Everything is an object in python: Mutable and immutable object
Introduction to mutable, immutable objects in python
As we have seen in different python programs, Python is a more accessible programming language widely used in various fields like data science, machine learning, artificial intelligence, and others.
One important concept that every developer must understand is the concept of mutable objects in Python.
First,
what is an object in Python?
In Python, everything is an object. Everything from simple data types like integers and strings to complex data structures like sets and lists are objects. An object in Python is an instance of a class. We can define A class as a well planned design for creating objects. In Python, we can create our own classes to define our own classes of objects with specific behaviors and properties.
A class defines the attributes and methods of an object. Attributes are properties that describe the object, while methods are functions that define the object's behavior.
To create an object in Python, you first need to define the class from which the object will be created. You can define a class by writing the class keyword followed by a capital letter of the name of that class
What is id and type function in Python?
Id and type are functions that are highly used by python developers in their regular programming.
Python is a good programming language that has more functions and libraries." type()" and "id()" are the most popular and common functions used in Python. These functions help to identify the type and identity of a variable, respectively. Understanding how to use these functions is very important in Python development, and it provides valuable insight during debugging and analyzing problems.
Type() Function
The "type()" function determines the type of an object in Python. In other words, it returns a variable's class or data type. For instance, if you want to know the kind of a variable "x," you can use the "type(x)" function. The output will be the class of the variable "x."
Example:
a = 'Hello world'
b = 34
c = 3.14
d = [1, 2, 3]
e = (4, 5, 6)
print(type(a)) # output: <class 'str'>
print(type(b)) # output: <class 'int'>
print(type(c)) # output: <class 'float'>
print(type(d)) # output: <class 'list'>
print(type(e)) # output: <class 'tuple'>
As seen in the example above, the "type()" function returns the variable type in string format. You can use this function to test for the type of object in a condition and perform different operations based on the results.
ID() Function
The "id()" function is used to obtain the identity of an object in Python. An object's identity is unique and immutable during its lifetime and is used to distinguish it from other objects. The "id()" function returns the unique identity of an object as an integer value.
Example:
a = 'Hello'
b = 'World'
c = a
print(id(a)) # output: 140421778306128
print(id(b)) # output: 140421778305728
print(id(c)) # output: 140421778306128
The example above shows that the "id()" function returns unique integer values for each object. When object "a" is assigned to object "c", both objects have the same identity, and thus, the same integer value.
You can also use the "id()" function to check if objects refer to the same memory location. If two objects have the same "id" value, then they are the same object.
According to the example below:
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Python treats mutable and immutable objects very differently, as the name suggests. Mutable objects are the ones that can be changed, while immutable objects are the ones that cannot be changed. Python assigns a fixed memory space to each object, and the contents of an immutable object cannot be altered once created. In contrast, the contents of a mutable object can change during program execution.
In Python, all the primitive data types, such as integers, strings, and floats, are immutable, whereas non-primitive data types, such as lists, sets, and dictionaries, are mutable. Generally when we create a value corresponding to a variable Python creates an object in memory that corresponds to that value. For immutable objects, the value is fixed once the object is created. For instance, if we assign a value of 5 to the variable 'a' Python will produce an integer object with a value of 5 and allocate memory for it. This object will always hold the value of 5 and cannot be changed. Any modification to the value of 'a' will create a new integer object with the new value, and the old object will be released from memory.
Contrarily, a mutable object can be modified in place without creating a new object. If we define a list named 'b' with the elements [1,2,3], we can modify its values by assigning new values to its elements. For example, we can change the second element of 'b' from '2' to '4' by writing b[1] = 4. This operation modifies the existing list object in memory instead of creating a new one. Therefore, any changes made through variable 'b' will be reflected in the original object in memory.
Another instance of how Python treats mutable and immutable objects differently is when they are used as function arguments. When we pass a mutable object as a function argument in Python, any changes made to the object within the function are reflected outside the function. In contrast, when we pass an immutable object as a function argument in Python, changes made to the object do not affect the original object outside the function. This behavior is because the immutable object creates a copy of itself before passing it to the function, while mutable objects pass by reference.
Despite their different treatment in Python, both mutable and immutable objects have their uses. Immutable objects are generally considered safe and predictable as they offer more control over the data flow in a program. On the contrary, mutable objects provide more flexibility in data manipulation and are preferred when data frequently changes, such as in a data structure like a list.
How argument are passed to functions, and what does that imply for mutable object?
In computer programming, functions are blocks of code that perform a specific task. In order for these functions to perform their intended task, they require input arguments that provide them with the necessary information needed for execution.
When we pass argument to function, it is typically evaluated and then stored in a variable that can be manipulated or accessed within the function. The value of an argument can be of any data type, including integers, strings, booleans, or even other objects.
