Real Python

The Real Python Podcast – Episode #28: Using Pylance to Write Better Python Inside of Visual Studio Code

2020-09-25T12:00:00+00:00

A big decision a developer has to make is what tool to use to write code? Would you like an editor that understands Python, and is there to help with suggestions, definitions, and analysis of your code? For many developers, its the free tool, Visual Studio Code. This week on the show, we have Savannah Ostrowski, program manager for the Python Language Server and Python in Visual Studio. We discuss Pylance, a new language server with fast, feature-rich language support for Python in VS Code.

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Python Community Interview With David Amos

2020-09-23T14:00:00+00:00

This week I’m joined by David Amos, the content technical lead here at Real Python.

In this interview, we talk about David’s love of LEGO and mathematics. We also talk about the Python Basics book, which is soon to be out of early access, and his involvement with PyCoder’s Weekly. So, without further ado, let’s get started.

Ricky: Thank you for joining me, David. Many of our readers and members may already know your background, but for those who don’t, let’s ask the inevitable questions: How did you get into programming, and when did you start using Python?

David: I discovered programming by accident when I came across the source code for the Gorillas game on my parents’ IBM 386 PS/2 computer. I guess I was about seven or eight years old. I found something called a .BAS file that opened up a program called QBasic and had all sorts of strange-looking text in it. I was instantly intrigued!

There was a note at the top of the file that explained how to adjust the game speed. I changed the value and ran the game. The effect was instantly noticeable. It was a thrilling experience.

I was obsessed with learning to program in QBasic. I made my own text adventure games. I even made a few animations using simple geometric shapes. It was tons of fun!

QBasic was a fantastic language for an eight-year-old kid to learn. It was challenging enough to keep me interested but easy enough to get quick results, which is really important for a child.

When I was around ten years old, I tried to teach myself C++. The ideas were too complex, and results came too slowly. After a few months of struggling, I stopped. But the idea of programming computers remained attractive to me—enough so that I took a web technology class in high school and learned the basics of HTML, CSS, and JavaScript.

In college, I decided to major in mathematics, but I needed a minor. I chose computer science because I thought having some experience with programming would make it easier to complete the degree requirements.

I learned about data structures with C++. I took an object-oriented programming class with Java. I studied operating systems and parallel computing with C. My programming horizons expanded vastly, and I found the whole subject pleasing both practically and intellectually.

At that time, I viewed programming as a tool to help me with mathematics research. In graduate school, I wrote programs to generate examples and test ideas for my research projects.

It was during graduate school, around 2013, that I found Python and pretty much instantly fell in love. I’d been using C++, MATLAB, and Mathematica as my primary research tools, but Python allowed me to focus on the research problem without getting caught up in the code.

And with Python’s awesome ecosystem of tools for scientific computing, like NumPy, SciPy, PuLP, and NetworkX, I had everything I needed to tackle problems like I would with MATLAB but in a much more expressive manner!

Ricky: You often hear the myth that a strong mathematics background is a prerequisite to be a programmer. While I think you’ll agree that it’s not always necessary for programmers to know advanced math, I’m curious to know how your math and data science background has helped you when writing code.

Read the full article at https://realpython.com/interview-david-amos/ »

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Working With Linked Lists in Python

2020-09-22T14:00:00+00:00

Linked lists are like a lesser-known cousin of lists. They’re not as popular or as cool, and you might not even remember them from your algorithms class. But in the right context, they can really shine. If you’re looking to brush up on your coding skills for a job interview, or if you want to learn more about Python data structures besides the usual dictionaries and lists, then you’ve come to the right place!

In this course, you’ll learn:

What linked lists are and when you should use them
How to use collections.deque for all of your linked list needs
How to implement your own linked lists
What the other types of linked lists are and what they can be used for

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Python Practice Problems: Get Ready for Your Next Interview

2020-09-21T14:00:00+00:00

Are you a Python developer brushing up on your skills before an interview? If so, then this tutorial will usher you through a series of Python practice problems meant to simulate common coding test scenarios. After you develop your own solutions, you’ll walk through the Real Python team’s answers so you can optimize your code, impress your interviewer, and land your dream job!

In this tutorial, you’ll learn how to:

Write code for interview-style problems
Discuss your solutions during the interview
Work through frequently overlooked details
Talk about design decisions and trade-offs

This tutorial is aimed at intermediate Python developers. It assumes a basic knowledge of Python and an ability to solve problems in Python. You can get skeleton code with failing unit tests for each of the problems you’ll see in this tutorial by clicking on the link below:

Download the sample code: Click here to get the code you'll use to work through the Python practice problems in this tutorial.

Each of the problems below shows the file header from this skeleton code describing the problem requirements. So download the code, fire up your favorite editor, and let’s dive into some Python practice problems!

Python Practice Problem 1: Sum of a Range of Integers#

Let’s start with a warm-up question. In the first practice problem, you’ll write code to sum a list of integers. Each practice problem includes a problem description. This description is pulled directly from the skeleton files in the repo to make it easier to remember while you’re working on your solution.

You’ll see a solution section for each problem as well. Most of the discussion will be in a collapsed section below that. Clone that repo if you haven’t already, work out a solution to the following problem, then expand the solution box to review your work.

Problem Description#

Here’s your first problem:

# integersums.py
""" Sum of Integers Up To n
    Write a function, add_it_up(), that takes a single integer as input
    and returns the sum of the integers from zero to the input parameter.

