Speaker 1: Welcome back to the deep dive. Today. We're getting into
Speaker 1: the foundations, specifically Python three programming.
Speaker 2: That's right, and we're basing this on some really solid
Speaker 2: material excerpts from a Beginner's Guide, part of the Undergraduate
Speaker 2: Topics and Computer Science series, peer reviewed expert.
Speaker 1: Author right, and the book itself is aimed at people
Speaker 1: starting from scratch, maybe zero programming knowledge, but wants to
Speaker 1: get them up to speed as capable Python developers exactly.
Speaker 2: So our goal here is to kind of pull out
Speaker 2: the key takeaways, give you that shortcut to the core
Speaker 2: ideas of Python.
Speaker 1: Three, distilled version from a trusted source.
Speaker 2: Precisely getting the best bits without reading the whole thing
Speaker 2: cover to cover.
Speaker 1: Okay, let's jump right in Python itself. Is it fundamentally
Speaker 1: and maybe what's a key thing that sets it apart
Speaker 1: early on?
Speaker 2: Sure? Well, at its heart it's a general purpose language.
Speaker 2: You hear it alongside CE plus plus T, JavaScript, Java,
Speaker 2: that kind of thing, and a really defining characteristic the
Speaker 2: book flags immediately is dynamic typing.
Speaker 1: Dynamic typing, okay, so that means a variable isn't locked
Speaker 1: into one data type.
Speaker 2: Exactly, that you can have a variable holding say a number,
Speaker 2: and then later on you can assign text a string
Speaker 2: to that same variable name.
Speaker 1: Python's fine with it, uh huh, unlike say Java or
Speaker 1: c sharp, where you declare the type up front and
Speaker 1: it's fixed.
Speaker 2: Right. That adds a lot of flexibility, which is a
Speaker 2: big part of Python's appeal.
Speaker 1: Interesting and the name Python it's not about snakes, h No.
Speaker 2: Not directly. It was created back in the eighties by
Speaker 2: Guido van Rossum in the Netherlands. Yeah, and he named
Speaker 2: it after Monty Python's flying Circus big fan.
Speaker 1: Seriously, that's brilliant.
Speaker 2: Yeah, and the book mentions you'll even find little nods
Speaker 2: to the show in Python's official documentation sometimes.
Speaker 1: Love that bit of personality definitely.
Speaker 2: And yeah, just to confirm we are laser focused on
Speaker 2: Python three here as that's what the.
Speaker 1: Source covers got it now. The book brings up programming paradigms,
Speaker 1: different ways to structure code. It starts with.
Speaker 2: Right, Procedural programming is sort of the classic approach. Think
Speaker 2: of it like a recipe, a sequence of explicit instructions.
Speaker 2: You break the steps down into procedures or functions and
Speaker 2: use things like if statements and loops to control the flow.
Speaker 1: Like see your Pascal exactly.
Speaker 2: Those are prime examples. It's a good starting point to
Speaker 2: understand how programs can be organized before we maybe get
Speaker 2: into other styles later like functional or object oriented.
Speaker 1: Makes sense, but okay, before writing any code, you need
Speaker 1: the tools. Setting up the environment.
Speaker 2: Yep, fundamental step. The book recommends the most direct route
Speaker 2: go to the official Python website Python dot org. Python
Speaker 2: dot org, I find the downloads link, grab the installer
Speaker 2: for your system, Windows, Mac, Linux, They're all.
Speaker 1: There, and part of installing is making sure your computer
Speaker 1: can find Python right. The path variable thing.
Speaker 2: Crucial point, especially on Windows. You need Python on your
Speaker 2: system's path, so you can just open a terminal or
Speaker 2: command prompt type Python and it works.
Speaker 1: Right, so it's accessible from anywhere exactly.
Speaker 2: The book also really pushes using a good code editor
Speaker 2: or even better, an ID an integrated development environment.
Speaker 1: So something more than just basic notepad.
Speaker 2: Definitely. It mentions Sublime text as a good text editor
Speaker 2: to start with. It understands Python syntax does color coding
Speaker 2: makes things much easier to read, okay, but for more
Speaker 2: involved projects. It flags a full ID as being really beneficial.
Speaker 1: And the recommendation there is pie Charm PI.
Speaker 2: Charm from Jet Brains. Yeah, it's described as a full workbench.
Speaker 2: It has code completion, debugging tools, integrated version control like get,
Speaker 2: lots of features that become really helpful as you progress.
Speaker 1: Jet Brain's website for that one, and the example code
Speaker 1: from the book itself.
Speaker 2: Two main ways suggested. If you've got GET installed, you
Speaker 2: can just use get clone with the GitHub link provided
Speaker 2: in the book, or if you don't have get, then
Speaker 2: you can usually just download the whole thing as a
Speaker 2: ZIP file directly from that same GitHub page. And as
Speaker 2: the book knows, IDEs like pie Charm often have Get
Speaker 2: built in any way, which makes cloning easier.
Speaker 1: Okay, environments now the traditional first program Hello World, the classic.
Speaker 2: It's really just a sanity check, you know, does the
Speaker 2: interpreter run? Does my editor save the file correctly? Are
Speaker 2: the settings okay?
Speaker 1: And Python files usually have the dot pi extension correct.
