50 Best Practices in Python

 

1. Write Readable Code: Use descriptive variable names and write comments where necessary.
2. Follow PEP 8: Adhere to Python's official style guide for formatting your code.
3. Use Virtual Environments: Isolate project dependencies using virtualenv or venv.
4. Keep Code DRY: Avoid duplication by creating reusable functions and modules.
5. Write Modular Code: Break your code into modules and packages.
6. Use List Comprehensions: For simple loops, prefer list comprehensions for readability and performance.
7. Handle Exceptions: Use try-except blocks to handle exceptions gracefully.
8. Use Context Managers: For resource management, use context managers (with statements).
9. Test Your Code: Write unit tests to ensure your code works as expected.
10. Leverage Built-in Functions: Python has a rich set of built-in functions; use them to simplify your code.
11. Optimize Imports: Import only what you need and organize imports logically.
12. Document Your Code: Write docstrings for modules, classes, and functions.
13. Use Meaningful Docstrings: Provide useful information in docstrings, including parameters, return values, and examples.
14. Adopt Version Control: Use git or another version control system to manage your code changes.
15. Automate Testing: Use CI/CD tools to automate your testing and deployment.
16. Use Type Hints: Add type hints to your function signatures to make your code more readable and maintainable.
17. Avoid Global Variables: Limit the use of global variables to reduce complexity.
18. Keep Functions Small: Write small, single-purpose functions.
19. Optimize Performance: Profile your code to find bottlenecks and optimize them.
20. Stay Updated: Keep your Python and library versions up to date.
21. Use Pythonic Idioms: Write code that takes advantage of Python’s features, such as tuple unpacking and the else clause in loops.
22. Practice Code Reviews: Regularly review code with peers to catch issues early and share knowledge.
23. Avoid Mutable Default Arguments: Default argument values should be immutable to avoid unexpected behavior.
24. Use Logging: Instead of print statements, use the logging module for better control over log output.
25. Be Careful with Floating Point Arithmetic: Understand the limitations and potential inaccuracies.
26. Leverage Generators: Use generators to handle large datasets efficiently.
27. Understand Variable Scope: Be aware of local and global scope and use variables appropriately.
28. Use Proper Indentation: Follow Python’s strict indentation rules to avoid syntax errors.
29. Encapsulate Data: Use classes and objects to encapsulate data and functionality.
30. Implement str and repr: Provide meaningful string representations for your classes.
31. Avoid Premature Optimization: Focus on readability and maintainability first; optimize when necessary.
32. Understand the GIL: Be aware of the Global Interpreter Lock and its impact on multithreading.
33. Use Efficient Data Structures: Choose the right data structure for the task (e.g., lists, sets, dictionaries).
34. Avoid Deep Nesting: Keep your code flat and avoid deep nesting of loops and conditionals.
35. Adopt a Consistent Naming Convention: Follow naming conventions for variables, functions, classes, and modules.
36. Use Enum for Constants: Use the Enum class to define constants.
37. Prefer f-Strings: Use f-strings for string formatting in Python 3.6+.
38. Leverage Dataclasses: Use dataclasses for simple data structures (Python 3.7+).
39. Handle Resources Properly: Ensure files and other resources are closed properly using with statements.
40. Understand List vs. Tuple: Use lists for mutable sequences and tuples for immutable sequences.
41. Use Decorators Wisely: Understand and use decorators to extend the behavior of functions and methods.
42. Optimize Memory Usage: Be mindful of memory usage, especially in large applications.
43. Adopt a Code Formatter: Use tools like Black to format your code automatically.
44. Use Static Analysis Tools: Employ tools like pylint, flake8, and mypy to catch potential issues early.
45. Understand Slicing: Use slicing effectively for lists, tuples, and strings.
46. Avoid Anti-patterns: Recognize and avoid common anti-patterns in Python programming.
47. Keep Learning: Continuously learn and stay updated with the latest Python features and libraries.
48. Contribute to Open Source: Contributing to open-source projects helps improve your skills and gives back to the community.
49. Write Secure Code: Be aware of security best practices and write code that minimizes vulnerabilities.
50. Refactor Regularly: Regularly refactor your code to improve its structure and readability.

Read More

Python Coding challenge - Day 212 | What is the output of the following Python Code?

 

let's break down and explain each part of this code:

my_dict = {1: 0, 0: [], True: False}

result = all(my_dict)

print(result)

Step-by-Step Explanation

Dictionary Creation:

my_dict = {1: 0, 0: [], True: False}

This line creates a dictionary my_dict with the following key-value pairs:

1: 0 - The key is 1 and the value is 0.