    The function should return 0 if a non-integer is passed in.
"""

Remember to run the unit tests until you get them passing!

Problem Solution#

Here’s some discussion of a couple of possible solutions.

Note: Remember, don’t open the collapsed section below until you’re ready to look at the answer for this Python practice problem!

How did writing the solution go? Ready to look at the answer?

For this problem, you’ll look at a few different solutions. The first of these is not so good:

# integersums.py
def first(n):
    num = 1
    sum = 0
    while num < n + 1:
        sum = sum + num
        num = num + 1
    return sum

In this solution, you manually build a while loop to run through the numbers 1 through n. You keep a running sum and then return it when you’ve finished the loop.

This solution works, but it has two problems:

It doesn’t display your knowledge of Python and how the language simplifies tasks like this.
It doesn’t meet the error conditions in the problem description. Passing in a string will result in the function throwing an exception when it should just return 0.

You’ll deal with the error conditions in the final answer below, but first let’s refine the core solution to be a bit more Pythonic.

The first thing to think about is that while loop. Python has powerful mechanisms for iterating over lists and ranges. Creating your own is usually unnecessary, and that’s certainly the case here. You can replace the while loop with a loop that iterates over a range():

# integersums.py
def better(n):
    sum = 0
    for num in range(n + 1):
        sum += num
    return sum

You can see that the for...range() construct has replaced your while loop and shortened the code. One thing to note is that range() goes up to but does not include the number given, so you need to use n + 1 here.

This was a nice step! It removes some of the boilerplate code of looping over a range and makes your intention clearer. But there’s still more you can do here.

Summing a list of integers is another thing Python is good at:

# integersums.py
def even_better(n):
    return sum(range(n + 1))

Wow! By using the built-in sum(), you got this down to one line of code! While code golf generally doesn’t produce the most readable code, in this case you have a win-win: shorter and more readable code.

There’s one problem remaining, however. This code still doesn’t handle the error conditions correctly. To fix that, you can wrap your previous code in a try...except block:

# integersums.py
def add_it_up(n):
    try:
        result = sum(range(n + 1))
    except TypeError:
        result = 0
    return result

This solves the problem and handles the error conditions correctly. Way to go!

Occasionally, interviewers will ask this question with a fixed limit, something like “Print the sum of the first nine integers.” When the problem is phrased that way, one correct solution would be print(45).

If you give this answer, however, then you should follow up with code that solves the problem step by step. The trick answer is a good place to start your answer, but it’s not a great place to end.

If you’d like to extend this problem, try adding an optional lower limit to add_it_up() to give it more flexibility!

Python Practice Problem 2: Caesar Cipher#

Read the full article at https://realpython.com/python-practice-problems/ »

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The Real Python Podcast – Episode #27: Preparing for an Interview With Python Practice Problems

2020-09-18T12:00:00+00:00

What is an effective way to prepare for a Python interview? Would you like a set of problems that increase in difficulty to practice and hone your Python skills? This week on the show, we have Jim Anderson to talk about his new Real Python article, "Python Practice Problems: Get Ready for Your Next Interview." This article provides several problems, which include skeleton code, unit tests, and solutions for you to compare your work.

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Numbers in Python

2020-09-16T14:00:00+00:00

You don’t need to be a math whiz to program well. The truth is, few programmers need to know more than basic algebra. Of course, how much math you need to know depends on the application you’re working on. In general, the level of math required to be a programmer is lower than you might expect. Although math and computer programming aren’t as correlated as some people might believe, numbers are an integral part of any programming language, and Python is no exception.

In this tutorial, you’ll learn how to:

Create integers and floating-point numbers
Round numbers to a given number of decimal places
Format and display numbers in strings

Let’s get started!

Note: This tutorial is adapted from the chapter “Numbers and Math” in Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you will see references to IDLE’s built-in debugging tools throughout this tutorial. However, you should have no problems running the example code from the editor and environment of your choice.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you'll need to take your Python skills to the next level.

Integers and Floating-Point Numbers#

Python has three built-in numeric data types: integers, floating-point numbers, and complex numbers. In this section, you’ll learn about integers and floating-point numbers, which are the two most commonly used number types. You’ll learn about complex numbers in a later section.

Integers#

An integer is a whole number with no decimal places. For example, 1 is an integer, but 1.0 isn’t. The name for the integer data type is int, which you can see with type():

>>>

>>> type(1)
<class 'int'>

You can create an integer by typing the desired number. For instance, the following assigns the integer 25 to the variable num:

>>>

>>> num = 25

When you create an integer like this, the value 25 is called an integer literal because the integer is literally typed into the code.

You may already be familiar with how to convert a string containing an integer to a number using int(). For example, the following converts the string "25" to the integer 25:

>>>

>>> int("25")
25

int("25") is not an integer literal because the integer value is created from a string.

When you write large numbers by hand, you typically group digits into groups of three separated by a comma or a decimal point. The number 1,000,000 is a lot easier to read than 1000000.

In Python, you can’t use commas to group digits in integer literals, but you can use underscores (_). Both of the following are valid ways to represent the number one million as an integer literal:

>>>

>>> 1000000
1000000

>>> 1_000_000
1000000

There’s no limit to how large an integer can be, which might be surprising considering that computers have a finite amount of memory. Try typing the largest number you can think of into IDLE’s interactive window. Python can handle it with no problem!

