Python get item

Python get item DEFAULT

__getitem__() in Python

In Python, everything is an object. There are a lot of ‘ordinary’ system call methods on these objects behind the scene which is not visible to the programmer. Here come what are called as magic methods. Magic methods in python are special methods that are invoked when we run any ordinary python code. To differentiate them with normal functions, they have surrounding double underscores.

If we want to add a and b, we write the following syntax:

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c = a + b

Internally it is called as:

c = a.__add__(b)

is a magic method in Python, which when used in a class, allows its instances to use the (indexer) operators. Say x is an instance of this class, then is roughly equivalent to .

The method defines behavior for when an item is accessed, using the notation . This is also part of both the mutable and immutable container protocols.







<class 'int'> 5 <class 'slice'> slice(5, 65, 5) <class 'str'> GeeksforGeeks <class 'tuple'> (1, 'x', 10.0) <class 'slice'> slice('a', 'z', 2) <class 'object'> <object object at 0x7f75bcd6d0a0>

Unlike some other languages, Python basically lets you pass any object into the indexer. You may be surprised that the actually parses. To the Python interpreter, that expression is equivalent to this: . As you can see, the 1, ‘x’, 10.0 part is implicitly parsed as a tuple. The expression makes use of Python’s slice syntax. It is equivalent to this expression: test[slice(5, 65, 5)].

The magic method is usually used for list indexing, dictionary lookup, or accessing ranges of values. Considering how versatile it is, it’s probably one of Python’s most underutilized magic methods.


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Using Python __getitem__ and __setitem__

I have always said that I am really fond of Python language. Today I am going to show you another big reason for the same. Python gives most of the flexibility for the developers to develop their code efficiently. And that is why Python allows its user to create their custom classes using the inbuilt classes such as list, str, etc.

Today I am going to demonstrate how to create your own custom datatype like list in Python. We are also going to add some custom fun texts to get things interesting. Now, before going for the actual implementation, consider the following code snippet:

As you can observe from above code, we can create a list and then set a value for a specified index and get the value at the specified index. We will be using the same functionality in our custom class that we are about to create.

Initially, we will be creating a simple class with a constructor that we initialize our list with zeros.

In the above code snippet, what I have done is I have created a simple class called . The constructor of the class i.e will initialize our my_custom_list with number of elements that user has specified.

The next step is to wite our own member function to print our my_custom_list. For that simply add the following code to the above code:

is called in Python. There are various magic methods in Python and we will be using some of them in a minute. But for now let us understand what is . So basically, is a special method which is used to specify the formatting of your class object. In simple words, whenever you call a on your class object, str specifies what should get printed on that time. Let us try to print our CustomList class now.

Wow! That’s something amazing right. We have successfully created our own class and now we can even print some custom text.

If you have observed in the first code snippet, Python lists can set a value for a specified index and get the value at the specified index. Now, we will be adding these functionalities to our class too. This is how its done:

If we run our program after adding above lines we get something similar as:

Ahaa! Now we can even set an item and retrieve an item from our CustomList class with custom text. Now, you can further play with other magic methods and create your own Custom classes.

Here’s the whole code:

Hope you enjoyed this small tutorial :)

Happy Coding!

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Put And Get Items From AWS DynamoDB Using Python

List Methods

Before starting, I should mention that the code in this blog post and in the video above is available on my github.

Defining a List

Lists are written within square brackets []

# Define a list
z = [3, 7, 4, 2]

Lists store an ordered collection of items which can be of different types. The list defined above has items that are all of the same type (int), but all the items of a list do not need to be of the same type as you can see below.

# Define a list
heterogenousElements = [3, True, 'Michael', 2.0]

The list contains an int, a bool, a string, and a float.

Access Values in a List

Each item in a list has an assigned index value. It is important to note that python is a zero indexed based language. All this means is that the first item in the list is at index 0.

# Define a list
z = [3, 7, 4, 2]# Access the first item of a list at index 0

Python also supports negative indexing. Negative indexing starts from the end. It can be more convienient at times to use negative indexing to get the last item in the list because you don’t have to know the length of the list to access the last item.

