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Python fluency Checklist with Practical Projects

Only topics that you need to practice to get to an advanced level in Python.Nothing more, Nothing less.

Variables

  1. Introduction to Variables
    There are several types of variables like Scalar variables which contains Integer, float and many other variable sub types. Variables help us to store information which we can then use in a program.

Control Flow

  1. If-else statements
    If-else statements help us to check whether certain conditions are met or not. Code inside the if statement only runs when the if condition is true. Otherwise, the code inside the else condition runs.
  2. While loops
    While loops run the code inside them repeatedly as long as the while condition evaluates to true. When the condition evaluates to false, the code inside the while loop stops running.
  3. For Loops
    When we want to run our code for a specific number of times, we use for loops. We can also use these loops for many other purposes.
  4. is and is not operators in Python
    The is and is not operators help in comparing values and objects in our programs.

Functions

  1. Introduction to Functions
    Instead of writing the same code repeatedly, we can create functions and use them whenever we need to throughout the program. This saves time in writing code. Functions also sometimes require the use of arguments which we need to pass into the function.
  2. Variable and Function Scope
    Whether we can access a variable throughout the program depends on its scope. This has important implications whenever we create any program. The same logic applies when we create any function. One of the most commonly used functions is the range function. The i variable in this function is always local-scoped.
  3. Lambda Functions
    Generally, we have to name our functions to use them. But we can also create nameless functions, also known as anonymous functions. In Python, we call them Lambda functions. We can also use these to shorten statements like if-else.

Modules

  1. What are modules?
    For any task like data analysis, machine learning, image or audio processing etc, specifically created and grouped utilities and functions exist, which we call as libraries. In Python, we call them modules. Many Python modules exist for a variety of purposes. An example of a module is the Random module, which generates random values in our programs.

Classes

  1. Introduction to Classes and Objects
    Classes are like templates for creating objects in Python. What are objects? Every element in Python is an object. Every object is an instance of some class. The concept of class variable discusses this in brief.
  2. Abstraction
    Abstraction helps us to represent objects in our programs with the required essential features and without unnecessary details. Abstract classes help us use abstraction in our programs.
  3. Decorators
    We can alter how classes or functions work in our programs using Decorators in Python.
  4. Descriptors
    As classes may have different class attributes, Descriptors help to create, control and manage such attributes.

Data Structures

  1. Lists
    Data structures like lists store objects of different data types together in one place. We can also access and change items inside lists using indexing.
  2. Arrays
    Arrays store objects of the same data type, which we can access later on in our programs.
  3. Tuples
    We can store multiple objects inside Tuples. Once we put items inside a tuple, we cannot change them.
  4. Dictionaries
    We can store information in key-value pairs using dictionaries. Like lists, we can also access and change items inside them.
  5. Set
    Sets help us store information like lists, the difference being that we cannot put duplicate values inside them. Also, sets will automatically sort the data we put inside them.
  6. Deque
    Double Ended Queue, also known as Deque, helps us store information which we can then modify from both sides of the queue, from the start or end.
  7. Mutable and Immutable Objects
    When we can modify objects, we call them mutable. Otherwise, we call them immutable. We have to design our programs carefully with a proper knowledge of mutable and immutable objects.
  8. Create a custom Binary Tree structure
    Binary trees are a popular data structure used widely in many applications. Learn how to create a binary tree in Python.

Regular Expressions

  1. Introduction to Regular Expressions
    Regular expressions help us to match or search for a particular pattern in data. We can also use these to shorten our code.

Unit Testing

  1. Introduction to Unit Testing
    For bug-free code, test driven development is of vital importance. Unit Testing helps us to test individual components in our program and ensure that it's error-free.

NumPy Library

  1. Arrays in NumPy
    Learn how to create arrays in NumPy and get a firm grasp on 2-D arrays. Also learn how to display NumPy arrays as Images.
  2. Array and Matrix Manipulation
    NumPy allows you to round numbers in matrices. Learn how to round elements of a matrix in NumPy. Also learn how to find absolute sum of elements in a matrix. Get a grasp on performing matrix multiplication and matrix multiplication by a scalar. Finally, learn about vector norm method in NumPy, which is widely used for machine learning.
  3. Errors in NumPy
    Running into errors is pretty common in programming. Learn how to solve the TypeError: only integer scalar arrays can be converted to a scalar index and the RuntimeError: module compiled against api version 0x10 but this version of numpy is 0xe in NumPy.

