FEAT: Add GuepardDataFrame for automated version tracking and rollback functionality

Author: kobbinour13

guepard_pandas/guepard_dataframe.py

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+import pandas as pd
+import requests
+
+class GuepardDataFrame(pd.DataFrame):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.api_url = "https://api.guepard.com"
+        self.dataset_id = kwargs.get('dataset_id', 'default')
+    
+    def commit(self, message=""):
+        version_id = self._generate_version_id()
+        data = self.to_parquet()
+        response = requests.post(f"{self.api_url}/datasets/{self.dataset_id}/versions",
+                                 files={"data": data},
+                                 data={"message": message, "version_id": version_id})
+        response.raise_for_status()
+        return version_id
+    
+    def list_versions(self):
+        response = requests.get(f"{self.api_url}/datasets/{self.dataset_id}/versions")
+        response.raise_for_status()
+        return response.json()
+    
+    def rollback(self, version_id):
+        response = requests.get(f"{self.api_url}/datasets/{self.dataset_id}/versions/{version_id}")
+        response.raise_for_status()
+        data = response.content
+        df = pd.read_parquet(data)
+        self.__init__(df)
+    
+    def next_version(self):
+        return self.commit()
+
+    def _generate_version_id(self):
+        from datetime import datetime
+        return datetime.now().strftime("%Y%m%d_%H%M%S")

guepard_pandas/readme.md

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+# Guepard-Pandas Wrapper
+
+## Introduction
+The Guepard-Pandas Wrapper is an extension of the Pandas DataFrame that integrates seamlessly with Guepard’s data versioning capabilities. This wrapper allows data engineers to use DataFrames as usual while automatically tracking versions, enabling rollback, and maintaining historical snapshots without additional effort.
+
+## Features
+- Automated version tracking for DataFrames.
+- Easy rollback to previous states.
+- Seamless integration with Guepard, ensuring efficient storage and retrieval.
+
+## Example Usage
+```python
+import pandas as pd
+from guepard_pandas.guepard_dataframe import GuepardDataFrame
+
+# Load a DataFrame
+df = GuepardDataFrame(pd.read_csv("data.csv"), dataset_id="1234")
+
+# Modify it
+df["new_col"] = df["existing_col"] * 2
+
+# Commit the changes
+df.commit("Added new column")
+
+# List versions
+print(df.list_versions())
+
+# Rollback to an older version
+df.rollback(version_id="20240326_123456")
+```
+
+## Implementation Plan
+1. Prototype Development
+   - Extend `pd.DataFrame` with versioning methods.
+   - Implement basic version storage using Parquet or Pickle.
+
+2. Integration with Guepard API
+   - Store versions directly in Guepard’s data management system.
+   - Optimize performance for large DataFrames.
+
+3. Testing & Optimization
+   - Benchmark storage and retrieval performance.
+   - Validate Pandas compatibility.
+
+## Conclusion
+This wrapper offers an elegant solution to integrate version control within Pandas using Guepard, enhancing data engineering workflows while maintaining full compatibility with Pandas.
+
+Next Steps: Review feedback and develop a proof of concept.