Become a Professional Python Programmer

Explore Python for the web in a fast crash course, covering basics, internet access, databases, and web apps built with Flask and natural language processing-based sentiment analysis.

Explore object oriented programming in Python, learn how modules use it, and understand what you can do with them and what you can't when interacting.

Explore the basics of object oriented programming in Python, covering classes, objects, and methods, with init and self, inheritance, and how to define and create objects.

Demonstrate object oriented programming in Python by creating objects, adding an upgrade method, and modifying internal attributes to show how modules and object interactions work.

Explore what Python modules are, why they save time, and how to install and use them with pip. Cover licensing, path issues, and wheel files for easy setup.

examine how python modules are programs and where they live—from local scripts to the standard library (lib) and site-packages, including third-party modules, and how to import and alias them.

Review the basics of object oriented programming in Python and how to interact with modules, download modules, and use Python namespaces, preparing for databases in the next tutorial.

Explore basic database concepts with SQLite in Python, and learn why databases outperform text files for long-term storage and web data persistence.

Discover how Eskew light enables lightweight databases, storing data in tables with columns and rows, using locking to prevent corruption, and understanding data types.

Import Eskew lights, connect to a new database tutorial.db, and create a cursor to manage data. Then define and create the example table with language, version, and skill level columns.

Insert data into a table using insert into values, manage the database connection, and explain how temporary files and the database log handle concurrent inserts in Python.

Learn to insert dynamic data into a database in python by simulating user input, collecting language, version, and skill level, and executing a dynamic insert with string formatting.

Learn to read data from a database in Python by executing select all from example, filtering with a where clause using input, and applying limits to queries.

Learn how to limit results, update records, and delete rows in SQLite, using select, update, delete statements, and commits to persist changes.

Recap basic sqlite concepts: database vs table, CRUD operations, and dynamic query formatting. Compare sqlite for local development with mysql for production, and preview Python web access and scraping topics.

Explore the built-in urllib module to access websites, fetch data, and customize requests and headers in Python, laying the foundation for internet programming.

Master urllib to perform http requests, read responses, and inspect raw html to extract meaningful data from web pages.

Learn to build a google search url with urllib and requests, add query parameters, handle spaces with percent encoding, and encode data in utf-8 while exploring get vs post.

Discover how urllib headers accompany web requests, including the user agent that identifies python urllib to servers. Learn why machines face blocking, and how APIs enable efficient programmatic access.

Explore XML and RSS fundamentals, including Extensible Markup Language, common RSS tags (channel, item, title, link, description), and how to parse feeds in Python while noting usage legalities.

Learn to parse XML and RSS feeds in Python using BeautifulSoup, install bs4 with pip, fetch data with requests, and extract item links for web scraping.

Cover urllib and requests for internet access, build and customize requests, explain API usage, and introduce BeautifulSoup for parsing HTML/XML and extracting data from web pages.

Learn the basic HTML page structure and common tags, and how to parse or scrape data by targeting elements such as paragraphs.

Learn the basic HTML document structure, including html, head, and body tags, and how to set a title; save files as .html in Notepad++ and view in a browser.

Explore the head section’s role in HTML pages, including meta tags and the meta description, which influences search results; learn to link stylesheets with rel=stylesheet and href paths.

Explore header and navigation bar structure using header and nav tags to create a consistent site banner and navigation across pages, and learn how headers aid search engine indexing.

The body tag contains the dynamic content of a web page, including header and footer sections. Headings from h1 to h6 organize content and reflect HTML standards.

Learn html basics for professional Python programmers: use comments to document code, add footers, and structure pages with div tags and containers; apply styling with style sheets and inline styles.

Learn to parse paragraph data from web pages using BeautifulSoup with requests, extracting text between p tags. Extract content from XML/RSS feeds, compare with regular expressions to improve scraping.

Learn the HTML structure—head, header, body, footer, and paragraph tags—and use Beautiful Soup to parse web page paragraphs, with notes on style sheets and Bootstrap.

Learn how to host Python web apps on a virtual private server, set up a VPS with Digital Ocean, and run scripts 24/7 with hourly payments.

Explore virtual private servers for running Python scripts 24/7, compare providers like Amazon Web Services and Digital Ocean, and set up a Ubuntu droplet with backups and SSH access.

Learn how to access a VPS using SSH, configure the root user and password, and install Python and pip to manage packages for server scripts.

Transfer files between your server and your computer using FileZilla with ftp or sftp. Navigate directories, edit with nano, and run python scripts on the server to verify changes.

Learn to transfer files with python using pysftp, set up with pycrypto and paramiko, and implement push and get functions to move files between local and server.

Create and manage a virtual private server, move files with file Zilah and S-sh, operate headless, and transfer data between home and servers, set up a database.

Learn to set up a MySQL database for Python web apps, compare it to SQLite, and explore its multiple interactions, speed, and robust documentation.

