Python is the dominant language in the data science environment. The primary causes of this dominance, in my opinion, are the simplicity of learning and the large range of data science tools.
Python is a versatile language that is not only useful for data science. The development of websites, mobile applications, and video games are some of Python's uses.
If you only use Python for data science-related tasks, you do not need to be a Python expert. However, there are a few fundamental concepts and traits that I think you should be aware of.
Libraries in general are not the focus of this article. They may be thought of as the basic Python for data science. You need to have a firm grasp of the principles of Python even if you only use Scikit-learn, Matplotlib, and Pandas. These libraries rely on your familiarity with Python's foundational concepts.
I will briefly illustrate each issue using a few examples. I'll also provide a link to a longer article for the majority of the topics.
Must-Know Python Topics for Data Science
1. Functions
Functions are the building blocks of Python. They give back a value with 0–N parameters. To create a function, we utilise the def keyword.
Each function should execute a single task. The creation of a function that performs many jobs defeats the purpose of using functions.
Additionally, we need to provide functions descriptive names so that we can understand what they do even without looking at the code.
- Keyword and positional arguments
When a function is defined, its parameters are stated.
Consider the multiply function that was created in the prior phase. We supply the two parameters with their corresponding values when the function is invoked.
Positional arguments can be recognised by their names alone.
Keyword parameters are declared using a name and a default value.
Positional arguments need values to be supplied when calling a function. Otherwise, we'll run into trouble. A keyword argument uses its default value if no value is provided.
- *args and **kwargs
Functions are the building blocks of Python. They give back a value with 0–N parameters. Python is fairly versatile when it comes to how parameters are passed to functions. The usage of *args and **kwargs simplifies and improves the handling of arguments.
The *args flag allows any number of positional arguments to be accepted by a function.
Thanks to kwargs, a function can take any amount of keyword arguments.
By default, the dictionary in kwargs is empty. Each unknown keyword argument is stored as a key-value pair in the **kwargs dictionary.
- Classes
The idea that objects belong to a certain type is the cornerstone of the object-oriented programming (OOP) paradigm. We can sort of explain the object based on its type.
Everything in Python is an object of a certain type, such as an integer, list, dictionary, function, and so forth. Classes enable us to describe various object types.
Classes include the following information:
What is necessary to create a class instance in data attributes?
Procedures (sometimes called procedural attributes): How we talk to the instances of a class.
- Lists
A list is a type of built-in data structure in Python. A collection of data points wrapped in square brackets is used to represent it. Lists can be used to hold any type of data or a mix of several sorts of data.
Lists may be changed, which is one of the reasons they are so popular. Consequently, we are able to remove and add items. The items in a list can also be updated.
- List comprehension
List comprehension is basically the creation of new lists based on existing lists, tuples, sets, and other iterables. It might also be considered a more attractive rendition of the for and if loops. List comprehensions are quicker than for loops in comparison.
- Dictionaries
A dictionary is an unorganised collection of key-value pairs. Each entry has a key and a value. A dictionary may be thought of as a list with a special index.
The keys must be distinctive and immutable. We can use tuples, strings, or numbers (int or float) as keys as a result. Any form can be taken by values.
Consider a circumstance where we would need to preserve a student's grade. They can be preserved in a dictionary or a list.
One method to create a dictionary is to write key-value pairs using curly brackets.
Any value in a dictionary can be accessed by its key.
- Sets
A set is an unordered collection of distinct hashable objects. This is how a set is defined in the official Python manual. Let's get going.
There are one or more elements, but not none, in this disorganised collection. A set's components are not organised in any specific order. Because of this, it does not support indexing or slicing like lists do.
Differentiated hashable objects: Each element of a set is unique. Immutable is described as hashable. Although the components of a set can change, they must all be immutable.
We can create a set by putting items in curly brackets and separating them with commas.
Sets don't include repeated members, thus even if we try to add the same member more than once, the resulting set will have unique elements.
- Tuples
A pair of values surrounded in parenthesis and divided by commas is referred to as a tuple. Unlike lists, tuples cannot be changed. One of the distinctive properties of tuples is their immutability.
A tuple is formed by values enclosed in parenthesis and separated by commas.
There are several ways to construct tuples than using parenthesis. A succession of values separated by commas makes up a tuple.
One of their most common applications is the use of tuples with functions that return multiple values.
- Lambda expressions
Lambda expressions are specialised sorts of functions. Normally, lambda expressions are used without a name.
Consider a method that is used just once or very infrequently. There are further variations of this method that differ somewhat from the original. To define a different function for each operation in this case is not the best course of action. An enormously more efficient approach to finish the jobs is by using lambda expressions.
Conclusion
We've covered some of Python's most significant concepts and ideas. Utilizing third-party tools and frameworks like Pandas, Matplotlib, Scikit-learn, TensorFlow, etc., the majority of data science-related activities are completed.
However, we must have a solid grasp of Python's essential actions and concepts in order to use such libraries successfully. They are assumed to be familiar with Python's basics.
