Python Programming - Beyond Basics to Applications

Explore Python GUI programming basics and a landscape of frameworks, from Tkinter and Qt to wxPython, web-based tools like Remi and PyWebView, and cross-platform design.

Explore Python tkinter GUI programming by building apps with widgets like frames, labels, buttons, entries, text, canvas, and menus, and handling events to create windows and dialog boxes.

Explore socket programming in Python by building a client-server model using TCP sockets, understanding IP addresses, ports, and the Python socket module through a hands-on example.

Learn to use the developer console to explore the http protocol, understand get requests and headers, and perform http requests in Python using sockets and the requests library.

Retrieve web pages and read HTML in Python by using http.client, urllib, and requests; learn to create connections, send requests, process responses, and view page HTML.

Learn to parse web pages in Python using Beautiful Soup, covering HTTP requests, web scraping, HTML parsing with lxml, data extraction, and ethical web scraping.

Explore crud operations with SQLite using DB Browser for SQLite, mastering create, read, update, and delete on a single table and basic SQL queries.

Create a sqlite3 table in Python by connecting to test.db and executing a create table statement for users with name and email fields.

Insert data into the users table in a SQLite database from Python using an insert into statement with name and email values, then commit to save.

Learn to retrieve data from a SQLite database in Python using sqlite3, creating a connection and cursor, executing select queries, iterating results, and displaying records with where conditions.

Learn how to update data in a SQLite database using Python, including writing update statements with set and where clauses, and committing changes.

Learn how to delete data in SQLite with Python, using the delete statement and commit, then verify results with a select query.

Explore open-source Python libraries for data processing, modeling, and visualization, including NumPy, SciPy, pandas, Keras, scikit-learn, PyTorch, TensorFlow, XGBoost, matplotlib, seaborn, Bokeh, Plotly, and BeautifulSoup.

Install numpy, create ndarrays with np.array, and inspect shape and dtype. Learn to use zeros, ones, empty, arange, and linspace, and see how numpy integrates with pandas, matplotlib, and scikit-learn.

Explore printing numpy arrays across dimensions, from 1-D rows to 3-D lists of matrices, using reshape to form matrices and set print options to control full or partial output.

Explore numpy universal functions (ufuncs) and apply elementwise operations such as exp and sqrt on arrays. Learn cross products of vectors and how axis parameters shape results.

Explore Matplotlib, the open source Python library for data visualizations, covering installation, figures and axes, pyplot usage, and simple plots like sine curves and scatter plots.

Explore matplotlib coding styles, including explicit object-oriented figure and axes management and implicit pyplot plots, with examples of linear, quadratic, and cubic data, labels, titles, legends, and helper functions.

Explore Matplotlib styling artists to control colors, line widths, and line styles, and learn to customize plots and scatter markers with edge and interior colors, plus legends.

Explore labeling plots in matplotlib by setting axis labels and titles, adding text, and using grids, annotations, and legends in histograms and line plots.

Explore Matplotlib axis scales and ticks, including linear and log scales, tick locators, and manual ticks; learn to add secondary axes and handle dates and strings.