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Jun-te Kim
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a751b8d361
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2 changed files with 96 additions and 40 deletions
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@ -11,6 +11,23 @@ monday = MondayClient(monday_key)
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# Northumberland LAD2 & HUG2
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board_ids = ["5121300882"]
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rate_card_data = {
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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", "Design Repetitive", "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"
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],
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"rate": [
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259, 125, 280, 125, 125,
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650, 195, 175, 135,
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120, "Post EPR Please Marianne", 85, 125, 60,
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25, 25, 25
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]
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}
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rate_card_df = pd.DataFrame(rate_card_data)
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for board in tqdm(board_ids):
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board_data = monday.boards.fetch_boards_by_id(board)
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columns = board_data["data"]["boards"][0]["columns"]
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@ -66,7 +83,7 @@ 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):
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def get_df(df, column_name, success_critera, job_name=None):
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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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@ -86,11 +103,11 @@ filtered_dfs.append(att)
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# V1 Coordination
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v1 = get_df(df, "lite ima status".lower(), [
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"rc complete",
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], "V1 Coordination")
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], "Coordination Stage 1 v1")
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filtered_dfs.append(v1)
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# V2 Coordination
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v2 = get_df(df, "ima-mtp status", ["ima-mtp completed"], "V2 Coordination")
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v2 = get_df(df, "ima-mtp status", ["ima-mtp completed"], "Coordination Stage 1 v2 remodel")
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filtered_dfs.append(v2)
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# # V3 Coordination
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@ -98,11 +115,11 @@ filtered_dfs.append(v2)
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# filtered_dfs.append(v3)
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# Coordination stage 2 Please complete
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# cors2 = df[
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# df["rc stg. 2"].str.lower().isin(["to invoice", "completed"])
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# ]
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# cors2["joby_type"] = "Coordination Stage 2"
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# filtered_dfs.append(cors2)
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cors2 = df[
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df["rc stg. 2"].str.lower().isin(["to invoice"])
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]
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cors2["joby_type"] = "Coordination Stage 2"
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filtered_dfs.append(cors2)
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# Design stage 1
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# design1 = get_df(df, "", ["done"], "Design")
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@ -123,7 +140,7 @@ filtered_dfs.append(lodg1)
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# Full Lodgement Phase
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full_lodgement = get_df(df, "trustmark lodgement".lower(), ["done"], "Full Lodgement")
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full_lodgement = get_df(df, "trustmark lodgement".lower(), ["done"], "Full lodgement phase 2")
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filtered_dfs.append(full_lodgement)
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# POST EPC
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@ -131,20 +148,17 @@ post_epc = get_df(df, "post-epc status", ["uploaded & completed", "to invoice"],
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filtered_dfs.append(post_epc)
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# # POST EPR
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# post_epr = df[
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# df["post-epc status"].str.lower().isin(["post epr completed"])
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# ].copy()
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# post_epr["job_type"] = "POST ATT"
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# filtered_dfs.append(post_epr)
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# POST EPR
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post_epr = df[
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df["post-epc status"].str.lower().isin(["post epr completed"])
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].copy()
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post_epr["job_type"] = "POST ATT"
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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 status", ["uploaded & completed", "to invoice"], "POST ATT")
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filtered_dfs.append(post_att)
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post_att = get_df(df, "post-test status", ["complete"], "POST ATT")
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filtered_dfs.append(post_att)
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# Retrofit Evaluation
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retro = get_df(df, "retrofit evaluation", ["done", "to invoice"], "Retrofit Evaluation")
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filtered_dfs.append(retro)
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@ -175,4 +189,13 @@ filtered_dfs.append(att_ns)
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final_df = pd.concat(filtered_dfs).reset_index(drop=True)
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final_df[['address', 'client', 'job_type']]
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final_df["job_type"] = final_df["job_type"].str.lower()
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rate_card_df["job_type"] = rate_card_df["job_type"].str.lower()
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# Now perform the merge
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combined_with_rates = final_df.merge(rate_card_df, on="job_type", how="left")
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import datetime
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timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
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attribute = ['address', 'client', 'job_type', 'rate']
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combined_with_rates[attribute].to_excel(f'Northumberland LAD2 & HUG2_{timestamp}.xlsx', index=False)
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@ -12,6 +12,23 @@ monday = MondayClient(monday_key)
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#Home Group Wave 2SP+
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board_ids = ["4254419092"]
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rate_card_data = {
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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", "Design Repetitive", "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"
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],
