Python Learning Resources

We, at The Programming Assignment Help have curated Learning Resources for you to learn the programming in a step-by-step manner and further excel in your academic projects. You can always reach out to our tutors for any queries. Please browse through each section below and embark on your learning journey!
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    Python Crash Course, 2nd Edition (Eric Matthes)

    Python Crash Course, 2nd Edition" by Eric Matthes is a friendly guide for beginners learning Python. It's like having a patient teacher who explains everything clearly. With fun projects and clear instructions, it helps you learn Python step by step, making programming easy and enjoyable.

    Automate the Boring Stuff with Python, 2nd Edition (Al Sweigart)

    Automate the Boring Stuff with Python, 2nd Edition" by Al Sweigart is like having a helpful friend show you how to use Python to simplify your daily tasks. It teaches you step by step how to write code to automate boring, repetitive jobs, making your life easier and more efficient.

    Head-First Python, 2nd Edition (Paul Barry)

    This visually engaging book presents Python concepts in a format ideal for beginners, providing an immersive learning experience. Through its visual approach, it offers a dynamic and captivating way to grasp fundamental Python principles, making it accessible and enjoyable for learners of all levels.

    Learning Python, 5th Edition (Mark Lutz)

    This comprehensive guide to Python programming covers everything from fundamental concepts to advanced topics, providing learners with a thorough understanding of the language's principles and capabilities. Whether you're a novice or an experienced programmer, this resource serves as an invaluable reference for mastering Python development.

    Fluent Python (Luciano Ramalho)

    This guide equips readers with the tools to craft Python code that is clear, concise, and effective. Through practical tips and examples, it empowers programmers to enhance their coding skills, ensuring that their Python projects are well-structured, maintainable, and efficient.

    Effective Python (Joshua Bloch)

    Considered a classic in the field, this book imparts invaluable insights into crafting clean, maintainable, and efficient Python code. By emphasizing best practices, it equips readers with the tools and techniques needed to elevate their programming skills and produce high-quality Python projects.

    Python for Everybody Specialization (University of Michigan)

    Coursera offers a series of online courses covering the fundamentals of Python programming. Through these courses, learners gain a solid understanding of Python's basics, including syntax, data structures, and problem-solving techniques, empowering them to embark on their programming journey with confidence.

    Think Python: How to Think Like a Computer Scientist (Allen B. Downey)

    This resource guides you to adopt a computer scientist's mindset, enabling you to write Python code that is not only well-organized but also efficient. By emphasizing problem-solving skills and best practices, it equips you with the tools to approach programming challenges systematically and produce high-quality code.

    Python Data Science Handbook (Jake VanderPlas)

    This comprehensive guide explores Python's applications in data science, covering data wrangling, analysis, and visualization in depth. It offers insights into various Python libraries and tools commonly used in the field, providing learners with the knowledge and skills needed to excel in data science projects.

    Deep Learning with Python (François Chollet)

    This introduction delves into deep learning concepts and demonstrates how to utilize Python libraries such as TensorFlow and Keras to construct deep learning models. By providing practical examples and insights, it equips learners with the essential skills to embark on their deep learning journey using Python.

