Chat GPT and Google Gemini Answer Programming Questions

notes

programming

Python

llm

Author

Stephen J. Mildenhall

Published

2024-02-13

Introduction

I believe that large language models (LLMs) are the most amazing thing that has happened in my lifetime—frankly, by a long stretch¹. While they are a bit of a cheat, because they incorporate everything that mankind knows as part of their training, their ability to understand is truly astonishing. I use these models multiple times an hour when I am working and find them incredibly helpful.

Google recently announced an upgrade, Gemini Advanced, of their model. I thought it would be interesting to compare it with ChatGPT4, which has been my go to model for the last few months. The examples below are a series of fairly technical object oriented programming questions in Python. In each case, I show the question first, followed by the two responses and then some commentary. The questions are as entered and contain typos—both models are tolerant of typos.

The comparison is not quite apples-to-apples because in GPT I have standing instructions. I can’t see how to enter those in Gemini yet.

Question 1

I am using quarto markdown to create a blog. I want to do a side by side comparison of various things. I think I can do that in markdown with :::{class info} using boostrap classes for columns? How?

Response Using GPT Answer

ChatGPT 4

To create a side-by-side comparison of various items in Quarto using markdown with Bootstrap classes, you can use the :::{.row} and :::{.col} syntax provided by Quarto to leverage Bootstrap’s grid system. This approach allows you to create responsive side-by-side layouts. Here’s how you can do it:

:::{.row}
:::{.col-md-6}
### Item 1
- Feature A
- Feature B
- Feature C
:::
:::{.col-md-6}
### Item 2
- Feature X
- Feature Y
- Feature Z
:::
:::

In this example: - :::{.row} starts a new row. - :::{.col-md-6} creates a column that spans 6 out of 12 possible column widths on medium-sized screens (and larger) for each item. This effectively divides the row into two columns for side-by-side comparison. - Inside each :::{.col-md-6} block, you can place the content you want to compare (e.g., ### Item 1 and ### Item 2 with their respective features listed).

Adjust the -md- part and the number (e.g., 6 in .col-md-6) based on your responsive design needs: - Use -sm-, -md-, -lg-, or -xl- to target different screen sizes. - Adjust the column span number (e.g., 4, 6, 8) to change the width of each column within the 12-column grid system.

Remember, this approach assumes your Quarto document is using Bootstrap or a theme that includes Bootstrap.

Google Gemini

Absolutely! You can use Quarto’s ability to incorporate custom CSS classes along with Bootstrap’s grid system to achieve side-by-side comparisons in your blog. Here’s how to do it:

1. Column Setup

Here’s the core Markdown structure using column layout divs:

:::{.columns}
::: {.column width="50%"}
**Content for the First Column**

Your comparison details, text, images, etc. for the first item would go here.
:::

::: {.column width="50%"}
**Content for the Second Column**

Your comparison details for the second item would go here.
:::
:::

Explanation:

:::{.columns}: Creates an outer container for the side-by-side columns.
::: {.column width="50%"}: Each of these defines an individual column. The width="50%" ensures they’ll take up half the available space, creating a two-column layout.

2. Adding Content

You can place any valid Markdown or HTML content within each .column div. For example:

:::{.columns}
::: {.column width="50%"}
## Item 1

* Feature A
* Feature B

[Image of Item 1](item1.jpg)
:::

::: {.column width="50%"}
## Item 2

* Feature X
* Feature Y

[Image of Item 2](item2.jpg)
:::
:::

3. (Optional) Styling with Bootstrap Classes

If you want more refined control using Bootstrap’s grid classes, you can adjust the .column divs accordingly. For example:

:::{.columns}
::: {.column .col-md-6}
   ...content...
:::
::: {.column .col-md-6}
   ...content...
:::
:::

Remember that for Bootstrap classes to work effectively, you’ll need to include the Bootstrap CSS in your Quarto document.

Tips

Experiment with the width percentages or Bootstrap’s column classes (col-sm-, col-md-, etc.) to get the exact side-by-side layout you need.
Consider adding extra CSS rules in your Quarto document to customize the appearance of the comparison further (e.g., borders, padding, etc.).

Let me know if you have any specific comparison scenarios, and I can help you tailor the code further!

Response Using Google’s Answer

ChatGPT 4

:::{.row}
:::{.col-md-6}
### Item 1
- Feature A
- Feature B
- Feature C
:::
:::{.col-md-6}
### Item 2
- Feature X
- Feature Y
- Feature Z
:::
:::

Etc.

