Introduction
Python is a high-level, general-purpose, dynamically typed, and interpreted programming language that is widely used for various applications, such as web development, data analysis, machine learning, automation, and more. Python is known for its simple and readable syntax, rich set of libraries and modules, and cross-platform compatibility.
download 3.7 python
Python 3.7 is a major release of the Python language that was released on June 27, 2018. It introduces several new features and improvements that make Python more powerful, expressive, and user-friendly. Some of the highlights of this version are:
Postponed evaluation of annotations: This feature allows you to use forward references and type hints without importing modules or executing code at definition time.
Built-in breakpoint() function: This function makes debugging easier by allowing you to insert a breakpoint in your code that will automatically launch a debugger when executed.
Data classes: This feature simplifies the creation of classes that mainly store data attributes and provide default methods for comparison, representation, and initialization.
Context variables: This feature enables you to manage context-specific state in a thread-safe way without using thread-local storage.
Other improvements in the standard library, CPython implementation, and documentation: These include new modules (such as contextvars and dataclasses), new functions (such as time.time_ns() and time.clock_gettime_ns()), new syntax features (such as PEP 562), performance enhancements (such as PEP 552), security fixes (such as PEP 476), documentation translations (such as PEP 545), and more.
In this article, we will explore how to download and install Python 3.7 on your machine, what are the main benefits of using this version, and what are some potential drawbacks or compatibility issues that you might encounter.
Downloading and installing Python 3.7
How to check your current Python version
Before you download and install Python 3.7, you might want to check if you already have Python on your machine and what version it is. To do so, you can use a command-line application, such as PowerShell on Windows or Terminal on macOS or Linux.
To check your Python version on Windows, follow these steps:
Press the Win key.
Type PowerShell.
Press Enter.
Type python --version or py --version in the PowerShell window.
Press Enter.
If you have Python installed, you should see something like this:
PS C:\Users\user> python --version Python 3.9.5
If you don't have Python installed or if you have an older version than 3.7, you should see something like this:
PS C:\Users\user> python --version python : The term 'python' is not recognized as the name of a cmdlet, function, script file, or operable program.
To check your Python version on macOS or Linux, follow these steps:
Open the Terminal application.
Type python --version or python3 --version in the Terminal window.
Press Enter.
If you have Python installed, you should see something like this:
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$ python --version Python 2.7.16 $ python3 --version Python 3.9.5
If you don't have Python installed or if you have an older version than 3.7, you should see something like this:
$ python --version -bash: python: command not found $ python3 --version -bash: python3: command not found
How to download Python 3.7 from the official website or the Windows Store
There are two main ways to download Python 3.7 on your machine: from the official Python website or from the Windows Store (if you are using Windows 10).
To download Python 3.7 from the official website, follow these steps:
Go to .
Scroll down to the Files section and choose the installer that matches your operating system and architecture (32-bit or 64-bit).
Click on the installer file to download it to your machine.
To download Python 3.7 from the Windows Store, follow these steps:
Go to .
Click on the Get button to open the Windows Store app.
Click on the Install button to download and install Python 3.7 on your machine.
How to install Python 3.7 on Windows, macOS, or Linux
Once you have downloaded Python 3.7, you need to install it on your machine. The installation process may vary depending on your operating system and the installer type that you chose.
To install Python 3.7 on Windows using the executable installer, follow these steps:
Double-click on the installer file that you downloaded.
Select Customize installation and click Next.
Select the optional features that you want to install, such as pip, IDLE, documentation, etc., and click Next.
Select the advanced options that you want to configure, such as adding Python to PATH, installing for all users, etc., and click Install.
Wait for the installation to complete and click Close.
To install Python 3.7 on Windows using the Windows Store app, follow these steps:
Open the Windows Store app and go to the Python 3.7 page.
Click on the Launch button to open Python 3.7 in a command prompt window.
Type python -m pip install --upgrade pip to update pip, the package manager for Python.
Type pip install to install any Python packages that you need.
To install Python 3.7 on macOS using the PKG installer, follow these steps:
Double-click on the installer file that you downloaded.
Follow the instructions on the screen and agree to the license agreement.
Select the destination disk where you want to install Python 3.7 and click Install.
Enter your administrator password and click Install Software.
Wait for the installation to complete and click Close.
To install Python 3.7 on macOS using the Homebrew package manager, follow these steps:
Open the Terminal application and type /bin/bash -c "$(curl -fsSL to install Homebrew if you don't have it already.
