This blog is to answer the numerous queries regarding how to start using Python for DS that have been posted on various forums.

So, to help you get started with Python programming, here are the foundations.

Let's examine each one and discover which fundamentals you should be familiar with.

1. Data Structures and Types

Understanding how Python understands data is the first step.

You should be familiar with integers (int), floats (float), strings (str), and booleans to start with because these are the most prevalent data types (bool). You should put these into practice.

I/O operations, typecasting, and type:

Using the type() method to determine the kind of data.

Values are stored in variables and input-output processes (a = 5.67).

When possible, typecasting involves changing a certain type of variable or data into another type. For instance, here's how to make an integer from a string of integers:

When working with the string data type, it helps to be familiar with textual data and its operators. Practice the following ideas:

• + Splitting and connecting the string with the split() and join() methods to concatenate strings
• Using the methods lower() and upper(), change the string's case.
• Utilizing a string's substrings.
• Data structures that are compound (lists, tuples, and dictionaries)

2. Compound data types, such as lists and tuples:

Lists are among the most significant and widely used data structures in Python. A list is a grouping of elements, which may be of the same data type or of different data types.

The ability to compute algebraic equations and statistical models on your collection of data will eventually be made possible by your understanding of lists.

The ideas you need to be knowledgeable with are listed below:

• How to store various data kinds in a Python list.
• accessing a particular list element or sub-list by indexing and slicing.
• auxiliary functions for copying, adding, removing, sorting, and reversing elements.

A set of objects in an unchanging order is called a triple. Tuples and lists are similar, but the main distinction is that tuples are immutable and lists are not.

Ideas to consider:

• Slicing and indexing (similar to lists).
• nesting tuples.
• Tuples and helper functions such as count() and index ().
• Dictionaries

These in Python are a different kind of collection. Dictionary entries are more like addresses while lists are integer indexed. Key-value pairs exist in dictionaries, and keys are comparable to list indexes.

3. Conditional statements, loops, and functions

Branching and Conditions

These boolean variables are used by Python to evaluate circumstances. Whenever a comparison or evaluation is made, boolean values are the outcome.

x = True

ptint(type(x))

# output: print(1 == 2) for the class bool.

False output

It's important to pay close attention to the comparison in the graphic since sometimes people mistake the assignment operator (=) with the comparison operator (==).

Operators for Booleans (or, and, not)

These are employed to evaluate several complex statements.

or — For the entire condition to be true, one of the numerous comparisons must be accurate.

and — For the complete condition to be true, all comparisons must be accurate.

not — Looks for the opposite of the provided comparison.

Loops

Loops are your best friend when it comes to reducing the overhead of redundant code because you'll need to perform repetitive tasks frequently. Loops are useful when you need to repeatedly iterate through each item in a list or dictionary. There are two sorts of loops: while and for.

Functions

Maintaining code can be quite tedious when working on a large project. Using functions is a practical approach to manage your code if it frequently completes identical tasks.

A function is a section of code that applies certain operations to data supplied and produces the intended result.

Programming with objects and utilizing external libraries

The helper methods for lists, dictionaries, and other data types that we have been utilizing come from where, though?

We are actually engaging with a list class object or a dict class object when we say list or dict. You can tell that a dictionary object is a class dict object by printing the object's type.

Utilizing functions improves readability, lessens repetition, makes the code reusable, and saves time.

Using Outside Libraries and Modules

One of the biggest benefits of using Python for data science is the incredible community that creates top-notch packages for a variety of domains and issues. When working on projects in Python, using external libraries and modules is essential.

We can use the defined classes, properties, and methods in these libraries and modules to carry out our duties. For instance, there are numerous mathematical functions in the math library that we can use to complete our computations. .py files represent the libraries.

End-Line

That ought to cover the Python basics and get you going with data science. As you work on more and more projects, you'll get to know a few additional features, functionalities, and data types.