Speaker 2: Dot PI is the standard and the key piece of
Speaker 2: code for Hello World is the print function that's.
Speaker 1: Built in part of Python itself.
Speaker 2: Yep, a fundamental built in function. It's how you tell
Speaker 2: your program to display output on the screen.
Speaker 1: Then the book makes it interactive pretty quickly with input.
Speaker 2: Right flips the script dot input. Let's the program ask you,
Speaker 2: the user, a question, it shows a prompt.
Speaker 1: Message whatever text you give it.
Speaker 2: Exactly, then a weads for you to type something and
Speaker 2: press enter. Whatever you typed comes back into the program
Speaker 2: as a string as text.
Speaker 1: Data, which means you need somewhere to store that input
Speaker 1: enter variables.
Speaker 2: Perfect lead in variables are essentially named storage spots in
Speaker 2: the computer's memory. You give a piece of data a name,
Speaker 2: a label so you can refer to it.
Speaker 1: Later username in the example exactly.
Speaker 2: And dining back to dynamic typing, that username variable holds
Speaker 2: the string from the input function, but you could theoretically
Speaker 2: assign a number to username later if you want it.
Speaker 1: So the sequence print Hello World, then username input under
Speaker 1: your name using the equals sign.
Speaker 2: The assignment operator right assigns the result of the input
Speaker 2: function to the variable username, and.
Speaker 1: Then print hello username. That shows print can handle multiple things.
Speaker 2: Yeah, you can give print multiple arguments separated by commas,
Speaker 2: like the literal text hello and the value stored in
Speaker 2: the username variable. It prints them out, usually with a
Speaker 2: space in between by default. A neat little demonstration.
Speaker 1: Simple but covers input, output, variables, assignment. Nice.
Speaker 2: It's those core building blocks.
Speaker 1: Okay, let's talk more about those building blocks, the basic
Speaker 1: data types.
Speaker 2: The source focuses on four key ones to start strings,
Speaker 2: text numbers, integers and floating point Boollian's true false, and
Speaker 2: this special value none.
Speaker 1: Strings first, text inside quotes yep.
Speaker 2: Single quotes like this or double quotes like this functionally
Speaker 2: identical in Python. The book mentioned single quotes are maybe
Speaker 2: slightly more common sort of the Pythonic convention, but both
Speaker 2: work fine.
Speaker 1: And strings aren't just static texts. They have operations methods.
Speaker 2: Oh yeah, lots of useful built in methods, things like
Speaker 2: split to break a string into a list of words,
Speaker 2: starts with or ends with to check.
Speaker 1: The beginning or end case checking two dot I supper
Speaker 1: eyes lower.
Speaker 2: Or dot isselfa to see if it's all letters. Very
Speaker 2: handy stuff, and case matters in comparisons.
Speaker 1: So Python is different from Python absolutely.
Speaker 2: If you want to compare strings regardless of case, the
Speaker 2: usual trick is to convert both to lower case first
Speaker 2: using dot lower so misstring dot lower Hello. Method names
Speaker 2: are also case sensitive, naturally.
Speaker 1: The book also mentions a way to format strings using
Speaker 1: string dot template.
Speaker 2: Yeah, that's one method. You create a template with placeholders
Speaker 2: like two hood and then use the dot substitute method
Speaker 2: to fill in those placeholders with actual values. There's also
Speaker 2: dot save substitute, which is useful because it won't raise
Speaker 2: an error if you forget to provide a value for
Speaker 2: a placeholder, It just leaves the placeholder texts.
Speaker 1: As is, moving on to numbers, integers and floats.
Speaker 2: Integers are whole numbers like ten naked five zero zero.
Speaker 2: Floats are numbers with a decimal point like three point
Speaker 2: one four next zero point five. Python uses a standard
Speaker 2: format called i EEE seven five to four for.
Speaker 1: Floats, and you can convert between them.
Speaker 2: Yes, using the end and float functions in three point
Speaker 2: nine O three boards. It truncates, just chops off the
Speaker 2: decimal part, doesn't round. Float five gives five point.
Speaker 1: Zero arithmetic a standard plus heat division.
Speaker 2: Okay, division's interesting. The single slash always results in a slot.
Speaker 2: Even four to two gives two point zero always a
Speaker 2: float always if you specifically want integer division the whole
Speaker 2: number result, throwing away any remainder you use the double
Speaker 2: slash move. So five two is two got it.
Speaker 1: For float division? For integer division, and.
Speaker 2: If you mix types like adding an integer in a float,
Speaker 2: the result will always be promoted to a float makes sense.
Speaker 1: Boolean's next true and.
Speaker 2: False the bedrock of decision making capital T capital F.
Speaker 2: The book points out a slightly quirky detail. Technically, there
Speaker 2: are subtype of integers really and true is one and
Speaker 2: false as zero, and conversely boole one is true, zero,
Speaker 2: zero is false. Non zero numbers are generally true, zero.
Speaker 1: Is false interesting connection, and the last one. None.
Speaker 2: None is Python's special way to signify the absence of
Speaker 2: a value. It's not zero, it's not an empty straying,
Speaker 2: it's just nothing. It has its own unique type none type.
Speaker 1: When would you use it?
Speaker 2: Often is a default value, or maybe a function returns
Speaker 2: none if it couldn't compute a result for some reason.
Speaker 1: And checking for it. The book says, use is none.
