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README.md

@@ -35,29 +35,29 @@ This dataset captures the evolution of GitHub's trending repositories over time,
 - ⭐ **89.8%** scraping success rate from Wayback Machine
 - 🏆 **Pre-processed monthly rankings** with weighted scoring
 
-## 🔧 Dataset Configurations
+## 🔧 Dataset Structure
 
-This dataset has **two configurations**:
+This dataset is organized into **two subdirectories**:
 
-### Configuration: `full` (Default)
-Complete daily trending data with 423,098 entries
+### `full/` - Complete Daily Data
+423,098 trending entries (2013-2025)
 
 ```python
 from datasets import load_dataset
-ds = load_dataset('ronantakizawa/github-top-projects', 'full')
+ds = load_dataset('ronantakizawa/github-top-projects', data_dir='full')
 ```
 
-### Configuration: `monthly`
-Top 25 repositories per month with 3,200 entries
+### `monthly/` - Monthly Top 25
+3,200 entries (top 25 per month with weighted scoring)
 
 ```python
 from datasets import load_dataset
-ds = load_dataset('ronantakizawa/github-top-projects', 'monthly')
+ds = load_dataset('ronantakizawa/github-top-projects', data_dir='monthly')
 ```
 
 ## 📁 Dataset Files
 
-### 1. `github-trending-projects-full.csv` (19 MB)
+### `full/data.csv` (19 MB)
 **Complete daily trending data** - All 423,098 entries
 
 | Column | Type | Description |
@@ -76,7 +76,7 @@ ds = load_dataset('ronantakizawa/github-top-projects', 'monthly')
 - Creating custom aggregations (weekly, yearly, etc.)
 - Studying viral repository behavior
 
-### 2. `github-top-projects-by-month.csv` (211 KB)
+### `monthly/data.csv` (211 KB)
 **Monthly top 25 repositories** - Pre-processed with weighted scoring
 
 | Column | Type | Description |
@@ -180,11 +180,11 @@ This rewards both **consistency** (frequent appearances) and **position** (highe
 from datasets import load_dataset
 
 # Load complete daily dataset (423,098 entries)
-ds_full = load_dataset('ronantakizawa/github-top-projects', 'full')
+ds_full = load_dataset('ronantakizawa/github-top-projects', data_dir='full')
 df_full = ds_full['train'].to_pandas()
 
 # Load monthly top 25 dataset (3,200 entries)
-ds_monthly = load_dataset('ronantakizawa/github-top-projects', 'monthly')
+ds_monthly = load_dataset('ronantakizawa/github-top-projects', data_dir='monthly')
 df_monthly = ds_monthly['train'].to_pandas()
 
 # Filter to 2020+ (with star data)
@@ -201,8 +201,8 @@ print(nov_2025[['rank', 'repository', 'star_count', 'ranking_appearances']])
 import pandas as pd
 
 # Download files from the dataset page, then:
-df_full = pd.read_csv('github-trending-projects-full.csv')
-df_monthly = pd.read_csv('github-top-projects-by-month.csv')
+df_full = pd.read_csv('full/data.csv')
+df_monthly = pd.read_csv('monthly/data.csv')
 
 # Get top trending projects of 2024
 df_2024 = df_full[df_full['date'].str.startswith('2024')]