Python is a powerful programming language that is widely used in many industries today. Python skills are in high demand due to its versatility and its ability to handle complex data structures. Python is commonly used in web development, scientific computing, artificial intelligence, and data science.
Python is a high-level, interpreted, general-purpose programming language, created on December 3, 1989 by Guido van Rossum with a design philosophy entitled “There’s only one way to do it, and that’s why it works.”
In the Python language, that means explicit is better than implicit. It also gives rise to the infamous Python telegraph pole analogy attributed to creator Guido van Rossum which goes like this:
There is beauty in π because it is perfect; there are no ugly numbers.” pi” refers to the Greek letter often used in mathematics to represent the ratio of a circle’s circumference to its diameter. The point of the analogy being that π (and by extension Python) are beautiful because they are elegant and concise.
File handling concepts
When working with files in Python, there are two main types that you will come across – text files and binary files. Text files are human readable and can be opened and edited in any text editor. Binary files, on the other hand, are not human readable and can only be opened and edited in specialized programs.
Python provides a number of built-in functions for working with both types of files. For example, the open() function can be used to open a file; while the read() function can be used to read data from a file. Similarly, the write() function can be used to write data to a file; while the close() function can be used to close an open file.
In addition to these basic functions, Python also provides a number of other file handling related functions such as seek(), tell(), truncate(), etc. which we will discuss later in this article.
Classes are the blueprint for creating objects. They define the properties and methods that an object will have. Objects are instances of a class. They can inherit the properties and methods of their parent class. Inheritance is when a child class inherits the attributes of its parent class. Polymorphism is when an object can take on many forms depending on how it is used. Data abstraction is the process of hiding information about an object so that only certain aspects of it are exposed to the outside world.
Python skills are in demand because they are versatile and powerful. Python can be used for web development, scientific computing, artificial intelligence, and more.
Python’s built-in data structures include lists, tuples, dictionaries, and sets. Each of these has its own strengths and weaknesses, which you should understand in order to make the most effective use of them.
Lists are perhaps the most versatile of Python’s data structures. They can be used to store both homogeneous and heterogeneous data types (i.e., data of different types can be stored in the same list). Lists are also mutable, which means they can be modified after they have been created.
Tuples are similar to lists in many respects, but they are immutable (i.e., they can not be modified after they have been created). This makes tuples particularly well-suited for storing data that should not be changed (such as database records).
Dictionaries are another important built-in data structure in Python. They differ from lists and tuples in that each element is mapped to a key rather than being indexed by a numeric value. This allows for more efficient lookup of dictionary values since the key can be used directly instead of having to search through an entire list or tuple for a matching value.
Sets are another useful built-in datatype in Python. Sets are similar to lists but without any particular order. This unordered nature makes sets ideal for storing groups of related values where duplicates are not allowed (such as unique IDs).
When you call a generator function, it doesn’t return a value-it returns an iterator object. This iterator object can be used in a for loop to iterate over the generated values. You can think of a generator as being like a factory that produces values one at a time, as they’re requested by the consumer (the for loop).
Generators are very powerful and can be used to create all sorts of iterables, including infinite sequences. For example, you could use a generator to generate all the prime numbers:
def primes(): num = 2 while True: # Infinite loop! for i in range(2, num): # Check if num is divisible by any number less than itself if (num % i) == 0: # If yes, then it’s not prime… move on to the next number break else: # Otherwise (if it isn’t divisible by anything less than itself), it IS prime! yield num break num +=