This article was published as a part of the Data Science Blogathon
For the last couple of decades, Python has created a name for itself in the domain of programming or scripting languages. The major reason why python is being heavily favoured is due to its extreme user-friendliness. Python is also used to handle complex programs or coding challenges. Emerging fields such as Machine Learning(ML), Artificial Intelligence(AI), and Data Science are also feeding to the high demand for learning this language. As compared to traditional languages such as Java, C#, and other languages, Python is a robust programming language that has quickly become a fast favourite for developers, data scientists, and AI/ML enthusiasts.
Python as a programming language has a number of use cases that draw in learners as well as experts in the IT industry. On a basic level, Python can be used as a programming language to practice data structure and algorithms or develop simple projects or games. The versatility of Python as a language allows its users to easily scale up their projects and create websites, software, or predictive models. Automation is taking over a majority of the IT industry and Python takes the lead as the favoured language used to automate tasks in data analytics or data science. Other than this, Python has a massive number of libraries and a robust community of programmers who continuously add more value to Python as a language.
One of the many reasons why beginners are drawn to python is due to its user-friendliness. Python has banished the feared semi-colon and uses a simple indented structure as its syntax. Python also finds a use case as an extension for applications that need a programmable interface. Some more benefits of Python include its most coveted feature that is its libraries. Python libraries are a vast resource that is used in a number of crucial code writing such as:
And all of these features are executable on many Unix, Linux, macOS, and Windows systems.
Over the years, Python has had a lot of upgrades and a lot of features have been added across the newer versions. Here, let us focus on two of the most recent versions that Python has added. Exploring the newer features helps you to work smoothly with it and of course, find a smarter way to work using newer libraries. All of the codes attached below are for educational purpose only and has been drawn from the original Python documentation that was released along with the new versions such as Python 3.9 and Python 3.10
A new module named zoneinfo has been created in Python 3.9. This module provides you access to the IANA or the Internet Assigned Numbers Authority time zone database. This module, by default, uses the system’s local time zone data.
print(datetime(2021, 7, 2, 12, 0).astimezone())
print(datetime(2021, 7, 2, 12, 0).astimezone().strftime("%Y-%m-%d %H:%M:%S %Z"))
print(datetime(2021, 7, 2, 12, 0).astimezone(timezone.utc))
2020-07-2 12:00:00-05:00 2020-07-2 12:00:00 EST 2020-07-2 17:00:00+00:00
Python 3.9 has added another cool feature that has attracted a lot of attention. Python 3.9 can now merge or update dictionaries using operators. The new operators i.e. ( | ) and ( |= ) have been added to Python 3.9 built-in dict class. You can access these operators to merge or update dictionaries using code similar to the ones tagged below.
>>> a = {‘v’: 1, 'art’: 2, 'py’: 3}
>>> b = {’v’: 'd’, 'topic’: 'python3.9’}
>>> a | b
{’art’: 2, 'py’: 3, ’v’:’d’, 'topic’: 'python3.9’}
>>> b | a
{’v’: 1,’art’: 2, 'py’: 3, 'topic’:’python3.9’ }
>>> a |= b
>>> a
{’art’: 2, 'py’: 3,’v’:’d’}
String handling problems are easier to solve with the new features added to Python 3.9. The codes tagged below are used to strip the prefixes and suffixes from sample strings. The new methods used in the sample codes below are:
These new methods have been created to replace the older strip() method due to the negative reviews from programmers about its buggy nature. Tagged below is a sample code that can help you understand the implementation of the two new methods.
print("Victor is playing outside".removeprefix("Victor "))
‘is playing outside’
The Python 3.9 release has enabled the support feature for the generic syntax for all standard collections which are currently available in the typing module. A generic type is often defined as a container, like for example a list. It is a type that can be easily parameterized. Usually, generic types have one or more types of parameters whereas a Parameterized generic is a specific instance of a generic data type with the container elements, for example, list or dictionary built-in collection types are the various types that are supported instead of specifically using the typing.Dict or typing.List
def print_value(input: str): # Specifying the passed value will be of type String
By using the following way, we will be able to find if the following input is a string or not
A new feature called Structural Pattern Matching has been introduced in the brand new Python 3.10. This matching process operates along with the same match-case logic but it also compares against a comparison object to trace a given pattern.
http_code = "419"
if http_code == "200":
print("OK")
elif http_code == "404":
print("Not Found Here")
elif http_code == "419":
print("Value Found")
else:
print("Code not found")
http_code = "419"
match http_code:
case "200":
print("Hi")
case "404":
print("Not Found")
case "419":
print("You Found Me")
case _: #Default Case
print("Code not found")
Loads of programmers face difficulties in error matching or debugging their code. Python 3.10 has added a very user-friendly feature called Associative Suggestion which is tagged with Syntax Error messages. This helps you quickly find a fix for the codes which have a bug or error in them.
named_car = 77 print(new_car)
NameError: name 'new_car' is not defined. Did you mean: named_car?
Upgrading from Python 3.9, we can assign multiple input types of a parameter without using the union keyword and by just using the OR symbol instead. It’s an easier way to define multiple input types for the same variable
def add(a: Union[int, float], b: Union[int, float]):
def add(a: int | float, b: int | float):
Context Managers help with the handling of resources such as files. You can now use multiple contexts in a single block. This will highly enhance your code as you no longer need multiple blocks or statements.
with open('output.log', 'rw') as fout:
fout.write('hello')
with (open('output.log', 'w') as fout, open('input.csv') as fin):
fout.write(fin.read())
So now we have discussed most of the updates that are there in Python 3.10 and which you will be using the most. The introduction of a switch case was something that was anticipated by many and it is finally here. So let me know if this article helped you in any way and drop me a text if you want me to write about a particular topic in my next article or want to add some information to my existing ones.
Thank you for reading till the end. Hope you are doing well and stay safe and are getting vaccinated soon or already are.
Data Engineer | Python, C/C++, AWS Developer | Freelance Tech Writer
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thanks Arnab for writing n sharing. side note - you may want to fix the output of a|=b dictionary merge example.
Information given is pertinent, highly useful