0: [] - The key is 0 (or (0) in another form) and the value is an empty list [].

True: False - The key is True and the value is False.

Note that in Python dictionaries, keys must be unique. If you try to define multiple key-value pairs with the same key, the last assignment will overwrite any previous ones. However, in this dictionary, the keys are unique even though 1 and True can be considered equivalent (True is essentially 1 in a boolean context).

Using the all Function:

result = all(my_dict)

The all function in Python checks if all elements in an iterable are True. When all is applied to a dictionary, it checks the truthiness of the dictionary's keys (not the values).

In this dictionary, the keys are 1, 0, and True.

The truthiness of the keys is evaluated as follows:

1 is True.

0 is False.

True is True.

Since one of the keys (0) is False, the all function will return False.

Printing the Result:

print(result)

This line prints the result of the all function. Given that the dictionary contains a False key (0), the output will be False.

Summary

Putting it all together, the code creates a dictionary and uses the all function to check if all the keys are true. Since one of the keys is 0 (which is False), the all function returns False, which is then printed.

So, when you run this code, the output will be:

False

Read More

Pdf To Audio using Python

 

# Importing necessary libraries

import PyPDF2

import pyttsx3

# Prompt user for the PDF file name

pdf_filename = input("Enter the PDF file name (including extension): ").strip()

# Open the PDF file

try:

    with open(pdf_filename, 'rb') as pdf_file:

        # Create a PdfFileReader object

        pdf_reader = PyPDF2.PdfReader(pdf_file)

        

        # Get an engine instance for the speech synthesis

        speak = pyttsx3.init()        

        # Iterate through each page and read the text

        for page_num in range(len(pdf_reader.pages)):

            page = pdf_reader.pages[page_num]

            text = page.extract_text()

            if text:

                speak.say(text)

                speak.runAndWait()       

        # Stop the speech engine

        speak.stop()      

        print("Audiobook creation completed.")

except FileNotFoundError:

    print("The specified file was not found.")

except Exception as e:

    print(f"An error occurred: {e}")

#clcoding.com

Explanation:

This script segment accomplishes several tasks:

Importing Necessary Libraries:

PyPDF2: This library is used to work with PDF files, allowing us to read the content of PDF documents.

pyttsx3: This library is used for text-to-speech conversion, enabling us to convert the text extracted from the PDF into spoken words.

Prompting User for PDF File Name:

The input() function is used to prompt the user to enter the name of the PDF file, including its extension (e.g., example.pdf).

The entered file name is stored in the variable pdf_filename.

Opening the PDF File:

The open() function is used to open the PDF file specified by the user. The file is opened in binary read mode ('rb').

This operation is wrapped in a try block to handle possible exceptions, such as the file not being found (FileNotFoundError) or other unexpected errors (Exception).

Reading PDF Content and Converting to Speech:

If the PDF file is successfully opened, a PdfReader object named pdf_reader is created using PyPDF2. This object is used to read the content of the PDF document.

An instance of the text-to-speech engine (speak) is initialized using pyttsx3.init().

The script iterates through each page of the PDF using a for loop and the range(len(pdf_reader.pages)) construct. For each page:

The text content is extracted from the page using page.extract_text().

If the extracted text is not empty, it is passed to the text-to-speech engine to be spoken aloud using speak.say(text) and speak.runAndWait().

After processing all pages, the text-to-speech engine is stopped using speak.stop().

Error Handling:

If the specified PDF file is not found (FileNotFoundError), the script prints a message indicating that the file was not found.

If any other unexpected error occurs during the execution of the script (Exception), the error message is printed.

Completion Message:

If the script executes successfully without encountering any errors, a message indicating the completion of audiobook creation is printed.

Overall, this script segment enables the user to specify a PDF file, reads its content, converts the text to speech, and creates an audiobook from the PDF content.

Read More

Python Coding challenge - Day 211 | What is the output of the following Python Code?

 

Code:

explain c = '13\t14' 

print(c.index('\t'))

Solution and Explanation:

Let's break down the code c = '13\t14' and print(c.index('\t')) to understand what it does:

c = '13\t14'

Here, we are assigning a string to the variable c.

The string '13\t14' contains the characters 1, 3, a tab character (\t), 1, and 4.

The \t is an escape sequence that represents a tab character.

print(c.index('\t'))

The index method is called on the string c.

c.index('\t') searches the string c for the first occurrence of the tab character (\t).

