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2 changed files with 221 additions and 146 deletions
8
.github/workflows/months_end.yml
vendored
8
.github/workflows/months_end.yml
vendored
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@ -1,10 +1,9 @@
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name: Months End
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on:
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# schedule:
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# # - cron: '0 17 * * 1-5'
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#adslfhjk asdlfjldsakjf
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schedule:
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- cron: '0 7 29 * *' # Runs at 7:00 AM UTC on the 29th of every month
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push:
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branches: [main, feature/month_end_automation_of_all]
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branches: [main]
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workflow_dispatch:
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jobs:
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@ -43,5 +42,6 @@ jobs:
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poetry run python etl/month_end_automation_wave_2_no_15.py
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poetry run python etl/month_end_automation_wave_2_no_16.py
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poetry run python etl/month_end_automation_wave_accent_housing.py
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poetry run python etl/month_end_automation_wave_3_layout.py
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env:
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PYTHONPATH: ${{ github.workspace }}
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@ -6,18 +6,37 @@ from etl.osmosis_complaince_address_to_files import get_all_items, extract_asset
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from pprint import pprint
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import pandas as pd
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import json
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import os
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from MonthEndUploader import upload_to_month_end_folder
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from enum import Enum
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monday_key = "eyJhbGciOiJIUzI1NiJ9.eyJ0aWQiOjQ5ODc2ODQxOCwiYWFpIjoxMSwidWlkIjozNjE3ODAzNCwiaWFkIjoiMjAyNS0wNC0xMVQxMToyMzoxNy40NjdaIiwicGVyIjoibWU6d3JpdGUiLCJhY3RpZCI6MTM5OTc4MjMsInJnbiI6InVzZTEifQ.-2Lit4s46ZF6AXuMW9t0TxIaFLkHqD4Yo-PyM9i2XZY"
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monday = MondayClient(monday_key)
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class BoardID(Enum):
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_9349630181 = "WCHG_WALKUPS_OPERATIONS"
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_8830772914 = "LQ_LONDON"
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_9601691730 = "CARDO_WALES_WEST_WAVE3"
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_9660895490 = "NORTHUMBERLAND_COUNTY_SHDF_WAVE3"
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_9641491000 = "WATFORD_WARM_HOMES"
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_9671463094 = "SEDDON"
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board_ids = [
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# "9349630181", # WCHG Walkups-Operations
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# "8830772914", # "L&Q London"
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# "9601691730", # Cardo Wales & West - Wave 3
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# "9660895490", # Northumberland County SHDF Wave 3
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"9641491000", #Watford Warm Homes
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"9349630181", # WCHG Walkups-Operations x
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"8830772914", # "L&Q London" x
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"9601691730", # Cardo Wales & West - Wave 3 x
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"9660895490", # Northumberland County SHDF Wave 3 x
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"9641491000", # Watford Warm Homes x
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"9671463094", # Seddon
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]
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empty = "Rate card info missing"
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rate_cards = {}
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rate_card_data_watford_warm_homes = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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@ -32,22 +51,9 @@ rate_card_data_watford_warm_homes = {
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empty, empty, empty, empty, empty,
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]
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}
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rate_cards.update({"9641491000": pd.DataFrame(rate_card_data_watford_warm_homes)})
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rate_card_data_2502_accent_housing = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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"Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
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"Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
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"RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
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],
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"rate": [
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empty, empty, empty, empty, empty,
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empty, empty, empty, empty, empty,
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empty, empty, empty, empty, empty,
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empty, empty, empty, 280, 150
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]
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}
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rate_card_data_l_and_q_london = {
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"job_type": [
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@ -64,22 +70,27 @@ rate_card_data_l_and_q_london = {
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]
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}
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rate_cards.update({"8830772914": pd.DataFrame(rate_card_data_l_and_q_london)})
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rate_card_data_northhumberland_country_shdf_wave_3 = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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"Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
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"Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Design Repetitive Complex", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
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"Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
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"RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
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],
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"rate": [
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empty, empty, empty, empty, empty,
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empty, empty, empty, empty, empty,
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empty, empty, empty, empty, empty,
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empty, empty, empty, 280, 130,
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259, 125, "280 or 310 depending on SH or LA", 125, 125,
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650, 415, 195, 225, 175,
