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Using Python Generators and yield: A Complete Guide

• February 15, 2023 February 15, 2023

In this tutorial, you’ll learn how to use generators in Python, including how to interpret the yield expression and how to use generator expressions . You’ll learn what the benefits of Python generators are and why they’re often referred to as lazy iteration. Then, you’ll learn how they work and how they’re different from normal functions.

Python generators provide you with the means to create your own iterator functions. These functions allow you to generate complex, memory-intensive operations. These operations will be executed lazily, meaning that you can better manage the memory of your Python program.

By the end of this tutorial, you’ll have learned:

• What Python generators are and how to use the yield expression
• How to use multiple yield keywords in a single generator
• How to use generator expressions to make generators simpler to write
• Some common use cases for Python generators

Table of Contents

Understanding Python Generators

Before diving into what generators are, let’s explore what iterators are. Iterators are objects that can be iterated upon, meaning that they return one action or item at a time . To be considered an iterator, objects need to implement two methods: __iter__() and __next__() . Some common examples of iterators in Python include for loops and list comprehensions .

Generators are a Pythonic implementation of creating iterators, without needing to explicitly implement a class with __iter__() and __next__() methods. Similarly, you don’t need to keep track of the object’s internal state. An important thing to note is that generators iterate over an object lazily, meaning they do not store their contents in memory .

The yield statement’s job is to control the flow of a generator function. The statement goes further to handle the state of the generator function, pausing it until it’s called again, using the next() function.

Creating a Simple Generator

In this section, you’ll learn how to create a basic generator. One of the key syntactical differences between a normal function and a generator function is that the generator function includes a yield statement .

Let’s see how we can create a simple generator function:

Let’s break down what is happening here:

• We define a function, return_n_values() , which takes a single parameter, n
• In the function, we first set the value of num to 0
• We then enter a while loop that evaluates whether the value of num is less than our function argument, n
• While that condition is True , we yield the value of num
• Then, we increment the value of num using the augmented assignment operator

Immediately, there are two very interesting things that happen:

• We use yield instead of return
• A statement follows the yield statement, which isn’t ignored

Let’s see how we can actually use this function:

In the code above, we create a variable values , which is the result of calling our generator function with an argument of 5 passed in. When we print the value of values , a generator object is returned.

So, how do we access the values in our generator object? This is done using the next() function, which calls the internal .__iter__() method. Let’s see how this works in Python:

We can see here that the value of 0 is returned. However, intuitively, we know that the values of 0 through 4 should be returned. Because a Python generator remembers the function’s state, we can call the next() function multiple times . Let’s call it a few more times:

In this case, we’ve yielded all of the values that the while loop will accept. Let’s see what happens when we call the next() function a sixth time:

We can see in the code sample above that when the condition of our while loop is no longer True , Python will raise StopIteration .

In the next section, you’ll learn how to create a Python generator using a for loop.

Creating a Python Generator with a For Loop

In the previous example, you learned how to create and use a simple generator. However, the example above is complicated by the fact that we’re yielding a value and then incrementing it. This can often make generators much more difficult for beginners and novices to understand.

Instead, we can use a for loop, rather than a while loop, for simpler generators . Let’s rewrite our previous generator using a for loop to make the process a little more intuitive:

In the code block above, we used a for loop instead of a while loop. We used the Python range() function to create a range of values from 0 through to the end of the values. This simplifies the generator a little bit, making it more approachable to readers of your code.

Unpacking a Generator with a For Loop

In many cases, you’ll see generators wrapped inside of for loops, in order to exhaust all possible yields. In these cases, the benefit of generators is less about remembering the state (though this is used, of course, internally), and more about using memory wisely.

In the code block above, we used a for loop to loop over each iteration of the generator. This implicitly calls the __next__() method. Note that we’re using the optional end= parameter of the print function, which allows you to overwrite the default newline character .

Creating a Python Generator with Multiple Yield Statements

A very interesting difference between Python functions and generators is that a generator can actually hold more than one yield expressions ! While, theoretically, a function can have more than one return keyword, nothing after the first will execute.

Let’s take a look at an example where we define a generator with more than one yield statement:

In the code block above, our generator has more than one yield statement. When we call the first next() function, it returns only the first yielded value. We can keep calling the next() function until all the yielded values are depleted. At this point, the generator will raise a StopIteration exception.

Understanding the Performance of Python Generators

One of the key things to understand is why you’d want to use a Python generator. Because Python generators evaluate lazily, they use significantly less memory than other objects.

For example, if we created a generator that yielded the first one million numbers, the generator doesn’t actually hold the values. Meanwhile, by using a list comprehension to create a list of the first one million values, the list actually holds the values. Let’s see what this looks like:

In the code block above, we import the sys library which allows us to access the getsizeof() function. We then print the size of both the generator and the list. We can see that the list is over 75,000 times larger.

In the following section, you’ll learn how to simplify creating generators by using generator expressions.

Creating Python Generator Expressions

When you want to create one-off generators, using a function can seem redundant. Similar to list and dictionary comprehensions , Python allows you to create generator expressions. This simplifies the process of creating generators, especially for generators that you only need to use once.

In order to create a generator expression, you wrap the expression in parentheses . Say you wanted to create a generator that yields the numbers from zero through four. Then, you could write (i for i in range(5)) .

In the example above, we used a generator expression to yield values from 0 to 4. We then call the next() function five times to print out the values in the generator.

In the following section, we’ll dive further into the yield statement.

Understanding the Python yield Statement

The Python yield statement can often feel unintuitive to newcomers to generators. What separates the yield statement from the return statement is that rather than ending the process, it simply suspends the current process.

The yield statement will suspend the process and return the yielded value. When the subsequent next() function is called, the process is resumed until the following value is yielded.

What is great about this is that the state of the process is saved. This means that Python will know where to pick up its iteration, allowing it to move forward without a problem.

How to Throw Exceptions in Python Generators Using throw

Python generators have access to a special method, .throw() , which allows them to throw an exception at a specific point of iteration . This can be helpful if you know that an erroneous value may exist in the generator.

Let’s take a look at how we can use the .throw() method in a Python generator:

Let’s break down how we can use the .throw() method to throw an exception in a Python generator:

• We create our generator using a generator expression
• We then use a for loop to loop over each value
• Within the for loop, we use an if statement to check if the value is equal to 3. If it is, we call the .throw() method, which raises an error

In some cases, you may simply want to stop a generator, rather than throwing an exception. This is what you’ll learn in the following section.

How to Stop a Python Generator Using stop

Python allows you to stop iterating over a generator by using the .close() function . This can be very helpful if you’re reading a file using a generator and you only want to read the file until a certain condition is met.