Speaker 2: Yes, variable is none, or variable is not none. That's
Speaker 2: the preferred, most reliable way to check if something holds
Speaker 2: the none value. Using none can sometimes work, but is
Speaker 2: safer and more idiomatic.
Speaker 1: Okay, data typees covered now. Making the program do things
Speaker 1: based on conditions or repeat actions control flow if statements first.
Speaker 2: Right, the fundamental decision maker if condition. If that condition
Speaker 2: evaluates too true, the indented block of code underneath it runs.
Speaker 1: If not it's skipped, and that indentation is crucial absolutely
Speaker 1: non negotiable in Python.
Speaker 2: Unlike languages that use curly braces, Python uses white space
Speaker 2: the indentation level to define which lines of code belong
Speaker 2: to the If block or else or loops or functions
Speaker 2: get it wrong, you get errors or incorrect logic.
Speaker 1: The standard is four spaces four.
Speaker 2: Spaces per indentation level is the strong convention.
Speaker 1: Yeah, okay, So if block runs code if true, what
Speaker 1: if it's fault?
Speaker 2: That's where else comes in ye if condition total one
Speaker 2: block runs if true, the other runs if.
Speaker 1: False, and for more than two options you use.
Speaker 2: Lf sure for else. If if condition one, l if
Speaker 2: condition two, l's condition three, I guess. Python checks each
Speaker 2: condition in order. The first one that's true gets executed,
Speaker 2: and the rest, including the final else, are skipped.
Speaker 1: Can you put if statements inside other if statements?
Speaker 2: Sure? That's called nesting really valid, though deep nesting can
Speaker 2: sometimes make code harder to read. The book also briefly
Speaker 2: mentions if expressions.
Speaker 1: What are those?
Speaker 2: It's like a compressed ifels that produces a value often
Speaker 2: used directly in assignments like result is value a true
Speaker 2: if condition else value a false it's a neat shorthand
Speaker 2: for simple cases.
Speaker 1: Okay, decisions covered. What about repetition loops.
Speaker 2: Two main kinds while and four. A while loop keeps
Speaker 2: executing its block of code as long as its condition remains.
Speaker 1: True, so you need to make sure that the condition
Speaker 1: eventually becomes false exactly.
Speaker 2: Otherwise you have an infinite loop, and you usually need
Speaker 2: to set up the variable used in the condition before
Speaker 2: the loop starts. And loops four loops are generally used
Speaker 2: when you want to iterate over a sequence of items,
Speaker 2: like all the elements in a list, where characters in
Speaker 2: a string or specific range of numbers.
Speaker 1: Often clearer when you know how many times you need
Speaker 1: to loop.
Speaker 2: Yeah, or when you're processing each item in a collection.
Speaker 2: The structure for item and collection is very common and
Speaker 2: readable in Python.
Speaker 1: What if your looping, say, searching for something, and you
Speaker 1: find it early, can you stop the loop?
Speaker 2: Yes, that's what the brake statement is for. It immediately
Speaker 2: exits the current innermost while or for loop.
Speaker 1: You're in okay, and the book mentions a weird four
Speaker 1: else construct.
Speaker 2: Ah, yes, the loop else. It's a bit unusual if
Speaker 2: you're coming from other languages. An else block attached to
Speaker 2: a for loop or while executes only if the loop
Speaker 2: completed normally, meaning it went through all its iterations without
Speaker 2: encountering a break statement.
Speaker 1: So if you break out the else's skipt correct.
Speaker 2: It's often used in search loops. If the loop finishes
Speaker 2: and the elf block runs, it means the break never happened.
Speaker 2: Therefore the item wasn't found.
Speaker 1: Huh, that's actually quite clever. Okay. As programs grow, just
Speaker 1: having one long sequence of code isn't practical. Functions are
Speaker 1: the answer.
Speaker 2: Absolutely essential. Functions let you package up a piece of code,
Speaker 2: give it a name, and reuse it. They're key to
Speaker 2: breaking down large, complex problems into smaller, more manageable, testable units.
Speaker 1: BOK connects this to something called functional decomposition.
Speaker 2: Right, that's a more formal term from software analysis. It's
Speaker 2: the process of identifying the main tasks your system needs
Speaker 2: to perform, representing them as functions, and then breaking down
Speaker 2: those functions into smaller subfunctions, focusing on inputs, outputs, and
Speaker 2: the transformation.
Speaker 1: Process and it mentions. Flow charts and data dictionaries alongside.
Speaker 2: This, Yeah, as related tools. Flow charts help visualize this
Speaker 2: sequence of steps and decisions in an algorithm. While data
Speaker 2: dictionaries provide a central place to define and describe all
Speaker 2: the data elements your system uses, useful for planning and documentation.
Speaker 1: So defining a function in Python starts with.
Speaker 2: Death deaf function name, parameters, followed by the colon and
Speaker 2: the indented block of code that makes up the function's body. Again,
Speaker 2: that four S based enden convention is standard, and the
Speaker 2: doc string oh very important for documentation. It's a string
Speaker 2: literal place right at the beginning of the function body
Speaker 2: inside cripple quotes. Usually it explains what the function does,
Speaker 2: what its parameters are, what a return, urns, et cetera.
Speaker 2: It's not just a comment. Tools can automatically extract this information.
