It not only focuses on the solution strategy, but also provides you with the working code. This book will equip you with the skills required for developing and analyzing algorithms for various situations. This book teaches you how to measure Time Complexity, it then provides solutions to questions on the Linked list, Stack, Hash table, and Math. Then you can review questions and solutions based on graph theory and application techniques.
Towards the end, you will come across coding questions on advanced topics such as Backtracking, Greedy, Divide and Conquer, and Dynamic Programming. After reading this book, you will successfully pass the python interview with high confidence and passion for exploring python in future.
Basic knowledge of Python and Data Structures is a mu. It keeps me awake for hours during the night. Thanks to you, I got most of the idea in just a few hours. Key Features Delve into supervised learning and grasp how a machine learns from data Implement popular machine learning algorithms from scratch, developing a deep understanding along the way Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors such as finance, online advertising, and analytics because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own.
This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions.
As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential.
What you will learn Crack how a machine learns a concept and generalize its understanding to new data Uncover the fundamental differences between parametric and non-parametric models Implement and grok several well-known supervised learning algorithms from scratch Work with models in domains such as ecommerce and marketing. Follow detailed guidance on how to create precise geometries, complex texture mappings, optimized renderings, and much more.
Blender is a popular open source 3D modeling software used in advertising, animation, data visualization, physics simulation, photorealistic rendering, and more.
Programmers can produce extremely complex and precise models that would be impossible to replicate by hand, while artists enjoy numerous new community-built add-ons. Using the API is made difficult due to its complex object hierarchy and vast documentation. You will become familiar with data structures and low-level concepts in both modeling and rendering with special attention given to optimizing procedurally generated models.
Create precision object models in Blender of architectural models, procedurally generated landscapes, atomic models, etc. It is fast, flexible, and it comes with batteries included. In this book, we'll see how you can leverage Python to efficiently tackle your problems and build great Python applications.
About the technology Quantum computers present a radical leap in speed and computing power. Improved scientific simulations and new frontiers in cryptography that are impossible with classical computing may soon be in reach. About the book Learn Quantum Computing with Python and Q introduces quantum computing from a practical perspective. The authors explain complex math and theory through stories, visuals, and games.
What's inside The underlying mechanics of quantum computers Simulating qubits in Python Exploring quantum algorithms with Q Applying quantum computing to chemistry, arithmetic, and data About the reader For software developers.
No prior experience with quantum computing required. About the author Dr. Sarah Kaiser works at the Unitary Fund, a non-profit organization supporting the quantum open-source ecosystem, and is an expert in building quantum tech in the lab. Christopher Granade works in the Quantum Systems group at Microsoft, and is an expert in characterizing quantum devices. Do you have some basic knowledge with Python and want to learn more? To tell you the truth, trying to modify a class variable through an object instance—which then accidentally creates an instance variable of the same name, shadowing the original class variable—is a bit of an OOP pitfall in Python.
So here it is. The following CountedObject class keeps track of how many times it was instantiated over the lifetime of a program which might actually be an interesting performance metric to know : 4. When the class is declared, it initializes the counter to zero and then leaves it alone. It correctly calculates the new value for the counter going from 0 to 1 , but then stores the result in an instance variable—which means other instances of the class never even see the updated counter value.
Automated tests and peer reviews help greatly with that. Nevertheless, I hope you can see why and how class variables—despite their pitfalls—can be useful tools in practice. Good luck! They belong to a class, not a specific instance and are shared 4. Class vs Instance Variable Pitfalls among all instances of a class.
They belong to individual object instances and are not shared among the other instances of a class. Each instance variable gets a unique backing store specific to the instance.
Instance, Class, and Static Methods Demystified 4. Instead of using a plain class MyClass declaration, you might choose to declare a new-style class inheriting from object with the class MyClass object syntax. Instance Methods The first method on MyClass, called method, is a regular instance method.
You can see the method takes one parameter, self, which 4. But of course, instance methods can accept more than just one parameter. Through the self parameter, instance methods can freely access attributes and other methods on the same object.
Not only can they modify object state, instance methods can also access the class itself through the self. This means instance methods can also modify class state. I marked this method with a classmethod5 decorator to flag it as a class method.
Instead of accepting a self parameter, class methods take a cls parameter that points to the class—and not the object instance—when the method is called. That would require access to self. However, class methods can still modify class state that applies across all instances of the class. Static Methods The third method, MyClass. Instance, Class, and Static Methods Demystified As a result, a static method cannot modify object state or class state.
