How I Code Faster - GitHub Copilot

Luke Barousse
3 Feb 202410:49

TLDRThe video discusses the use of GitHub Copilot to enhance Python coding efficiency. The speaker shares their experience using the tool over a year, highlighting its benefits for average and below-average coders by providing coding recommendations directly in popular code editors like VS Code. They recount building a Python web app with Copilot's assistance, particularly during integration with a live SQL database. A study is mentioned, indicating that users of such tools complete more tasks in less time. However, concerns are raised about a decline in code quality due to over-reliance on AI assistance. The video also covers the setup process for GitHub Copilot, including subscription options and editor integration. The speaker demonstrates building a data analytics project with Copilot, from generating a dataset to analyzing it in a Python notebook and creating a README. Despite encountering errors and needing to manually troubleshoot, the video concludes by emphasizing Copilot's role in speeding up the coding workflow and offering valuable insights into programming, even for those new to the field.

Takeaways

  • 🚀 GitHub Copilot is a tool that integrates into popular code editors like VS Code to provide coding recommendations and speed up the coding workflow.
  • 🎓 It's particularly useful for less experienced coders, as it can offer insights and solutions without the need for an open chat window.
  • 🛠️ The tool has been credited with not only speeding up task completion but also increasing developer fulfillment, according to a study analyzing its impact.
  • ⚠️ However, there are drawbacks, including a potential decrease in code quality due to over-reliance on AI assistance, as indicated by an increase in code churn.
  • 📈 Despite concerns, GitHub Copilot leads the field of AI coding assistants, with competitors like TabNine and Code Whisperer showing a trend of users switching to Copilot.
  • 💰 It requires a subscription for regular use, but is free for students and offers a business account for secure data handling in a professional setting.
  • 📚 The speaker used GitHub Copilot to build a Python web app from scratch, overcoming integration issues with a live SQL database.
  • 💡 The tool can generate a robust amount of code for data analysis, but it may require human intervention to fix errors, especially when the generated code doesn't match the dataset.
  • 📈 GitHub Copilot can help in generating visualizations and explanations for code, aiding new programmers in understanding the steps taken during data analysis.
  • 📝 For troubleshooting errors, it's suggested to keep relevant data and notebooks open to provide more context to the AI, which can improve the accuracy of its suggestions.
  • 🔍 The speaker mentions that while GitHub Copilot uses the advanced GPT-4 model, it sometimes reverts to GPT-3.5 Turbo, which may explain some inconsistencies in error resolution.
  • ⏱️ The entire project, from data analysis to generating a README, can be completed in less than 10 minutes with GitHub Copilot, significantly speeding up the workflow.

Q & A

  • What is GitHub Copilot and where can it be found?

    -GitHub Copilot is an AI-powered code assistant that provides coding recommendations. It is integrated into popular code editors, such as Visual Studio Code (VS Code).

  • How does GitHub Copilot help in coding?

    -GitHub Copilot assists developers by generating code suggestions, helping to speed up the coding workflow, and even addressing issues with live SQL database integration by providing relevant code snippets.

  • What are some potential drawbacks of using GitHub Copilot?

    -There can be a decrease in code quality due to over-reliance on the AI tool, leading to more code being reverted or updated shortly after being authored. This issue is projected to double by 2024 compared to the pre-AI baseline.

  • How can GitHub Copilot be installed for use in Visual Studio Code?

    -To install GitHub Copilot in VS Code, you need to enable it by going to the extensions section and install two extensions: one for using GitHub Copilot within the text editor and another for the chat interface.

  • What are the benefits of using GitHub Copilot for building a project?

    -GitHub Copilot can help build a project from scratch by generating code for data analysis, creating Python notebooks, and even summarizing results in a README file, significantly speeding up the development process.

  • How does the speaker handle errors generated during code execution with GitHub Copilot?

    -The speaker uses the 'fix' command provided by GitHub Copilot to address errors. If the suggested fixes do not resolve the issue, the speaker may manually troubleshoot or use external resources to find a solution.

