Как складывать переменные в python

Что такое + в Python?

Я пишу первую простую программу на Python и мне сложно понять, что такое в нем ‘+’, почему он так по-разному используется. Он может складывать переменные с типом int (целые числа). Но, как я понимаю он и с другими категориями используется. И str , и bool , и т.д. Он же там по-разному действует? Как так получается?

Дополню ответы здесь.

Первое, введем терминологию на примере a + b

1. a — левый операнд
2. + — оператор сложения
3. b — правый операнд

От простого к сложному. Действие оператора сложения зависит от типов данных, с которым их используют.

Для числовых типов данных ( int , float , complex )

Оператор сложения работает, как обычное математическое сложение

Здесь стоит обратить внимание на комплексную запись числа (1 + 1j) . Оператор сложения определяет тип данных, как complex .

Для последовательностей ( str , list , tuple , dict )

В данных типах данных появляется такой термин, как конкатенация. Что буквально переводится с латыни присоединение цепями; сцепле́ние

Конкатенировать можно только строку со строкой. В любом другом случае, вы будете получать ошибку

list , tuple

Операция сложения для списков и кортежей тоже называется конкатенацией

Если складывать с другими типами данных, результатом будет исключение TypeError

Не поддерживается операция сложения.

Булевый тип ( bool )

Здесь надо принять такую условность True = 1 , False = 0 . И оператор сложения с данным типом данных можно рассматривать, как сложение с числами 1 и 0

Чуть сложнее.

При использовании оператора сложения в python вызывается метод __add__() . Буквально, для выражения a + b , где a — это экземпляр класса, в котором определен метод __add__() , будет вызываться следующая конструкция a.__add__(b) .

У каждого типа данных можно спросить документацию по данному методу

Небольшой пример переопределения __add__()

В примере наглядно показано, что переопределение метода __add__() влияет на оператор сложения. Важно отметить, что здесь важен порядок слева направо. То есть метод __add__() должен быть определен у левого операнда

Если поменяем порядок

То есть у типа данных int определен метод __add__() , но ему не известно, что делать с моим типом данных

Python's sum(): The Pythonic Way to Sum Values

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Summing Values the Pythonic Way With sum()

Python’s built-in function sum() is an efficient and Pythonic way to sum a list of numeric values. Adding several numbers together is a common intermediate step in many computations, so sum() is a pretty handy tool for a Python programmer.

As an additional and interesting use case, you can concatenate lists and tuples using sum() , which can be convenient when you need to flatten a list of lists.

In this tutorial, you’ll learn how to:

• Sum numeric values by hand using general techniques and tools
• Use Python’s sum() to add several numeric values efficiently
• Concatenate lists and tuples with sum()
• Use sum() to approach common summation problems
• Use appropriate values for the arguments in sum()
• Decide between sum() and alternative tools to sum and concatenate objects

This knowledge will help you efficiently approach and solve summation problems in your code using either sum() or other alternative and specialized tools.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Understanding the Summation Problem

Summing numeric values together is a fairly common problem in programming. For example, say you have a list of numbers [1, 2, 3, 4, 5] and want to add them together to compute their total sum. With standard arithmetic, you’ll do something like this:

1 + 2 + 3 + 4 + 5 = 15

As far as math goes, this expression is pretty straightforward. It walks you through a short series of additions until you find the sum of all the numbers.

It’s possible to do this particular calculation by hand, but imagine some other situations where it might not be so possible. If you have a particularly long list of numbers, adding by hand can be inefficient and error-prone. What happens if you don’t even know how many items are in the list? Finally, imagine a scenario where the number of items you need to add changes dynamically or unpredictably.

In situations like these, whether you have a long or short list of numbers, Python can be quite useful to solve summation problems.

If you want to sum the numbers by creating your own solution from scratch, then you can try using a for loop:

Here, you first create total and initialize it to 0 . This variable works as an accumulator in which you store intermediate results until you get the final one. The loop iterates through numbers and updates total by accumulating each successive value using an augmented assignment.

