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Implementation:Mbzuai oryx Awesome LLM Post training Pd Read Csv Keywords

From Leeroopedia


Knowledge Sources
Domains Data_Collection, Data_Ingestion
Last Updated 2026-02-08 07:30 GMT

Overview

Concrete tool for loading categorized research keywords from a CSV file using pandas for trend analysis queries.

Description

The pd.read_csv call in future_research_data.py loads a CSV file containing two columns: Category (research area grouping) and Research Keyword (specific query term). The resulting DataFrame is iterated row by row in the main processing loop, with each keyword driving a set of yearly API queries against Semantic Scholar.

Usage

Call this at the start of the research trend analysis pipeline. The CSV file must exist at the specified path and must contain the required columns. The loaded DataFrame drives all subsequent API queries.

Code Reference

Source Location

Signature

# Wrapper usage of pandas.read_csv
csv_path = "assets/Keywords.csv"
prompts_df = pd.read_csv(csv_path)
# prompts_df columns: ['Category', 'Research Keyword']

Import

import pandas as pd

I/O Contract

Inputs

Name Type Required Description
csv_path str Yes Path to the keywords CSV file (hardcoded as "assets/Keywords.csv")

Required CSV Schema:

Column Type Description
Category str Research area grouping (e.g., "Reinforcement Learning", "NLP")
Research Keyword str Specific query term for Semantic Scholar search

Outputs

Name Type Description
prompts_df pandas.DataFrame DataFrame with rows of category-keyword pairs, iterated in the main loop

Usage Examples

Loading Keywords for Trend Analysis

import pandas as pd

# Load research keywords from CSV
csv_path = "assets/Keywords.csv"
prompts_df = pd.read_csv(csv_path)

# Iterate over keywords
for index, row in prompts_df.iterrows():
    category = row['Category']
    keyword = row['Research Keyword']
    print(f"Processing: '{keyword}' in '{category}'")
    # ... query API for each keyword

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