Install mysql server and client, create and use epic db, build a programmers table with name, language, and email fields, and run show databases and describe table queries.

Master hands-on MySQL basics by inserting into a programmers table, querying with select, and applying updates, deletes, and table operations like show tables, describe, truncate, and drop.

Build a reusable Python database connection by creating a db_connect module that returns a connection and cursor, using host, user, password, and database, then test it on the server.

Fetch data from an rss feed with beautiful soup and automatically insert time-stamped links into a database, including table creation, script setup, and commit operations.

Learn to extend a Python script that parses RSS feed links and inserts them into a database, adding logic to avoid duplicates by checking existing links before insertion.

Explore running a Python script 24/7 on a server using nohup, python insertion to PI, and an ampersand to background the process, then monitor with top or htop.

Learn how to schedule scripts with cron, using minutes, hours, days, weeks, and months fields, with asterisk patterns, full paths, and how to edit, run, and restart cron.

Recap of setting up a MySQL database with Python: install client and server, insert and read data, ingest RSS links, prevent duplicates, schedule with cron, and preview Flask.

Learn to set up a Python's Flask web framework project as a web server accessible via IP, build a website quickly, and apply Bootstrap styling for a polished look.

Learn to set up a Flask app on a Linux server with Apache and mod_wsgi, configure /var/www, create static and templates, and define a hello world route in __init__.py.

Set up a Flask backend using a virtual environment, install Flask with pip, and prepare an Apache WSGI configuration to serve on port 80.

Configure a Flask app with a WSGI file in Apache, set directory and static paths, enable logs, protect a secret key, restart Apache, and verify site on server IP.

Learn Flask web development basics, set up live editing, handle errors, and render templates with render_template from an index.html in a templates directory, then introduce Jinja templating for variables.

Explore Jinja templating with Flask to pass variables via render_template, render dynamic HTML, use for loops and if statements, and display values with curly braces.

Learn how to integrate Bootstrap with a Flask web app to style pages, manage static assets, and leverage the grid system and components for responsive layouts.

Explore Bootstrap layout concepts, including 12-column grids, responsive design, and ready-made components like forms, glyph icons, dropdowns, navigation bars, and the integration of jQuery and JavaScript.

Expand your flask web development by adding new pages and linking them in the nav bar, then extend templates to keep nav bar and render with app route and render_template.

Learn to extend templates in a Flask app by using extends and blocks, and render page1 and about pages with Bootstrap.

Learn to handle 404 errors in a Flask web app with a custom error handler. Render a dedicated 404 template and enhance pages with Bootstrap buttons and containers.

Explore a complete Python web workflow, from object oriented programming and modules to building with Flask, scraping with Beautiful Soup, and storing data in SQLite and MySQL.

Download map plot lib for your Python version, install with pip, verify by importing in idle, and prepare for the next section on basic graph types.

Explore the basics of matplotlib in python for data visualization, including graph requirements, titles, labels, legends, loading data from csv files, and charts: line, scatter, bar, histogram, stacked, and pie.

Learn basic Matplotlib plotting with pyplot, creating line graphs from X and Y data, rendering graphs with show, and common X-Y length errors to troubleshoot.

Add x and y labels and a title to a Python matplotlib graph to clarify the data, then explore figure window controls for panning, zooming, configuring subplots, and saving figures.

Learn to add a basic legend in MAP plot lib plots with multiple lines, labeling each line via the plot method, and explore moving the legend outside the graph.

Learn to create bar charts with matplotlib by plotting x and y data, customizing colors and labels, and adding a legend and titles for clear data visualization.

Explore how histograms reveal data distributions and differ from bar charts. Learn to set bins, use built-in histogram functions, and adjust bar width and cumulative options with test scores.

Use scatter plots to explore correlations and group differences between time spent on tests and test scores, and learn to label axes, titles, and multiple data sets.

Learn how stack plots visualize a whole and its parts over time in Python, using a company budget example with taxes, overhead, and entertainment, and customize colors, legends, and labels.

Learn to create pie charts in map plot live in Python, including sizes, labels, and colors. Adjust start angle, show percentages, add shadows, and apply explode effects for emphasis.

Load data from a csv file in python with the csv module, using a comma delimiter to build x and y, then plot with matplotlib.

learn how to load data with NumPy from text or csv files, use a delimiter, and perform unpacking to separate x and y for efficient data import.

Review the basics of Matplotlib, how it works, and how to add titles to graphs, while covering line graphs, scatter plots, bar charts, and histograms.

Explore basic customization for data visualization with map plot lib, covering palettes, labels, dates, ticks, lines, fills, alpha, subplots, spines, and creating your own styles or style sheets.

Explore fetching stock time-series data from Yahoo Finance API, manage multiple data series (open, high, low, close, volume), convert dates for map plot lib, and apply moving averages.