Floating-Point Numbers#

A floating-point number, or float for short, is a number with a decimal place. 1.0 is a floating-point number, as is -2.75. The name of the floating-point data type is float:

>>>

>>> type(1.0)
<class 'float'>

Like integers, floats can be created from floating-point literals or by converting a string to a float with float():

Read the full article at https://realpython.com/python-numbers/ »

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Command Line Interfaces in Python

2020-09-15T14:00:00+00:00

Adding the capability of processing Python command line arguments provides a user-friendly interface to your text-based command line program. It’s similar to what a graphical user interface is for a visual application that’s manipulated by graphical elements or widgets.

Python exposes a mechanism to capture and extract your Python command line arguments. These values can be used to modify the behavior of a program. For example, if your program processes data read from a file, then you can pass the name of the file to your program, rather than hard-coding the value in your source code.

By the end of this course, you’ll know:

The origins of Python command line arguments
The underlying support for Python command line arguments
The standards guiding the design of a command line interface
The basics to manually customize and handle Python command line arguments
The libraries available in Python to ease the development of a complex command line interface

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Plot With Pandas: Python Data Visualization for Beginners

2020-09-14T14:00:00+00:00

Whether you’re just getting to know a dataset or preparing to publish your findings, visualization is an essential tool. Python’s popular data analysis library, pandas, provides several different options for visualizing your data with .plot(). Even if you’re at the beginning of your pandas journey, you’ll soon be creating basic plots that will yield valuable insights into your data.

In this tutorial, you’ll learn:

What the different types of pandas plots are and when to use them
How to get an overview of your dataset with a histogram
How to discover correlation with a scatter plot
How to analyze different categories and their ratios

Free Bonus: Click here to get access to a Conda cheat sheet with handy usage examples for managing your Python environment and packages.

Set Up Your Environment#

You can best follow along with the code in this tutorial in a Jupyter Notebook. This way, you’ll immediately see your plots and be able to play around with them.

You’ll also need a working Python environment including pandas. If you don’t have one yet, then you have several options:

If you have more ambitious plans, then download the Anaconda distribution. It’s huge (around 500 MB), but you’ll be equipped for most data science work.
If you prefer a minimalist setup, then check out the section on installing Miniconda in Setting Up Python for Machine Learning on Windows.
If you want to stick to pip, then install the libraries discussed in this tutorial with pip install pandas matplotlib. You can also grab Jupyter Notebook with pip install jupyterlab.
If you don’t want to do any setup, then follow along in an online Jupyter Notebook trial.

Once your environment is set up, you’re ready to download a dataset. In this tutorial, you’re going to analyze data on college majors sourced from the American Community Survey 2010–2012 Public Use Microdata Sample. It served as the basis for the Economic Guide To Picking A College Major featured on the website FiveThirtyEight.

First, download the data by passing the download URL to pandas.read_csv():

>>>

In [1]: import pandas as pd

In [2]: download_url = (
   ...:     "https://raw.githubusercontent.com/fivethirtyeight/"
   ...:     "data/master/college-majors/recent-grads.csv"
   ...: )

In [3]: df = pd.read_csv(download_url)

In [4]: type(df)
Out[4]: pandas.core.frame.DataFrame

By calling read_csv(), you create a DataFrame, which is the main data structure used in pandas.

Note: You can follow along with this tutorial even if you aren’t familiar with DataFrames. But if you’re interested in learning more about working with pandas and DataFrames, then you can check out Using Pandas and Python to Explore Your Dataset and The Pandas DataFrame: Make Working With Data Delightful.

Now that you have a DataFrame, you can take a look at the data. First, you should configure the display.max.columns option to make sure pandas doesn’t hide any columns. Then you can view the first few rows of data with .head():

>>>

In [5]: pd.set_option("display.max.columns", None)

In [6]: df.head()

You’ve just displayed the first five rows of the DataFrame df using .head(). Your output should look like this:

The default number of rows displayed by .head() is five, but you can specify any number of rows as an argument. For example, to display the first ten rows, you would use df.head(10).

Create Your First Pandas Plot#

Your dataset contains some columns related to the earnings of graduates in each major:

"Median" is the median earnings of full-time, year-round workers.
"P25th" is the 25th percentile of earnings.
"P75th" is the 75th percentile of earnings.
"Rank" is the major’s rank by median earnings.

Let’s start with a plot displaying these columns. First, you need to set up your Jupyter Notebook to display plots with the %matplotlib magic command:

>>>

In [7]: %matplotlib
Using matplotlib backend: MacOSX

The %matplotlib magic command sets up your Jupyter Notebook for displaying plots with Matplotlib. The standard Matplotlib graphics backend is used by default, and your plots will be displayed in a separate window.

Note: You can change the Matplotlib backend by passing an argument to the %matplotlib magic command.

For example, the inline backend is popular for Jupyter Notebooks because it displays the plot in the notebook itself, immediately below the cell that creates the plot:

>>>

In [7]: %matplotlib inline

There are a number of other backends available. For more information, check out the Rich Outputs tutorial in the IPython documentation.

Read the full article at https://realpython.com/pandas-plot-python/ »

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The Real Python Podcast – Episode #26: 5 Years Podcasting Python with Michael Kennedy: Growth, GIL, Async, and More

2020-09-11T12:00:00+00:00

Why is Python pulling in so many new programmers? Maybe some of that growth is from Python being a full-spectrum language. This week on the show we have Michael Kennedy, the host of the podcast "Talk Python to Me". Michael reflects on five years of podcasting about Python, and many of the changes he has seen in the Python landscape.