The index method returns the index (position) of the first occurrence of the specified value.

If the specified value is not found, it raises a ValueError.

Let's put it all together:

The string c is '13\t14'. Visually, it can be represented as:

13<TAB>14

where <TAB> is a single tab character.

When we call c.index('\t'), we are looking for the index of the tab character in the string c.

In the string '13\t14', the tab character is located at index 2 (considering zero-based indexing):

'1' is at index 0

'3' is at index 1

'\t' (tab) is at index 2

'1' is at index 3

'4' is at index 4

Therefore, the statement print(c.index('\t')) will output 2 because the tab character is found at index 2 in the string c.

Read More

Box and Whisker plot using Python Libraries

Step 1: Install Necessary Libraries

First, make sure you have matplotlib and seaborn installed. You can install them using pip:

pip install matplotlib seaborn

#clcoding.com

Step 2: Import Libraries

Next, import the necessary libraries in your Python script or notebook.

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

Step 3: Create Sample Data

Create some sample data to plot. This can be any dataset you have, but for demonstration purposes, we will create a simple dataset using NumPy.

# Generate sample data

np.random.seed(10)

data = [np.random.normal(0, std, 100) for std in range(1, 5)]

Step 4: Create the Box and Whisker Plot

Using matplotlib and seaborn, you can create a basic Box and Whisker plot.

# Create a boxplot

plt.figure(figsize=(10, 6))

plt.boxplot(data, patch_artist=True)

# Add title and labels

plt.title('Box and Whisker Plot')

plt.xlabel('Category')

plt.ylabel('Values')

# Show plot

plt.show()

Step 5: Enhance the Plot with Seaborn

For more advanced styling, you can use seaborn, which provides more aesthetic options.

# Set the style of the visualization

sns.set(style="whitegrid")

# Create a boxplot with seaborn

plt.figure(figsize=(10, 6))

sns.boxplot(data=data)

# Add title and labels

plt.title('Box and Whisker Plot')

plt.xlabel('Category')

plt.ylabel('Values')

# Show plot

plt.show()

Read More

Python Coding challenge - Day 210 | What is the output of the following Python Code?

 

Code:

lst = ['P', 20, 'Q', 4.50]

item = iter(lst)

print(next(item))

Solution and Explanation: 

 Let's break down the code and explain each part in detail:

lst = ['P', 20, 'Q', 4.50]

item = iter(lst)

print(next(item))

Explanation

Creating the List:

lst = ['P', 20, 'Q', 4.50]

This line defines a list named lst with four elements:

A string 'P'

An integer 20

Another string 'Q'

A float 4.50

Creating an Iterator:

item = iter(lst)

The iter() function is called with the list lst as an argument.

This converts the list into an iterator object. An iterator is an object that allows you to traverse through all the elements of a collection (in this case, the list lst).

The iterator item is now ready to iterate over the elements of the list one by one.

Accessing the First Element Using next():

print(next(item))

The next() function is called on the iterator item.

The next() function retrieves the next element from the iterator. Since this is the first call to next(item), it returns the first element of the list lst, which is 'P'.

After retrieving the element, the iterator advances to the next element in the list.

The print() function then prints this retrieved element ('P') to the console.

Output

When you run the code, the output will be: P

This is because 'P' is the first element in the list lst. The next(item) function call retrieves and returns this first element, which is then printed by the print() function.

Read More

Python List Comprehensions

List comprehensions in Python provide a powerful and concise way to create lists. They are an essential part of any Python programmer's toolkit. Ready to make your code more Pythonic? Let's dive in!

What is a List Comprehension?

A list comprehension is a compact way to process all or part of the elements in a sequence and return a list with the results. The syntax is simple yet powerful:

[expression for item in iterable if condition]

Basic Example

Let's start with a basic example. Suppose we want a list of squares for numbers from 0 to 9. With list comprehensions, it's a one-liner:

squares = [x**2 for x in range(10)]

print(squares)

Output:

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Adding a Condition

What if we want only even squares? Just add an if condition at the end:

even_squares = [x**2 for x in range(10) if x % 2 == 0]

print(even_squares)

Output:

[0, 4, 16, 36, 64]

Nested Comprehensions

You can also nest comprehensions for multidimensional lists. Here’s an example to flatten a 2D list:

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

flattened = [num for row in matrix for num in row]

print(flattened)

Output:

[1, 2, 3, 4, 5, 6, 7, 8, 9]

Dictionary Comprehensions

List comprehensions aren’t just for lists. You can create dictionaries too!

