Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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Word document manipulation with python-docx - handling split placeholders, headers/footers, nested tables
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
The #1 issue with Word templates: Word often splits placeholder text across multiple XML runs. For example, {{CANDIDATE_NAME}} might be stored as:
{{CANDIDATE_NAME}}This happens due to spell-check, formatting changes, or Word's internal XML structure.
# DON'T DO THIS - won't find split placeholders
for para in doc.paragraphs:
for run in para.runs:
if '{{NAME}}' in run.text: # Won't match if split!
run.text = run.text.replace('{{NAME}}', value)
import re
def replace_placeholder_robust(paragraph, placeholder, value):
"""Replace placeholder that may be split across runs."""
full_text = paragraph.text
if placeholder not in full_text:
return False
# Find all runs and their positions
runs = paragraph.runs
if not runs:
return False
# Build mapping of character positions to runs
char_to_run = []
for run in runs:
for char in run.text:
char_to_run.append(run)
# Find placeholder position
start_idx = full_text.find(placeholder)
end_idx = start_idx + len(placeholder)
# Get runs that contain the placeholder
if start_idx >= len(char_to_run):
return False
start_run = char_to_run[start_idx]
# Clear all runs and rebuild with replacement
new_text = full_text.replace(placeholder, str(value))
# Preserve first run's formatting, clear others
for i, run in enumerate(runs):
if i == 0:
run.text = new_text
else:
run.text = ''
return True
import re
from docx import Document
def replace_all_placeholders(doc, data):
"""Replace all {{KEY}} placeholders with values from data dict."""
def replace_in_paragraph(para):
"""Replace placeholders in a single paragraph."""
text = para.text
# Find all placeholders
pattern = r'\{\{([A-Z_]+)\}\}'
matches = re.findall(pattern, text)
if not matches:
return
# Build new text with replacements
new_text = text
for key in matches:
placeholder = '{{' + key + '}}'
if key in data:
new_text = new_text.replace(placeholder, str(data[key]))
# If text changed, rebuild paragraph
if new_text != text:
# Clear all runs, put new text in first run
runs = para.runs
if runs:
runs[0].text = new_text
for run in runs[1:]:
run.text = ''
# Process all paragraphs
for para in doc.paragraphs:
replace_in_paragraph(para)
# Process tables (including nested)
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
for para in cell.paragraphs:
replace_in_paragraph(para)
# Handle nested tables
for nested_table in cell.tables:
for nested_row in nested_table.rows:
for nested_cell in nested_row.cells:
for para in nested_cell.paragraphs:
replace_in_paragraph(para)
# Process headers and footers
for section in doc.sections:
for para in section.header.paragraphs:
replace_in_paragraph(para)
for para in section.footer.paragraphs:
replace_in_paragraph(para)
Headers/footers are separate from main document body:
from docx import Document
doc = Document('template.docx')
# Access headers/footers through sections
for section in doc.sections:
# Header
header = section.header
for para in header.paragraphs:
# Process paragraphs
pass
# Footer
footer = section.footer
for para in footer.paragraphs:
# Process paragraphs
pass
Tables can contain other tables. Must recurse:
def process_table(table, data):
"""Process table including nested tables."""
for row in table.rows:
for cell in row.cells:
# Process paragraphs in cell
for para in cell.paragraphs:
replace_in_paragraph(para, data)
# Recurse into nested tables
for nested_table in cell.tables:
process_table(nested_table, data)
For {{IF_CONDITION}}...{{END_IF_CONDITION}} patterns:
def handle_conditional(doc, condition_key, should_include, data):
"""Remove or keep conditional sections."""
start_marker = '{{IF_' + condition_key + '}}'
end_marker = '{{END_IF_' + condition_key + '}}'
for para in doc.paragraphs:
text = para.text
if start_marker in text and end_marker in text:
if should_include:
# Remove just the markers
new_text = text.replace(start_marker, '').replace(end_marker, '')
# Also replace any placeholders inside
for key, val in data.items():
new_text = new_text.replace('{{' + key + '}}', str(val))
else:
# Remove entire content between markers
new_text = ''
# Apply to first run
if para.runs:
para.runs[0].text = new_text
for run in para.runs[1:]:
run.text = ''
from docx import Document
import json
import re
def fill_template(template_path, data_path, output_path):
"""Fill Word template handling all edge cases."""
# Load data
with open(data_path) as f:
data = json.load(f)
# Load template
doc = Document(template_path)
def replace_in_para(para):
text = para.text
pattern = r'\{\{([A-Z_]+)\}\}'
if not re.search(pattern, text):
return
new_text = text
for match in re.finditer(pattern, text):
key = match.group(1)
placeholder = match.group(0)
if key in data:
new_text = new_text.replace(placeholder, str(data[key]))
if new_text != text and para.runs:
para.runs[0].text = new_text
for run in para.runs[1:]:
run.text = ''
# Main document
for para in doc.paragraphs:
replace_in_para(para)
# Tables (with nesting)
def process_table(table):
for row in table.rows:
for cell in row.cells:
for para in cell.paragraphs:
replace_in_para(para)
for nested in cell.tables:
process_table(nested)
for table in doc.tables:
process_table(table)
# Headers/Footers
for section in doc.sections:
for para in section.header.paragraphs:
replace_in_para(para)
for para in section.footer.paragraphs:
replace_in_para(para)
doc.save(output_path)
# Usage
fill_template('template.docx', 'data.json', 'output.docx')
doc.paragraphscell.tables{{IF_...}} markers after processing아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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Redact text from PDF documents for blind review anonymization
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Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
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Analyze failed GitHub Action jobs for a pull request.
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Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
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