    Reference Materials

    Python Built-in Functions




    Returns absolute value of a number


    Checks if any element of an iterable is True


    Returns True when all elements in iterable are True


    Returns string containing printable representation


    Converts integer to binary string


    Converts a value to Boolean


    Returns array of given byte size


    Checks if the object is callable


    Returns immutable bytes object


    Returns a character (a string) from an integer


    Returns a Python code object


    Returns class method for a given function


    Creates a complex number


    Deletes attribute from the object


    Creates a dictionary


    Tries to return attributes of an object


    Returns a tuple of quotient and remainder


    Returns an enumerated object


    Transforms a method into a static method


    Constructs iterator from elements which are true


    Runs Python code within a program


    Returns floating point number from number or string


    Returns formatted representation of a value


    Returns immutable frozenset object


    Returns value of named attribute of an object


    Returns dictionary of current global symbol table


    Executes dynamically created program


    Returns whether object has named attribute


    Invokes the built-in Help System


    Converts an integer to hexadecimal


    Returns hash value of an object


    Reads and returns a line of string


    Returns identity of an object


    Checks if an object is an instance of a class


    Returns integer from a number or string


    Checks if a class is a subclass of another class


    Returns an iterator


    Creates a list in Python


    Returns dictionary of the current local symbol table


    Returns length of an object


    Returns the largest item


    Returns the smallest value


    Applies function and returns a list


    Retrieves next item from the iterator


    Returns memory view of an argument


    Creates a featureless object


    Returns the octal representation of an integer


    Returns an integer of the Unicode character


    Returns a file object


    Returns the power of a number


    Prints the given object


    Returns the property attribute


    Returns a sequence of numbers


    Returns a printable representation of the object


    Returns the reversed iterator of a sequence


    Rounds a number to specified decimals


    Constructs and returns a set


    Sets the value of an attribute of an object


    Returns a slice object


    Returns a sorted list from the given iterable


    Returns the string version of the object


    Adds items of an iterable


    Returns a tuple


    Returns the type of the object


    Returns the __dict__ attribute


    Returns an iterator of tuples


    Function called by the import statement


    Returns a proxy object of the base class

    Python Dictionary Methods




    Removes all items


    Returns the shallow copy of a dictionary


    Creates a dictionary from a given sequence


    Returns the value of the key


    Returns a view of the dictionary's (key, value) pairs


    Returns a view object of all keys


    Returns and removes the latest element from the dictionary


    Inserts a key with a value if the key is not present


    Removes and returns the element having the given key


    Returns a view of all values in the dictionary


    Updates the dictionary

    Python List Methods




    Adds a single element to the end of the list


    Adds iterable elements to the end of the list


    Inserts an element into the list


    Removes an item from the list


    Returns the index of the element in the list


    Returns the count of the element in the list


    Removes and returns the element at the given index


    Reverses the list


    Sorts elements of a list


    Returns a shallow copy of the list


    Removes all items from the list

    Python Set Methods




    Removes the specified element from the set


    Adds an element to a set


    Returns a shallow copy of a set


    Removes all elements from a set


    Returns the difference of two sets


    Updates the calling set with the difference of sets


    Removes an element from the set


    Returns the intersection of two or more sets


    Updates the calling set with the intersection of sets


    Checks if two sets are disjoint


    Checks if a set is a subset of another set


    Checks if a set is a superset of another set


    Removes an arbitrary element from the set


    Returns the symmetric difference of sets


    Updates the set with the symmetric difference


    Returns the union of sets


    Adds elements to the set


    Returns an immutable frozenset object

    Python String Methods




    Converts first character to capital letter


    Pads string with specified character


    Converts to case folded strings


    Returns occurrences of substring in string


    Checks if string ends with the specified suffix


    Replaces tab character with spaces


    Returns encoded string of given string


    Returns the index of first occurrence of substring


    Formats string into nicer output


    Returns index of substring


    Checks alphanumeric character


    Checks if all characters are alphabets


    Checks decimal characters


    Checks digit characters


    Checks for valid identifier


    Checks if all alphabets in a string are lowercase


    Checks numeric characters


    Checks printable character


    Checks whitespace characters


    Checks for titlecased string


    Checks if all characters are uppercase


    Returns a concatenated string


    Returns left-justified string of given width


    Returns right-justified string of given width


    Returns lowercased string


    Returns uppercased string


    Swaps uppercase characters to lowercase; vice versa


    Removes leading characters


    Removes trailing characters


    Removes both leading and trailing characters


    Returns a tuple


    Returns a translation table


    Returns a tuple


    Returns mapped charactered string


    Replaces substring inside


    Returns the highest index of substring


    Returns highest index of substring


    Splits string into a list of substrings


    Splits string from right


    Splits string at line boundaries


    Checks if string starts with the specified string


    Returns a title cased string


    Returns a copy of the string padded with zeros


    Formats the string using dictionary

    Python Tuple Methods




    Returns count of the element in the tuple


    Returns the index of the element in the tuple

    Example Project
    1. timer2: A Simplified Python Function Scheduler

    timer2 is a Python module designed to streamline scheduling of functions at specific times or at regular intervals. It offers a user-friendly alternative to the built-in `threading.Timer` module.

    Key Features:

    Single-Threaded Efficiency: Unlike `threading.Timer`, timer2 operates within a single thread, ensuring efficient resource management. This makes it ideal for managing numerous scheduled tasks without introducing unnecessary overhead.

    Focus on Lightweight Tasks: timer2 is best suited for scheduling non-resource intensive operations. For computationally expensive tasks, consider offloading them to a separate execution pool (threads, multiprocessing, or message queues) using `timer2.apply_after` as demonstrated in the provided example.

    In essence, timer2 empowers you to efficiently schedule Python functions without the complexity of managing multiple threads, making it a valuable tool for streamlining task automation in your applications.


