Implementation:Mbzuai oryx Awesome LLM Post training Json Normalize Excel Export

Knowledge Sources	Awesome-LLM-Post-training pandas.json_normalize DataFrame.to_excel
Domains	Data_Collection, Data_Export
Last Updated	2026-02-08 07:30 GMT

Overview

Concrete tool for exporting collected paper data to JSON and flattened Excel formats using pandas.

Description

The export block in deep_collection_sementic.py performs two operations: first, it writes the complete data list to a JSON file using json.dump with indent formatting; second, it uses pd.json_normalize to flatten the nested paper dictionaries into a tabular DataFrame and exports it to Excel via df.to_excel. This produces both a machine-readable JSON archive and a human-browsable spreadsheet of the collected corpus.

Usage

Execute this export step after the main collection loop completes. It requires the complete data list of paper detail dictionaries produced by the fetch pipeline.

Code Reference

Source Location

• Repository: Awesome-LLM-Post-training
• File: scripts/deep_collection_sementic.py
• Lines: 130-144

Signature

# Final export block (not a function; inline script logic)

# JSON export
json_filename = "papers.json"
with open(json_filename, "w", encoding="utf-8") as f:
    json.dump(data, f, indent=4)

# Excel export via pandas normalization
df = pd.json_normalize(data)
df.to_excel("papers.xlsx", index=False)

Import

import json
import pandas as pd

I/O Contract

Inputs

Name	Type	Required	Description
data	list[dict]	Yes	Complete list of paper detail dicts from the collection pipeline

Outputs

Name	Type	Description
papers.json	File	Full JSON export with nested structure preserved (indent=4)
papers.xlsx	File	Flattened Excel spreadsheet with one row per paper, nested fields as dotted column names

Usage Examples

Standard Export After Collection

import json
import pandas as pd

# Assume 'data' is the collected list of paper dicts
json_filename = "papers.json"
with open(json_filename, "w", encoding="utf-8") as f:
    json.dump(data, f, indent=4)

print(f"JSON saved: {json_filename}")

# Flatten nested structure and export to Excel
df = pd.json_normalize(data)
df.to_excel("papers.xlsx", index=False)
print(f"Excel saved: papers.xlsx ({len(df)} rows)")

Related Pages

Implements Principle

• Principle:Mbzuai_oryx_Awesome_LLM_Post_training_Data_Export_Pipeline

Requires Environment

• Environment:Mbzuai_oryx_Awesome_LLM_Post_training_Python_Requests