Let’s repeat our previous example, though we’ll stop the generator rather than throwing an exception:

In the code block above we used the .close() method to stop the iteration. While the example above is simple, it can be extended quite a lot. Imagine reading a file using Python – rather than reading the entire file, you may only want to read it until you find a given line.

In this tutorial, you learned how to use generators in Python, including how to interpret the yield expression and how to use generator expressions . You learned what the benefits of Python generators are and why they’re often referred to as lazy iteration. Then, you learned how they work and how they’re different from normal functions.

Additional Resources

To learn more about related topics, check out the resources below:

• Understanding and Using Functions in Python for Data Science
• Python: Return Multiple Values from a Function
• Python Built-In Functions
• Python generators: Official Documentation

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

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Functional Programming HOWTO ¶

A. M. Kuchling

In this document, we’ll take a tour of Python’s features suitable for implementing programs in a functional style. After an introduction to the concepts of functional programming, we’ll look at language features such as iterator s and generator s and relevant library modules such as itertools and functools .

Introduction ¶

This section explains the basic concept of functional programming; if you’re just interested in learning about Python language features, skip to the next section on Iterators .

Programming languages support decomposing problems in several different ways:

Most programming languages are procedural : programs are lists of instructions that tell the computer what to do with the program’s input. C, Pascal, and even Unix shells are procedural languages.

In declarative languages, you write a specification that describes the problem to be solved, and the language implementation figures out how to perform the computation efficiently. SQL is the declarative language you’re most likely to be familiar with; a SQL query describes the data set you want to retrieve, and the SQL engine decides whether to scan tables or use indexes, which subclauses should be performed first, etc.

Object-oriented programs manipulate collections of objects. Objects have internal state and support methods that query or modify this internal state in some way. Smalltalk and Java are object-oriented languages. C++ and Python are languages that support object-oriented programming, but don’t force the use of object-oriented features.

Functional programming decomposes a problem into a set of functions. Ideally, functions only take inputs and produce outputs, and don’t have any internal state that affects the output produced for a given input. Well-known functional languages include the ML family (Standard ML, OCaml, and other variants) and Haskell.

The designers of some computer languages choose to emphasize one particular approach to programming. This often makes it difficult to write programs that use a different approach. Other languages are multi-paradigm languages that support several different approaches. Lisp, C++, and Python are multi-paradigm; you can write programs or libraries that are largely procedural, object-oriented, or functional in all of these languages. In a large program, different sections might be written using different approaches; the GUI might be object-oriented while the processing logic is procedural or functional, for example.

In a functional program, input flows through a set of functions. Each function operates on its input and produces some output. Functional style discourages functions with side effects that modify internal state or make other changes that aren’t visible in the function’s return value. Functions that have no side effects at all are called purely functional . Avoiding side effects means not using data structures that get updated as a program runs; every function’s output must only depend on its input.

Some languages are very strict about purity and don’t even have assignment statements such as a=3 or c = a + b , but it’s difficult to avoid all side effects, such as printing to the screen or writing to a disk file. Another example is a call to the print() or time.sleep() function, neither of which returns a useful value. Both are called only for their side effects of sending some text to the screen or pausing execution for a second.

Python programs written in functional style usually won’t go to the extreme of avoiding all I/O or all assignments; instead, they’ll provide a functional-appearing interface but will use non-functional features internally. For example, the implementation of a function will still use assignments to local variables, but won’t modify global variables or have other side effects.

Functional programming can be considered the opposite of object-oriented programming. Objects are little capsules containing some internal state along with a collection of method calls that let you modify this state, and programs consist of making the right set of state changes. Functional programming wants to avoid state changes as much as possible and works with data flowing between functions. In Python you might combine the two approaches by writing functions that take and return instances representing objects in your application (e-mail messages, transactions, etc.).

Functional design may seem like an odd constraint to work under. Why should you avoid objects and side effects? There are theoretical and practical advantages to the functional style:

Formal provability.

Modularity.

Composability.

Ease of debugging and testing.

Formal provability ¶

A theoretical benefit is that it’s easier to construct a mathematical proof that a functional program is correct.

For a long time researchers have been interested in finding ways to mathematically prove programs correct. This is different from testing a program on numerous inputs and concluding that its output is usually correct, or reading a program’s source code and concluding that the code looks right; the goal is instead a rigorous proof that a program produces the right result for all possible inputs.

The technique used to prove programs correct is to write down invariants , properties of the input data and of the program’s variables that are always true. For each line of code, you then show that if invariants X and Y are true before the line is executed, the slightly different invariants X’ and Y’ are true after the line is executed. This continues until you reach the end of the program, at which point the invariants should match the desired conditions on the program’s output.

Functional programming’s avoidance of assignments arose because assignments are difficult to handle with this technique; assignments can break invariants that were true before the assignment without producing any new invariants that can be propagated onward.

Unfortunately, proving programs correct is largely impractical and not relevant to Python software. Even trivial programs require proofs that are several pages long; the proof of correctness for a moderately complicated program would be enormous, and few or none of the programs you use daily (the Python interpreter, your XML parser, your web browser) could be proven correct. Even if you wrote down or generated a proof, there would then be the question of verifying the proof; maybe there’s an error in it, and you wrongly believe you’ve proved the program correct.

Modularity ¶

A more practical benefit of functional programming is that it forces you to break apart your problem into small pieces. Programs are more modular as a result. It’s easier to specify and write a small function that does one thing than a large function that performs a complicated transformation. Small functions are also easier to read and to check for errors.

Ease of debugging and testing ¶

Testing and debugging a functional-style program is easier.

Debugging is simplified because functions are generally small and clearly specified. When a program doesn’t work, each function is an interface point where you can check that the data are correct. You can look at the intermediate inputs and outputs to quickly isolate the function that’s responsible for a bug.

Testing is easier because each function is a potential subject for a unit test. Functions don’t depend on system state that needs to be replicated before running a test; instead you only have to synthesize the right input and then check that the output matches expectations.

Composability ¶

As you work on a functional-style program, you’ll write a number of functions with varying inputs and outputs. Some of these functions will be unavoidably specialized to a particular application, but others will be useful in a wide variety of programs. For example, a function that takes a directory path and returns all the XML files in the directory, or a function that takes a filename and returns its contents, can be applied to many different situations.

Over time you’ll form a personal library of utilities. Often you’ll assemble new programs by arranging existing functions in a new configuration and writing a few functions specialized for the current task.

Iterators ¶

I’ll start by looking at a Python language feature that’s an important foundation for writing functional-style programs: iterators.