Google Gemini

Absolutely! You can use Quarto’s ability to incorporate custom CSS classes along with Bootstrap’s grid system to achieve side-by-side comparisons in your blog. Here’s how to do it:

1. Column Setup

Here’s the core Markdown structure using column layout divs:

:::{.columns}
::: {.column width="50%"}
**Content for the First Column**

Your comparison details, text, images, etc. for the first item would go here.
:::

::: {.column width="50%"}
**Content for the Second Column**

Your comparison details for the second item would go here.
:::
:::

Etc.

Commentary

GPT answered the question because it used Bootstrap, but the answer didn’t work very well. The Google answer achieved the desired effect. The rest of this post uses the Google answer tweaked to have space between the columns.

Question 2

I have the following two classes. Is it correct that B is derived from A? In the constructor for B how do I call the constructor for A with argument x?

class A():
  def __init__(self, x):
    self.x = x

class B(A):
  def __init__(self, x, y):
    self.y = y
   ?? constructor for A

Responses

ChatGPT 4

Yes, class B is derived from class A in your example. To call the constructor of class A from the constructor of class B, you use the super() function. Here’s how you can do it:

class A():
    def __init__(self, x):
        self.x = x

class B(A):
    def __init__(self, x, y):
        super().__init__(x)  # This calls the constructor of class A with the argument x
        self.y = y

The super().__init__(x) call within B’s constructor invokes the constructor of the superclass A, passing x as the argument. This way, A’s constructor is responsible for handling the x attribute, while B’s constructor handles the additional attributes or operations specific to B, such as setting the y attribute in this case.

Google Gemini

Absolutely, you’re correct! Here’s a breakdown of why and how to call the parent class constructor:

Inheritance

The syntax class B(A) signifies that class B is derived from class A. This is the principle of inheritance in object-oriented programming. The derived class (B) inherits the attributes and methods of the base class (A).

Constructor Calling

To properly initialize an instance of B, you need to ensure that the attributes from both class A and class B are set up. Here’s how you call A’s constructor from within B’s constructor:

class B(A):
    def __init__(self, x, y):
        super().__init__(x)  # Call A's constructor
        self.y = y

Explanation

super(): This function gives you access to the parent class (A) from within the derived class (B).
super().__init__(x): This line explicitly calls the parent class’s constructor (__init__) and passes the argument x to it. This ensures that the x attribute in class A is initialized correctly.

Key Points

In Python, it’s important to call the parent class’s constructor when creating a derived class. This guarantees that the inherited attributes are properly set up.
The super() function makes working with inheritance much easier.

Let me know if you’d like a more elaborate example or further explanations of object-oriented concepts!

Commentary

Both answers on point and informative. My standing instructions to GPT favor shorter responses.

Question 3

Pls tell me more aout super. I find it very mysterious. How does it work if B is dervied from A1 and A2 and A3, for example? Or from A and C and C is in turn dervied from D?

Responses

ChatGPT 4

The super() function in Python is used to give access to methods in a superclass from a subclass that inherits from it. It’s particularly powerful and somewhat complex when dealing with multiple inheritance, where a class is derived from more than one base class.

Basic Usage

In a single inheritance scenario, super() is straightforward: it allows you to call methods of the superclass in your subclass. The common use case is to extend the functionality of the superclass method in the subclass by either calling the superclass method before executing additional logic in the subclass method or doing so afterward.

Multiple Inheritance

Python supports multiple inheritance, where a class can inherit from more than one class. This introduces complexity, especially in how super() behaves. Python’s method resolution order (MRO) comes into play in these scenarios.

Method Resolution Order (MRO)

Python uses the C3 linearization algorithm to establish an MRO in multiple inheritance scenarios. The MRO determines the order in which base classes are searched when executing a method. You can view the MRO of a class using the .__mro__ attribute or the mro() method.

