Python Homework Help | Do My Python Homework

Python programming language has been introduced in computer science courses by many colleges and machine learning courses. It is one of the most important programming languages to be learned & hence gets weekly homework & assignments in every coursework.

When you learn Python, you get a chance to learn various other programming languages & analysis tools. 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? 
• JSON and XML files, and learn HTTP methods such as GET, POST, PUT, and DELETE.
• Databases - create and process XML files, Interact with SQLite databases, read, write and process CSV files, create and process log messages from Python programs, etc
• How to create Graphical User Interfaces (GUIs) in Python using the tkinter package
• Standardization, coding conventions & best practices in Python.
• 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 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.

What is Python? Why do students study Python?

Python is an object-oriented programming language that is high-level and interactive to develop web apps. The language has an easy syntax that everyone can understand. 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. You can also use this to make system calls on all operating systems and run code that is written in C and C++. With the ability of Python to run on all system architectures, you can use this language for developing various apps.

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 programming language that has wide applications right from machine learning to data science.

• Simple for everyone to understand - Python language is easier for everyone to understand and use. The syntax is easier to remember. Due to its simplicity, it becomes easier to write lengthy code with ease.
• Free and open-source - The open-source programming language allows developers or beginners to work on this without having to pay anything. You can also download the source code, modify the code and distribute the new version of Python. 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 Python Homework & Assignments

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 information that is unseen.
• 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. With this coding will turn easy and becomes easier for you to grasp concepts.

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 is the base for data science that allows data analysts to use this language to perform complicated statistical calculations, create the best visualizations, create machine learning algorithms, analyze data and do data-related tasks. Using Python, you can build different data visualizations such as line and bar graphs, pie charts, histograms, and 3D plots. There are many libraries that let you write programs for machine learning and analyzing data briskly and efficiently with the help of Keras and TensorFlow.

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:

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 hold 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 who 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 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.