    Image to Sound Converter - Python Project

    This Python project converts photos to auditory sounds. It makes use of both speech synthesis and optical character recognition (OCR) to do this. This is how it works.

    1. Image Processing: The project most likely extracts text from the image using an OCR package pytesseract
    2. Text to Speech: The extracted text is then converted into spoken audio using a speech synthesis library like gTTS.
    3. Output: The final audio file can be played or saved for later use.

    This description provides a concise overview of your project's functionality and setup instructions.


    Tkinter-Based Classroom Management System

    This is a Python application you developed to assist teachers and tutors in managing their classrooms. Here are some key features:

    • User Interface: Built with Tkinter, providing a graphical interface for easy interaction.
    • Student Management:
      • Create and manage student profiles.
      • Potentially store student data in a database (details not mentioned in the description).
    • Performance Tracking:
      • Generate reports on student performance (details not mentioned in the description).
      • Visualize student and class performance using pie charts and bar graphs.
    • Additional Features:
      • Percentage calculator (likely for calculating grades).
      • Option to create accounts within the software (for user management?).


    Skin Disease Detection using Convolutional Neural Networks (CNNs)

    The purpose of this project is to use Convolutional Neural Networks (CNNs) to diagnose skin problems using image analysis.

    Problem: Treatment for skin problems must begin as soon as possible with accuracy.

    Solution: This research suggests employing CNNs, a form of deep learning algorithm, to analyze photos and detect skin problems.

    Benefits of CNNs:

    • High Accuracy:strong> Literature suggests CNNs achieve high accuracy in various tasks, including medical image analysis.
    • Image Analysis: CNNs excel at extracting relevant information from photos, making them ideal for detecting skin diseases.

    This project delves into the application of CNNs to enhance the precision of skin disease identification, perhaps resulting in prompt diagnosis and improved patient results.


    Facial Recognition System with Tkinter GUI (Python & OpenCV)

    This project uses Python and OpenCV to construct a facial recognition system with an intuitive Tkinter GUI. Below is a summary of its features:

    Training Phase:

    1. User Input: The script prompts the user to enter an ID and name.
    2. Image Capture: Clicking the "Take Images" button opens the camera and captures 60 image samples of the person.
    3. Data Storage: Captured images are saved in the "TrainingImage" folder. User information (ID and name) is stored in a CSV file named "StudentDetails.csv" within the "StudentDetails" folder.
    4. Model Training: Clicking the "Train Image" button trains the model using the captured images. The trained model is saved as "Trainner.yml" in the "TrainingImageLabel" folder.

    Recognition Phase:

    1. Face Detection: Clicking the "Track Image" button opens the camera again for real-time face detection.
    2. Recognition: If a face is recognized, the system displays the person's ID and name on the image.
    3. Attendance Recording: Upon program exit (pressing 'q' or 'Q'), a CSV file containing the recognized person's details (name, ID, date, and time) is saved in the "Attendance" folder. This information is also displayed in the GUI window.


    • Enter the base currency details (e.g., "USD").
    • Provide the target currency details (e.g., "EUR").
    • Enter the exchange rate and the amount of the base currency to convert.
    • View the converted amount along with the target currency symbol.
    • Press "ENTER" to exit the program.

    Overall, this project offers a user-friendly facial recognition solution complete with training, face detection, attendance tracking, and data storage features.


    Missing Person Tracking System with Image Processing and Machine Learning

    This Python project tackles the critical issue of missing persons, particularly children, in India. It uses a PostgreSQL database, machine learning, and image processing to help law enforcement with their search operations.

    Problem: Although successful, traditional investigative techniques take a lot of time and might not be applicable to people who have migrated.

    Timely response is impeded by the tedious chore of manually examining CCTV footage for incidents involving missing persons.


    1. Case Registration:
      • A user-friendly GUI application built with PyQt5 allows law enforcement to register missing person cases.
      • It captures relevant details and stores them securely in a PostgreSQL database.
    2. Public Participation:
      • A separate mobile application empowers citizens to anonymously submit photos of people they suspect might be missing or found begging.
      • This anonymity protects users from potential local threats.
    3. Matching Images::
      • The system utilizes the K-Nearest Neighbors (KNN) algorithm to compare registered missing person images with user-submitted photos.
      • Potential matches are flagged for further investigation by law enforcement.

    This project offers a technological approach to expedite missing person searches and potentially reunite families.


    Combating Email Spam with Machine Learning (College Project)

    With the goal of providing college students with the resources they need for email security, this project addresses the issue of email spam, which is only becoming worse.