Speaker 1: Parameters are in the definition. Arguments are what you pass
Speaker 1: when you call it.
Speaker 2: Correct parameters are the names used inside the function. Arguments
Speaker 2: are the actual value supplied when the function is called.
Speaker 2: The book then gets into some powerful argument handling features
Speaker 2: ARGs and quarks.
Speaker 1: Right for when you don't know how many arguments will be.
Speaker 2: Passed Exactlygs inside the function definition collects any extra positional
Speaker 2: arguments one's passed just by position into a a tupo okay,
Speaker 2: and quary's collects any extra keyword arguments one's past is
Speaker 2: name value into a dictionary. You can use them together.
Speaker 2: The book highlights this as really useful for library authors
Speaker 2: who want to offer flexibility to their users.
Speaker 1: And Lambda functions.
Speaker 2: Lambdas are for creating small, anonymous functions in line. If
Speaker 2: you need a simple function just for one quick use,
Speaker 2: maybe as an argument to another function, lambda lets you
Speaker 2: define it right there without a full deaf statement.
Speaker 1: The book suggest us using the number guessing game as
Speaker 1: an exercise for function refactoring.
Speaker 2: Yeah, taking a simple script and breaking its logic down
Speaker 2: into well defined functions is a great way to practice this.
Speaker 1: Structuring functions also introduce the idea of variable scope, where
Speaker 1: a variable.
Speaker 2: Lives crucial concept. Variables defined right at the top level
Speaker 2: of a script outside any function have global scope. They
Speaker 2: can in principle be accessed from anywhere.
Speaker 1: The variables inside a function.
Speaker 2: They have local scope. They only exist and are accessible
Speaker 2: within that function. If you have a local variable with
Speaker 2: the same name as a global variable, the local one
Speaker 2: takes precedence inside the function. They are distinct.
Speaker 1: What if you need to change a global variable from
Speaker 1: inside a function.
Speaker 2: You have to explicitly tell Python that's your intention using
Speaker 2: the global keyword global, we will bovar at the start
Speaker 2: of the function says when I use my global of
Speaker 2: ar here, I mean the global one, not a new
Speaker 2: local one.
Speaker 1: Okay, and non local. That's where nested functions.
Speaker 2: Right. If you define a function inside another one function,
Speaker 2: nesting non local is used in the inner function to
Speaker 2: refer to a variable in the directly enclosing function scope,
Speaker 2: but not the global scope. The example in the book
Speaker 2: shows an interfunction modifying a variable belonging to the outer
Speaker 2: function using non local.
Speaker 1: That clarifies the difference. Now a potentially tricky concept recursion.
Speaker 2: Ah recursion a function calling itself. It's a way to
Speaker 2: solve problems by breaking them down into smaller, self similar
Speaker 2: sub problems.
Speaker 1: The key is having an escape patch right. A base
Speaker 1: case absolutely essential.
Speaker 2: The base case or termination condition is the simplest version
Speaker 2: of the problem that the function can solve directly without
Speaker 2: calling itself again. This stops the chain of calls. Without it,
Speaker 2: you get infinite recursion and eventually a recursion er.
Speaker 1: Factorial is the textbook example.
Speaker 2: It is factorial n is n factorial n one until
Speaker 2: you hit the base case factorial one, which is just
Speaker 2: one the book traces how the calls stack up and
Speaker 2: then unwind to produce the final result.
Speaker 1: How does it stack up against just using a loop iteration?
Speaker 2: Recursion can often lead to a very elegant, concise code
Speaker 2: that mirrors the mathematical definition of a problem. However, iteration
Speaker 2: using loops is generally more efficient in terms of speed
Speaker 2: and memory usage in Python and many other languages. The
Speaker 2: book does mention that some languages, particularly functional ones, can
Speaker 2: optimize tail recursion into iteration, removing the overhead.
Speaker 1: Speaking of functional, let's shift to that programming style. How
Speaker 1: does the book contrast it with procedural.
Speaker 2: It's presented as a different mindset. The core idea in
Speaker 2: functional programming is to think about computation more like evaluating
Speaker 2: mathematical functions. A key principle is avoiding side effects.
Speaker 1: Side effects meaning changes to.
Speaker 2: State outside the function itself. A pure functions output depends
Speaker 2: only on its input arguments and it doesn't modify anything
Speaker 2: external like a global variable or a file. This makes
Speaker 2: code easier to reason about and test because you know
Speaker 2: exactly what it does based on its inputs alone. This
Speaker 2: relates to ref prential transparency.
Speaker 1: Which means you can replace the function call with its
Speaker 1: result pretty much.
Speaker 2: If a function is pure, substituting its call with its
Speaker 2: return value for the same inputs won't change the program's
Speaker 2: overall behavior. Big win for predictability.
Speaker 1: And immutable data is encouraged.
Speaker 2: Yes, using data structures that cannot be changed after creation.
Speaker 2: Python strings are a good example. When you seem to
Speaker 2: modify a string, you're actually creating a new string object.
Speaker 2: Working with immutable data helps prevent accidental side effects. Because
Speaker 2: functions can't secretly change data that other parts of the
Speaker 2: program rely on, they return new data.
Speaker 1: Instead, and recursion fits naturally here.