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Find & Fix Code Bugs in Python: Debug With IDLE

2020-09-09T14:00:00+00:00

Everyone makes mistakes—even seasoned professional developers! Python’s interactive interpreter, IDLE, is pretty good at catching mistakes like syntax errors and runtime errors, but there’s a third type of error that you may have already experienced. Logic errors occur when an otherwise valid program doesn’t do what was intended. Logic errors cause unexpected behaviors called bugs. Removing bugs is called debugging.

A debugger is a tool that helps you hunt down bugs and understand why they’re happening. Knowing how to find and fix bugs in your code is a skill that you’ll use for your entire coding career!

In this tutorial, you’ll:

Learn how to use IDLE’s Debug Control window
Practice debugging on a buggy function
Learn alternative methods for debugging your code

Note: This tutorial is adapted from the chapter “Finding and Fixing Code Bugs” in Python Basics: A Practical Introduction to Python 3.

The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you will see references to IDLE’s built-in debugging tools throughout this tutorial. However, you should be able to apply the same concepts to the debugger of your choice.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you'll need to take your Python skills to the next level.

Use the Debug Control Window#

The main interface to IDLE’s debugger is the Debug Control window, or the Debug window for short. You can open the Debug window by selecting Debug→Debugger from the menu in the interactive window. Go ahead and open the Debug window.

Note: If the Debug menu is missing from your menu bar, then make sure to bring the interactive window into focus by clicking it.

Whenever the Debug window is open, the interactive window displays [DEBUG ON] next to the prompt to indicate that the debugger is open. Now open a new editor window and arrange the three windows on your screen so that you can see all of them simultaneously.

In this section, you’ll learn how the Debug window is organized, how to step through your code with the debugger one line at a time, and how to set breakpoints to help speed up the debugging process.

The Debug Control Window: An Overview#

To see how the debugger works, you can start by writing a simple program without any bugs. Type the following into the editor window:

 1 for i in range(1, 4):
 2     j = i * 2
 3     print(f"i is {i} and j is {j}")

Save the file, then keep the Debug window open and press F5. You’ll notice that execution doesn’t get very far.

The Debug window will look like this:

Notice that the Stack panel at the top of the window contains the following message:

> '__main__'.<module>(), line 1: for i in range(1, 4):

This tells you that line 1 (which contains the code for i in range(1, 4):) is about to be run but hasn’t started yet. The '__main__'.module() part of the message refers to the fact that you’re currently in the main section of the program, as opposed to being, for example, in a function definition before the main block of code has been reached.

Below the Stack panel is a Locals panel that lists some strange looking stuff like __annotations__, __builtins__, __doc__, and so on. These are internal system variables that you can ignore for now. As your program runs, you’ll see variables declared in the code displayed in this window so that you can keep track of their value.

There are five buttons located at the top left-hand corner of the Debug window: Go, Step, Over, Out, and Quit. These buttons control how the debugger moves through your code.

In the following sections, you’ll explore what each of these buttons does, starting with Step.

The Step Button#

Go ahead and click Step at the top left-hand corner of the Debug window. The Debug window changes a bit to look like this:

Read the full article at https://realpython.com/python-debug-idle/ »

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Exploring HTTPS and Cryptography in Python

2020-09-08T14:00:00+00:00

Have you ever wondered why it’s okay for you to send your credit card information over the Internet? You may have noticed the https:// on URLs in your browser, but what is it, and how does it keep your information safe? Or perhaps you want to create a Python HTTPS application, but you’re not exactly sure what that means.

In this course, you’ll get a working knowledge of the various factors that combine to keep communications over the Internet safe. You’ll see concrete examples of how a Python HTTPS application keeps information secure.

In this course, you’ll learn how to:

Monitor and analyze network traffic
Apply cryptography to keep data safe
Describe the core concepts of Public Key Infrastructure (PKI)
Create your own Certificate Authority
Build a Python HTTPS application
Identify common Python HTTPS warnings and errors

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Video Subtitles & Transcripts Now Available on Real Python

2020-09-07T14:00:00+00:00

Hey there,

I’ve got a big update to share today:

Real Python video courses now have full subtitles and transcripts!

I think this is going to do a lot for accessibility and making your favorite Python learning resources easier to review & more searchable.

Let’s do a quick demo. Video lessons now come with full subtitles that you can turn on and off at your convenience:

Below each video you’ll also find an interactive transcript that animates along with the video to show you what section of the video is currently playing:

Read the full article at https://realpython.com/video-subtitles-transcripts-now-available/ »

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The Real Python Podcast – Episode #25: Data Version Control in Python and Real Python Video Transcripts

2020-09-04T12:00:00+00:00

Wouldn't it be nice to a use a form of version control for data? Something that would allow you to track and version your datasets and models. Well, that's what the tool called DVC is designed to do. This week on the show, David Amos is here and he's brought another batch of PyCoder’s Weekly articles and projects.

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PyTorch vs Tensorflow for Your Python Deep Learning Project

2020-09-02T14:00:00+00:00

PyTorch vs TensorFlow: What’s the difference? Both are open source Python libraries that use graphs to perform numerical computation on data. Both are used extensively in academic research and commercial code. Both are extended by a variety of APIs, cloud computing platforms, and model repositories.

If they’re so similar, then which one is best for your project?