squares_dict = {x: x**2 for x in range(10)}

print(squares_dict)

Output:

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}

Set Comprehensions

And even sets! This creates a set of unique squares:

unique_squares = {x**2 for x in range(10)}

print(unique_squares)

Output:

{0, 1, 4, 9, 16, 25, 36, 49, 64, 81}

Advanced Use - Function Application

You can apply functions within comprehensions. Here’s an example using str.upper():

words = ["hello", "world", "python"]

upper_words = [word.upper() for word in words]

print(upper_words)

Output:

['HELLO', 'WORLD', 'PYTHON']

Comprehensions with Multiple Conditions

You can add multiple conditions. Let’s filter numbers divisible by 2 and 3:

filtered = [x for x in range(20) if x % 2 == 0 if x % 3 == 0]

print(filtered)

Output:

[0, 6, 12, 18]

In Conclusion

List comprehensions make your code cleaner and more readable. They’re efficient and Pythonic. Practice and integrate them into your projects to see the magic!

Read More

Introduction to Modern Statistics free pdf

 

Introduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches. Other highlights include:

Web native book. The online book is available in HTML, which offers easy navigation and searchability in the browser. The book is built with the bookdown package and the source code to reproduce the book can be found on GitHub. Along with the bookdown site, this book is also available as a PDF and in paperback.

Tutorials. While the main text of the book is agnostic to statistical software and computing language, each part features 4-8 interactive R tutorials (for a total of 32 tutorials) that walk you through the implementation of the part content in R with the tidyverse for data wrangling and visualisation and the tidyverse-friendly infer package for inference. The self-paced and interactive R tutorials were developed using the learnr R package, and only an internet browser is needed to complete them.

Labs. Each part also features 1-2 R based labs. The labs consist of data analysis case studies and they also make heavy use of the tidyverse and infer packages.

Datasets. Datasets used in the book are marked with a link to where you can find the raw data. The majority of these point to the openintro package. You can install the openintro package from CRAN or get the development version on GitHub.

Hard Copy: Introduction to Modern Statistics

Free PDF: Introduction to Modern Statistics

Read More

Python Coding challenge - Day 209 | What is the output of the following Python Code?

 

Code:

h = [5, 6, 7, 8]

h.pop()

h.pop(0)

print(h)

Solution and Explanation: 

Let's break down the given Python code and explain what each line does:

h = [5, 6, 7, 8]

This line creates a list h with the elements 5, 6, 7, and 8.

h = [5, 6, 7, 8]

h.pop()

The pop() method removes and returns the last item from the list. Since no argument is provided, it removes the last element, which is 8.

Before h.pop():

h = [5, 6, 7, 8]

After h.pop():

h = [5, 6, 7]

h.pop(0)

The pop(0) method removes and returns the item at index 0 from the list. This means it removes the first element, which is 5.

Before h.pop(0):

h = [5, 6, 7]

After h.pop(0):

h = [6, 7]

print(h)

This line prints the current state of the list h to the console.

print(h)

The output will be:

[6, 7]

Summary

Initially, the list h is [5, 6, 7, 8].

The first h.pop() removes the last element, resulting in [5, 6, 7].

The second h.pop(0) removes the first element, resulting in [6, 7].

Finally, print(h) outputs [6, 7].

So the final state of the list h after these operations is [6, 7].

Read More

Common Python Errors and How to Fix Them

 

ZeroDivisionError: division by zero

This happens when you try to divide a number by zero. Always check the divisor before dividing.

# Correct way

result = 10 / 2

# Incorrect way

result = 10 / 0  # ZeroDivisionError

#clcoding.com 

IndentationError: unexpected indent

Python uses indentation to define code blocks. This error occurs when there’s a misalignment in the indentation.

# Correct way

if True:

    print("Hello")

# Incorrect way

if True:

  print("Hello")  # IndentationError

#clcoding.com 

ImportError: No module named 'module'

This error means Python can’t find the module you’re trying to import. Check if the module is installed and the name is correct.

# Install the module first

# pip install requests

import requests  # Correct way

import non_existent_module  # ImportError

#clcoding.com 

ValueError: invalid literal for int() with base 10: 'text'

This occurs when you try to convert a string that doesn’t represent a number to an integer. Ensure the string is numeric.