Speaker 2: Functional programming often emphasizes recursion as a primary control structure,
Speaker 2: fitting the mathematical function model well, though as we said,
Speaker 2: iteration is still possible and often more performant in practice
Speaker 2: in Python.
Speaker 1: What advantages does this style offer, according to.
Speaker 2: The book, potential for less code, code that's easier to
Speaker 2: understand and test due to purity, easier parallelization since pure
Speaker 2: functions don't interfere with each other, and sometimes very elegant
Speaker 2: retursive solutions.
Speaker 1: Any downsides mentioned.
Speaker 2: Things like handling continuous input output or interactive systems can
Speaker 2: feel less natural with a purely functional approach. Historically, there
Speaker 2: were performance concerns, though that's less true now, and the
Speaker 2: style the idioms might feel less intuitive at first if
Speaker 2: you're used to procedural or ooprogramming, sometimes perceived as a
Speaker 2: bit academic, though functional features are increasingly mainstream.
Speaker 1: Got it. This leads into higher order functions. What makes
Speaker 1: a function higher order.
Speaker 2: It comes from the fact that functions in Python are
Speaker 2: first class objects. You can assign them to variables, store
Speaker 2: them in data structures, pass them as arguments to other functions,
Speaker 2: and return them from functions, just.
Speaker 1: Like any other data type like a number or a string.
Speaker 2: Exactly. A higher order function is simply a function that
Speaker 2: either takes one or more functions as arguments or returns
Speaker 2: a function as its result, or both.
Speaker 1: Key consop functional programming, very much so.
Speaker 2: The book shows examples like passing a calculation function into
Speaker 2: another function to tax salary tax funk, or a function
Speaker 2: that takes an action function applied data.
Speaker 1: Action funk, and functions returning functions that leads to closures.
Speaker 2: Yes, when a function defined inside another function an inner
Speaker 2: function is returned, it carries with it the environment of
Speaker 2: the scope in which it was created. It closes over
Speaker 2: any variables that references from.
Speaker 1: That outer scope, so it remembers variables from the outer
Speaker 1: function even after the outer function has finished.
Speaker 2: Precisely, the reset function example in the book with the
Speaker 2: inner lambda shows that the return lambda still has access
Speaker 2: to the addition variable from reset functions.
Speaker 1: Scope and currying related but different.
Speaker 2: Related. Currying, named after Haskell curry is a technique where
Speaker 2: you take a function that accepts multiple arguments and transform
Speaker 2: it into a sequence of functions that each take a
Speaker 2: single argument. More practically, in Python, it often means creating
Speaker 2: a new function by pre filling or binding some of
Speaker 2: the arguments of an existing function.
Speaker 1: Like taking multiply ab and creating double multiply.
Speaker 2: Two B's essentially yes, yeah, you create a specialized version
Speaker 2: by fixing one or more parameters. It's about creating reusable
Speaker 2: function variations.
Speaker 1: Okay, let's switch gears completely to the other major paradigm,
Speaker 1: object oriented programming OP.
Speaker 2: Right, a very different way to structure things. The core
Speaker 2: idea here is to bundle data called attributes or properties,
Speaker 2: and the operations that work on that data called methods,
Speaker 2: together into units called classes.
Speaker 1: And you work with instances of these classes exactly.
Speaker 2: You create objects, which are instances of a class. So
Speaker 2: you might have an employee class, and then John and
Speaker 2: Jane could be specific employee objects, each with their own name, ID,
Speaker 2: et cetera, but sharing the methods defined in the employee class,
Speaker 2: like calculate pay.
Speaker 1: So design involves identifying these objects.
Speaker 2: Yeah, you look at the problem domain and identify the
Speaker 2: key entities. What data do they need to store? Their
Speaker 2: still eight, what actions can they perform or have performed
Speaker 2: on them? Their methods or interface objects then interact by
Speaker 2: calling methods on each other sending messages. The pump system
Speaker 2: example illustrates how this can create modular systems where obvious
Speaker 2: only need to know about their direct collaborators.
Speaker 1: Defining a class uses the class keyword in Python.
Speaker 2: Simple as class name. Then inside you define attributes and methods.
Speaker 1: And creating an object an instance, you call.
Speaker 2: The class name as if it were a function. P
Speaker 2: one person elis thirty. Now P one is a variable
Speaker 2: holding a reference to a person object in memory.
Speaker 1: What if I do P two equals P one? Does
Speaker 1: that make a copy?
Speaker 2: No, that's a crucial point the book makes. It copies
Speaker 2: the reference the memory address. P one and P two
Speaker 2: now point to the exact same person object. Changing the
Speaker 2: object via P one will also be reflected when you
Speaker 2: look at it via P two. The ID function can
Speaker 2: show they have the same memory id.
Speaker 1: If you just print P one you get something cryptic.
Speaker 2: Usually yeah. The default is the class name and its
Speaker 2: memory address. To get something meaningful, you need to define
Speaker 2: a special method called stye self within the class. That
Speaker 2: method should return the string representation you want print to use.
Speaker 1: Python handles cleaning up these objects automatically.
Speaker 2: Yes, that's garbage collection, a huge advantage over language is
Speaker 2: like C plus plus where you have to manage memory manually.
Speaker 2: Python keeps track of references, and when an object has
Speaker 2: no more references pointing to it, it gets automatically cleaned up.
