Head First Python. 3rd Edition Paul Barry

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• Other books in OReillys Head First series
• Table of Contents (the real thing)
• How to use this Book: Intro
  • Who is this book for?
    • Who should probably back away from this book?
  • We know what youre thinking
  • We know what your brain is thinking
  • Metacognition: thinking about thinking
  • Heres what WE did:
  • Read Me
  • Lets install the latest Python
    • Installing on Windows
    • Installing on macOS
    • Installing on Linux
  • Python on its own is not enough
    • Install the latest Jupyter Notebook backend
    • Install the latest release of VS Code
  • Configure VS Code to your taste
  • Add two required extensions to VS Code
  • VS Codes Python support is state-of-the-art
  • The Technical Review Team
  • Acknowledgments
• 1. Why Python?: Similar but Different
  • Getting ready to run some code
  • Preparing for your first Jupyter experience
  • Lets pop some code into your notebook editor
  • Press Shift+Enter to run your code
    • So Python code really is easy to read and run
  • What if you want more than one card?
  • Take a closer look at the card drawing code
  • The Big 4: list, tuple, dictionary, and set
  • Model your deck of cards with a set
  • The print dir combo mambo
  • Getting help with dirs output
  • Populate the set with cards
  • This feels like a deck of cards now
  • What exactly is card?
  • Need to find something?
  • Lets pause and take stock
  • Python ships with a rich standard library
  • With Python youll only write the code you need
    • Pythons package ecosystem is to die for
  • Just when you thought you were done
• 2. Diving in: Lets Make a Splash
  • How is the Coach working right now?
  • The Coach needs a more capable stopwatch
  • Cubicle Conversation
  • The file and the spreadsheet are related
  • Our first task: Extract the filenames data
    • Everything is an object in Python
  • A string is an object with attributes
    • Take a moment to appreciate what youre looking at here
  • Extract the swimmers data from the filename
  • Dont try to guess what a method does
  • Splitting (aka, breaking apart) a string
  • Theres still some work to do
  • Read error messages from the bottom up
  • Be careful when combining method calls
  • Cubicle Conversation
  • Lets try another string method
  • All that remains is to create some variables
    • Multiple assignment (aka unpacking)
  • Task #1 is done!
  • Task #2: Process the data in the file
• 3. Lists of Numbers: Processing List Data
  • Task #2: Process the data in the file
  • Grab a copy of the Coachs data
  • The open BIF works with files
  • Cubicle Conversation
  • Using with to open (and close) a file
  • Variables are created dynamically, as needed
  • The files data is what you really want
  • We have the swimmers data from the file
  • Lets take stock of our progress so far
    • Your new best friend, Pythons colon
  • What needs to happen next feels familiar
  • The previous chapter is paying dividends
  • Converting a time string into a time value
  • Convert the times to hundredths of seconds
  • To hundredths of seconds with Python
  • A quick review of Pythons for loop
  • The gloves are off for loops vs. while loops
  • Youre cruising now and making great progress!
  • Lets keep a copy of the conversions
  • Creating a new, empty list
  • Displaying a list of your lists methods
  • Its time to calculate the average
  • Convert the average to a swim time string
  • Its time to bring everything together
  • Task #2 (finally) gets over the line!
• 4. List of Files: Functions, Modules & Files
  • Cubicle Conversation
  • You already have most of the code you need
  • How to create a function in Python
  • Save your code as often as you wish
    • Add the code you want to share to the function
  • Simply copying code is not enough
  • Be sure to copy all the code you need
    • Update and save your code before continuing
  • Use modules to share code
  • Bask in the glory of your returned data
  • Functions return a tuple when required
  • Lets get a list of the Coachs filenames
  • Its time for a bit of detective work
  • What can you do to lists?
  • Is the issue with your data or your code?
  • Cubicle Conversation
  • Decisions, decisions, decisions
  • Lets look for the colon in the string
  • Did you end up with 60 processed files?
  • The Coachs code is taking shape
• 5. Formatted String Literals: Make Charts from Data
  • Cubicle Conversation
  • Create simple bar charts with HTML and SVG
    • Lets match up your HTML and SVG to the output you see on screen:
  • Getting from a simple chart to a Coach chart
  • Build the strings your HTML needs in code
  • String concatenation doesnt scale
  • f-strings are a very popular Python feature
  • Generating SVG is easy with f-strings!