You can also wrap the for loop in a function. This way, you can reuse the code for different lists:

In sum_numbers() , you take an iterable—specifically, a list of numeric values—as an argument and return the total sum of the values in the input list. If the input list is empty, then the function returns 0 . The for loop is the same one that you saw before.

You can also use recursion instead of iteration. Recursion is a functional programming technique where a function is called within its own definition. In other words, a recursive function calls itself in a loop:

When you define a recursive function, you take the risk of running into an infinite loop. To prevent this, you need to define both a base case that stops the recursion and a recursive case to call the function and start the implicit loop.

In the above example, the base case implies that the sum of a zero-length list is 0 . The recursive case implies that the total sum is the first value, numbers[0] , plus the sum of the rest of the values, numbers[1:] . Because the recursive case uses a shorter sequence on each iteration, you expect to run into the base case when numbers is a zero-length list. As a final result, you get the sum of all the items in your input list, numbers .

Note: In this example, if you don’t check for an empty input list (your base case), then sum_numbers() will never run into an infinite recursive loop. When your numbers list reaches a length of 0 , the code tries to access an item from the empty list, which raises an IndexError and breaks the loop.

With this kind of implementation, you’ll never get a sum from this function. You’ll get an IndexError every time.

Another option to sum a list of numbers in Python is to use reduce() from functools . To get the sum of a list of numbers, you can pass either operator.add or an appropriate lambda function as the first argument to reduce() :

You can call reduce() with a reduction, or folding, function along with an iterable as arguments. Then reduce() uses the input function to process iterable and returns a single cumulative value.

In the first example, the reduction function is add() , which takes two numbers and adds them together. The final result is the sum of the numbers in the input iterable . As a drawback, reduce() raises a TypeError when you call it with an empty iterable .

In the second example, the reduction function is a lambda function that returns the addition of two numbers.

Since summations like these are commonplace in programming, coding a new function every time you need to sum some numbers is a lot of repetitive work. Additionally, using reduce() isn’t the most readable solution available to you.

Python provides a dedicated built-in function to solve this problem. The function is conveniently called sum() . Since it’s a built-in function, you can use it directly in your code without importing anything.

Getting Started With Python’s sum()

Readability is one of the most important principles behind Python’s philosophy. Visualize what you are asking a loop to do when summing a list of values. You want it to loop over some numbers, accumulate them in an intermediate variable, and return the final sum. However, you can probably imagine a more readable version of summation that doesn’t need a loop. You want Python to take some numbers and sum them together.

Now think about how reduce() does summation. Using reduce() is arguably less readable and less straightforward than even the loop-based solution.

This is why Python 2.3 added sum() as a built-in function to provide a Pythonic solution to the summation problem. Alex Martelli contributed the function, which nowadays is the preferred syntax for summing a list of values:

Wow! That’s neat, isn’t it? It reads like plain English and clearly communicates the action you’re performing on the input list. Using sum() is way more readable than a for loop or a reduce() call. Unlike reduce() , sum() doesn’t raise a TypeError when you provide an empty iterable. Instead, it understandably returns 0 .

You can call sum() with the following two arguments:

1. iterable is a required argument that can hold any Python iterable. The iterable typically contains numeric values but can also contain lists or tuples.
2. start is an optional argument that can hold an initial value. This value is then added to the final result. It defaults to 0 .

Internally, sum() adds start plus the values in iterable from left to right. The values in the input iterable are normally numbers, but you can also use lists and tuples. The optional argument start can accept a number, list, or tuple, depending on what is passed to iterable . It can’t take a string.

In the following two sections, you’ll learn the basics of using sum() in your code.

The Required Argument: iterable

Accepting any Python iterable as its first argument makes sum() generic, reusable, and polymorphic. Because of this feature, you can use sum() with lists, tuples, sets, range objects, and dictionaries:

In all these examples, sum() computes the arithmetic sum of all the values in the input iterable regardless of their types. In the two dictionary examples, both calls to sum() return the sum of the keys of the input dictionary. The first example sums the keys by default and the second example sums the keys because of the .keys() call on the input dictionary.

