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    Python Homework Help | Do My Python Homework

    Python programming language has been introduced as a key subject in computer science, programming, data science, and Artificial Intelligence courses by many universities. It is one of the most important programming languages to be learned & hence gets weekly homework & assignments. Some of the popular Python Homework Help concepts & what you get to learn from them are listed below:

    • Basic Network Programming Concepts in Python - How to communicate with RESTful APIs using CRUD methods? How to use sockets? 
    • Handling JSON and XML files and mastering HTTP methods like GET, POST, PUT, and DELETE.
    • Database manipulation, including XML file creation and processing, SQLite database interaction, CSV file reading, writing, and processing, as well as generating and managing Python program log messages.
    • Creating Graphical User Interfaces (GUIs) using the tkinter package in Python.
    • Embracing Python standardization, adhering to coding conventions, and implementing best practices. 
    • Basic Python Concepts -  Shallow and deep operations, composition, inheritance, polymorphism, classes, instances, attributes, and methods; abstract classes, subclassing, encapsulation, advanced techniques of exception handling, serialization of Python objects, metaprogramming etc.

    The Programming Assignment Help website offers Python Homework Help and tutoring services to relieve your tension if you are concerned about your programming homework. Our top-tier Python programmers thoroughly research your homework and provide you with accurate Python code solutions. With encouraging live online Python assistance, students can try to finish their homework and get the queries solved via live sessions & chats.

    Why is Python an important Subject in Programming Coursework?

    Python, a widely utilized interactive and high-level object-oriented programming language, finds extensive application in web app development. Its approachable syntax ensures accessibility for a diverse range of users. It allows users to code efficiently. The portable programming languages are supported on various operating systems such as Windows, Unix, and Linux. It has a high-level data structure and allows dynamic typing and dynamic binding. With this, you can develop complicated apps. Python's versatility extends to making system calls on all operating systems and executing code written in C and C++. Its cross-system compatibility allows for diverse app development across various system architectures.

    It is open-source, powerful, and makes programming fun. Python coders can type the variables dynamically without having to explain each one of them. The syntax is readable and often is used in analytics, machine learning, and web development. The interpreter used in this language will help you find bugs.

    Why do students study Python?

    Python is a versatile programming language with applications spanning from machine learning to data science.

    • User-Friendly Simplicity - Python is designed for universal ease of understanding and utilization. Its straightforward syntax is easy to recall, facilitating the creation of extensive code with convenience.
    • Free and open-source - Being an open-source language, Python offers a cost-free platform for developers and beginners alike. It empowers users to access, modify, and distribute the source code freely.  Companies will modify this open source and build a version that is beneficial for all.
    • High productivity - Various applications can be created using this programming language. With access to a lot of libraries, users can try out new things. Many programming languages will lack the freedom and flexibility that Python does.
    • A lot of libraries - Python has a lot of libraries while the standard library is immense and includes every function.

    Concepts used in Solving Python Homework

    IDEs used in Python Homework & Assignments

    The IDE is the place where you edit and write the code. Following are the IDEs that allow you to write Python code and execute

    • Sublime Text 3 - Sublime Text is a popular editor that allows you to write code in different languages including Python. It is quick, easy to customize and has a large community. When you encounter any issue with the editor, you can get quick help. It offers the required support for Python. Various packages that you can install such as code linting, auto-completion and debugging. It creates the best Python development environment for you to code.
    • PyCharm - It is an IDE for professional developers and has all the key features that an IDE will have such as code completion, inspection, highlighting the errors, fixes, debugging, version control system and code refactoring.
    • VS code - It is a free and open-source IDE from Microsoft that is used for Python development. You can also add extensions to create a Python environment. It is lightweight and packed with various features, which is why many Python developers love using it.
       

    Popular Libraries used in Python Homework & Assignments

    Following are the Python libraries that you can use:

    • TensorFlow Homework Help - Machine learning projects in Python would use this library, which is open-source. TensorFlow will be a part of every Google app that is used for machine learning. It acts like a computational library to write algorithms that will have different tensor operations. You can even execute computational graphs with this library. It is easy to train on CPU and GPU for distributed computing.
    • Scikit-Learn Homework Help -  This Python library is linked to SciPy and NumPy. It is the best library to work with complex data. Many changes are made to this library and one is cross-validation. There are various cross-validation features you can use to check the accuracy of supervised models on the unseen information.
    • NumPy Homework Help - It is a popular machine-learning library that allows you to perform operations on different tensors. It is interactive and easier to use. Using this, you can easily perform complicated mathematical implementations. 