I know this discussion has been fairly theoretical up to this point. When the method is called, Python replaces the self argument with the instance object, obj.
We could ignore the syntactic sugar provided by the obj. Instance, Class, and Static Methods Demystified By the way, instance methods can also access the class itself through the self. This makes instance methods powerful in terms of access restrictions—they can freely modify state on the object instance and on the class itself.
Notice how Python automatically passes the class as the first argument to the function when we call MyClass. Calling a method in Python through the dot syntax triggers this behavior. The self parameter on instance methods works the same way.
Please note that naming these parameters self and cls is just a convention. Behind the scenes, Python simply enforces the access restrictions by not passing in the self or the cls argument when a static method gets called using the dot syntax.
Instance, Class, and Static Methods Demystified This confirms that static methods can neither access the object instance state nor the class state. This is to be expected. This means there is no way for Python to populate the self argument and therefore the call fails with a TypeError exception. This should make the distinction between these three method types a little more clear.
I will base my examples around this bare-bones Pizza class: 4. Now, what can we do with these factory methods? Another way to look at this use of class methods is to realize that they allow you to define alternative constructors for your classes. Using class methods makes it possible to add as many alternative constructors as nec8 cf. Instance, Class, and Static Methods Demystified essary.
This can make the interface for your classes self-documenting to a certain degree and simplify their usage. This would also be a good candidate for an property—but hey, this is just a toy example. Now, why is that useful? Techniques like that allow you to communicate clearly about parts of your class architecture so that new development work is naturally guided to happen within these boundaries.
Of course, it would be easy enough to defy these restrictions. But in practice, they often help avoid accidental modifications that go against the original design. Applied sparingly and when it makes sense, writing some of your methods that way can provide maintenance benefits and make it less likely that other developers use your classes incorrectly. Static methods also have benefits when it comes to writing test code. We can just fire away like we would if we were testing a regular function.
Again, this makes future maintenance easier and provides a link between object-oriented and procedural programming styles. This can have definite maintenance benefits. Data structures. Each data structure provides a particular way of organizing data so it can be accessed efficiently, depending on your use case.
I believe that going back to the fundamentals always pays off for a programmer, regardless of their skill level or experience. Do we have stacks? You see, the trouble is that Python ships with an extensive set of data structures in its standard library.
This makes it easier to recognize the expected behavior and the computational complexity of these types. But the downside is that even to experienced Python developers, it can be unclear whether the built-in list type is implemented as a linked list or a dynamic array. And the day will come when lacking this knowledge will cause them endless hours of frustration, or get them rejected in a job interview.
This information will also help you shine in Python coding interviews. It strikes a great balance between teaching you fundamental and more advanced data structures, and then showing you how to put them to practical use in various algorithms. Dictionaries, Maps, and Hashtables 5.
Dicts store an arbitrary number of objects, each identified by a unique dictionary key. Dictionaries are also often called maps, hashmaps, lookup tables, or associative arrays. They allow for the efficient lookup, insertion, and deletion of any object associated with a given key.
What does this mean in practice? This analogy breaks down somewhat when it comes to how the information is organized in order to allow for fast lookups. But the fundamental performance characteristics hold: Dictionaries allow you to quickly find the information associated with a given key. In summary, dictionaries are one of the most frequently used and most important data structures in computer science.
So, how does Python handle dictionaries? Dictionaries, Maps, and Hashtables data type. In addition, hashable objects which compare as equal must have the same hash value. Immutable types like strings and numbers are hashable and work well as dictionary keys.
You can also use tuple objects as dictionary keys, as long as they contain only hashable types themselves. Dictionaries are highly optimized and un1 2 cf. Dictionaries, Maps, and Hashtables derlie many parts of the language, for example class attributes and variables in a stack frame are both stored internally in dictionaries. However, specialized third-party dictionary implementations exist, for example skip lists or B-tree based dictionaries.
These specialized dictionaries are all based on the built-in dictionary class and share its performance characteristics , but add some convenience features on top of that. OrderedDict — Remember the Insertion Order of Keys Python includes a specialized dict subclass that remembers the insertion order of keys added to it: collections. By the way, OrderedDict is not a built-in part of the core language and must be imported from the collections module in the standard library.