# Correct way

number = int("123")

# Incorrect way

number = int("abc")  # ValueError

#clcoding.com 

AttributeError: 'object' has no attribute 'attribute'

This happens when you try to use an attribute or method that doesn’t exist for an object. Ensure you are calling the correct method or attribute.

class MyClass:

    def __init__(self):

        self.value = 10

obj = MyClass()

# Correct way

print(obj.value)

# Incorrect way

print(obj.price)  # AttributeError

#clcoding.com 

KeyError: 'key'

This error occurs when you try to access a dictionary key that doesn’t exist. Use .get() method or check if the key exists.

my_dict = {"name": "Alice"}

# Correct way

print(my_dict.get("age", "Not Found"))

# Incorrect way

print(my_dict["age"])  # KeyError

#clcoding.com 

IndexError: list index out of range

This occurs when you try to access an index that doesn't exist in a list. Always check the list length before accessing an index.

my_list = [1, 2, 3]

# Correct way

print(my_list[2])  # Prints 3

# Incorrect way

print(my_list[5])  # IndexError

#clcoding.com 

TypeError: unsupported operand type(s)

This happens when you perform an operation on incompatible types. Check the data types of your variables.

# Correct way

result = 5 + 3

# Incorrect way

result = 5 + "3"  # Can't add integer and string

#clcoding.com 

NameError: name 'variable' is not defined

This occurs when you try to use a variable or function before it's declared. Ensure that all variables and functions are defined before use.

# Correct way

name = "Alice"

print(name)

# Incorrect way

print(name)

name = "Alice"

#clcoding.com 

SyntaxError: invalid syntax

This usually means there's a typo or a mistake in the code structure. Check for missing colons, parentheses, or indentation errors. Example:

print("Hello World")

# Missing parenthesis can cause SyntaxError

print "Hello World"

#clcoding.com 

Read More

Python Coding challenge - Day 208 | What is the output of the following Python Code?

 

Code:

g = [1, 2, 3]

h = [1, 2, 3]

print(g is h)  

print(g == h) 

Solution and Explanation:

In Python, the expressions g = [1, 2, 3] and h = [1, 2, 3] create two separate list objects that contain the same elements. When we use print(g is h) and print(g == h), we are comparing these two lists in different ways.

g is h

The is operator checks for object identity. It returns True if both operands refer to the exact same object in memory.

g = [1, 2, 3]

h = [1, 2, 3]

print(g is h)

In this case, g and h are two different objects that happen to have the same contents. Since they are distinct objects, g is h will return False.

g == h

The == operator checks for value equality. It returns True if the operands have the same value, which for lists means that they have the same elements in the same order.

g = [1, 2, 3]

h = [1, 2, 3]

print(g == h)

Here, g and h have the same elements in the same order, so g == h will return True.

Summary

g is h: Checks if g and h are the same object in memory (identity). Result: False.

g == h: Checks if g and h have the same contents (equality). Result: True.

Thus, the output will be:

False

True

Read More

Python Coding challenge - Day 207 | What is the output of the following Python Code?

 

Code:

str_a = "hello"

str_b = "hello"

print(str_a is str_b)

print(str_a == str_b)

Solution and Explanation:  

In Python, the statements str_a = "hello" and str_b = "hello" both create string objects that contain the same sequence of characters. Let's break down what happens when you use the is and == operators to compare these two strings.

is Operator

The is operator checks for object identity. It returns True if both operands refer to the same object in memory.

== Operator

The == operator checks for value equality. It returns True if the values of the operands are equal, regardless of whether they are the same object in memory.

Now, let's analyze the code:

str_a = "hello"

str_b = "hello"

print(str_a is str_b)  # This checks if str_a and str_b are the same object

print(str_a == str_b)  # This checks if the values of str_a and str_b are equal

str_a is str_b

In Python, small strings and some other immutable objects are sometimes interned for efficiency. String interning means that identical strings may refer to the same object in memory. Because "hello" is a short, commonly used string, Python often interns it. As a result, str_a and str_b will likely refer to the same interned string object. Therefore, str_a is str_b will typically return True because they are the same object in memory.

str_a == str_b

The == operator compares the values of str_a and str_b. Since both strings contain the same sequence of characters "hello", str_a == str_b will return True regardless of whether they are the same object in memory.

Summary

str_a is str_b checks if str_a and str_b refer to the exact same object. In this case, it will likely be True due to string interning.

str_a == str_b checks if str_a and str_b have the same value. This will be True because both strings contain "hello".

So, the output of the code will be:

True

True

This illustrates both object identity and value equality for these string variables in Python.