Speaker 2: Freeing the memory lets you focus on logic, not memory leaks.
Speaker 1: Can attributes belong to the class itself, not just individual objects.
Speaker 2: They can. Class attributes are shared by all instances of
Speaker 2: the class. Useful for constants related to the class, or
Speaker 2: maybe tracking things like how many instances have been created.
Speaker 2: Account dot instance count Instance attributes are specific to each object.
Speaker 2: My account dot balance.
Speaker 1: And those special methods like in it and stray have
Speaker 1: double underscores.
Speaker 2: Right, and it's a constructor called automatically when you create
Speaker 2: an object stave sopra r for printing. There are many
Speaker 2: others so cautions. You generally shouldn't name your own methods
Speaker 2: like something unless you're intentionally overriding one of Python's built
Speaker 2: in behaviors.
Speaker 1: Are there methods that work on the class directly.
Speaker 2: Yes. Class methods often decorated with at class method and
Speaker 2: static methods at static method. They can be called on
Speaker 2: the class itself, my class dot class method rather than
Speaker 2: an instance. Useful for factory methods. Alternative ways to create
Speaker 2: instances or utility functions related to the class.
Speaker 1: How do classes connect? Inheritance seems key in OOP.
Speaker 2: Inheritance is a cornerstone. It lets you create a new class, subclass,
Speaker 2: or child class that inherits properties, attributes, and methods from
Speaker 2: an existing class, superclass, or parent class. It promotes code
Speaker 2: reuse and establishes is a relationships.
Speaker 1: Like a manager is an employee, which is a person.
Speaker 2: Perfect example, the manager inherits from employee, gaining its attributes
Speaker 2: and methods, and employee inherits from person. The subclass can
Speaker 2: use the inherited features directly, it can override them, provide
Speaker 2: its own version, and it can add completely new features
Speaker 2: of its own.
Speaker 1: If a child class needs its own in it, how
Speaker 1: does it make sure the parents in it also runs.
Speaker 2: The standard way is to call super dot end inside
Speaker 2: the child's in it. Super gives you a reference to
Speaker 2: the parent class, allowing you to call its methods, including
Speaker 2: the constructor. To ensure proper initialization.
Speaker 1: And overwriting a method is just redefining it in the
Speaker 1: child Yes.
Speaker 2: If person has a display method and employee needs a
Speaker 2: different display, you just define display again an employee. If
Speaker 2: you still need the parents behavior inside the overridden method,
Speaker 2: you can call it using super dot display.
Speaker 1: The book offers advice on when inheritance is appropriate.
Speaker 2: Yeah. It suggests asking does the child really use or
Speaker 2: specialize the parent's features? Does the parent's core functionality make
Speaker 2: sense in the child's context. The dinghy is a conveyance
Speaker 2: example highlights thinking about whether the relationship truly fits the
Speaker 2: is model meaningfully.
Speaker 1: Python supports inheriting from multiple parents. Multiple inheritance it does.
Speaker 2: You can list multiple parent classes slay my class PARENTA
Speaker 2: parent B. This can be powerful, but also adds complexity,
Speaker 2: particularly regarding which parent's method gets called if both define
Speaker 2: a method with the same name. Python has a defined
Speaker 2: method resolution order MRO to handle this, but it's something
Speaker 2: to be aware of the order you list the parents matters, okay.
Speaker 1: Making our custom objects work with standard operators like plus
Speaker 1: a that's operator overloading exactly.
Speaker 2: It allows you to define how standard operators should behave
Speaker 2: when applied to instances of your custom classes. The goal
Speaker 2: is to make code using your objects more intuitive and readable.
Speaker 1: Like vector one plus vector two instead of vector one dot.
Speaker 2: Ad vector Decisely, you achieve this by implementing more of
Speaker 2: those special double underscore methods add for plus subframeke mole
Speaker 2: for a cup, a q for ltf for eight, and
Speaker 2: many others.
Speaker 1: And inside adds self other self is the left side,
Speaker 1: other is the right.
Speaker 2: Correct Inside that method you define what it means to
Speaker 2: add other to self. The book shows you can use
Speaker 2: is instance other to check the type of other and
Speaker 2: potentially handle addition with different types, like adding an integer
Speaker 2: to your custom object.
Speaker 1: What about properties and decorators like AT property.
Speaker 2: Decorators using the AT syntax are a way to modify
Speaker 2: or enhance functions or methods in Python. AT property is
Speaker 2: a specific built in decorator that lets you define a
Speaker 2: method but access it as if it were a simple attribute, So.
Speaker 1: My object dot value might actually call a method behind
Speaker 1: the scenes exactly.
Speaker 2: It's often used to create getters. You can put logic
Speaker 2: inside the method, maybe calculate the value on the fly,
Speaker 2: or check permissions whenever someone accesses my object dot value.
Speaker 1: Can you control assignment too, Like my object dot value
Speaker 1: equals ten.
Speaker 2: Yes. Using me at property name dot setter decorator, you
Speaker 2: define another method decorated with at value dot setter properties value,
Speaker 2: and that method will be called whenever someone tries to
Speaker 2: assign to my object dot value. Great for validation or
Speaker 2: triggering actions on.
Speaker 1: Change and at delet for delay.