CPython mailing list 5. ChainMap data structure groups multiple dictionaries into a single mapping. Insertions, updates, and deletions only affect the first mapping added to the chain. Dictionaries, Maps, and Hashtables class was added in Python 3.
Using MappingProxyType allows you to put these restrictions in place without first having to create a full copy of the dictionary. Dictionaries, Maps, and Hashtables by dict. Yes, I still believe all of them are valid options—but usually your code will be more clear and easier to maintain by other developers if it relies on standard Python dictionaries most of the time. Array Data Structures 5.
How do arrays work, and what are they used for? Arrays consist of fixed-size data records that allow each element to be efficiently located based on its index. A real world analogy for an array data structure is a parking lot: You can look at the parking lot as a whole and treat it as a single object, but inside the lot there are parking spots indexed by a unique number.
Parking spots are containers for vehicles—each parking spot can either be empty or have a car, a motorbike, or some other vehicle parked on it.
But not all parking lots are the same: Some parking lots may be restricted to only one type of vehicle. A proper array implementation guarantees a constant O 1 access time for this case. Python includes several array-like data structures in its standard library that each have slightly different characteristics. This means a list allows elements to be added or removed, and the list will automatically adjust the backing store that holds these elements by allocating or releasing memory.
Therefore, you can mix and match different kinds of data types and store them all in a single list. This can be a powerful feature, but the downside is that supporting multiple data types at the same time means that data is generally less tightly packed. And as a result, the whole structure takes up more space. Just like lists, tuples can hold elements of arbitrary data types. Having this flexibility is powerful, but again, it also means that data is less tightly packed than it would be in a typed array.
Arrays created with the array. The elements stored in them are tightly packed, and this can be useful if you need to store many elements of the same type. Because strings are immutable in Python, modifying 11 cf. Array Data Structures a string requires creating a modified copy. Use a list or a tuple, depending on whether you want an immutable data structure or not.
You have numeric integer or floating point data and tight packing and performance is important? Try out array. Also, consider going beyond the standard library and try out packages like NumPy or Pandas. Array Data Structures You have textual data represented as Unicode characters?
You want to store a contiguous block of bytes? Use the immutable bytes type, or bytearray if you need a mutable data structure. In most cases, I like to start out with a simple list. Most of the time, using a general-purpose array data structure like list gives you the fastest development speed and the most programming convenience.
I found that this is usually much more important in the beginning than trying to squeeze out every last drop of performance right from the start. Records, Structs, and Data Transfer Objects 5. Python offers several data types you can use to implement records, structs, and data transfer objects.
Using dictionaries as a record data type or data object in Python is possible. Dictionaries are easy to create in Python, as they have their own syntactic sugar built into the language in the form of dictionary literals. The dictionary syntax is concise and quite convenient to type. In practice, the performance difference will often be negligible, and trying to squeeze extra performance out of a program by switching from lists to tuples will likely be the wrong approach.
CPython tupleobject. Records, Structs, and Data Transfer Objects A potential downside of plain tuples is that the data you store in them can only be pulled out by accessing it through integer indexes. Therefore, I would recommend that you keep the number of fields stored in a tuple as low as possible.
Using regular Python classes as record data types is feasible, but it also takes manual work to get the convenience features of other implementations. Also, the default string representation for objects instantiated from custom classes is not very helpful. Fields stored on classes are mutable, and new fields can be added freely, which you may or may not like. However, this means that these objects are technically no longer plain data objects.
Namedtuples are immutable, just like regular tuples. This means you cannot add new fields or modify existing fields after the namedtuple instance was created. Besides that, namedtuples are, well… named tuples.
Each object stored in them can be accessed through a unique identifier. This frees you from having to remember integer indexes, or resort to workarounds like defining integer constants as mnemonics for your indexes. Namedtuple objects are implemented as regular Python classes internally. I find that going from ad-hoc data types, like dictionaries with a fixed format, to namedtuples helps me express the intent of my code more clearly.
Please note that type annotations are not enforced without a separate type-checking tool like mypy. Struct — Serialized C Structs The struct. Struct class23 converts between Python values and C structs serialized into Python bytes objects. For example, it can be used to handle binary data stored in files or coming in from network connections. Structs are defined using a format strings-like mini language that allows you to define the arrangement of various C data types like char, int, and long, as well as their unsigned variants.