# print last item in the list

As a reminder, you could also access the same item using positive indexes (as seen below).

Slice of Lists

Slices are good for getting a subset of values in your list. For the example code below, it will return a list with the items from index 0 up to and not including index 2.

# Define a list
z = [3, 7, 4, 2]print(z[0:2])
# everything up to but not including index 3

The code below returns a list with items from index 1 to the end of the list

# index 1 to end of list

Update Item in a List

Lists in Python are mutable. All that means is that after defining a list, it is possible to update the individual items in a list.

# Defining a list
z = [3, 7, 4, 2]# Update the item at index 1 with the string "fish"
z[1] = "fish"

Python lists have different methods that help you modify a list. This section of the tutorial just goes over various python list methods.

Index Method

# Define a list
z = [4, 1, 5, 4, 10, 4]

The index method returns the first index at which a value occurs. In the code below, it will return 0.


You can also specify where you start your search.

print(z.index(4, 3))

Count Method

The count method works just like how it sounds. It counts the number of times a value occurs in a list

random_list = [4, 1, 5, 4, 10, 4]

Sort Method

The sort method sorts and alters the original list in place.

z = [3, 7, 4, 2]

The code above sorts a list from low to high. The code below shows that you can also sort a list from high to low.

# Sorting and Altering original list
# high to low
z.sort(reverse = True)

As an aside, I should mention that you can also sort a list of strings from a-z and z-a as you can see here.

Append Method

The append method adds an element to the end of a list. This happens inplace.

z = [7, 4, 3, 2]

Remove Method

The remove method removes the first occurrence of a value in a list.

z = [7, 4, 3, 2, 3]

Pop Method

The pop method removes an item at the index you provide. This method will also return the item you removed from the list. If you don’t provide an index, it will by default remove the item at the last index.

z = [7, 4, 3, 3]

Extend Method

The method extends a list by appending items. The benefit of this is you can add lists together.

z = [7, 3, 3]

Alternatively, the same thing could be accomplished by using the + operator.

print([1,2] + [3,4])

Insert Method

The insert method inserts an item before the index you provide

z = [7, 3, 3, 4, 5]
z.insert(4, [1, 2])

Closing Remarks

Please let me know if you have any questions either here or in the comments section of the youtube video or through Twitter! Next post reviews python dictionaries. If you want to learn how to utilize the Pandas, Matplotlib, or Seaborn libraries, please consider taking my Python for Data Visualization LinkedIn Learning course. Here is a free preview video.


Item python get

Python dictionary get() Method


Python dictionary method get() returns a value for the given key. If key is not available then returns default value None.


Following is the syntax for get() method −

dict.get(key, default = None)


  • key − This is the Key to be searched in the dictionary.

  • default − This is the Value to be returned in case key does not exist.

Return Value

This method return a value for the given key. If key is not available, then returns default value None.


The following example shows the usage of get() method.

Live Demo

#!/usr/bin/python dict = {'Name': 'Zabra', 'Age': 7} print "Value : %s" % dict.get('Age') print "Value : %s" % dict.get('Education', "Never")

When we run above program, it produces following result −

Value : 7 Value : Never


Python OOP Tutorial 5: Special (Magic/Dunder) Methods

3. Data model

3.3. Special method names¶

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named , and is an instance of this class, then is roughly equivalent to . Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically or ).

Setting a special method to indicates that the corresponding operation is not available. For example, if a class sets to , the class is not iterable, so calling on its instances will raise a (without falling back to ). 2

When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the interface in the W3C’s Document Object Model.)

3.3.1. Basic customization¶

(cls[, ...])¶

Called to create a new instance of class cls. is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of should be the new object instance (usually an instance of cls).

Typical implementations create a new instance of the class by invoking the superclass’s method using with appropriate arguments and then modifying the newly-created instance as necessary before returning it.

If is invoked during object construction and it returns an instance of cls, then the new instance’s method will be invoked like , where self is the new instance and the remaining arguments are the same as were passed to the object constructor.

If does not return an instance of cls, then the new instance’s method will not be invoked.

is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation.

