50 Days of Learning 50 Pro Level Python Concepts

Explore Python's built-in hashing with the hashlib module, generating fixed-size hashes for strings using sha256 and other algorithms, and verify determinism, one-way properties, and the avalanche effect.

Explore Python's hashing module by implementing sha-2, sha-3, shake, and Blake algorithms, compare security levels, and note md5 and sha-1 as insecure for new apps.

Hash large files efficiently by processing data in four kilobyte chunks, streaming bytes into a sha-256 hasher, and outputting the hexadecimal digest.

Learn to serialize Python objects to JSON using dumps and loads, then save to JSON files, recognizing JSON's lightweight, human readable format and its use in web APIs and configurations.

Learn how to deserialize JSON into Python objects using the JSON module, converting JSON strings or files into Python objects for data manipulation.

Discover how to send json data over a post request, including username and password, and learn to handle nested json and save preferences to settings.json with json.dump.

Explore Python turtle graphics by importing the built-in turtle module, drawing on a canvas with a pen, and using forward, backward, left, and right commands to create shapes.

Master turtle graphics in Python with pen up and down, pen size, color, and fill for circles and squares. Position with go to, set heading, and home for dynamic drawings.

Explore advanced turtle features in Python: draw circles with extent, use undo and speed, query position and coordinates, measure distance, manage pen state, and customize shape and size.

BeautifulSoup is a Python library that simplifies web scraping by extracting data from HTML and XML, using parsers like lxml or HTML5 lib.

Learn to scrape a web page with BeautifulSoup in Python. Import bs4's BeautifulSoup, create a soup object, and use prettify and find to extract titles and div content.

Learn to scrape and print specific data from a web page with BeautifulSoup, selecting div.article, extracting headings and descriptions via find, find_all, and for loops.

Learn to use BeautifulSoup to extract targeted text from article divs by using find_all and indexing, navigating h2 and a tags, and printing Katy Perry or the second paragraph.

Learn to scrape the 50 most beautiful places in the world using Beautiful Soup and requests, extracting titles and descriptions from the site and printing them to the console.

Discover how a server provides content to clients, processes requests, and delivers responses, while a proxy server acts as a gateway with caching.

Compare TCP and UDP protocols, highlighting TCP's reliable, ordered delivery through connections, acknowledgments, and timeouts, versus UDP's faster but unreliable, connectionless delivery for video conferencing, live streaming, and online games.

learn how sockets enable client–server communication, built from an IP address and port, and how DNS translates domain names to IP addresses for connecting to servers.

Create a server using sockets on localhost:8080, bind and listen for TCP client requests, accept connections, and send the encoded message before closing the connection.

Develop an understanding of building a Python client that connects to a local server using a TCP socket, receives, decodes, and prints messages in a loop before closing the connection.

Develop a file server that receives a requested file name from the client, opens and reads the file, and sends the content back, or returns a file not found error.

Build a file client and server using socket programming to request and transfer a file by name, handle not found cases, and print the decoded content.

Build a chatroom application with a Python server that accepts client connections, manages a list of connected clients, and broadcasts messages to all clients using threading.

Learn to build mobile apps in Python with Kivy. Create labels, buttons, images, and text inputs, and design layouts with grid, box, float, anchor, while handling keyboard, mouse events, and clocks.

Explore f-strings in Python, the formatted string literals, a concise way to embed variables and expressions inside strings. Learn about basic insertion, multiline f-strings, and debugging benefits.

Discover how to manage quotes and backslashes in f-strings, using single, double, or triple quotes and escaping, with path examples using raw strings.

Explore type conversion in f-strings, converting numbers to hexadecimal, binary, octal, and more, formatting with commas, underscores, percentages, and signs, plus lambda functions with f-strings.

Learn how to embed lambda functions in f-strings in Python, including simple examples and readability considerations, with guidance on when to avoid complex lambdas.

Explore OpenCV, the open source computer vision library, and learn its use in image processing, real-time video analysis, and Python-based applications like face detection, text recognition, and brain tumor detection.

Explore how OpenCV uses the HSV color model in Python to represent colors, explaining hue, saturation, and value and how they relate to brightness and rgb.

Install OpenCV by running pip install opencv-python in your command line or PyCharm terminal, using the right package for Python 3.x or 2.x, and ensure internet access.

Learn to read, display, and save images with OpenCV in Python using cv2.imread, cv2.imshow, waitKey, and cv2.imwrite, including color and grayscale options.

Learn how to handle keyboard events in OpenCV by capturing key presses, including exiting with escape (27). Use ord to trigger writing an image named two.png and destroy windows.

Explore drawing geometric shapes on images with OpenCV, using line, rectangle, circle, and text functions to annotate the Lena image in color, with thickness, color, and font options.

Capture live video with OpenCV using VideoCapture to read and display frames from the default camera with imshow, then exit on Q, release resources, and check frame width and height.

Save captured video with OpenCV by using the video writer, selecting a fourcc code like xvid, and writing frames at 20 fps in a 640x480 frame.

Learn to overlay dynamic text on video frames with OpenCV by displaying frame width and height, and current date and time using cv2.putText, font settings, and BGR colors.

Explore mouse events in OpenCV by listing available events, filtering input actions such as left button down, left button up, double click, mouse movement, and wheel events with cv2.

Learn to implement mouse events in OpenCV with Python by using setMouseCallback, handling left and right clicks and double-clicks, and overlaying text on images with putText.

Explore simple image thresholding in OpenCV to separate an object from its background by thresholding pixels. See binary and binary inverse thresholds and how a gradient image demonstrates the result.

OpenCV object detection demonstrates detecting red color by converting BGR to HSV, masking with a lower and upper bound, and applying bitwise and to reveal results.

Explore NumPy, the numerical Python library that powers scientific computing with fast multi-dimensional arrays and matrices, enabling efficient data operations in Python for SciPy, scikit-learn, and OpenCV.

install numpy for python with pip install numpy from the terminal or command line. PyCharm users may need a manual install, while Spyder or Jupyter often include numpy by default.

Install and import numpy, create and print a one-dimensional array, access items by index (including negative indices), and explore size, item size, nbytes, dtype, and updating items.

Create a two-dimensional numpy array A2 with three rows and four columns, using zero-based indexing. Inspect shape, size, dtype, and itemsize, and update a value by specifying row and column.

Learn to perform element-wise arithmetic on 1D and 2D numpy arrays, use indexing and slicing to access data, and manage dtype conversions for clear results.

Learn how to use numpy built-in functions such as zeros, arrange, linspace, reshape, and concatenate to create, reshape, and combine arrays with practical examples.

Explore how to compare NumPy arrays and their elements, use array_equal to compare complete arrays, and apply logical operations like logical_or, logical_and, xor, and not on arrays.

Master numpy’s mathematical functions (cos, sin, tan, arc sine) and array operations (add, abs, power, log, reduce, accumulate), plus max, min, arg max, sqrt, append, and random integers.