Speaker 2: Right at value dot deleter defines the method called the
Speaker 2: end of myobject dot value is executed. The book often
Speaker 2: shows these us with a private backing attribute like value,
Speaker 2: with a single underscore to store the actual data, enforcing
Speaker 2: access through the property methods for encapsulation.
Speaker 1: Moving into more advanced concepts, protocols, polymorphism descriptors. What's the
Speaker 1: idea of a protocol? In Python?
Speaker 2: A protocol is like an informal interface. It's not something
Speaker 2: you formally declare. It's defined by the expected methods in behavior.
Speaker 2: If an object has the necessary methods like itter and
Speaker 2: next for the iteration protocol, it's considered to adhere to
Speaker 2: that protocol, regardless of his actual class or inheritance.
Speaker 1: This sounds like duck typing.
Speaker 2: It's the heart of duck typing. If it walks like
Speaker 2: a duck and quacks like a duck, then it must
Speaker 2: be a duck. Python often cares more about whether an
Speaker 2: object can do what's needed, does it have the right methods,
Speaker 2: rather than what its specific type is.
Speaker 1: And polymorphism flows from this.
Speaker 2: Yes, polymorphism means many forms. It's the ability for different
Speaker 2: objects potentially of different types, maybe not even related by inheritance,
Speaker 2: but adhering to the same protocol to respond to the
Speaker 2: same message the same method call in their own specific way.
Speaker 2: Len works on strings, lists, dictionaries because they all implement
Speaker 2: the len method part of the size protocol, but how
Speaker 2: they calculate their length differs.
Speaker 1: The context manager protocol enables the with statement exactly.
Speaker 2: Any object with enter and exit methods supports the context
Speaker 2: manager protocol. The width object is variable. Statement automatically calls
Speaker 2: object dot enter the start. It's return value goes into
Speaker 2: variable and guarantees object dot exit is called at the end.
Speaker 2: Even if errors occur within the width block. Essential for
Speaker 2: resource management like files or network connections.
Speaker 1: And descriptors. They sound complex, they.
Speaker 2: Are a more advanced mechanism. A descriptor is an object
Speaker 2: attributed to a class that defines special behavior when that
Speaker 2: attribute is accessed on an instance. They control access using
Speaker 2: methods like get, set, and delete defined on the descript
Speaker 2: object itself. Properties are actually implemented using descriptors. The book
Speaker 2: also mentions related attribute access methods. Get it is called
Speaker 2: only as a last resort if an attribute isn't found
Speaker 2: through normal means. Get attribute is called for every attribute
Speaker 2: access use with extreme care to avoid infinite recursion, and
Speaker 2: set attry is called for every attribute assignment.
Speaker 1: Okay, let's dive deep into iteration, iterables, iterators, generators, quarantines.
Speaker 2: What's the difference, okay and itterable is anything you could
Speaker 2: loop over with a for loop. Think lists, strains, dictionaries.
Speaker 2: The key requirement is that it must have an itter method.
Speaker 2: An gives you an iterator. The iterator is the object
Speaker 2: that actually keeps track of the position and produces the
Speaker 2: next value. It must have a next method that returns
Speaker 2: the next item and raises a stop iteration exception. When
Speaker 2: there are no more items. Iterators usually also have an
Speaker 2: inter method that just returns self.
Speaker 1: So four loops work by getting an iterator from the
Speaker 1: iterable then calling next on it repeatedly exactly.
Speaker 2: The inner tool's module in Python provides lots of pre
Speaker 2: built efficient iterators.
Speaker 1: Where do generators fit int.
Speaker 2: Are a super convenient way to create iterators. Instead of
Speaker 2: writing a full class with iter and next, you write
Speaker 2: a function that uses.
Speaker 1: The yield keyword yield instead of return.
Speaker 2: Right when you call a generator function, it doesn't run immediately.
Speaker 2: It returns a special generator object, which is an iterator.
Speaker 2: Each time you call next on the generator object or
Speaker 2: use it in a for loop, the function's code runs
Speaker 2: until it hits a yield statement. It sends the yielded
Speaker 2: value back, and then pauses its execution right there, preserving its.
Speaker 1: Local state lazy evaluation exactly.
Speaker 2: It only generates the next value when asked. This is
Speaker 2: incredibly memory efficient for large sequences or even infinite ones,
Speaker 2: because you don't store everything in memory at once. The
Speaker 2: even subto example in the book likely demonstrates this.
Speaker 1: Okay, so generators yield values out. How are courotines different.
Speaker 2: Core routines are like advanced generators. They can also yield
Speaker 2: a pause, but the key difference is they can also
Speaker 2: receive value sent into them. Well paused yield can act
Speaker 2: as an expression that sieves of value passed via the
Speaker 2: send method, so they become little processing units you can
Speaker 2: push data into. They often need to be primed first,
Speaker 2: calling next or send none to run up to the
Speaker 2: first yield before you can send data. The book mentions
Speaker 2: a rep example suggesting a coroutine that receives lines of
Speaker 2: text and processes them. They also have ways to handle being.
Speaker 1: Closed fascinating distinction. Let's quickly run through Python's main built
Speaker 1: in collections sure.
Speaker 2: The four work horses mentioned are lists, tuples, sets.
Speaker 1: And dictionaries check mutable, sequence.
Speaker 2: Ordered, unchangeable, allow duplicates, parentheses.