     

    Why Is Python Homework Challenging to Solve?

    Python assignments can pose certain challenges, and here are some common questions students might encounter while working on them:

    • Creating a 3D Space: To create a 3D space in Python, employ the Matplotlib library. Begin by importing the Axes3D module from Matplotlib, then utilize the axes method to generate a 3D plot. Incorporate the plot method to incorporate data into the plot.
    • Calculating Series in Python: Python provides built-in libraries like NumPy and Pandas for series calculations. NumPy assists in mathematical operations on arrays, while Pandas helps in creating and manipulating dataframes for series calculations.
    • Fitting Data to Gaussian Distribution: For fitting data to a Gaussian distribution in Python, rely on the SciPy library. Import the stats module from SciPy, create a normal distribution using the norm method, and use the fit method to align the distribution with your data.
    • Returning an Integer Array: To return an integer array in Python, utilize the numpy library. Formulate an array and then apply slicing or indexing to extract a specific segment or element.
    • Combining Bytes: To combine bytes in Python, employ the b'' syntax to form a byte array. Merge byte arrays using the + operator or the join() method. Convert the byte array to a mutable integer sequence using the bytearray() method.
    • Obtaining Output as a List: For acquiring output as a list in Python, apply the list() function. Pass the output to this function to convert it into a list. Alternatively, leverage list comprehension to shape a list from the output.
    • Plotting x/y/z Data: To plot x/y/z data, rely on the Matplotlib library. Establish a 3D plot using the Axes3D method and integrate your data using the plot_surface method.
    • Saving Multiple Models: Save multiple models in Python using the joblib library. Import the dump() method from joblib to save models to a file. Use the load() method to reload models from the file.
    • Creating a New Binary Variable: Forge a new binary variable in Python by creating a boolean variable. Convert the boolean variable to an integer using the astype() method, generating a binary variable where True equals 1 and False equals 0.
    • Gaining Insights from Data: Extract insights from data with Python using analysis and visualization libraries like NumPy, Pandas, and Matplotlib. Employ exploratory data analysis for pattern identification and visualization techniques to communicate insights effectively.
    • Displaying Clusters: To display clusters in Python, construct scatter plots with the Matplotlib library. Assign colors based on cluster membership and differentiate clusters using shapes or sizes.
    • Obtaining an Image Matrix: Utilize the OpenCV library to obtain an image matrix in Python. Read the image using imread() and convert it to a matrix using the cvtColor() method. Manipulate the matrix as needed for analysis.

    These questions touch on technical concepts often encountered in Python assignments, and mastering them can lead to the successful completion of your tasks. 

    Why should you hire someone to do Python Homework?

    Students when assigned to write a program in Python based on the specifications given by the professors, end up struggling a lot. They spend sleepless nights, but could not get the desired output. However, we have a team of Python developers who have enough experience and a good amount of knowledge in writing Python homework. The programs or solutions done by our team will help you secure good grades in the examination.

    Data Analysis and Machine Learning Homework Help

    Python serves as the foundation for data science, providing data analysts with a versatile language for executing intricate statistical computations, crafting compelling visual representations, formulating machine learning algorithms, conducting data analysis, and executing various data-related tasks. Python's capabilities encompass the creation of diverse data visualizations, ranging from fundamental line and bar graphs to intricate pie charts, histograms, and 3D plots. Additionally, the language boasts an array of libraries, including Keras and TensorFlow, which streamline the creation of efficient programs for machine learning and robust data analysis.

    Web Development Homework Help

    Python develops the backend of the website and application which users do not see. The role of Python in developing a web app or website is to send and receive data from the server, process data and communicate with the database. Various frameworks are offered by this language for web development and the main ones include Flask and Django.

    Some of the popular topics in Python on which our programming assignment & homework experts work on a daily basis are listed below:

     Machine Learning  Object-Oriented Programming
     Data Science  Python Shell
     Data Analysis  GUI Programming in Python (TkInter)
     Operators in Python  Working with RESTful APIs
     String Manipulation  SQLite
     Loops in Python  Network Programming in Python

     

    Ask 'The Programming Assignment Help' to - Do My Python Homework

    The Programming Assignment Help offers the best Python homework help for students who are struggling to complete the task. We possess extensive knowledge of various concepts in Python and use them to write the program effectively and get the output as expected.