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135, 120, "60 - check", "85 or 90 depending on SH or LA", "110 or 125 depending on SH or LA",
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60, 45, 45, 45, empty, empty
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]
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}
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pd.DataFrame(rate_card_data_northhumberland_country_shdf_wave_3)
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rate_cards.update({"9660895490": pd.DataFrame(rate_card_data_northhumberland_country_shdf_wave_3)})
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rate_card_data_walk_ups = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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@ -95,14 +106,59 @@ rate_card_data_walk_ups = {
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]
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}
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rate_card_df = pd.DataFrame(rate_card_data_watford_warm_homes)
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# rate_card_df = pd.DataFrame(rate_card_data_example)
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# rate_card_df = pd.DataFrame(rate_card_data_2502_accent_housing)
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# rate_card_df = pd.DataFrame(rate_card_data_l_and_q_london)
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# rate_card_df = pd.DataFrame(rate_card_data_northhumberland_country_shdf_wave_3)
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# rate_card_df = pd.DataFrame(rate_card_data_walk_ups)
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rate_cards.update({"9349630181": pd.DataFrame(rate_card_data_walk_ups)})
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rate_card_data_empty = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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"Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
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"Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
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"RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
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],
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"rate": [
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empty, empty, empty, empty, empty,
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empty, empty, empty, empty, empty,
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empty,empty , empty, empty, empty,
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empty, empty, empty, empty, empty,
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]
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}
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rate_card_data_cardo = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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"Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple","Design Repetitive Complex", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
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"Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
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"RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
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],
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"rate": [
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259, 125, 260, 125, 125,
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415, 415, 225, 225, 175,
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135, 120 , "60 - check", 85, 125,
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60, 45, 45, 45, empty, empty,
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]
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}
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rate_cards.update({"9601691730": pd.DataFrame(rate_card_data_cardo)})
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rate_card_data_seddon = {
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"job_type": [
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"RA", "ATT", "Coordination Stage 1 v1", "Coordination Stage 1 v2 remodel", "Coordination Stage 1 v3 remodel",
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"Design Archetype Complex", "Design Archetype Simple", "Design Repetitive Simple","Design Repetitive Complex", "Coordination Stage 2", "Lodgement phase 1", "Full lodgement phase 2",
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"Post EPR", "Post EPC", "Post ATT", "retrofit evaluation",
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"RA no show", "ATT no show", "post EPC no show", "Full cost MTP", "measure modelling"
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],
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"rate": [
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259, 125, 280, 125, 125,
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650, 415, 195, 225, 175,
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135, 120 , "60 - check", 85, 125,
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60, 45, 45, 45, empty, empty,
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]
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}
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rate_cards.update({"9671463094": pd.DataFrame(rate_card_data_seddon)})
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board_to_record = {}
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for board in tqdm(board_ids):
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print(f"working on board {board}")
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board_data = monday.boards.fetch_boards_by_id(board)
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@ -133,150 +189,169 @@ for board in tqdm(board_ids):
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reversed_col_id_map[col.get("id")]: col.get("text")
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})
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all_records.append(data)
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board_to_record.update({board: all_records})
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# Convert to DataFrame
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df = pd.DataFrame(all_records)
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filtered_dfs = []
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for board, all_records in board_to_record.items():
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df = pd.DataFrame(all_records)
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filtered_dfs = []
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def get_df(df, column_name, success_critera, job_name=None):
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_ = pd.DataFrame()
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if column_name in col_id_map:
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_ = df[
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df[column_name].str.lower().isin(success_critera)
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].copy()
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if job_name:
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_["job_type"] = job_name
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return _
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def get_df(df, column_name, success_critera, job_name=None):
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_ = pd.DataFrame()
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if column_name in col_id_map:
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_ = df[
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df[column_name].str.lower().isin(success_critera)
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].copy()
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if job_name:
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_["job_type"] = job_name
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return _