In this tutorial, you’ll learn:

What the differences are between PyTorch and TensorFlow
What tools and resources are available for each
How to choose the best option for your specific use case

You’ll start by taking a close look at both platforms, beginning with the slightly older TensorFlow, before exploring some considerations that can help you determine which choice is best for your project. Let’s get started!

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

What Is TensorFlow?#

TensorFlow was developed by Google and released as open source in 2015. It grew out of Google’s homegrown machine learning software, which was refactored and optimized for use in production.

The name “TensorFlow” describes how you organize and perform operations on data. The basic data structure for both TensorFlow and PyTorch is a tensor. When you use TensorFlow, you perform operations on the data in these tensors by building a stateful dataflow graph, kind of like a flowchart that remembers past events.

Who Uses TensorFlow?#

TensorFlow has a reputation for being a production-grade deep learning library. It has a large and active user base and a proliferation of official and third-party tools and platforms for training, deploying, and serving models.

After PyTorch was released in 2016, TensorFlow declined in popularity. But in late 2019, Google released TensorFlow 2.0, a major update that simplified the library and made it more user-friendly, leading to renewed interest among the machine learning community.

Code Style and Function#

Before TensorFlow 2.0, TensorFlow required you to manually stitch together an abstract syntax tree—the graph—by making tf.* API calls. It then required you to manually compile the model by passing a set of output tensors and input tensors to a session.run() call.

A Session object is a class for running TensorFlow operations. It contains the environment in which Tensor objects are evaluated and Operation objects are executed, and it can own resources like tf.Variable objects. The most common way to use a Session is as a context manager.

In TensorFlow 2.0, you can still build models this way, but it’s easier to use eager execution, which is the way Python normally works. Eager execution evaluates operations immediately, so you can write your code using Python control flow rather than graph control flow.

To see the difference, let’s look at how you might multiply two tensors using each method. Here’s an example using the old TensorFlow 1.0 method:

>>>

>>> import tensorflow as tf

>>> tf.compat.v1.disable_eager_execution()

>>> x = tf.compat.v1.placeholder(tf.float32, name = "x")
>>> y = tf.compat.v1.placeholder(tf.float32, name = "y")

>>> multiply = tf.multiply(x, y)

>>> with tf.compat.v1.Session() as session:
...     m = session.run(
...         multiply, feed_dict={x: [[2., 4., 6.]], y: [[1.], [3.], [5.]]}
...     )
...     print(m)
[[ 2.  4.  6.]
 [ 6. 12. 18.]
 [10. 20. 30.]]

This code uses TensorFlow 2.x’s tf.compat API to access TensorFlow 1.x methods and disable eager execution.

Read the full article at https://realpython.com/pytorch-vs-tensorflow/ »

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Editing Excel Spreadsheets in Python With openpyxl

2020-09-01T14:00:00+00:00

Excel spreadsheets are one of those things you might have to deal with at some point. Either it’s because your boss loves them or because marketing needs them, you might have to learn how to work with spreadsheets in Python, and that’s when knowing openpyxl comes in handy!

In this course, you’ll learn how to use openpyxl to:

Read Excel spreadsheets and iterate through the data
Manipulate speadsheet data using Python data structures
Create simple or more complex spreadsheets
Format workbooks using styles, filters, and conditional formatting
Enhance spreadsheets by adding images and charts

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Python 3 Installation & Setup Guide

2020-08-31T16:15:29+00:00

Installing or updating Python on your computer is the first step to becoming a Python programmer. There are a multitude of installation methods: you can download official Python distributions from Python.org, install from a package manager, and even install specialized distributions for scientific computing, Internet of Things, and embedded systems.

This tutorial focuses on official distributions, as they’re generally the best option for getting started with learning to program in Python.

In this tutorial you’ll learn how to:

Check which version of Python, if any, is installed on your machine
Install or update Python on Windows, macOS, and Linux
Use Python on mobile devices like phones or tablets
Use Python on the Web with online interpreters

No matter what operating system you’re on, this tutorial has you covered. Find your operating system below and dive in!

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

How to Install Python on Windows#

There are three installation methods on Windows:

The Microsoft Store
The full installer
Windows Subsystem for Linux

In this section, you’ll learn how to check which version of Python, if any, is installed on your Windows computer. You’ll also learn which of the three installation methods you should use.

How to Check Your Python Version on Windows#

To check if you already have Python on your Windows machine, first open a command-line application, such as PowerShell.

Tip: Here’s how you open PowerShell:

Press the Win key.
Type PowerShell.
Press Enter.

Alternatively, you can right-click the Start button and select Windows PowerShell or Windows PowerShell (Admin).

You can also use cmd.exe or Windows Terminal.

With the command line open, type in the following command and press Enter:

C:\> python --version
Python 3.8.4

Using the --version switch will show you the version that’s installed. Alternatively, you can use the -V switch:

C:\> python -V
Python 3.8.4

In either case, if you see a version less than 3.8.4, which was the most recent version at the time of writing, then you’ll want to upgrade your installation.

Note: If you don’t have a version of Python on your system, then both of the above commands will launch the Microsoft Store and redirect you to the Python application page. You’ll see how to complete the installation from the Microsoft Store in the next section.

If you’re interested in where the installation is located, then you can use the where.exe command in cmd.exe or PowerShell:

C:\> where.exe python
C:\Users\mertz\AppData\Local\Programs\Python\Python37-32\python.exe

Note that the where.exe command will work only if Python has been installed for your user account.

