Что делает функция len

Python len() Function

The len() function in Python is super convenient and one that you will use all the time when making programs. The input to len() is either a sequence or a collection and it returns the length when the function is called. You can use len() to get the length of anything you want in Python. For example, it can be used with strings, lists, tuples, and dictionaries. Another good use case for using len() is in combination with loops or the range() function. Let’s look at several ways to use the len() function now.

Using len() with strings

Starting off quite simply, we can use len() to check the length of a string. Here are several examples of doing just that.

len() of string practical example

In the examples below, we store a sequence of numbers as a string to represent zip codes. We can use len() to determine if the zip codes are of the correct length. Since all zip codes need to be 5 characters in length, we can use len() to determine if a given zip code is valid or invalid.

Using len() with lists

The len() function is super useful when working with lists in Python. There are countless examples of when you need to know the length of a list in both simple and complex programs. Here are several examples of using len() with a list.

len() of lists practical examples

Using len() in a loop

In the example below, we have a list of yard tools. We can loop over the names of each yard tool, and print out the length of each one using the len() function.

We can do a similar thing with the Simpsons, and f-strings.

len() in a list comprehension

The len() function also works well with list comprehensions in Python.

Using len() with range()

This example shows how to use the len() function with the Python range() function. This is a useful approach because the code in the loop can access the variable i index as well as the value at the index. The nice thing about using len() with range() is that no matter how few or how many items are in the list, the code iterates over them accurately with no need for counters.

Note as we change the size of the list here, the code still handles it correctly.

Counting command line args with len()

This neat use of the len() function lets us count the number of command-line arguments that have been passed to a python file. We can also count the total length of all command-line arguments.

len() with tuples, dictionaries, and sets

You can also use the len() function with the tuple, dictionary, and set data types. Here are a few examples of that.

Python len() Function Summary

We’ve seen plenty of examples of how to use the len() function in different ways. You’ll find yourself using this handy function all the time in your Python programs.

Using the len() Function in Python

In many situations, you’ll need to find the number of items stored in a data structure. Python’s built-in function len() is the tool that will help you with this task.

There are some cases in which the use of len() is straightforward. However, there are other times when you’ll need to understand how this function works in more detail and how to apply it to different data types.

In this tutorial, you’ll learn how to:

Find the length of built-in data types using len()
Use len() with third-party data types
Provide support for len() with user-defined classes

By the end of this article, you’ll know when to use the len() Python function and how to use it effectively. You’ll know which built-in data types are valid arguments for len() and which ones you can’t use. You’ll also understand how to use len() with third-party types, such as ndarray in NumPy and DataFrame in pandas, and with your own classes.

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Getting Started With Python’s len()

The function len() is one of Python’s built-in functions. It returns the length of an object. For example, it can return the number of items in a list. You can use the function with many different data types. However, not all data types are valid arguments for len() .

You can start by looking at the help for this function:

The function takes an object as an argument and returns the length of that object. The documentation for len() goes a bit further:

Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set). (Source)

When you use built-in data types and many third-party types with len() , the function doesn’t need to iterate through the data structure. The length of a container object is stored as an attribute of the object. The value of this attribute is modified each time items are added to or removed from the data structure, and len() returns the value of the length attribute. This ensures that len() works efficiently.

In the following sections, you’ll learn about how to use len() with sequences and collections. You’ll also learn about some data types that you cannot use as arguments for the len() Python function.

Using len() With Built-in Sequences

A sequence is a container with ordered items. Lists, tuples, and strings are three of the basic built-in sequences in Python. You can find the length of a sequence by calling len() :

When finding the length of the string greeting , the list office_days , and the tuple london_coordinates , you use len() in the same manner. All three data types are valid arguments for len() .

The function len() always returns an integer as it’s counting the number of items in the object that you pass to it. The function returns 0 if the argument is an empty sequence:

In the examples above, you find the length of an empty string, an empty list, and an empty tuple. The function returns 0 in each case.

A range object is also a sequence that you can create using range() . A range object doesn’t store all the values but generates them when they’re needed. However, you can still find the length of a range object using len() :

This range of numbers includes the integers from 1 to 19 with increments of 2 . The length of a range object can be determined from the start, stop, and step values.

In this section, you’ve used the len() Python function with strings, lists, tuples, and range objects. However, you can also use the function with any other built-in sequence.

Using len() With Built-in Collections

At some point, you may need to find the number of unique items in a list or another sequence. You can use sets and len() to achieve this:

You generate the list numbers using a list comprehension, and it contains twenty random numbers ranging between 1 and 20 . The output will be different each time the code runs since you’re generating random numbers. In this particular run, there are eleven unique numbers in the list of twenty randomly generated numbers.

Another built-in data type that you’ll use often is the dictionary. In a dictionary, each item consists of a key-value pair. When you use a dictionary as an argument for len() , the function returns the number of items in the dictionary:

The output from the first example shows that there are three key-value pairs in this dictionary. As was the case with sequences, len() will return 0 when the argument is either an empty dictionary or an empty set. This leads to empty dictionaries and empty sets being falsy.

Exploring len() With Other Built-in Data Types

You can’t use all built-in data types as arguments for len() . For data types that don’t store more than one item within them, the concept of length isn’t relevant. This is the case with numbers and Boolean types:

The integer, float, Boolean, and complex types are examples of built-in data types that you can’t use with len() . The function raises a TypeError when the argument is an object of a data type that doesn’t have a length.

You can also explore whether it’s possible to use iterators and generators as arguments for len() :

You’ve already seen that a list has a length, meaning you can use it as an argument in len() . You create an iterator from the list using the built-in function iter() . In an iterator, each item is fetched whenever it’s required, such as when the function next() is used or in a loop. However, you can’t use an iterator in len() .

You get a TypeError when you try to use an iterator as an argument for len() . As the iterator fetches each item as and when it’s needed, the only way to measure its length is to exhaust the iterator. An iterator can also be infinite, such as the iterator returned by itertools.cycle() , and therefore its length can’t be defined.

You can’t use generators with len() for the same reason. The length of these objects can’t be measured without using them up.

Exploring len() Further With Some Examples

In this section, you’ll learn about some common use cases for len() . These examples will help you understand better when to use this function and how to use it effectively. In some of the examples, you’ll also see cases where len() is a possible solution but there may be more Pythonic ways of achieving the same output.

Verifying the Length of a User Input

A common use case of len() is to verify the length of a sequence input by a user:

In this example, you use an if statement to check if the integer returned by len() is greater than or equal to 4 and less than or equal to 10 . You can run this script and you’ll get an output similar to the one below:

The username is nine characters long in this case, so the condition in the if statement evaluates to True . You can run the script again and input an invalid username:

In this case, len(username) returns 2 , and the condition in the if statement evaluates to False .

Ending a Loop Based on the Length of an Object

You’ll use len() if you need to check when the length of a mutable sequence, such as a list, reaches a specific number. In the following example, you ask the user to enter three username options, which you store in a list:

You’re now using the result from len() in the while statement. If the user enters an invalid username, you don’t keep the input. When the user enters a valid string, you append it to the list usernames . The loop repeats until there are three items in the list.