If your dictionary stores numbers in its values and you would like to sum these values instead of the keys, then you can do this by using .values() just like in the .keys() example.

You can also use sum() with a list comprehension as an argument. Here’s an example that computes the sum of the squares of a range of values:

Python 2.4 added generator expressions to the language. Again, sum() works as expected when you use a generator expression as an argument:

This example shows one of the most Pythonic techniques to approach the summation problem. It provides an elegant, readable, and efficient solution in a single line of code.

The Optional Argument: start

The second and optional argument, start , allows you to provide a value to initialize the summation process. This argument is handy when you need to process cumulative values sequentially:

Here, you provide an initial value of 100 to start . The net effect is that sum() adds this value to the cumulative sum of the values in the input iterable. Note that you can provide start as a positional argument or as a keyword argument. The latter option is way more explicit and readable.

If you don’t provide a value to start , then it defaults to 0 . A default value of 0 ensures the expected behavior of returning the total sum of the input values.

Summing Numeric Values

The primary purpose of sum() is to provide a Pythonic way to add numeric values together. Up to this point, you’ve seen how to use the function to sum integer numbers. Additionally, you can use sum() with any other numeric Python types, such as float , complex , decimal.Decimal , and fractions.Fraction .

Here are a few examples of using sum() with values of different numeric types:

Here, you first use sum() with floating-point numbers. It’s worth noting the function’s behavior when you use the special symbols inf and nan in the calls float(«inf») and float(«nan») . The first symbol represents an infinite value, so sum() returns inf . The second symbol represents NaN (not a number) values. Since you can’t add numbers with non-numbers, you get nan as a result.

The other examples sum iterables of complex , Decimal , and Fraction numbers. In all cases, sum() returns the resulting cumulative sum using the appropriate numeric type.

Concatenating Sequences

Even though sum() is mostly intended to operate on numeric values, you can also use the function to concatenate sequences such as lists and tuples. To do that, you need to provide an appropriate value to start :

In these examples, you use sum() to concatenate lists and tuples. This is an interesting feature that you can use to flatten a list of lists or a tuple of tuples. The key requirement for these examples to work is to select an appropriate value for start . For example, if you want to concatenate lists, then start needs to hold a list.

In the examples above, sum() is internally performing a concatenation operation, so it works only with those sequence types that support concatenation, with the exception of strings:

When you try to use sum() to concatenate strings, you get a TypeError . As the exception message suggests, you should use str.join() to concatenate strings in Python. You’ll see examples of using this method later on when you get to the section on Using Alternatives to sum() .

Practicing With Python’s sum()

So far, you’ve learned the basics of working with sum() . You’ve learned how to use this function to add numeric values together and also to concatenate sequences such as lists and tuples.

In this section, you’ll look at some more examples of when and how to use sum() in your code. With these practical examples, you’ll learn that this built-in function is quite handy when you’re performing computations that require finding the sum of a series of numbers as an intermediate step.

You’ll also learn that sum() can be helpful when you’re working with lists and tuples. A special example you’ll look at is when you need to flatten a list of lists.

Computing Cumulative Sums

The first example you’ll code has to do with how to take advantage of the start argument for summing cumulative lists of numeric values.

Say you’re developing a system to manage the sales of a given product at several different points of sale. Every day, you get a sold units report from each point of sale. You need to systematically compute the cumulative sum to know how many units the whole company sold over the week. To solve this problem, you can use sum() :

By using start , you set an initial value to initialize the sum, which allows you to add successive units to the previously computed subtotal. At the end of the week, you’ll have the company’s total count of sold units.

Calculating the Mean of a Sample

Another practical use case of sum() is to use it as an intermediate calculation before doing further calculations. For example, say you need to calculate the arithmetic mean of a sample of numeric values. The arithmetic mean, also known as the average, is the total sum of the values divided by the number of values, or data points, in the sample.

