Adventures in Machine Learning

Python 310: New Features and Upgrading Considerations for Developers

Better Error Messages and

Structural Pattern Matching: What You Need to Know

As a programmer, you know that writing code is hard, and debugging it can be even harder. However, two new features in Python 3.10 aim to make your life easier: better error messages and structural pattern matching.

Better Error Messages

Firstly, let’s talk about better error messages. Have you ever encountered an error message that left you scratching your head?

Perhaps it was a generic “SyntaxError” that didn’t provide any helpful suggestions on how to fix your code. Or maybe you spent hours trying to figure out where a missing quotation mark or curly brace was.

Well, with the new improvements in error messages, those issues will be a thing of the past. Python 3.10 will provide more precise error messages, telling you exactly where the problem occurred in your code.

For example, if you have a missing quotation mark, it will show you the exact line where the error occurred and place a caret under the offending character.

Moreover, the improved error messages also suggest ways to fix the issue.

For instance, if you have an EOL (end of line) error, Python 3.10 will tell you to “add a backslash to continue the previous line.” This kind of specificity is crucial for saving time and frustration when debugging your code. Additionally, Python 3.10 includes syntax highlighting, which makes it easier to identify errors in your code.

Syntax highlighting helps you visually distinguish between different parts of your code, including comments, strings, and keywords. Moreover, it makes problematic lines more apparent, which saves you time from having to comb through your entire script to find the source of an error.

Structural Pattern Matching

Another new feature in Python 3.10 is structural pattern matching. This feature allows you to match and destructure data structures such as lists, dictionaries, and tuples.

You can use the “match … case” syntax to match values and patterns and execute the corresponding code.

The “match” statement is followed by the value to be matched, and the “case” statements define the patterns to check for.

For example, let’s say you want to process a dictionary containing people’s contact information.

You can use structural pattern matching to get the email addresses of the people in the dictionary as shown below:


contacts = {‘John’: {’email’: ‘[email protected]’, ‘phone’: ‘555-1234’}, ‘Jane’: {’email’: ‘[email protected]’, ‘phone’: ‘555-5678’}}

for name, info in contacts.items():

match info:

case {’email’: email, ‘phone’: phone}:

print(f”{name}: {email}”)


In this example, the “match” statement checks for a dictionary containing the “email” and “phone” keys. If the pattern matches, it assigns the corresponding values to the “email” and “phone” variables.

This way, you can extract the email addresses efficiently, even if the structure of the dictionary changes. Moreover, structural pattern matching supports different types of patterns such as mapping, sequence, literal, and wildcard patterns.

Mapping patterns match dictionaries where certain keys must exist and may have specific values. Sequence patterns match lists and tuples, allowing you to match each element against a pattern.

Literal patterns match a specific value, and wildcard patterns match anything. AS patterns let you assign any part of a pattern a name to be used in the matching code.


In summary, Python 3.10 introduces two exciting new features: better error messages and structural pattern matching. Better error messages provide detailed information about syntax errors and suggest ways to fix them, saving you time and frustration when debugging your code.

Structural pattern matching allows you to match and destructure different data structures efficiently, making your code cleaner and more readable. By familiarizing yourself with these features, you can write more robust and efficient code that is easier to debug and maintain.

Type Unions,

Aliases, and

Guards: Enhancing Flexibility in Python

Python 3.10 introduces several new features that enhance the flexibility of the language, including type unions, aliases, and guards. These features provide more robust options for working with data types, enabling you to write more concise, readable, and maintainable code.

Type Unions

Type unions are a new way of specifying that variables or arguments may have more than one acceptable data type. Traditionally, you would use if statements and isinstance() or type() functions to check the type of the data.

Type unions reduce this type-checking code by allowing you to group multiple data types under a single name. In Python 3.10, you can create a type union using the Union[] annotation.

This syntax allows you to define which types a given parameter or variable may be. You can also use the “|” operator to create the union.

The resulting type can be any of the given types. For example, let’s say you have a function that takes either a string or an integer as an argument.

You can use a type union to specify this as shown below:


from typing import Union

def print_value(value: Union[str, int]):



In this example, the Union[] annotation is used to specify that the value argument can be either a string or an integer. This makes the function more flexible, allowing users to pass either data type without triggering a type error.