Read More

Interesting facts about Dictionaries

 Dictionary Methods

Dictionaries come with several handy methods such as setdefault, update, pop, popitem, and clear.

my_dict = {'name': 'Alice', 'age': 25}

# setdefault

my_dict.setdefault('city', 'Unknown')

print(my_dict)  

# update

my_dict.update({'age': 26, 'city': 'New York'})

print(my_dict)  

# pop

age = my_dict.pop('age')

print(age)  # Output: 26

print(my_dict)  

# popitem

item = my_dict.popitem()

print(item) 

print(my_dict)  

# clear

my_dict.clear()

print(my_dict)  

#clcoding.com

{'name': 'Alice', 'age': 25, 'city': 'Unknown'}

{'name': 'Alice', 'age': 26, 'city': 'New York'}

26

{'name': 'Alice', 'city': 'New York'}

('city', 'New York')

{'name': 'Alice'}

{}

Ordered Dictionaries

As of Python 3.7, dictionaries maintain insertion order by default. The OrderedDict from the collections module was used for this purpose in earlier versions.

my_dict = {'first': 1, 'second': 2, 'third': 3}

print(my_dict)  

#clcoding.com

{'first': 1, 'second': 2, 'third': 3}

Merging Dictionaries

Starting with Python 3.9, you can use the | operator to merge dictionaries.

dict1 = {'a': 1, 'b': 2}

dict2 = {'b': 3, 'c': 4}

merged_dict = dict1 | dict2

print(merged_dict)  

#clcoding.com

{'a': 1, 'b': 3, 'c': 4}

Dictionary Views

The keys, values, and items methods return dictionary view objects, which are dynamic and reflect changes in the dictionary.

my_dict = {'name': 'Alice', 'age': 25}

keys_view = my_dict.keys()

print(keys_view)  

my_dict['city'] = 'New York'

print(keys_view) 

#clcoding.com

dict_keys(['name', 'age'])

dict_keys(['name', 'age', 'city'])

Default Values with get and defaultdict

Using the get method, you can provide a default value if the key is not found.

my_dict = {'name': 'Alice'}

print(my_dict.get('age', 'Not Found'))  

Not Found

With defaultdict from the collections module, you can provide default values for missing keys.

from collections import defaultdict

dd = defaultdict(int)

dd['a'] += 1

print(dd)  

#clcoding.com

defaultdict(<class 'int'>, {'a': 1})

Dictionary Comprehensions

Just like list comprehensions, you can create dictionaries using dictionary comprehensions.

squares = {x: x*x for x in range(6)}

print(squares)  

#clcoding.com

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Iterating Through Dictionaries

You can iterate through keys, values, or key-value pairs in a dictionary.

my_dict = {'name': 'Alice', 'age': 25}

for key in my_dict:

    print(key)  

for value in my_dict.values():

    print(value)  

for key, value in my_dict.items():

    print(key, value)  

#clcoding.com

name

age

Alice

25

name Alice

age 25

Keys Must Be Immutable and Unique

The keys in a dictionary must be immutable types (like strings, numbers, or tuples) and must be unique.

my_dict = {(1, 2): 'tuple key', 'name': 'Alice', 3.14: 'pi'}

print(my_dict)  

#clcoding.com

{(1, 2): 'tuple key', 'name': 'Alice', 3.14: 'pi'}

Efficient Lookup Time

Dictionaries have average O(1) time complexity for lookups, insertions, and deletions due to their underlying hash table implementation.

my_dict = {'a': 1, 'b': 2, 'c': 3}

print(my_dict['b'])  

#clcoding.com

2

Dynamic and Mutable

Dictionaries are mutable, which means you can change their content without changing their identity.

my_dict = {'name': 'Alice', 'age': 25}

my_dict['age'] = 26

my_dict['city'] = 'New York'

print(my_dict)  

#clcoding.com

{'name': 'Alice', 'age': 26, 'city': 'New York'}

Read More

Python Coding challenge - Day 206 | What is the output of the following Python Code?

 

Code:

dict_a = {"a": 1, "b": 2}

dict_b = {"a": 1, "b": 2}

print(dict_a is dict_b)

print(dict_a == dict_b)

Solution and Explanation: 

This code creates two dictionaries, dict_a and dict_b, with identical key-value pairs. Then it prints the results of two different comparisons:

print(dict_a is dict_b): This checks whether dict_a and dict_b refer to the same object in memory. In this case, they are two separate dictionary objects, so the output will be False.

print(dict_a == dict_b): This checks whether the contents of dict_a and dict_b are the same. Since they have the same key-value pairs, the output will be True.

Read More