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README.md

@@ -35,6 +35,26 @@ 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
+
+This dataset has **two configurations**:
+
+### Configuration: `full` (Default)
+Complete daily trending data with 423,098 entries
+
+```python
+from datasets import load_dataset
+ds = load_dataset('ronantakizawa/github-top-projects', 'full')
+```
+
+### Configuration: `monthly`
+Top 25 repositories per month with 3,200 entries
+
+```python
+from datasets import load_dataset
+ds = load_dataset('ronantakizawa/github-top-projects', 'monthly')
+```
+
 ## 📁 Dataset Files
 
 ### 1. `github-trending-projects-full.csv` (19 MB)
@@ -154,38 +174,40 @@ This rewards both **consistency** (frequent appearances) and **position** (highe
 
 ## 💡 Usage Examples
 
-### Load Full Dataset (Python)
+### Load with Hugging Face Datasets (Recommended)
 
 ```python
-import pandas as pd
+from datasets import load_dataset
 
-# Load complete dataset
-df = pd.read_csv('github-trending-projects-full.csv')
+# Load complete daily dataset (423,098 entries)
+ds_full = load_dataset('ronantakizawa/github-top-projects', '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')
+df_monthly = ds_monthly['train'].to_pandas()
 
 # Filter to 2020+ (with star data)
-df_recent = df[df['date'] >= '2020-01-01']
+df_recent = df_full[df_full['date'] >= '2020-01-01']
 
-# Get top trending projects of 2024
-df_2024 = df[df['date'].str.startswith('2024')]
-top_2024 = df_2024.groupby(['repo_owner', 'name']).size().sort_values(ascending=False).head(10)
-print(top_2024)
+# Get November 2025 top 10
+nov_2025 = df_monthly[df_monthly['month'] == '2025-11'].head(10)
+print(nov_2025[['rank', 'repository', 'star_count', 'ranking_appearances']])
 ```
 
-### Load Monthly Top (Python)
+### Load Directly from CSV
 
 ```python
 import pandas as pd
 
-# Load pre-processed monthly rankings
+# 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')
 
-# Get November 2025 top 10
-nov_2025 = df_monthly[df_monthly['month'] == '2025-11'].head(10)
-print(nov_2025[['rank', 'repository', 'star_count', 'ranking_appearances']])
-
-# Find projects that consistently rank #1
-rank_1_projects = df_monthly[df_monthly['rank'] == 1]['repository'].value_counts()
-print(rank_1_projects.head(10))
+# Get top trending projects of 2024
+df_2024 = df_full[df_full['date'].str.startswith('2024')]
+top_2024 = df_2024.groupby(['repo_owner', 'name']).size().sort_values(ascending=False).head(10)
+print(top_2024)
 ```
 
 ### Time Series Analysis