Python Interview Questions

  1. Median of Two Sorted Arrays
    Median of two sorted arrays is a common problem that uses basic algorithmic thinking concepts. Learn how solve it using different approaches and using the binary search method.
  2. Longest substring without repeating characters
    Longest substring without repeating characters is an important algorithm which teaches practical applications of dictionaries and loops in Python.
  3. Merge K sorted Linked Lists
    The concepts of classes and functions find their applications in the merge K sorted linked lists algorithm. It also teaches how to use a heap (a type of binary tree) data structure in Python.
  4. Intersection of two arrays
    The algorithm of intersection of two arrays teaches how to use lists in python for effective problem solving.
  5. Predict the Output Questions
    Test your knowledge of Python with over 50 Predict the Output Python interview questions.
  6. Lambda Questions
    Lambda functions offer a simple way to create anonymous functions in Python. Find your knowledge gaps and practice using over 40 Lambda interview questions in Python.
  7. NumPy Questions
    NumPy is a vital Python library for machine learning, scientific computing, and data analysis purposes. Evalute your NumPy skills with a comprehensive list of Numpy interview questions.

Scripts

  1. Open CSV files in Python
    For daily data or analysis tasks working with CSV files in Python is a handy tool.
  2. Convert PDF to Image
    Learn how to convert PDF to image in Python and gain an understanding of an image library.
  3. Convert video to images
    Build a simple video to images converter in Python. Also learn how to use the OpenCV library.
  4. Create a script to search web using Google Custom Search API
    Improve your skills by creating a custom Python script to search the web using Google Custom Search API.
  5. Get IP Address
    Learn what public and private IP addresses are and create a script to get IP address in Python.
  6. Retweet recent tweets with a particular hashtag
    Learn about APIs and build a script to retweet recent tweets with a particular hashtag on Twitter.
  7. Post a tweet
    Create a python script to post your tweets using twitter API.
  8. Get details on CPU and Ram Usage
    Implement a script in Python to get CPU and Ram Usage details of your computer.
  9. Open Webpage and Login
    Learn how to open a webpage and log in using Python and some other applications. Also learn about web scraping in brief.
  10. Refresh URL/Tab using Python
    Create a Python script to refresh any URL or tab in your browser.
  11. Scroll on a webpage
    Understand how to simulate a scroll on any webpage using Python.
  12. Control cursor using Python
    Learn how to build a Python script that can control your cursor and simulate actions like click, drag etc.
  13. Control Keyboard using Python
    Implement a script in Python that can control a keyboard and simulate typing.
  14. Create a GitHub repository
    Learn how to create a github repository using GitHub REST API.
  15. Create GitHub issues
    Implement a script in Python to create github issues and other actions like commenting, adding a label etc.
  16. Get currency exchange rates
    Learn how to get currency exchange rates and create a basic graph in Python.
  17. Create archive for large files
    Large files take up a lot of space and archiving each file can be tedious. Learn how to create an archive for large files using Python scripting.
  18. Read and write JSON file
    Accessing data objects requires handling of JSON files. Understand how to read and write JSON file using Python.
  19. Send email in Gmail
    Learn how to send email in Gmail using a Python script with the help of the Smtplib and the email modules.
  20. Read email from Gmail
    Implement a script that can read any email from Gmail using Python along with the email and the imaplib modules.
  21. Send file/attachments in Gmail
    Manually sending many files or attachments in Gmail may become time consuming. Learn how to send files and attachments in Gmail using a script in Python.
  22. Generate Wiki Summary
    Learn how to generate a summary of any Wikipedia article using Python and the Wikipedia module.
  23. Send WhatsApp Message
    Understand how you can send any WhatsApp message using a Python script with the help of PyWhatKit library and Selenium web driver tool.
  24. Send Bulk WhatsApp Messages
    You can also create a script send bulk WhatsApp messages using Python. You will also use the PyWhatKit library along with Selenium and the ChromeDriver tool.
  25. Download files from Google Drive
    Learn how to download files from Google Drive using Python and the Google Drive API.
  26. Get CO2 Emissions data
    Implement a script in Python to access CO2 emissions data using an API.
  27. Get Current Stock Price
    Learn how to get the current price of any stock from Yahoo Finance using Python.
  28. Wait for Page Load
    Implement a script to wait for any page to load and simulate the loading time in Python.
  29. Delete unused files
    Learn how to save space and create a script to delete files not accessed for a long time using Python.
  30. Zip a File
    Understand how to create a script that zips any file using Python along with the zipfile and the shutil modules.