Example with Multiple Inheritance

Consider the following classes:

class A:
    def __init__(self):
        print("A", end=" ")
        super().__init__()

class B(A):
    def __init__(self):
        print("B", end=" ")
        super().__init__()

class C(A):
    def __init__(self):
        print("C", end=" ")
        super().__init__()

class D(B, C):
    def __init__(self):
        print("D", end=" ")
        super().__init__()

In this case, D is derived from both B and C. If you instantiate D, the output will illustrate the MRO:

d = D()

Output might be: D B C A

This output is the result of the C3 linearization that Python uses to resolve the order in which methods should be called. You can check the MRO by:

print(D.mro())

`super()` in Multiple Inheritance

When you call super() in a class that’s part of a multiple inheritance hierarchy, Python follows the MRO to determine which superclass method to invoke. This ensures that each method in the hierarchy is called in a specific, predictable order, avoiding the problem of a method being called more than once.

`super()` with Arguments

In Python 3, super() called without arguments is equivalent to super(ThisClass, self), automatically passing the class and instance to super(). In complex inheritance hierarchies, super() works with the MRO to ensure the correct superclass method is called next.

Summary

super() is essential for accessing superclass methods in a subclass.
In multiple inheritance scenarios, the MRO determines the order in which superclass methods are called.
Python’s use of the C3 linearization algorithm ensures a consistent and predictable MRO.
Understanding super() and MRO is crucial for correctly implementing and extending methods in complex inheritance hierarchies.

Google Gemini

You’re right! super() can seem a bit mysterious, especially in scenarios with multiple inheritance. Let’s unravel that mystery:

How super() Works

The core idea behind super() is to help navigate the inheritance hierarchy in a dynamic way. Here’s what happens under the hood:

Method Resolution Order (MRO): When you use super(), Python doesn’t just look at the immediate parent class of B. It analyzes the entire inheritance chain using an algorithm called the Method Resolution Order (MRO). The MRO determines the order in which classes are searched when you try to access a method (like __init__).
Delegation: Inside your B class’s method, super() essentially says, “Don’t execute the method here; find the next version of this method up the inheritance chain and execute that one.”

Example: Multiple Inheritance

Let’s say you have this structure:

class A1:
    def __init__(self):
        print("A1 init")

class A2:
    def __init__(self):
        print("A2 init")

class A3:
    def __init__(self):
        print("A3 init")

class B(A1, A2, A3):
    def __init__(self):
        super().__init__()

If you create an instance of B, the super().__init__() call would follow the MRO to initialize classes in a specific order, You might see output like this, reflecting the order in which constructors are called:

A1 init
A2 init
A3 init

Example: Deeper Inheritance

Now, consider this scenario:

class D:
    def __init__(self):
        print("D init")

class C(D):
    def __init__(self):
        super().__init__()
        print("C init")

class A:
    def __init__(self):
        print("A init")

class B(A, C):
    def __init__(self):
        super().__init__()

Again, super() would follow the MRO, and your output might look like this:

D init
C init
A init

Key Points

super()’s behavior is determined by the Method Resolution Order, ensuring a predictable chain of method calls even in complex inheritance relationships.
This dynamic resolution is what makes super() particularly useful in multiple inheritance scenarios where the exact inheritance structure might change.

Let me know if you want a specific example with some attributes and method calls to make it even more concrete!

Commentary

GPT brings up the C3 linearization algorithm, which is a level of detail I appreciate. Basic GPT example has more depth, but Gemini follows up with a better nested inheritance structure. GPT touches on the arguments to super.

Question 4

If B derives from A, you call B the subclass and A the super class?

Responses

ChatGPT 4

Yes, that’s correct. In the context of inheritance in object-oriented programming:

A is referred to as the superclass (or base class, parent class), because it is the class from which another class (the subclass) inherits.
B, which derives from A, is called the subclass (or derived class, child class) because it inherits from the superclass.

The subclass extends or modifies the behavior defined in the superclass. This mechanism allows for code reuse and polymorphism, making it easier to maintain and extend the functionality of your software.

Google Gemini

Absolutely! You got the terminology right. In the inheritance relationship where B derives from A:

B is the subclass (also called derived class or child class). It inherits properties and behaviors from its parent.
A is the superclass (also called base class or parent class). It provides the foundation for the subclass to build upon.

Let me know if you have any other questions on the topic!

Commentary

I just find the terminology confusing and neither answer convinced me it is logical. It’s just the child’s question, “But, why is a cow a cow?”

Question 5

that feels upsidedown to me!

