Python, one of the most popular programming languages in the world, provides a wide range of data types to handle different kinds of information. Whether you’re working with numbers, text, or complex structures like lists and dictionaries, Python has got you covered! But understanding how these data types work and when to use them can be daunting for beginners. Fear not though – in this blog post we’ll break down all the basics of Python’s built-in data types so you can start using them confidently in your own code. So buckle up and let’s dive into the fascinating world of data types in Python!
What are data types in Python?
In Python, there are three main types of data: strings, numbers, and tuples.
Strings are just like any other type in Python. They can contain any text you want, and they can be accessed through the string variable. To create a string, you simply need to use the str function.
Numbers in Python are simple enough. You can just use the number variable to store any number you want. To create a number, you can use the numeral function or the random function.
This is where tuples come in handy. A tuple is basically a collection of two or more items. You can create a tuple by using the empty tuple () as an argument to the list() function. You can also create a tuple by using two variables together: first name and last name for example.
How do we use data types in Python?
In Python, data types determine how a value is interpreted. Values can be integers, string literals, floating-point numbers, or lists of one or more of those types.
Integer data type
An integer in Python is a whole number without any decimal parts. An integer can be any nonzero value from 2 to 363829529842784000000000000000♠1022 (that’s 10 raised to the 22nd power!). To declare an integer variable, use the int keyword:
variable = int(input)
To assign an integer value to a variable, use the syntax int x = y :
variable = 5
float data type
A float in Python is a number with decimal parts. A float can be any real number from -3.402823E+38 (-3.402823₂±0.000000₃) to 3.402823E+38 (3.402823₂±0.000000₃). To declare a float variable, use the float keyword:
To assign a float value to a variable, use the syntax float x = y :
What are the different data types in Python?
In Python, there are a few different data types that can be used to store information. These data types include integers, floating point numbers, strings, tuples (a two-item list), lists (a four-item list), dictionaries (a collection of named items), and sets (a collection of unique items).
Integer Numbers
An integer in Python is a whole number that can be written without any decimal points. For example, 4 and -5 are both integers. Integer numbers can only store positive values.
Floating Point Numbers
A floating point number in Python is a number that includes a fractional part as well as a whole number part. For example, 12.3 and 3.14 are both floating point numbers. Floating point numbers can store any real value, including negative values.
String Data Type
A string in Python is just like it sounds: it’s a series of characters that you can use to represent text. You can create strings using the string module or by calling the str() function on an object that contains string data. Strings can contain any Unicode character sequence you want.
Tuple Data Type
A tuple in Python is a two-item list object. Tuples are useful for storing multiple related pieces of information together. For example, the name and email addresses fields in your user account might be stored as tuples because they require two separate pieces of data to work correctly: one for the name field and one
Conclusion
In this article, we have discussed the different data types that are available in Python and how to use them. We have also explained when to use which type of data and provided some examples. Overall, this article has given you a good understanding of the different data types that are available in Python and how to use them. If you would like to learn more about any of the data types mentioned in this article, be sure to consult our other articles on the subject.