What Your Options Are#

As mentioned earlier, there are three ways to install the official Python distribution on Windows:

Read the full article at https://realpython.com/installing-python/ »

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The Real Python Podcast – Episode #24: Options for Packaging Your Python Application: Wheels, Docker, and More

2020-08-28T12:00:00+00:00

Have you wondered, how should I package my Python code? You've written the application, but now you need to distribute it to the machines it's intended to run on. It depends on what the code is, the libraries it depends on, and with whom do you want to share it. This week on the show we have Itamar Turner-Trauring, creator of the website pythonspeed.com. We discuss his article "Options for Packaging Your Python Code: Wheels, Conda, Docker, and More," covering the how of sharing your code.

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Common Python Data Structures (Guide)

2020-08-26T14:00:00+00:00

Data structures are the fundamental constructs around which you build your programs. Each data structure provides a particular way of organizing data so it can be accessed efficiently, depending on your use case. Python ships with an extensive set of data structures in its standard library.

However, Python’s naming convention doesn’t provide the same level of clarity that you’ll find in other languages. In Java, a list isn’t just a list—it’s either a LinkedList or an ArrayList. Not so in Python. Even experienced Python developers sometimes wonder whether the built-in list type is implemented as a linked list or a dynamic array.

In this tutorial, you’ll learn:

Which common abstract data types are built into the Python standard library
How the most common abstract data types map to Python’s naming scheme
How to put abstract data types to practical use in various algorithms

Note: This tutorial is adapted from the chapter “Common Data Structures in Python” in Python Tricks: The Book. If you enjoy what you read below, then be sure to check out the rest of the book.

Free Bonus: Click here to get access to a chapter from Python Tricks: The Book that shows you Python's best practices with simple examples you can apply instantly to write more beautiful + Pythonic code.

Dictionaries, Maps, and Hash Tables#

In Python, dictionaries (or dicts for short) are a central data structure. Dicts store an arbitrary number of objects, each identified by a unique dictionary key.

Dictionaries are also often called maps, hashmaps, lookup tables, or associative arrays. They allow for the efficient lookup, insertion, and deletion of any object associated with a given key.

Phone books make a decent real-world analog for dictionary objects. They allow you to quickly retrieve the information (phone number) associated with a given key (a person’s name). Instead of having to read a phone book front to back to find someone’s number, you can jump more or less directly to a name and look up the associated information.

This analogy breaks down somewhat when it comes to how the information is organized to allow for fast lookups. But the fundamental performance characteristics hold. Dictionaries allow you to quickly find the information associated with a given key.

Dictionaries are one of the most important and frequently used data structures in computer science. So, how does Python handle dictionaries? Let’s take a tour of the dictionary implementations available in core Python and the Python standard library.

`dict`: Your Go-To Dictionary#

Because dictionaries are so important, Python features a robust dictionary implementation that’s built directly into the core language: the dict data type.

Python also provides some useful syntactic sugar for working with dictionaries in your programs. For example, the curly-brace ({ }) dictionary expression syntax and dictionary comprehensions allow you to conveniently define new dictionary objects:

>>>

>>> phonebook = {
...     "bob": 7387,
...     "alice": 3719,
...     "jack": 7052,
... }

>>> squares = {x: x * x for x in range(6)}

>>> phonebook["alice"]
3719

>>> squares
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

There are some restrictions on which objects can be used as valid keys.

Python’s dictionaries are indexed by keys that can be of any hashable type. A hashable object has a hash value that never changes during its lifetime (see __hash__), and it can be compared to other objects (see __eq__). Hashable objects that compare as equal must have the same hash value.

Immutable types like strings and numbers are hashable and work well as dictionary keys. You can also use tuple objects as dictionary keys as long as they contain only hashable types themselves.

For most use cases, Python’s built-in dictionary implementation will do everything you need. Dictionaries are highly optimized and underlie many parts of the language. For example, class attributes and variables in a stack frame are both stored internally in dictionaries.

Python dictionaries are based on a well-tested and finely tuned hash table implementation that provides the performance characteristics you’d expect: O(1) time complexity for lookup, insert, update, and delete operations in the average case.

There’s little reason not to use the standard dict implementation included with Python. However, specialized third-party dictionary implementations exist, such as skip lists or B-tree–based dictionaries.

Besides plain dict objects, Python’s standard library also includes a number of specialized dictionary implementations. These specialized dictionaries are all based on the built-in dictionary class (and share its performance characteristics) but also include some additional convenience features.

Let’s take a look at them.

`collections.OrderedDict`: Remember the Insertion Order of Keys#

Read the full article at https://realpython.com/python-data-structures/ »

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Django Redirects

2020-08-25T14:00:00+00:00

When you build web applications in Python using the Django framework, you’ll likely need to redirect the user from one URL to another. This course covers what you need to know about redirecting in Django. All the way from the low-level details of the HTTP protocol to the high-level way of dealing with them in Django.

By the end of this course you’ll learn:

How HTTP Redirects work
The difference between temporary and permanent redirects
How to use query strings with a redirect
How to avoid common problems

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Python mmap: Improved File I/O With Memory Mapping

2020-08-24T14:00:00+00:00

The Zen of Python has a lot of wisdom to offer. One especially useful idea is that “There should be one—and preferably only one—obvious way to do it.” Yet there are multiple ways to do most things in Python, and often for good reason. For example, there are multiple ways to read a file in Python, including the rarely used mmap module

Python’s mmap provides memory-mapped file input and output (I/O). It allows you to take advantage of lower-level operating system functionality to read files as if they were one large string or array. This can provide significant performance improvements in code that requires a lot of file I/O.

