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Robust Markdown table parsing and DataFrame extraction.
"""
import io
import re
from typing import Optional, Tuple, List
import pandas as pd
def extract_markdown_table_block(text: str) -> Optional[str]:
"""Extracts the markdown table section from generated model text.
Handles markdown code blocks (```markdown ... ``` or ``` ... ```) and
raw pipe-delimited table patterns.
"""
if not text or not isinstance(text, str):
return None
# Check for markdown code fences first
code_block_match = re.search(r"```(?:markdown|table)?\s*([\s\S]*?)\s*```", text, re.IGNORECASE)
candidate_text = code_block_match.group(1) if code_block_match else text
# Look for pipe-separated table lines (| ... | ... |)
lines = candidate_text.strip().split("\n")
table_lines: List[str] = []
in_table = False
for line in lines:
stripped = line.strip()
if stripped.startswith("|") and stripped.endswith("|") and "|" in stripped[1:-1]:
in_table = True
table_lines.append(stripped)
elif in_table:
# End of table encountered
break
if len(table_lines) >= 2:
return "\n".join(table_lines)
return None
def parse_markdown_to_dataframe(text: str) -> Tuple[Optional[pd.DataFrame], bool]:
"""Parses a markdown table string into a pandas DataFrame.
Returns:
Tuple of (DataFrame or None, is_valid_boolean)
"""
table_str = extract_markdown_table_block(text)
if not table_str:
return None, False
try:
lines = [line.strip() for line in table_str.strip().split("\n") if line.strip()]
if len(lines) < 2:
return None, False
# Header line
header_line = lines[0]
header_cells = [c.strip() for c in header_line.split("|")[1:-1]]
if not header_cells:
return None, False
# Check separator line (e.g. |---|---| or |:---|---:|)
separator_line = lines[1]
if not re.match(r"^\|?(\s*:?-+:?\s*\|?)+\s*$", separator_line):
# Not a standard separator, but let's check if rows start immediately
data_start_idx = 1
else:
data_start_idx = 2
data_rows = []
for line in lines[data_start_idx:]:
row_cells = [c.strip() for c in line.split("|")[1:-1]]
if not row_cells:
continue
# Pad or truncate to match header length
if len(row_cells) < len(header_cells):
row_cells.extend([""] * (len(header_cells) - len(row_cells)))
elif len(row_cells) > len(header_cells):
row_cells = row_cells[:len(header_cells)]
data_rows.append(row_cells)
if not data_rows:
return None, False
df = pd.DataFrame(data_rows, columns=header_cells)
# Attempt to convert numeric columns to float
for col in df.columns:
try:
# Remove common formatting like commas or units if applicable
cleaned_col = df[col].astype(str).str.replace(",", "", regex=False)
df[col] = pd.to_numeric(cleaned_col)
except Exception:
pass
return df, True
except Exception:
return None, False
def dataframe_to_markdown(df: pd.DataFrame) -> str:
"""Converts a pandas DataFrame into a clean Markdown table."""
return df.to_markdown(index=False)
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