An iterator is an object representing a stream of data; this object returns the data one element at a time. A Python iterator must support a method called __next__() that takes no arguments and always returns the next element of the stream. If there are no more elements in the stream, __next__() must raise the StopIteration exception. Iterators don’t have to be finite, though; it’s perfectly reasonable to write an iterator that produces an infinite stream of data.

The built-in iter() function takes an arbitrary object and tries to return an iterator that will return the object’s contents or elements, raising TypeError if the object doesn’t support iteration. Several of Python’s built-in data types support iteration, the most common being lists and dictionaries. An object is called iterable if you can get an iterator for it.

You can experiment with the iteration interface manually:

Python expects iterable objects in several different contexts, the most important being the for statement. In the statement for X in Y , Y must be an iterator or some object for which iter() can create an iterator. These two statements are equivalent:

Iterators can be materialized as lists or tuples by using the list() or tuple() constructor functions:

Sequence unpacking also supports iterators: if you know an iterator will return N elements, you can unpack them into an N-tuple:

Built-in functions such as max() and min() can take a single iterator argument and will return the largest or smallest element. The "in" and "not in" operators also support iterators: X in iterator is true if X is found in the stream returned by the iterator. You’ll run into obvious problems if the iterator is infinite; max() , min() will never return, and if the element X never appears in the stream, the "in" and "not in" operators won’t return either.

Note that you can only go forward in an iterator; there’s no way to get the previous element, reset the iterator, or make a copy of it. Iterator objects can optionally provide these additional capabilities, but the iterator protocol only specifies the __next__() method. Functions may therefore consume all of the iterator’s output, and if you need to do something different with the same stream, you’ll have to create a new iterator.

Data Types That Support Iterators ¶

We’ve already seen how lists and tuples support iterators. In fact, any Python sequence type, such as strings, will automatically support creation of an iterator.

Calling iter() on a dictionary returns an iterator that will loop over the dictionary’s keys:

Note that starting with Python 3.7, dictionary iteration order is guaranteed to be the same as the insertion order. In earlier versions, the behaviour was unspecified and could vary between implementations.

Applying iter() to a dictionary always loops over the keys, but dictionaries have methods that return other iterators. If you want to iterate over values or key/value pairs, you can explicitly call the values() or items() methods to get an appropriate iterator.

The dict() constructor can accept an iterator that returns a finite stream of (key, value) tuples:

Files also support iteration by calling the readline() method until there are no more lines in the file. This means you can read each line of a file like this:

Sets can take their contents from an iterable and let you iterate over the set’s elements:

Generator expressions and list comprehensions ¶

Two common operations on an iterator’s output are 1) performing some operation for every element, 2) selecting a subset of elements that meet some condition. For example, given a list of strings, you might want to strip off trailing whitespace from each line or extract all the strings containing a given substring.

List comprehensions and generator expressions (short form: “listcomps” and “genexps”) are a concise notation for such operations, borrowed from the functional programming language Haskell ( https://www.haskell.org/ ). You can strip all the whitespace from a stream of strings with the following code:

You can select only certain elements by adding an "if" condition:

With a list comprehension, you get back a Python list; stripped_list is a list containing the resulting lines, not an iterator. Generator expressions return an iterator that computes the values as necessary, not needing to materialize all the values at once. This means that list comprehensions aren’t useful if you’re working with iterators that return an infinite stream or a very large amount of data. Generator expressions are preferable in these situations.

Generator expressions are surrounded by parentheses (“()”) and list comprehensions are surrounded by square brackets (“[]”). Generator expressions have the form:

Again, for a list comprehension only the outside brackets are different (square brackets instead of parentheses).

The elements of the generated output will be the successive values of expression . The if clauses are all optional; if present, expression is only evaluated and added to the result when condition is true.

Generator expressions always have to be written inside parentheses, but the parentheses signalling a function call also count. If you want to create an iterator that will be immediately passed to a function you can write:

The for...in clauses contain the sequences to be iterated over. The sequences do not have to be the same length, because they are iterated over from left to right, not in parallel. For each element in sequence1 , sequence2 is looped over from the beginning. sequence3 is then looped over for each resulting pair of elements from sequence1 and sequence2 .

To put it another way, a list comprehension or generator expression is equivalent to the following Python code:

This means that when there are multiple for...in clauses but no if clauses, the length of the resulting output will be equal to the product of the lengths of all the sequences. If you have two lists of length 3, the output list is 9 elements long:

To avoid introducing an ambiguity into Python’s grammar, if expression is creating a tuple, it must be surrounded with parentheses. The first list comprehension below is a syntax error, while the second one is correct:

Generators ¶

Generators are a special class of functions that simplify the task of writing iterators. Regular functions compute a value and return it, but generators return an iterator that returns a stream of values.

You’re doubtless familiar with how regular function calls work in Python or C. When you call a function, it gets a private namespace where its local variables are created. When the function reaches a return statement, the local variables are destroyed and the value is returned to the caller. A later call to the same function creates a new private namespace and a fresh set of local variables. But, what if the local variables weren’t thrown away on exiting a function? What if you could later resume the function where it left off? This is what generators provide; they can be thought of as resumable functions.

Here’s the simplest example of a generator function:

Any function containing a yield keyword is a generator function; this is detected by Python’s bytecode compiler which compiles the function specially as a result.

When you call a generator function, it doesn’t return a single value; instead it returns a generator object that supports the iterator protocol. On executing the yield expression, the generator outputs the value of i , similar to a return statement. The big difference between yield and a return statement is that on reaching a yield the generator’s state of execution is suspended and local variables are preserved. On the next call to the generator’s __next__() method, the function will resume executing.

Here’s a sample usage of the generate_ints() generator:

You could equally write for i in generate_ints(5) , or a, b, c = generate_ints(3) .

Inside a generator function, return value causes StopIteration(value) to be raised from the __next__() method. Once this happens, or the bottom of the function is reached, the procession of values ends and the generator cannot yield any further values.

You could achieve the effect of generators manually by writing your own class and storing all the local variables of the generator as instance variables. For example, returning a list of integers could be done by setting self.count to 0, and having the __next__() method increment self.count and return it. However, for a moderately complicated generator, writing a corresponding class can be much messier.

The test suite included with Python’s library, Lib/test/test_generators.py , contains a number of more interesting examples. Here’s one generator that implements an in-order traversal of a tree using generators recursively.

Two other examples in test_generators.py produce solutions for the N-Queens problem (placing N queens on an NxN chess board so that no queen threatens another) and the Knight’s Tour (finding a route that takes a knight to every square of an NxN chessboard without visiting any square twice).