  • What is the speaker's opinion on the effectiveness of GitHub Copilot in fixing errors?

    -The speaker finds that GitHub Copilot is not always effective in fixing errors and sometimes requires manual intervention from the programmer to understand and correct the issue.

  • What are the subscription options for GitHub Copilot?

    -GitHub Copilot offers a yearly subscription for $100, a free option for students, and a business account for work with secure data that includes enterprise-grade security, safety, and privacy.

  • How does the speaker use GitHub Copilot to generate a Python notebook?

    -The speaker uses a slash command to prompt GitHub Copilot to generate a new notebook, including steps for importing libraries, loading the CSV, and performing exploratory data analysis.

  • What is the speaker's experience with GitHub Copilot's error troubleshooting?

    -The speaker has experienced frustration with GitHub Copilot's error troubleshooting, noting that a significant portion of their time is spent fixing errors rather than writing new code.

  • What are the speaker's final thoughts on GitHub Copilot?

    -Despite the frustrations, the speaker acknowledges that GitHub Copilot has significantly sped up their workflow and helped with troubleshooting. They wish they had the tool when they first started coding.

  • How does the speaker use GitHub Copilot to generate a README file?

    -The speaker uses a prompt to instruct GitHub Copilot to generate text for a README file, detailing the contents, requirements, and usage of the project.

Outlines

00:00

🤖 Introduction to GitHub Copilot for Python Coding

The speaker shares their experience using GitHub Copilot to enhance their Python coding workflow over the past year. They explain that GitHub Copilot is an AI-powered tool integrated into popular code editors like Visual Studio Code (VS Code), which provides coding recommendations and can be particularly beneficial for less experienced coders. The speaker also mentions the tool's chat interface for asking questions and keeping them private. They recount building a Python web app with the help of Copilot, especially when encountering difficulties with integrating a live SQL database. They reference a study showing that users of such tools complete more tasks in less time and express more job satisfaction. However, they caution about potential drawbacks, such as decreased code quality due to overreliance on AI assistance. The video also includes a disclaimer about sponsorship and a promotion for the speaker's data analytics course.

05:01

💻 Setting Up and Using GitHub Copilot for a Data Analytics Project

The speaker provides a step-by-step guide on setting up GitHub Copilot within VS Code by installing necessary extensions and linking the account. They then demonstrate how to use Copilot to build a data analytics project from scratch. The project involves three main components: a dataset, a Python notebook for analysis, and a README file to summarize results. The speaker uses their own dataset about data analyst job postings in the US as an example. They show how to generate a new notebook with exploratory data analysis code and address an error encountered during the process. They discuss the limitations of Copilot in error troubleshooting and the importance of providing context to the AI for better performance. The speaker also emphasizes the need to cite sources when using others' code. Despite some frustrations, they acknowledge that Copilot is a leading AI coding assistant according to the 2023 developer survey. The video concludes with a demonstration of analyzing a specific column of the dataset and generating a README file.

10:03

📈 Finalizing the Project with GitHub Copilot

The speaker wraps up the video by detailing the final steps in completing the data analytics project with GitHub Copilot. They focus on analyzing the 'title' column of the dataset to understand the variety of job roles. Using Copilot, they generate a bar chart displaying the top job roles, with data analyst being the most frequent. The speaker then guides on generating a README file that details the project's contents, requirements, and usage. They successfully create the README using Copilot's assistance and paste it into their project. The video concludes with the speaker reflecting on the value of using Copilot to speed up their coding workflow and help with troubleshooting. They encourage viewers to like the video if they found it helpful and tease the next video in the series.

Mindmap

Keywords

💡GitHub Copilot

GitHub Copilot is an AI-powered code generation tool developed by GitHub and OpenAI. It integrates within popular code editors like Visual Studio Code (VS Code) to provide coding recommendations, making the coding process faster and more efficient. In the video, the speaker discusses their experience using GitHub Copilot to speed up their Python coding workflow and build a web app.

