![]() Ranges: The range type is used to represent an immutable sequence of numbers. You can, however, define a new tuple using the original tuple: John = person Output: TypeError: 'tuple' object does not support item assignment We can slice the tuple to view individual values, but cannot delete or reassign: person = 25 An example with heterogeneous values: person = ('John', 'Smith', 23, 'Jane', 'Smith', 26) Rather than square brackets, they are denoted with parentheses: ( ). This is typically the case when a sequence consists of heterogeneous values. Because of this, they are ideally suited for use cases that need to preserve the sequence throughout operations. Tuples: Tuples are nearly identical in concept to lists, except that they are immutable. Technically, the shopping list is a list of sequences, as strings are sequences themselves (more on this later). List operations allow for quick sorting, accessing of individual values (known as slicing), or reassigning and deleting: print(shopping_list) For example, we can create a shopping list: shopping_list = They can be created in several ways, but are always denoted with square brackets. age of students in a class, number of pitches in an inning, or items to buy at the store, etc), or when values need to be added or removed recursively. They are ideal for use cases where the values are all of the same category (e.g. Lists: Lists are mutable sequences that typically hold homogeneous data. Let’s take a closer look at each sequence type: A mutable sequence can be changed after it has been created, while an immutable sequence cannot. Sequences can either be immutable or mutable. The first three sequence types are able to hold any type of data values, while Strings are limited exclusively to text. The values could be individual words, phrases, numbers, or even a series within a series. Python Data Types: SequencesĪ sequence describes a series of values. greater than, less than, equal, not equal, etc.). In mathematical operations, they behave exactly like 1 and 0, but can also be used in the context of boolean operations and comparisons (i.e. Boolean values are a special case of numeric types used to express True and False. Python Data Types: Booleansįollowing numeric types, perhaps the most common data type encountered are boolean types. A full list of operations for each data type can be found in the documentation. Many operations overlap between types, but some are unique. The following table lists the operations that can be performed with/on numeric types: OperationĮach data type can undergo certain operations. Addition is not the only operation that numeric types can undergo. If your data is complex, it makes sense to use complex numbers. If your application requires more than one significant digit, using integers won’t cut it. ![]() The output is the same: x = 2Ĭhoosing whether to use an integer, a float, or a complex number is pretty straightforward. The constructors int(), float(), and complex() are used to produce numbers of each type in Python: x = int(2)Īlternatively, Python will automatically define a number as in integer if you do not include a decimal as a float number if you do and as a complex number if you use the form a+bj where j indicates the imaginary part. Complex numbers consist of a real and an imaginary component, both represented as floating numbers.Floating numbers are real numbers represented in decimal form with a predefined precision.Integers are whole numbers that can be negative, zero, or positive.Each are equivalent to their mathematical counterpart: Numeric types consist of integers, floating type numbers (or floats), and complex numbers. ![]() Installation instructions can also be found here.Īll set? Let’s go. I’ll be using a free, pre-built distribution of Python 3.6 called ActivePython, which you can download here. To follow along with the exercises in this tutorial, you’ll need to have a recent version of Python installed. This tutorial reviews the basics of how and when to use each, and (for those migrating from Python 2 to 3) will also point out some of the differences between Python 2 usage versus Python 3 usage. In any language, there are often multiple ways of accomplishing the same goal, but that does not always mean each solution is equally efficient. Learning how and when to use each is the first thing one should do when encountering a new programming language. There are a few others, but these are the most important and most frequently used.Įvery programming language is built upon fundamental constituents that provide the building blocks for constructing the more sophisticated programming-based tools. The principle built-in Python data types include: Understanding Python data types allows you to take full advantage of the language’s design and program as efficiently and effectively as possible.
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