Passing values into a generator ¶

In Python 2.4 and earlier, generators only produced output. Once a generator’s code was invoked to create an iterator, there was no way to pass any new information into the function when its execution is resumed. You could hack together this ability by making the generator look at a global variable or by passing in some mutable object that callers then modify, but these approaches are messy.

In Python 2.5 there’s a simple way to pass values into a generator. yield became an expression, returning a value that can be assigned to a variable or otherwise operated on:

I recommend that you always put parentheses around a yield expression when you’re doing something with the returned value, as in the above example. The parentheses aren’t always necessary, but it’s easier to always add them instead of having to remember when they’re needed.

( PEP 342 explains the exact rules, which are that a yield -expression must always be parenthesized except when it occurs at the top-level expression on the right-hand side of an assignment. This means you can write val = yield i but have to use parentheses when there’s an operation, as in val = (yield i) + 12 .)

Values are sent into a generator by calling its send(value) method. This method resumes the generator’s code and the yield expression returns the specified value. If the regular __next__() method is called, the yield returns None .

Here’s a simple counter that increments by 1 and allows changing the value of the internal counter.

And here’s an example of changing the counter:

Because yield will often be returning None , you should always check for this case. Don’t just use its value in expressions unless you’re sure that the send() method will be the only method used to resume your generator function.

In addition to send() , there are two other methods on generators:

throw(value) is used to raise an exception inside the generator; the exception is raised by the yield expression where the generator’s execution is paused.

close() raises a GeneratorExit exception inside the generator to terminate the iteration. On receiving this exception, the generator’s code must either raise GeneratorExit or StopIteration ; catching the exception and doing anything else is illegal and will trigger a RuntimeError . close() will also be called by Python’s garbage collector when the generator is garbage-collected.

If you need to run cleanup code when a GeneratorExit occurs, I suggest using a try: ... finally: suite instead of catching GeneratorExit .

The cumulative effect of these changes is to turn generators from one-way producers of information into both producers and consumers.

Generators also become coroutines , a more generalized form of subroutines. Subroutines are entered at one point and exited at another point (the top of the function, and a return statement), but coroutines can be entered, exited, and resumed at many different points (the yield statements).

Built-in functions ¶

Let’s look in more detail at built-in functions often used with iterators.

Two of Python’s built-in functions, map() and filter() duplicate the features of generator expressions:

f(iterA[0], iterB[0]), f(iterA[1], iterB[1]), f(iterA[2], iterB[2]), ... .

You can of course achieve the same effect with a list comprehension.

filter(predicate, iter) returns an iterator over all the sequence elements that meet a certain condition, and is similarly duplicated by list comprehensions. A predicate is a function that returns the truth value of some condition; for use with filter() , the predicate must take a single value.

This can also be written as a list comprehension:

enumerate(iter, start=0) counts off the elements in the iterable returning 2-tuples containing the count (from start ) and each element.

enumerate() is often used when looping through a list and recording the indexes at which certain conditions are met:

sorted(iterable, key=None, reverse=False) collects all the elements of the iterable into a list, sorts the list, and returns the sorted result. The key and reverse arguments are passed through to the constructed list’s sort() method.

(For a more detailed discussion of sorting, see the Sorting Techniques .)

The any(iter) and all(iter) built-ins look at the truth values of an iterable’s contents. any() returns True if any element in the iterable is a true value, and all() returns True if all of the elements are true values:

zip(iterA, iterB, ...) takes one element from each iterable and returns them in a tuple:

It doesn’t construct an in-memory list and exhaust all the input iterators before returning; instead tuples are constructed and returned only if they’re requested. (The technical term for this behaviour is lazy evaluation .)

This iterator is intended to be used with iterables that are all of the same length. If the iterables are of different lengths, the resulting stream will be the same length as the shortest iterable.

You should avoid doing this, though, because an element may be taken from the longer iterators and discarded. This means you can’t go on to use the iterators further because you risk skipping a discarded element.

The itertools module ¶

The itertools module contains a number of commonly used iterators as well as functions for combining several iterators. This section will introduce the module’s contents by showing small examples.

The module’s functions fall into a few broad classes:

Functions that create a new iterator based on an existing iterator.

Functions for treating an iterator’s elements as function arguments.

Functions for selecting portions of an iterator’s output.

A function for grouping an iterator’s output.

Creating new iterators ¶

itertools.count(start, step) returns an infinite stream of evenly spaced values. You can optionally supply the starting number, which defaults to 0, and the interval between numbers, which defaults to 1:

itertools.cycle(iter) saves a copy of the contents of a provided iterable and returns a new iterator that returns its elements from first to last. The new iterator will repeat these elements infinitely.

itertools.repeat(elem, [n]) returns the provided element n times, or returns the element endlessly if n is not provided.

itertools.chain(iterA, iterB, ...) takes an arbitrary number of iterables as input, and returns all the elements of the first iterator, then all the elements of the second, and so on, until all of the iterables have been exhausted.

itertools.islice(iter, [start], stop, [step]) returns a stream that’s a slice of the iterator. With a single stop argument, it will return the first stop elements. If you supply a starting index, you’ll get stop-start elements, and if you supply a value for step , elements will be skipped accordingly. Unlike Python’s string and list slicing, you can’t use negative values for start , stop , or step .

itertools.tee(iter, [n]) replicates an iterator; it returns n independent iterators that will all return the contents of the source iterator. If you don’t supply a value for n , the default is 2. Replicating iterators requires saving some of the contents of the source iterator, so this can consume significant memory if the iterator is large and one of the new iterators is consumed more than the others.

Calling functions on elements ¶

The operator module contains a set of functions corresponding to Python’s operators. Some examples are operator.add(a, b) (adds two values), operator.ne(a, b) (same as a != b ), and operator.attrgetter('id') (returns a callable that fetches the .id attribute).

itertools.starmap(func, iter) assumes that the iterable will return a stream of tuples, and calls func using these tuples as the arguments:

Selecting elements ¶

Another group of functions chooses a subset of an iterator’s elements based on a predicate.

itertools.filterfalse(predicate, iter) is the opposite of filter() , returning all elements for which the predicate returns false:

itertools.takewhile(predicate, iter) returns elements for as long as the predicate returns true. Once the predicate returns false, the iterator will signal the end of its results.

itertools.dropwhile(predicate, iter) discards elements while the predicate returns true, and then returns the rest of the iterable’s results.

itertools.compress(data, selectors) takes two iterators and returns only those elements of data for which the corresponding element of selectors is true, stopping whenever either one is exhausted:

Combinatoric functions ¶

The itertools.combinations(iterable, r) returns an iterator giving all possible r -tuple combinations of the elements contained in iterable .

