3.7 Python Download: How to Install and Use Python 3.7 on Your Computer
Python is one of the most popular and versatile programming languages in the world. It is used for web development, data science, machine learning, automation, scripting, and many more applications.
Python 3.7 is the latest stable release of Python as of June 2018. It introduces many new features and improvements that make coding in Python easier, faster, and more fun.
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In this article, you will learn:
What is Python 3.7 and why you should use it
How to check your current Python version
How to download and install Python 3.7 on Windows, macOS, and Linux
How to run Python 3.7 code and use its features
</ul What is Python 3.7 and why you should use it
Python 3.7 is the latest stable version of Python, which was released on June 27, 2018. It is the result of more than 18 months of development and includes over 500 bug fixes and enhancements.
Some of the main features and benefits of Python 3.7 are:
A new built-in function breakpoint() that makes debugging easier by invoking the Python debugger at any point in your code.
A new module dataclasses that simplifies the creation of classes that store data and provide common methods like __init__(), __repr__(), and __eq__().
A new feature called postponed evaluation of annotations that allows you to use type hints without importing them or causing circular dependencies.
New high-precision timing functions in the time module that provide better accuracy and resolution for performance measurement.
Improved Unicode support, including the addition of over 100 new language scripts and emoji characters.
Improved performance, especially for startup time, method calls, attribute access, and generator expressions.
Python 3.7 is also compatible with most of the existing Python libraries and frameworks, such as Django, Flask, NumPy, Pandas, TensorFlow, and more. You can use Python 3.7 to develop web applications, data analysis, machine learning, automation, scripting, and more.
Python 3.7 is not only faster and more powerful than previous Python versions, but also more fun to code with. You can enjoy the new features and improvements that make your code more readable, concise, and expressive.
How to check your current Python version
Before you download and install Python 3.7, you may want to check your current Python version on your computer. This will help you decide if you need to upgrade or not.
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To check your current Python version, you can use one of the following methods:
Use the command-line or terminal. Type python --version or python -V and press Enter. You should see something like Python 3.6.5 or Python 2.7.15. The first number indicates the major version, the second number indicates the minor version, and the third number indicates the patch level.
Use the interactive interpreter. Type python or python3 and press Enter. You should see something like Python 3.6.5 (default, Apr 1 2018, 05:46:30). The first line shows the Python version and the date of release. You can also type import sys; print(sys.version) to get more information about your Python installation.
If you have multiple Python versions installed on your computer, you may need to specify which one you want to use. For example, you can type python3.7 or py -3.7 to run Python 3.7, or python2.7 or py -2.7 to run Python 2.7.
How to download and install Python 3.7 on Windows, macOS, and Linux
The easiest way to download and install Python 3.7 on your computer is to use the official installer from the Python website. You can find the installer for your operating system on the . You can also use other methods, such as the Microsoft Store, Homebrew, or the package manager of your Linux distribution.
Here are the steps to download and install Python 3.7 on different operating systems:
Windows
Go to the and choose the installer for Windows. You can choose either the 32-bit or the 64-bit version, depending on your system architecture.
Run the installer and follow the instructions. You can choose to install Python for all users or just for yourself, and customize the installation location and options. Make sure to check the box that says "Add Python 3.7 to PATH" to make Python accessible from anywhere.
Verify that Python 3.7 is installed by opening a command prompt and typing python --version. You should see something like Python 3.7.0.
You can also use the Microsoft Store to install Python 3.7 on Windows 10. Just search for "Python 3.7" in the store and click on "Get". This will install Python in a separate location and add it to your PATH automatically.
If you want to use Linux tools and commands on Windows, you can use the Windows Subsystem for Linux (WSL). This will allow you to run a Linux distribution, such as Ubuntu, on your Windows machine. You can then use the package manager of your Linux distribution to install Python 3.7.
macOS
Go to the and choose the installer for macOS. You can choose either the 32-bit or the 64-bit version, depending on your system architecture.
Run the installer and follow the instructions. You can customize the installation location and options, but you don't need to add Python to your PATH manually.
Verify that Python 3.7 is installed by opening a terminal and typing python3 --version. You should see something like Python 3.7.0.
You can also use Homebrew to install Python 3.7 on macOS. Homebrew is a popular package manager that allows you to install various software and tools on your Mac. To use Homebrew, you need to have Xcode and the Command Line Tools installed on your Mac.