You could even use len() to check when a sequence is empty:

You use the list method .pop() to remove the first item from the list in each iteration until the list is empty. If you’re using this method on large lists, you should remove items from the end of the list as this is more efficient. You can also use the deque data type from the collections built-in module, which allows you to pop from the left efficiently.

There’s a more Pythonic way of achieving the same output by using the truthiness of sequences:

An empty list is falsy. This means that the while statement interprets an empty list as False . A non-empty list is truthy, and the while statement treats it as True . The value returned by len() determines the truthiness of a sequence. A sequence is truthy when len() returns any non-zero integer and falsy when len() returns 0 .

Finding the Index of the Last Item of a Sequence

Imagine you want to generate a sequence of random numbers in the range 1 to 10 and you’d like to keep adding numbers to the sequence until the sum of all the numbers exceeds 21 . The following code creates an empty list and uses a while loop to populate the list:

You append random numbers to the list until the sum exceeds 21 . The output you’ll get will vary as you’re generating random numbers. To display the last number in the list, you use len(numbers) and subtract 1 from it since the first index of the list is 0 . Indexing in Python allows you to use the index -1 to obtain the last item in a list. Therefore, although you can use len() in this case, you don’t need to.

You want to remove the last number in the list so that the sum of all numbers in the list doesn’t exceed 21 . You use len() again to work out the index of the last item in the list, which you use as an argument for the list method .pop() . Even in this instance, you could use -1 as an argument for .pop() to remove the last item from the list and return it.

Splitting a List Into Two Halves

If you need to split a sequence into two halves, you’ll need to use the index that represents the midpoint of the sequence. You can use len() to find this value. In the following example, you’ll create a list of random numbers and then split it into two smaller lists:

In the assignment statement where you define first_half , you use the slice that represents the items from the beginning of numbers up to the midpoint. You can work out what the slice represents by breaking down the steps you use in the slice expression:

First, len(numbers) returns the integer 10 .
Next, 10 // 2 returns the integer 5 as you use the integer division operator.
Finally, 0:5 is a slice that represents the first five items, which have indices 0 to 4 . Note that the endpoint is excluded.

In the next assignment, where you define second_half , you use the same expression in the slice. However, in this case, the integer 5 represents the start of the range. The slice is now 5: to represent the items from index 5 up to the end of the list.

If your original list contains an odd number of items, then half of its length will no longer be a whole number. When you use integer division, you obtain the floor of the number. The list first_half will now contain one less item than second_half .

You can try this out by creating an initial list of eleven numbers instead of ten. The resulting lists will no longer be halves, but they’ll represent the closest alternative to splitting an odd sequence.

Using the len() Function With Third-Party Libraries

You can also use Python’s len() with several custom data types from third-party libraries. In the last section of this tutorial, you’ll learn how the behavior of len() depends on the class definition. In this section, you’ll look at examples of using len() with data types from two popular third-party libraries.

NumPy’s ndarray

The NumPy module is the cornerstone of all quantitative applications of programming in Python. The module introduces the numpy.ndarray data type. This data type, along with functions within NumPy, is ideally suited for numerical computations and is the building block for data types in other modules.

Before you can start using NumPy, you’ll need to install the library. You can use Python’s standard package manager, pip , and run the following command in the console:

You’ve installed NumPy, and now you can create a NumPy array from a list and use len() on the array:

The NumPy function np.array() creates an object of type numpy.ndarray from the list you pass as an argument.

However, NumPy arrays can have more than one dimension. You can create a two-dimensional array by converting a list of lists into an array:

The list numbers consists of two lists, each containing five integers. When you use this list of lists to create a NumPy array, the result is an array with two rows and five columns. The function returns the number of rows in the array when you pass this two-dimensional array as an argument in len() .

To get the size of both dimensions, you use the property .shape , which is a tuple showing the number of rows and columns. You obtain the number of dimensions of a NumPy array either by using .shape and len() or by using the property .ndim .

In general, when you have an array with any number of dimensions, len() returns the size of the first dimension:

In this example, you create a three-dimensional array with the shape (2, 3, 4) where each element is a random integer between 1 and 20 . You use the function np.random.randint() to create an array this time. The function len() returns 2 , which is the size of the first dimension.

Pandas’ DataFrame

The DataFrame type in the pandas library is another data type that is used extensively in many applications.

Before you can use pandas, you’ll need to install it by using the following command in the console:

You’ve installed the pandas package, and now you can create a DataFrame from a dictionary:

The dictionary’s keys are strings representing the names of students in a class. The value of each key is a list with the marks for three subjects. When you create a DataFrame from this dictionary, you define the index using a list containing the subject names.

The DataFrame has three rows and four columns. The function len() returns the number of rows in the DataFrame. The DataFrame type also has a .shape property, which you can use to show that the first dimension of a DataFrame represents the number of rows.

You’ve seen how len() works with a number of built-in data types and also with some data types from third-party modules. In the following section, you’ll learn how to define any class so that it’s usable as an argument for the len() Python function.

Using len() on User-Defined Classes

When you define a class, one of the special methods you can define is .__len__() . These special methods are called dunder methods as they have double underscores at the beginning and end of the method names. Python’s built-in len() function calls its argument’s .__len__() method.

In the previous section, you’ve seen how len() behaves when the argument is a pandas DataFrame object. This behavior is determined by the .__len__() method for the DataFrame class, which you can see in the module’s source code in pandas.core.frame :

This method returns the length of the DataFrame’s .index property using len() . This dunder method defines the length of a DataFrame to be equal to the number of rows in the DataFrame as represented by .index .

You can explore the .__len__() dunder method further with the following toy example. You’ll define a class named YString . This data type is based on the built-in string class, but objects of type YString give the letter Y more importance than all the other letters:

The .__init__() method of YString initializes the object using the .__init__() method of the parent str class. You achieve this using the function super() . The .__str__() method defines the way the object is displayed. The functions str() , print() , and format() all call this method. For this class, you represent the object as an all-lowercase string with the exception of the letter Y, which you display as uppercase.

For this toy class, you define the object’s length as the number of occurrences of the letter Y in the string. Therefore, the .__len__() method returns the count of the letter Y.

You can create an object of class YString and find its length. The module name used for the example above is ystring.py :

You create an object of type YString from an object of type str and show the representation of the object using print() . You then use the object message as an argument for len() . This calls the class’s .__len__() method, and the result is the number of occurrences of the letter Y in message . In this case, the letter Y appears four times.

The YString class is not a very useful one, but it helps illustrate how you can customize the behavior of len() to suit your needs. The .__len__() method must return a non-negative integer. Otherwise, it raises an error.

Another special method is the .__bool__() method, which determines how an object can be converted to a Boolean. The .__bool__() dunder method is not normally defined for sequences and collections. In these cases, the .__len__() method determines the truthiness of an object:

The variable first_string doesn’t have a Y in it. As shown by the output from bool() , the string is truthy as it’s non-empty. However, when you create an object of type YString from this string, the new object is falsy as there are no Y letters in the string. Therefore, len() returns 0 . In contrast, the variable second_string does include the letter Y, and so both the string and the object of type YString are truthy.

