Mapping Table References in JSON to Excel Values with Python
This article explores how to efficiently map table references from a JSON file to their corresponding values in an Excel file using Python. We'll address a common problem faced by data analysts and developers who need to seamlessly integrate data from various sources.
The Challenge
Imagine you have an Excel file containing a list of requirements, some of which reference tables defined in a separate JSON file. Your task is to automatically extract the relevant table data and integrate it into the Excel file, ultimately generating a LaTeX document for easy reporting.
Leveraging Python and Stack Overflow Insights
To solve this, we'll use Python's powerful data manipulation libraries like Pandas and JSON handling capabilities. We'll also draw inspiration from Stack Overflow answers to guide our solution. Let's break down the process:
1. Data Loading
We begin by loading the Excel and JSON files using pandas and json, respectively:
import pandas as pd
import json
# Read Excel file
df = pd.read_excel('input.xlsx')
# Read JSON file
with open('tables.json', 'r') as file:
tables = json.load(file)
2. Identifying Table References
We need to identify table references within the Excel data. Here, we'll use regular expressions:
import re
# Extract requirement column
requirements = df.iloc[:, 0]
# Regular expression to find table references
pattern = r"table (\d+)"
# Create a new column to store table references
df['Table Reference'] = requirements.apply(lambda req: re.search(pattern, req) if re.search(pattern, req) else None)
3. Mapping References to Values
We'll map the extracted table references to their corresponding values from the JSON file:
# Extract table number from the match
df['Table Number'] = df['Table Reference'].apply(lambda match: int(match.group(1)) if match else None)
# Map table numbers to values
df['Table Data'] = df['Table Number'].apply(lambda table_number: tables[f"table {table_number}"] if table_number else None)
4. LaTeX Generation
Finally, we can create a LaTeX document containing the integrated information:
import openpyxl
# Create a new Excel workbook
wb = openpyxl.Workbook()
ws = wb.active
# Write the data to the Excel sheet
for row in range(len(df)):
for col in range(len(df.columns)):
ws.cell(row=row+1, column=col+1).value = df.iloc[row, col]
# Save the Excel file
wb.save('output.xlsx')
# Generate LaTeX code
latex_code = r"""
\documentclass{article}
\begin{document}
\section{Requirements}
\begin{tabular}{|l|l|l|}
\hline
Requirement & Table Reference & Table Data \\
\hline
"""
# Iterate through the DataFrame and add data to the LaTeX table
for index, row in df.iterrows():
latex_code += f"{row['Requirement']} & {row['Table Reference']} & {row['Table Data']} \\\\ \hline"
latex_code += r"""
\end{tabular}
\end{document}
"""
# Write the LaTeX code to a file
with open('output.tex', 'w') as file:
file.write(latex_code)
Key Takeaways
- Regular expressions are powerful tools for pattern matching in text data.
- Pandas simplifies data manipulation, enabling easy integration of data from different sources.
- JSON is a versatile format for storing structured data.
Further Enhancements
- You can extend this code to handle cases where multiple table references exist within a single requirement.
- You can customize the LaTeX output to achieve specific formatting and table styles.
- Consider adding error handling to gracefully manage cases where table references are invalid or missing.
This guide provides a practical framework for mapping table references in JSON to corresponding values in Excel using Python. By adapting this approach, you can efficiently automate data integration and reporting processes for a wide range of applications.