    • Increased internet usage causes an increase in spam emails, which are frequently used for illicit purposes including fraud and phishing.
    • Spammers utilize cunning strategies to deceive naive users by masquerading as legitimate emails.

    Solution - Machine Learning for Spam Detection:

    • This project explores machine learning techniques to identify spam emails.
    • Students will delve into various machine learning algorithms and apply them to email datasets.
    • By evaluating performance metrics like precision and accuracy, the project will identify the most effective algorithm for spam detection.

    Learnings and Benefits:

    This project empowers students with valuable skills in machine learning and email security. The developed model can potentially be used to filter spam emails and enhance user safety. This project provides a valuable learning experience for college students, equipping them to combat the growing menace of email spam.


    Machine Learning-Based Disease Prediction System (Python)

    This study explores the potential applications of machine learning for disease prediction using user-reported symptoms.

    Important Technologies:

    Artificial Intelligence: In order to find trends in medical data, this initiative analyzes it using machine learning techniques.

    Scikit-learn ,or Sklearn: This robust Python library has features for a number of machine learning uses, including classification (in this case, illness prognosis).

    Data: A dataset including information on over 4,000 diseases and their accompanying symptoms is used by the system.


    • Users can input their symptoms into the system.
    • The machine learning model analyzes the provided symptoms against the vast disease dataset.
    • The system predicts potential disease matches based on the learned patterns.



    3rdiSlideshow is an application designed to seamlessly stream all 3rdi Images to a video output, displayed in fullscreen mode and synchronized with the original images' timing.

    • Install Ubuntu 22.04 for Desktop. Note: Creating the bootable USB stick on Windows is necessary for proper functioning.
    • Enter the boot menu by pressing F7 or Delete, then select the USB for boot.
    • Enable Remote Desktop in Settings.
    • Disable the keyring by setting the password to empty. Reboot the system and ensure that screen sharing functions properly (test using the Microsoft Remote Desktop app).


    Carbon Emission Prediction using Machine Learning

    This study investigates the concerning rise of carbon emissions in developing countries,


    • Analyze CO2 emission trends in a country.
    • Identify key factors influencing CO2 emissions related to energy resources.
    • Utilize machine learning algorithms to predict future emission levels.


    Data Analysis: Employ descriptive methods to identify trends and relationships between CO2 emissions and related factors.

    Machine Learning: Apply machine learning algorithms to develop predictive models for future CO2 emission levels.


    Coventry University Attendance System (CUAS) - Alternative Approach to Student Sign-In


    Students forgetting their student cards can hinder their ability to sign in for lectures, creating administrative burdens and potentially inaccurate attendance records.

    Proposed Solution: CUAS (Coventry University Attendance System)

    CUAS offers a convenient and alternative method for student sign-in, addressing the issue of forgotten cards.

    Student Sign-In via QR Codes:

    • Students use their smartphones' cameras to scan QR codes displayed in classrooms.
    • A dedicated mobile app (Tracker) facilitates the QR code scanning process.

    Web Application for Lecturers:

    • Lecturers can utilize a web application to monitor student attendance in real-time.
    • This streamlines attendance tracking and reduces manual processes.

    Overall, CUAS presents a practical and user-friendly solution for student sign-in, enhancing the attendance management process at Coventry University.


    Birthday Paradox Simulation in Python

    This project uses Python simulations to investigate the intriguing Birthday Paradox. The Birthday Paradox is the counterintuitive idea that there's a good chance that two or more persons in a group of remarkably small numbers will have the same birthday.

    Project Components:
    Theoretical Probability Calculation (

    • This script calculates the theoretical probability of a birthday match based on mathematical formulas.
    • It prompts the user for the number of people and outputs the calculated probability.

    Birthday Paradox Simulation (

    • This script simulates the Birthday Paradox by generating random birthdays for a specified number of people.
    • It checks if there are any shared birthdays within the simulated group.
    • The script runs multiple simulations and calculates the probability of a birthday match based on the simulation results.
    • By comparing the theoretical and simulated probabilities, this project provides an engaging way to understand the Birthday Paradox.


    Sophia: Your Friendly Telegram Chatbot (Python Project)

    Looking to streamline customer service for your business? Look no further than Sophia, your friendly Python-powered chatbot designed for Telegram!

    What Sophia Does:

    Answers Common Questions: Equipped with a library of predefined responses, Sophia can handle routine inquiries, freeing up your time for more complex issues.

    Moderates Inappropriate Messages: Sophia helps maintain a positive chat environment by filtering out offensive language.

