Python Tutorial

Introduction Python Features Python Applications Python System requirements Python Installation Python Examples Python Basics Python Indentation Python Variables Python Data Types Python IDE Python Keywords Python Operators Python Comments Python Pass Statement

Python Conditional Statements

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Python Loops

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Python Arrays

Python Array Python Matrix

Python Strings

Python Strings Python Regex

Python Built-in Data Structure

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Python Functions

Python Function Python min() function Python max() function Python User-define Functions Python Built-in Functions Python Recursion Anonymous/Lambda Function in Python apply() function in python Python lambda() Function

Python File Handling

Python File Handling Python Read CSV Python Write CSV Python Read Excel Python Write Excel Python Read Text File Python Write Text File Read JSON File in Python

Python Exception Handling

Python Exception Handling Python Errors and exceptions Python Assert

Python OOPs Concept

OOPs Concepts in Python Classes & Objects in Python Inheritance in Python Polymorphism in Python Python Encapsulation Python Constructor Python Super function Python Static Method Static Variables in Python Abstraction in Python

Python Iterators

Iterators in Python Yield Statement In Python Python Yield vs Return

Python Generators

Python Generator

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Python Decorator

Python Functions and Methods

Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods

Python Modules

Python Modules Python Datetime Module Python Math Module Python Import Module Python Time ModulePython Random Module Python Calendar Module CSV Module in Python Python Subprocess Module

Python MySQL

Python MySQL Python MySQL Client Update Operation Delete Operation Database Connection Creating new Database using Python MySQL Creating Tables Performing Transactions

Python MongoDB

Python MongoDB

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Python Data Structure Implementation

Python Stack Python Queue Python Linked List Python Hash Table Python Graph

Python Advance Topics

Speech Recognition in Python Face Recognition in Python Python Linear regression Python Rest API Python Command Line Arguments Python JSON Python Subprocess Python Virtual Environment Type Casting in Python Python Collections Python Attributes Python Commands Python Data Visualization Python Debugger Python DefaultDict Python Enumerate

Python 2

What is Python 2

Python 3

Anaconda in Python 3 Anaconda python 3 installation for windows 10 List Comprehension in Python3

How to

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Sorting

Python Sort List Sort Dictionary in Python Python sort() function Python Bubble Sort

Programs

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Questions

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Differences

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Python Kivy

Kivy Architecture Kivy Buttons Kivy Layouts Kivy Widgets What is Kivy in Python

Python Tkinter

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Python PyQt5

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Misc

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_dict_ in Python

An unordered collection of data values known as a dictionary can be used in Python to store data values similar to a map. Dictionaries can also store a key: value pair, in contrast to other data types, which can only store a single value for each element. An option to increase the dictionary's efficiency is Key-Value.

Regarding attribute access, most users only have experience with the dot "." (as in x.any attribute).

Attribute access, very simply, is the process of acquiring an object that is associated with the one you currently possess. Without getting too deep into the specifics, it might appear to someone who uses Python to be extremely straightforward. For this very straightforward operation, there are numerous activities taking place in the background.

Introduction to  _dict _ :

Each module has a unique attribute called dict that holds the module's symbol table. An item's (writable) attributes are kept in a dictionary or another mapping object.

Every Python object contains a property denoted by the symbol __dict__. Additionally, this object possesses all of the attributes that have been listed for it. __dict__ can also be referred to as a mapping proxy object. By giving a class object the __dict__ property, we can use the dictionary.

Syntax:

object.__dict__  

Code 1:

# class 'vegetables' is defined here
class vegetables:  
    def __init__(self): # constructor  here
    # keys initialized here
        self.Carrot = 'Orange'  
        self.Ladyfinger = 'Green'  
        self.Tomato = 'Red'  
    def Show(self):  
        print("The Dictionary from object fields belongs to the class vegetables :")
        # object vegetable of class vegetables here
vegetable= vegetables()  
# calling Show function  
vegetable.Show()  
# calling the attribute __dict__ on vegetables 
# making it print it  
print(vegetable.__dict__)

Output:

_dict_ Python

Code 2:

class Grade1_Class:  
    def __init__(self,Name,age):  
        self.Name = Name  
        self.age = age  
    def feature(self):  
        if self.age == "7":  
            return True  
        else:  
            return False  
Object1 = Grade1_Class('Anika','7')  
print(Object1.__dict__)

Output:

_dict_ Python

Using Python's Dictionary Without Using __dict__ :

To Create a Dictionary:

Python allows you to create dictionaries from lists of entries by enclosing them in curly brackets and separating them with commas. Dictionary pairs consisting of a key and a key:value pair are saved as pairs, each of which is stored separately. Values in dictionaries can be any data and duplicated, unlike keys, which cannot be repeated and must be immutable.

 In curly brackets, the structure is enclosed, the elements are separated by commas, and each key and value are separated by colons (:). A dictionary without any elements is possible.

The dictionary's contents can be of any type, but its keys must be immutable, such as integers, tuples, or strings.

Code 3:

# Creating a Dictionary here
dictionary = {1: 'I', 2: 'Love', 3: 'Myself'}
print("\nCreating a Dictionary by using Integer Keys : ")
print(dictionary)  
# Creating a Dictionary
# various Mixed keys   here
dic1= {'Address': 'Rajasthan', 7: [1, 2, 3, 4]}  
print("\nCreating a Dictionary by using Mixed Keys : ")  
print(dic1)

Output:

_dict_ Python

Note: The built-in method dict() can also be used to create dictionaries. Remember a dictionary will be empty if two curly braces are simply combined.