The elements within each tuple remain in the same order as iterable returned them. For example, the number 1 is always before 2, 3, 4, or 5 in the examples above. A similar function, itertools.permutations(iterable, r=None) , removes this constraint on the order, returning all possible arrangements of length r :

If you don’t supply a value for r the length of the iterable is used, meaning that all the elements are permuted.

Note that these functions produce all of the possible combinations by position and don’t require that the contents of iterable are unique:

The identical tuple ('a', 'a', 'b') occurs twice, but the two ‘a’ strings came from different positions.

The itertools.combinations_with_replacement(iterable, r) function relaxes a different constraint: elements can be repeated within a single tuple. Conceptually an element is selected for the first position of each tuple and then is replaced before the second element is selected.

Grouping elements ¶

The last function I’ll discuss, itertools.groupby(iter, key_func=None) , is the most complicated. key_func(elem) is a function that can compute a key value for each element returned by the iterable. If you don’t supply a key function, the key is simply each element itself.

groupby() collects all the consecutive elements from the underlying iterable that have the same key value, and returns a stream of 2-tuples containing a key value and an iterator for the elements with that key.

groupby() assumes that the underlying iterable’s contents will already be sorted based on the key. Note that the returned iterators also use the underlying iterable, so you have to consume the results of iterator-1 before requesting iterator-2 and its corresponding key.

The functools module ¶

The functools module contains some higher-order functions. A higher-order function takes one or more functions as input and returns a new function. The most useful tool in this module is the functools.partial() function.

For programs written in a functional style, you’ll sometimes want to construct variants of existing functions that have some of the parameters filled in. Consider a Python function f(a, b, c) ; you may wish to create a new function g(b, c) that’s equivalent to f(1, b, c) ; you’re filling in a value for one of f() ’s parameters. This is called “partial function application”.

The constructor for partial() takes the arguments (function, arg1, arg2, ..., kwarg1=value1, kwarg2=value2) . The resulting object is callable, so you can just call it to invoke function with the filled-in arguments.

Here’s a small but realistic example:

functools.reduce(func, iter, [initial_value]) cumulatively performs an operation on all the iterable’s elements and, therefore, can’t be applied to infinite iterables. func must be a function that takes two elements and returns a single value. functools.reduce() takes the first two elements A and B returned by the iterator and calculates func(A, B) . It then requests the third element, C, calculates func(func(A, B), C) , combines this result with the fourth element returned, and continues until the iterable is exhausted. If the iterable returns no values at all, a TypeError exception is raised. If the initial value is supplied, it’s used as a starting point and func(initial_value, A) is the first calculation.

If you use operator.add() with functools.reduce() , you’ll add up all the elements of the iterable. This case is so common that there’s a special built-in called sum() to compute it:

For many uses of functools.reduce() , though, it can be clearer to just write the obvious for loop:

A related function is itertools.accumulate(iterable, func=operator.add) . It performs the same calculation, but instead of returning only the final result, accumulate() returns an iterator that also yields each partial result:

The operator module ¶

The operator module was mentioned earlier. It contains a set of functions corresponding to Python’s operators. These functions are often useful in functional-style code because they save you from writing trivial functions that perform a single operation.

Some of the functions in this module are:

Math operations: add() , sub() , mul() , floordiv() , abs() , …

Logical operations: not_() , truth() .

Bitwise operations: and_() , or_() , invert() .

Comparisons: eq() , ne() , lt() , le() , gt() , and ge() .

Object identity: is_() , is_not() .

Consult the operator module’s documentation for a complete list.

Small functions and the lambda expression ¶

When writing functional-style programs, you’ll often need little functions that act as predicates or that combine elements in some way.

If there’s a Python built-in or a module function that’s suitable, you don’t need to define a new function at all:

If the function you need doesn’t exist, you need to write it. One way to write small functions is to use the lambda expression. lambda takes a number of parameters and an expression combining these parameters, and creates an anonymous function that returns the value of the expression:

An alternative is to just use the def statement and define a function in the usual way:

Which alternative is preferable? That’s a style question; my usual course is to avoid using lambda .

One reason for my preference is that lambda is quite limited in the functions it can define. The result has to be computable as a single expression, which means you can’t have multiway if... elif... else comparisons or try... except statements. If you try to do too much in a lambda statement, you’ll end up with an overly complicated expression that’s hard to read. Quick, what’s the following code doing?

You can figure it out, but it takes time to disentangle the expression to figure out what’s going on. Using a short nested def statements makes things a little bit better:

But it would be best of all if I had simply used a for loop:

Or the sum() built-in and a generator expression:

Many uses of functools.reduce() are clearer when written as for loops.

Fredrik Lundh once suggested the following set of rules for refactoring uses of lambda :

Write a lambda function.

Write a comment explaining what the heck that lambda does.

Study the comment for a while, and think of a name that captures the essence of the comment.

Convert the lambda to a def statement, using that name.

Remove the comment.

I really like these rules, but you’re free to disagree about whether this lambda-free style is better.

Revision History and Acknowledgements ¶

The author would like to thank the following people for offering suggestions, corrections and assistance with various drafts of this article: Ian Bicking, Nick Coghlan, Nick Efford, Raymond Hettinger, Jim Jewett, Mike Krell, Leandro Lameiro, Jussi Salmela, Collin Winter, Blake Winton.

Version 0.1: posted June 30 2006.

Version 0.11: posted July 1 2006. Typo fixes.

Version 0.2: posted July 10 2006. Merged genexp and listcomp sections into one. Typo fixes.

Version 0.21: Added more references suggested on the tutor mailing list.

Version 0.30: Adds a section on the functional module written by Collin Winter; adds short section on the operator module; a few other edits.

References ¶

Structure and Interpretation of Computer Programs , by Harold Abelson and Gerald Jay Sussman with Julie Sussman. The book can be found at https://mitpress.mit.edu/sicp . In this classic textbook of computer science, chapters 2 and 3 discuss the use of sequences and streams to organize the data flow inside a program. The book uses Scheme for its examples, but many of the design approaches described in these chapters are applicable to functional-style Python code.

https://www.defmacro.org/ramblings/fp.html : A general introduction to functional programming that uses Java examples and has a lengthy historical introduction.

https://en.wikipedia.org/wiki/Functional_programming : General Wikipedia entry describing functional programming.

https://en.wikipedia.org/wiki/Coroutine : Entry for coroutines.

https://en.wikipedia.org/wiki/Partial_application : Entry for the concept of partial function application.

https://en.wikipedia.org/wiki/Currying : Entry for the concept of currying.

Python-specific ¶

https://gnosis.cx/TPiP/ : The first chapter of David Mertz’s book Text Processing in Python discusses functional programming for text processing, in the section titled “Utilizing Higher-Order Functions in Text Processing”.