Open a terminal and type brew install python to install the latest version of Python, which should be Python 3.7.
Verify that Python 3.7 is installed by typing python3 --version. You should see something like Python 3.7.0.
Linux
Linux distributions usually come with Python pre-installed, but it may not be the latest version. You can use the package manager of your Linux distribution to install or update Python 3.7, or you can build it from source if you want more control over the installation process.
Here are the steps to install Python 3.7 on some of the most common Linux distributions:
Ubuntu
Open a terminal and type sudo apt update to update your package list.
Type sudo apt install python3.7 to install Python 3.7.
Verify that Python 3.7 is installed by typing python3.7 --version. You should see something like Python 3.7.0.
</ul Debian
Open a terminal and type sudo apt update to update your package list.
Type sudo apt install python3.7 to install Python 3.7.
Verify that Python 3.7 is installed by typing python3.7 --version. You should see something like Python 3.7.0.
Fedora
Open a terminal and type sudo dnf update to update your package list.
Type sudo dnf install python3.7 to install Python 3.7.
Verify that Python 3.7 is installed by typing python3.7 --version. You should see something like Python 3.7.0.
CentOS
Open a terminal and type sudo yum update to update your package list.
Type sudo yum install python3.7 to install Python 3.7.
Verify that Python 3.7 is installed by typing python3.7 --version. You should see something like Python 3.7.0.
To run Python 3.7 code from an IDE, you can use one of the many available options, such as PyCharm, Visual Studio Code, Spyder, or Thonny. You can choose the IDE that suits your preferences and needs, and configure it to use Python 3.7 as the interpreter. You can then write, edit, debug, and run your Python 3.7 code from the IDE.
Python 3.7 offers many new features and improvements that you can use to enhance your code and make it more readable, concise, and expressive. Here are some examples of how to use some of the new features of Python 3.7:
Use the breakpoint() function for debugging
Python 3.7 introduces a new built-in function called breakpoint() that allows you to easily invoke the Python debugger at any point in your code. The debugger lets you inspect the state of your program, execute commands, set breakpoints, and step through your code.
To use the breakpoint() function, you simply need to insert it in your code where you want to pause the execution and launch the debugger. For example:
def factorial(n): if n == 0: return 1 else: breakpoint() # pause here and launch the debugger return n * factorial(n-1) print(factorial(5))
When you run this code, you will see something like this:
--Return-- > <stdin>(6)factorial()->1 (Pdb)
This means that the debugger has paused at the breakpoint() line and is waiting for your input. You can type various commands to interact with the debugger, such as p to print a variable, n to execute the next line, c to continue the execution, or h to get help.
The breakpoint() function is a convenient way to debug your code without having to import or configure any external modules or tools. You can also customize the behavior of the breakpoint() function by setting the environment variable PYTHONBREAKPOINT to a different debugger or function.
Use data classes for simple class creation
Python 3.7 introduces a new module called dataclasses that simplifies the creation of classes that store data and provide common methods. Data classes are similar to named tuples, but they are mutable and more flexible.
To use data classes, you need to import the dataclasses module and use the @dataclass decorator on your class definition. For example:
from dataclasses import dataclass @dataclass class Point: x: float y: float def distance(self, other): return ((self.x - other.x) 2 + (self.y - other.y) 2) 0.5
This will automatically generate the __init__(), __repr__(), and __eq__() methods for your class, as well as other methods depending on the parameters of the decorator. You can then create and use instances of your class as usual:
p1 = Point(1.0, 2.0) p2 = Point(3.0, 4.0) print(p1) # Point(x=1.0, y=2.0) print(p1 == p2) # False print(p1.distance(p2)) # 2.8284271247461903
Data classes are a convenient way to create simple classes that store data and provide common methods without having to write a lot of boilerplate code. You can also customize the behavior of data classes by using different parameters for the decorator or overriding the generated methods.
Use postponed evaluation of annotations for type hinting
Type hinting is a feature that allows you to annotate your code with type information that can help you catch errors, improve readability, and enable tools like static analysis or type checkers.