Responses

ChatGPT 4

It’s understandable that the terminology might feel counterintuitive at first! The terms “superclass” and “subclass” can indeed seem a bit abstract or even upside down, especially when you’re starting to grasp the concepts of inheritance and object-oriented programming. Here’s a way to think about it that might help:

Superclass (Base Class, Parent Class): Think of the superclass as the “foundation” or “base” upon which subclasses are built. It’s “super” in the sense that it provides the basic attributes and methods from which other classes can inherit. It’s a general, broader classification.
Subclass (Derived Class, Child Class): The subclass is considered “below” the superclass in the hierarchy, not in importance, but because it specializes or extends the superclass. It inherits from the superclass, adding more specific features or overriding existing ones to differentiate itself. It’s a more specific, detailed classification built on top of the superclass.

In a family tree analogy, the superclass is like a parent, while the subclass is like a child. The child inherits traits from the parent but can also have its unique characteristics or override inherited traits with new ones.

Despite the initial confusion, these terms are widely used in object-oriented programming to describe relationships between classes in a hierarchy, emphasizing the direction of inheritance from the more general to the more specific.

Google Gemini

I totally understand why that might feel upside-down! Here’s a couple of perspectives that might help you see why these terms make sense:

1. Building Blocks:

Think of the superclass as the foundation or the base component upon which other classes are built. It’s on top in the sense that it lays the groundwork.
The subclass is like a more specialized version of the base class. It adds upon or refines what the superclass provides, and thus sits ‘below’ the foundation in the hierarchy.

2. Tree Analogy:

Visualize an inheritance hierarchy as an upside-down family tree.
The superclass would be like a grandparent at the very top of the tree.
Subclasses would be like their children, and any further derived classes would be like grandchildren, branching downwards.

3. “Super” as in “Superset”:

The superclass holds a ‘superset’ of the characteristics that all its subclasses inherit. That’s why the word ‘super’ makes sense; it encompasses the broader properties.
Subclasses are ‘subsets’ within this broader class structure.

It’s Not Perfect

It’s worth noting that no analogy is perfect. Terms like “super” and “sub” sometimes get mixed up due to their connotations in everyday language. The core to remember is the direction of inheritance – subclasses build upon and extend from the foundation of the superclass.

Would you like some more visual examples to help solidify the concept?

Commentary

Gotta love Gemini’s “It’s on top in the sense that it lays the groundwork.” Possible hint of non-human writing. I still find the nomenclature confusing.

Question 6

Again, I have the following situation. I have a = A() existing. I then want to create a B object that “wraps” that A, something like b = B(a). How can I do that? A expects a string (say), not a class A instance. Do you see what I mean?

class A():
    def __init__(self, x):
        self.x = x

class B(A):
    def __init__(self, x, y):
        super().__init__(x)  # This calls the constructor of class A with the argument x
        self.y = y

Responses

ChatGPT 4

If you want to create a class B that “wraps” an instance of class A rather than inheriting from A, you shouldn’t inherit B from A. Instead, B should take an instance of A as an argument and store it as an attribute. This way, B can use or modify A’s behavior or data as needed. Here’s how you can do it:

class A():
    def __init__(self, x):
        self.x = x

class B():
    def __init__(self, a_instance, y):
        self.a_instance = a_instance  # Store the instance of A
        self.y = y

    # Example method to demonstrate accessing A's attributes/methods from B
    def show_x_and_y(self):
        print("A's x:", self.a_instance.x)
        print("B's y:", self.y)

In this design, B does not inherit from A, so it doesn’t call A’s constructor with super().__init__(x). Instead, B takes an existing instance of A as an argument along with its own data (y) and stores it. This allows B to “wrap” A, accessing or modifying A’s attributes and methods through the stored instance (self.a_instance).

Here’s how you could use these classes:

# Create an instance of A
a = A("some string")

# Create an instance of B, wrapping the instance 'a' of A
b = B(a, "another string")

# Use B's method to demonstrate access to both A and B's data
b.show_x_and_y()

This approach allows B to act as a wrapper for A, providing additional functionality or storing additional data while still having access to the wrapped A instance’s attributes and methods. This pattern is useful in various scenarios, such as when you want to extend the functionality of a class without modifying it directly (a form of composition).

Google Gemini

You’re right! There seems to be a mismatch in your goal and the code structure. Let me explain and offer a solution:

The Issue

Inheritance vs. Composition: Your original question phrasing (“wraps that A”) suggests composition, rather than inheritance. When one object “wraps” another, it has an instance of the other object as a part of it, not is an instance of it.
Type Mismatch: Class A’s constructor expects a string, but if you use inheritance as shown, you’re trying to pass it an existing instance of A.

