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https://github.com/Hestia-Homes/survey-extraction.git
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month end
This commit is contained in:
Jun-te Kim
parent
9c7e7ecdd9
commit
a751b8d361
4 changed files with 189 additions and 69 deletions
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@ -11,6 +11,24 @@ monday = MondayClient(monday_key)
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# NCHA Derbyshire Dales (DDDCC) SHDF
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board_ids = ["6947307148"]
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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 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"
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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, 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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@ -86,7 +104,7 @@ filtered_dfs.append(att)
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# V1 Coordination
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v1 = get_df(df, "coordination 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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@ -98,15 +116,15 @@ filtered_dfs.append(v1)
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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, "design upload to sharepoint", ["done"], "Design")
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filtered_dfs.append(design1)
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# # Design stage 1
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# design1 = get_df(df, "design upload to sharepoint", ["done"], "Design")
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# filtered_dfs.append(design1)
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# Design revision
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# design2 = get_df(df, "design revision invoice", [
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@ -123,7 +141,7 @@ filtered_dfs.append(lodg1)
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# Full Lodgement Phase
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full_lodgement = get_df(df, "full lodgement invoice status".lower(), ["to invoice"], "Full Lodgement")
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full_lodgement = get_df(df, "full lodgement invoice 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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@ -131,12 +149,12 @@ post_epc = get_df(df, "lodged epc", ["complete"], "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["lodged epc"].str.lower().isin(["post epr completed"])
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].copy()
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post_epr["job_type"] = "Post EPR"
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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", ["done"], "POST ATT")
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@ -176,4 +194,15 @@ 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[['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'NCHA Derbyshire Dales (DDDCC) SHDF_{timestamp}.xlsx', index=False)
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@ -12,6 +12,23 @@ monday = MondayClient(monday_key)
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# ECO 4 NCHA Almshouses Operations
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board_ids = ["9136254638"]
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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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@ -87,39 +104,50 @@ filtered_dfs.append(att)
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v1 = df[
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df["coordination status"].str.lower().isin(["ioe/mtp complete"])
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].copy()
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v1["job_type"] = "V1 Coordination"
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v1["job_type"] = "Coordination Stage 1 v1"
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filtered_dfs.append(v1)
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# V2 Coordination
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# _ = df[df["mtp v2 invoiced"].str.lower().isin(['done', 'needs to be invoiced'])].copy()
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# _["job_type"] = "V2 Coordination"
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# _["job_type"] = "Coordination Stage 1 v2 remodel"
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# filtered_dfs.append(_)
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# V3 Coordination
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# v3 = df[df["v3 rc status"].str.lower().isin(['uploaded'])].copy()
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# v3["job_type"] = "V3 Coordination"
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# v3["job_type"] = "Coordination Stage 1 v3 remodel"
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# filtered_dfs.append(_)
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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 = df[
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df["retrofit design status"].str.lower().isin(["to invoice"])
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].copy()
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design1["job_type"] = "Design"
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design1 = design1[design1["design type"].str.lower().isin(["archetype"])].copy()
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design1["job_type"] = "Design Archetype"
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filtered_dfs.append(design1)
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# Design stage 2
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# design2 = df[
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# df[""].str.lower().isin(["to invoice"])
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# ].copy()
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# design2["job_type"] = "Design Revision"
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# filtered_dfs.append(design2)
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design2 = df[
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df["retrofit design status"].str.lower().isin(["to invoice"])
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].copy()
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design2 = design2[design2["design type"].str.lower().isin(["repetitive"])].copy()
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design2["job_type"] = "Design Repetitive"
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filtered_dfs.append(design2)
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# Design revision
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design2 = df[
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df["retrofit design status"].str.lower().isin(["to invoice"])
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].copy()
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design2 = design2[design2["design revision"].str.lower().isin(["A", "B", "C"])].copy()
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design2["job_type"] = "Design Repetitive"
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filtered_dfs.append(design2)
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# Lodgement Phase 1
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# lodg1 = df[
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@ -132,7 +160,7 @@ filtered_dfs.append(design1)
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_ = df[
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df["trustmark lodgement"].str.lower().isin(["done"])
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].copy()
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_["job_type"] = "Full Lodgement"
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_["job_type"] = "Full lodgement phase 2"
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filtered_dfs.append(_)
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# POST EPC
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@ -143,12 +171,12 @@ post_epc["job_type"] = "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 = df[
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@ -177,7 +205,7 @@ filtered_dfs.append(ra_ns)
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# ATT NO Show
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att_ns = df[