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# RA
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ra = get_df(df, "ra invoicing status", ["to invoice"], "RA")
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if not ra.empty:
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filtered_dfs.append(ra)
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# RA
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ra = get_df(df, "ra invoicing status", ["to invoice"], "RA")
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if not ra.empty:
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filtered_dfs.append(ra)
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att = get_df(df, "post att invoicing status", ["to invoice"], "ATT")
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if not att.empty:
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filtered_dfs.append(att)
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att = get_df(df, "post att invoicing status", ["to invoice"], "ATT")
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if not att.empty:
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filtered_dfs.append(att)
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modeling = get_df(df, "mtp invoicing status", ["modelling to invoice"], "Measure Modelling")
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if not modeling.empty:
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filtered_dfs.append(modeling)
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modeling = get_df(df, "mtp invoicing status", ["modelling to invoice"], "Measure Modelling")
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if not modeling.empty:
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filtered_dfs.append(modeling)
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try:
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# Only needed for one board in wave 3
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full_cost = get_df(df, "mtp invoicing status", ["(V1) Full cost MTP to invoice (no previous modelling)".lower()], "full cost mtp")
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if not full_cost.empty:
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filtered_dfs(full_cost)
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except Exception as e:
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print(e)
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try:
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# Only needed for one board in wave 3
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full_cost = get_df(df, "mtp invoicing status", ["(V1) Full cost MTP to invoice (no previous modelling)".lower()], "full cost mtp")
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if not full_cost.empty:
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filtered_dfs(full_cost)
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except Exception as e:
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print(e)
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v1 = get_df(df, "mtp invoicing status", ["(v1) ioe/mtp to invoice"], "Coordination Stage 1 v1")
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if not v1.empty:
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filtered_dfs.append(v1)
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v1 = get_df(df, "mtp invoicing status", ["(v1) ioe/mtp to invoice"], "Coordination Stage 1 v1")
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if not v1.empty:
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filtered_dfs.append(v1)
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v2 = get_df(df, "mtp invoicing status", ["(v2) ioe/mtp to invoice"], "Coordination Stage 1 v2 remodel")
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if not v2.empty:
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filtered_dfs.append(v2)
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v2 = get_df(df, "mtp invoicing status", ["(v2) ioe/mtp to invoice"], "Coordination Stage 1 v2 remodel")
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if not v2.empty:
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filtered_dfs.append(v2)
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v3 = get_df(df, "mtp invoicing status", ["(v3) ioe/mtp to invoice"], "Coordination Stage 1 v3 remodel")
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if not v3.empty:
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filtered_dfs.append(v3)
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v3 = get_df(df, "mtp invoicing status", ["(v3) ioe/mtp to invoice"], "Coordination Stage 1 v3 remodel")
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if not v3.empty:
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filtered_dfs.append(v3)
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# Coordination stage 2 Please complete
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cors2 = get_df(df, "rc stage 2", ["to invoice"], "Coordination Stage 2")
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if not cors2.empty:
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filtered_dfs.append(cors2)
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# Coordination stage 2 Please complete
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cors2 = get_df(df, "rc stage 2", ["to invoice"], "Coordination Stage 2")
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if not cors2.empty:
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filtered_dfs.append(cors2)
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# Design archetype complex
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design = get_df(df, "design invoicing status", ["to invoice"])
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design1 = get_df(design, "design invoice type", ["archetype (complex)"], "Design Archetype Complex")
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if not design1.empty :
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filtered_dfs.append(design1)
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# Design archetype complex
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design = get_df(df, "design invoicing status", ["to invoice"])
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design1 = get_df(design, "design invoice type", ["archetype (complex)"], "Design Archetype Complex")
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if not design1.empty :
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filtered_dfs.append(design1)
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# Design archetype simple
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design1 = get_df(design, "design invoice type", ["archetype (simple)"], "Design Archetype Simple")
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if not design1.empty:
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filtered_dfs.append(design1)
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# Design archetype simple
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design1 = get_df(design, "design invoice type", ["archetype (simple)"], "Design Archetype Simple")
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if not design1.empty:
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filtered_dfs.append(design1)
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# Design repetitive simple
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design1 = get_df(design, "design invoice type", ["archetype (simple)"], "Design Archetype repetitive")
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if not design1.empty:
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filtered_dfs.append(design1)
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# Design repetitive simple
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design1 = get_df(design, "design invoice type", ["archetype (simple)"], "Design Archetype repetitive")
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if not design1.empty:
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filtered_dfs.append(design1)
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# Design repetitive complex
