Code:
def rem(a, b):
return a % b
print(rem(3,7))
Python Coding April 29, 2024 Python Coding Challenge No comments
def rem(a, b):
return a % b
print(rem(3,7))
Python Coding April 28, 2024 Python Coding Challenge No comments
name = "Jane Doe"
def myFunction(parameter):
value = "First"
value = parameter
print (value)
myFunction("Second")
Python Coding April 28, 2024 Python Coding Challenge No comments
my_string = '0x1a'
my_int = int(my_string, 16)
print(my_int)
Solution and Explanation:s = 'clcoding'
print(s[1:6][1:3])
Let's break down the expression s[1:6][1:3] step by step:
s[1:6]: This part of the expression extracts a substring from the original string s. The slice notation [1:6] indicates that we want to start from index 1 (inclusive) and end at index 6 (exclusive), effectively extracting characters from index 1 to index 5 (0-based indexing). So, after this step, the substring extracted is 'lcodi'.
[1:3]: This part further slices the substring obtained from the previous step. The slice notation [1:3] indicates that we want to start from index 1 (inclusive) and end at index 3 (exclusive) within the substring 'lcodi'. So, after this step, the substring extracted is 'co'.
Putting it all together, when you execute print(s[1:6][1:3]), it extracts a substring from the original string s starting from index 1 to index 5 ('lcodi'), and then from this substring, it further extracts a substring starting from index 1 to index 2 ('co'). Therefore, the output of the expression is:
co
Python Coding April 28, 2024 Python Coding Challenge No comments
When delving into the world of Python programming, you'll inevitably come across the concept of namespaces. At first glance, it might seem like just another technical jargon, but understanding namespaces is crucial for writing clean, organized, and maintainable code in Python. In this blog post, we'll unravel the mystery behind namespaces, explore how they work, and discuss their significance in Python programming.
What are Namespaces?
In Python, a namespace is a mapping from names to objects. It serves as a mechanism to organize and manage names in a program. Think of it as a dictionary where the keys are the names of variables, functions, classes, and other objects, and the values are the corresponding objects themselves. Namespaces are used to avoid naming conflicts and to provide a context for the names used in a program.
Types of Namespaces
In Python, there are several types of namespaces:
Built-in Namespace: This namespace contains built-in functions, exceptions, and other objects that are available by default in Python. Examples include print(), len(), and ValueError.
Global Namespace: This namespace includes names defined at the top level of a module or script. These names are accessible throughout the module or script.
Local Namespace: This namespace consists of names defined within a function or method. It is created when the function or method is called and is destroyed when the function or method exits.
Enclosing Namespace: This namespace is relevant for nested functions. It includes names defined in the outer function's scope that are accessible to the inner function.
Class Namespace: This namespace holds attributes and methods defined within a class. Each class has its own namespace.
How Namespaces Work
When you reference a name in Python, the interpreter looks for that name in a specific order across the available namespaces. This order is known as the "LEGB" rule:
Local: The interpreter first checks the local namespace, which contains names defined within the current function or method.
Enclosing: If the name is not found in the local namespace, the interpreter looks in the enclosing namespaces, starting from the innermost and moving outward.
Global: If the name is still not found, the interpreter searches the global namespace, which includes names defined at the top level of the module or script.
Built-in: Finally, if the name is not found in any of the above namespaces, the interpreter searches the built-in namespace, which contains Python's built-in functions and objects.
If the interpreter fails to find the name in any of the namespaces, it raises a NameError.
Significance of Namespaces
Namespaces play a crucial role in Python programming for the following reasons:
Preventing Name Collisions: Namespaces help avoid naming conflicts by providing a unique context for each name. This makes it easier to organize and manage code, especially in large projects with multiple modules and packages.
Encapsulation: Namespaces promote encapsulation by controlling the visibility and accessibility of names. For example, names defined within a function are not visible outside the function, which helps prevent unintended interactions between different parts of the code.
Modularity: Namespaces facilitate modularity by allowing developers to define and organize code into reusable modules and packages. Each module or package has its own namespace, which helps maintain separation of concerns and promotes code reuse.