Solution: Composition

Here’s how you can modify your code to achieve the “wrapping” behavior using composition:

class A:
    def __init__(self, x):
        self.x = x

class B:
    def __init__(self, a_obj):
        self.a_obj = a_obj  # Store the A object

# Create an A object
a = A("some string")

# Create a B object that wraps the A object
b = B(a)

# Access the value of x through the B object
print(b.a_obj.x)  # Output: "some string"

Explanation

No Inheritance: We’ve removed the inheritance relationship (class B(A)). Now, B is an independent class.
Storing the Object: Instead of calling A’s constructor inside B, we now accept an instance of A (a_obj) in B’s constructor and store it as an attribute (self.a_obj).

Benefits of Composition

Flexibility: B can now work with any instance of A, not just ones directly created inside its constructor.
Clearer Separation: This avoids tight coupling between the classes that can arise from inheritance, enhancing reusability.

Let me know if you have any further questions or want to explore more complex scenarios of how different classes can interact with each other!

Commentary

Both suggesting a member variable, which Gemini calls composition.

Question 7

I see that, but A exposes a lot of methods that I want to be able to call directly in B, not self.Aobject.method, just self.method. THis must be a standard thing is OOP, that you want to dynamically extend an object? I don’t want a big A with too many methods, as I only need B occasionally. What is the best pattern to use? Really looking for you help here! THis is a problem I faced on numerous occasions!

Responses

ChatGPT 4

Given your requirements, it sounds like you’re looking for a way to dynamically extend an object’s functionality in a flexible manner, without permanently adding more methods to the base class (A) and while maintaining the ability to call A’s methods directly from an instance of B as if they were its own. This scenario calls for a design pattern that allows for such flexibility, typically achieved through composition or the use of a proxy pattern.

One approach that aligns well with your needs is the Proxy Pattern, specifically a variation known as the Delegation Pattern. Instead of inheriting from class A, class B holds an instance of A and delegates calls to it. You can automate delegation in Python using the __getattr__ method to dynamically forward method calls to the A instance.

Here’s how you might implement this:

class A:
    def __init__(self, x):
        self.x = x

    def method_a(self):
        print(f"Method in A, x = {self.x}")

class B:
    def __init__(self, a_instance, y):
        self._a_instance = a_instance
        self.y = y

    def __getattr__(self, name):
        """Delegate attribute access to the A instance if attribute not found in B."""
        return getattr(self._a_instance, name)

    # B's specific methods can be defined here
    def method_b(self):
        print(f"Method in B, y = {self.y}")

# Usage
a = A("Hello from A")
b = B(a, "Hello from B")

b.method_a()  # This will call A's method_a through B
b.method_b()  # This will directly call B's method_b

In this implementation, B does not inherit from A. Instead, it “wraps” an instance of A. When you try to access an attribute or method on an instance of B that doesn’t exist in B, Python’s __getattr__ method is called. Here, __getattr__ is used to delegate those calls to the wrapped A instance. This allows you to call methods of A directly on an instance of B without the need for B to explicitly define or know about those methods.

Key Points:

Flexibility: This approach allows B to extend A dynamically, without hardcoding method names or altering the structure of A.
Maintainability: It keeps A’s implementation clean and focused, only adding functionality in B when necessary.
Transparency: To the user of class B, it appears as if B has all methods of A, providing a seamless interface.

Considerations:

Performance: Using __getattr__ introduces a slight overhead because of the dynamic method resolution.
Debugging: Debugging can be slightly more complex due to the indirection added by __getattr__.
Method Overriding: If B needs to override a method from A and also call the original method from A, you’ll have to handle that explicitly in B.

This pattern provides a powerful way to dynamically extend objects in Python, accommodating scenarios where inheritance might be too rigid or when you want to avoid bloating base classes with methods that are only occasionally needed.

Google Gemini

Absolutely, your requirement makes perfect sense! There are indeed a few standard OOP patterns that help you dynamically extend existing objects with minimal fuss. Let’s explore the most suitable ones:

1. The Proxy Pattern

Concept: You create a “proxy” object that mimics the interface of the original object (A in your case). This proxy internally holds a reference to the original object, and then delegates actual method calls to that internal object.
How it Helps:
- Selective Exposure: The proxy object lets you control which methods from A you want to directly expose, keeping B’s interface clean.
- Lazy Creation: You could instantiate the internal A object only when needed, saving resources if B’s capabilities are used occasionally.

