Implementation:Mbzuai oryx Awesome LLM Post training Pd ExcelWriter Export

Knowledge Sources	Awesome-LLM-Post-training pandas.ExcelWriter
Domains	Data_Export, Trend_Analysis
Last Updated	2026-02-08 07:30 GMT

Overview

Concrete tool for exporting research trend results to a multi-sheet Excel workbook using pandas ExcelWriter with openpyxl.

Description

The export block in future_research_data.py uses pd.ExcelWriter with the openpyxl engine to create a single .xlsx file where each research keyword gets its own sheet. For each keyword in results_dict, the Data list is converted to a DataFrame and written to a sheet named after the keyword (truncated to 31 characters for Excel compatibility). The workbook is finalized when the context manager exits.

Usage

Execute this export after all keywords have been processed and results_dict is fully populated. Requires the openpyxl package to be installed.

Code Reference

Source Location

Repository: Awesome-LLM-Post-training
File: scripts/future_research_data.py
Lines: 93-101

Signature

# Multi-sheet Excel export block
excel_path = os.path.join(output_dir, "research_trends.xlsx")
with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:
    for keyword, info in results_dict.items():
        df = pd.DataFrame(info["Data"])
        sheet_name = keyword[:31]  # Excel sheet name limit
        df.to_excel(writer, sheet_name=sheet_name, index=False)

Import

import os
import pandas as pd
# openpyxl must be installed (used as engine)

I/O Contract

Inputs

Name	Type	Required	Description
results_dict	dict	Yes	Dict keyed by keyword, each value has "Category" and "Data" (list of year-count dicts)
output_dir	str	Yes	Directory path for the output Excel file

Outputs

Name	Type	Description
research_trends.xlsx	File	Multi-sheet Excel workbook with one sheet per keyword, each containing Year and Papers Published columns

Usage Examples

Export Trend Data to Excel

import os
import pandas as pd

output_dir = "results"
excel_path = os.path.join(output_dir, "research_trends.xlsx")

# results_dict populated from trend analysis
results_dict = {
    "RLHF": {
        "Category": "Reinforcement Learning",
        "Data": [
            {"Year": 2020, "Papers Published": 50},
            {"Year": 2021, "Papers Published": 120},
            {"Year": 2022, "Papers Published": 340},
        ]
    },
    "Direct Preference Optimization": {
        "Category": "Alignment",
        "Data": [
            {"Year": 2020, "Papers Published": 0},
            {"Year": 2021, "Papers Published": 5},
            {"Year": 2022, "Papers Published": 45},
        ]
    }
}

with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:
    for keyword, info in results_dict.items():
        df = pd.DataFrame(info["Data"])
        sheet_name = keyword[:31]
        df.to_excel(writer, sheet_name=sheet_name, index=False)

print(f"Excel saved: {excel_path}")

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