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The verdict is in: ChatGPT is not just hype, it is a useful tool that everyone can take advantage of in some way.
Keep in mind what I No Say: ChatGPT is not responsive. It is not an artificial general intelligence. ChatGPT is not a panacea that can and will solve all problems. No take everyone’s job.
Understanding that ChatGPT is a tool at our disposal, let’s see how it can help us learn Python. Let’s make it a bit meta too: we’re going to have ChatGPT give us a series of prompts that we can then use to interact with ChatGPT to learn Python.
Make sense?
This is how we started.
Immediate: I want to learn Python. I am new to programming. I would like you to be my coach. Write down 25 prompts that I could use to interact with you and learn all about Python programming.
And here is the list that ChatGPT provided me:
- What is Python and why is it popular?
- How do I install Python on my computer?
- What are the basic data types in Python?
- How do I declare variables in Python?
- What are the different operators available in Python?
- How do I use if-else statements in Python?
- What are loops in Python and how do I use them?
- How do I define and call functions in Python?
- What are lists and how do I use them in Python?
- What are tuples and how are they different from lists in Python?
- What are dictionaries and how do I use them in Python?
- What are sets and how do I use them in Python?
- What are the built-in functions in Python and how do I use them?
- How do I work with strings in Python?
- How do I work with files in Python?
- What is exception handling in Python and how do I use it?
- How do I use modules and packages in Python?
- How do I create and use classes and objects in Python?
- What is inheritance and polymorphism in Python?
- What are decorators and how do I use them in Python?
- How do I use regular expressions in Python?
- How do I work with databases in Python?
- How do I use the NumPy library in Python?
- How do I use the Pandas library in Python?
- How do I use the Matplotlib library in Python for data visualization?
This is especially helpful as it provided topics for prompts in a (somewhat) logical progression as far as learning is concerned. For example, you’ll definitely want to know how to install Python (message #2) before delving into NumPy (message #23).
In any case, all these indications seem reasonable. Let’s look at the next step: using prompts. Let’s start from the beginning.
Immediate: What is Python and why is it popular?
Python is a high-level interpreted programming language that was first released in 1991. It is a popular language due to its easy-to-read syntax, dynamic typing, and interpreted nature, making it a versatile and accessible language for beginners and experts alike. Python is also widely used for various applications, including web development, scientific computing, data analysis, artificial intelligence, and more. Additionally, Python has a large and active community that develops and maintains a large number of useful libraries and modules, which add functionality to the language and further increase its popularity.
OK, that’s a good high-level overview. Now let’s get more information.
Immediate: Can you give more details about this?
Sure! Let me break down a few key points:
High-level language: Python is considered a high-level language because it abstracts away many of the underlying details of the computer, such as memory management, which are handled automatically by the language interpreter. This makes it easier to write and understand code, as well as faster to develop.
Easy-to-read syntax – Python’s syntax is designed to be simple, readable, and intuitive, making it a great choice for beginners just starting to learn programming. This is because the syntax is closer to natural language and doesn’t require a lot of symbols or special characters like other programming languages do.
Dynamic typing: Python uses dynamic typing, which means that you don’t need to specify a variable’s type before using it. This makes the code more flexible, but it also means the interpreter needs to work more behind the scenes to keep track of what each variable is.
Interpreted Language: Python is an interpreted language, which means that the interpreter executes the code line by line, rather than compiling it into machine code beforehand. This allows for faster development, since you can test small chunks of code without having to compile the entire program, but it also means that the code will run slower than if it had been compiled.
Versatile: Python is used for a wide range of tasks, from web development and scientific computing to data analysis and artificial intelligence. This versatility is due in part to its large, active community that has developed many libraries and modules that add functionality to the language.
Great Community – As mentioned, the Python community is large and active, which means there are many resources available for learning the language, getting help with specific problems, and contributing to the development of new libraries and modules. This also helps ensure that the language continues to evolve and improve over time.
These are just some of the reasons why Python is so popular and widely used.
That’s a bit more useful. Let’s go deeper into a particular point.
Immediate: Explain dynamic typing in more detail.
Dynamic typing is a feature of the Python programming language that allows the type of a variable to change during the course of program execution. This is in contrast to statically typed languages, such as C or Java, where a variable’s type must be specified when it is declared and cannot change thereafter.
With dynamic typing, you can declare a variable and assign a value to it, without having to specify the type of the value beforehand. For example:
x = 42In this case, the type of x is automatically determined to be int based on the value that is assigned to it. Later in the program, you can change the value of x to be a string, for example:
x = "Hello, World!"And the type of x will automatically change to str.
Dynamic typing makes your code more flexible and easier to write because you don’t have to keep track of the type of each variable and can focus only on values and operations. However, it also means that the interpreter has to work more behind the scenes to keep track of types, and it can also make it difficult to catch certain types of errors, such as type mismatches, until runtime.
In general, dynamic typing is one of the features that makes Python a popular choice for many tasks, as it allows for faster development and more flexible code.
Suddenly it seems like we are getting somewhere and learning some important points about Python and programming in general.
ChatGPT can be further investigated to clarify, rephrase, and elaborate points as needed, which you can always do. This is not just a valid way to learn Python, you can take advantage of ChatGPT’s strengths to help you learn any subject in the same way.
And note what we didn’t have to do: figure out what to look for online; search results online; comb the results for useful resources; analyze the information in each resource, consume it, and finally find out if it was helpful or not.
And this is the true power of ChatGPT. The other side of the coin is that relying only on what ChatGPT shares can leave you susceptible to bias, misinformation, and related issues. In this particular case, that may not be as potentially harmful as in others; however, supplementing this information with some follow-up internet searches can help ensure that ChatGPT provides accurate and unbiased information.
As ChatGPT could tell you (if prompted):
Learning is a great accomplishment and taking the time to expand your knowledge is commendable. Keep up the good work! Every little step you take brings you closer to mastering the new topic. Remember that the journey is as important as the destination. Don’t be discouraged if things seem challenging at times, that’s a normal part of the learning process. Keep asking questions and looking for resources, and you’ll find success in no time. Believe in yourself and your abilities, and have fun with the learning experience!
Matthew May (@mattmayo13) is a data scientist and editor-in-chief of KDnuggets, the essential online resource for data science and machine learning. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew has an MS in Computer Science and a Post Graduate Diploma in Data Mining. He can be reached at editor1 on kdnuggets[dot]com.