If you have the sample [2, 3, 4, 2, 3, 6, 4, 2] and you want to calculate the arithmetic mean by hand, then you can solve this operation:

(2 + 3 + 4 + 2 + 3 + 6 + 4 + 2) / 8 = 3.25

If you want to speed this up by using Python, you can break it up into two parts. The first part of this computation, where you are adding together the numbers, is a task for sum() . The next part of the operation, where you are dividing by 8, uses the count of numbers in your sample. To calculate your divisor, you can use len() :

Here, the call to sum() computes the total sum of the data points in your sample. Next, you use len() to get the number of data points. Finally, you perform the required division to calculate the sample’s arithmetic mean.

In practice, you may want to turn this code into a function with some additional features, such as a descriptive name and a check for empty samples:

Inside average() , you first check if the input sample has any data points. If not, then you raise a ValueError with a descriptive message. In this example, you use the walrus operator to store the number of data points in the variable num_points so that you won’t need to call len() again. The return statement computes the sample’s arithmetic mean and sends it back to the calling code.

Note: Computing the mean of a sample of data is a common operation in statistics and data analysis. The Python standard library provides a convenient module called statistics to approach these kinds of calculations.

In the statistics module, you’ll find a function called mean() :

The statistics.mean() function has very similar behavior to the average() function you coded earlier. When you call mean() with a sample of numeric values, you’ll get the arithmetic mean of the input data. When you pass an empty list to mean() , you’ll get a statistics.StatisticsError .

Note that when you call average() with a proper sample, you’ll get the desired mean. If you call average() with an empty sample, then you get a ValueError as expected.

Finding the Dot Product of Two Sequences

Another problem you can solve using sum() is finding the dot product of two equal-length sequences of numeric values. The dot product is the algebraic sum of products of every pair of values in the input sequences. For example, if you have the sequences (1, 2, 3) and (4, 5, 6), then you can calculate their dot product by hand using addition and multiplication:

1 × 4 + 2 × 5 + 3 × 6 = 32

To extract successive pairs of values from the input sequences, you can use zip() . Then you can use a generator expression to multiply each pair of values. Finally, sum() can sum the products:

With zip() , you generate a list of tuples with the values from each of the input sequences. The generator expression loops over each tuple while multiplying the successive pairs of values previously arranged by zip() . The final step is to add the products together using sum() .

The code in the above example works. However, the dot product is defined for sequences of equal length, so what happens if you provide sequences with different lengths? In that case, zip() ignores the extra values from the longest sequence, which leads to an incorrect result.

To deal with this possibility, you can wrap the call to sum() in a custom function and provide a proper check for the length of the input sequences:

Here, dot_product() takes two sequences as arguments and returns their corresponding dot product. If the input sequences have different lengths, then the function raises a ValueError .

Embedding the functionality in a custom function allows you to reuse the code. It also gives you the opportunity to name the function descriptively so that the user knows what the function does just by reading its name.

Flattening a List of Lists

Flattening a list of lists is a common task in Python. Say you have a list of lists and need to flatten it into a single list containing all the items from the original nested lists. You can use any of several approaches to flattening lists in Python. For example, you can use a for loop, as in the following code:

Inside flatten_list() , the loop iterates over all the nested lists contained in a_list . Then it concatenates them in flat using an augmented assignment operation ( += ). As the result, you get a flat list with all the items from the original nested lists.

But hold on! You’ve already learned how to use sum() to concatenate sequences in this tutorial. Can you use that feature to flatten a list of lists like you did in the example above? Yes! Here’s how:

That was quick! A single line of code and matrix is now a flat list. However, using sum() doesn’t seem to be the fastest solution.

Using a list comprehension is another common way to flatten a list of list in Python:

This new version of flatten_list() uses a comprehension instead of a regular for loop. However, the nested comprehensions can be challenging to read and understand.