    • Experienced Python developers - We have a team of developers who have enough experience and knowledge in writing code in Python language. They can answer all your academic-related queries effectively and instantly.
    • Round-the-clock support - We have a dedicated team that offers you round-clock support. You can contact the team anytime through email, call or live chat from any place globally to get quality solutions.
    • On-Time delivery - We assure you to deliver the homework on time. This helps you to get enough time to check the solution and get back to us for any changes to be done.

     

    Example of A Simple Python Code Written By Our Programming Homework Help Expert

     

    Code for: the setosa vs. versicolor learning problem,

    Solution:

    
    import numpy as np
    
    class Perceptron(object):
        """Perceptron
    
        Parameters
        ------------
        eta : float
          Learning rate (between 0.0 and 1.0)
        n_iter : int
          Passes over the training dataset.
        random_state : int
          Random weight initialization seed for a random number generator.
    
        Attributes
        -----------
        w : 1d-array
          Weights after fitting.
        errors : list
          Number of updates (misclassifications) during each epoch.
    
        """
        def __init__(self, eta=0.01, n_iter=50, random_state=1):
            self.eta = eta
            self.n_iter = n_iter
            self.random_state = random_state
    
        def fit(self, X, y):
            """Fit training data.
    
            Parameters
            ----------
            X : {array-like}, shape = [n_samples, n_features]
              Training vectors, with n_samples denoting the sample count and
              n_features is the number of features.
            y : array-like, shape = [n_samples]
              Target values.
    
            Returns
            -------
            self : object
    
            """
            rgen = np.random.RandomState(self.random_state)
            self.w = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])
            self.errors = []
    
            for i in range(self.n_iter):
                errors = 0
                for xi, target in zip(X, y):
                    update = self.eta * (target - self.predict(xi))
                    self.w[1:] += update * xi
                    self.w[0] += update
                    errors += int(update != 0.0)
                self.errors.append(errors)
            return self
    
        def net_input(self, X):
            """Calculate net input"""
            return np.dot(X, self.w[1:]) + self.w[0]
    
        def predict(self, X):
            """Return class label after unit step"""
            return np.where(self.net_input(X) >= 0.0, 1, -1)
    
    # ### Reading-in the Iris data
    import pandas as pd
    pd.options.mode.chained_assignment = None  # default='warn'
    
    df = pd.read_csv('https://archive.ics.uci.edu/ml/'
    'machine-learning-databases/iris/iris.data', header=None)
    
    # We are selecting first 100 samples only
    
    (as both Versicolor and Sentosa categories are present in these 100 samples only)
    df2 = df.head(100)
    # Representing Sentosa as 1 and Versicolor as -1
    y = np.array([1]*50 + [-1]*50)
    # Drop the last column
    
    (which represents target and store the remaining which are the features)
    X_all = df2.drop([4],axis=1)
    
    print('\nFeature-1 is left out\n')
    X = X_all[[1,2,3]].to_numpy()
    # Fitting the model with X as input and y as labels and only 4 iterations
    model = Perceptron(n_iter=4)
    model.fit(X,y)
    # Calculating the sum of the errors
    sum_of_errors = sum(model.errors)
    print('Sum of errors:',sum_of_errors)
    
    print('\nFeature-2 is left out\n')
    X = X_all[[0,2,3]].to_numpy()
    # Fitting the model with X as input and y as labels and only 4 iterations
    model = Perceptron(n_iter=4)
    model.fit(X,y)
    # Calculating the sum of the errors
    sum_of_errors = sum(model.errors)
    print('Sum of errors:',sum_of_errors)
    
    print('\nFeature-3 is left out\n')
    X = X_all[[0,1,3]].to_numpy()
    # Fitting the model with X as input and y as labels and only 4 iterations
    model = Perceptron(n_iter=4)
    model.fit(X,y)
    # Calculating the sum of the errors
    sum_of_errors = sum(model.errors)
    print('Sum of errors:',sum_of_errors)
    
    print('\nFeature-4 is left out\n')
    X = X_all[[0,1,2]].to_numpy()
    # Fitting the model with X as input and y as labels and only 4 iterations
    model = Perceptron(n_iter=4)
    model.fit(X,y)
    # Calculating the sum of the errors
    sum_of_errors = sum(model.errors)
    print('Sum of errors:',sum_of_errors)
    
    """####
    
    When we train the model for setosa and versicolor, we can see this,
    the sum of the errors after training the model is minimum in case when we exclude either
    feature-1 or feature-4 (both have same sum of errors = 5).

    So we can exclude any one feature. We will exclude feature-4 after 4 iterations!"""
     

    If you need help in writing Python homework, Python assignments & projects, then contact us right today.