You can read more about using object-oriented programming and defining classes in Object-Oriented Programming (OOP) in Python 3.

Conclusion

You’ve explored how to use len() to determine the number of items in sequences, collections, and other data types that hold several items at a time, such as NumPy arrays and pandas DataFrames.

The len() Python function is a key tool in many programs. Some of its uses are straightforward, but there’s a lot more to this function than its most basic use cases, as you’ve seen in this tutorial. Knowing when you can use this function and how to use it effectively will help you write neater code.

In this tutorial, you’ve learned how to:

Find the length of built-in data types using len()
Use len() with third-party data types
Provide support for len() with user-defined classes

You now have a good foundation for understanding the len() function. Learning more about len() helps you understand the differences between data types better. You’re ready to use len() in your algorithms and to improve the functionality of some of your class definitions by enhancing them with the .__len__() method.

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Как работает метод len() в Python? [закрыт]

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Закрыт 2 года назад .

Метод len() вернет длину массива, это ясно. Когда смотрю примеры кода, частенько вижу использование for in len(arr) — 1 или for in len(arr) . Пожалуйста, объясните, как это работает или зачем это юзать в цикле .

Все понятно

Допустим, у вас есть список: lst = [10, 20, 30, 40]

И вы хотите пройтись по всем его элементам, допустим, для итерации. С помощью функции len() измеряется длина списка. range() предоставляет последовательность целых чисел, которые можно использовать как индексы для элементов того же списка. Теперь можно пройтись по элементам в цикле:

Теперь в вашем списке элементы выглядят так: [11, 21, 31, 41] .

Погуглите "Python введение в цикл for".

Если нужно произвести количество итераций равное по длине итерируемому объекту, то используется len.

Допустим вам нужно пройти по списку сколько — то раз и прибавить к каждому числу в списке + 1. Вы не знаете какой длиной этот список. Как раз для этого вам и нужен len() . С помощью len() вы узнаёте длину массива и таким образом проходите по списку столько раз и делает что вам нужно.

Note: Вообще надо стараться избегать такого кода т.к.
часто это плохой код стайл и написание в лоб. Обычно можно делать так: (пример)

Вам уже объяснили что это, но повторю:

Проитерироваться по списку, получая в i индекс текущего элемента

Built-in Functions¶

The Python interpreter has a number of functions and types built into it that are always available. They are listed here in alphabetical order.

Return the absolute value of a number. The argument may be an integer, a floating point number, or an object implementing __abs__() . If the argument is a complex number, its magnitude is returned.

Return an asynchronous iterator for an asynchronous iterable . Equivalent to calling x.__aiter__() .

Note: Unlike iter() , aiter() has no 2-argument variant.

New in version 3.10.

Return True if all elements of the iterable are true (or if the iterable is empty). Equivalent to:

When awaited, return the next item from the given asynchronous iterator , or default if given and the iterator is exhausted.

This is the async variant of the next() builtin, and behaves similarly.

This calls the __anext__() method of async_iterator, returning an awaitable . Awaiting this returns the next value of the iterator. If default is given, it is returned if the iterator is exhausted, otherwise StopAsyncIteration is raised.

New in version 3.10.

Return True if any element of the iterable is true. If the iterable is empty, return False . Equivalent to:

As repr() , return a string containing a printable representation of an object, but escape the non-ASCII characters in the string returned by repr() using \x , \u , or \U escapes. This generates a string similar to that returned by repr() in Python 2.

Convert an integer number to a binary string prefixed with “0b”. The result is a valid Python expression. If x is not a Python int object, it has to define an __index__() method that returns an integer. Some examples:

If the prefix “0b” is desired or not, you can use either of the following ways.

See also format() for more information.

Return a Boolean value, i.e. one of True or False . x is converted using the standard truth testing procedure . If x is false or omitted, this returns False ; otherwise, it returns True . The bool class is a subclass of int (see Numeric Types — int, float, complex ). It cannot be subclassed further. Its only instances are False and True (see Boolean Values ).

Changed in version 3.7: x is now a positional-only parameter.

This function drops you into the debugger at the call site. Specifically, it calls sys.breakpointhook() , passing args and kws straight through. By default, sys.breakpointhook() calls pdb.set_trace() expecting no arguments. In this case, it is purely a convenience function so you don’t have to explicitly import pdb or type as much code to enter the debugger. However, sys.breakpointhook() can be set to some other function and breakpoint() will automatically call that, allowing you to drop into the debugger of choice. If sys.breakpointhook() is not accessible, this function will raise RuntimeError .

Raises an auditing event builtins.breakpoint with argument breakpointhook .

New in version 3.7.

Return a new array of bytes. The bytearray class is a mutable sequence of integers in the range 0 <= x < 256. It has most of the usual methods of mutable sequences, described in Mutable Sequence Types , as well as most methods that the bytes type has, see Bytes and Bytearray Operations .

The optional source parameter can be used to initialize the array in a few different ways:

If it is a string, you must also give the encoding (and optionally, errors) parameters; bytearray() then converts the string to bytes using str.encode() .

If it is an integer, the array will have that size and will be initialized with null bytes.

If it is an object conforming to the buffer interface , a read-only buffer of the object will be used to initialize the bytes array.

If it is an iterable, it must be an iterable of integers in the range 0 <= x < 256 , which are used as the initial contents of the array.

Without an argument, an array of size 0 is created.

class bytes ( source = b» ) class bytes ( source , encoding ) class bytes ( source , encoding , errors )

Return a new “bytes” object which is an immutable sequence of integers in the range 0 <= x < 256 . bytes is an immutable version of bytearray – it has the same non-mutating methods and the same indexing and slicing behavior.

Accordingly, constructor arguments are interpreted as for bytearray() .

Bytes objects can also be created with literals, see String and Bytes literals .

Return True if the object argument appears callable, False if not. If this returns True , it is still possible that a call fails, but if it is False , calling object will never succeed. Note that classes are callable (calling a class returns a new instance); instances are callable if their class has a __call__() method.

New in version 3.2: This function was first removed in Python 3.0 and then brought back in Python 3.2.

Return the string representing a character whose Unicode code point is the integer i. For example, chr(97) returns the string ‘a’ , while chr(8364) returns the string ‘€’ . This is the inverse of ord() .

The valid range for the argument is from 0 through 1,114,111 (0x10FFFF in base 16). ValueError will be raised if i is outside that range.

Transform a method into a class method.

A class method receives the class as an implicit first argument, just like an instance method receives the instance. To declare a class method, use this idiom:

The @classmethod form is a function decorator – see Function definitions for details.

A class method can be called either on the class (such as C.f() ) or on an instance (such as C().f() ). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument.

Class methods are different than C++ or Java static methods. If you want those, see staticmethod() in this section. For more information on class methods, see The standard type hierarchy .

Changed in version 3.9: Class methods can now wrap other descriptors such as property() .

Changed in version 3.10: Class methods now inherit the method attributes ( __module__ , __name__ , __qualname__ , __doc__ and __annotations__ ) and have a new __wrapped__ attribute.

Changed in version 3.11: Class methods can no longer wrap other descriptors such as property() .