Pulls stock data from the internet in Python, decodes bytes to strings, and parses newline and comma delimited lines (date, open, high, low, close, volume) into stock_data list for visualization.

Develop a date conversion function to enable plotting stock data in Python using matplotlib, converting date strings to numeric values and rendering a line plot with date formatting.

Learn to customize stock charts by adding a grid, rotating tick labels to prevent overlap, and tweaking axis options and subplots to improve readability.

Convert unix time stamps to readable dates and adjust subplot layouts to improve time-series visualizations in Python, using date conversion and subplot configuration techniques.

Learn to customize ticks and axis labels in Python plots, set axis label colors, and control y-ticks with precise values, while preparing for legends and alpha-based fills later.

Explore how to create filled areas under lines with Matplotlib, control opacity via alpha, customize edge colors, and apply conditional fills to highlight regions above or below a threshold.

Customize spines in your plot by changing color and thickness, and remove them as needed; draw horizontal lines to highlight thresholds while adjusting axes for clearer visuals.

Create open high low close candlestick charts with matplotlib finance tools, prepare data arrays including open, high, low, close, and volume, and customize colors and date formatting for financial visualization.

upgrade matplotlib to use style sheets that theme plots with presets like ggplot and dark_background, learn where style files live, and preview creating your own style sheet.

Create and apply your own styles in map plot lib to customize fonts, colors, axes, and grid, building a style file from defaults and saved backups for consistent visuals.

Learn to create live updating graphs in Python using matplotlib animation, reading data from a file, and refreshing the plot with a clear, interval-driven update.

Learn to add and place text in Python data visualizations, including plotting data, customizing a font dict, and dynamically annotating specific points on charts.

Learn to annotate a specific point on a stock plot in Python, using text, arrows, and axis fractions to highlight events like oil spills and price moves.

Learn to dynamically annotate the last price on stock charts with a map plot library, using x-y positioning and styled boxes to highlight live values.

Explore extensive customization in Python data visualization with the map plot lib module, covering colors, spines, live plots, open-high-low-close candlestick charts, subplot parameters, spacing, and reusable style sheets.

Learn how to create sub plots in map plot lib to display multiple related plots on the same figure, with options for different x and y axes or shared axes.

Learn how to create and customize multiple subplots using a figure and add_subplot, exploring one-by-one, two-by-one, and three-by-two layouts, and compare subplot to grid for complex arrangements.

Explains subplot2grid for flexible multi-plot layouts, setting grid size, start points, and row and column spans; compares it to basic subplots and shows customization for indicators.

Learn to build a candlestick chart suite in Python using subplot to grid for open-high-low-close data with top, middle, and bottom indicators. Tune axes, labels, and annotations while selecting styles.

Create moving averages from data using a quick function and np.convolve to smooth prices over a window, align dates, and plot on multiple axes.

Add a high minus low indicator to the price chart to track volatility from daily highs and lows in Python, using mapping and list conversion for plotting.

Learn how to customize x-axis date labels in Python data visualization, remove extraneous dates, and visualize moving averages with custom red and green fills using fill_between.

Add axis labels and tune x and y ticks with custom locators to prevent overlap, then prune and bin markers for cleaner plots, and prepare for volume and legend enhancements.

Share x axes across multiple charts to zoom together, align data with a calculated start, and adjust visuals; explore moving averages and preparing for y-axis sharing and multiple axes.

Learn to plot volume on a price chart by adding a second y axis, sharing the x axis, and visualizing volume with a filled area while tuning grid and scale.

Add and customize legends in Python plotting by labeling age, volume, and moving averages on candlesticks, set location and columns, and adjust font, box, padding, and alpha.

Master subplots in MAP plot lib to display multiple graphs in one figure, using add subplots or grid-based subplot syntax. Preview planning geographically with base map.

Plot geographic data with Python using Basemap to convert latitude and longitude into map plots, exploring pre-installed maps including globes and flat maps.

Learn to download, extract, and install Basemap for Python map plotting using SourceForge or unofficial binaries, install via Python 3.4 and pip, and verify by importing Basemap.

Add a base map with the basemap toolkit to visualize coastlines and continents using a Miller cylindrical projection, and convert latitude-longitude to coordinates for map boundaries.

Explore customizing map projections in a base map, switching from Miller to other projections, setting bounding coordinates, adjusting resolution, and adding tiles and a title to the map.

Learn to customize base maps in Python plotting by adjusting boundaries, colors, and fills for continents, lakes, and topography, and by drawing countries, states, and counties.

Plot coordinates on a base map using a plotting library, flip latitude and longitude to x and y, and tune markers while performing a land check.

Learn to connect coordinates on a base map by plotting points, drawing a line, and adding a legend and color to visualize geographic routes.

Explore geographical plotting with the basemap extension of map plot lib, feeding latitude and longitude to visualize globe and flat maps and zoom into specific countries.