Using .append() is another way to flatten a list of lists:

In this version of flatten_list() , someone reading your code can see that the function iterates over every sublist in a_list . In the second for loop, the function iterates over each item in sublist to finally populate the new flat list with .append() . Arguably, readability can be an advantage of this solution.

Using Alternatives to sum()

As you’ve already learned, sum() is helpful for working with numeric values in general. However, when it comes to working with floating-point numbers, Python provides an alternative tool. In math , you’ll find a function called fsum() that can help you improve the general precision of your floating-point computations.

You might have a task where you want to concatenate or chain several iterables so that you can work with them as one. For this scenario, you can look to the itertools module’s function chain() .

You might also have a task where you want to concatenate a list of strings. You’ve learned in this tutorial that there’s no way to use sum() for concatenating strings. This function just wasn’t built for string concatenation. The most Pythonic alternative is to use str.join() .

Summing Floating-Point Numbers: math.fsum()

If your code is constantly summing floating-point numbers with sum() , then you should consider using math.fsum() instead. This function performs floating-point computations more carefully than sum() , which improves the precision of your computation.

According to its documentation, fsum() “avoids loss of precision by tracking multiple intermediate partial sums.” The documentation provides the following example:

With fsum() , you get a more precise result. However, you should note that fsum() doesn’t solve the representation error in floating-point arithmetic. The following example uncovers this limitation:

In these examples, both functions return the same result. This is due to the impossibility of accurately representing both values 0.1 and 0.2 in binary floating-point:

Unlike sum() , however, fsum() can help you reduce floating-point error propagation when you add very large and very small numbers together:

Wow! The second example is pretty surprising and totally defeats sum() . With sum() , you get 0.0 as a result. This is quite far away from the correct result of 20000.0 , as you get with fsum() .

Concatenating Iterables With itertools.chain()

If you’re looking for a handy tool for concatenating or chaining a series of iterables, then consider using chain() from itertools . This function can take multiple iterables and build an iterator that yields items from the first one, from the second one, and so on until it exhausts all the input iterables:

When you call chain() , you get an iterator of the items from the input iterables. In this example, you access successive items from numbers using next() . If you want to work with a list instead, then you can use list() to consume the iterator and return a regular Python list.

chain() is also a good option for flattening a list of lists in Python:

To flatten a list of lists with chain() , you need to use the iterable unpacking operator ( * ). This operator unpacks all the input iterables so that chain() can work with them and generate the corresponding iterator. The final step is to call list() to build the desired flat list.

Concatenating Strings With str.join()

As you’ve already seen, sum() doesn’t concatenate or join strings. If you need to do so, then the preferred and fastest tool available in Python is str.join() . This method takes a sequence of strings as an argument and returns a new, concatenated string:

Using .join() is the most efficient and Pythonic way to concatenate strings. Here, you use a list of strings as an argument and build a single string from the input. Note that .join() uses the string on which you call the method as a separator during the concatenation. In this example, you call .join() on a string that consists of a single space character ( » » ), so the original strings from greeting are separated by spaces in your final string.

Conclusion

You can now use Python’s built-in function sum() to add multiple numeric values together. This function provides an efficient, readable, and Pythonic way to solve summation problems in your code. If you’re dealing with math computations that require summing numeric values, then sum() can be your lifesaver.

In this tutorial, you learned how to:

• Sum numeric values using general techniques and tools
• Add several numeric values efficiently using Python’s sum()
• Concatenate sequences using sum()
• Use sum() to approach common summation problems
• Use appropriate values for the iterable and start arguments in sum()
• Decide between sum() and alternative tools to sum and concatenate objects

With this knowledge, you’re now able to add multiple numeric values together in a Pythonic, readable, and efficient way.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Summing Values the Pythonic Way With sum()

Get a short & sweet Python Trick delivered to your inbox every couple of days. No spam ever. Unsubscribe any time. Curated by the Real Python team.