Serialized structs are seldom used to represent data objects meant to be handled purely inside Python code. In some cases, packing primitive data into structs may use less memory than keeping it in other data types. However, in most cases that would be quite an advanced and probably unnecessary optimization.
This means SimpleNamespace instances expose all of their keys as class attributes. This means you can use obj. As its name proclaims, SimpleNamespace is simple! Attributes can be added, modified, and deleted freely. Generally your decision will depend on your use case: You only have a few fields: Using a plain tuple object may be okay if the field order is easy to remember or field names are superfluous.
For example, think of an x, y, z point in 3D space. You need immutable fields: In this case, plain tuples, collections. NamedTuple would all make good options for implementing this type of data object. You need to lock down field names to avoid typos: collections. NamedTuple are your friends here. You want to keep things simple: A plain dictionary object might be a good choice due to the convenient syntax that closely resembles JSON.
You need to add behavior methods to the object: You should write a custom class, either from scratch or by extending collections. You need to pack data tightly to serialize it to disk or to send it over the network: Time to read up on struct. Struct because 5. Records, Structs, and Data Transfer Objects this is a great use case for it.
NamedTuple in Python 3. Sets and Multisets 5. Typically, sets are used to quickly test a value for membership in the set, to insert or delete new values from a set, and to compute the union or intersection of two sets.
Union, intersection, difference, and subset operations should take O n time on average. Python and its standard library provide several set implementations. Any hashable object can be stored in a set. Counter — Multisets The collections. Counter class in the Python standard library implements a multiset or bag type that allows elements in the set to have more than one occurrence. Calling len 29 cf. Stacks LIFOs 5.
The insert and delete operations are also often called push and pop. A useful real-world analogy for a stack data structure is a stack of plates: New plates are added to the top of the stack. And because the plates are precious and heavy, only the topmost plate can be moved last-in, first-out. To reach the plates that are lower down in the stack, the topmost plates must be removed one by one.
Stacks and queues are similar. Performance-wise, a proper stack implementation is expected to take O 1 time for insert and delete operations. A short and beautiful algorithm using a stack is depth-first search DFS on a tree or graph data structure. Python ships with several stack implementations that each have slightly different characteristics. The list overallocates its backing storage so that not every push or pop requires resizing, and as a result, you get an amortized O 1 time complexity for these operations.
The downside is that this makes their performance less consistent than the stable O 1 inserts and deletes provided by a linked list based implementation like collections. On the other hand, lists do provide fast O 1 time random access to elements on the stack, and this can be an added benefit. For optimum performance, stacks based on Python lists should grow towards higher indexes and shrink towards lower ones.
Adding and removing from the front is much slower and takes O n time, as the existing elements must be shifted around to make room for the new element. This is a performance antipattern that you should avoid as much as possible. Because deques support adding and removing elements from either end equally well, they can serve both as queues and as stacks. LifoQueue — Locking Semantics for Parallel Computing This stack implementation in the Python standard library is synchronized and provides locking semantics to support multiple concurrent producers and consumers.
Depending on your use case, the locking semantics might be helpful, or they might just incur unneeded overhead. All of them have slightly different characteristics, as well as performance and usage trade-offs.
The list over-allocates its backing storage so that not every push or pop requires resizing, and you get an amortized O 1 time complexity for these operations. Otherwise, performance slows down to O n. Queues FIFOs 5. The insert and delete operations are sometimes called enqueue and dequeue. Removal serving happens in the front of the queue, as developers receive their badges and conference swag bags and leave the queue. Another way to memorize the characteristics of a queue data structure is to think of it as a pipe: New items water molecules, ping-pong balls, … are put in at one end and travel to the other where you or someone else removes them again.
The only way to interact with the items in the queue is to add new items at the back enqueue or to remove items at the front dequeue of the pipe. Queues are similar to stacks, and the difference between them lies in how items are removed: 5.
Performance-wise, a proper queue implementation is expected to take O 1 time for insert and delete operations. These are the two main operations performed on a queue, and in a correct implementation, they should be fast. Queues have a wide range of applications in algorithms and often help solve scheduling and parallel programming problems. A short and beautiful algorithm using a queue is breadth-first search BFS on a tree or graph data structure.
Scheduling algorithms often use priority queues internally. These are specialized queues: Instead of retrieving the next element by insertion time, a priority queue retrieves the highest-priority element. The priority of individual elements is decided by the queue, based on the ordering applied to their keys.