Aliases are another new feature in Python 3.10 that allow you to simplify your code by creating type aliases. These aliases are essentially new names for existing types.

They allow you to create more readable code by having a descriptive name for a complex type. To create a type alias, you use the TypeAlias[] annotation.

This annotation allows you to give your type alias a meaningful name that you can use in your code instead of the original type. For example, let’s say you have a complex dictionary that you use frequently in your code.

You can simplify your code by creating an alias for this type, as shown below:


from typing import TypeAlias

PersonDict = TypeAlias[dict[str, Union[str, int]]]


In this example, we use the TypeAlias[] annotation to create the PersonDict alias. This alias is a dictionary with string keys and values that can either be strings or integers.

Now you can refer to this dictionary type easier using its alias.


Guards are a new way of refining variable types within functions.

Guards are a logical expression added in the “if” clause that can conditionally refine the type of a variable.

You can use guards to specify conditions that must be met before a variable can be used in a particular way.

Here’s an example:


def get_first_element(items: list) -> Union[int, str]:

first = items[0]

if isinstance(first, str):

return first.upper()

return first


In this example, the function takes a list as an argument and returns the first element of the list.

However, if the first element is a string, the guard will convert the string element to uppercase before returning it.

Guards are also useful in checking variable values before operating on them.

Stricter Zipping of Sequences

Another enhancement in Python 3.10 is stricter zipping of sequences. This feature improves the zip() function by providing more stringent checking when you call the function with sequences or iterables of different lengths.

Previously, the zip() function would silently ignore any excess items in the longer sequence during zipping, leaving you with data loss. In Python 3.10, zip() can raise a ValueError if the sequences to be zipped are not of the same length.

This error provides more visibility into the data you are working with. Additionally, Python 3.10 supports iterator unpacking inside the zip() function with the “next()” function, allowing you to avoid unpacking the entire iterable.

Here’s an example:


names = [‘Alice’, ‘Bob’, ‘Charlie’]

ages = [25, 30]


list(zip(names, ages))

except ValueError as e:

print(f”Error: {e}”)

# Output: Error: zip argument #2 must support iteration


In this example, the “ages” list is shorter than the “names” list, which would result in a loss of data in previous versions of Python. Now, in Python 3.10, the ValueError is raised because zipping the sequences together results in an incomplete dataset.


Python 3.10 introduces several new features that enhance the flexibility and readability of the language. Type unions, aliases, and guards allow you to write more concise, readable, and maintainable code, while stricter zipping of sequences provides more visibility into the data you are working with.

By familiarizing yourself with these features, you can write more robust and efficient code that is easier to debug and maintain. New Features and Enhancements in Python 3.10

Python 3.10 comes with several new features and enhancements that improve the functionality and usability of the language.

From new functions in the statistics module to updates in SSL security, Python 3.10 has everything you need to write robust and efficient code. In this article, we’ll dive into some of the latest features and explore how they can enhance your programming experience.

New Functions in the statistics Module

The statistics module in Python 3.10 has received several exciting new functions that can help you calculate and analyze data quickly. Here’s a brief overview of the new functions:


fmean(): This function calculates the arithmetic mean of floating-point numbers faster than mean() function, which is a slower binary float implementation.


geometric_mean(): This function calculates the geometric mean of a sequence of numbers, which is useful in various scientific and mathematical applications. 3.

harmonic_mean(): This function calculates the harmonic mean of a sequence of numbers. The harmonic mean is useful for finding average rates of speed and other situations where the average is influenced by the rate of change.

4. multimode(): This function returns a list of the most frequently occurring values in a sequence.

It is useful for determining which values occur most often in a dataset.

Updated Functions in the statistics Module

The statistics module also includes updates for some of its existing functions, including mean(). Mean() now uses fmean() by default on an iterable if the iterable contains floating point numbers.

The update means higher accuracy and quicker performance when working with float data types.

Other Pretty Cool Features

Apart from the enhancements in the statistics module, Python 3.10 features many new additions. Let’s take a closer look at what else is new.

Default Text Encodings

Python 3.10 introduces new functions to control Python’s default text encoding. By default, the new ASCII compatibility mode is enabled, making Python’s default text encoding UTF-8 for all strings using ASCII characters exclusively.