About Leodanis Pozo Ramos

Leodanis is an industrial engineer who loves Python and software development. He's a self-taught Python developer with 6+ years of experience. He's an avid technical writer with a growing number of articles published on Real Python and other sites.

Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. The team members who worked on this tutorial are:

6. Expressions¶

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

and no semantics are given, the semantics of this form of name are the same as for othername .

6.1. Arithmetic conversions¶

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

If either argument is a complex number, the other is converted to complex;

otherwise, if either argument is a floating point number, the other is converted to floating point;

otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

6.2. Atoms¶

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

6.2.1. Identifiers (Names)¶

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

Private name mangling: When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier __spam occurring in a class named Ham will be transformed to _Ham__spam . This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done.

6.2.2. Literals¶

Python supports string and bytes literals and various numeric literals:

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating point number, complex number) with the given value. The value may be approximated in the case of floating point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

6.2.3. Parenthesized forms¶

A parenthesized form is an optional expression list enclosed in parentheses:

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries¶

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

either the container contents are listed explicitly, or

they are computed via a set of looping and filtering instructions, called a comprehension.

Common syntax elements for comprehensions are:

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)] .

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator . A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions. If a comprehension contains either async for clauses or await expressions or other asynchronous comprehensions it is called an asynchronous comprehension. An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530.

New in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

6.2.5. List displays¶

A list display is a possibly empty series of expressions enclosed in square brackets:

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays¶

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with <> ; this literal constructs an empty dictionary.

6.2.7. Dictionary displays¶

A dictionary display is a possibly empty series of key/datum pairs enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of key/datum pairs is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding datum. This means that you can specify the same key multiple times in the key/datum list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking. Its operand must be a mapping . Each mapping item is added to the new dictionary. Later values replace values already set by earlier key/datum pairs and earlier dictionary unpackings.

New in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448.

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy . (To summarize, the key type should be hashable , which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last datum (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572.

6.2.8. Generator expressions¶

A generator expression is a compact generator notation in parentheses:

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)) .

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression. An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators ).

New in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

6.2.9. Yield expressions¶

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions .

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None . Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError , while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration , or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

The proposal for adding generators and the yield statement to Python.

PEP 342 — Coroutines via Enhanced Generators

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

PEP 380 — Syntax for Delegating to a Subgenerator

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

6.2.9.1. Generator-iterator methods¶

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None . The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__() ’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

generator. send ( value ) ¶

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

generator. throw ( value ) ¶ generator. throw ( type [ , value [ , traceback ] ] )

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

6.2.9.2. Examples¶

Here is a simple example that demonstrates the behavior of generators and generator functions:

For examples using yield from , see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

6.2.9.3. Asynchronous generator functions¶

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None . Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break , the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks() . When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py.

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

6.2.9.4. Asynchronous generator-iterator methods¶

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

coroutine agen. __anext__ ( ) ¶

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

coroutine agen. asend ( value ) ¶

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration , or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

coroutine agen. athrow ( value ) ¶ coroutine agen. athrow ( type [ , value [ , traceback ] ] )

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

coroutine agen. aclose ( ) ¶

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

6.3. Primaries¶

Primaries represent the most tightly bound operations of the language. Their syntax is:

6.3.1. Attribute references¶

An attribute reference is a primary followed by a period and a name:

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. This production can be customized by overriding the __getattr__() method. If this attribute is not available, the exception AttributeError is raised. Otherwise, the type and value of the object produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

6.3.2. Subscriptions¶

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__() . When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__ , see __class_getitem__ versus __getitem__ .

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__() :

Mappings. If the primary is a mapping , the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.

Sequences. If the primary is a sequence , the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str , list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences . However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x . The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters. A character is not a separate data type but a string of exactly one character.

6.3.3. Slicings¶

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy ) whose start , stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

6.3.4. Calls¶

A call calls a callable object (e.g., a function ) with a possibly empty series of arguments :

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None , it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable . Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4) , if y evaluates to a sequence y1, …, yM, this is equivalent to a call with M+4 positional arguments x1, x2, y1, …, yM, x3, x4.