In this tutorial, you’ll learn:

What kinds of computer memory exist
What problems you can solve with mmap
How use memory mapping to read large files faster
How to change a portion of a file without rewriting the entire file
How to use mmap to share information between multiple processes

Free Download: Get a sample chapter from CPython Internals: Your Guide to the Python 3 Interpreter showing you how to unlock the inner workings of the Python language, compile the Python interpreter from source code, and participate in the development of CPython.

Understanding Computer Memory#

Memory mapping is a technique that uses lower-level operating system APIs to load a file directly into computer memory. It can dramatically improve file I/O performance in your program. To better understand how memory mapping improves performance, as well as how and when you can use the mmap module to take advantage of these performance benefits, it’s useful to first learn a bit about computer memory.

Computer memory is a big, complicated topic, but this tutorial focuses only on what you need to know to use the mmap module effectively. For the purposes of this tutorial, the term memory refers to random-access memory, or RAM.

There are several types of computer memory:

Physical
Virtual
Shared

Each type of memory can come into play when you’re using memory mapping, so let’s review each one from a high level.

Physical Memory#

Physical memory is the least complicated type of memory to understand because it’s often part of the marketing associated with your computer. (You might remember that when you bought your computer, it advertised something like 8 gigabytes of RAM.) Physical memory typically comes on cards that are connected to your computer’s motherboard.

Physical memory is the amount of volatile memory that’s available for your programs to use while running. Physical memory should not be confused with storage, such as your hard drive or solid-state disk.

Virtual Memory#

Virtual memory is a way of handling memory management. The operating system uses virtual memory to make it appear that you have more memory than you do, allowing you to worry less about how much memory is available for your programs at any given time. Behind the scenes, your operating system uses parts of your nonvolatile storage, such as your solid-state disk, to simulate additional RAM.

In order to do this, your operating system must maintain a mapping between physical memory and virtual memory. Each operating system uses its own sophisticated algorithm to map virtual memory addresses to physical ones using a data structure called a page table.

Luckily, most of this complication is hidden from your programs. You don’t need to understand page tables or logical-to-physical mapping to write performant I/O code in Python. However, knowing a little bit about memory gives you a better understanding of what the computer and libraries are taking care of for you.

mmap uses virtual memory to make it appear that you’ve loaded a very large file into memory, even if the contents of the file are too big to fit in your physical memory.

Shared Memory#

Shared memory is another technique provided by your operating system that allows multiple programs to access the same data simultaneously. Shared memory can be a very efficient way of handling data in a program that uses concurrency.

Python’s mmap uses shared memory to efficiently share large amounts of data between multiple Python processes, threads, and tasks that are happening concurrently.

Digging Deeper Into File I/O#

Now that you have a high-level view of the different types of memory, it’s time to understand what memory mapping is and what problems it solves. Memory mapping is another way to perform file I/O that can result in better performance and memory efficiency.

In order to fully appreciate what memory mapping does, it’s useful to consider regular file I/O from a lower-level perspective. A lot of things happen behind the scenes when reading a file:

Read the full article at https://realpython.com/python-mmap/ »

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The Real Python Podcast – Episode #23: Python Wheels and Pass by Reference in Python

2020-08-21T12:00:00+00:00

Have you wondered what are Python wheels? How are they used to package Python code? Does Python use pass by value or pass by reference? This week on the show, David Amos is here to help answer these questions, and he has brought another batch of PyCoder’s Weekly articles and projects.

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Real Python Office Hours

2020-08-18T14:00:00+00:00

The Real Python Office Hours is a weekly hangout where members of Real Python get the chance to meet fellow Pythonistas to chat about your learning progress, ask questions, and discuss Python tips & tricks via screen sharing.

In this video course, you can:

View previous office hours meetings
Download the chat transcript from each meeting
Explore resources and bonus materials discussed during each meeting

Office Hours Schedule & Registration: Join us live for the next members-only Q&A session with the Real Python Team: View upcoming Office Hours events »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #22: Create Cross-Platform Python GUI Apps With BeeWare

2020-08-14T12:00:00+00:00

Do you want to distribute your Python applications to other users who don't have or even use Python? Maybe you're interested in seeing your Python application run on iOS or Android mobile devices. This week on the show we have Russell Keith-Magee, the founder and maintainer of the BeeWare project. Russell talks about Briefcase, a tool that converts a Python application into native installers on macOS, Windows, Linux, and mobile devices.

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Identify Invalid Python Syntax

2020-08-11T14:00:00+00:00

Python is known for its simple syntax. However, when you’re learning Python for the first time or when you’ve come to Python with a solid background in another programming language, you may run into some things that Python doesn’t allow. If you’ve ever received a SyntaxError when trying to run your Python code, then this guide can help you. Throughout this course, you’ll see common examples of invalid syntax in Python and learn how to resolve the issue.

