Head First Python. 3rd Edition

Head First Python. 3rd Edition - Opis

What will you learn from this book?Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, you'll quickly grasp Python's fundamentals by working with built-in data structures and functions. You'll build your very own web app, which-once it's ready for prime time-runs in the cloud. You'll learn how to wrangle data with Python, scrape data from the web, feed data to pandas, and interact with databases. This third edition is a complete learning experience that will help you become a bona fide Python programmer in no time.What's so special about this book?If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. With this book, you'll learn Python through a multisensory experience that engages your mind-rather than a text-heavy approach that puts you to sleep. Spis treści:
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 (...) więcej 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 O autorze: Paul Barry pochodzi z Belfastu. Obecnie mieszka wraz z rodziną w niewielkim miasteczku Carlow w Irlandii Północnej. Od 1997 roku wykłada w Instytucie Technologicznym w Carlow. Przedtem (w Irlandii i Kanadzie) zajmował się rozwiązaniami IT dla opieki zdrowotnej. Barry jest ekspertem w dziedzinie programowania w Pythonie i w Perlu. W przeszłości był stałym współpracownikiem „Linux Journal Magazine”, napisał także kilka książek o programowaniu. mniej