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This article mainly explains "how to check the memory address of variables in Python". The explanation in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to view the memory address of variables in Python".

1. Python variable

In most languages, when giving a name to a value, this behavior is referred to as "assigning a value to a variable" or "storing a value in a variable". However, Python differs from many other computer languages in that instead of storing values in variables, it is like "pasting" the name on top of the value (technically, binding the name to an object). So, some Python programmers will say that Python has no variables, only names, and find the value it represents by name.

Variables in Python are different from those in other development languages (such as C):

In C language, a variable is similar to a "container", and the value assigned to it is contained in the container.

In python, a variable is similar to a name tag "affixed" to a value, finding the value it represents by name.

1.1 first point

Let's first explain the first point: the implementation of variables are as follows: reference semantics and value semantics.

The variable in python language is implemented by reference semantics, in which the reference to the value (object) is stored (the address of the memory space where the value is located). In this way, the storage space required for the variable is the same, because only one reference needs to be saved. Some languages (such as c) do not use this approach, they store variables directly in the storage area of variables, which is called value semantics. In this way, a variable of an integer type needs to hold the space needed to hold an integer (for example, the int type in the c language takes up 4 bytes).

The reference relationship between variables and objects in python is similar to that between pointer variables and pointing addresses in c language.

In the data structure of python, objects are divided into mutable objects and immutable objects. Basic data types such as int, float, meta-ancestor tuple and str are immutable objects; list (list), dict (dictionary) and set (collection) are mutable objects, and the references of elements stored in mutable objects do not change, but change the values that their references point to.

Using reference semantics to store only the memory address where the value of a variable is located, not the value of the variable itself.

1.2 second point

Now explain the second point: the relationship among variables, objects, and references in Python.

In Python, everything is an object. In Python, an object has three elements: identity, type, and value.

☆ logo (identity):

Used to uniquely identify an object, usually corresponding to the address of the object in computer memory. Use the built-in function id (obj) to return the unique identity of the object.

☆ type (type):

Type can limit the range of values of an object and the actions that can be performed. Use the built-in function type (obj) to return the type to which the object belongs.

The object contains the standard header information: the type identifier. Identifies the type of object that represents the type of data stored by the object.

Each object has two standard header information:

1. Type identifier to identify the (data) type of the object

two。 A reference counter that records the number of references to the current object.

(recycling mechanism: the reference counter of the variable is 0, which cleans up automatically. Smaller integer objects have a caching mechanism. )

☆ value (value):

Information that represents the data stored by the object. You can print values directly using the built-in function print (obj).

In Python, variables are used to point to arbitrary objects and are references to objects. Python variables are more like pointers (or Python variables are more like "stickers") than data storage areas (rather than data "containers").

Variables in Python are not "containers" containing objects, but "labels" affixed to objects-- assign values to a variable, attach the label to one object, and re-assign it, tearing off the label and attaching it to another object.

In python, variables hold references to objects (values). In this way, each initialization of a variable opens up a new space to assign the address of the new content to the variable. The id () function can get the address of the variable in memory. When we assign different values to variables, the address changes, and the same value address does not change. The following is an example:

In Python, values can be placed somewhere in memory (address), variables are used to reference them, assign a new value to the variable, the original value is not overwritten by the new value, the variable just refers to the new value. By the way, Python's garbage collection mechanism automatically cleans up values that are no longer used, so you don't have to worry that your computer's memory is full of invalid values that are "discarded."

1.3 third point

Now explain the third point: mutable (mutable) type objects, immutable (immutable) type objects

A mutable type object that can change its value while its id () remains fixed.

An immutable type of object, an object with a fixed value. Immutable objects include numbers (numbers), strings (strings), and tuples (tuples). Such objects cannot be changed. If you must store a different value, you must create a new object. Immutable objects are not allowed to modify their own content. If we assign an immutable object, we actually generate a new object and let the variable point to it. Even if the object is as simple as the numbers 0 and 1.

Because variables in Python hold object references, for immutable objects, although the object itself is immutable, the object reference of the variable is mutable. The use of such a mechanism can sometimes make people confused and it seems that the variable object has changed. Such as the following code:

I = 73

I + = 2

The characteristics of immutable objects have not changed, they are still immutable objects, but only create new objects and change the object references of variables.

For mutable objects, the contents of their objects can be changed. When the content of the object changes, the object reference of the variable does not change. Such as the following example.

M = [5pr 9]

Masks = [6]

II. Summary

The Python variable refers to the name that binds an object (binding is to associate an object with a name).

When binding, the variable is the name.

When used, the variable represents a reference to the object.

The only thing that changes the variable is the binding relationship.

Supplementary note:

For complex data types (lists, collections, dictionaries), if you add an element or several elements, it will not change its own address, but will only change the address reference of its internal elements, but if it is re-assigned, the address will be re-assigned to cover the address, and the address will be changed. The sample code is as follows:

List_ = print (list_, id (list_)) list_.append (5) print (list_, id (list_)) # as the above code, because the list_ before and after append is still the same object, but the value of the object has changed, so the address remains the same. # such as the following code print (list_, id (list_), id (list_ [1])) # print the list, the address of the list, the address of the second element list_ [1] = 'aaa' # modification list print (list_, id (list_)) Id (list_ [1]) # print the list, the address of the list, the address of the second element # it is not difficult to find: the list has changed, the address of the list has not changed, the internal elements of the list have changed, and the address of the internal elements of the list has changed. thank you for reading. The above is the content of "how to view the memory address of variables in Python". After the study of this article, I believe you have a deeper understanding of how to view the memory address of variables in Python, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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