Explore 3D graphing with map plot live, adding a third dimension to visualize three axes and compare three features at once, including 3D scatter plots, wire frames, and barge shards.

Explore 3D plotting in map plot lib by creating a 3D axes, plotting a wireframe with X, Y, Z data, and labeling axes for immersive interaction.

Illustrate three-dimensional scatter plots using x, y, and z coordinates in visualization, apply color and markers to separate groups, adjust alpha for depth, and add labels and legends for interpretation.

Learn to render 3D bar charts by supplying x, y, z starting points and the bars' height, width, and depth—a six-piece data input for a 3D bar plotting function.

Explore how to create and customize a 3d wireframe in Python by using axes 3d, get_test_data, and plot_wireframe, adjusting rstride and cstride for a smoother, more complex grid.

Explore three-dimensional representations of data sets with Python's Map plot lib, creating scatter plots, bar charts, wireframes, and 3d lines from a vanilla install.

Explore the full spectrum of data visualization with Python, from line, scatter, and bar charts to 3D and geographic plots, including date stamps and labels.

Explore pandas, a fast, C-optimized Python data analysis tool using data frames, handling diverse data types and sources like SQL, CSV, JSON, and HDF, with integration to Matplotlib and scikit-learn.

Install and import the pandas module using wheel files or pip, and learn core concepts: series, data frame, index, and slicing.

Learn the basics of working with pandas, navigate around a data frame, and visualize data to understand how a data frame is set up.

Create a dataframe from a dictionary using pandas as pd and name it df. View the data, access columns with df.column or df['column'], and inspect data types and index.

Explore inspecting a data frame with head and tail, perform slicing by column and index, and handle moving averages while clarifying indexing rules.

Explore how to set a pandas data frame index from a column, handle return values versus in-place modification, and view changes with head after indexing.

Learn to visualize data in a pandas dataframe with matplotlib, using Yahoo Finance stock data (open, high, low, close, adjusted close) and plotting multiple series with clear styling.

Convert data to a Python list or a Pandas series and compare usage for iteration and storage. Access the last value using indexing, including negative indices, on both structures.

Explore core pandas data frame concepts with slicing, indexing, and basic visualization, then practice input/output formats, type conversions, and applying custom functions via map or apply.

Explore how pandas handles input and output across multiple data types, loading data into a dataframe and converting between json, csv, and pickle formats, including pandas vs Python pickle.

Load a CSV file and save it after modifying the dataframe with pandas, including setting the year as the index and using to_csv for output.

Explore input and output in python data analysis by reading csv files, setting headers and index columns, renaming columns, and saving results with flexible io options.

Learn to read and write hdf5 files with pandas and PyTables, create hdf stores, and manage datasets using read_hdf and to_hdf.

Learn to load JSON data into pandas dataframes and export dataframes to JSON, while handling API responses and Bitcoin depth data for practical data analysis.

Learn to read and write Python pickles to serialize any object (including data frames) in binary form, and compare speed with pandas' pickle methods.

Learn how pandas handles input and output with multiple formats, convert between data types, and manage headers, column names, and date parsing to streamline data analysis in Python.

Explore basic pandas data frame operations, including organizing data, applying logic, and computing rolling statistics and moving averages, and learn when to rely on built-in functions over custom ones.

Learn how to manipulate pandas DataFrame columns with operations and new column creation, using the Yahoo API to compute open, high, low, and close.

Master column and dataframe logical categorization in Python with pandas, using boolean logic to compare close versus open and filter data frames by conditions.

Use describe to generate count, mean, standard deviation, min, max, and percentiles from a data frame. Interpret open prices and volumes to spot unusual movements and anticipate moving statistics.

Apply moving and rolling statistics in pandas to compute windowed moving averages and standard deviation for a panel data frame, then visualize results with Matplotlib.

Define a custom function and apply rolling apply to a pandas data frame to compute a moving average, then compare it with pandas' mean for verification.

Explore basic data manipulation in pandas, including column operations, new columns, descriptive statistics, and rolling statistics like moving averages with rolling apply, with upcoming coverage of missing data and outliers.

Learn to handle missing data and detect outliers in real-world datasets with Python. Build strategies for dropping rows or columns, or filling values, and distinguish outliers from errors.

Explore handling not available data in pandas by dropping NaN rows with dropna, using in-place options, and optionally filling with forward or backward methods.

Learn how to handle missing data in pandas by filling forward and backward, dropping rows, or replacing with a static value like negative 9999, with limits to control changes.

Detect outliers in pandas data analysis using descriptive statistics, rolling standard deviation, and filtering to isolate or remove anomalous measurements, including practical examples from sensor data and bridge readings.

Master handling missing data and outliers in Python by deciding when to fill, drop, or flag invalid values, and prepare data frames for merging.

Learn how to combine data frames in pandas by appending, merging on shared keys such as username, and aligning indices to create fast, unified datasets for analysis.