    Engages in Conversations (Private & Groups): Capable of interacting in both individual and group chats, Sophia provides a convenient touchpoint for your customers.


    Text-to-Speech with Voice Style Control (Python/PyTorch App)

    This Python/PyTorch application empowers you to synthesize realistic human speech with customizable voice styles!

    Key Features:

    • Voice Model Training (Optional): Train your own voice model to achieve a specific voice style (details not mentioned).
    • Pre-Trained Voice Models: Utilize pre-trained voice models for immediate speech synthesis.
    • Vocoder Integration: Integrate a vocoder (like HiFiGAN) to convert the model's output into high-quality audio. You can import your own vocoder models from external sources
    • Text Input Options:
      • Single Line: Synthesize a single sentence.
      • Multi Line: Synthesize multiple sentences, preserving the line breaks between them.
      • Paragraph: Automatically split a paragraph into sentences for natural-sounding speech synthesis.
    • Synthesis Control: Select the desired voice model (checkpoint), language, and vocoder from the Settings menu.

    Overall, this project offers an adaptable framework for producing speech that is similar to that of a human using custom voice styles. It serves both consumers who wish to use pre-trained models for rapid text-to-speech generation and developers who want to train custom voice models.


    Expert Answers

    When you need information from the user in your Python program, just use `input()`. It shows a message, waits for the user to type something, then gives you what they typed as text. For example, ask "What's your name?" and whatever they type will be stored for you to use later. It's that simple and friendly!

    In Python, `==` compares the values of two objects to see if they're equal, while `is` checks if two variables point to the same object in memory. `==` compares content, while `is` compares identity, meaning whether they're exactly the same object in memory.

    In Python, you can easily determine if a number is odd or even using the modulo operator `%`. If `num % 2 == 0`, it's even because there's no remainder when divided by 2. Otherwise, if `num % 2` gives a non-zero result, it's odd.

    In Python, `elif` stands for "else if" and is used alongside `if` statements to assess multiple conditions sequentially. If the condition associated with the initial `if` statement isn't met, Python moves on to evaluate the condition linked with the `elif` statement. If this condition holds true, the corresponding code block executes. Essentially, `elif` enables handling of various alternative conditions after the initial `if` statement.

    To turn a string into lowercase in Python, simply employ the `lower()` method, designed for strings. This function creates a replica of the string with all alphabetic characters changed to lowercase. For instance, if `my_string` holds a string, you can transform it to lowercase with `my_string.lower()`.

    The `TypeError: 'int' object is not iterable` error happens when you attempt to loop over an integer, which isn't allowed in Python. Loops or constructs like `for` expect iterable objects such as lists or strings, which can provide a sequence of elements. Since integers don't fit this criteria, Python throws an error. To fix it, make sure you're using the right data type or iterate over an appropriate iterable object like a list or string.

    In Python, conjuring random numbers is a whimsical task, courtesy of the versatile `random` module. Among its treasures lies `random.randint(a, b)`, a spellbinding function that bestows upon you a random integer betwixt the enchanted realms of `a` and `b`, inclusive. Behold! With but a flick of code, `random.randint(1, 10)` summons forth a mystical integer, dancing betwixt the shadows of 1 and 10.

    In the realm of Python, behold the humble pass statement – a silent sentinel, a placeholder of nullity. When Python's syntax demands a statement but no deeds need be done, pass arises as a subtle placeholder. Encounter it when laying the foundation of classes, functions, or conditional domains where future enchantments shall take shape. It is the whisper of anticipation, the promise of code yet to be written.

    In the realm of Python, banishing duplicates from a list is a task both elegant and swift. By harnessing the power of sets – ethereal collections where uniqueness reigns supreme – duplicates dissolve like mist before the sun. With a mere transmutation, a list becomes a set, shedding its duplicative shackles. Then, should you desire the familiar form of a list once more, fear not! Through the incantation of the `list()` constructor, the set may once again assume the guise of a list, its duplicates exiled to the void. Thus, each element emerges unscathed, appearing but once in the new order of things.

    In the realm of Python lists, two methods, `append()` and `extend()`, offer pathways to expansion, yet their ways diverge. The `append()` method, akin to a solitary traveler, adds its lone argument as a singular element to the end of the list. Meanwhile, the `extend()` method, a journeyman of iteration, traverses the realms of its iterable argument, such as a list, adding each element individually to the list's end. Thus, while `append()` bestows upon the list a solitary gift, `extend()` opens the floodgates, ushering forth a multitude of elements into the ever-expanding realm of the list.