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https://github.com/Hestia-Homes/Model.git
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working on eco eligibility code
This commit is contained in:
Khalim Conn-Kowlessar
parent
6693ab4ca6
commit
8b48dbac9e
1 changed files with 122 additions and 31 deletions
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@ -725,6 +725,13 @@ class DataLoader:
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def load(self):
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# Get the december figures, which is just a csv
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self.december_figures = pd.read_csv(self.december_figures_filepath)
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# Remove the spaces in HA Name
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self.december_figures["HA Name"] = self.december_figures["HA Name"].str.replace(" ", "")
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self.december_figures["ECO4"] = self.december_figures["ECO4"].astype("Int64")
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self.december_figures["GBIS"] = self.december_figures["GBIS"].astype("Int64")
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if self.use_cache:
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self.data = read_pickle_from_s3(
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bucket_name="retrofit-datalake-dev",
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@ -732,11 +739,6 @@ class DataLoader:
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)
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return
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# Get the december figures, which is just a csv
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self.december_figures = pd.read_csv(self.december_figures_filepath)
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# Remove the spaces in HA Name
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self.december_figures["HA Name"] = december_figures["HA Name"].str.replace(" ", "")
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data = {}
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for filepath in self.directories:
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ha_name = filepath.split("/")[2]
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@ -768,46 +770,135 @@ class DataLoader:
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This function will return a dictionary of facts and figures for each HA
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:return:
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"""
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scheme_map = {
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"ECO4": "ECO4",
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"AFFORDABLE WARMTH": "ECO4",
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}
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eco_eligibility_map = {
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"not eligble": "not eligible"
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}
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ha_facts_and_figures = []
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for ha_name, data_assets in self.data.items():
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asset_list = data_assets["asset_list"].copy()
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survey_list = data_assets["survey_list"].copy()
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ciga_list = data_assets["ciga_list"].copy()
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asset_list["ECO Eligibility"].value_counts()
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# Change the column name if it's ECO eligibility
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asset_list = asset_list.rename(columns={"ECO eligibility": "ECO Eligibility"})
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# Remove surplus whitespace from the ECO Eligibility column
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asset_list["ECO Eligibility"] = asset_list["ECO Eligibility"].str.strip()
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# Push to lower case
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asset_list["ECO Eligibility"] = asset_list["ECO Eligibility"].str.lower()
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# Remap
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asset_list["ECO Eligibility"] = asset_list["ECO Eligibility"].map(eco_eligibility_map)
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# We merge on ciga and update the status to reflect if it has failed ciga or not
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# If Guarantee is Yes, this means that there is a guarantee in place, and the property failed the CIGA
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# check
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asset_list = asset_list.merge(
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ciga_list[["asset_list_row_id", "Guarantee"]],
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how='left',
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on="asset_list_row_id"
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)
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if not ciga_list.empty:
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# We merge on ciga and update the status to reflect if it has failed ciga or not
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# If Guarantee is Yes, this means that there is a guarantee in place, and the property failed the CIGA
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# check
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asset_list = asset_list.merge(
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ciga_list[["asset_list_row_id", "Guarantee"]],
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how='left',
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on="asset_list_row_id"
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)
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asset_list["ECO Eligibility"].value_counts()
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asset_list["ECO Eligibility"].value_counts()
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asset_list["ECO Eligibility"] = np.where(
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(
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asset_list["ECO Eligibility"].str.contains("(Subject to CIGA)", regex=False) &
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(asset_list["Guarantee"] == "Yes")
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),
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"Failed CIGA",
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asset_list["ECO Eligibility"]
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)
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asset_list["ECO Eligibility"] = np.where(
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(
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asset_list["ECO Eligibility"].str.contains("(subject to ciga)", regex=False) &
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(asset_list["Guarantee"] == "Yes")
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),
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"failed ciga",
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asset_list["ECO Eligibility"]
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)
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# We replace any remaining "Subject to CIGA" with pass Ciga
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asset_list["ECO Eligibility"] = np.where(
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asset_list["ECO Eligibility"].str.contains("Subject to CIGA", regex=False),
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"Pass CIGA",
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asset_list["ECO Eligibility"]
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)
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# We replace any remaining "Subject to CIGA" with pass Ciga
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asset_list["ECO Eligibility"] = np.where(
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asset_list["ECO Eligibility"].str.contains("Subject to CIGA", regex=False),
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"eco4 - passed ciga",
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asset_list["ECO Eligibility"]
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)
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asset_list = asset_list.drop(columns=["Guarantee"])
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asset_list = asset_list.drop(columns=["Guarantee"])
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# Update the asset list with the categorisations
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# Update the asset list with the categorisations and rename changes
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self.data[ha_name]["asset_list"] = asset_list
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# Report on sales
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sales_report = {}
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if not survey_list.empty:
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scheme_column = survey_list.columns[0]
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# We clean up the survey list installation or cancelled
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survey_list["installed_or_cancelled_clean"] = survey_list["INSTALLED OR CANCELLED"].str.lower()
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# Remove all punctuation
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survey_list["installed_or_cancelled_clean"] = survey_list["installed_or_cancelled_clean"].str.replace(
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r'[^\w\s]', '', regex=True
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)
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# Remove double spaces
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survey_list["installed_or_cancelled_clean"] = survey_list["installed_or_cancelled_clean"].str.replace(
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r'\s+', ' ', regex=True
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)
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# Remove trailing spaces
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survey_list["installed_or_cancelled_clean"] = survey_list["installed_or_cancelled_clean"].str.strip()
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# Remap the values in the scheme column
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survey_list[scheme_column] = survey_list[scheme_column].map(scheme_map)
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survey_list["installation_status"] = None
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survey_list["installation_status"] = np.where(
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survey_list["installed_or_cancelled_clean"].isin(["installed", "installed see notes"]),
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"installed",
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survey_list["installation_status"]
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)
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survey_list["installation_status"] = np.where(
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survey_list["installed_or_cancelled_clean"].isin(["cancelled"]),
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"cancelled",
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survey_list["installation_status"]
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)
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# Find partial installations
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survey_list["installation_status"] = np.where(
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survey_list["installed_or_cancelled_clean"].str.contains("still to be installed"),
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"partially installed",
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survey_list["installation_status"]
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)
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# Find partial cancellations
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# TODO: We might have more indications of partial cancellations
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survey_list["installation_status"] = np.where(
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survey_list["installed_or_cancelled_clean"].isin(["loft cancelled"]),
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"partially cancelled",
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survey_list["installation_status"]
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)
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# Finally, for other cases, we set the status to "in progress"
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survey_list["installation_status"] = survey_list["installation_status"].fillna("in progress")
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# We concatenate the scheme name with the installation status
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survey_list["installation_status"] = (
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survey_list[scheme_column] + " - " + survey_list["installation_status"]
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)
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# We get the sales
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sales_report = survey_list["installation_status"].value_counts().to_dict()
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ha_facts_and_figures.append(
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{
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"HA Name": ha_name,
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**asset_list["ECO Eligibility"].value_counts().to_dict(),
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**sales_report
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}
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)
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ha_facts_and_figures = pd.DataFrame(ha_facts_and_figures)
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ha_facts_and_figures = ha_facts_and_figures.drop(
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columns=["not eligible"]
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)
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ha_facts_and_figures = self.december_figures.merge(ha_facts_and_figures, how="inner", on="HA Name")
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return ha_facts_and_figures
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