})
+                else:
+                    data.update({
+                        reversed_col_id_map[col.get("id")]: col.get("text")
+                    })
+        all_records.append(data)
+
+# Convert to DataFrame
+df = pd.DataFrame(all_records)
+
+filtered_dfs = []
+
+
+def get_df(df, column_name, success_critera, job_name=None):
+    _ = pd.DataFrame()
+    if column_name in col_id_map:
+        _ = df[
+            df[column_name].str.lower().isin(success_critera)
+        ].copy()
+        if job_name:
+            _["job_type"] = job_name
+    
+    
+    return _
+
+
+
+modeling = get_df(df, "mtp invoicing status", ["modelling to invoice"], "First half of mtp")
+if not modeling.empty:
+    filtered_dfs.append(modeling)
+
+second_payment = get_df(df, "mtp invoicing status", ["(V1) Remainder IOE/MTP to invoice".lower()], "second half of mtp")
+if not second_payment.empty:
+    filtered_dfs.append(second_payment)
+
+full_cost = get_df(df, "mtp invoicing status", ["(v1) full cost mtp to invoice (no previous modelling)"], "full cost mtp")
+if not full_cost.empty:
+    filtered_dfs(full_cost)
+
+final_df = pd.concat(filtered_dfs).reset_index(drop=True)
+
+final_df["job_type"] = final_df["job_type"].str.lower()
+rate_card_df["job_type"] = rate_card_df["job_type"].str.lower()
+
+# Now perform the merge
+combined_with_rates = final_df.merge(rate_card_df, on="job_type", how="left")
+import datetime
+timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
+
+attribute = ['address', 'client', 'job_type', 'rate']
+combined_with_rates[attribute].to_excel(f'2502 Accent housing {timestamp}.xlsx', index=False)