Develop skills in combining data frames with pandas using concat, align by axis, and add new columns gracefully while handling missing values in evolving datasets.

Append data frames in pandas using append, with emphasis on index handling and ignore index when combining data. Note that appending is slow and avoided in favor of database updates.

Explore how to merge and join data frames in pandas by combining two frames on a common column, such as temperature, and view how index choice affects joins.

Learn to join dataframes in pandas by setting a username index and using join or merge to combine keys, handle missing rows, and choose left, right, outer, or inner joins.

Combine data frames in pandas using concatenation, appending, joining, and merging, and learn resampling and open-high-low-close techniques for efficient analysis of large datasets.

Explore advanced pandas operations in Python, including resampling for visualization, correlation and covariance tables, buffering data for RAM-limited datasets, and mapping functions toward database work.

Learn to sort data in python with pandas by single or multiple columns, using ascending or descending rules, and pull data from quanto for sorting by date or prices.

Learn to sort data by multiple rules using pandas, sorting by grade, then by age, then by name, with ascending and descending options.

learn how to resample time series data in python using pandas to aggregate into different frequencies, applying mean, sum, or open high low close, and manage gaps.

Learn how to resample stock price data to open, high, low, and close using pandas, handle missing data with forward fill, and address weekend gaps in daily to multi-day intervals.

Learn to compute correlation and covariance across a pandas data frame and build tables that reveal relationships among crime rate, employment rate, and income.

Learn to compute correlation and covariance in Pandas, combine multiple data frames, rename columns, set indices, and build a comprehensive correlation table while noting correlation is not causation.

Learn to write and map a custom function to a pandas DataFrame column to compute percentage differences between income segments and visualize results with matplotlib.

Compare percentage changes across income groups by graphing bottom, middle, and top fifths using one-liner calculations; separate calculations from plotting to build clear, labeled visuals.

Master buffering basics to handle massive data by reading in chunks instead of loading the full dataset into memory, using pandas and Bitcoin price data as examples.

Explore buffering data into HDF5 stores using chunked reads and writes to balance memory and speed, compare with SQLite databases, and learn how to name columns and handle chunked appends.

Explore advanced pandas operations, including resampling to downsize data frames and OHLC calculations, apply basic statistical operations. Buffer large datasets beyond memory with pandas and prepare for database workflows.

This section shows how to use pandas for data analysis with Python to work with a database, load datasets, output data, create or modify databases, and re-enter data through examples.

Learn to use pandas to populate a simple database from a data frame and read back into a data frame with read_sql. Use chunking and append semantics.

Convert unix time to pandas datetime, set the date index, and resample to an ohlc format for candlestick plotting, preparing a graph-ready dataset.

Finish building an open high low close graph and candlestick chart with 10-day and 50-day moving averages, using pandas and matplotlib to plot daily OHLC data.

Wrap up the pandas with Python for data analysis series by exploring data frames and series, performing calculations, handling missing data, and resampling data.

This practical introduction to Django from scratch builds foundational concepts and rapid, robust features with automatic migrations and built-in views, while encouraging proactive Python learning.

See how to build mystery books, an online Django bookstore, with adding and listing books, book pages, reviews, a Google map, a cart, Stripe checkout, and emails.

Learn to set up a Django project in a Python virtual environment, install Django, and run basic management commands, while exploring models, databases, migrations, and simple views and templates.

Set up a Python 2.7 virtual environment, install Django with pip, create a Django project, configure database settings, run initial migrations, and start the development server to verify the setup.

Explore how Django uses the MTV approach to model, template, and view data, and solidify development workflow with git version control, commits, and project organization.

explore basic django views and templates within a project and app, render templates from a view using a request, and configure urls with include and regex rules.

Create and manage Django models and databases using migrations and the admin interface; define a Book model, run migrations, and register models for admin.

Recap the Django section by setting up a virtual environment, installing Django, creating a project and app, building templates and URLs, and applying migrations via the admin interface.

Learn to implement a user authentication system using a third-party module to handle registration, activation, and emails, then add cookies and session variables, plus social registration with Facebook or Google.

Learn to set up a Django user authentication system with registration redux, including sign up, login, activation emails, and templates, plus project setup with virtual environments and migrations.

Register users, activate accounts via email, and manage login and session variables with templates and simple settings. Observe how session data drives personalized UI.

Master social registration in a Django app by integrating Python-social-auth, enabling sign-up with Facebook, Google, and Twitter, and configuring backends, keys, and login templates.

Explore a simple user registration system with full authentication and login capabilities. Learn how cookies, session variables, and Django enable secure sign-ins and social registration via Facebook.

Explore Django's front-end features, enhance website aesthetics, and integrate Twitter Bootstrap for styling. Learn to manage static assets, compress and optimize loading, and refactor templates to improve layout.