Type brew update to update Homebrew.
Type brew install python@3.7 to install Python 3.7 using Homebrew.
Type brew link --overwrite python@3.7 to link Python 3.7 to your system path.
To install Python 3.7 on Linux using the source code, follow these steps:
Download the source code archive from .
Extract the archive to a directory of your choice, such as /python-3.7.9.
Open the Terminal application and navigate to the directory where you extracted the archive, such as cd /python-3.7.9.
Type ./configure to configure the build options.
Type make to compile the source code.
Type sudo make install to install Python 3.7 on your system.
Features and benefits of Python 3.7
PEP 563: Postponed evaluation of annotations
Annotations are metadata that can be attached to variables, parameters, functions, classes, and modules in Python. They are usually used to provide type hints, which are optional suggestions for the expected types of values or arguments. For example, you can write a function like this:
def add(x: int, y: int) -> int: return x + y
This function has annotations that indicate that x and y should be integers, and that the return value should also be an integer. However, these annotations are not enforced by Python, and you can still pass values of other types without getting an error.
In Python 3.7, annotations are evaluated at definition time, which means that they are executed as expressions when the code is parsed. This can cause some problems, such as:
Performance overhead: Evaluating annotations can slow down the loading of modules or scripts that use them extensively.
Forward references: You cannot use a name that is not yet defined in an annotation, unless you wrap it in a string. For example, you cannot write a class like this:
class Node: def __init__(self, value: int, next: Node): self.value = value self.next = next
This will raise a NameError because Node is not defined yet when the annotation is evaluated. You have to write it like this:
class Node: def __init__(self, value: int, next: 'Node'): self.value = value self.next = next
Circular imports: You cannot import a module that imports your module in an annotation, unless you use a string or a delayed import. For example, you cannot write a module like this:
# mod1.py from mod2 import Foo def bar(x: Foo) -> None: pass
# mod2.py from mod1 import bar class Foo: def __init__(self): bar(self)
This will raise an ImportError because mod1 and mod2 depend on each other. You have to write it like this:
# mod1.py from typing import TYPE_CHECKING if TYPE_CHECKING: from mod2 import Foo def bar(x: 'Foo') -> None: pass
# mod2.py from mod1 import bar class Foo: def __init__(self): bar(self)
To solve these problems, Python 3.7 introduces a new feature called postponed evaluation of annotations. This feature allows you to defer the evaluation of annotations until they are needed, such as by a type checker or a runtime introspection tool. To enable this feature, you have to add this line at the top of your module:
from __future__ import annotations
This will make all annotations in your module be stored as strings, rather than being evaluated as expressions. This will improve the performance, allow forward references, and avoid circular imports. You can still access the original expressions by using the typing.get_type_hints() function or the __annotations__ attribute of objects.
PEP 553: Built-in breakpoint() function
Debugging is an essential skill for any programmer, as it allows you to find and fix errors in your code. One of the most common debugging tools is a breakpoint, which is a point in your code where the execution stops and allows you to inspect the state of your program.
In Python 3.7, there is a new built-in function called breakpoint() that makes it easier to insert breakpoints in your code. You can simply call this function anywhere in your code where you want to pause the execution and launch a debugger. For example, you can write something like this:
def factorial(n): if n == 0: return 1 else: breakpoint() return n * factorial(n - 1) This will stop the execution when the function is called with a positive argument and open a debugger in your terminal. You can then use the debugger commands to inspect the variables, step through the code, evaluate expressions, etc.
The breakpoint() function is customizable, as you can configure which debugger to use and how to launch it. By default, it uses the pdb module, which is the standard Python debugger. However, you can change this by setting the PYTHONBREAKPOINT environment variable to the name of another debugger module or function. For example, you can use ipdb, which is a wrapper around pdb that provides an interactive IPython shell. To do so, you can type this in your terminal before running your script:
export PYTHONBREAKPOINT=ipdb.set_trace
You can also disable the breakpoint() function by setting the PYTHONBREAKPOINT environment variable to 0. This will make the function do nothing when called. This can be useful when you want to run your code without stopping at breakpoints.
PEP 557: Data classes
Data classes are a new feature in Python 3.7 that simplify the creation of classes that mainly store data attributes and provide default methods for comparison, representation, and initialization. Data classes are similar to namedtuple or struct in other languages, but they are more flexible and powerful.