  • The data is all there, or is it?
  • Make sure you return all the data you need
  • You have numbers now, but are they usable?
  • Scaling numeric values so they fit
  • All thats left is the end of your webpage
  • Writing to files, like reading, is painless
  • Its time to display your handiwork
  • All thats left are two aesthetic tweaks
  • Cubicle Conversation
  • Its time for another custom function
  • Lets add another function to your module
  • Whats with that hundredths value?
  • Rounding is not what you want (in this case)
  • One more minor formatting tweak
  • Things are progressing well
• 6. Getting Organized: Data Structure Choices
  • Get to know the data youll be working with
  • Lets extract a list of swimmers names
  • The list-set-list duplicate removing trick
  • The Coach now has a list of names
  • A small change makes a big difference
  • Every tuple is unique
  • Perform super fast lookups with dictionaries
  • Dictionaries are key/value lookup stores
  • Anatomy of building a dictionary
  • Dictionaries are optimized for speedy lookup
  • Display the entire dictionary
  • The pprint module prett y-prints your data
  • Your dictionary-of-lists is easily processed
  • This is really stating to come together
• 7. Building a Webapp: Web Development
  • Lets build the Coachs webapp with Flask
  • Install Flask from PyPI
  • Prepare your folder to host your webapp
  • The Flask MVP
  • You have options when working with your code
    • How does your browser and your Flask-based webapp communicate?
  • Building your webapp, bit by bit
    • Spoiler Alert!
  • Whats the deal with that NameError?
  • Cubicle Conversation
  • Flask includes built-in session support
  • Flasks session technology is a dictionary
    • Fixing your quick fix
  • Adjusting your code with the better fix
    • Use render_template to display web pages
    • That list of swimmers needs to be a drop-down list
  • Building Jinja2 templates saves you time
    • Lets get to know a bit about Jinja2s markup extensions to HTML
  • Extend base.html to create more pages
  • Dynamically creating a drop-down list
  • Selecting a swimmer
  • You need to somehow process the forms data
  • Your forms data is available as a dictionary
  • Youre inching closer to a working system
  • Functions support default parameter values
  • Default parameter values are optional
  • The final version of your code, 1 of 2
  • The final version of your code, 2 of 2
  • As a first webapp goes, this is looking good
  • The Coachs system is ready for prime time
• 8. Deployment: Run Your Code Anywhere
  • Theres always more than one way to do something
  • Theres still something that doesnt feel right
  • Jinja2 executes code between {{ and }}
  • Cubicle Conversation
  • The ten steps to cloud deployment
  • A beginner account is all you need
  • Theres nothing stopping you from starting
  • When in doubt, stick with the defaults
  • The placeholder webapp doesnt do much
  • Deploying your code to PythonAnywhere
  • Extract your code in the console
  • Configure the Web tab to point to your code
  • Edit your webapps WSGI file
  • Your cloud-hosted webapp is ready!
• 9. Working with HTML: Web Scraping
  • The Coach needs more data
  • Cubicle Conversation
  • Get to know your data before scraping
  • We need a plan of action
  • A step-by-step guide to web scraping
    • Lets take the Coachs advice and go with a three/two split
  • Its time for some HTML-parsing technology
    • Its time for some em eh cold soup!
  • Grab the raw HTML page from Wikipedia
  • Get to know your scraped data
  • You can copy a slice from any sequence
  • Its time for some HTML parsing power
  • Searching your soup for tags of interest
    • The gazpacho defaults can sometimes trip you up
  • The returned soup is also searchable
  • Which table contains the data you need?
  • Four big tables and four sets of world records
  • Its time to extract the actual data
  • Extract data from all the tables, 1 of 2
  • Extract data from all the tables, 2 of 2
  • That nested loop did the trick!
• 10. Working with Data: Data Manipulation
  • Bending your data to your will
  • You now have the data you need
  • Apply what you already know
  • Is there too much data here?
  • Filtering on the relay data
  • Youre now ready to update your bar charts
  • Cubicle Conversation
  • Python ships with a built-in JSON library
  • JSON is textual, but far from pretty
    • Importing JSON data
  • Getting to the webapp integration
  • All thats needed: an edit and a copynpaste
  • Adding the world records to your bar chart
  • Is your latest version of the webapp ready?