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping , the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings ( * ), and keyword arguments may follow dictionary unpackings ( ** ). Originally proposed by PEP 448.

A call always returns some value, possibly None , unless it raises an exception. How this value is computed depends on the type of the callable object.

a user-defined function:

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions . When the code block executes a return statement, this specifies the return value of the function call.

a built-in function or method:

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

A new instance of that class is returned.

a class instance method:

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

a class instance:

The class must define a __call__() method; the effect is then the same as if that method was called.

6.4. Await expression¶

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function .

New in version 3.5.

6.5. The power operator¶

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1 .

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100 , but 10**-2 returns 0.01 .

Raising 0.0 to a negative power results in a ZeroDivisionError . Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError .)

This operation can be customized using the special __pow__() method.

6.6. Unary arithmetic and bitwise operations¶

All unary arithmetic and bitwise operations have the same priority:

The unary — (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

(invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1) . It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

6.7. Binary arithmetic operations¶

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

New in version 3.5.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

This operation can be customized using the special __truediv__() and __floordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34 .) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand 1.

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y) . Floor division and modulo are also connected with the built-in function divmod() : divmod(x, y) == (x//y, x%y) . 2.

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting .

The modulo operation can be customized using the special __mod__() method.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The — (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() method.

6.8. Shifting operations¶

The shifting operations have lower priority than the arithmetic operations:

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

This operation can be customized using the special __lshift__() and __rshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n) . A left shift by n bits is defined as multiplication with pow(2,n) .

6.9. Binary bitwise operations¶

Each of the three bitwise operations has a different priority level:

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

6.10. Comparisons¶

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

Comparisons yield boolean values: True or False . Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a, b, c, …, y, z are expressions and op1, op2, …, opN are comparison operators, then a op1 b op2 c . y opN z is equivalent to a op1 b and b op2 c and . y opN z , except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c, so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

6.10.1. Value comparisons¶

The operators < , > , == , >= , <= , and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object , they inherit the default comparison behavior from object . Types can customize their comparison behavior by implementing rich comparison methods like __lt__() , described in Basic customization .

The default behavior for equality comparison ( == and != ) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y ).

A default order comparison ( < , > , <= , and >= ) is not provided; an attempt raises TypeError . A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

Numbers of built-in numeric types ( Numeric Types — int, float, complex ) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

The not-a-number values float(‘NaN’) and decimal.Decimal(‘NaN’) are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float(‘NaN’) , 3 < x , x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not , never the equality operators.

Binary sequences (instances of bytes or bytearray ) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.

Strings (instances of str ) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord() ) of their characters. 3

Strings and binary sequences cannot be directly compared.

Sequences (instances of tuple , list , or range ) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError .

Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

Lexicographical comparison between built-in collections works as follows:

For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).

Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y ). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).

Mappings (instances of dict ) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

Order comparisons ( < , > , <= , and >= ) raise TypeError .

Sets (instances of set or frozenset ) can be compared within and across their types.

They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets <1,2>and <2,3>are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min() , max() , and sorted() produce undefined results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

Equality comparison should be reflexive. In other words, identical objects should compare equal:

x is y implies x == y

Comparison should be symmetric. In other words, the following expressions should have the same result:

x == y and y == x

x != y and y != x

x < y and y > x

x <= y and y >= x

Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

x > y and y > z implies x > z

x < y and y <= z implies x < z

Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

x == y and not x != y

x < y and not x >= y (for total ordering)

x > y and not x <= y (for total ordering)

The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

6.10.2. Membership test operations¶

The operators in and not in test for membership. x in s evaluates to True if x is a member of s, and False otherwise. x not in s returns the negation of x in s . All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y) .

For the string and bytes types, x in y is True if and only if x is a substring of y. An equivalent test is y.find(x) != -1 . Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True .