(self[, ...])¶

Called after the instance has been created (by ), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an method, the derived class’s method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: .

Because and work together in constructing objects ( to create it, and to customize it), no non- value may be returned by ; doing so will cause a to be raised at runtime.


Called when the instance is about to be destroyed. This is also called a finalizer or (improperly) a destructor. If a base class has a method, the derived class’s method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance.

It is possible (though not recommended!) for the method to postpone destruction of the instance by creating a new reference to it. This is called object resurrection. It is implementation-dependent whether is called a second time when a resurrected object is about to be destroyed; the current CPython implementation only calls it once.

It is not guaranteed that methods are called for objects that still exist when the interpreter exits.


doesn’t directly call — the former decrements the reference count for by one, and the latter is only called when ’s reference count reaches zero.

CPython implementation detail: It is possible for a reference cycle to prevent the reference count of an object from going to zero. In this case, the cycle will be later detected and deleted by the cyclic garbage collector. A common cause of reference cycles is when an exception has been caught in a local variable. The frame’s locals then reference the exception, which references its own traceback, which references the locals of all frames caught in the traceback.

See also

Documentation for the module.


Due to the precarious circumstances under which methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to instead. In particular:

  • can be invoked when arbitrary code is being executed, including from any arbitrary thread. If needs to take a lock or invoke any other blocking resource, it may deadlock as the resource may already be taken by the code that gets interrupted to execute .

  • can be executed during interpreter shutdown. As a consequence, the global variables it needs to access (including other modules) may already have been deleted or set to . Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the method is called.


Called by the built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form should be returned. The return value must be a string object. If a class defines but not , then is also used when an “informal” string representation of instances of that class is required.

This is typically used for debugging, so it is important that the representation is information-rich and unambiguous.


Called by and the built-in functions and to compute the “informal” or nicely printable string representation of an object. The return value must be a string object.

This method differs from in that there is no expectation that return a valid Python expression: a more convenient or concise representation can be used.

The default implementation defined by the built-in type calls .


Called by bytes to compute a byte-string representation of an object. This should return a object.

(self, format_spec

Called by the built-in function, and by extension, evaluation of formatted string literals and the method, to produce a “formatted” string representation of an object. The format_spec argument is a string that contains a description of the formatting options desired. The interpretation of the format_spec argument is up to the type implementing , however most classes will either delegate formatting to one of the built-in types, or use a similar formatting option syntax.

See Format Specification Mini-Language for a description of the standard formatting syntax.

The return value must be a string object.

Changed in version 3.4: The __format__ method of itself raises a if passed any non-empty string.

Changed in version 3.7: is now equivalent to rather than .

(self, other
(self, other
(self, other
(self, other
(self, other
(self, other

These are the so-called “rich comparison” methods. The correspondence between operator symbols and method names is as follows: calls , calls , calls , calls , calls , and calls .

A rich comparison method may return the singleton if it does not implement the operation for a given pair of arguments. By convention, and are returned for a successful comparison. However, these methods can return any value, so if the comparison operator is used in a Boolean context (e.g., in the condition of an statement), Python will call on the value to determine if the result is true or false.

By default, implements by using , returning in the case of a false comparison: . For , by default it delegates to and inverts the result unless it is . There are no other implied relationships among the comparison operators or default implementations; for example, the truth of does not imply . To automatically generate ordering operations from a single root operation, see .

See the paragraph on for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, and are each other’s reflection, and are each other’s reflection, and and are their own reflection. If the operands are of different types, and right operand’s type is a direct or indirect subclass of the left operand’s type, the reflected method of the right operand has priority, otherwise the left operand’s method has priority. Virtual subclassing is not considered.


Called by built-in function and for operations on members of hashed collections including , , and . should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example:



truncates the value returned from an object’s custom method to the size of a . This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object’s must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is with .

If a class does not define an method it should not define a operation either; if it defines but not , its instances will not be usable as items in hashable collections. If a class defines mutable objects and implements an method, it should not implement , since the implementation of hashable collections requires that a key’s hash value is immutable (if the object’s hash value changes, it will be in the wrong hash bucket).