To create a data class, you have to use the @dataclass decorator from the dataclasses module and define the attributes of your class as class variables with type annotations. For example, you can write a data class like this:
from dataclasses import dataclass @dataclass class Point: x: float y: float
This will automatically generate an __init__() method that takes x and y as arguments and assigns them to self.x and self.y, respectively. It will also generate an __eq__() method that compares two instances of Point by their attributes, an __repr__() method that returns a string representation of an instance of Point, and a __hash__() method that returns a hash value of an instance of Point.
You can create and use instances of Point like this:
p1 = Point(1.0, 2.0) p2 = Point(3.0, 4.0) print(p1) # prints Point(x=1.0, y=2.0) print(p1 == p2) # prints False print(hash(p1)) # prints 3713081631934410656
Data classes also support some additional features, such as:
Default values: You can assign default values to your attributes by using the = operator. For example, you can write a data class like this:
@dataclass class Rectangle: width: float = 0.0 height: float = 0.0
This will make the width and height arguments optional when creating an instance of Rectangle.
Field options: You can customize the behavior of your attributes by using the field() function from the dataclasses module. For example, you can write a data class like this:
@dataclass class Circle: radius: float area: float = field(init=False)
This will make the area attribute not be included in the __init__() method, but it will still be generated in the other methods. You can also use other options, such as repr, compare, hash, etc., to control how your attributes are used in different methods.
Post-init processing: You can define a special method called __post_init__() in your data class that will be executed after the __init__() method. This can be useful for performing some additional processing or validation on your attributes. For example, you can write a data class like this:
@dataclass class Circle: radius: float area: float = field(init=False) def __post_init__(self): self.area = 3.14 * self.radius 2
This will calculate and assign the area attribute based on the radius attribute after creating an instance of Circle.
PEP 567: Context variables
Context variables are a new feature in Python 3.7 that enable you to manage context-specific state in a thread-safe way without using thread-local storage. Context variables are similar to global variables, but they are local to a context, which is a set of values that are associated with a certain execution flow.
To create and use context variables, you have to use the ContextVar class from the contextvars module and the contextvars.copy _context() function from the same module. For example, you can write something like this:
from contextvars import ContextVar, copy_context # Create a context variable name = ContextVar('name') # Set a value for the context variable name.set('Alice') # Get the value of the context variable print(name.get()) # prints Alice # Create a copy of the current context ctx = copy_context() # Modify the value of the context variable in the copy ctx.run(name.set, 'Bob') # Get the value of the context variable in the copy print(ctx.run(name.get)) # prints Bob # Get the value of the context variable in the original context print(name.get()) # prints Alice
This example shows how you can create a context variable called name, set and get its value in the current context, make a copy of the current context, modify and get the value of the context variable in the copy, and get the value of the context variable in the original context. As you can see, the value of the context variable is different in different contexts, but it is consistent within each context.
Context variables are useful for situations where you need to store some state that is relevant to a specific execution flow, such as a request, a task, a coroutine, etc., without affecting other flows or threads. For example, you can use context variables to store information such as user identity, locale, timezone, logging level, etc.
Other improvements in the standard library, CPython implementation, and documentation
Python 3.7 also introduces many other improvements and enhancements in various aspects of the language, such as:
New modules: Python 3.7 adds two new modules to the standard library: contextvars and dataclasses. The former provides support for context variables, as we have seen before. The latter provides support for data classes, as we have seen before.
New functions: Python 3.7 adds several new functions to existing modules in the standard library. Some of them are:
time.time_ns() and time.clock_gettime_ns(): These functions return the current time or clock time as an integer number of nanoseconds.
os.cpu_count(): This function returns the number of CPUs in the system.
math.comb(n, k): This function returns the number of ways to choose k items from n items without repetition and without order.
math.dist(p, q): This function returns the Euclidean distance between two points p and q.
math.isqrt(n): This function returns the integer square root of n.
statistics.fmean(data): This function returns the arithmetic mean of a sequence of numbers as a float.
statistics.geometric_mean(data): This function returns the geometric mean of a sequence of numbers as a float.
statistics.multimode(data): This function returns a list of the most frequently occurring values in a sequence of numbers.
New syntax features: Python 3.7 adds some new syntax features to enhance the readability and expressiveness of the language. Some of them are:
PEP 562: Customization of module attributes: This feature allows you to define a special function called __getattr__() in your module that will be called when an attribute of your module is accessed but not found. This can be useful for implementing lazy loading, deprecation warnings, or proxying attributes to other modules.
