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new ha analysis wip

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Khalim Conn-Kowlessar 2024-03-15 10:09:26 +00:00
parent 479a2b08c3
commit cc319ab911
1 changed files with 5 additions and 13 deletions
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18
etl/eligibility/ha_15_32/ha_analysis_batch_3.py
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@ -5210,7 +5210,7 @@ def fml_analysis(loader):
] ]
# TODO: There will be some properties that are subject to CIGA that do not look like they ned a CIGA check! pass # TODO: There will be some properties that are subject to CIGA that do not look like they ned a CIGA check! pass
# them! # them! Non-invasices will have checked the wall though
codes = [ codes = [
"HA39", "HA14", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA7", "HA39", "HA14", "HA24", "HA15", "HA32", "HA28", "HA6", "HA1", "HA7",
@ -5352,16 +5352,11 @@ def fml_analysis(loader):
] ]
# Characterise no CIGA check needed # Characterise no CIGA check needed
# TODO: WHAT ABOUT PASSED CIGA - don't need to apply the further deduction
ciga_check_needed = had_survey[ ciga_check_needed = had_survey[
had_survey["ECO Eligibility"].str.contains("subject to ciga") had_survey["ECO Eligibility"].str.contains("subject to ciga")
].copy() ].copy()
ciga_check_passed = had_survey[ ciga_check_passed = had_survey[had_survey["ECO Eligibility"] == "eco4 - passed ciga"]
had_survey["ECO Eligibility"] == "eco4 - passed ciga"
]
# These should be treated the same as one that have passed their ciga checks, from a detection perspective # These should be treated the same as one that have passed their ciga checks, from a detection perspective
ciga_check_passed_eligible = ciga_check_passed[ ciga_check_passed_eligible = ciga_check_passed[
(ciga_check_passed["walls-description"].str.lower().str.contains("cavity") == True) & (ciga_check_passed["walls-description"].str.lower().str.contains("cavity") == True) &
@ -5469,18 +5464,15 @@ def fml_analysis(loader):
"HA Name": ha_name, "HA Name": ha_name,
"Original ECO4 Estimate - Remaining": original_remaining, "Original ECO4 Estimate - Remaining": original_remaining,
"Postcode List - Remaining": postcode_list_remaining, "Postcode List - Remaining": postcode_list_remaining,
"Of which sold": sales_since_nov, # "Of which sold": sales_since_nov,
"Of which ECO4 Eligible - Remaining": int(total_expectation), "Of which ECO4 Eligible - Remaining": int(total_expectation),
"Of which GBIS Eligibile - Remaining": int(total_gbis_expectation), "Of which GBIS Eligibile - Remaining": int(total_gbis_expectation),
"Proportion with a survey": proportion_with_survey, # "Proportion with a survey": proportion_with_survey,
} }
) )
results_df = pd.DataFrame(results) results_df = pd.DataFrame(results)
results_df.to_csv("analysis - revised.csv")
wall_descriptions = list(set(wall_descriptions))
from pprint import pprint
pprint(wall_descriptions)
# results_df["Delta vs November"] = 100 * ( # results_df["Delta vs November"] = 100 * (
# results_df["Of which ECO4 Eligible - Remaining"] - results_df["Original ECO4 Estimate - Remaining"] # results_df["Of which ECO4 Eligible - Remaining"] - results_df["Original ECO4 Estimate - Remaining"]