Compile the source into a code or AST object. Code objects can be executed by exec() or eval() . source can either be a normal string, a byte string, or an AST object. Refer to the ast module documentation for information on how to work with AST objects.

The filename argument should give the file from which the code was read; pass some recognizable value if it wasn’t read from a file ( ‘<string>’ is commonly used).

The mode argument specifies what kind of code must be compiled; it can be ‘exec’ if source consists of a sequence of statements, ‘eval’ if it consists of a single expression, or ‘single’ if it consists of a single interactive statement (in the latter case, expression statements that evaluate to something other than None will be printed).

The optional arguments flags and dont_inherit control which compiler options should be activated and which future features should be allowed. If neither is present (or both are zero) the code is compiled with the same flags that affect the code that is calling compile() . If the flags argument is given and dont_inherit is not (or is zero) then the compiler options and the future statements specified by the flags argument are used in addition to those that would be used anyway. If dont_inherit is a non-zero integer then the flags argument is it – the flags (future features and compiler options) in the surrounding code are ignored.

Compiler options and future statements are specified by bits which can be bitwise ORed together to specify multiple options. The bitfield required to specify a given future feature can be found as the compiler_flag attribute on the _Feature instance in the __future__ module. Compiler flags can be found in ast module, with PyCF_ prefix.

The argument optimize specifies the optimization level of the compiler; the default value of -1 selects the optimization level of the interpreter as given by -O options. Explicit levels are 0 (no optimization; __debug__ is true), 1 (asserts are removed, __debug__ is false) or 2 (docstrings are removed too).

This function raises SyntaxError if the compiled source is invalid, and ValueError if the source contains null bytes.

If you want to parse Python code into its AST representation, see ast.parse() .

Raises an auditing event compile with arguments source and filename . This event may also be raised by implicit compilation.

When compiling a string with multi-line code in ‘single’ or ‘eval’ mode, input must be terminated by at least one newline character. This is to facilitate detection of incomplete and complete statements in the code module.

It is possible to crash the Python interpreter with a sufficiently large/complex string when compiling to an AST object due to stack depth limitations in Python’s AST compiler.

Changed in version 3.2: Allowed use of Windows and Mac newlines. Also, input in ‘exec’ mode does not have to end in a newline anymore. Added the optimize parameter.

Changed in version 3.5: Previously, TypeError was raised when null bytes were encountered in source.

New in version 3.8: ast.PyCF_ALLOW_TOP_LEVEL_AWAIT can now be passed in flags to enable support for top-level await , async for , and async with .

Return a complex number with the value real + imag*1j or convert a string or number to a complex number. If the first parameter is a string, it will be interpreted as a complex number and the function must be called without a second parameter. The second parameter can never be a string. Each argument may be any numeric type (including complex). If imag is omitted, it defaults to zero and the constructor serves as a numeric conversion like int and float . If both arguments are omitted, returns 0j .

For a general Python object x , complex(x) delegates to x.__complex__() . If __complex__() is not defined then it falls back to __float__() . If __float__() is not defined then it falls back to __index__() .

When converting from a string, the string must not contain whitespace around the central + or — operator. For example, complex(‘1+2j’) is fine, but complex(‘1 + 2j’) raises ValueError .

Changed in version 3.6: Grouping digits with underscores as in code literals is allowed.

Changed in version 3.8: Falls back to __index__() if __complex__() and __float__() are not defined.

This is a relative of setattr() . The arguments are an object and a string. The string must be the name of one of the object’s attributes. The function deletes the named attribute, provided the object allows it. For example, delattr(x, ‘foobar’) is equivalent to del x.foobar . name need not be a Python identifier (see setattr() ).

class dict ( ** kwarg ) class dict ( mapping , ** kwarg ) class dict ( iterable , ** kwarg )

Create a new dictionary. The dict object is the dictionary class. See dict and Mapping Types — dict for documentation about this class.

For other containers see the built-in list , set , and tuple classes, as well as the collections module.

dir ( ) ¶ dir ( object )

Without arguments, return the list of names in the current local scope. With an argument, attempt to return a list of valid attributes for that object.

If the object has a method named __dir__() , this method will be called and must return the list of attributes. This allows objects that implement a custom __getattr__() or __getattribute__() function to customize the way dir() reports their attributes.

If the object does not provide __dir__() , the function tries its best to gather information from the object’s __dict__ attribute, if defined, and from its type object. The resulting list is not necessarily complete and may be inaccurate when the object has a custom __getattr__() .

The default dir() mechanism behaves differently with different types of objects, as it attempts to produce the most relevant, rather than complete, information:

If the object is a module object, the list contains the names of the module’s attributes.

If the object is a type or class object, the list contains the names of its attributes, and recursively of the attributes of its bases.

Otherwise, the list contains the object’s attributes’ names, the names of its class’s attributes, and recursively of the attributes of its class’s base classes.

The resulting list is sorted alphabetically. For example:

Because dir() is supplied primarily as a convenience for use at an interactive prompt, it tries to supply an interesting set of names more than it tries to supply a rigorously or consistently defined set of names, and its detailed behavior may change across releases. For example, metaclass attributes are not in the result list when the argument is a class.

Take two (non-complex) numbers as arguments and return a pair of numbers consisting of their quotient and remainder when using integer division. With mixed operand types, the rules for binary arithmetic operators apply. For integers, the result is the same as (a // b, a % b) . For floating point numbers the result is (q, a % b) , where q is usually math.floor(a / b) but may be 1 less than that. In any case q * b + a % b is very close to a, if a % b is non-zero it has the same sign as b, and 0 <= abs(a % b) < abs(b) .

enumerate ( iterable , start = 0 ) ¶

Return an enumerate object. iterable must be a sequence, an iterator , or some other object which supports iteration. The __next__() method of the iterator returned by enumerate() returns a tuple containing a count (from start which defaults to 0) and the values obtained from iterating over iterable.

The arguments are a string and optional globals and locals. If provided, globals must be a dictionary. If provided, locals can be any mapping object.

The expression argument is parsed and evaluated as a Python expression (technically speaking, a condition list) using the globals and locals dictionaries as global and local namespace. If the globals dictionary is present and does not contain a value for the key __builtins__ , a reference to the dictionary of the built-in module builtins is inserted under that key before expression is parsed. That way you can control what builtins are available to the executed code by inserting your own __builtins__ dictionary into globals before passing it to eval() . If the locals dictionary is omitted it defaults to the globals dictionary. If both dictionaries are omitted, the expression is executed with the globals and locals in the environment where eval() is called. Note, eval() does not have access to the nested scopes (non-locals) in the enclosing environment.

The return value is the result of the evaluated expression. Syntax errors are reported as exceptions. Example:

This function can also be used to execute arbitrary code objects (such as those created by compile() ). In this case, pass a code object instead of a string. If the code object has been compiled with ‘exec’ as the mode argument, eval() ‘s return value will be None .

Hints: dynamic execution of statements is supported by the exec() function. The globals() and locals() functions return the current global and local dictionary, respectively, which may be useful to pass around for use by eval() or exec() .

If the given source is a string, then leading and trailing spaces and tabs are stripped.

