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Table parser + computation recipes — save scripts to /tmp
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Table parser + computation recipes — save scripts to /tmp
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Review board — verify the analyst's answer before final submission
Python computation formulas for Treasury data analysis. Use for percent change, CAGR, stdev, kurtosis, VaR, regression, Theil index, and other statistical calculations.
CPI-U annual averages (1929-2024) for inflation adjustment. Use when question mentions inflation, CPI, constant dollars, or real dollars.
How to read Treasury Bulletin data files in /app/resources/. Use when exploring resources or when JSON parsing fails.
U.S. fiscal year boundaries and calendar year conversions. Use when question mentions fiscal year, FY, or when you need to determine which months belong to a fiscal year.
How to accurately extract values from Treasury Bulletin tables. Use when reading tables with many columns, similar row labels, or when you need to verify you have the right cell.
| name | officeqa-tools |
| description | Table parser + computation recipes — save scripts to /tmp |
import re,sys,difflib
def clean(v):
v=re.sub(r'\s*[0-9]+/\s*$','',v) # strip footnotes like 3/
v=re.sub(r'^[rp]/\s*','',v) # strip r/ p/ prefixes
m=re.match(r'^\(([0-9,.]+)\)$',v) # (123) = -123
if m:v='-'+m.group(1)
return v.strip()
def to_num(v):
v=clean(v).replace(',','').replace('$','').replace('%','')
try:return float(v)
except:return None
def parse(lines):
p=sum(l.count('|') for l in lines);t=sum(l.count('\t') for l in lines)
d='|' if p>t else '\t'
rows=[]
for n,l in enumerate(lines):
pts=[c.strip() for c in l.split(d) if c.strip()]
if len(pts)>=2:rows.append((n,pts))
if not rows:return [],[],''
h=rows[0][1];data=rows[1:]
units=''
for l in lines[:15]:
m=re.search(r'(millions?|billions?|thousands?|percent)',l,re.I)
if m:units=m.group(0).lower();break
return h,data,units
args=sys.argv[1:];fp=args[0]
rf=cf=lr=None;sr=sc=fu=False;i=1
while i<len(args):
if args[i]=='--row':rf=args[i+1].lower();i+=2
elif args[i]=='--col':cf=args[i+1].lower();i+=2
elif args[i]=='--lines':m=re.match(r'(\d+)-(\d+)',args[i+1]);lr=(int(m[1])-1,int(m[2])) if m else None;i+=2
elif args[i]=='--rows':sr=True;i+=1
elif args[i]=='--cols':sc=True;i+=1
elif args[i]=='--fuzzy':fu=True;i+=1
else:i+=1
with open(fp,errors='replace') as f:al=f.readlines()
lines=al[lr[0]:lr[1]] if lr else al
h,data,units=parse(lines)
if not h:print(''.join(lines[:30]));sys.exit()
if units:print(f'[units: {units}]')
if sc:
for j,c in enumerate(h):print(f'[{j}] {c}')
elif sr:
for n,pts in data:print(f'[{n+1}] {pts[0]}')
else:
for n,pts in data:
label=pts[0]
if rf:
if fu:
ratio=difflib.SequenceMatcher(None,rf,label.lower()).ratio()
if ratio<0.5:continue
elif rf not in label.lower():continue
for j in range(1,len(pts)):
cn=h[j] if j<len(h) else f'c{j}'
if cf and cf not in cn.lower():continue
raw=pts[j] if j<len(pts) else ''
if not raw or raw=='nan':continue
num=to_num(raw)
if num is not None:print(f'{label} | {cn} = {num}')
else:print(f'{label} | {cn} = {clean(raw)}')
import re,sys,os
q=sys.argv[1];d=sys.argv[2] if len(sys.argv)>2 else '/app/resources'
for fp in sorted(os.listdir(d)):
if not fp.endswith('.txt'):continue
with open(os.path.join(d,fp),errors='replace') as f:lines=f.readlines()
for i,l in enumerate(lines):
if re.search(q,l,re.I):
ctx=''.join(lines[max(0,i-1):i+2]).rstrip()
print(f'[{fp}:{i+1}]\n{ctx}\n')
python3 /tmp/e.py FILE --cols # list columns
python3 /tmp/e.py FILE --rows # list rows with line numbers
python3 /tmp/e.py FILE --row "defense" --col "1953" # exact cell
python3 /tmp/e.py FILE --row "defense" # all cols for row
python3 /tmp/e.py FILE --row "natl def" --fuzzy # fuzzy match
python3 /tmp/g.py "keyword" /app/resources # search files
python3 /tmp/batch.py "metric" /app/resources # extract row from ALL files at once
If a file has multiple tables, find the right section first:
python3 /tmp/e.py FILE --rows # scan all row labels — find the section you need
grep -n "Table\|TABLE" FILE # find table boundaries by line number
python3 /tmp/e.py FILE --lines START-END --row "target" # extract within that section only
import re,sys,os
row=sys.argv[1];d=sys.argv[2] if len(sys.argv)>2 else '/app/resources'
for fp in sorted(os.listdir(d)):
if not fp.endswith('.txt'):continue
full=os.path.join(d,fp)
with open(full,errors='replace') as f:lines=f.readlines()
for l in lines:
if re.search(row,l,re.I):
vals=re.findall(r'[\d,]+\.?\d*',l)
if vals:print(f'{fp}: {" | ".join(vals)}')
break
Usage: python3 /tmp/batch.py "Rural Electrification" /app/resources
Gets the target row from ALL files in one call — saves turns on multi-file questions.
For multi-year: count extracted values vs expected count before computing.
python3 -c "
x=YOUR_RAW_VALUE; n=DECIMAL_PLACES
print(f'raw={x}')
print(f'rounded={round(x,n)}')
print(f'truncated={int(x*10**n)/10**n}')
"
If rounded and truncated differ, prefer truncated — Treasury legacy systems use fixed-point truncation. Write the truncated value to answer.txt.
Sum monthly values: extract 12 months → python3 -c "print(sum([v1,...,v12]))"
Pct change: extract old, new → python3 -c "print((NEW-OLD)/OLD*100)"
Stdev (sample): extract N values → python3 -c "import statistics; print(statistics.stdev([...]))"
Stdev (population): → statistics.pstdev([...])
Geometric mean: N positive values → statistics.geometric_mean([...])
Regression: paired (x,y) → r=statistics.linear_regression(xs,ys); print(r.slope,r.intercept)
Correlation: → statistics.correlation(xs,ys)
Theil index: N values → import math; v=[...]; m=sum(v)/len(v); print(sum((x/m)*math.log(x/m) for x in v)/len(v))
Z-score: series + target → print((TARGET-statistics.mean(v))/statistics.stdev(v))
Expected shortfall 95%: → v=sorted([...]); c=max(1,int(len(v)*0.05)); print(sum(v[:c])/c)
CAGR: start,end,years → print(((END/START)**(1/YEARS)-1)*100)
Compounded growth: → import math; print(math.log(END/START)/YEARS)