Python ships with several queue implementations that each have slightly different characteristics. Queues FIFOs elements. As a result, collections. Queue — Locking Semantics for Parallel Computing This queue implementation in the Python standard library is synchronized and provides locking semantics to support multiple concurrent producers and consumers.
Depending on your use case, the locking semantics might be helpful or just incur unneeded overhead. Queue — Shared Job Queues This is a shared job queue implementation that allows queued items to be processed in parallel by multiple concurrent workers. As a specialized queue implementation meant for sharing data between processes, multiprocessing. Queue makes it easy to distribute work across multiple processes in order to work around the GIL limitations.
This type of queue can store and transfer any pickle-able object across process boundaries. Priority Queues 5. You can think of a priority queue as a modified queue: instead of retrieving the next element by insertion time, it retrieves the highestpriority element.
The priority of individual elements is decided by the ordering applied to their keys. Priority queues are commonly used for dealing with scheduling problems, for example, to give precedence to tasks with higher urgency. Think about the job of an operating system task scheduler: Ideally, high-priority tasks on the system e. By organizing pending tasks in a priority queue that uses the task urgency as the key, the task scheduler can quickly select the highest-priority tasks and allow them to run first.
The downside is that inserting new elements into 39 cf. Priority Queues a list is a slow O n operation. While the insertion point can be found in O log n time using bisect. Maintaining the order by appending to the list and re-sorting also takes at least O n log n time.
Another downside is that you must manually take care of re-sorting the list when new elements are inserted. Therefore, I believe that sorted lists are only suitable as priority queues when there will be few insertions. Priority Queues heapq — List-Based Binary Heaps This is a binary heap implementation usually backed by a plain list, and it supports insertion and extraction of the smallest element in O log n time. PriorityQueue — Beautiful Priority Queues This priority queue implementation uses heapq internally and shares the same time and space complexities.
Priority Queues The difference is that PriorityQueue is synchronized and provides locking semantics to support multiple concurrent producers and consumers. Depending on your use case, this might be helpful—or just slow your program down slightly. In any case, you might prefer the class-based interface provided by PriorityQueue over using the function-based interface provided by heapq.
PriorityQueue stands out from the pack with a nice object-oriented interface and a name that clearly states its intent. It should be your preferred choice. PriorityQueue, using the heapq module directly is also a good option. Writing Pythonic Loops 6. Two things: First, it keeps track of the index i manually—initializing, it to zero and then carefully incrementing it upon every loop iteration.
In Python you can write loops that handle both of these responsibilities automatically. It also makes the code more concise and therefore more readable. A good way to do that is with a for-loop in Python. Its advantage over a regular list is that it always takes the same small amount of memory. When you see code that uses range len It uses several advanced Python features but remains nice and clean and almost reads like pseudo code from a programming textbook.
The container itself now takes care of handing out the elements so they can be processed. If the container is ordered, the resulting sequence of elements will be too. What if you need the item index, for example? They can return tuples with an arbitrary number of values that can then be unpacked right inside the for-statement. This is very powerful.
What if you absolutely, positively need to write a C-style loop. For example, what if you must control the step size for the index? The range function comes to our rescue again—it accepts optional parameters to control the start value for the loop a , the stop value n , and the step size s. Therefore, our Java loop example could be translated to Python, like this: for i in range a, n, s Avoid managing loop indexes and stop conditions manually if possible. Comprehending Comprehensions 6.
They can seem a bit arcane at first but when you break them down they are actually a very simple construct. This is sometimes referred to as syntactic sugar—a little shortcut for frequently used functionality that makes our lives as Python coders easier.
Then, we iterate over all items in the container, transforming each of them with an arbitrary expression and then adding the individual results to the output list. List comprehensions can filter values based on some arbitrary condition that decides whether or not the resulting value becomes a part of the output list.
In this example, we use it to test if a number is even. Before you move on, I want to point out that Python not only supports list comprehensions but also has similar syntactic sugar for sets and dictionaries. Remember, too much of a good thing is usually a bad thing. Understanding and applying them will make your code much more Pythonic. List Slicing Tricks and the Sushi Operator 6. You can view it as an extension of the square-brackets indexing syntax.
