apb-pandas-utils

v1.2.5 suspicious
5.0
Medium Risk

Pandas and geopandas utils

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high network risk due to unexpected network calls, potentially indicating unauthorized access or data exfiltration attempts. Despite this, other risks such as shell execution, obfuscation, and credential harvesting are minimal.

  • High network risk
  • Unexpected network calls to auth endpoints
Per-check LLM notes
  • Network: The package makes unexpected network calls to auth endpoints, which may indicate unauthorized access attempts or data exfiltration.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, suggesting they may be new or less active, but no other red flags are present.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_geopandas_utils.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (357 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Separate author ("Ernesto Arredondo Martínez") and maintainer ("Port de Barcelona") listed
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 24 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in portdebarcelona/PLANOL-generic_python_packages
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • logic. """ session = requests.Session() retry_strategy = Retry( total=max_retries,
  • epo GIS') resp_auth = requests.post( f'{self.url_base}/repo_gis_pg/auth/acces-token'
  • Escales') resp_auth = requests.post( f'{self.url_base}/adm_escales/auth/acces-token'
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository portdebarcelona/PLANOL-generic_python_packages appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Ernesto Arredondo Martínez" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apb-pandas-utils 
Create a geographical data analysis tool using Python that leverages the 'apb-pandas-utils' library. Your application should enable users to upload CSV files containing geographical data (latitude/longitude or addresses), perform various analyses such as calculating distances between points, identifying clusters of locations, and visualizing the data on a map. Here’s a step-by-step guide on how to develop this mini-app:

1. **Setup Environment**: Ensure you have Python installed along with 'pandas', 'geopandas', 'matplotlib', 'folium', and 'apb-pandas-utils'.
2. **Data Input**: Develop a feature where users can upload a CSV file containing at least columns for latitude and longitude.
3. **Data Cleaning**: Utilize 'apb-pandas-utils' to clean and preprocess the uploaded data, handling missing values and ensuring all records are valid geographical points.
4. **Geographical Analysis**: Implement functionality to calculate distances between points using 'apb-pandas-utils', identify clusters using k-means clustering, and provide insights into the spatial distribution of the data.
5. **Visualization**: Use 'folium' to create an interactive map displaying the uploaded points, clusters, and paths between points. Allow users to customize the map view.
6. **Report Generation**: Enable users to generate PDF reports summarizing the analysis performed, including charts and maps created.
7. **User Interface**: Develop a simple web interface using Flask or Django where users can interact with your app, upload data, and visualize results.
8. **Documentation**: Provide comprehensive documentation detailing how to use the application, including API documentation if applicable.

Throughout the development process, focus on making the application user-friendly and efficient, leveraging 'apb-pandas-utils' for its powerful data manipulation and geographical utilities.

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