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"""
Table Generator - Generate tables from text data
"""
import re
from typing import Dict, List, Optional, Tuple
import logging
logger = logging.getLogger(__name__)
class TableGenerator:
"""
Automatically generate tables from text content.
"""
def __init__(self):
"""Initialize table generator."""
pass
def extract_table_data(self, text: str) -> List[List[str]]:
"""
Extract potential table data from text.
Args:
text: Input text
Returns:
List of rows (each row is list of cells)
"""
# Look for tabular patterns or lists
lines = text.split("\n")
table_data = []
for line in lines:
# Split by multiple spaces or commas
cells = re.split(r"\s{2,}|,\s*", line.strip())
if len(cells) > 1 and all(cell.strip() for cell in cells):
table_data.append([cell.strip() for cell in cells])
return table_data if table_data else self._create_default_table()
def _create_default_table(self) -> List[List[str]]:
"""Create a default example table."""
return [
["Item", "Description", "Value"],
["Example 1", "Sample data", "100"],
["Example 2", "Sample data", "200"],
]
def generate_summary_table(self, text: str) -> List[List[str]]:
"""
Generate summary table from text content.
Args:
text: Text to summarize in table format
Returns:
Table data
"""
# Extract key points and create summary table
sentences = re.split(r"(?<=[.!?])\s+", text)[:5] # First 5 sentences
table = [["Point", "Description"]]
for i, sentence in enumerate(sentences, 1):
# Truncate long sentences
desc = sentence[:50] + "..." if len(sentence) > 50 else sentence
table.append([f"Point {i}", desc])
return table
def generate_comparison_table(self, items: List[str], attributes: List[str], data: Dict) -> List[List[str]]:
"""
Generate comparison table.
Args:
items: Items to compare
attributes: Comparison attributes
data: Data dictionary {item: {attribute: value}}
Returns:
Comparison table
"""
table = [["Item"] + attributes]
for item in items:
row = [item]
for attr in attributes:
value = data.get(item, {}).get(attr, "-")
row.append(str(value))
table.append(row)
return table
def generate_statistics_table(self, data_points: List[float]) -> List[List[str]]:
"""
Generate statistics summary table.
Args:
data_points: List of numerical data points
Returns:
Statistics table
"""
if not data_points:
return [["Metric", "Value"], ["Average", "N/A"], ["Min", "N/A"], ["Max", "N/A"]]
avg = sum(data_points) / len(data_points)
min_val = min(data_points)
max_val = max(data_points)
med_val = sorted(data_points)[len(data_points) // 2]
return [
["Metric", "Value"],
["Count", str(len(data_points))],
["Average", f"{avg:.2f}"],
["Minimum", f"{min_val:.2f}"],
["Maximum", f"{max_val:.2f}"],
["Median", f"{med_val:.2f}"],
]
def format_as_markdown(self, table: List[List[str]]) -> str:
"""
Format table as Markdown.
Args:
table: Table data
Returns:
Markdown table string
"""
if not table:
return ""
# Create header
md_table = "| " + " | ".join(table[0]) + " |\n"
md_table += "|" + "|".join(["---" for _ in table[0]]) + "|\n"
# Add rows
for row in table[1:]:
md_table += "| " + " | ".join(str(cell) for cell in row) + " |\n"
return md_table
def format_as_html(self, table: List[List[str]]) -> str:
"""
Format table as HTML.
Args:
table: Table data
Returns:
HTML table string
"""
if not table:
return "<table></table>"
html = "<table border='1' cellpadding='10'>\n"
# Header row
html += "<thead><tr>"
for cell in table[0]:
html += f"<th>{cell}</th>"
html += "</tr></thead>\n"
# Body rows
html += "<tbody>\n"
for row in table[1:]:
html += "<tr>"
for cell in row:
html += f"<td>{cell}</td>"
html += "</tr>\n"
html += "</tbody>\n"
html += "</table>"
return html
def format_as_csv(self, table: List[List[str]]) -> str:
"""
Format table as CSV.
Args:
table: Table data
Returns:
CSV string
"""
import csv
import io
output = io.StringIO()
writer = csv.writer(output)
for row in table:
writer.writerow(row)
return output.getvalue()
def generate_from_dataframe(self, df_dict: Dict) -> List[List[str]]:
"""
Generate table from dataframe-like dictionary.
Args:
df_dict: Dictionary with column names as keys and data lists as values
Returns:
Table data
"""
if not df_dict:
return []
# Create header
headers = list(df_dict.keys())
table = [headers]
# Get number of rows
num_rows = max(len(v) for v in df_dict.values()) if df_dict.values() else 0
# Create rows
for i in range(num_rows):
row = []
for col_name in headers:
value = df_dict[col_name][i] if i < len(df_dict[col_name]) else "-"
row.append(str(value))
table.append(row)
return table
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