By the end of this course, you’ll be able to:

Identify invalid syntax in Python
Make sense of SyntaxError tracebacks
Resolve invalid syntax or prevent it altogether

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #21: Exploring K-means Clustering and Building a Gradebook With Pandas

2020-08-07T12:00:00+00:00

Do you want to learn the how and when of implementing K-means clustering in Python? Would you like to practice your pandas skills with a real-world project? This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Practical Recipes for Working With Files in Python

2020-08-04T14:00:00+00:00

Python has several built-in modules and functions for handling files. These functions are spread out over several modules such as os, os.path, shutil, and pathlib, to name a few. This course gathers in one place many of the functions you need to know in order to perform the most common operations on files in Python.

In this course, you’ll learn how to:

Retrieve file properties
Create directories
Match patterns in filenames
Traverse directory trees
Make temporary files and directories
Delete files and directories
Copy, move, or rename files and directories
Create and extract ZIP archives

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The Real Python Podcast – Episode #20: Building PDFs in Python with ReportLab

2020-07-31T12:00:00+00:00

Have you wanted to generate advanced reports as PDFs using Python? Maybe you want to build documents with tables, images, or fillable forms. This week on the show we have Mike Driscoll to talk about his book "ReportLab - PDF Processing with Python."

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Python's None: Null in Python

2020-07-28T14:00:00+00:00

If you have experience with other programming languages, like C or Java, then you’ve probably heard of the concept of null. Many languages use this to represent a pointer that doesn’t point to anything, to denote when a variable is empty, or to mark default parameters that you haven’t yet supplied. null is often defined to be 0 in those languages, but null in Python is different.

Python uses the keyword None to define null objects and variables. While None does serve some of the same purposes as null in other languages, it’s another beast entirely. As the null in Python, None is not defined to be 0 or any other value. In Python, None is an object and a first-class citizen!

In this course, you’ll learn:

What None is and how to test for it
When and why to use None as a default parameter
What None and NoneType mean in your traceback
How to use None in type checking
How null in Python works under the hood

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #19: Advanced Python Import Techniques and Managing Users in Django

2020-07-24T12:00:00+00:00

Would you like to clearly understand what's happening when you use the Python import keyword? Do you want to use modules more effectively to structure your code? Or maybe you're ready to move to the next level with your Django project by adding user management. This week on the show, David Amos is back with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Mastering Python's Built-in time Module

2020-07-21T14:00:00+00:00

The Python time module provides many ways of representing time in code, such as objects, numbers, and strings. It also provides functionality other than representing time, like waiting during code execution and measuring the efficiency of your code.

This course will walk you through the most commonly used functions and objects in time.

By the end of this course, you’ll be able to:

Understand core concepts at the heart of working with dates and times, such as epochs, time zones, and daylight savings time
Represent time in code using floats, tuples, and struct_time
Convert between different time representations
Suspend thread execution
Measure code performance using perf_counter()

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Real Python

The Real Python Podcast – Episode #28: Using Pylance to Write Better Python Inside of Visual Studio Code

Python Community Interview With David Amos

Working With Linked Lists in Python

Python Practice Problems: Get Ready for Your Next Interview

Python Practice Problem 1: Sum of a Range of Integers#

Problem Description#

Problem Solution#

Python Practice Problem 2: Caesar Cipher#

The Real Python Podcast – Episode #27: Preparing for an Interview With Python Practice Problems

Numbers in Python

Integers and Floating-Point Numbers#

Integers#

Floating-Point Numbers#

Command Line Interfaces in Python

Plot With Pandas: Python Data Visualization for Beginners

Set Up Your Environment#

Create Your First Pandas Plot#

The Real Python Podcast – Episode #26: 5 Years Podcasting Python with Michael Kennedy: Growth, GIL, Async, and More

Find & Fix Code Bugs in Python: Debug With IDLE

Use the Debug Control Window#

The Debug Control Window: An Overview#

The Step Button#

Exploring HTTPS and Cryptography in Python

Video Subtitles & Transcripts Now Available on Real Python

The Real Python Podcast – Episode #25: Data Version Control in Python and Real Python Video Transcripts

PyTorch vs Tensorflow for Your Python Deep Learning Project

What Is TensorFlow?#

Who Uses TensorFlow?#

Code Style and Function#

Editing Excel Spreadsheets in Python With openpyxl

Python 3 Installation & Setup Guide

How to Install Python on Windows#

How to Check Your Python Version on Windows#

What Your Options Are#

The Real Python Podcast – Episode #24: Options for Packaging Your Python Application: Wheels, Docker, and More

Common Python Data Structures (Guide)

Dictionaries, Maps, and Hash Tables#

dict: Your Go-To Dictionary#

collections.OrderedDict: Remember the Insertion Order of Keys#

Django Redirects

Python mmap: Improved File I/O With Memory Mapping

Understanding Computer Memory#

Physical Memory#

Virtual Memory#

Shared Memory#

Digging Deeper Into File I/O#

The Real Python Podcast – Episode #23: Python Wheels and Pass by Reference in Python

Real Python Office Hours

The Real Python Podcast – Episode #22: Create Cross-Platform Python GUI Apps With BeeWare

Identify Invalid Python Syntax

The Real Python Podcast – Episode #21: Exploring K-means Clustering and Building a Gradebook With Pandas

Practical Recipes for Working With Files in Python

The Real Python Podcast – Episode #20: Building PDFs in Python with ReportLab

Python's None: Null in Python

The Real Python Podcast – Episode #19: Advanced Python Import Techniques and Managing Users in Django

Mastering Python's Built-in time Module

`dict`: Your Go-To Dictionary#

`collections.OrderedDict`: Remember the Insertion Order of Keys#