See ast.literal_eval() for a function that can safely evaluate strings with expressions containing only literals.

Raises an auditing event exec with the code object as the argument. Code compilation events may also be raised.

exec ( object , globals = None , locals = None , / , * , closure = None ) ¶

This function supports dynamic execution of Python code. object must be either a string or a code object. If it is a string, the string is parsed as a suite of Python statements which is then executed (unless a syntax error occurs). 1 If it is a code object, it is simply executed. In all cases, the code that’s executed is expected to be valid as file input (see the section File input in the Reference Manual). Be aware that the nonlocal , yield , and return statements may not be used outside of function definitions even within the context of code passed to the exec() function. The return value is None .

In all cases, if the optional parts are omitted, the code is executed in the current scope. If only globals is provided, it must be a dictionary (and not a subclass of dictionary), which will be used for both the global and the local variables. If globals and locals are given, they are used for the global and local variables, respectively. If provided, locals can be any mapping object. Remember that at the module level, globals and locals are the same dictionary. If exec gets two separate objects as globals and locals, the code will be executed as if it were embedded in a class definition.

If the globals dictionary does not contain a value for the key __builtins__ , a reference to the dictionary of the built-in module builtins is inserted under that key. That way you can control what builtins are available to the executed code by inserting your own __builtins__ dictionary into globals before passing it to exec() .

The closure argument specifies a closure–a tuple of cellvars. It’s only valid when the object is a code object containing free variables. The length of the tuple must exactly match the number of free variables referenced by the code object.

Raises an auditing event exec with the code object as the argument. Code compilation events may also be raised.

The built-in functions globals() and locals() return the current global and local dictionary, respectively, which may be useful to pass around for use as the second and third argument to exec() .

The default locals act as described for function locals() below: modifications to the default locals dictionary should not be attempted. Pass an explicit locals dictionary if you need to see effects of the code on locals after function exec() returns.

Читать:

Вследствие чего кванты рентгеновского излучения не наводят остаточной радиации

Changed in version 3.11: Added the closure parameter.

Construct an iterator from those elements of iterable for which function is true. iterable may be either a sequence, a container which supports iteration, or an iterator. If function is None , the identity function is assumed, that is, all elements of iterable that are false are removed.

Note that filter(function, iterable) is equivalent to the generator expression (item for item in iterable if function(item)) if function is not None and (item for item in iterable if item) if function is None .

See itertools.filterfalse() for the complementary function that returns elements of iterable for which function is false.

Return a floating point number constructed from a number or string x.

If the argument is a string, it should contain a decimal number, optionally preceded by a sign, and optionally embedded in whitespace. The optional sign may be ‘+’ or ‘-‘ ; a ‘+’ sign has no effect on the value produced. The argument may also be a string representing a NaN (not-a-number), or positive or negative infinity. More precisely, the input must conform to the floatvalue production rule in the following grammar, after leading and trailing whitespace characters are removed:

Here digit is a Unicode decimal digit (character in the Unicode general category Nd ). Case is not significant, so, for example, “inf”, “Inf”, “INFINITY”, and “iNfINity” are all acceptable spellings for positive infinity.

Otherwise, if the argument is an integer or a floating point number, a floating point number with the same value (within Python’s floating point precision) is returned. If the argument is outside the range of a Python float, an OverflowError will be raised.

For a general Python object x , float(x) delegates to x.__float__() . If __float__() is not defined then it falls back to __index__() .

If no argument is given, 0.0 is returned.

Changed in version 3.6: Grouping digits with underscores as in code literals is allowed.

Changed in version 3.7: x is now a positional-only parameter.

Changed in version 3.8: Falls back to __index__() if __float__() is not defined.

Convert a value to a “formatted” representation, as controlled by format_spec. The interpretation of format_spec will depend on the type of the value argument; however, there is a standard formatting syntax that is used by most built-in types: Format Specification Mini-Language .

The default format_spec is an empty string which usually gives the same effect as calling str(value) .

A call to format(value, format_spec) is translated to type(value).__format__(value, format_spec) which bypasses the instance dictionary when searching for the value’s __format__() method. A TypeError exception is raised if the method search reaches object and the format_spec is non-empty, or if either the format_spec or the return value are not strings.

Changed in version 3.4: object().__format__(format_spec) raises TypeError if format_spec is not an empty string.

Return a new frozenset object, optionally with elements taken from iterable. frozenset is a built-in class. See frozenset and Set Types — set, frozenset for documentation about this class.

For other containers see the built-in set , list , tuple , and dict classes, as well as the collections module.

getattr ( object , name ) ¶ getattr ( object , name , default )

Return the value of the named attribute of object. name must be a string. If the string is the name of one of the object’s attributes, the result is the value of that attribute. For example, getattr(x, ‘foobar’) is equivalent to x.foobar . If the named attribute does not exist, default is returned if provided, otherwise AttributeError is raised. name need not be a Python identifier (see setattr() ).

Since private name mangling happens at compilation time, one must manually mangle a private attribute’s (attributes with two leading underscores) name in order to retrieve it with getattr() .

Return the dictionary implementing the current module namespace. For code within functions, this is set when the function is defined and remains the same regardless of where the function is called.

The arguments are an object and a string. The result is True if the string is the name of one of the object’s attributes, False if not. (This is implemented by calling getattr(object, name) and seeing whether it raises an AttributeError or not.)

Return the hash value of the object (if it has one). Hash values are integers. They are used to quickly compare dictionary keys during a dictionary lookup. Numeric values that compare equal have the same hash value (even if they are of different types, as is the case for 1 and 1.0).

For objects with custom __hash__() methods, note that hash() truncates the return value based on the bit width of the host machine. See __hash__() for details.

Invoke the built-in help system. (This function is intended for interactive use.) If no argument is given, the interactive help system starts on the interpreter console. If the argument is a string, then the string is looked up as the name of a module, function, class, method, keyword, or documentation topic, and a help page is printed on the console. If the argument is any other kind of object, a help page on the object is generated.

Note that if a slash(/) appears in the parameter list of a function when invoking help() , it means that the parameters prior to the slash are positional-only. For more info, see the FAQ entry on positional-only parameters .

This function is added to the built-in namespace by the site module.

Changed in version 3.4: Changes to pydoc and inspect mean that the reported signatures for callables are now more comprehensive and consistent.

Convert an integer number to a lowercase hexadecimal string prefixed with “0x”. If x is not a Python int object, it has to define an __index__() method that returns an integer. Some examples:

If you want to convert an integer number to an uppercase or lower hexadecimal string with prefix or not, you can use either of the following ways:

See also format() for more information.

