api-to-dataframe

v2.2.0 safe
4.0
Medium Risk

A package to convert API responses to pandas dataframe

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe due to its minimal risk scores across various categories, including no presence of shell execution, obfuscation, or credential harvesting patterns. However, the low activity and limited maintainer history slightly elevate the metadata risk.

  • Low risk in network, shell, obfuscation, and credential areas.
  • Potential unreliability due to low maintainer activity.
Per-check LLM notes
  • Network: Making network calls to endpoints is common for packages that fetch data from APIs and convert them into dataframes.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low activity and the maintainer has limited history with PyPI, indicating potential unreliability.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/IvanildoBarauna/api-to-dataframe/blob/mai
  • Detailed PyPI description (3089 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 3 type-annotated function signatures (partial)
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in IvanildoBarauna/api-to-dataframe
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • he request response = requests.get(endpoint, timeout=connection_timeout, headers=headers)
✓ 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: outlook.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "IvanildoBarauna" 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 api-to-dataframe 
Create a fully-functional mini-app that fetches financial data from an API and converts it into a pandas DataFrame using the 'api-to-dataframe' package. This app will allow users to input a stock ticker symbol and a date range, then retrieve historical stock prices for that specific period. Here are the steps and features you need to implement:

1. **Setup**: Begin by installing the necessary packages including 'api-to-dataframe', 'pandas', and any other dependencies needed for handling API requests.
2. **API Integration**: Integrate an API that provides historical stock price data. Popular choices include Alpha Vantage or IEX Cloud. Ensure you have the appropriate API key for authentication.
3. **User Input**: Design a simple user interface where users can enter a stock ticker symbol and select a start and end date for the data retrieval.
4. **Data Retrieval & Conversion**: Use the 'api-to-dataframe' package to convert the retrieved JSON data from the API into a pandas DataFrame. This step involves parsing the API response and converting it into a structured format suitable for analysis.
5. **Data Analysis**: Implement basic data analysis functions such as calculating daily returns, plotting the closing prices over time, and identifying peaks and troughs in the stock price movement.
6. **Visualization**: Visualize the data using matplotlib or seaborn libraries to provide graphical representations of the stock price movements within the specified date range.
7. **Output Options**: Allow users to export the analyzed DataFrame into different formats like CSV or Excel for further offline analysis.
8. **Error Handling**: Ensure robust error handling to manage issues such as invalid inputs, network errors, or API rate limits.
9. **Documentation**: Write clear documentation explaining how to use the app, including setup instructions and examples of how to interpret the output.

By following these steps, your mini-app will not only demonstrate the power of 'api-to-dataframe' but also provide practical value to anyone interested in analyzing historical stock market data.

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