Code 4:

# Creating - an empty Dictionary  
Edict = {}  
print("An Empty Dictionary: ")  
print(Edict)  
# Creating Dictionary - using the dict() method  
dictionary = dict({1: 'One', 2: 'Two', 3: 'Three'})  
print("\nCreating a Dictionary by using the dict() method : ")  
print(dictionary)  
# Creating a Dictionary - using each item as a different Pair  
dict2 = dict([(1, 'First'), (2, 'Second')])  
print("\nCreating a Dictionary by using each item as a different pair : ")  
print(dict2)


Output:

_dict_ Python

Adding new Elements to it:

A Python dictionary can be expanded with elements using a number of different methods. Combining the key and value results in Dict[Key] = "Value," which may be used to add one value at a time to a dictionary. An existing value in a Dictionary can be changed using the built-in update() function. An existing dictionary can also be expanded using nested key values.

It should be noted that when a value is added if the key-value pair already exists, the value is updated. If not, the dictionary receives a new key and value.

Code 5:

Dict1 = {} # Empty Dictionary  
# Adding only one element at a time  
Dict1[0] = 'Nice'  
Dict1[1] = 'To'  
Dict1[2] = 'Cook'  
print("\nDictionary after the addition of some elements: ")  
print(Dict1)  
# A group of values are added to a certain Key.
Dict1['Grade'] = 7, 8, 9  
print("\nDictionary after the adding a set of values to a key: ")  
print(Dict1)  
# Updating the existing Key's Value  
Dict1[2] = 'Dance'  
print("\nDictionary after Updated key value: ")  
print(Dict1)  
# Adding Nested Key value
Dict1[3] = {'Nested' :{'A' : 'Sing', 'B' : 'Play'}}  
print("\n After Addition of a Nested Key our dictionary looks like: ")  
print(Dict1)

Output:

_dict_ Python

Dictionary Elements Accessing:

A dictionary employs keys, whereas other data structures require indexing to access values. Keys can be used in square brackets [ or with the get() method.

If a key is entered using square brackets [], KeyError is produced if the dictionary does not include the key. In contrast, if the key cannot be found, the get() function returns None.

Code 6:

# Dictionary is created  
dictionary = {1: 'My', '2': 'Name', 3: 'is', 4: 'Ruzela'}  
# using key - # accessing an element 
print("Accessing an element using the key:")  
print(dictionary['2'])  
print("Accessing some other element using the key:")  
print(dictionary[1])  
#  using the get() method - accessing an element 
print("Accessing an using the get() method:")  
print(dictionary.get(4))  
print("Accessing another using the get() method:")  
print(dictionary.get(3))

Output:

_dict_ Python

To obtain the value of a key that already exists in the nested dictionary, we can use the indexing [] technique.

Code 7:

# Dictionary created 
Dictionary = {'dict1': {1: 'Apple'},  
        'dict2': {2: 'Orange'}}  
# Accessing the elements using the key  
print(Dictionary['dict1'])  
print(Dictionary['dict1'][1])  
print(Dictionary['dict2'][2])

Output:

_dict_ Python

Differentiating between a List and a Dictionary

Lists and dictionaries are two examples of data structures that are fundamentally different. A list can contain an ordered sequence of elements such that we can index into it or iterate over it. Lists can be altered after they have been created since they are a changeable type. The Python dictionary implements hash tables and key-value storage. It doesn't require any particular order and calls for hashable keys. Additionally, key lookups are completed quickly.

The Following Characteristics are Found in a List's Elements:

  • Unless otherwise specified, they remain in the existing order (for instance, by sorting the list).
  • They could be of any kind or even a mix of several kinds.

We can access them by using numerical (zero-based) indexes.

The Following are the Features of Dictionary Elements:

  • There are values and keys for each entry.
  • There is no warrant for orders.
  • To access elements, key values are needed.
  • Key values may be of any hashtable type (other than a dict), and types may be concatenated.
  • Any value type is acceptable, including other dicts, and types can be merged.

Note: When we have a collection of unique keys and values, we use a dictionary; however, when we have an ordered collection of items, we use a list.

Code 8: Code Containing Some Build-In Dictionary Methods:

# Dictionary created
dict1={1:"My",2:"Name",3:"is",4:"Ruzela"}  
dict2=dict1.copy()  #copy method  
print(dict2) 
dict1.clear()  #clear method  
print(dict1) 
print(dict2.items())  #items method
print(dict2.keys()) #keys method 
print(dict2.get(1)) #get method  
dict2.pop(4)  #pop method 
print(dict2)  
dict2.popitem() #popitem method  
print(dict2)  
print(dict2.values())  #values method 
dict2.update({4:"Rupsha"})  #update method 
print(dict2)

Output:

_dict_ Python

Conclusion:

  • Dictionaries are a type of data structure used in computer languages to store information that is somehow connected.
  • Each module has a special attribute called __dict__.
  • The symbol table for the module is located in __dict__.
  • A mapping object contains the attributes of an element.
  • In Python, each object has a property denoted by the symbol __dict__.
  • The term "mapping proxy object" is another name for the object __dict__.
  • Keys and Values are the two terms for a Python dictionary's two parts.
  • You might not receive your data back in the exact order you supplied because dictionaries don't maintain their data in any particular order.
  • Keys will only have one component.
  • Values may take the form of integers, lists, lists within lists, etc.
  • The dictionary's keys must be immutable, such as integers, tuples, or strings, but its contents can be of any type. Duplication of keys is not permitted; each key may only be used for one entry.
  • Since dictionary keys are case-sensitive, the same key name spelt differently in Python dictionaries is viewed as a separate key.