In conclusion, understanding namespaces is essential for writing clean, organized, and maintainable code in Python. By leveraging namespaces effectively, developers can avoid naming conflicts, promote encapsulation, and enhance the modularity of their codebase. So, the next time you write Python code, remember the importance of namespaces and how they contribute to the structure and functionality of your programs. Happy coding!
Python Coding April 28, 2024 Coursera No comments
Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.
Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.
Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.
Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.
Specialization - 4 course series
Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.
This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.
By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an
AI-powered future
This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning.
Younes Bensouda Mourri
is an Instructor of AI at Stanford University who also helped build the
Deep Learning Specialization
Łukasz Kaiser
is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Applied Learning Project
This Specialization will equip you with machine learning basics and state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:
• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use locality-sensitive hashing to approximate nearest neighbors.
• Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.
• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.
• Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering, and to build chatbots. Learn T5, BERT, transformer, reformer, and more with 🤗 Transformers!
Python Coding April 27, 2024 Python Coding Challenge No comments
def fred():
print("Zap")
def jane():
print("ABC")
jane()
fred()
jane()
Python Coding April 27, 2024 Python Coding Challenge No comments
# Prompt the user to enter a number
num = int(input("Enter a number: "))
# Create a string representation of the multiplication table
table = '\n'.join([f"{num} x {i} = {num * i}" for i in range(1, 11)])
# Print the multiplication table
print(table)
#clcoding.com
Python Coding April 27, 2024 Python Coding Challenge No comments
Introduction to Python Classes:
Classes are the building blocks of object-oriented programming (OOP) in Python. They encapsulate data and functionality into objects, promoting code reusability and modularity. At its core, a class is a blueprint for creating objects, defining their attributes (variables) and methods (functions).
class ClassName:
# Class variables
class_variable = value
# Constructor
def __init__(self, parameters):
self.instance_variable = parameters
# Instance method
def method_name(self, parameters):
# method body
#clcoding.com
Class Variables: Variables shared by all instances of a class. Constructor (init): Initializes instance variables when an object is created. Instance Variables: Variables unique to each instance. Instance Methods: Functions that operate on instance variables.
Selection deleted
class Car:
# Class variable
wheels = 4
# Constructor
def __init__(self, brand, model):
# Instance variables
self.brand = brand
self.model = model
# Instance method
def display_info(self):
print(f"{self.brand} {self.model} has {self.wheels} wheels.")
# Creating objects of Car class
car1 = Car("Toyota", "Camry")
car2 = Car("Honda", "Accord")
# Accessing instance variables and calling methods
car1.display_info()
car2.display_info()
Toyota Camry has 4 wheels.
Honda Accord has 4 wheels.
Python Coding April 27, 2024 Python No comments
Python programming language has been gaining immense popularity in recent years, and for good reason. Whether you're a beginner looking to dive into the world of coding or an experienced developer seeking to expand your skill set, learning Python offers a myriad of benefits and opportunities. In this blog post, we'll explore some compelling reasons why you should consider learning Python.
Simplicity and Readability:
One of Python's most appealing features is its simplicity and readability. With its clean and concise syntax, Python code resembles plain English, making it easy to understand and write. This simplicity not only reduces the time spent on coding but also makes Python an excellent choice for beginners who are just starting their programming journey.Rich Ecosystem of Libraries and Frameworks:
Python boasts a vast ecosystem of libraries and frameworks that streamline development and empower developers to achieve their goals more efficiently. From web frameworks like Django and Flask to data science libraries such as pandas and NumPy, Python offers robust solutions for virtually any task or project. These libraries and frameworks not only accelerate development but also ensure code reliability and maintainability.Python Coding April 26, 2024 Python Coding Challenge No comments
def test(a, b = 5):
print(a, b)
test(-3)
Python Coding April 26, 2024 Data Science No comments
Free Books Python Programming for Beginnershttps://t.co/uzyTwE2B9O
— Python Coding (@clcoding) September 11, 2023
Top 10 Python Data Science book
— Python Coding (@clcoding) July 9, 2023
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Top 4 free Mathematics course for Data Science ! pic.twitter.com/s5qYPLm2lY
— Python Coding (@clcoding) April 26, 2024
Web Development using Python
— Python Coding (@clcoding) December 2, 2023
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