One key aspect of arguments in programming is that they can either be passed by reference or passed by value. When arguments are passed by value, this means that a copy of the data is created and passed to the function. Any changes made to the argument within the process do not affect the original value of the view outside of the function.
On the other hand, when arguments are passed by reference, the function can modify the original data directly. This is because the function is provided with a respect or pointer to the original data rather than a copy of it. Any changes made to the data within the function will be reflected in the original data outside the function.
In addition to this, another important concept related to arguments is that of mutability. In programming, objects can be mutable or immutable, depending on whether or not their values can be changed after they have been created.
When an argument is passed to a mutable object, such as a list or dictionary, this means that the function can modify the data within the thing directly. Any changes made to the object within the function will be reflected in the original object outside of the function. For example, if a function modifies a list that was passed as an argument, the original list that was passed to the function will also be modified.
In contrast, when an argument is passed to an immutable object like a string or tuple, the function cannot directly modify the data within the object. Any attempted changes will result in a new object being created rather than modifying the original value of the argument.
Tuples and frozen sets can contain mutable object while they are mutable object.
Tuples and Frozen Sets are immutable in nature because their contents cannot be modified once they have been created. However, this does not mean that the contents of these data types cannot be mutable. The content of a tuple or frozen set can be mutable, the tuple or set itself is still immutable. This means that the size and order of the tuple or set cannot be changed.
In Python, tuples are lists of values that are separated by commas enclosed in parentheses. Tuples are used to group together related data, such as a person's name and age or coordinates. Tuples are faster than lists and take up less memory, which makes them a great option when dealing with large amounts of data.
On the other hand, Frozen Sets are similar to sets, but unlike normal sets, they are immutable. This means that set is created, its length and elements cannot be ch,anged. A frozen set is created by calling the frozenset() function, which takes an iterable argument, such as a list or a tuple.
One of the major benefits of tuples and frozen sets is that they are hashable, which means that they can be used as keys in a dictionary. This is because they are both immutable, and their content cannot be changed, which makes them perfect for use as keys in a dictionary.
However, one thing to keep in mind is that if a tuple or frozen set contains mutable objects, then those objects can be modified, even though the tuple or frozen set itself is immutable. For example, if we have a tuple containing a list, we can modify the contents of the list even though we cannot modify the contents of the tuple. This is because the list is mutable and can be changed, but the tuple itself is immutable, so we cannot change its contents.
How arguments passed to functions, and how does it imply to immutable objects?
Regardless of the method used to pass arguments, many programming languages treat these arguments as immutable. This means that if a function receives a variable as an argument, it cannot modify it. The function can calculate the value but must return a new value or result calculated from the original variable.
This approach to function arguments follows the basic principles of functional programming, emphasizing separating concerns and minimizing unexpected side effects. Treating function arguments as immutable reduces the scope for unforeseen changes and bugs in a program. It also enhances the reusability of the code as the function can be called with a wide range of input values without modifying the original data
what is difference between assignment and referencing?
python as it is object oriented programming allows programmers to create new variables, perform calculations, and manipulate data, among other things. When working with Python, programmers often encounter two concepts - assignment and referencing.
Assignment is a process in which a value or object is stored in a variable name. For instance, we can assign a number to the variable "x" by writing "x=5". This means that the interpreter will create a memory space for the value 5 and label it with the name "x". Once assigned, we can use the variable "x" to perform various operations such as computations, comparisons or print statements without knowing the value of "x" beforehand. So, in essence, assignment in Python is a way to give a name to a value.
Referencing on the other hand, involves creating a second name that refers to the same value in memory that was created during the assignment. To create a reference to an existing value, we use the "=" sign followed by the name of the variable already assigned. For instance, if we have already assigned the value 5 to "x", we can create a reference to it by writing "y = x". This means that "y" now refers to the same memory space as "x". It is important to note that referencing does not create a new object, but rather creates an alias that can be used to access the object.
So the main difference between assignment and referencing is that assignment creates a new object and assigns it a name, while referencing creates a new name for an existing object. In other words, when we assign a value to a variable, we are giving it an identity, whereas referencing creates an alias for an existing identity.
Integer pre-allocation in python.:exploring the first 265 integers when cpython created
When writing computer programs, optimizing code for efficiency is a vital consideration. One such optimization technique is pre-allocating variables to reduce overhead in the allocation of resources.
In Python, integers are objects that occupy a certain amount of memory. In CPython, the most widely used implementation of Python, the first 262 integers are pre-allocated for optimization purposes. This means that whenever an integer within this range is instantiated, it will simply reference an already pre-allocated object in memory rather than requiring additional memory allocation.
What is NSMALLPOSINTS and NSMALLNEGINTS, and how are they used?