PEP 563: Postponed evaluation of annotations: This feature allows you to defer the evaluation of annotations until they are needed, as we have seen before.
PEP 564: Add new time functions with nanosecond resolution: This feature adds new functions to the time module that support nanosecond resolution, as we have seen before.
PEP 565: Show DeprecationWarning in __main__: This feature makes DeprecationWarning messages visible by default when running a script directly, rather than only when running it through a test runner or an interactive session. This can help developers to notice and fix deprecated features more easily.
Performance enhancements: Python 3.7 improves the performance and efficiency of various aspects of the language, such as:
PEP 552: Deterministic pyc files: This feature makes the bytecode files generated by Python (.pyc files) deterministic, meaning that they will have the same content and hash value regardless of the time or machine where they are generated. This can improve the reproducibility, security, and caching of Python code.
PEP 560: Core support for typing module and generic types: This feature improves the implementation and performance of the typing module, which provides support for type hints and static type checking in Python. It also adds support for generic types, which are types that can be parameterized by other types, such as List[int] or Dict[str, float].
PEP 564: Add new time functions with nanosecond resolution: This feature adds new functions to the time module that support nanosecond resolution, as we have seen before.
Other optimizations: Python 3.7 also includes other optimizations, such as faster method calls, faster dict operations, faster attribute access, faster exception handling, etc.
Security fixes: Python 3.7 fixes some security issues that affect previous versions of the language, such as:
PEP 476: Enabling certificate verification by default for stdlib http clients: This feature enables SSL/TLS certificate verification by default for the http.client, urllib.request, and urllib.parse modules, which are used to make HTTP requests in Python. This can prevent man-in-the-middle attacks and other security risks when communicating with HTTPS servers.
PEP 578: Python Runtime Audit Hooks: This feature allows you to register audit hooks that can monitor and log events that occur during the execution of Python code, such as importing modules, opening files, executing commands, etc. This can help you to detect and prevent malicious or unauthorized activities in your Python applications.
Other fixes: Python 3.7 also includes other security fixes, such as disabling HTTP header injections, preventing buffer overflows, fixing memory leaks, etc.
Documentation translations: Python 3.7 adds support for translating the official Python documentation into different languages, such as French, Japanese, Korean, Spanish, etc. This can help non-English speakers to learn and use Python more easily and effectively.
Drawbacks and compatibility issues of Python 3.7
While Python 3.7 offers many benefits and improvements over previous versions of the language, it also has some drawbacks and compatibility issues that you should be aware of before using it. Some of them are:
async and await are now reserved keywords: In Python 3.7, async and await are reserved keywords that cannot be used as identifiers or attribute names. This means that you cannot name your variables, functions, classes, or modules as async or await. This can break some existing code that uses these names, especially in asynchronous programming frameworks or libraries.
Some security fixes may break backward compatibility: Some of the security fixes that are introduced in Python 3.7 may cause some existing code to fail or behave differently than before. For example, enabling certificate verification by default for HTTP clients may raise SSL errors for some servers that use self-signed or expired certificates. You may need to update your code or configuration to handle these changes.
The C API is not affected by the compatibility mode: The C API is the interface that allows you to write extensions or embed Python in other applications using the C language. The C API is not affected by the __future__ import statements that enable some features in Python 3.7, such as postponed evaluation of annotations or deterministic pyc files. This means that you may need to use different techniques or macros to access these features from C code.
Some packages may not support Python 3.7 yet: Some third-party packages or libraries that you use in your Python projects may not support Python 3.7 yet or may have some bugs or issues with this version. You may need to check the compatibility and update status of these packages before using them with Python 3.7.
Conclusion
In this article, we have learned how to download and install Python 3.7 on your machine, what are the main features and benefits of this version, and what are some potential drawbacks or compatibility issues that you might encounter.
Python 3.7 is a major release of the Python language that introduces several new features and improvements that make Python more powerful, expressive, and user-friendly. Some of the highlights of this version are postponed evaluation of annotations, built-in breakpoint() function, data classes, context variables, and other enhancements in the standard library, CPython implementation, and documentation.
However, Python 3.7 also has some drawbacks and compatibility issues that you should be aware of before using it. Some of them are async and await being reserved keywords, some security fixes breaking backward compatibility, the C API not being affected by the compatibility mode, and some packages not supporting Python 3.7 yet.
