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1264273 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | from __future__ import annotations
from pathlib import Path
from typing import Dict, List, Tuple
import pandas as pd
from pyscbwrapper import SCB
# ===================== #
# ---- CONFIG AREA ---- #
# ===================== #
TAX_ID = "ssyk2012" # or "ssyk96"
TABLES: Dict[str, Tuple[str, ...]] = {
"ssyk2012_tab": ("en", "AM", "AM0208", "AM0208E", "YREG51BAS"),
"ssyk96_tab": ("en", "AM", "AM0208", "AM0208E", "YREG33"),
}
def get_project_root() -> Path:
try:
return Path(__file__).resolve().parents[1]
except NameError:
return Path.cwd().resolve().parents[1]
# ========================= #
# ---- CORE FUNCTIONS ---- #
# ========================= #
def latest_year_from_vars(vars_: Dict[str, List[str]]) -> str:
years = []
for y in vars_.get("year", []):
try:
years.append(int(y))
except Exception:
pass
return str(max(years))
def fetch_scb_metadata_and_year(tax_id: str):
scb = SCB(*TABLES[f"{tax_id}_tab"])
vars_ = scb.get_variables()
occupations_key, occupations = next(iter(vars_.items()))
clean_key = occupations_key.replace(" ", "") # wrapper quirk
latest_year = latest_year_from_vars(vars_)
return scb, clean_key, occupations, latest_year
def fetch_data_for_year(
scb: SCB, occ_key: str, occupations: List[str], year: str
) -> Tuple[List[dict], Dict[str, str]]:
scb.set_query(**{occ_key: occupations, "year": [year]})
scb_data = scb.get_data()
scb_fetch = scb_data["data"]
codes = scb.get_query()["query"][0]["selection"]["values"]
occ_dict = dict(zip(codes, occupations))
return scb_fetch, occ_dict
def build_weights_df(scb_fetch: List[dict], occ_dict: Dict[str, str]) -> pd.DataFrame:
# raw records
records = []
for r in scb_fetch:
code, year = r["key"][:2] # occupation code, year
name = occ_dict.get(code, code)
value = r["values"][0]
records.append(
{"code_4": code, "occupation": name, "year": year, "value": value}
)
df = pd.DataFrame(records)
df = df[df["code_4"] != "0002"].reset_index(drop=True) ## remove unidentified group
# keep year as string, codes as strings; value numeric
df["year"] = df["year"].astype(str)
df["value"] = pd.to_numeric(df["value"], errors="coerce")
df = df.dropna(subset=["value"]).reset_index(drop=True)
df["value"] = df["value"].astype(int)
df = df[df["value"] > 0].reset_index(drop=True)
# code hierarchy
df["code_4"] = (
df["code_4"]
.astype(str)
.str.replace(r"\D", "", regex=True)
.str.slice(0, 4)
.str.zfill(4)
)
df["code_3"] = df["code_4"].str[:3]
df["code_2"] = df["code_4"].str[:2]
df["code_1"] = df["code_4"].str[0]
# aggregated totals per level (by year) — sums are ints; codes remain strings
for level in ["code_1", "code_2", "code_3"]:
df[f"value_{level[-1]}"] = (
df.groupby(["year", level])["value"].transform("sum").astype(int)
)
# weights (shares) within each higher level
df["weight_in_code_3"] = (df["value"] / df["value_3"]).round(6)
df["weight_in_code_2"] = (df["value"] / df["value_2"]).round(6)
df["weight_in_code_1"] = (df["value"] / df["value_1"]).round(6)
# final columns for export
df = df[
[
"code_4",
"occupation",
"value",
"weight_in_code_3",
"weight_in_code_2",
"weight_in_code_1",
"year",
]
]
return df
def write_weights(df: pd.DataFrame, tax_id: str, year: str) -> Path:
root = get_project_root()
out_path = root / "data" / "scb_weights" / f"{tax_id}_weights_en_{year}.csv"
out_path.parent.mkdir(parents=True, exist_ok=True)
df.to_csv(out_path, index=False)
return out_path
# ===================== #
# -------- CLI -------- #
# ===================== #
def main(tax_id: str = TAX_ID):
scb, occ_key, occupations, latest_year = fetch_scb_metadata_and_year(tax_id)
scb_fetch, occ_dict = fetch_data_for_year(scb, occ_key, occupations, latest_year)
df = build_weights_df(scb_fetch, occ_dict)
out_path = write_weights(df, tax_id, latest_year)
print(f"✅ Wrote: {out_path.resolve()}")
return df
if __name__ == "__main__":
main()
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