User-defined classes have and methods by default; with them, all objects compare unequal (except with themselves) and returns an appropriate value such that implies both that and .

A class that overrides and does not define will have its implicitly set to . When the method of a class is , instances of the class will raise an appropriate when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checking .

If a class that overrides needs to retain the implementation of from a parent class, the interpreter must be told this explicitly by setting .

If a class that does not override wishes to suppress hash support, it should include in the class definition. A class which defines its own that explicitly raises a would be incorrectly identified as hashable by an call.


By default, the values of str and bytes objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.

This is intended to provide protection against a denial-of-service caused by carefully-chosen inputs that exploit the worst case performance of a dict insertion, O(n2) complexity. See for details.

Changing hash values affects the iteration order of sets. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).

See also .

Changed in version 3.3: Hash randomization is enabled by default.


Called to implement truth value testing and the built-in operation ; should return or . When this method is not defined, is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither nor , all its instances are considered true.

3.3.2. Customizing attribute access¶

The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of ) for class instances.

(self, name

Called when the default attribute access fails with an (either raises an because name is not an instance attribute or an attribute in the class tree for ; or of a name property raises ). This method should either return the (computed) attribute value or raise an exception.

Note that if the attribute is found through the normal mechanism, is not called. (This is an intentional asymmetry between and .) This is done both for efficiency reasons and because otherwise would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the method below for a way to actually get total control over attribute access.

(self, name

Called unconditionally to implement attribute accesses for instances of the class. If the class also defines , the latter will not be called unless either calls it explicitly or raises an . This method should return the (computed) attribute value or raise an exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, .


This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup.

For certain sensitive attribute accesses, raises an auditing event with arguments and .

(self, name, value

Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). name is the attribute name, value is the value to be assigned to it.

If wants to assign to an instance attribute, it should call the base class method with the same name, for example, .

For certain sensitive attribute assignments, raises an auditing event with arguments , , .

(self, name

Like but for attribute deletion instead of assignment. This should only be implemented if is meaningful for the object.

For certain sensitive attribute deletions, raises an auditing event with arguments and .


Called when is called on the object. A sequence must be returned. converts the returned sequence to a list and sorts it. Customizing module attribute access¶

Special names and can be also used to customize access to module attributes. The function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an . If an attribute is not found on a module object through the normal lookup, i.e. , then is searched in the module before raising an . If found, it is called with the attribute name and the result is returned.

The function should accept no arguments, and return a sequence of strings that represents the names accessible on module. If present, this function overrides the standard search on a module.

For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the attribute of a module object to a subclass of . For example:

importsysfromtypesimportModuleTypeclassVerboseModule(ModuleType):def__repr__(self):returnf'Verbose {self.__name__}'def__setattr__(self,attr,value):print(f'Setting {attr}...')super().__setattr__(attr,value)sys.modules[__name__].__class__=VerboseModule


Defining module and setting module only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected.

Changed in version 3.5: module attribute is now writable.

New in version 3.7: and module attributes.

See also

PEP 562 - Module __getattr__ and __dir__

Describes the and functions on modules. Implementing Descriptors¶

The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’ .

(self, instance, owner=None

Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional owner argument is the owner class, while instance is the instance that the attribute was accessed through, or when the attribute is accessed through the owner.

This method should return the computed attribute value or raise an exception.

PEP 252 specifies that is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own implementation always passes in both arguments whether they are required or not.

(self, instance, value

Called to set the attribute on an instance instance of the owner class to a new value, value.

Note, adding or changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors for more details.

(self, instance

Called to delete the attribute on an instance instance of the owner class.

The attribute is interpreted by the module as specifying the class where this object was defined (setting this appropriately can assist in runtime introspection of dynamic class attributes). For callables, it may indicate that an instance of the given type (or a subclass) is expected or required as the first positional argument (for example, CPython sets this attribute for unbound methods that are implemented in C). Invoking Descriptors¶

In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: , , and . If any of those methods are defined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, has a lookup chain starting with , then , and continuing through the base classes of excluding metaclasses.

However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called.