Code Example (Simplified):

class A:
    def method1(self):
        # ...
    def method2(self):
        # ...

class B:
    def __init__(self):
        self._a = None  # Placeholder for A object

    def method1(self):
        if not self._a:
            self._a = A()  # Create A on demand
        return self._a.method1()  # Delegate

2. The Adapter Pattern

Concept: When you have an existing object (A) but its interface doesn’t quite align with how you want to use it in B. The adapter provides a “translation layer”.
How it Helps
- Interface Adaptation: An adapter class can take calls designed for B and transform them into the appropriate actions on A, even if the method names don’t match perfectly.

3. Mixins

Concept: Small, reusable classes designed for adding specific functionality. Can be ‘mixed into’ an inheritance hierarchy. (Note: Python’s multiple inheritance makes this easier).
How it Helps:
- Focused Features: Break down specific behaviors of A that you need in B into mixin classes, and include only those needed for B.

Choosing the Right Pattern

Proxy: Useful if you want direct access to most of A’s methods and potential lazy creation.
Adapter: Better when there’s a mismatch in how A’s methods work and how you want to use them in B.
Mixins: A good choice if you can factor out the required pieces of functionality from A into well-defined, reusable mixin classes.

Important Note: Overusing any of these patterns can lead to complexity. Assess how many of A’s methods you truly need in B and make a design choice based on simplicity and clarity.

Let me know if you’d like a more detailed example of any of these patterns in action!

Commentary

Both get to the Proxy Pattern, but offer slightly different solutions. The GPT solution is exactly what I wanted, and seems very nice. It leverages built in Python and avoid having to name methods. The Gemini solution is less general.

Question 8

Isn’t there a getattr and getattribute with subtly different properties? can you explain?

Note: Gemini hadn’t brought this up previously.

Responses

ChatGPT 4

Yes, in Python, both __getattr__ and __getattribute__ methods exist, and they do have subtly different behaviors and use cases when it comes to attribute access in classes.

`getattribute`

Always Called: __getattribute__ is called for every attempt to access an attribute, regardless of whether the attribute exists or not. It’s a part of the lookup chain for any attribute access on an object.
Override with Caution: Because __getattribute__ is called for every attribute access, overriding it requires careful handling to avoid infinite recursion. When you override __getattribute__, you typically use super().__getattribute__(name) within it to safely access attributes of the superclass without recursion.
Use Case: You might override __getattribute__ if you need to intercept every attribute access, which could be useful for debugging, logging, or implementing proxies where you need to handle all attribute accesses dynamically.

Example:

class A:
    def __init__(self):
        self.x = 'X'

    def __getattribute__(self, name):
        print(f"Accessing {name}")
        return super().__getattribute__(name)

a = A()
print(a.x)  # This will print "Accessing x" followed by "X"

`getattr`

Called as a Fallback: __getattr__ is only called if the attribute was not found by the usual means. It acts as a fallback method for attribute access, which makes it useful for catching attempts to access missing attributes without affecting normal attribute access.
Use Case: __getattr__ is suitable when you want to provide a default behavior for missing attributes, such as returning a default value, generating attributes on the fly, or forwarding attribute access to another object (as in delegation or proxy patterns).

Example:

class B:
    def __getattr__(self, name):
        return f"{name} does not exist"

b = B()
print(b.some_missing_attribute)  # This will print "some_missing_attribute does not exist"

Key Differences

Invocation: __getattribute__ is called for every attribute access, making it very powerful but also risky if not handled correctly due to the potential for infinite recursion. __getattr__ is called only when an attribute is not found by the normal lookup process.
Purpose: __getattribute__ can be used to intercept all attribute accesses, which is useful for low-level control or proxying. __getattr__ is more suited for providing fallback behavior for missing attributes or for cases where attributes are to be generated dynamically.

Choosing between __getattribute__ and __getattr__ depends on your specific needs for attribute access and interception, and understanding their differences is key to using them effectively.