Speaker 1: Check immutable sequence.
Speaker 2: Ordered unchangeable, n duplicates, curly braces.
Speaker 1: Test stuck mutable collection of unique items great for membership testing, dictionaries,
Speaker 1: unordered mostly ordered since three point seven, key value pairs,
Speaker 1: n duplicate keys, curly braces que check.
Speaker 2: Key based lookup mapping keys to values conceptually based on
Speaker 2: hash tables for efficiency. The book also points to the
Speaker 2: collections module for more specialized types. Like counter recounting things,
Speaker 2: and order addict if you need guaranteed insertion order.
Speaker 1: The guide also touches on abstract data types ADTs like
Speaker 1: queues and stacks.
Speaker 2: Right ADTs are more about the concept the behavior rather
Speaker 2: than a specific implementation.
Speaker 1: Que first in, first out FIFO like a line.
Speaker 2: YEP, add to the back, remove from the front, order preserved.
Speaker 1: Stack last in first out LIFO like plates add to the.
Speaker 2: Top, removed from the top, order also preserved. The book
Speaker 2: shows how you can use Python lists to mimic thesele's
Speaker 2: a penpop for a stack, a pen pop a queue,
Speaker 2: but notes pop is inefficient for lists. It then provides
Speaker 2: proper Q and stack class examples using an internal list
Speaker 2: but exposing the correct ADT methods on key toq or push.
Speaker 1: Pop Circling back to functionalideas, dot map and filter two.
Speaker 2: Very useful higher order functions for working with iterables. Filter
Speaker 2: function iterable takes a function that returns true or false.
Speaker 2: It applies this test to every item in the iterable
Speaker 2: and gives you back an iterator containing only the items
Speaker 2: for which the function returned true.
Speaker 1: Good for selecting data exactly.
Speaker 2: And map function iterable one applies a given function to
Speaker 2: every item in the iterable or corresponding items if you
Speaker 2: pass multiple itterables and returns an iterator of the results.
Speaker 2: Good for transforming data precisely applying an operation across a
Speaker 2: whole collection. The examples likely show things like squaring every
Speaker 2: number in a list or extracting a specific piece of
Speaker 2: data from a list of objects.
Speaker 1: Powerful tools for data manipulation in a functional style.
Speaker 2: Definitely encourages thinking about operations on collections as transformations rather
Speaker 2: than modifying things in place.
Speaker 1: Lastly, the book mentions a tic tac toe example as
Speaker 1: a way to tie things together.
Speaker 2: Yeah, it's presented as a mini case study. It likely
Speaker 2: shows how concepts like classes board player, game inheritance, human player,
Speaker 2: computer player inheriting from player data structures, maybe a list
Speaker 2: of lists for the board and methods all combined to
Speaker 2: build a recognizable application and administrates putting the pieces into practice.
Speaker 1: Wow. Okay, that was quite the journey through this beginner's guide.
Speaker 1: We really covered the spectrum. Started with the basics Python's
Speaker 1: dynamic nature setting up.
Speaker 2: Right, Then the fundamental building blocks data types like strings, number, booleans, none,
Speaker 2: control flow using if lf else, and loops like while
Speaker 2: and four, and the critical role of indentation.
Speaker 1: Instruction code with functions, understanding parameters, arguments, arcs, quarks, scope, rules, global,
Speaker 1: non local, and even recursion.
Speaker 2: We explored different philosophies to the functional approach, with its
Speaker 2: focus on pure functions, immutability, referential transparency, and tools like
Speaker 2: higher order functions, closures and currying.
Speaker 1: And contrasted that with object orientation classes, objects, attributes, methods,
Speaker 1: the power of inheritance, operator overlooting for natural syntax, and
Speaker 1: niceties like properties and decorators.
Speaker 2: We touched on some deeper Python mechanics too, like protocols
Speaker 2: enabling duct typing and polymorphism, context managers for reliable resource
Speaker 2: handling with descriptions behind the scenes, and the nuances of attribute.
Speaker 1: Access methods, plus advanced iteration patterns iterables giving iterators, the
Speaker 1: memory efficiency of generators using yield, and the data receiving
Speaker 1: capabilities of corotines, and of.
Speaker 2: Course Python's core collections lists, tuples, sets, dictionaries, along with
Speaker 2: conceptual ADTs like queues and stacks, and functional hopers like
Speaker 2: map and filter.
Speaker 1: It feels like this deep dive really extracted the essence
Speaker 1: from that guide, laying out a roadmap from basic syntax
Speaker 1: to some pretty sophisticated concepts needed to really get good
Speaker 1: at Python.
Speaker 2: Yeah, it covers a lot of ground necessary to move
Speaker 2: from beginner to experience developer.
Speaker 1: Reflecting on all of this from the simplest variable assignment
Speaker 1: right up to complex things like metaclasses implicitly enabling descriptors,
Speaker 1: or choosing between recursion and iteration, or deciding between a
Speaker 1: functional or object oriented design. Python gives you so many tools,
Speaker 1: so many ways to think. It really makes you wonder,
Speaker 1: doesn't it. As you gain experience, how do you actually
Speaker 1: choose when faced with a new problem? How do you
Speaker 1: navigate this rich landscape and select the most effective, the
Speaker 1: most elegant, the most pythonic approach from all these possibilities