Open file and return a corresponding file object . If the file cannot be opened, an OSError is raised. See Reading and Writing Files for more examples of how to use this function.

file is a path-like object giving the pathname (absolute or relative to the current working directory) of the file to be opened or an integer file descriptor of the file to be wrapped. (If a file descriptor is given, it is closed when the returned I/O object is closed unless closefd is set to False .)

mode is an optional string that specifies the mode in which the file is opened. It defaults to ‘r’ which means open for reading in text mode. Other common values are ‘w’ for writing (truncating the file if it already exists), ‘x’ for exclusive creation, and ‘a’ for appending (which on some Unix systems, means that all writes append to the end of the file regardless of the current seek position). In text mode, if encoding is not specified the encoding used is platform-dependent: locale.getencoding() is called to get the current locale encoding. (For reading and writing raw bytes use binary mode and leave encoding unspecified.) The available modes are:

open for reading (default)

open for writing, truncating the file first

open for exclusive creation, failing if the file already exists

open for writing, appending to the end of file if it exists

text mode (default)

open for updating (reading and writing)

The default mode is ‘r’ (open for reading text, a synonym of ‘rt’ ). Modes ‘w+’ and ‘w+b’ open and truncate the file. Modes ‘r+’ and ‘r+b’ open the file with no truncation.

As mentioned in the Overview , Python distinguishes between binary and text I/O. Files opened in binary mode (including ‘b’ in the mode argument) return contents as bytes objects without any decoding. In text mode (the default, or when ‘t’ is included in the mode argument), the contents of the file are returned as str , the bytes having been first decoded using a platform-dependent encoding or using the specified encoding if given.

Python doesn’t depend on the underlying operating system’s notion of text files; all the processing is done by Python itself, and is therefore platform-independent.

buffering is an optional integer used to set the buffering policy. Pass 0 to switch buffering off (only allowed in binary mode), 1 to select line buffering (only usable in text mode), and an integer > 1 to indicate the size in bytes of a fixed-size chunk buffer. Note that specifying a buffer size this way applies for binary buffered I/O, but TextIOWrapper (i.e., files opened with mode=’r+’ ) would have another buffering. To disable buffering in TextIOWrapper , consider using the write_through flag for io.TextIOWrapper.reconfigure() . When no buffering argument is given, the default buffering policy works as follows:

Binary files are buffered in fixed-size chunks; the size of the buffer is chosen using a heuristic trying to determine the underlying device’s “block size” and falling back on io.DEFAULT_BUFFER_SIZE . On many systems, the buffer will typically be 4096 or 8192 bytes long.

“Interactive” text files (files for which isatty() returns True ) use line buffering. Other text files use the policy described above for binary files.

encoding is the name of the encoding used to decode or encode the file. This should only be used in text mode. The default encoding is platform dependent (whatever locale.getencoding() returns), but any text encoding supported by Python can be used. See the codecs module for the list of supported encodings.

errors is an optional string that specifies how encoding and decoding errors are to be handled—this cannot be used in binary mode. A variety of standard error handlers are available (listed under Error Handlers ), though any error handling name that has been registered with codecs.register_error() is also valid. The standard names include:

‘strict’ to raise a ValueError exception if there is an encoding error. The default value of None has the same effect.

‘ignore’ ignores errors. Note that ignoring encoding errors can lead to data loss.

‘replace’ causes a replacement marker (such as ‘?’ ) to be inserted where there is malformed data.

‘surrogateescape’ will represent any incorrect bytes as low surrogate code units ranging from U+DC80 to U+DCFF. These surrogate code units will then be turned back into the same bytes when the surrogateescape error handler is used when writing data. This is useful for processing files in an unknown encoding.

‘xmlcharrefreplace’ is only supported when writing to a file. Characters not supported by the encoding are replaced with the appropriate XML character reference &#nnn; .

‘backslashreplace’ replaces malformed data by Python’s backslashed escape sequences.

‘namereplace’ (also only supported when writing) replaces unsupported characters with \N <. >escape sequences.

newline determines how to parse newline characters from the stream. It can be None , » , ‘\n’ , ‘\r’ , and ‘\r\n’ . It works as follows:

When reading input from the stream, if newline is None , universal newlines mode is enabled. Lines in the input can end in ‘\n’ , ‘\r’ , or ‘\r\n’ , and these are translated into ‘\n’ before being returned to the caller. If it is » , universal newlines mode is enabled, but line endings are returned to the caller untranslated. If it has any of the other legal values, input lines are only terminated by the given string, and the line ending is returned to the caller untranslated.

When writing output to the stream, if newline is None , any ‘\n’ characters written are translated to the system default line separator, os.linesep . If newline is » or ‘\n’ , no translation takes place. If newline is any of the other legal values, any ‘\n’ characters written are translated to the given string.

If closefd is False and a file descriptor rather than a filename was given, the underlying file descriptor will be kept open when the file is closed. If a filename is given closefd must be True (the default); otherwise, an error will be raised.

A custom opener can be used by passing a callable as opener. The underlying file descriptor for the file object is then obtained by calling opener with (file, flags). opener must return an open file descriptor (passing os.open as opener results in functionality similar to passing None ).

The newly created file is non-inheritable .

The following example uses the dir_fd parameter of the os.open() function to open a file relative to a given directory:

The type of file object returned by the open() function depends on the mode. When open() is used to open a file in a text mode ( ‘w’ , ‘r’ , ‘wt’ , ‘rt’ , etc.), it returns a subclass of io.TextIOBase (specifically io.TextIOWrapper ). When used to open a file in a binary mode with buffering, the returned class is a subclass of io.BufferedIOBase . The exact class varies: in read binary mode, it returns an io.BufferedReader ; in write binary and append binary modes, it returns an io.BufferedWriter , and in read/write mode, it returns an io.BufferedRandom . When buffering is disabled, the raw stream, a subclass of io.RawIOBase , io.FileIO , is returned.

See also the file handling modules, such as fileinput , io (where open() is declared), os , os.path , tempfile , and shutil .

Raises an auditing event open with arguments file , mode , flags .

The mode and flags arguments may have been modified or inferred from the original call.

Changed in version 3.3:

The opener parameter was added.

The ‘x’ mode was added.

IOError used to be raised, it is now an alias of OSError .

FileExistsError is now raised if the file opened in exclusive creation mode ( ‘x’ ) already exists.

Changed in version 3.4:

The file is now non-inheritable.

Changed in version 3.5:

If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

The ‘namereplace’ error handler was added.

Changed in version 3.6:

Support added to accept objects implementing os.PathLike .

On Windows, opening a console buffer may return a subclass of io.RawIOBase other than io.FileIO .

Changed in version 3.11: The ‘U’ mode has been removed.

Given a string representing one Unicode character, return an integer representing the Unicode code point of that character. For example, ord(‘a’) returns the integer 97 and ord(‘€’) (Euro sign) returns 8364 . This is the inverse of chr() .

Return base to the power exp; if mod is present, return base to the power exp, modulo mod (computed more efficiently than pow(base, exp) % mod ). The two-argument form pow(base, exp) is equivalent to using the power operator: base**exp .

The arguments must have numeric types. With mixed operand types, the coercion rules for binary arithmetic operators apply. For int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, pow(10, 2) returns 100 , but pow(10, -2) returns 0.01 . For a negative base of type int or float and a non-integral exponent, a complex result is delivered. For example, pow(-9, 0.5) returns a value close to 3j .

For int operands base and exp, if mod is present, mod must also be of integer type and mod must be nonzero. If mod is present and exp is negative, base must be relatively prime to mod. In that case, pow(inv_base, -exp, mod) is returned, where inv_base is an inverse to base modulo mod.