Google Gemini

Absolutely! You’re referring to two special methods in Python that are important for handling attribute access:

1. __getattribute__

The Gatekeeper: __getattribute__ is called for every attribute access attempt for an object, regardless of whether the attribute actually exists or not. It has ultimate control over how attributes are fetched.
Default Behavior: If you don’t define __getattribute__ in your class, Python has a default implementation that does the standard attribute lookup through an instance’s __dict__ (its internal dictionary of attributes) and walks up the inheritance chain if needed.
Customization: If you do define __getattribute__, you override this default behavior, allowing you to intercept all attribute access and implement custom logic.

2. __getattr__

Fallback Handler: __getattr__ is called only when the normal attribute lookup has failed. This means the attribute wasn’t found in the object’s __dict__ or through its inheritance chain.
Providing Defaults: A common use for __getattr__ is to provide default values when certain attributes don’t exist.
Dynamic Attributes: It’s also useful for creating objects with attributes that can be generated on the fly.

Relationship & Example

class MyClass:
    def __init__(self):
        self.val = 10

    def __getattribute__(self, name):
        print("Calling __getattribute__")
        return super().__getattribute__(name)

    def __getattr__(self, name):
        print("Calling __getattr__")
        return f"Default value for '{name}'"

obj = MyClass()

print(obj.val)           # Calls __getattribute__
print(obj.nonexistent)   # Calls __getattribute__, then __getattr__

Key Differences

Feature	`__getattribute__`	`__getattr__`
Scope	Called for every attribute access attempt	Called only when attribute is not found
Priority	Highest priority	Falls back when `__getattribute__` doesn’t find attribute
Default Behavior	Python has a built-in default	No default implementation
Common Use Case	Dynamic attribute lookup; controlling access	Providing default values; dynamic attribute creation

Let me know if you’d like a more advanced use case demonstration of how these special methods can be used for powerful things like metaprogramming!

Commentary

Matching comments. Gemini’s table is helpful.

Conclusions

Google won on two columns, but otherwise GPT has the slight edge for me, including naming the MRO algorithm and offering a better implementation of the delegation pattern. However, both are really, really useful. I find a question-and-answer style of learning is most effective and LLMs enable it well.

What a great time to be a programmer! It will age me, but I remember struggling to learn C with just Kernighan and Ritchie (my copy a svelte 272 pages) and the Unix manual pages. Problem solving could take a long time! LLMs have completely replaced Stack Overflow for me. LLMs must (should) be having a massive impact on programmer productivity for more mundane implementation.

Finally, I love Markdown! It’s great to be able to cut and paste answers and have the formatting just come out right.

Appendix: ChatGPT Standing Instructions

I have a PhD in mathematics, am a qualified general insurance actuary. I have written several research papers in math, probability, risk theory, and actuarial science. I am an expert Python programmer.

I like shorter responses. I will ask followup questions if needed. When providing code samples do not provide an explanation. I will ask if needed.

Python code: always use pathlib and Path for file manipulations. Never repeat code you have already shown, just show the new code. I only need installation instructions for Windows. If I want other operating systems, I will ask.

Windows only! Shell scripts: just provide Windows cmd script. I will ask if I want others. Installation instructions for software and other software advice only needed for Windows operating system.

Always use SI units. If units are involved and are complex, consider providing a unit-analysis in addition to a numerical example.

Use ISO standard dates YYYY-MM-DD.

Use Markdown.

Footnotes

Previous winner: LIGO and the detection of gravitational waves.↩︎

Introduction

Question 1

Response Using GPT Answer

ChatGPT 4

Google Gemini

Response Using Google’s Answer

ChatGPT 4

Google Gemini

Commentary

Question 2

Responses

ChatGPT 4

Google Gemini

Commentary

Question 3

Responses

ChatGPT 4

Basic Usage

Multiple Inheritance

Method Resolution Order (MRO)

Example with Multiple Inheritance

super() in Multiple Inheritance

super() with Arguments

Summary

Google Gemini

Commentary

Question 4

Responses

ChatGPT 4

Google Gemini

Commentary

Question 5

Responses

ChatGPT 4

Google Gemini

Commentary

Question 6

Responses

ChatGPT 4

Google Gemini

Commentary

Question 7

Responses

ChatGPT 4

Key Points:

Considerations:

Google Gemini

Commentary

Question 8

Responses

ChatGPT 4

__getattribute__

__getattr__

Key Differences

Google Gemini

Commentary

Conclusions

Appendix: ChatGPT Standing Instructions

Footnotes

`super()` in Multiple Inheritance

`super()` with Arguments

`getattribute`

`getattr`