    • But are you really done?
  • Cubicle Conversation
  • PythonAnywhere has you covered
  • You need to upload your utility code, too
  • Deploy your latest webapp to PythonAnywhere
  • Tell PythonAnywhere to run your latest code
  • Test your utilities before cloud deployment
  • Lets run your task daily at 1:00am
• 11. Working with: elephants dataframes: Tabular Data
  • The elephant in the room or is it a panda?
  • A dictionary of dictionaries with pandas?
  • Start by conforming to convention
  • A list of pandas dataframes
  • Selecting columns from a dataframe
  • Dataframe to dictionary, attempt #1
  • Removing unwanted data from a dataframe
  • Negating your pandas conditonal expression
  • Dataframe to dictionary, attempt #2
  • Dataframe to dictionary, attempt #3
  • Its another dictionary of dictionaries
  • Comparing gazpacho to pandas
  • It was only the shortest of glimpses
• 12. Databases: Getting Organized
  • The Coach has been in touch
  • Cubicle Conversation
  • It pays to plan ahead
  • Task #1: Decide on your database structure
  • The napkin structure + data
  • Installing the DBcm module from PyPI
    • Do this to follow along
  • Getting started with DBcm and SQLite
  • DBcm works alongside the with statement
  • Use triple-quoted strings for your SQL
  • Not all SQL returns results
    • Create the events and times tables
  • Your tables are ready (and Task #1 is done)
  • Determining the list of swimmers files
  • Task #2: Adding data to a database table
  • Stay safe with Pythons SQL placeholders
  • Lets repeat this process for the events
  • All thats left is your times table
  • The times are in the swimmers files
  • A database update utility, 1 of 2
  • A database update utility, 2 of 2
  • Task #2 is (finally) done
• 13. List Comprehensions: Database Integrations
  • Four queries to grab the data you need
  • Lets explore the queries in a new notebook
  • Five lines of loop code become one
  • Getting from five lines of code to one
  • A nondunder combo mambo
  • One query down, three to go
  • Two queries down, two to go
  • The last, but not least (query)
  • The database utilities code, 1 of 2
  • The database utilities code, 2 of 2
    • Using a data module supports future refactoring activities
  • Its nearly time for the database integration
  • Cubicle Conversation
  • Its time to integrate your database code!
  • Updating your existing webapps code
  • Review your template(s) for changes
  • So whats the deal with your template?
  • Lets display a list of events
  • All thats left is to draw the bar chart
  • Reviewing the most-recent swimclub.py code
  • Meet the SVG-generating Jinja2 template
  • Code is read more than its written.
  • The convert_utils module
  • list zip what?!?
  • Your database integrations are complete!
• 14. Deployment Revisited: The Finishing Touches
  • Cubicle Conversation
  • Migrating to MariaDB
    • Configuring MariaDB for the Coachs webapp
  • Moving the Coachs data to MariaDB
    • Reusing your tables, 1 of 2
  • Apply three edits to schema.sql
  • Reusing your tables, 2 of 2
  • Lets check your tables are defined correctly
  • Copying your existing data to MariaDB
  • Make your queries compatible with MariaDB
  • Your database utility code need edits, too
  • Create a new database on PythonAnywhere
  • Adjust your database credentials dictionary
    • Edit data_utils.py to support multiple locations
  • Copying everything to the cloud
    • Preparing your code and data for upload
  • Update your webapp with your latest code
  • Just a few more steps
  • Populate your cloud database with data
  • Its time for a PythonAnywhere Test Drive
  • Is something wrong with PythonAnywhere?
  • Cubicle Conversation
  • The Coach is a happy chappy!
• A. The Top Ten Things We Didnt Cover
  • 1. Classes
    • Its not that were against classes
    • But, what if you cant do without a custom class?
    • What does Python class code look like?
    • Playing cards with a class
  • 2. Exceptions
  • 3. Testing
  • 4. The walrus operator
  • 5. Wheres the switch? What switch?
  • 6. Advanced language features
  • 7. Concurrency
  • 8. Type Hints
  • 9. Virtual Environments
  • 10. Tools
  • Programmers code editors (and IDEs)
  • Code formatters
    • Taking notebooks to the next level

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