October 15, 2018
1. When was Python developed?
Contrary to its recent popularity, Python is not as new as some may think. In fact, Python was first developed and implemented in the late 1980s by Guido van Rossum of Centrum Wiskunde & Informatica (CWI), who created and introduced this easy-to-use and extremely powerful open source programming language that many of us have come to adore.
Python’s syntax is simple to read and originates from C-language, but this is because it is the language Python was written in. In contrast to C, which uses curly braces and semicolons to group and organize code, Python takes advantage of the whitespace to delimit the amount of code need, ultimately making Python significantly easier to read and understand.
So, with all of this diversity and programming control, is there a best time or situation to determine when you should use Python or not?
2. When should you use Python?
Finding applicable uses for Python in any development field is actually quite simple. For a long time, Python was considered a come-and-gone dead language, not receiving much recognition until the world was ready for it. For far too long, the premise and idea of Python was thought to be too big for the technical field to grasp. Rossum had developed a language that would take nearly 30 years before it became incredibly essential, but why?
There are several other languages, some of them well known like the C series, SQL, Java, HTML and Ruby to name a few. While these languages have their roles and accompanying levels of programming power, Python’s versatile code exists beyond desktop and web applications.
Yes, in fact, Python’s growth exploded with the rise of the data science boom, machine learning, and of course the consistent headlining AI trends. Python has well over 100,000 libraries of language ready to be integrated into a wide range of fields. From teaching a machine to sort through products, images, or something else to finding use in medical research labs for genetic sequencing. So, I’ll ask again, “When should you use Python?”
3. When was the latest Python version released?
Python undergoes frequent version updates, with each release advancing the strength and power of it, and what was once a misunderstood language, has quickly become one of the most in-demand languages to date. Currently, Python is running the Python 3.7.0 which provides small fixes to prevent buffer overrunning and the Regexes vulnerabilities in difflib and poplib, leading to detrimental backtracking and the formation of DOS vectors (REDOS). These have been refactored and solutions implemented in the latest downloadable version.
4. When should you use classes with Python?
Some debate that classes are not needed in Python’s object-oriented programming language, but it’s quite contrary to that belief as most of the code is executed, integrated, or implemented using the specialized class structure for object state identification. Need an example?
Let’s say we have two people or objects we need to put into our coding. This is where your classes come in. Identifying each of the people as a person, they are now placed into a class, “Person” that will be used during coding to refer to the people, but there’s something else that makes classes special.
You can initiate attributes assigned to your classes, so the two people placed in the Person class can be directly targeted by adding values. One person may be a welder, while the other a doctor. You can set variables by assigning them to the Person class, see example:
|>>> # two variables are instantiated
>>> Welder = Person(“Welder”)
>>> Doctor = Person(“Doctor”)
>>> # print the names of the two variables
5. “When” statements with Python
“When” statements or conditional statements are coding statements set to trigger when something happens. It is the if/then effect. If this happens then execute this. This is essential for writing useful programs, especially with machine learning. Conditional statements in Python act as outlining parameters set that your program will follow. Using “when” statements in Python can be used with data analysis set with algorithms and programmed to look and reacted when certain metrics and guides are achieved or triggered. This is a core function for early building blocks for machine learning programs.
6. When should you use generators?
Using generators in Python is not suitable for all cases, but is extremely beneficial when it comes to needing iterations created in a pythonic manner. Iterators are commonly known as “lazy evaluation” which means that it does not continuously run, but rather acts when an object iteration is called upon. Iterators help when needing to sort, decipher, and organize large data sets.
Iterators can be made without generators, however, using generators in code instead increase functionality and expressions. Generators act much like the return code in, where it returns a value. Generators, however, bring the yield statement to Python, meaning that it creates a state-save feature that allows the function to continue where it left off instead of having to recompute from the start each time.
7. When should you use static methods?
The use of @staticmethod in Python is best described as the implementation of function specific criterion within a class. The static method can be called on both the class ( c.f() ) or on an instance ( c().f() ), which the instance is ignored, but the class is not.
Static methods are closely similar to those used in Java and C++ and are callable without instantiating the class before. This means that it can access class, but it cannot make any changes to the information inside of the class parameter.
8. When should you use assert?
Assertion statements in Python are lifesavers when it comes to spot checking and making sure your program runs smoothly without error. Commonly used as a Debugging aid that basically fact checks certain conditions in the program. If the Assert statement is true, the program will keep functioning as normal. When the Assert statement is false, you’ll start to understand the true value of using this statement for testing when developing in Python. A false statement will return an AssertionError and typically has an optional error message written to let you know why the statement is not true and is evaluated as an error.
To make this more clear, the Assert statement is not a tool used to handle run-time errors, but instead is a debugging aid that helps developers locate the root cause of bugs in an efficient manner. Assertions should only ever be Raised if and only if there is a bug in the program.
9. When should you use tuples?
Tuples are often confused with lists, and at a quick glance, sure, I can see it happening, but really? Are they that similar? When programming, coding or developing in Python, you’ll often use a combination of tuples and lists, but the important significance of a tuple is that they are heterogeneous datatypes that cannot be augmented once an element of the tuple has been assigned. Lists, on the other hand, can change.
Let’s clarify a little further. A list usually contains data, information, or other content that relate or coincide with the other elements on a list. A Tuple is categorizing different elements that do not share a correlation. Why is a Tuple better than a list?
Since tuples need exceptions in order to be changed, the iteration through tuples are faster than lists, which means there is a small performance increase. Also, immutable tuples can serve as a key for a dictionary, which is not achievable through a list. Another useful tip of using a tuple is when you have datatypes that do not vary or change, the implementation of a tuple can act as a safeguard to guarantee the stored data continues to remain write-protected.
10. When are you going to start using Python?
Lastly, I ask, “When will you start using Python?” From basic and simple coding to intricate and highly detailed graphical interactive learning-capable programs, Python is an extremely powerful language. If you’ve made it this far and still have questions, then feel free to reach out to our professional developers who specialize in Python.