The starting point for descriptor invocation is a binding, . How the arguments are assembled depends on :

Direct Call

The simplest and least common call is when user code directly invokes a descriptor method: .

Instance Binding

If binding to an object instance, is transformed into the call: .

Class Binding

If binding to a class, is transformed into the call: .

Super Binding

If is an instance of , then the binding searches for the base class immediately preceding and then invokes the descriptor with the call: .

For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of , and . If it does not define , then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines and/or , it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both and , while non-data descriptors have just the method. Data descriptors with and (and/or ) defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances.

Python methods (including and ) are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class.

The function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property. __slots__¶

__slots__ allow us to explicitly declare data members (like properties) and deny the creation of __dict__ and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)

The space saved over using __dict__ can be significant. Attribute lookup speed can be significantly improved as well.

This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance. Notes on using __slots__
  • When inheriting from a class without __slots__, the __dict__ and __weakref__ attribute of the instances will always be accessible.

  • Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises . If dynamic assignment of new variables is desired, then add to the sequence of strings in the __slots__ declaration.

  • Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add to the sequence of strings in the __slots__ declaration.

  • __slots__ are implemented at the class level by creating descriptors (Implementing Descriptors) for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment.

  • The action of a __slots__ declaration is not limited to the class where it is defined. __slots__ declared in parents are available in child classes. However, child subclasses will get a __dict__ and __weakref__ unless they also define __slots__ (which should only contain names of any additional slots).

  • If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.

  • Nonempty __slots__ does not work for classes derived from “variable-length” built-in types such as , and .

  • Any non-string iterable may be assigned to __slots__. Mappings may also be used; however, in the future, special meaning may be assigned to the values corresponding to each key.

  • __class__ assignment works only if both classes have the same __slots__.

  • Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise .

  • If an iterator is used for __slots__ then a descriptor is created for each of the iterator’s values. However, the __slots__ attribute will be an empty iterator.

3.3.3. Customizing class creation¶

Whenever a class inherits from another class, __init_subclass__ is called on that class. This way, it is possible to write classes which change the behavior of subclasses. This is closely related to class decorators, but where class decorators only affect the specific class they’re applied to, solely applies to future subclasses of the class defining the method.

classmethod (cls

This method is called whenever the containing class is subclassed. cls is then the new subclass. If defined as a normal instance method, this method is implicitly converted to a class method.

Keyword arguments which are given to a new class are passed to the parent’s class . For compatibility with other classes using , one should take out the needed keyword arguments and pass the others over to the base class, as in:


The default implementation does nothing, but raises an error if it is called with any arguments.


The metaclass hint is consumed by the rest of the type machinery, and is never passed to implementations. The actual metaclass (rather than the explicit hint) can be accessed as .

When a class is created, scans the class variables and makes callbacks to those with a hook.

(self, owner, name

Automatically called at the time the owning class owner is created. The object has been assigned to name in that class:

classA:x=C()# Automatically calls: x.__set_name__(A, 'x')

If the class variable is assigned after the class is created, will not be called automatically. If needed, can be called directly:

classA:passc=C()A.x=c# The hook is not calledc.__set_name__(A,'x')# Manually invoke the hook

See Creating the class object for more details. Resolving MRO entries¶

If a base that appears in class definition is not an instance of , then an method is searched on it. If found, it is called with the original bases tuple. This method must return a tuple of classes that will be used instead of this base. The tuple may be empty, in such case the original base is ignored.

See also

PEP 560 - Core support for typing module and generic types Preparing the class namespace¶

Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a attribute, it is called as (where the additional keyword arguments, if any, come from the class definition). The method should be implemented as a . The namespace returned by is passed in to , but when the final class object is created the namespace is copied into a new .

If the metaclass has no attribute, then the class namespace is initialised as an empty ordered mapping.

See also

PEP 3115 - Metaclasses in Python 3000

Introduced the namespace hook Executing the class body¶

The class body is executed (approximately) as . The key difference from a normal call to is that lexical scoping allows the class body (including any methods) to reference names from the current and outer scopes when the class definition occurs inside a function.