Here’s an example of computing an inverse for 38 modulo 97 :

Changed in version 3.8: For int operands, the three-argument form of pow now allows the second argument to be negative, permitting computation of modular inverses.

Changed in version 3.8: Allow keyword arguments. Formerly, only positional arguments were supported.

Print objects to the text stream file, separated by sep and followed by end. sep, end, file, and flush, if present, must be given as keyword arguments.

All non-keyword arguments are converted to strings like str() does and written to the stream, separated by sep and followed by end. Both sep and end must be strings; they can also be None , which means to use the default values. If no objects are given, print() will just write end.

The file argument must be an object with a write(string) method; if it is not present or None , sys.stdout will be used. Since printed arguments are converted to text strings, print() cannot be used with binary mode file objects. For these, use file.write(. ) instead.

Whether the output is buffered is usually determined by file, but if the flush keyword argument is true, the stream is forcibly flushed.

Changed in version 3.3: Added the flush keyword argument.

Return a property attribute.

fget is a function for getting an attribute value. fset is a function for setting an attribute value. fdel is a function for deleting an attribute value. And doc creates a docstring for the attribute.

A typical use is to define a managed attribute x :

If c is an instance of C, c.x will invoke the getter, c.x = value will invoke the setter, and del c.x the deleter.

If given, doc will be the docstring of the property attribute. Otherwise, the property will copy fget’s docstring (if it exists). This makes it possible to create read-only properties easily using property() as a decorator :

The @property decorator turns the voltage() method into a “getter” for a read-only attribute with the same name, and it sets the docstring for voltage to “Get the current voltage.”

A property object has getter , setter , and deleter methods usable as decorators that create a copy of the property with the corresponding accessor function set to the decorated function. This is best explained with an example:

This code is exactly equivalent to the first example. Be sure to give the additional functions the same name as the original property ( x in this case.)

The returned property object also has the attributes fget , fset , and fdel corresponding to the constructor arguments.

Changed in version 3.5: The docstrings of property objects are now writeable.

Rather than being a function, range is actually an immutable sequence type, as documented in Ranges and Sequence Types — list, tuple, range .

Return a string containing a printable representation of an object. For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval() ; otherwise, the representation is a string enclosed in angle brackets that contains the name of the type of the object together with additional information often including the name and address of the object. A class can control what this function returns for its instances by defining a __repr__() method. If sys.displayhook() is not accessible, this function will raise RuntimeError .

Return a reverse iterator . seq must be an object which has a __reversed__() method or supports the sequence protocol (the __len__() method and the __getitem__() method with integer arguments starting at 0 ).

round ( number , ndigits = None ) ¶

Return number rounded to ndigits precision after the decimal point. If ndigits is omitted or is None , it returns the nearest integer to its input.

For the built-in types supporting round() , values are rounded to the closest multiple of 10 to the power minus ndigits; if two multiples are equally close, rounding is done toward the even choice (so, for example, both round(0.5) and round(-0.5) are 0 , and round(1.5) is 2 ). Any integer value is valid for ndigits (positive, zero, or negative). The return value is an integer if ndigits is omitted or None . Otherwise, the return value has the same type as number.

For a general Python object number , round delegates to number.__round__ .

The behavior of round() for floats can be surprising: for example, round(2.675, 2) gives 2.67 instead of the expected 2.68 . This is not a bug: it’s a result of the fact that most decimal fractions can’t be represented exactly as a float. See Floating Point Arithmetic: Issues and Limitations for more information.

Return a new set object, optionally with elements taken from iterable. set is a built-in class. See set and Set Types — set, frozenset for documentation about this class.

For other containers see the built-in frozenset , list , tuple , and dict classes, as well as the collections module.

This is the counterpart of getattr() . The arguments are an object, a string, and an arbitrary value. The string may name an existing attribute or a new attribute. The function assigns the value to the attribute, provided the object allows it. For example, setattr(x, ‘foobar’, 123) is equivalent to x.foobar = 123 .

name need not be a Python identifier as defined in Identifiers and keywords unless the object chooses to enforce that, for example in a custom __getattribute__() or via __slots__ . An attribute whose name is not an identifier will not be accessible using the dot notation, but is accessible through getattr() etc..

Since private name mangling happens at compilation time, one must manually mangle a private attribute’s (attributes with two leading underscores) name in order to set it with setattr() .

Return a slice object representing the set of indices specified by range(start, stop, step) . The start and step arguments default to None . Slice objects have read-only data attributes start , stop , and step which merely return the argument values (or their default). They have no other explicit functionality; however, they are used by NumPy and other third-party packages. Slice objects are also generated when extended indexing syntax is used. For example: a[start:stop:step] or a[start:stop, i] . See itertools.islice() for an alternate version that returns an iterator.

Return a new sorted list from the items in iterable.

Has two optional arguments which must be specified as keyword arguments.

key specifies a function of one argument that is used to extract a comparison key from each element in iterable (for example, key=str.lower ). The default value is None (compare the elements directly).

reverse is a boolean value. If set to True , then the list elements are sorted as if each comparison were reversed.

Use functools.cmp_to_key() to convert an old-style cmp function to a key function.

The built-in sorted() function is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade).

The sort algorithm uses only < comparisons between items. While defining an __lt__() method will suffice for sorting, PEP 8 recommends that all six rich comparisons be implemented. This will help avoid bugs when using the same data with other ordering tools such as max() that rely on a different underlying method. Implementing all six comparisons also helps avoid confusion for mixed type comparisons which can call reflected the __gt__() method.

For sorting examples and a brief sorting tutorial, see Sorting HOW TO .

Transform a method into a static method.

A static method does not receive an implicit first argument. To declare a static method, use this idiom:

The @staticmethod form is a function decorator – see Function definitions for details.

A static method can be called either on the class (such as C.f() ) or on an instance (such as C().f() ). Moreover, they can be called as regular functions (such as f() ).

Static methods in Python are similar to those found in Java or C++. Also, see classmethod() for a variant that is useful for creating alternate class constructors.

Like all decorators, it is also possible to call staticmethod as a regular function and do something with its result. This is needed in some cases where you need a reference to a function from a class body and you want to avoid the automatic transformation to instance method. For these cases, use this idiom:

For more information on static methods, see The standard type hierarchy .

Changed in version 3.10: Static methods now inherit the method attributes ( __module__ , __name__ , __qualname__ , __doc__ and __annotations__ ), have a new __wrapped__ attribute, and are now callable as regular functions.

Return a str version of object. See str() for details.

str is the built-in string class . For general information about strings, see Text Sequence Type — str .

Sums start and the items of an iterable from left to right and returns the total. The iterable’s items are normally numbers, and the start value is not allowed to be a string.

For some use cases, there are good alternatives to sum() . The preferred, fast way to concatenate a sequence of strings is by calling ».join(sequence) . To add floating point values with extended precision, see math.fsum() . To concatenate a series of iterables, consider using itertools.chain() .

Changed in version 3.8: The start parameter can be specified as a keyword argument.

Return a proxy object that delegates method calls to a parent or sibling class of type. This is useful for accessing inherited methods that have been overridden in a class.