Projects

  1. CRUD application using Django
    CRUD applications make use of basic functions of create, read, update and delete. These form the building blocks of any application. Learn how to create a simple CRUD app in Python using the Django framework. Also gain an understanding of the try-else statements.
  2. 2048 Game
    Learn how to create your own 2048 game. This will also give you a firm grasp on loops.
  3. Flappy Bird Game
    Gain a conceptual understanding of programming and python concepts by building your own flappy bird game.
  4. Snake Game
    Get a firm grasp on the concept of classes and learn how to develop a snake game using Python.
  5. Minesweeper Game
    Implement your own version of the minesweeper game. Learn how to handle events using the Tkinter module.
  6. Tic Tac Toe Game
    Learn how to build your own Tic Tac Toe game using Python. You will also learn about getting input from users and error handling.
  7. Command Line Countdown Timer
    Build on your understanding of object oriented programming by creating a command line countdown timer.
  8. Typing Speed Test
    Learn how to create your own typing speed test right inside your terminal. Develop a deeper understanding of the curses module.
  9. Simple Text Editor
    Create a simple text editor using Python with the help of the Tkinter library.
  10. Online C Code Compiler Using Flask
    Learn how to create an online C Code compiler using Flask. You will get a solid grasp of the flask web framework, the subprocess module and the os module.
  11. Live Sketching App
    Build a live sketching app in Python using OpenCV and NumPy libraries.
  12. Static webpage application using Flask
    Learn how to create a static webpage application using Flask. This project also uses basic HTML, JavaScript and CSS knowledge.
  13. Create Login Page using Flask
    Implement a basic version of a login page using Flask. Also learn how to use the sessions extension in Flask.
  14. Deploy a Python Web app on Heroku
    Deploying your apps is important so that others can interact with your project. Learn how to deploy a simple Python Web app on Heroku.
  15. A Static Portfolio Website
    Learn how to create a static website for your portfolio using the Django framework. This project also requires basic understanding of HTML and CSS.
  16. File Hosting Service in Django
    Build a file hosting service in Django where you can upload your files and get a link to share them.
  17. Reconstructing Face using PCA
    Learn how to reconstruct a face from a set of faces using PCA. Principal Component Analysis (PCA) is a common machine learning technique used for handling large datasets and deducing patterns from such datasets.
  18. Time Series Forecasting
    Python is widely used for forecasting and building data models. Learn how to perform time series forecasting using NIFTY50 stock market data and analyse stock market trends of HDFC.
  19. Time Series Classification
    Get a grasp on the topic of time series classification and create a time series classification project. Make use of Pandas, NumPy and the Sklearn libraries along with the Tsfresh Python package.
  20. Predicting Air Pollution Levels
    Perform an analysis of the air pollution levels in New Delhi using Python and Jupyter Notebook.
  21. EEG Signal Analysis
    EEG stands for Electroencephalography. It is a technique used to record brain activity, and helpful in evaluating brain disorders. Learn how to perform EEG signal analysis with the help of the MNE-Python library.
  22. Image Captioning using Keras
    Learn how to implement your own deep learning project by building an image captioning project using Keras. Keras is a popular API used for deep learning projects.
  23. Implement Document Clustering using K Means
    Get a practical understanding of document clustering by implementing your own document clustering using K means project. You will use the popular Scikit-Learn machine learning library along with NumPy, Pandas and the CSV module.
  24. TextRank for Text Summarization
    In Natural Language Processing, TextRank is an important technique for producing document summaries. Learn how to implement textrank for text summarization using modules like math and future along with NumPy and Summarizer libraries.
  25. NLP Project: Compare Text Summarization Models
    Learn more about the concept of Text Summarization and build a project comparing text summarization models. You will use libraries like Gensim, Transformers, Pytorch, NLTK, Sumy and Rouge along with the SentencePiece tokenizer and the JSON module.
  26. Flask Web App for a Machine Learning model
    Learn how to deploy a machine learning model by creating a web application using Flask. This project uses NumPy, Pandas and the Sklearn libraries along with the Pickle module.
  27. Differentiating Fake Faces using Simple ML and Computer Vision
    Fake images and deepfakes are a common problem on the internet. Learn how to differentiate fake faces using machine learning and computer vision. This project uses Jupyter Notebook along with OpenCV, NumPy, Matplotlib and Scikit-Learn libraries.
  28. Build and Use an Image Denoising Autoencoder model in Keras
    Learn how to build an Image Denoising Autoencoder model using Python and libraries like Keras, NumPy and Matplotlib.
  29. Build ShuffleNet using Python
    Implement a neural network called ShuffleNet using Python. Utilize libraries and modules like Pytorch, NumPy, os, math, datetime, tqdm, tarfile, warning, Matplotlib and PIL.
  30. Native Language Identification (NLI)
    In Natural Language Processing (NLP), NLI is an important concept. Get a grasp on NLI by building a native language identification model that identifies the native language of the author. This model makes use of the Sklearn library and the re module for regular expressions.

Generated by OpenGenus. Updated on 2023-11-27