Mertz also wrote a 3-part series of articles on functional programming for IBM’s DeveloperWorks site; see part 1 , part 2 , and part 3 ,

Python documentation ¶

Documentation for the itertools module.

Documentation for the functools module.

Documentation for the operator module.

PEP 289 : “Generator Expressions”

PEP 342 : “Coroutines via Enhanced Generators” describes the new generator features in Python 2.5.

Table of Contents

• Formal provability
• Ease of debugging and testing
• Composability
• Data Types That Support Iterators
• Generator expressions and list comprehensions
• Passing values into a generator
• Built-in functions
• Creating new iterators
• Calling functions on elements
• Selecting elements
• Combinatoric functions
• Grouping elements
• The operator module
• Small functions and the lambda expression
• Revision History and Acknowledgements
• Python-specific
• Python documentation

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Python Generators

• What is a Generator?

A Python generator is a function that produces a sequence of results. It works by maintaining its local state, so that the function can resume again exactly where it left off when called subsequent times. Thus, you can think of a generator as something like a powerful iterator.

The state of the function is maintained through the use of the keyword yield , which has the following syntax:

This Python keyword works much like using return , but it has some important differences, which we'll explain throughout this article.

Generators were introduced in PEP 255 , together with the yield statement. They have been available since Python version 2.2.

• How do Python Generators Work?

In order to understand how generators work, let's use the simple example below:

The code above defines a generator named numberGenerator , which receives a value n as an argument, and then defines and uses it as the limit value in a while loop. In addition, it defines a variable named number and assigns the value zero to it.

Calling the "instantiated" generator ( myGenerator ) with the next() method runs the generator code until the first yield statement, which returns 1 in this case.

Even after returning a value to us, the function then keeps the value of the variable number for the next time the function is called and increases its value by one. So the next time this function is called, it will pick up right where it left off.

Calling the function two more times, provides us with the next 2 numbers in the sequence, as seen below:

If we were to have called this generator again, we would have received a StopIteration exception since it had completed and returned from its internal while loop.

This functionality is useful because we can use generators to dynamically create iterables on the fly. If we were to wrap myGenerator with list() , then we'd get back an array of numbers (like [0, 1, 2] ) instead of a generator object, which is a bit easier to work with in some applications.

• The Difference Between return and yield

The keyword return returns a value from a function, at which time the function then loses its local state. Thus, the next time we call that function, it starts over from its first statement.

On the other hand, yield maintains the state between function calls, and resumes from where it left off when we call the next() method again. So if yield is called in the generator, then the next time the same generator is called we'll pick right back up after the last yield statement.

• Using return in a Generator

A generator can use a return statement, but only without a return value, that is in the form:

When the generator finds the return statement, it proceeds as in any other function return.

As the PEP 255 states:

Note that return means "I'm done, and have nothing interesting to return", for both generator functions and non-generator functions.

Let's modify our previous example by adding an if-else clause, which will discriminate against numbers higher than 20. The code is as follows:

In this example, since our generator won't yield any values it will be an empty array, as the number 30 is higher than 20. Thus, the return statement is working similarly to a break statement in this case.

This can be seen below:

If we would have assigned a value less than 20, the results would have been similar to the first example.

• Using next() to Iterate through a Generator

We can parse the values yielded by a generator using the next() method, as seen in the first example. This method tells the generator to only return the next value of the iterable , but nothing else.

For example, the following code will print on the screen the values 0 to 9.

The code above is similar to the previous ones, but calls each value yielded by the generator with the function next() . In order to do this, we must first instantiate a generator g , which is like a variable that holds our generator state.

When the function next() is called with the generator as its argument, the Python generator function is executed until it finds a yield statement. Then, the yielded value is returned to the caller and the state of the generator is saved for later use.

Running the code above will produce the following output:

Note : There is, however, a syntax difference between Python 2 and 3. The code above uses the Python 3 version. In Python 2, the next() can use the previous syntax or the following syntax:

• What is a Generator Expression?

Generator expressions are like list comprehensions , but they return a generator instead of a list. They were proposed in PEP 289, and became part of Python after version 2.4.

The syntax is similar to list comprehensions, but instead of square brackets, they use parenthesis.

For example, our code from before could be modified using generator expressions as follows:

The results will be the same as in our first few examples:

Generator expressions are useful when using reduction functions such as sum() , min() , or max() , as they reduce the code to a single line. They're also much shorter to type than a full Python generator function. For example, the following code will sum the first 10 numbers:

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After running this code, the result will be:

• Managing Exceptions

One important thing to note is that the yield keyword is not permitted in the try part of a try/finally construct. Thus, generators should allocate resources with caution.

However, yield can appear in finally clauses, except clauses, or in the try part of try/except clauses.

For example, we could have created the following code:

In the code above, as a result of the finally clause, the number 10 is included in the output, and the result is a list of numbers from 0 to 10. This normally wouldn't happen since the conditional statement is number < n . This can be seen in the output below:

• Sending Values to Generators

Generators have a powerful tool in the send() method for generator-iterators. This method was defined in PEP 342, and is available since Python version 2.5.

The send() method resumes the generator and sends a value that will be used to continue with the next yield . The method returns the new value yielded by the generator.

The syntax is send(value) . On the initial call, this method should be called with None as a value. The result will be that the generator advances its execution to the first yield expression.

If the generator exits without yielding a new value (like by using return ), the send() method raises StopIteration .

The following example illustrates the use of send() . In the first and third lines of our generator, we ask the program to assign the variable number the value previously yielded. In the first line after our generator function, we instantiate the generator, and we generate a first yield in the next line by calling the next function. Thus, in the last line we send the value 5, which will be used as input by the generator, and considered as its previous yield.

Note : Because there is no yielded value when the generator is first created, before using send() , we must make sure that the generator yields a value using next() or send(None) . In the example above, we execute the next(g) line for just this reason, otherwise we'd get an error saying "TypeError: can't send non-None value to a just-started generator".

After running the program, it prints on the screen the value 5, which is what we sent to it:

The third line of our generator from above also shows a new Python feature introduced in the same PEP: yield expressions. This feature allows the yield clause to be used on the right side of an assignment statement. The value of a yield expression is None , until the program calls the method send(value) .

• Connecting Generators

Since Python 3.3, a new feature allows generators to connect themselves and delegate to a sub-generator.

The new expression is defined in PEP 380, and its syntax is:

where <expression> is an expression evaluating an iterable, which defines the delegating generator.