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"rate": [
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259, 125, 280, 125, 125,
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650, 195, 175, 135,
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120, "Post EPR Please Marianne", 85, 125, 60,
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25, 25, 25
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]
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}
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rate_card_df = pd.DataFrame(rate_card_data)
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for board in tqdm(board_ids):
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board_data = monday.boards.fetch_boards_by_id(board)
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@ -68,11 +85,12 @@ 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):
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def get_df(df, column_name, success_critera, job_name=None):
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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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_["job_type"] = job_name
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if job_name:
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_["job_type"] = job_name
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return _
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@ -86,13 +104,13 @@ att = get_df(df, "pre-att", ["completed"], "ATT")
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filtered_dfs.append(att)
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# V1 Coordination
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v1 = get_df(df, "osmosis rc status".lower(), [
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v1 = get_df(df, "osmosis rc status".lower(), [
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"rc completed",
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], "V1 Coordination")
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], "Coordination Stage 1 v1")
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filtered_dfs.append(v1)
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# V2 Coordination
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v2 = get_df(df, "v2 ioe mtp", ["completed"], "V2 Coordination")
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v2 = get_df(df, "v2 ioe mtp", ["completed"], "Coordination Stage 1 v2 remodel")
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filtered_dfs.append(v2)
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# # V3 Coordination
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@ -100,14 +118,20 @@ filtered_dfs.append(v2)
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# # filtered_dfs.append(v3)
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# Coordination stage 2 Please complete
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# cors2 = df[
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# df["rc stg. 2"].str.lower().isin(["to invoice", "completed"])
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# ]
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# cors2["joby_type"] = "Coordination Stage 2"
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# filtered_dfs.append(cors2)
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cors2 = df[
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df["rc stage 2"].str.lower().isin(["to invoice"])
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]
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cors2["joby_type"] = "Coordination Stage 2"
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filtered_dfs.append(cors2)
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# Design stage 1
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design1 = get_df(df, "design invoice status", ["to invoice"], "Design")
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# Design stage Archetype
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design1 = get_df(df, "design invoice status", ["to invoice"])
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design1 = get_df(design1, "design type for invoicing", ["archetype"], "Design Archetype")
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filtered_dfs.append(design1)
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# Design stage Repetitive
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design1 = get_df(df, "design invoice status", ["to invoice"])
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design1 = get_df(design1, "design type for invoicing", ["repetitive"], "Design Repetitive")
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filtered_dfs.append(design1)
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# Design revision
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@ -120,11 +144,11 @@ filtered_dfs.append(design1)
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# filtered_dfs.append(design2)
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# Lodgement Phase 1
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lodg1 = get_df(df, "TM Phase 1 Invoicing Status".lower(), ["done", "to invoice"], "Lodgement Phase 1")
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lodg1 = get_df(df, "TM Phase 1 Invoicing Status".lower(), ["done", "to invoice"], "Lodgement phase 1")
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filtered_dfs.append(lodg1)
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# Full Lodgement Phase
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full_lodgement = get_df(df, "Jun-te TM Phase 2 Invoicing Status".lower(), ["to invoice"], "Full Lodgement")
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full_lodgement = get_df(df, "Jun-te TM Phase 2 Invoicing Status".lower(), ["to invoice"], "Full lodgement phase 2")
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filtered_dfs.append(full_lodgement)
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# POST EPC
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@ -132,12 +156,12 @@ post_epc = get_df(df, "post-epc status", ["complete & uploaded"], "POST EPC")
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filtered_dfs.append(post_epc)
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# # POST EPR
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# post_epr = df[
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# df["post-epc status"].str.lower().isin(["post epr completed"])
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# ].copy()
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# post_epr["job_type"] = "POST ATT"
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# filtered_dfs.append(post_epr)
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# POST EPR
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post_epr = df[
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df["post-epc status"].str.lower().isin(["post epr completed"])
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].copy()
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post_epr["job_type"] = "POST ATT"
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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 status", ["complete & uploaded"], "POST ATT")
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@ -176,4 +200,13 @@ filtered_dfs.append(epc_ns)
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final_df = pd.concat(filtered_dfs).reset_index(drop=True)
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final_df[['address', 'client', 'job_type']]
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final_df["job_type"] = final_df["job_type"].str.lower()
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rate_card_df["job_type"] = rate_card_df["job_type"].str.lower()
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# Now perform the merge
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combined_with_rates = final_df.merge(rate_card_df, on="job_type", how="left")
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import datetime
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timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')
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attribute = ['address', 'client', 'job_type', 'rate']
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combined_with_rates[attribute].to_excel(f'HomeGroup Wave 2SP+_{timestamp}.xlsx', index=False)
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