However, even when the class definition occurs inside the function, methods defined inside the class still cannot see names defined at the class scope. Class variables must be accessed through the first parameter of instance or class methods, or through the implicit lexically scoped reference described in the next section. Creating the class object¶

Once the class namespace has been populated by executing the class body, the class object is created by calling (the additional keywords passed here are the same as those passed to ).

This class object is the one that will be referenced by the zero-argument form of . is an implicit closure reference created by the compiler if any methods in a class body refer to either or . This allows the zero argument form of to correctly identify the class being defined based on lexical scoping, while the class or instance that was used to make the current call is identified based on the first argument passed to the method.

CPython implementation detail: In CPython 3.6 and later, the cell is passed to the metaclass as a entry in the class namespace. If present, this must be propagated up to the call in order for the class to be initialised correctly. Failing to do so will result in a in Python 3.8.

When using the default metaclass , or any metaclass that ultimately calls , the following additional customization steps are invoked after creating the class object:

  1. The method collects all of the attributes in the class namespace that define a method;

  2. Those methods are called with the class being defined and the assigned name of that particular attribute;

  3. The hook is called on the immediate parent of the new class in its method resolution order.

After the class object is created, it is passed to the class decorators included in the class definition (if any) and the resulting object is bound in the local namespace as the defined class.

When a new class is created by , the object provided as the namespace parameter is copied to a new ordered mapping and the original object is discarded. The new copy is wrapped in a read-only proxy, which becomes the attribute of the class object.

See also

PEP 3135 - New super

Describes the implicit closure reference

3.3.4. Customizing instance and subclass checks¶

The following methods are used to override the default behavior of the and built-in functions.

In particular, the metaclass implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs.

(self, instance

Return true if instance should be considered a (direct or indirect) instance of class. If defined, called to implement .

(self, subclass

Return true if subclass should be considered a (direct or indirect) subclass of class. If defined, called to implement .

Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class.

3.3.5. Emulating generic types¶

One can implement the generic class syntax as specified by PEP 484 (for example ) by defining a special method:

classmethod (cls, key

Return an object representing the specialization of a generic class by type arguments found in key.

This method is looked up on the class object itself, and when defined in the class body, this method is implicitly a class method. Note, this mechanism is primarily reserved for use with static type hints, other usage is discouraged.

See also

PEP 560 - Core support for typing module and generic types

3.3.6. Emulating callable objects¶

(self[, args...])¶

Called when the instance is “called” as a function; if this method is defined, roughly translates to .

3.3.7. Emulating container types¶

The following methods can be defined to implement container objects. Containers usually are sequences (such as lists or tuples) or mappings (like dictionaries), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers k for which where N is the length of the sequence, or slice objects, which define a range of items. It is also recommended that mappings provide the methods , , , , , , , , , and behaving similar to those for Python’s standard dictionary objects. The module provides a abstract base class to help create those methods from a base set of , , , and . Mutable sequences should provide methods , , , , , , , and , like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods , , , , and described below; they should not define other numerical operators. It is recommended that both mappings and sequences implement the method to allow efficient use of the operator; for mappings, should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the method to allow efficient iteration through the container; for mappings, should iterate through the object’s keys; for sequences, it should iterate through the values.


Called to implement the built-in function . Should return the length of the object, an integer 0. Also, an object that doesn’t define a method and whose method returns zero is considered to be false in a Boolean context.

CPython implementation detail: In CPython, the length is required to be at most . If the length is larger than some features (such as ) may raise . To prevent raising by truth value testing, an object must define a method.


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Python Dictionary get()

The method returns the value for the specified key if the key is in the dictionary.


Syntax of Dictionary get()

The syntax of is:

dict.get(key[, value])

get() Parameters

method takes maximum of two parameters:

  • key - key to be searched in the dictionary
  • value (optional) - Value to be returned if the is not found. The default value is .

Return Value from get()

method returns:

  • the value for the specified if is in the dictionary.
  • if the is not found and is not specified.
  • if the is not found and is specified.

Example 1: How does get() work for dictionaries?


Python get() method Vs dict[key] to Access Elements

method returns a default value if the is missing.

However, if the is not found when you use , exception is raised.



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