Let's see this with an example:

The code above defines three different generators. The first, named myGenerator1 , has an input parameter, which is used to specify the limit in a range. The second, named myGenerator2 , is similar to the previous one, but contains two input parameters, which specify the two limits allowed in the range of numbers. After this, myGenerator3 calls myGenerator1 and myGenerator2 to yield their values.

The last three lines of code print on the screen three lists generated from each of the three generators previously defined. As we can see when we run the program below, the result is that myGenerator3 uses the yields obtained from myGenerator1 and myGenerator2 , in order to generate a list that combines the previous three lists.

The example also shows an important application of generators: the capacity to divide a long task into several separate parts, which can be useful when working with big sets of data.

As you can see, thanks to the yield from syntax, generators can be chained together for more dynamic programming.

• Benefits of Generators
• Simplified code

As seen in the examples shown in this article, generators simplify code in a very elegant manner. These code simplifications and elegance are even more evident in generator expressions, where a single line of code replaces an entire block of code.

• Better performance

Generators work on lazy (on-demand) generation of values. This results in two advantages. First, lower memory consumption. However, this memory saving will work in our benefit if we use the generator only once. If we use the values several times, it may be worthwhile to generate them at once and keep them for later use.

The on-demand nature of generators also means we may not have to generate values that won't be used, and thus would have been wasted cycles if they were generated. This means your program can use only the values needed without having to wait until all of them have been generated.

• When to use Generators

Generators are an advanced tool present in Python. There are several programming cases where generators can increase efficiency. Some of these cases are:

• Processing large amounts of data: generators provide calculation on-demand, also called lazy evaluation. This technique is used in stream processing.
• Piping: stacked generators can be used as pipes, in a manner similar to Unix pipes.
• Concurrency: generators can be used to generate (simulate) concurrency.
• Wrapping Up

Generators are a type of function that generates a sequence of values. As such they can act in a similar manner to iterators. Their use results in a more elegant code and improved performance.

These aspects are even more evident in generator expressions, where one line of code can summarize a sequence of statements.

Generators' working capacity has been improved with new methods, such as send() , and enhanced statements, such as yield from .

As a result of these properties, generators have many useful applications, such as generating pipes, concurrent programming, and helping in creating streams from large amounts of data.

As a consequence of these improvements, Python is becoming more and more the language of choice in data science.

What have you used generators for? Let us know in the comments!

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Home » Advanced Python » Python Generator Expressions

Python Generator Expressions

Summary : in this tutorial, you’ll learn about the Python generator expression to create a generator object.

Introduction to generator expressions

A generator expression is an expression that returns a generator object.

Basically, a generator function is a function that contains a yield statement and returns a generator object.

For example, the following defines a generator function:

The squares generator function returns a generator object that produces square numbers of integers from 0 to length - 1 .

Because a generator object is an iterator , you can use a for loop to iterate over its elements:

A generator expression provides you with a more simple way to return a generator object.

The following example defines a generator expression that returns square numbers of integers from 0 to 4:

Since the squares is a generator object, you can iterate over its elements like this:

As you can see, instead of using a function to define a generator function, you can use a generator expression.

A generator expression is like a list comprehension in terms of syntax. For example, a generator expression also supports complex syntaxes including:

• if statements
• Multiple nested loops
• Nested comprehensions

However, a generator expression uses the parentheses () instead of square brackets [] .

Generator expressions vs list comprehensions

The following shows how to use the list comprehension to generate square numbers from 0 to 4:

And this defines a square number generator:

In terms of syntax, a generator expression uses parentheses () while a list comprehension uses the square brackets [] .

2) Memory utilization

A list comprehension returns a list while a generator expression returns a generator object.

It means that a list comprehension returns a complete list of elements upfront. However, a generator expression returns a list of elements, one at a time, based on request.

A list comprehension is eager while a generator expression is lazy.

In other words, a list comprehension creates all elements right away and loads all of them into the memory.

Conversely, a generator expression creates a single element based on request. It loads only one single element to the memory.

3) Iterable vs iterator

A list comprehension returns an iterable . It means that you can iterate over the result of a list comprehension again and again.

However, a generator expression returns an iterator , specifically a lazy iterator. It becomes exhausting when you complete iterating over it.

• Use a Python generator expression to return a generator.

Python Enhancement Proposals

• Python »
• PEP Index »

PEP 289 – Generator Expressions

Bdfl pronouncements, the details, early binding versus late binding, reduction functions, acknowledgements.

This PEP introduces generator expressions as a high performance, memory efficient generalization of list comprehensions PEP 202 and generators PEP 255 .

Experience with list comprehensions has shown their widespread utility throughout Python. However, many of the use cases do not need to have a full list created in memory. Instead, they only need to iterate over the elements one at a time.

For instance, the following summation code will build a full list of squares in memory, iterate over those values, and, when the reference is no longer needed, delete the list:

Memory is conserved by using a generator expression instead:

Similar benefits are conferred on constructors for container objects:

Generator expressions are especially useful with functions like sum(), min(), and max() that reduce an iterable input to a single value:

Generator expressions also address some examples of functionals coded with lambda:

These simplify to:

List comprehensions greatly reduced the need for filter() and map(). Likewise, generator expressions are expected to minimize the need for itertools.ifilter() and itertools.imap(). In contrast, the utility of other itertools will be enhanced by generator expressions:

Having a syntax similar to list comprehensions also makes it easy to convert existing code into a generator expression when scaling up application.

Early timings showed that generators had a significant performance advantage over list comprehensions. However, the latter were highly optimized for Py2.4 and now the performance is roughly comparable for small to mid-sized data sets. As the data volumes grow larger, generator expressions tend to perform better because they do not exhaust cache memory and they allow Python to re-use objects between iterations.

This PEP is ACCEPTED for Py2.4.

(None of this is exact enough in the eye of a reader from Mars, but I hope the examples convey the intention well enough for a discussion in c.l.py. The Python Reference Manual should contain a 100% exact semantic and syntactic specification.)

is equivalent to:

Only the outermost for-expression is evaluated immediately, the other expressions are deferred until the generator is run:

changes to:

where testlist_gexp is almost the same as listmaker, but only allows a single test after ‘for’ … ‘in’:

• The rule for arglist needs similar changes.

This means that you can write:

but you would have to write:

i.e. if a function call has a single positional argument, it can be a generator expression without extra parentheses, but in all other cases you have to parenthesize it.

The exact details were checked in to Grammar/Grammar version 1.49.

For example:

Unfortunately, there is currently a slight syntactic difference. The expression:

is legal, meaning:

But generator expressions will not allow the former version:

is illegal.

The former list comprehension syntax will become illegal in Python 3.0, and should be deprecated in Python 2.4 and beyond.