In previous versions of Python, type annotations were evaluated at runtime, which could cause some problems with forward references or circular dependencies. For example:
# This code will raise a NameError because Node is not defined yet class Tree: def __init def __init__(self, value: int, left: Node, right: Node): self.value = value self.left = left self.right = right class Node(Tree): pass
Python 3.7 introduces a new feature called postponed evaluation of annotations that allows you to use type hints without importing them or causing circular dependencies. To use this feature, you need to add a special comment at the top of your module:
from __future__ import annotations # Now this code will work fine because Node is not evaluated until needed class Tree: def __init__(self, value: int, left: Node, right: Node): self.value = value self.left = left self.right = right class Node(Tree): pass
This will make the type annotations be stored as strings and only evaluated when needed by tools like type checkers or doc generators. This will also make your code more consistent and compatible with future versions of Python.
Postponed evaluation of annotations is a convenient way to use type hints without worrying about import errors or circular dependencies. You can also use the typing module to define more complex types, such as unions, generics, or callables.
Use high-precision timing functions for performance measurement
Python 3.7 introduces two new high-precision timing functions in the time module that provide better accuracy and resolution for performance measurement. These functions are time.perf_counter_ns() and time.process_time_ns().
The time.perf_counter_ns() function returns the value of a performance counter in nanoseconds. This is the most precise and reliable way to measure the elapsed time between two events in your code. For example:
import time start = time.perf_counter_ns() # do some computation end = time.perf_counter_ns() elapsed = end - start print(f"The computation took elapsed nanoseconds")
The time.process_time_ns() function returns the value of the process time in nanoseconds. This is the amount of time that your process has spent on the CPU, excluding the time spent on sleeping or waiting for I/O. This is useful for measuring the CPU time of your code. For example:
import time start = time.process_time_ns() # do some computation end = time.process_time_ns() cpu_time = end - start print(f"The computation used cpu_time nanoseconds of CPU time")
These functions are more accurate and consistent than the previous time.perf_counter() and time.process_time() functions, which returned floating-point numbers in seconds. You can use these functions to measure and compare the performance of your code and optimize it accordingly.
Conclusion: Summary and benefits of Python 3.7
In this article, you learned how to download and install Python 3.7 on your computer and how to use some of its new features and improvements.
You learned that Python 3.7 offers many benefits, such as:
A new built-in function breakpoint() that makes debugging easier by invoking the Python debugger at any point in your code.
A new module dataclasses that simplifies the creation of classes that store data and provide common methods.
A new feature called postponed evaluation of annotations that allows you to use type hints without importing them or causing circular dependencies.
New high-precision timing functions in the time module that provide better accuracy and resolution for performance measurement.
Improved Unicode support, including the addition of over 100 new language scripts and emoji characters.
Improved performance, especially for startup time, method calls, attribute access, and generator expressions.
Python 3.7 is not only faster and more powerful than previous Python versions, but also more fun to code with. You can enjoy the new features and improvements that make your code more readable, concise, and expressive.
If you want to learn more about Python 3.7, you can read the official documentation, the What's New in Python 3.7 guide, or the Real Python tutorials on Python 3.7 features. You can also watch the video course on Setting Up Python by Real Python.
If you have any questions or issues with Python 3.7, you can use the official mailing lists, forums, or IRC channels to ask for help or get feedback from other Python users and developers. You can also use the official bug tracker to report bugs or request features for Python 3.7.
Python 3.7 is a great choice for your next project, whether it is web development, data science, machine learning, automation, scripting, or anything else. You can download and install Python 3.7 today and start coding with it right away.
FAQs
Q: Is Python 3.7 compatible with older Python versions?
A: Python 3.7 is mostly compatible with Python 3.6 and below, but some syntax changes and new features may require some code modifications. You can use tools like 2to3 or pyupgrade to help you migrate your code.
Q: How do I update my existing Python installation to Python 3.7?
A: Depending on your installation method, you may need to download and run the new installer, use a package manager, or build from source. You can also use tools like pyenv or virtualenv to manage multiple Python versions on your machine.
Q: What are some of the best resources to learn more about Python 3.7?
A: You can read the official documentation, the What's New in Python 3.7 guide, or the Real Python tutorials on Python 3.7 features. You can also watch the video course on Setting Up Python by Real Python.
Q: How do I get help or report bugs for Python 3.7?
A: You can use the official mailing lists, forums, or IRC channels to ask questions or get help from other Python users and developers. You can also use the official bug tracker to report issues or request features for Python 3.7.
Q: Where can I download other versions of Python?
A: You can visit the official download page or the release history page to find links to other versions of Python, including prereleases, legacy versions, and alternative implementations.
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