For user-defined classes which define the __contains__() method, x in y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__() , x in y is True if some value z , for which the expression x is z or x == z is true, is produced while iterating over y . If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__() , x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i] , and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in .

6.10.3. Identity comparisons¶

The operators is and is not test for an object’s identity: x is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. 4

6.11. Boolean operations¶

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False , None , numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True , but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or ‘foo’ yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not ‘foo’ produces False rather than » .)

6.12. Assignment expressions¶

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier , while also returning the value of the expression .

One common use case is when handling matched regular expressions:

Or, when processing a file stream in chunks:

Assignment expressions must be surrounded by parentheses when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert and with statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

New in version 3.8: See PEP 572 for more details about assignment expressions.

6.13. Conditional expressions¶

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x. If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

6.14. Lambdas¶

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

6.15. Expression lists¶

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking. Its operand must be an iterable . The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

New in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448.

The trailing comma is required only to create a single tuple (a.k.a. a singleton); it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: () .)

6.16. Evaluation order¶

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

6.17. Operator precedence¶

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

Binding or parenthesized expression, list display, dictionary display, set display

Python: Переменные и конкатенация

Попробуем использовать переменные с конкатенацией, при этом синтаксически ничего не поменяется. Мы умеем конкатенировать две строки:

Значит, мы сумеем склеить строку и одну переменную, в которой записана строка:

А еще можно конкатенировать две переменные, в которых записаны строки:

Переменные — важный инструмент в программировании. Они упрощают сложные вычисления и таким образом облегчают разработку. Но чтобы успешно работать с переменными, надо не только правильно использовать их, но и правильно называть. Об этом поговорим уже в следующем уроке.

Задание

Сайты постоянно посылают письма своим пользователям. Типичная задача — сделать автоматическую отправку персонального письма, где в заголовке будет имя пользователя. Если где-то в базе сайта хранится имя человека в виде строки, то задача генерации заголовка сводится к конкатенации: например, нужно склеить строку Здравствуйте со строкой, где записано имя.

Напишите программу, которая будет генерировать заголовок и тело письма, используя уже готовые переменные, и выводить получившиеся строки на экран.

Для заголовка используйте переменные first_name и greeting , запятую и восклицательный знак. Выведите это на экран в правильном порядке.

Для тела письма используйте переменные info и intro , при этом второе предложение должно быть на новой строке.

Результат на экране будет выглядеть так:

Выполните задание, используя только два print() .

Если вы зашли в тупик, то самое время задать вопрос в «Обсуждениях». Как правильно задать вопрос:

• Обязательно приложите вывод тестов, без него практически невозможно понять что не так, даже если вы покажете свой код. Программисты плохо исполняют код в голове, но по полученной ошибке почти всегда понятно, куда смотреть.

Тесты устроены таким образом, что они проверяют решение разными способами и на разных данных. Часто решение работает с одними входными данными, но не работает с другими. Чтобы разобраться с этим моментом, изучите вкладку «Тесты» и внимательно посмотрите на вывод ошибок, в котором есть подсказки.

Это нормально ��, в программировании одну задачу можно выполнить множеством способов. Если ваш код прошел проверку, то он соответствует условиям задачи.

В редких случаях бывает, что решение подогнано под тесты, но это видно сразу.

Создавать обучающие материалы, понятные для всех без исключения, довольно сложно. Мы очень стараемся, но всегда есть что улучшать. Если вы встретили материал, который вам непонятен, опишите проблему в «Обсуждениях». Идеально, если вы сформулируете непонятные моменты в виде вопросов. Обычно нам нужно несколько дней для внесения правок.

Кстати, вы тоже можете участвовать в улучшении курсов: внизу есть ссылка на исходный код уроков, который можно править прямо из браузера.

Полезное

Подумайте, с какой строкой и в каком порядке нужно склеивать переменные, чтобы получить такой двухстрочный вывод тела письма.

Помните, что можно создать строку, которая содержит только управляющую последовательность \n . Вы можете конкатенировать эту строку с переменными для правильного форматирования текста.