List comprehensions also “leak” their loop variable into the surrounding scope. This will also change in Python 3.0, so that the semantic definition of a list comprehension in Python 3.0 will be equivalent to list(<generator expression>). Python 2.4 and beyond should issue a deprecation warning if a list comprehension’s loop variable has the same name as a variable used in the immediately surrounding scope.

After much discussion, it was decided that the first (outermost) for-expression should be evaluated immediately and that the remaining expressions be evaluated when the generator is executed.

Asked to summarize the reasoning for binding the first expression, Guido offered [1] :

Various use cases were proposed for binding all free variables when the generator is defined. And some proponents felt that the resulting expressions would be easier to understand and debug if bound immediately.

However, Python takes a late binding approach to lambda expressions and has no precedent for automatic, early binding. It was felt that introducing a new paradigm would unnecessarily introduce complexity.

After exploring many possibilities, a consensus emerged that binding issues were hard to understand and that users should be strongly encouraged to use generator expressions inside functions that consume their arguments immediately. For more complex applications, full generator definitions are always superior in terms of being obvious about scope, lifetime, and binding [2] .

The utility of generator expressions is greatly enhanced when combined with reduction functions like sum(), min(), and max(). The heapq module in Python 2.4 includes two new reduction functions: nlargest() and nsmallest(). Both work well with generator expressions and keep no more than n items in memory at one time.

• Raymond Hettinger first proposed the idea of “generator comprehensions” in January 2002.
• Peter Norvig resurrected the discussion in his proposal for Accumulation Displays.
• Alex Martelli provided critical measurements that proved the performance benefits of generator expressions. He also provided strong arguments that they were a desirable thing to have.
• Phillip Eby suggested “iterator expressions” as the name.
• Subsequently, Tim Peters suggested the name “generator expressions”.
• Armin Rigo, Tim Peters, Guido van Rossum, Samuele Pedroni, Hye-Shik Chang and Raymond Hettinger teased out the issues surrounding early versus late binding [1] .
• Jiwon Seo single-handedly implemented various versions of the proposal including the final version loaded into CVS. Along the way, there were periodic code reviews by Hye-Shik Chang and Raymond Hettinger. Guido van Rossum made the key design decisions after comments from Armin Rigo and newsgroup discussions. Raymond Hettinger provided the test suite, documentation, tutorial, and examples [2] .

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0289.rst

Last modified: 2023-09-09 17:39:29 GMT

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Data Science

Generating iterables or objects that allow stepping over them is considered to be a burdensome task. But, in Python , the implementation of this painful task just gets really smooth. So let’s go ahead and take a closer look at Generators in Python.

Here is a list of all the topics covered in this article:

• What are Generators?

Advantages of using Generators

• Normal Functions vs Generator Functions
• Using Generator Functions
• Generators with loops
• Generator Expressions
• Generating Fibonacci Series
• Generating Numbers

So let’s begin. :)

What are Generators in Python?

Generators are basically functions that return traversable objects or items. These functions do not produce all the items at once, rather they produce them one at a time and only when required. Whenever the for statement is included to iterate over a set of items, a generator function is run. Generators have a number of advantages as well.

Without Generators in Python, producing iterables is extremely difficult and lengthy.

Generators easy to implement as they automatically implement __iter__(), __next__() and StopIteration which otherwise, need to be explicitly specified.

Memory is saved as the items are produced as when required, unlike normal Python functions . This fact becomes very important when you need to create a huge number of iterators. This is also considered as the biggest advantage of generators.

Can be used to produce an infinite number of items.

They can also be used to pipeline a number of operations

Normal Functions vs Generator Functions:

Generators in Python are created just like how you create normal functions using the ‘def’ keyword. But, Generator functions make use of the yield keyword instead of return. This is done to notify the interpreter that this is an iterator. Not just this, Generator functions are run when the next() function is called and not by their name as in case of normal functions. Consider the following example to understand it better:

OUTPUT: [1, 2, 3] 

As you can see, in the above output, func() is making use of the yield keyword and the next function for its execution. But, for normal function you will need the following piece of code:

If you look at the above example, you might be wondering why to use a Generator function when the normal function is also returning the same output. So let’s move on and see how to use Generators in Python.

Using Generator functions:

As mentioned earlier, Generators in Python produce iterables one at a time. Take a look at the following example:

When you execute the following function, you will see the following output:

Here, one iterable object has been returned satisfying the while condition. After execution, the control is transferred to the caller. In case more items are needed, the same function needs to be executed again by calling the next() function.

On further executions, the function will return 6,7, etc. Generator functions in Python implement the __iter__() and __next__() methods automatically. Therefore, you can iterate over the objects by just using the next() method. When the item generation should terminate, Generator functions implement the StopIteration internally without having to worry the caller. Here is another example of this:

Generators with loops:

In case you want to execute the same function at once, you can make use of the ‘for’ loop. This loop helps iterate over the objects and after all implementations it executes StopIteration. 

OUTPUT: 

You can also specify expressions to generate iterable objects.

Generator Expressions:

You can also use expressions along with the for loop to produce iterators. This usually makes the generation iterables much easy. Generator expression resemble list comprehensions and like lambda functions , generator expressions create anonymous generator functions.

Take a look at the example below:

List Comprehension:[2, 3, 4, 5, 6, 7]

Generator expression:

<generator object <genexpr> at 0x0000016362944480>

As you can see, in the above output, the first expression is a list comprehension which is specified within [] brackets. List comprehension produces the complete list of items at once. The next is a generator expression which returns the same items but one at a time. It is specified using () brackets.

Generator functions can be used within other functions as well. For example:

Generator expression 2

The above program prints the min value when the above expression as applied to the values of a.

Let us use Generators in Python to:

• Generate Fibonacci Series

Generating Fibonacci Series: 

Fibonacci series as we all know is a series of numbers wherein each number is a sum of preceding two numbers. The first two numbers are 0 and 1. Here is a generator program to generate Fibonacci series:

The above output shows the Fibonacci series with values less than 50. Let’s now take a look at how to generate a list of numbers.

Generating Numbers:

In case you want to generate specified list numbers, you can do it using generator functions. Take a look a look at the following example:

<generator object <genexpr> at 0x000001CBE1602DE0>

0 1 2 3 4 5 6 7 8 9

<generator object <genexpr> at 0x000001CBE1623138> 2 4 6 8

The above program has returned even numbers from 2 to 10. This brings us to the end of this article on Generators in Python. I hope you have understood all the topics.

Got a question for us? Please mention it in the comments section of this “Generators in Python” blog and we will get back to you as soon as possible.

To get in-depth knowledge on Python along with its various applications, you can enroll for live  Python Certification Training  with 24/7 support and lifetime access. 

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