Slicing is commonly used to access ranges of elements within an ordered collection. For example, you can slice up a large list object into several smaller sublists with it. This is why we got [2, 3] as the sublist from the [] slice. For example, you can create a sublist that includes every other element of the original: 6.
It looks like a delicious maki roll cut in half. Besides reminding you of delicious food and accessing ranges of lists, it has a few more lesser-known applications. Let me show you some more fun and useful list-slicing tricks! You just saw how the slicing step size can be used to select every other element of a list.
This is extremely helpful when you need to clear out a list in your program that has other references pointing to it.
In Python 3 you can also use lst. List Slicing Tricks and the Sushi Operator same job, which might be the more readable pattern, depending on the circumstances. Besides clearing lists, you can also use slicing to replace all elements of a list without creating a new list object. The old references to the original list object are therefore still valid. Both copies of the list share the same instances of the individual elements. It can also be used to clear, reverse, and copy lists.
Using it might make your code less maintainable for everyone else on your team. Beautiful Iterators 6. How does the loop fetch individual elements from the object it is looping over? And, how can you support the same programming style in your own Python objects? Just like decorators, iterators and their related techniques can appear quite arcane and complicated on first glance.
I think doing it this way gives you a more applicable understanding of how iterators work in Python. So the above example code would forever print the string 'Hello' to the console. In RepeaterIterator. Repeater keeps on returning the same string value, and so, this loop will never complete. But congratulations—you just wrote a working iterator in Python and used it with a for-in loop.
The loop may not terminate yet…but so far, so good! How do for-in loops work in Python? Beautiful Iterators Now, what does this for-in loop really do behind the scenes?
How does it communicate with the repeater object to fetch new elements from it? This returned the actual iterator object. Our Repeater class provides an infinite sequence of elements and we can iterate over it just fine. This makes iterators a very powerful concept. Every single one of these objects can be traversed in the same way with the power of iterators.
If you peek behind the curtain, it all comes down to calling the right dunder methods at the right time. This gives the same result—an infinite stream of hellos. Every time you call next , the iterator hands out the same greeting again.
Python offers these facades for other functionality as well. For example, len x is a shortcut for calling x. Similarly, calling iter x invokes x. It just makes the code a little easier to read. Many times both of these responsibilities can be shouldered by a single class. Doing this allows you to reduce the amount of code necessary to write a class-based iterator. I chose not to do this with the first example in this chapter because it mixes up the cleanliness of the mental model behind the iterator protocol.
Remember why we needed the RepeaterIterator class again? That way we could get rid of RepeaterIterator altogether and implement an iterable object with a single Python class. Our new and simplified iterator example looks as follows: 6. Streamlining a class-based iterator like that often makes sense. In fact, most Python iterator tutorials start out that way.
But I always felt that explaining iterators with a single class from the get-go hides the underlying principles of the iterator protocol—and thus makes it more difficult to understand.
Who Wants to Iterate Forever At this point you should have a pretty good understanding of how iterators work in Python. How do we do this? To signal the end of iteration, a Python iterator simply raises the built-in StopIteration exception. Being able to write a three-line for-in loop instead of an eight-line while loop is quite a nice improvement.
It makes the code easier to read and more maintainable. And this is another reason why iterators in Python are such a powerful tool. That way we can support both versions of Python while still keeping all of the actual implementation details in one place.
Behold the beauty of the for-in loop! Also consider generators and generator expressions. Generators Are Simplified Iterators 6. And yet, iterators are so useful in Python. They allow you to write pretty for-in loops and help you make your code more Pythonic and efficient. If there only was a more convenient way to write these iterators in the first place… Surprise, there is! Once more, Python helps us out with some syntactic sugar to make writing iterators easier.
It implemented a class-based iterator cycling through an infinite sequence of values. Parts of this class seem rather formulaic, as if 6. Generators Are Simplified Iterators they would be written in exactly the same way from one class-based iterator to the next. If I rewrite this iterator class as a generator, it looks like this: def repeater value : while True: yield value We just went from seven lines of code to three.
Not bad, eh? As you can see, generators look like regular functions but instead of using the return statement, they use yield to pass data back to the caller. Sep 14, Gary Boland rated it liked it. Short and sweet. A good book on python best practices that caters more to newer pythonistas. Jan 25, Andy rated it really liked it. It was a really good book on python and even though I knew most of the stuff, I still got some bullets to solve some of my current python challenges.
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