Learn to master Django's template language by using variables, tags, filters, and blocks, and implement static files with load static for Bootstrap integration and template inheritance.

Learn to integrate Twitter Bootstrap 3 into a Django project using template inheritance, static assets, and Bootstrap themes to create a responsive, polished web interface.

Refactor registration and activation templates using bootstrap, implement template blocks for reuse, and enable static file compression with Django compressor by updating settings and static assets.

Review Django template language and static files, explore integrating Twitter bootstrap for a simple responsive user interface, and examine static file compression and template refactoring to optimize content delivery.

Build a functional e-commerce storefront by implementing a shopping cart and integrating PayPal and Stripe via AJAX, bringing the app to life.

Create and link author and review models, display in-stock books on a tiled storefront, and build a product detail page with reviews, stock info, and linked routes.

Learn to build a Django shopping cart from scratch by modeling a cart and book order with user, active status, order date, and payment fields, plus add and remove methods.

Learn to wire PayPal payments into a Python app's shopping cart by creating a PayPal app, using sandbox accounts, installing the PayPal REST SDK, and building the checkout flow.

Learn to implement Stripe checkout with Ajax in a Django app, including token handling, cents conversion, and updating orders. Protect requests with CSRF, use test mode, and manage redirects.

Review the storefront grid tiles, add a shopping cart, integrate PayPal, and explore credit card payments using ajax-powered interactions to enhance user experience.

Expand a practical Python project by adding image uploads for book covers, enabling review submissions via forms, and integrating an enterprise mail service for professional emailing.

Create a new branch for file uploading, configure a media route, add an image field with per-book upload paths, and update templates and migrations with pillow support.

Create a Django review form, handle post vs get requests, validate input, and save a review linked to the user and the book to display on the book details page.

Upgrade emailing by switching from Gmail to Mailgun and configuring SMTP settings in a Django app. Send discount codes after user reviews using email templates with text and html formats.

Upload images to replace the default book cover and finalize the reviews module with a Gengel form. Use the meal guide’s external cloud emailing service for automatic post-purchase emails.

Learn geolocation and map integration by embedding a Google map, showing the reviewer’s location, and storing latitude and longitude to build waypoint markers for all reviews.

Add a Google map on detail page using geo location to detect the user’s city from IP, and render latitude and longitude on a map canvas with Maps API fallback.

Explore advanced map usage in a Django app by adding latitude and longitude fields, saving spatial data in reviews, and rendering markers with geolocation on a dynamic map.

Integrate google maps with a geolocation module to show the user’s location on the map for review and book detail pages, dropping pins for each review using latitude and longitude.

Power up Django with practical add-ons and build an api with tasty pie to serve structured data for third-party use and map integration, while exploring post save signals for carts.

Install Tastypie and set up a Django API to expose book, review, and user resources. Use migrations, authentication, and a v-1 API endpoint to serve data to the front end.

Explore django signals by wiring a post_save receiver to auto adjust stock after a purchase, decrementing inventory and sending a purchase confirmation email.

Explore an API-driven workflow powering a Google Maps integration, with front-end separation from back-end logic, and use the post_save signal to adjust stock after purchase and send a thank-you email.

Enable the Jenko debug toolbar to run unit tests and programmatically test the site, then introduce logging to record Django events.

Install and enable the Django debug toolbar on the development server to inspect requests, queries, headers, and static files, and use its panels to debug and profile Django behavior.

Explore unit testing in Django by building test cases, setting up a separate test database, and validating views, models, and cart flows from login to checkout.

Explore how to implement robust logging in a Django app, using the Django debug toolbar for in-place debugging, and configure formatters, handlers, and rotating file logs.

Learn to use the Jenga debug toolbar to observe traffic and events behind the scenes, and implement logging to capture application events in a file for ongoing testing and insight.

Deploy your Django web app, strengthen testing, and refine features like maps and user systems. Consult two scoops of Django and full stack Python resources to deepen your learning.

Use Python and pygame to create a basic helicopter game. The course covers installation, importing pygame, initializing, creating a display surface, handling events, and a 60 fps game loop.

Learn graphics setup in pygame with Python 3 by loading a helicopter image, defining rgb colors, and drawing it with surface.blit in a game loop using 0,0 origin and fps.

Implement input controls in a Pygame helicopter game by handling key down and key up events to move the helicopter, apply gravity, and enforce screen boundaries to trigger game over.

Learn to implement a pygame game over screen with a replay option, a reusable message surface, and dynamic text rendering to center prompts on a resizable window.

Become a Python programmer teaches drawing two obstacle blocks with pygame to create a gap for a helicopter to pass through, with configurable block size, gap, and speed.

Explore game logic for obstacle generation in a helicopter game, learning to spawn new blocks as others leave the screen, adjust gaps, and fine-tune timing and boundaries.

Implement collision logic to determine when the helicopter hits a block or flies through a gap, using x and y coordinates, block dimensions, and boundary checks.

Implement a Pygame-based score system by detecting gap crossings, incrementing the score, and rendering it on screen, with initialization and display updates.

demonstrates adding colors and scaling difficulty in a pygame game by defining color palettes, coloring blocks, and increasing speed and decreasing gaps as the score rises.

Learn to build and design sophisticated Python applications using modern object oriented programming, covering classes, objects, data attributes, constants, constructors, and access modifiers.

Install the Python idle by downloading the official Python idle from python.org, selecting the Windows 32-bit installer for version 3.4.2, and completing the installation to run Python programs.

Explore essential Python constructs, including classes, the default constructor, attributes, methods, and class variables, with hands-on practice and a guided, step-by-step project.

Learn class constructions in Python by declaring classes with the class keyword and colon. Distinguish class data attributes from object data attributes and implement methods that operate on internal data.

Explore the __init__ method as the constructor that initializes data attributes when a new object is created, using a Python class example such as employee with def and self.

Learn how to declare and initialize data attributes in a Python class using the constructor, self, and the dot operator, assigning first name, last name, and user ID.

Explore class methods, defined with def and self, that operate on a class's data, and learn to implement __init__ and indentation-based code blocks in Python.

Learn how to declare and use class variables in a Python class, such as employee count and pay rate, and access them with the class name dot operator.

Explore how to access class data attributes in Python and display values—first name, last name, age, and user id—using the class name and the dot operator in a print statement.

Explore how the Python main function starts and executes a program as a standalone function, while defining a class with an __init__ constructor and invoking main to run the script.

Explore core Python object oriented concepts: the class, the special method as the default constructor, attributes, methods, and class attributes, and how to initialize and use data.

Create a simple project example from section two by setting a title and displaying an employee database with three data attributes—first name, last name, and age—using hardcoded values.

Define a simple Python class for a person with first name, last name, and age, then print these attributes in main to simulate an employee database.

Explore creating Python objects from classes, understand their role in object-oriented programming, and manipulate data attributes and methods through practical examples and a small project.

Define a Python class with an __init__ constructor, set attributes (first name, last name, user id) via self, then create three employee objects in main, each with its memory reference.

Define an employee class with a default constructor, set attributes like first name, last name, and user id, and use getters to access them via the dot operator.

Define a Python class with a default constructor and initialize object attributes. Modify object attribute values using the dot operator and assignment, then reprint updated attributes to see changes.

Learn how to access object methods in Python by building a class with an initializer, and implement getter and setter (accessor) methods for first name, last name, and user id.

Learn to access object methods in Python using the dot operator to call getters and setters, and to display and modify attributes like first name, last name, and user id.

Explore how to create and manipulate a Python object, including accessing and changing attribute values, and using object methods to perform operations and display results.

Demonstrate the section 3 project life by initializing attributes to not available, then updating first name, last name, user id, and position type in a Python program, displaying results.

The lecture explains building a Python class with a constructor setting default values, implementing setters and getters, and a main method that creates, prints, modifies, and reprints an employee object.

Explore the constructor and destructor magic methods, learn why constructors matter for creating unique Python objects, and examine access modifiers and class attributes with hands-on coding projects.

Learn to pass arguments to the constructor, initialize object attributes with self, and map first name, last name, and user id; implement a details method to display them.

Declare and call the main method, define the employee constructor, create objects with first name, last name, and user id, and show employee details.

Learn how deconstructors free memory in Python by destroying objects and triggering the del keyword; observe the garbage collector freeing space.

Demonstrate how public visibility allows accessing object attributes outside the class, reveal the dangers, and set up the next lesson on protecting bank account balance values.

Learn how to implement private data attributes in a bank account class with double underscores, expose access via a get balance method, and modify balance through class methods only.

Explore Python constructor and destructor magic methods, initialize objects, create unique instances with distinct attributes, manage memory, and control access with public and private modifiers.

Watch a live demonstration of creating three bank accounts with first name, last name, account number, and balance, printing data while using private attributes for security.

Define a Python bank accounts class with an init method to set first name, last name, account number, balance, and private data, and a get_account_details method to print each account.

Master class inheritance in Python by building child and parent classes, linking them, importing from files, and overriding methods to create a simple guided project.

Learn to create a child class that inherits from a bank account, using a private balance and super constructor to initialize and extend with a savings account.

Create a savings account object and display its balance. Use inherited bank account methods to deposit 300, withdraw 450, and verify the balance updates from 200 to 500 to 50.

Create a checking account child class that inherits from the bank account, initializes the balance via the parent constructor, and provides deposit and withdraw methods.

Explore inheritance by creating a bank account parent class with a balance attribute and methods like deposit, withdraw, and get balance, then extend it with a child class.

Explore building a bank account class with deposit and withdrawal methods, handling initial and current balances, enforcing funds checks with if-else logic, and previewing inheritance to child classes.

