Become a Professional Python Programmer

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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 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.

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.

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.

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 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.

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

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.

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 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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.