💡VS Code

Visual Studio Code (VS Code) is a popular, open-source code editor developed by Microsoft. It is known for its rich ecosystem of extensions, which enhance its functionality. In the context of the video, VS Code is the code editor of choice for the speaker, and it is where they use the GitHub Copilot extension to assist with coding.

💡AI coding assistance

AI coding assistance refers to the use of artificial intelligence to aid in the coding process. This can include generating code snippets, providing suggestions, and even fixing errors. The video discusses the impact of AI coding assistance on code quality and productivity, noting that it can lead to faster completion of tasks but also potential issues with code quality.

💡Data analytics

Data analytics is the process of examining raw data with the goal of drawing insights, making informed decisions, and solving problems. In the video, the speaker builds a data analytics project from scratch using GitHub Copilot, which involves analyzing a dataset of data analyst job postings in the US.

💡Python notebook

A Python notebook is an interactive document that allows users to write and execute Python code, along with narrative text, equations, and visualizations. It is often used for data analysis and machine learning. The video demonstrates the creation of a Python notebook using GitHub Copilot to showcase data analysis to non-coders.

💡Exploratory data analysis

Exploratory data analysis (EDA) is an approach to analyze data sets to summarize their main characteristics, often using visual methods. It is a crucial step in understanding the data and informing the subsequent modeling process. The video script mentions performing EDA as part of the data analytics project.

💡Code quality

Code quality refers to the level of excellence of source code in terms of its readability, efficiency, maintainability, and overall effectiveness. The video discusses the potential negative impact of AI coding assistance on code quality, citing an increase in code churn and the need for more frequent updates and revisions.

💡Chat interface

A chat interface is a feature that allows users to interact with a system or application through natural language conversation. In the context of the video, GitHub Copilot includes a chat interface that enables users to ask questions and receive responses, which can be quickly cleared to maintain privacy.

💡Error troubleshooting

Error troubleshooting is the process of identifying, analyzing, and resolving issues or 'errors' in a software program. The video script describes the speaker's experience troubleshooting errors during the coding process, noting that while GitHub Copilot can assist, it may not always be the best tool for fixing errors.

💡Readme file

A readme file, often named 'README.md', is a text file that provides information about other files in a directory or project. It typically includes details about the project's purpose, contents, requirements, and usage instructions. In the video, the speaker uses GitHub Copilot to generate a readme file for their data analytics project.

💡Large language model

A large language model refers to a complex and extensive artificial intelligence model designed to understand and generate human-like language. GitHub Copilot is powered by such a model, which is used to provide coding suggestions and answer questions through the chat interface. The video mentions the use of the model and its limitations when dealing with certain coding errors.

Highlights

The use of GitHub Copilot has significantly sped up the Python coding workflow for the speaker.

GitHub Copilot provides coding recommendations directly within popular code editors like VS Code.

The tool is particularly beneficial for average and below-average coders, as it eliminates the need for a separate chat window.

GitHub Copilot includes a chat interface for asking questions and can be quickly cleared for privacy.

The speaker built a Python web app from scratch with the assistance of GitHub Copilot, especially for integrating with a live SQL database.

A study found that users of GitHub Copilot completed more tasks in less than half the time.

Three out of four programmers reported increased fulfillment in their work due to using GitHub Copilot.

There are concerns about a decrease in code quality due to reliance on AI coding assistance.

GitHub Copilot is not always the best at fixing errors, as experienced by the speaker during the video.

The speaker suggests keeping the dataset open in an editor for better context when using GitHub Copilot.

GitHub Copilot uses different models, sometimes reverting to GPT 3.5 Turbo instead of the more advanced GPT 4.

Despite frustrations, GitHub Copilot leads the 2023 developer survey as the top AI coding assistant.

The speaker generated a robust amount of code to analyze a dataset using GitHub Copilot.

GitHub Copilot can help explain code to those new to programming through the 'explain' command.

The speaker was able to build a full data analytics project in less than 10 minutes using GitHub Copilot.

GitHub Copilot can generate a README file detailing the contents, requirements, and usage of a project.

The speaker recommends GitHub Copilot for speeding up the coding workflow and aiding in troubleshooting.