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(df["pre att no show evidence"].fillna(-9999) != df["pre att no show invoice"].fillna(-9999)) &
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(df["pre att no show evidence"] ! = 0)
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(df["pre att no show evidence"] != 0)
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].copy()
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att_ns["job_type"] = "ATT NO SHOW"
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filtered_dfs.append(att_ns)
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@ -193,4 +221,13 @@ 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'ECO 4 NCHA Almshouses Operations_{timestamp}.xlsx', index=False)
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@ -12,6 +12,22 @@ monday = MondayClient(monday_key)
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#ECO 4 Wates Operations
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board_ids = ["9520779048"]
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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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@ -89,25 +105,25 @@ v1 = get_df(df, "Coordination Status IOE/MTP".lower(), [
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"(V1) IOE/MTP Complete".lower(),
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"(V2) IOE/MTP Complete".lower(),
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"(V3) IOE/MTP Complete".lower(),
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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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# _ = df[df["mtp v2 invoiced"].str.lower().isin(['done', 'needs to be invoiced'])].copy()
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# _["job_type"] = "V2 Coordination"
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# _["job_type"] = "Coordination Stage 1 v2 remodel"
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# filtered_dfs.append(_)
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# V3 Coordination
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# v3 = df[df["v3 rc status"].str.lower().isin(['uploaded'])].copy()
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# v3["job_type"] = "V3 Coordination"
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# v3["job_type"] = "Coordination Stage 1 v3 remodel"
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# filtered_dfs.append(_)
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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 status"].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, "retrofit design status", ["completed"], "Design")
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@ -119,7 +135,7 @@ design2 = get_df(df, "design revision invoice", [
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"Rev. B to invoice".lower(),
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"Rev. C to invoice".lower(),
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"Rev. D to invoice".lower(),
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], "Design Revision")
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], "Design Repetitive")
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filtered_dfs.append(design2)
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# Lodgement Phase 1
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@ -130,7 +146,7 @@ filtered_dfs.append(design2)
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# filtered_dfs.append(lodg1)
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# Full Lodgement Phase
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full_lodgement = get_df(df, "full lodgement", ["completed"], "Full Lodgement")
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full_lodgement = get_df(df, "full lodgement", ["completed"], "Full lodgement phase 2")
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filtered_dfs.append(full_lodgement)
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# POST EPC
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@ -138,12 +154,12 @@ post_epc = get_df(df, "post epc & evaluation status", ["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 & evaluation 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"], "POST ATT")
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@ -151,7 +167,7 @@ filtered_dfs.append(post_att)
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# Retrofit Evaluation
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retro = get_df(df, "retrofit evaluation", ["done"], "Retrofit Evaluation")
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retro = get_df(df, "post epc & evaluation status", ["uploaded"], "Retrofit Evaluation")
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filtered_dfs.append(retro)
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# RA NO Show
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@ -179,4 +195,13 @@ filtered_dfs.append(retro)
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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'ECO 4 Wates Operations_{timestamp}.xlsx', index=False)
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@ -11,6 +11,23 @@ monday = MondayClient(monday_key)
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# NCHA Almshouses
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board_ids = ["5423364294"]
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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 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"
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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, 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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@ -87,23 +104,24 @@ filtered_dfs.append(att)
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# V1 Coordination
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v1 = get_df(df, "coordination status (mtp)".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, "v2 coordination status", ["rc v2 complete", "uploaded"], "V2 Coordination")
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# v2 = get_df(df, "v2 coordination status", ["rc v2 complete", "uploaded"], "Coordination Stage 1 v2 remodel")
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# filtered_dfs.append(v2)
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# # V3 Coordination
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# Coordination Stage 1 v3 remode
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# v3 = get_df(df, "")
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# # filtered_dfs.append(v3)
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||||
# Coordination stage 2 Please complete
|
||||
# cors2 = df[
|
||||
# df["rc stg. 2"].str.lower().isin(["to invoice", "completed"])
|
||||
# ]
|
||||
# cors2["joby_type"] = "Coordination Stage 2"
|
||||
# filtered_dfs.append(cors2)
|
||||
cors2 = df[
|
||||
df["rc stage 2"].str.lower().isin(["to invoice"])
|
||||
].copy()
|
||||
cors2["joby_type"] = "Coordination Stage 2"
|
||||
filtered_dfs.append(cors2)
|
||||
|
||||
# Design stage 1
|
||||
# design1 = get_df(df, "rd invoiced", ["to invoice"], "Design")
|
||||
|
|
@ -124,7 +142,7 @@ filtered_dfs.append(v1)
|
|||
|
||||
|
||||
# Full Lodgement Phase
|
||||
full_lodgement = get_df(df, "trustmark lodgement".lower(), ["done"], "Full Lodgement")
|
||||
full_lodgement = get_df(df, "trustmark lodgement".lower(), ["done"], "Full lodgement phase 2")
|
||||
filtered_dfs.append(full_lodgement)
|
||||
|
||||
# POST EPC
|
||||
|
|
@ -159,7 +177,7 @@ filtered_dfs.append(ra_ns)
|
|||
|
||||
# ATT NO Show
|
||||
att_ns = df[
|
||||
(df(df["att no show evidence"].fillna(-9999) != df["att no show invoice"].fillna(-9999))) &
|
||||
(df["att no show evidence"].fillna(-9999) != df["att no show invoice"].fillna(-9999)) &
|
||||
(df["att no show evidence"] != 0)
|
||||
].copy()
|
||||
att_ns["job_type"] = "ATT NO SHOW"
|
||||
|
|
@ -170,9 +188,20 @@ filtered_dfs.append(att_ns)
|
|||
# epc_ns = df[
|
||||
# df["post epc no show evidence"].fillna(-9999) != df["post epc no show invoice"].fillna(-9999)
|
||||
# ].copy()
|
||||
# epc_ns["job_type"] = "EPC NO SHOW"
|
||||
# epc_ns["job_type"] = "post EPC no show"
|
||||
# filtered_dfs.append(epc_ns)
|
||||
|
||||
final_df = pd.concat(filtered_dfs).reset_index(drop=True)
|
||||
|
||||
final_df[['address', 'client', 'job_type']]
|
||||
final_df[['address', 'client', 'job_type']]
|
||||
|
||||
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'NCHA Almshouses_{timestamp}.xlsx', index=False)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue