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AI continues to evolve and become more ubiquitous in nearly every industry. One of the most prominent and widely used AI applications is Open AI’s ChatGPT, which can generate answers and even code based on natural language prompts.
If you code in Python, you may be wondering if ChatGPT can be used in your workflows. The short answer is yes. The long answer is yes, but it’s important to understand its capabilities and limitations. In today’s blog, we’ll answer the question as to whether or not ChatGPT can code in Python and give you the rest of the long answer. Read on to learn more!
Understanding AI-Language Models
Before diving into the capabilities of ChatGPT, it’s important to understand AI language models. Artificial intelligence language models can analyze and process massive amounts of text data, allowing them to learn patterns and relationships between words and phrases.
ChatGPT, which stands for Generative Pre-Trained Transformer, is an example of an AI language model. When fed a prompt, it can generate humanlike text. These natural language processing capabilities have made AI more accessible than ever. Rather than having to understand programming to provide AI with a command, you can simply ask it as if you were having a conversation.
These AI language models can be trained on any type of language data. You could train it on books, online forums, articles and much more. As you might guess, this can lead to problems like bias, inaccuracies and inappropriate responses. However, it can also lead to realistic humanlike responses if it is trained on the right data.
While there are plenty of entertainment uses for ChatGPT, there are real, practical applications. One such application is using it as a coding assistant. Let’s talk about how that works.
ChatGPT as a Coding Assistant for Python
ChatGPT can be used as a coding assistant for several popular programming languages. As one of the most widely used programming languages, ChatGPT has a decent amount of experience and training data for working in Python. This makes it a viable option as a coding assistant for Python programmers.
Users can easily interface and interact with ChatGPT through conversational input, which makes it perfect for beginners trying to learn to code. However, it can also be useful for expert programmers.
Some use cases for ChatGPT as a coding assistant may include asking it for suggestions or having it review code for errors. You can even have it generate code for simple tasks that you don’t have time to do. This leads to more productivity since you don’t necessarily have to code everything manually. Also, ChatGPT is constantly learning. It can learn your coding patterns and preferences and become a better assistant the more you use it.
It’s worth noting that ChatGPT is far from perfect and can be prone to errors. It’s far from being a viable replacement for humans at this point. There are ethical considerations to consider as well, but we’ll touch on those later.
Generating Python with ChatGPT
As mentioned, ChatGPT even has the capability of generating code. How does this work?
Essentially, you can give the model an input or prompt, and it will generate code based on the information given. You can ask it to create code snippets, functions and even entire programs. Often, it takes seconds for it to provide you with your requested input.
Also, the more structured and detailed your prompt is, the better. If you give ChatGPT a good enough prompt, you may get exactly what you want the first time. However, you can also ask it to make adjustments after it spits out a response, further refining the generated code.
For simple coding tasks, this can be incredibly beneficial. Programmers can automate repetitive or tedious coding tasks and even improve their code. Again, it’s important to note that ChatGPT is far from infallible. The model can often be highly accurate and create syntactically correct code, but that code may not always be efficient or optimized. It’s still worth reviewing generated code carefully before implementing it.
That said, let’s do a quick example to see how ChatGPT Python generation works.
A Quick Example
For this example, we’ve copy-pasted an excerpt from an interaction with ChatGPT. We’ll ask it to create a simple age calculator app in Python.
Prompt: Create a Python script that calculates my age based on a birthday input
ChatGPT: Certainly! Here's a Python script that calculates your age based on a birthday input:
today = datetime.date.today()
age = today.year - birthdate.year
# Check if the birthday has already occurred this year
if today < birthdate.replace(year=today.year):
age -= 1
# Get the user's birthday as input in the format "YYYY-MM-DD"
birthday_input = input("Enter your birthday (YYYY-MM-DD): ")
# Convert the input string to a datetime object
birthday = datetime.datetime.strptime(birthday_input, "%Y-%m-%d").date()
age = calculate_age(birthday)
print("Your age is:", age)
print("Invalid date format. Please enter the birthday in the format YYYY-MM-DD.")
To use this script, simply run it in a Python environment, and it will prompt you to enter your birthday in the format YYYY-MM-DD. After entering your birthday, it will calculate and display your age.
Note: This script assumes that the input birthday is in a valid format (YYYY-MM-DD). If an invalid date format is entered, it will display an error message.<p>
Benefits and Use Cases
As you can see from our example, code generation can be incredibly simple. It took less than 30 seconds for us to prompt ChatGPT and generate that code. While it might not be perfect, you can see how it might be beneficial for simpler tasks.
With that being said, let’s take a look at some of the main use cases of ChatGPT for Python programmers:
- Save time - The fastest coder will admit that writing code can be a time-consuming process, no matter how simple that code may be. ChatGPT can help you be more productive by generating sections of tedious code and automating some of your processes.
- Simplify tasks - Adding to the time-saving benefit, many tasks can be simplified if they can’t be outright automated. For instance, you can generate code snippets for a particular function rather than writing code from scratch. Or, you can have ChatGPT check your code for errors, debug code and make intelligent suggestions to optimize it.
- Documentation - You can save a lot of time in the documentation process by simply feeding your code to ChatGPT and telling it to create documentation to explain the code’s functionality. You can even dictate what type of tone or skill level it should write the documentation with.
- Learning - Beginners can use ChatGPT to help them learn Python code. You can observe generated code, run it in a Python environment and see how it works. You can also ask it to review your code and make suggestions on what you did wrong or what you can improve.
- Research - You can use ChatGPT to research coding solutions or treat the model as a search engine to look up the information you need quickly.<p>
What Are the Limitations?
As great as the above use cases and benefits are, ChatGPT has more than a few limitations when it comes to generating Python code. Let’s review some of those limitations so you can use the tool effectively:
- Accuracy - As we’ve talked about, ChatGPT can not be trusted to be 100% accurate. It has made mistakes in the past, and it will continue to make mistakes. Also, you shouldn’t expect that it will always generate code in the most optimized way possible. While it may be sufficient, it can sometimes fall short in this respect. Make sure to always review the code generated by ChatGPT before implementation.
- Complex tasks - While ChatGPT isn’t completely incapable of complex tasks, there are plenty of times when it may not be able to perform up to expectations. If your program has complicated requirements or nuanced contexts, ChatGPT likely won’t be able to generate that type of script better than an experienced programmer.
- Ethical considerations - Lastly, as we learned, AI language models generate responses based on the language they’re trained on. This means they aren’t devoid of bias. The potential ethical concerns are worth a deeper dive into, so let’s touch on these considerations in the next section.
Ethical Implications and Code Quality
At this point, ChatGPT isn’t advanced enough to completely get rid of bias, disinformation and other concerns in its algorithm. While this doesn’t mean ChatGPT should necessarily be dismissed as a coding assistant altogether, it is important to be aware of these shortcomings so you can get the most out of the technology.
First, let’s address the question of bias. Biased code can lead to discriminatory and unethical outcomes when applied to programs where bias can be harmful. It’s especially crucial to remain vigilant and identify biases in ChatGPT-generated code.
Of course, code quality is a concern as well. With rigorous testing and reviews by human developers, you can ensure that the code quality from ChatGPT is up to expectations. Industries where security is a major concern, such as finance and healthcare, should be especially vigilant when using AI for coding-related tasks.
Mito Is a Notebook-Native AI Tool Built for Python
While ChatGPT can be helpful for certain coding applications, there are AI tools that are better suited for certain situations. Mito, for instance, is a Python-based spreadsheet app that is ideal for Python analytics and data science.
Mito is especially useful for those looking to upskill from Excel or Google Sheets to the more robust Python-based data analytics. Mito can automatically generate corresponding Pandas code as you edit and manipulate data in a spreadsheet. Want to learn more about Mito and how it can improve your spreadsheet analytics? Try it out today.
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