The object_or_type determines the method resolution order to be searched. The search starts from the class right after the type.

For example, if __mro__ of object_or_type is D -> B -> C -> A -> object and the value of type is B , then super() searches C -> A -> object .

The __mro__ attribute of the object_or_type lists the method resolution search order used by both getattr() and super() . The attribute is dynamic and can change whenever the inheritance hierarchy is updated.

If the second argument is omitted, the super object returned is unbound. If the second argument is an object, isinstance(obj, type) must be true. If the second argument is a type, issubclass(type2, type) must be true (this is useful for classmethods).

There are two typical use cases for super. In a class hierarchy with single inheritance, super can be used to refer to parent classes without naming them explicitly, thus making the code more maintainable. This use closely parallels the use of super in other programming languages.

The second use case is to support cooperative multiple inheritance in a dynamic execution environment. This use case is unique to Python and is not found in statically compiled languages or languages that only support single inheritance. This makes it possible to implement “diamond diagrams” where multiple base classes implement the same method. Good design dictates that such implementations have the same calling signature in every case (because the order of calls is determined at runtime, because that order adapts to changes in the class hierarchy, and because that order can include sibling classes that are unknown prior to runtime).

For both use cases, a typical superclass call looks like this:

In addition to method lookups, super() also works for attribute lookups. One possible use case for this is calling descriptors in a parent or sibling class.

Note that super() is implemented as part of the binding process for explicit dotted attribute lookups such as super().__getitem__(name) . It does so by implementing its own __getattribute__() method for searching classes in a predictable order that supports cooperative multiple inheritance. Accordingly, super() is undefined for implicit lookups using statements or operators such as super()[name] .

Also note that, aside from the zero argument form, super() is not limited to use inside methods. The two argument form specifies the arguments exactly and makes the appropriate references. The zero argument form only works inside a class definition, as the compiler fills in the necessary details to correctly retrieve the class being defined, as well as accessing the current instance for ordinary methods.

For practical suggestions on how to design cooperative classes using super() , see guide to using super().

class tuple class tuple ( iterable )

Rather than being a function, tuple is actually an immutable sequence type, as documented in Tuples and Sequence Types — list, tuple, range .

With one argument, return the type of an object. The return value is a type object and generally the same object as returned by object.__class__ .

The isinstance() built-in function is recommended for testing the type of an object, because it takes subclasses into account.

With three arguments, return a new type object. This is essentially a dynamic form of the class statement. The name string is the class name and becomes the __name__ attribute. The bases tuple contains the base classes and becomes the __bases__ attribute; if empty, object , the ultimate base of all classes, is added. The dict dictionary contains attribute and method definitions for the class body; it may be copied or wrapped before becoming the __dict__ attribute. The following two statements create identical type objects:

Keyword arguments provided to the three argument form are passed to the appropriate metaclass machinery (usually __init_subclass__() ) in the same way that keywords in a class definition (besides metaclass) would.

Changed in version 3.6: Subclasses of type which don’t override type.__new__ may no longer use the one-argument form to get the type of an object.

Return the __dict__ attribute for a module, class, instance, or any other object with a __dict__ attribute.

Objects such as modules and instances have an updateable __dict__ attribute; however, other objects may have write restrictions on their __dict__ attributes (for example, classes use a types.MappingProxyType to prevent direct dictionary updates).

Without an argument, vars() acts like locals() . Note, the locals dictionary is only useful for reads since updates to the locals dictionary are ignored.

A TypeError exception is raised if an object is specified but it doesn’t have a __dict__ attribute (for example, if its class defines the __slots__ attribute).

zip ( * iterables , strict = False ) ¶

Iterate over several iterables in parallel, producing tuples with an item from each one.

More formally: zip() returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument iterables.

Another way to think of zip() is that it turns rows into columns, and columns into rows. This is similar to transposing a matrix.

zip() is lazy: The elements won’t be processed until the iterable is iterated on, e.g. by a for loop or by wrapping in a list .

One thing to consider is that the iterables passed to zip() could have different lengths; sometimes by design, and sometimes because of a bug in the code that prepared these iterables. Python offers three different approaches to dealing with this issue:

By default, zip() stops when the shortest iterable is exhausted. It will ignore the remaining items in the longer iterables, cutting off the result to the length of the shortest iterable:

zip() is often used in cases where the iterables are assumed to be of equal length. In such cases, it’s recommended to use the strict=True option. Its output is the same as regular zip() :

Unlike the default behavior, it raises a ValueError if one iterable is exhausted before the others:

Without the strict=True argument, any bug that results in iterables of different lengths will be silenced, possibly manifesting as a hard-to-find bug in another part of the program.

Shorter iterables can be padded with a constant value to make all the iterables have the same length. This is done by itertools.zip_longest() .

Edge cases: With a single iterable argument, zip() returns an iterator of 1-tuples. With no arguments, it returns an empty iterator.

Tips and tricks:

The left-to-right evaluation order of the iterables is guaranteed. This makes possible an idiom for clustering a data series into n-length groups using zip(*[iter(s)]*n, strict=True) . This repeats the same iterator n times so that each output tuple has the result of n calls to the iterator. This has the effect of dividing the input into n-length chunks.

zip() in conjunction with the * operator can be used to unzip a list:

Changed in version 3.10: Added the strict argument.

This is an advanced function that is not needed in everyday Python programming, unlike importlib.import_module() .

This function is invoked by the import statement. It can be replaced (by importing the builtins module and assigning to builtins.__import__ ) in order to change semantics of the import statement, but doing so is strongly discouraged as it is usually simpler to use import hooks (see PEP 302) to attain the same goals and does not cause issues with code which assumes the default import implementation is in use. Direct use of __import__() is also discouraged in favor of importlib.import_module() .

The function imports the module name, potentially using the given globals and locals to determine how to interpret the name in a package context. The fromlist gives the names of objects or submodules that should be imported from the module given by name. The standard implementation does not use its locals argument at all and uses its globals only to determine the package context of the import statement.

level specifies whether to use absolute or relative imports. 0 (the default) means only perform absolute imports. Positive values for level indicate the number of parent directories to search relative to the directory of the module calling __import__() (see PEP 328 for the details).

When the name variable is of the form package.module , normally, the top-level package (the name up till the first dot) is returned, not the module named by name. However, when a non-empty fromlist argument is given, the module named by name is returned.

For example, the statement import spam results in bytecode resembling the following code:

The statement import spam.ham results in this call:

Note how __import__() returns the toplevel module here because this is the object that is bound to a name by the import statement.

On the other hand, the statement from spam.ham import eggs, sausage as saus results in

Here, the spam.ham module is returned from __import__() . From this object, the names to import are retrieved and assigned to their respective names.

If you simply want to import a module (potentially within a package) by name, use importlib.import_module() .

Changed in version 3.3: Negative values for level are no longer supported (which also changes the default value to 0).

Changed in version 3.9: When the command line options -E or -I are being used, the environment variable PYTHONCASEOK is now ignored.

Note that the parser only accepts the Unix-style end of line convention. If you are reading the code from a file, make sure to use newline conversion mode to convert Windows or Mac-style newlines.