Environment:Mbzuai oryx Awesome LLM Post training Python Pandas
| Knowledge Sources | |
|---|---|
| Domains | Data_Processing, Data_Export |
| Last Updated | 2026-02-08 08:00 GMT |
Overview
Python environment with pandas and openpyxl for tabular data processing, CSV reading, JSON normalization, and multi-sheet Excel export.
Description
This environment provides the data processing layer for the research trend analysis pipeline. It includes pandas for reading CSV keyword files (pd.read_csv), creating DataFrames from query results, normalizing nested JSON structures (pd.json_normalize), and exporting to Excel. The openpyxl engine is required for pd.ExcelWriter to produce .xlsx files with multiple sheets. The json standard library module is used for progressive JSON checkpoint saving.
Usage
Use this environment for any data processing or export workflow. It is required by Pd_Read_Csv_Keywords (loading keyword CSV), Pd_ExcelWriter_Export (multi-sheet Excel export), and Json_Dump_Progressive (progressive JSON saving during trend analysis).
System Requirements
| Category | Requirement | Notes |
|---|---|---|
| OS | Any (Linux, macOS, Windows) | No OS-specific dependencies |
| Hardware | Standard CPU | No GPU required; moderate RAM for large DataFrames |
| Disk | 500MB free | For Excel and JSON output files |
Dependencies
System Packages
- No system-level packages required beyond Python itself
Python Packages
- `python` >= 3.6
- `pandas` (any recent version)
- `openpyxl` (required as Excel engine for pd.ExcelWriter)
Credentials
No credentials required for data processing operations.
Quick Install
# Install all required packages
pip install pandas openpyxl
Code Evidence
CSV loading from `scripts/future_research_data.py:27-28`:
csv_path = "assets/Keywords.csv"
prompts_df = pd.read_csv(csv_path)
Multi-sheet Excel export from `scripts/future_research_data.py:93-99`:
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"])
# Excel sheet names have a maximum of 31 characters
sheet_name = keyword[:31]
df.to_excel(writer, sheet_name=sheet_name, index=False)
JSON normalization from `scripts/deep_collection_sementic.py:142`:
df = pd.json_normalize(data)
df.to_excel("papers.xlsx", index=False)
Common Errors
| Error Message | Cause | Solution |
|---|---|---|
| `ModuleNotFoundError: No module named 'pandas'` | pandas not installed | `pip install pandas` |
| `ModuleNotFoundError: No module named 'openpyxl'` | openpyxl not installed | `pip install openpyxl` |
| `FileNotFoundError: assets/Keywords.csv` | Input CSV file missing from expected path | Ensure the Keywords.csv file exists at assets/Keywords.csv with Category and Research Keyword columns |
| `InvalidWorksheetName` | Excel sheet name exceeds 31 characters | The code truncates with `keyword[:31]` but very long keywords may produce collisions |
Compatibility Notes
- All platforms: Works on Linux, macOS, and Windows without modification.
- Excel limitations: Sheet names are capped at 31 characters by the Excel format specification. The scripts handle this via truncation (`keyword[:31]`).
- openpyxl engine: Explicitly specified as `engine='openpyxl'` in ExcelWriter. The default engine varies by pandas version, so explicit specification ensures consistent behavior.
- CSV encoding: The scripts use default encoding when reading CSV. Non-ASCII keywords may require specifying `encoding='utf-8'` in `pd.read_csv()`.