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Code Interpreters

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

GitHub Copilot

GitHub Copilot is an AI pair programmer that helps you write code faster and with less work. It draws context from comments and code to suggest individual lines and whole functions instantly. GitHub Copilot is powered by a generative AI model developed by GitHub, OpenAI, and Microsoft.

GitHub Copilot VS Code Extension

Continue.dev

Continue.dev is an open-source AI code assistant that integrates seamlessly with Visual Studio Code. It provides intelligent code completions, refactoring suggestions, and documentation generation to help you write code more efficiently.

Continue VS Code Extension

Codeium

Codeium is a free AI-powered code completion tool that supports over 20 programming languages. It offers a Visual Studio Code extension that provides features such as intelligent code completions, inline suggestions, and documentation generation.

Codeium VS Code Extension

ChatGPT Code Interpreter

OpenAI ChatGPT has integrated a Code Interpreter into its Assistants API. Code Interpreter can also be accessed from Azure AI.

Azure AI Copilot

Azure AI App Templates

Programmer Q/A

Phind.com - is a search engine optimized for developers and technical questions with simple explanations and relevant code snippets from the web, drawing from sources like StackOverFlow.

Writing code

Key Features:

  • Code Completion: Suggests code completions as you type, from single lines to entire functions.
  • Code Generation from Comments: Generates code based on natural language descriptions in comments.
  • Multiple Language Support: Works with a wide range of programming languages, including Python, JavaScript, TypeScript, Ruby, Go, Java, C++, C#, and more.
  • IDE Integration: Integrates seamlessly with popular IDEs like Visual Studio Code, Neovim, JetBrains IDEs, and more.
  • Context Awareness: Considers the surrounding code and comments to provide relevant suggestions.
  • Learning and Adaptation: Continuously learns from your coding style and improves its suggestions over time.

IDE Integration

  • Start Typing: Begin writing code as you normally would. In VS Code, Copilot will analyze the context and offer suggestions in grayed-out text.
  • Accepting Suggestions:
    • Tab: Press Tab to accept the entire suggestion.
    • Partial Acceptance: Use Ctrl+Right Arrow (Cmd+Right Arrow on macOS) to accept the next word of a suggestion.
    • Cycle through suggestions: Press Alt+] or Alt+[ (Option+] or Option+[ on macOS) to view other suggestions from Copilot.
  • Ignoring Suggestions: Simply keep typing to ignore a suggestion.
  • Triggering Suggestions Manually:
    • Press Ctrl+Enter (Cmd+Enter on macOS) to open the Copilot completion panel with a list of suggestions to choose from.
  • Writing Comments: Write comments in natural language to describe what you want the code to do. Copilot will attempt to generate the corresponding code.

Chat Use Cases

Linux Guru

ChatGPT is trained on common data science languages, like Python, Julia, and R. Use ChatGPT to help develop basic code or to explain and debug code you're trying to write.

Using ChatGPT can be a time savings, reducing the time it takes to look for the answers yourself over conventional search.

I want you to act as a humble data scientist who works a lot with Python and scientific visualization

Create a Python script which generates a visually pleasing and compelling heat map for a CSV dataset

Explain Code

You can also Code Interpreters to summarize code or to help explain its operation

I want you to act as a humble data scientist who works a lot with Linux 

Explain to me what the following code does:

$ find /home/www \( -type d -name .git -prune \) -o -type f -print0 | xargs -0 sed -i 's/subdomainA\.example\.com/subdomainB.example.com/g'
Write Regular Expressions

Regular Expressions, or regex is a bane of many programmers. GPT can write, edit, and explain complex regex

I want you to act as a regex generator. Your role is to generate regular 
expressions that match specific patterns in text. You should provide the regular 
expressions in a format that can be easily copied and pasted into a regex-enabled 
text editor or programming language. Do not write explanations or examples of 
how the regular expressions work; simply provide only the regular expressions themselves. 

remove any numbers from a string and replace them with a capital X
DevOps

ChatGPT can automate tasks and write tests

=== Prompt

```markdown
I want you to act as a DevOps engineer who specializes in SQL and Docker.

I am running an Ubuntu 22.04 server with a dockerized web service which appears to have a memory leak. Write a unit test for the PostgreSQL web server using SQL, JSON, or Python that can find the process which is causing the leak and restart the docker-compose service.
```
Optimize for Performance 

Make this code run faster and use less memory using BASH
import os

folder path

dir_path = os.getcwd()

list to store files

res = []

Iterate directory

for path in os.listdir(dir_path): # check if current path is a file if os.path.isfile(os.path.join(dir_path, path)): res.append(path) print(res)

Data Analysis

Imagine you are a researcher with a dataset of student performance metrics. You want to understand the correlation between study hours and exam scores. You could use a Code Interpreter as follows:

Data Analysis with Code Interpreter 
Analyze the attached CSV file 'student_data.csv'.

1.  Load the dataset into a Pandas DataFrame.
2.  Clean the data by handling any missing values.
3.  Generate a scatter plot to visualize the relationship between 'study_hours' and 'exam_scores'.
4.  Calculate the Pearson correlation coefficient between these two variables.
5.  Interpret the results and provide a brief summary.

a Code Interpreter should:

  1. Write Python code that loads the CSV file using the Pandas library.
  2. Implement data cleaning procedures, such as imputing or removing missing values.
  3. Use a library like Matplotlib or Seaborn to create a scatter plot.
  4. Calculate the correlation coefficient using appropriate statistical methods.
  5. Generate a textual interpretation of the results, explaining the correlation in the context of the research question.

Benefits of Using Code Interpreters

  • Accessibility: Enables researchers without extensive programming skills to perform complex data analyses.
  • Efficiency: Automates the coding process, saving time and reducing the potential for manual coding errors.
  • Reproducibility: Provides a clear record of the analysis steps, enhancing the reproducibility of research findings.
  • Iterative Analysis: Allows for quick iteration through different analysis approaches, facilitating exploratory data analysis.

Limitations

While Code Interpreters are powerful tools, they should be used with an understanding of their limitations. The generated code should be reviewed for correctness and appropriateness, especially in the context of complex or nuanced analyses. Researchers should also be aware of potential biases in data and interpretations.

Effective Use

  • Write Clear Comments: Well-written comments that clearly describe the intent of your code will significantly improve the quality of Copilot's suggestions.
  • Break Down Complex Tasks: For complex functions, break down the logic into smaller steps using comments. This helps Copilot understand the structure and generate more accurate code.
  • Iterate and Refine: Don't be afraid to reject suggestions and try different approaches. Copilot is a tool to assist you, not replace your own coding skills.
  • Review and Test: Always review and test the code generated by Copilot to ensure it meets your requirements and doesn't contain errors.
  • Provide Feedback: Use the feedback features within your IDE to rate suggestions. This helps improve Copilot over time.
  • Disable/Enable as Needed: If you find Copilot distracting or unhelpful for a particular task, you can temporarily disable it in your IDE settings.

Ethical Considerations

  • Code Originality: While Copilot is trained on a massive dataset of public code, it's important to be aware of potential issues related to code originality and licensing. Always review the generated code and ensure it complies with applicable licenses.
  • Security: Be cautious when using Copilot to generate code that handles sensitive data or interacts with external systems. Review the code carefully for potential security vulnerabilities.
  • Bias: Like any AI model, Copilot can reflect biases present in the training data. Be mindful of potential biases in the generated code and strive to write inclusive and unbiased code.

Further Resources