Understanding Data Types in Python || Lesson 8 || Python For Beginners ||

Publicado em: 04 Junho 2026
no canal de: Wisdomers - Placements & Programming

In this class we understand Data Types in Python

0:00 Intro
0:22 Introduction to data types
0:26 Numeric data types
1:40 sequence types
3:55 Mapping Datatype
5:16 boolean and none type

Welcome, Wisdomers. In programming, we work with different data types. It is crucial to understand that each type is stored differently in memory. We will dive into those memory-level details in our upcoming classes. In this video, we will introduce the different data types available in Python.

In Python, numeric data is categorized into three primary types: int, float, and complex.
Integer (int): This type represents whole numbers, including positive, negative, and zero (e.g., 1, 99, -9, 0). Crucially, integers do not contain a fractional or decimal component. For example, in the assignment x = 99, x is a variable of the integer data type.
Floating-Point : Often referred to as 'float,' these represent real numbers containing one or more decimals (e.g., 1.2, 2.0, 99.94). If we define y = 8.75, y is identified as a float variable.
Complex: This type is used for numbers with a real and an imaginary part, where the imaginary part is denoted by the suffix 'j'. For example, z = 3 + 4j represents a complex number.

Next, let's look at Sequence Types: Strings, Lists, and Tuples. The word 'sequence' simply refers to an ordered arrangement, much like items standing in a queue. This 'linear' storage is why we call them sequence types. Stay tuned, as we are about to visualize exactly how this looks at the memory level.

Let's dive into strings. Simply put, a string is a sequence of individual characters. Take the example x = "hello". Here, the word 'hello' is a string defined by double quotes. To help you understand how the computer actually 'sees' this, we’ve provided two visualizations: a vertical memory view and a horizontal memory view. Moving forward, we will focus on the horizontal representation, as it is much more intuitive for understanding how data is organized and accessed in modern computer architecture.

Python formally classifies the string as a Text Sequence Type because it organizes text in a sequential order. As demonstrated, strings are defined using either single or double quotes; both are syntactically same in Python. However, when handling text that spans multiple lines, we utilize Triple Quotes (three sets of double or single quotes).

Now, let’s examine Lists and Tuples. At this stage, our focus is strictly on their basic definitions, as we will learn much more about them in later classes. A List is defined as an ordered sequence of elements; as shown in the example, these are put inside square brackets. Similarly, a Tuple is an ordered sequence of elements, but it is shown using standard parentheses. Please look at the examples to see the difference in their syntax. We will dive deeper into the technical differences between these two types in our future lessons.

Our next focus is the Mapping Data Type, commonly referred to in Python as the Dictionary (dict). In this session, we will focus on its fundamental definition and syntax, leaving a deep dive for our comming classes. A dictionary is formally defined as a collection of key-value pairs. Syntactically, dictionaries are enclosed in curly braces (often called flower brackets). As illustrated in the video, 'name' and 'age' serve as the unique keys, while 'Mohan' and '25' are their corresponding values.

Let’s dive into Sets. Simply put, a set is a collection where every element must be unique. Take a look at the syntax on your screen—we use curly braces to define them. Notice that in our first example, all elements are different. Now, look at what happens when we include the number '5' two times in the same set. Even though we wrote it twice, Python only stores it once. This is the core power of a set: it automatically handles duplicates for you.

Finally, let’s examine the Boolean and None data types. The Boolean type represents logical states and consists of only two possible values: True and False. It is important to note that Python is case-sensitive, so these must always be capitalized. For example, x = True or z = False are used to handle binary logic in our programs.
Our last category is the None data type. In Python, None signifies the absence of a value or a null state. If we define x = None, it indicates that the variable x is currently empty or has no data assigned to it. This is distinct from zero or an empty string; it is a dedicated type representing 'nothingness'

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