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design1 = get_df(design, "design invoice type", ["archetype (complex)"], "Design Archetype complex")
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if not design1.empty:
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filtered_dfs.append(design1)
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# Design repetitive complex
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design1 = get_df(design, "design invoice type", ["archetype (complex)"], "Design Archetype complex")
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if not design1.empty:
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filtered_dfs.append(design1)
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# Design Revision
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revision_letter = ['a', 'b', 'c', 'd']
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for letter in revision_letter:
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design = get_df(df, "design revision invoice", [f"rev. {letter} to invoice"], "Design Revision")
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if not design.empty:
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filtered_dfs.append(design)
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# Design Revision
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revision_letter = ['a', 'b', 'c', 'd']
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for letter in revision_letter:
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design = get_df(df, "design revision invoice", [f"rev. {letter} to invoice"], "Design Revision")
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if not design.empty:
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filtered_dfs.append(design)
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# Lodgement Phase 1
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lodg1 = get_df(df, "lodgement phase 1 invoicing status", ["to invoice"], "Lodgement Phase 1")
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if not lodg1.empty:
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filtered_dfs(lodg1)
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# Lodgement Phase 1
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lodg1 = get_df(df, "lodgement phase 1 invoicing status", ["to invoice"], "Lodgement Phase 1")
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if not lodg1.empty:
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filtered_dfs(lodg1)
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# Full Lodgement Phase
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lodg2 = get_df(df, "full lodgement invoicing status", ["to invoice"], "Full lodgement phase 2")
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if not lodg2.empty:
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filtered_dfs.append(lodg2)
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# Full Lodgement Phase
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lodg2 = get_df(df, "full lodgement invoicing status", ["to invoice"], "Full lodgement phase 2")
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if not lodg2.empty:
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filtered_dfs.append(lodg2)
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# POST EPC
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post_epc = get_df(df, "post epc & eval. invoicing status", ["epc to invoice"], "POST EPC")
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if not post_epc.empty:
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filtered_dfs.append(post_epc)
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# POST EPC
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post_epc = get_df(df, "post epc & eval. invoicing status", ["epc to invoice"], "POST EPC")
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if not post_epc.empty:
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filtered_dfs.append(post_epc)
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# POST EPR
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post_epr = get_df(df, "post epc & eval. invoicing status", ["epr to invoice"], "POST EPR")
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if not post_epr.empty:
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filtered_dfs.append(post_epr)
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# POST EPR
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post_epr = get_df(df, "post epc & eval. invoicing status", ["epr to invoice"], "POST EPR")
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if not post_epr.empty:
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filtered_dfs.append(post_epr)
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# post att
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post_att = get_df(df, "post att invoicing status", ["to invoice"], "POST ATT")
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if not post_att.empty:
|
||||
filtered_dfs.append(post_epc)
|
||||
# post att
|
||||
post_att = get_df(df, "post att invoicing status", ["to invoice"], "POST ATT")
|
||||
if not post_att.empty:
|
||||
filtered_dfs.append(post_epc)
|
||||
|
||||
# Retrofit Evaluation
|
||||
rc = get_df(df, "rc stage 2 invoicing status", ["to invoice"], "retrofit evaluation")
|
||||
if not rc.empty:
|
||||
filtered_dfs.append(rc)
|
||||
# Retrofit Evaluation
|
||||
rc = get_df(df, "rc stage 2 invoicing status", ["to invoice"], "retrofit evaluation")
|
||||
if not rc.empty:
|
||||
filtered_dfs.append(rc)
|
||||
|
||||
# RA NO Show
|
||||
ra_ns = get_df(df,"ra no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "RA NO SHOW")
|
||||
if not ra_ns.empty:
|
||||
filtered_dfs.append(ra_ns)
|
||||
# RA NO Show
|
||||
ra_ns = get_df(df,"ra no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "RA NO SHOW")
|
||||
if not ra_ns.empty:
|
||||
filtered_dfs.append(ra_ns)
|
||||
|
||||
|
||||
# ATT NO Show
|
||||
att_ns = get_df(df, "pre att no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "ATT NO SHOW")
|
||||
if not att_ns.empty:
|
||||
filtered_dfs.append(att_ns)
|
||||
# ATT NO Show
|
||||
att_ns = get_df(df, "pre att no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "ATT NO SHOW")
|
||||
if not att_ns.empty:
|
||||
filtered_dfs.append(att_ns)
|
||||
|
||||
|
||||
# Post visit no show
|
||||
epc_ns = get_df(df, "post works no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "post EPC NO SHOW")
|
||||
if not epc_ns.empty:
|
||||
filtered_dfs.append(epc_ns)
|
||||
# Post visit no show
|
||||
epc_ns = get_df(df, "post works no show invoice", ["to invoice","to invoice (+1 previous no show)", "to invoice (+2 previous no shows)"], "post EPC NO SHOW")
|
||||
if not epc_ns.empty:
|
||||
filtered_dfs.append(epc_ns)
|
||||
|
||||
final_df = pd.concat(filtered_dfs).reset_index(drop=True)
|
||||
if len(filtered_dfs) != 0:
|
||||
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()
|
||||
final_df["job_type"] = final_df["job_type"].str.lower()
|
||||
rate_card_df = rate_cards[board]
|
||||
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')
|
||||
# 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'Watford Warm Homes {timestamp}.xlsx', index=False)
|
||||
|
||||
# Upload to sharepoint
|
||||
attribute = ['address', 'client', 'job_type', 'rate']
|
||||
master_folder_name = BoardID[f"_{board}"].value
|
||||
file_name = f"{master_folder_name}_{timestamp}.xlsx"
|
||||
combined_with_rates[attribute].to_excel(file_name, index=False)
|
||||
|
||||
file_path = os.path.abspath(file_name)
|
||||
|
||||
upload_to_month_end_folder(file_name, file_path, master_folder_name)
|
||||
|
||||
invoice_name = "rate_card.xlsx"
|
||||
file_path = os.path.abspath(invoice_name)
|
||||
rate_card_df.to_excel(invoice_name, index=False)
|
||||
upload_to_month_end_folder(invoice_name, file_path, master_folder_name)
|
||||
|
||||
attribute = ['address', 'client', 'job_type', 'rate']
|
||||
combined_with_rates[attribute].to_excel(f'Watford Warm Homes {timestamp}.xlsx', index=False)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue