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.

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.

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

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.

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

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.

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 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 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 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.

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.

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.

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.

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 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 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.

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 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.

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.

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.

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.

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.

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.

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

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 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.

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.

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 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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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

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.

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.

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.

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 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.

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.

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.

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

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.

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.

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.

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 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 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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.