NSMALLPOSINTS and NSMALLNEGINTS are two Python macros used to define the number of small positive and negative integers used in a Python program. These macros are critical in optimizing the performance of Python programs, mainly when dealing with lists or arrays.
In Python, integers are objects that occupy a certain amount of memory depending on their size. Small integers, which are integers between -5 and 256, are stored in a dedicated memory area to improve performance. This is because these small integers are commonly used as array indices, loop counters, and other frequently used small integer values.
To further optimize the performance of Python programs, NSMALLPOSINTS and NSMALLNEGINTS macros can be used. The NSMALLPOSINTS macro defines the number of small positive integers that will be allocated in the dedicated memory area, while the NSMALLNEGINTS macro defines the number of small negative integers that will be allocated in the same area.
By default, Python sets these values to 257 for NSMALLPOSINTS and -5 for NSMALLNEGINTS, which means that the first 256 small positive integers and the first 5 small negative integers are stored in the dedicated memory area. However, if a Python program is designed to use more than 256 small positive integers or more than 5 small negative integers, it is necessary to increase the values of these macros to avoid creating unnecessary Python objects and improve program performance.
To increase the values of these macros, they can be set in the Python interpreter using the sys. setallocatedvars() method. For example, the following code increases the number of small positive integers to 500 and small negative integers to 50:
import sys
sys.setallocatedvars(NSMALLPOSINTS=500, NSMALLNEGINTS=50)
It is essential to note that setting these macros to excessively high values can lead to excessive memory usage and reduced performance. Therefore, it is advisable to set these macros to appropriate values for the specific Python program and avoid unnecessary object creation.
Mechanism of aliases.
Alliase is a mechanism used in programming languages to define aliases or shortcuts for complex identifiers. Python is a high-level, dynamic programming language that provides developers a rich set of features. The alliase feature in Python serves no exception in that regard, as it allows developers to define shortcuts for importing or referencing modules, classes, functions, or variables.
Let's begin by understanding what alliase means. In Python, an alliase is a name defined by the developer that is bound to another name, in the same or another namespace, for ease of use or code readability. It helps save typing and provides a consistent naming convention. The alliase feature in Python is essential when using frequently or lengthy variable or module names.
To create an alliase in Python, one must use the "as" keyword. This keyword allows renaming a module, class, function, or variable. For example, to create an alliase for a module, one can use the "import as" syntax. Here's an example:
```
Import pandas as pd
```
In this example, we are importing the pandas module and creating an alliase named "pd." It allows us to use the functions or methods of the pandas module by simply typing "pd" rather than "pandas" in our code.
Similarly, we can use the alliase feature for class names. Here's a code example:
```
from tkinter import Tk as tk
```
In this example, we import the Tk class from the tkinter module and create an alliase named "tk." It allows us to create an instance of the Tk class by simply typing "tk()" rather than "Tk()" in our code.
The alliase feature is also prevalent in function and variable names. Here's an example:
```
from math, import pi as PI
radius = 5
area_of_circle = PI * radius ** 2
```
In this example, we are creating an alliase named "PI" for the constant pi from the math module. We are then using the "PI" alliase to calculate the area of a circle with a radius of 5.
In conclusion, the alliase feature in Python is a powerful tool that helps developers improve code readability and reduce typing. It enables creating shortcuts for long or complicated modules, classes, functions, or variable names. Understanding the mechanics of alliase in Python is crucial for writing clean and efficient code. As developers become proficient with Python, they will undoubtedly use this powerful feature in their projects.
Memory schema in python
Memory schema in Python is a valuable tool for organizing and retrieving information from long-term memory. A blueprint or framework structures our memories into meaningful and coherent patterns.
OOP is a prominent example of memory schema. OOP is a programming methodology that models real-world objects and their interactions. It represents objects as instances of classes that encapsulate their state and behavior. In memory schema terms, OOP provides a mental model for organizing information about things based on their properties, methods, and relationships.
For instance, consider a Python class that represents a car. The class can have attributes such as make, model, year, and color and methods like accelerate and brake. By storing this information in memory schema, we can easily recall the properties and behavior of a car object and manipulate it accordingly. We can also use inheritance to create sub-classes that inherit the attributes and behavior of the parent class.
Another example of memory schema in Python is data structures such as lists and dictionaries. Lists are collections of elements that are ordered and mutable, while dictionaries are collections of key-value pairs that are unordered and mutable. These data structures provide a mental model for organizing and manipulating data based on their properties and relationships.
For instance, consider a list of numbers. We can use memory schema to remember the position and value of each element in the list and the operations we can perform on them, such as sorting, appending, and slicing. Similarly, we can use memory schema to remember the keys and values in a dictionary and how to access or modify them using the corresponding key.
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