Use import to split code into separate files, improving maintainability. Build a parent class bank account and child classes savings account and checking account, initialize balances, and print them.

Explore how to override parent class methods in a child class to achieve polymorphism, using the same method name with different behavior while still accessing the parent method when needed.

discover how class inheritance enables building a child class that reuses a parent class's pre-existing code, extends functionality, and allows overriding methods like deposit.

Set up two accounts—savings and checking—with initial deposits of 100 and 500, then deposit into each to update savings to 400 and checking to 575, and display the results.

Learn to structure a Python project with parent and child classes for savings and checking accounts, initialize balances, perform deposits, and display updated balances.

watch a live demo of building an inheritance-based bank account system with checking and savings accounts. deposit initial balances and transfer funds from checking to savings to demonstrate behavior.

Explore building the final project by combining bank account, savings, and checking classes through inheritance in one file, with deposits, withdrawals, balance displays, and transfers between accounts.

Demonstrates a Python final project workflow for transferring funds from a checking to a savings account, including balance checks, deposits, and updating balances.

Learn the basic concepts of Python's object-oriented programming and access free resources and thorough documentation to deepen your understanding.

Explore how Scrappy crawls web pages to pull links, text, images, and titles with CSS selectors and XPath, install pip, run your spider, and output JSON or CSV.

Extract text from craigslist ads using a Scrapy spider and XPath, building a Python project named SEO pets and pulling headline text from each listing.

Build and run a scrapy spider to crawl pages, extract pet names with XPath, and output results to CSV or JSON for easy verification.

Learn to build two scrapy spiders for scraping quotes from quote by, first with XPath then with CSS selectors, extracting quote text, author, and tags using div.quote, span.author, and div.tags.

Build a new Scrapy spider to extract quotes, tags, and authors using XPath and CSS selectors. Compare XPath and CSS extraction methods, run tests, and confirm results.

Extract book titles and URLs from two python book resource pages using XPath, identifying the common site item structure and targeting title and URL fields for each book.

Explore a project solution that sets up a spider, uses a jupiter notebook, and parses two urls with xpath to extract name and url, saving results as json or csv.

Learn to extract text using XPath, then tackle extractions with XPath and CSS; complete the caption project by extracting text from two pages and storing them in an array.

Explore Python basics by importing modules, generating random numbers and matrices, computing square roots, and building simple add, subtract, multiply, and divide functions with division-by-zero handling.

Define a norm function and a max-norm finder in Python, generate a linspace array, and compute the maximum norm across subarrays defined by start and end indices.

Learn to compute integrals with code, starting from a simple 0 to 4.5 area under a function, then explore Gaussian and Euler–Poisson integrals, debugging in Jupyter.

Use double integration to compute the area of a right triangle with x from 0 to 10 and y from 0 to 10−x, yielding 50.

Explore statistics programming by constructing a binomial model to assess test outcomes, using the probability mass function to compute exact and expected points for correct and wrong answers.

Develop and manipulate a 4x4 matrix in Python by setting row 3 and column 3 to one, then derive A minus C and multiply by C to yield five outputs.

Learn to install the library, write custom functions, and perform complex computations in Python, including integration, double integration, algebra, dot matrix calculations, and statistics, with a hands-on project walkthrough.

Brett introduces the become a professional Python programmer course, compares it with alternatives, covers simple installation, matrix calculations and arrays, eigenvalues, determinants, covariance, and outlines a project with a walkthrough.

Discover what NumPy is and how it enables efficient array computations for scientific computing, and learn how to install it on Mac or Windows.

Explore basic Python math in this course, using pi, random numbers, and functions like square root and power, then build and expand a matrix with seeding, append, and sign.

Take a tour of numpy basics: import numpy as np, create and print arrays, convert units, and generate evenly spaced values with arange and linspace, noting endpoint behavior.

Learn to create and manipulate numpy arrays, including 3-D shapes, slicing, and reshaping a 0 to 23 range into four by six, plus generating zeros and an identity matrix.

Explore creating numpy arrays of zeros and ones, building constant and two-by-two identity matrices, and populating with random values while managing dimensions and data types.

Create a 2x2 matrix and compute the standard deviation of the entire matrix. Compute the standard deviation of the rows (axis=0) and the columns (axis=1).

Explains how to compute eigenvalues for two matrices using Python and NumPy, including the definition via determinant of (A - λI) and practical code demonstrations.

Compute the covariance of two gaussian arrays using a normalized centralized gaussian function with sigma values 1 and 2, and print the resulting covariance in Python.

Create matrices A, B, and C in Python and compute their determinants with the determinant function. Compare det(A) and det(C) with and without dot product using the Benay Kouachi formula.

Review eigenvalues, determinants, and covariance matrices, including covariance of multiple matrices and diagonal matrices, plus a module project with a walkthrough using the non-pilot library for Python.