Therefore, we recommend that you use Python 3.7 if you want to take advantage of the new features and improvements that it offers, but also be prepared to deal with some challenges and changes that it may bring. You should also keep your Python version and packages up to date to ensure the best performance and security of your Python projects.
FAQs
Q: How do I update my Python version to 3.7?
A: The easiest way to update your Python version to 3.7 is to download and install it from the official website or the Windows Store, as we have explained before. However, you should also check if your existing code and packages are compatible with Python 3.7 before updating, as some changes may break your code or cause errors. You can use tools such as pyenv, virtualenv, or pipenv to manage multiple Python versions and environments on your machine.
Q: How do I use type hints in Python 3.7?
A: Type hints are optional annotations that you can use to indicate the expected types of values or arguments in your Python code. They are mainly used for static type checking, which is a process of verifying that your code follows the type rules at compile time or before running it. To use type hints in Python 3.7, you have to import the typing module and use the predefined types or create your own types using generics or aliases. For example, you can write a function like this:
from typing import List, Dict def count_words(text: str) -> Dict[str, int]: words = text.split() counts = for word in words: counts[word] = counts.get(word, 0) + 1 return counts
This function has type hints that indicate that text should be a string, and that the return value should be a dictionary of strings and integers. You can use tools such as mypy, pytype, or pyre to check your code for type errors using these type hints.
Q: How do I debug my Python code using the breakpoint() function?
A: The breakpoint() function is a new built-in function in Python 3.7 that allows you to insert a breakpoint in your code that will automatically launch a debugger when executed. To use the breakpoint() function, you have to call it anywhere in your code where you want to pause the execution and launch a debugger. For example, you can write something like this:
def factorial(n): if n == 0: return 1 else: breakpoint() return n * factorial(n - 1)
This will stop the execution when the function is called with a positive argument and open a debugger in your terminal. You can then use the debugger commands to inspect the variables, step through the code, evaluate expressions, etc. By default, the breakpoint() function uses the pdb module, which is the standard Python debugger. However, you can change this by setting the PYTHONBREAKPOINT environment variable to the name of another debugger module or function.
Q: How do I create and use data classes in Python 3.7?
A: Data classes are a new feature in Python 3.7 that simplify the creation of classes that mainly store data attributes and provide default methods for comparison, representation, and initialization. To create a data class, you have to use the @dataclass decorator from the dataclasses module and define the attributes of your class as class variables with type annotations. For example, you can write a data class like this:
from dataclasses import dataclass @dataclass class Point: x: float y: float
This will automatically generate an __init__() method that takes x and y as arguments and assigns them to self.x and self.y, respectively. It will also generate an __eq__() method that compares two instances of Point by their attributes, an __repr__() method that returns a string representation of an instance of Point, and a __hash__() method that returns a hash value of an instance of Point. You can create and use instances of Point like this:
p1 = Point(1.0, 2.0) p2 = Point(3.0, 4.0) print(p1) # prints Point(x=1.0, y=2.0) print(p1 == p2) # prints False print(hash(p1)) # prints 3713081631934410656
Q: How do I manage context-specific state using context variables in Python 3.7?
A: Context variables are a new feature in Python 3.7 that enable you to manage context-specific state in a thread-safe way without using thread-local storage. Context variables are similar to global variables, but they are local to a context, which is a set of values that are associated with a certain execution flow.
To create and use context variables, you have to use the ContextVar class from the contextvars module and the contextvars.copy_context() function from the same module. For example, you can write something like this:
from contextvars import ContextVar, copy_context # Create a context variable name = ContextVar('name') # Set a value for the context variable name.set('Alice') # Get the value of the context variable print(name.get()) # prints Alice # Create a copy of the current context ctx = copy_context() # Modify the value of the context variable in the copy ctx.run(name.set, 'Bob') # Get the value of the context variable in the copy print(ctx.run(name.get)) # prints Bob # Get the value of the context variable in the original context print(name.get()) # prints Alice
This example shows how you can create a context variable called name, set and get its value in the current context, make a copy of the current context, modify and get the value of the context variable in the copy, and get the value of the context variable in the original context. As you can see, the value of the context variable is different in different contexts, but it is consistent within each context.
Context variables are useful for situations where you need to store some state that is relevant to a specific execution flow, such as a request, a task, a coroutine, etc., without affecting other flows or threads. For example, you can use context variables to store information such as user identity, locale, timezone, logging level, etc.
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