Non-ASCII characters will trigger UnicodeEncodeErrors or UnicodeDecodeErrors when used with encoding methods that don’t support them. To better accommodate the new defaults, Python 3.10 adds UTF-8 mode, which enables UTF-8 by default for all strings, including those containing non-ASCII characters.

Asynchronous Iteration

Python 3.10 introduces the “async for” statement and asynchronous iteration. Together with async/await support, these features allow you to write asynchronous programs in Python more naturally, making it easier for developers to write high-performance, asynchronous, and concurrent Python code.

Context Manager Syntax

Context managers can control how Python’s runtime behaves when entering or exiting a code block or when dealing with resources. Python 3.10 introduces a new syntax for context managers, using the “with ..

as” statement. The new syntax makes it more natural and easier to manage resources and execute exit code in Python.

Modern and Secure SSL

Python 3.10 also updates the SSL implementation used in the library to OpenSSL 1.1.1, reflecting modern encryption and security standards. In particular, Python 3.10 supports TLS 1.3, the latest protocol for SSL/TLS communications.

As a result, Python 3.10 improves the performance of secure networking applications.

More Information About Your Python Interpreter

The sys module now includes the pycache_prefix variable, which allows you to see the path to the cache directory in which compiled bytecode files are stored. This new feature is available for both regular and virtual environments and provides more insight into what’s happening behind the scenes when you run your Python code.


Python 3.10 is a significant update to the language, introducing many new features and enhancements, including new functions in the statistics module, asynchronous iteration and async/await, context manager syntax, and improved SSL security. Additionally, Python 3.10 includes default text encoding settings and provides more information about the interpreter with the pycache_prefix variable.

By utilizing these new features, Python developers can write more efficient, readable, and secure code.

Future Annotations and Upgrading to Python 3.10: What You Need to Know

Python 3.10 comes with many new features and enhancements that improve the functionality and usability of the language. The new features include context managers, asynchronous iteration, and regular expressions.

Moreover, several third-party libraries, such as NumPy and Pandas, have added support for the new features in Python 3.10. But what about backward compatibility?

In this article, we will look at future annotations and upgrading to Python 3.10.

Future Annotations

Future annotations are a new feature in Python 3.10 that allows you to specify that you are using Python 3.10-only syntax. These annotations help code maintainers and future versions of Python to recognize Python 3.10-specific syntax elements.

One of the most significant benefits of future annotations is that they allow you to write more forward-looking code. You can use Python 3.10’s new syntax knowing that your code will become more relevant as more developers and projects upgrade to Python 3.10.

Detecting Python 3.10 at Runtime

You can use the sys.version_info variable to detect the version of Python your program is running on, including Python 3.10. This feature is useful for writing code that needs to run on different versions of Python, ensuring that the code behaves accordingly.

Here’s an example:


import sys

if sys.version_info >= (3, 10):

# use future syntax here


# use an alternative syntax


Upgrade to Python 3.10

Upgrading to a new version of Python is always a significant decision, and Python 3.10 is no exception. While Python 3.10 offers many benefits and new features, it’s important to consider several factors before committing to an upgrade.

Upgrade Considerations

Backward Compatibility: Python 3.10 is a major release and could break your existing code. It’s wise to test your code with Python 3.10 to identify potential problems before committing to an upgrade.

You can use tools like 2to3 to perform an automated code migration; however, this tool is not perfect, and you should review the resulting code manually. Third-Party Libraries: Before upgrading to Python 3.10, check that any third-party libraries you use are compatible with the new version.

If a dependency is not compatible, it may cause issues or break your code. Asynchronous Programming: If your code relies heavily on asynchronous programming, you may want to exercise caution before upgrading to Python 3.10.

Although Python 3.10 provides new features to simplify asynchronous programming, it also introduces breaking changes to asyncio. Review the changes in asyncio before upgrading to Python 3.10.

Benefits of Upgrading

Despite the potential challenges, upgrading to Python 3.10 offers several benefits, including:

1. Improved Error Messages: Python 3.10 comes with better error messages, making it easier to debug your code.


Structural Pattern Matching: Python 3.10 introduces structural pattern matching with multiple pattern types that can make your code more readable and efficient.

3. fmean(): The new fmean() function calculates the arithmetic mean of floating-point numbers faster than the mean() function.

4. multimode(): The new multimode() function returns a list of the most frequently occurring values

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