Data types python set

Python Sets

Sets are used to store multiple items in a single variable.

Set is one of 4 built-in data types in Python used to store collections of data, the other 3 are List, Tuple, and Dictionary, all with different qualities and usage.

A set is a collection which is unordered, unchangeable*, and unindexed.

* Note: Set items are unchangeable, but you can remove items and add new items.

Sets are written with curly brackets.

Example

Note: Sets are unordered, so you cannot be sure in which order the items will appear.

Set Items

Set items are unordered, unchangeable, and do not allow duplicate values.

Unordered

Unordered means that the items in a set do not have a defined order.

Set items can appear in a different order every time you use them, and cannot be referred to by index or key.

Unchangeable

Set items are unchangeable, meaning that we cannot change the items after the set has been created.

Once a set is created, you cannot change its items, but you can remove items and add new items.

Duplicates Not Allowed

Sets cannot have two items with the same value.

Example

Duplicate values will be ignored:

Note: The values True and 1 are considered the same value in sets, and are treated as duplicates:

Example

True and 1 is considered the same value:

Get the Length of a Set

To determine how many items a set has, use the len() function.

Example

Get the number of items in a set:

Set Items — Data Types

Set items can be of any data type:

Example

String, int and boolean data types:

A set can contain different data types:

Example

A set with strings, integers and boolean values:

type()

From Python’s perspective, sets are defined as objects with the data type ‘set’:

Example

What is the data type of a set?

The set() Constructor

It is also possible to use the set() constructor to make a set.

Example

Using the set() constructor to make a set:

Python Collections (Arrays)

There are four collection data types in the Python programming language:

• List is a collection which is ordered and changeable. Allows duplicate members.
• Tuple is a collection which is ordered and unchangeable. Allows duplicate members.
• Set is a collection which is unordered, unchangeable*, and unindexed. No duplicate members.
• Dictionary is a collection which is ordered** and changeable. No duplicate members.

*Set items are unchangeable, but you can remove items and add new items.

**As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered.

When choosing a collection type, it is useful to understand the properties of that type. Choosing the right type for a particular data set could mean retention of meaning, and, it could mean an increase in efficiency or security.

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Data Types¶

The modules described in this chapter provide a variety of specialized data types such as dates and times, fixed-type arrays, heap queues, double-ended queues, and enumerations.

Python also provides some built-in data types, in particular, dict , list , set and frozenset , and tuple . The str class is used to hold Unicode strings, and the bytes and bytearray classes are used to hold binary data.

The following modules are documented in this chapter:

• datetime — Basic date and time types
  • Aware and Naive Objects
  • Constants
  • Available Types
    • Common Properties
    • Determining if an Object is Aware or Naive
    • Examples of usage: timedelta
    • Examples of Usage: date
    • Examples of Usage: datetime
    • Examples of Usage: time
    • strftime() and strptime() Format Codes
    • Technical Detail
    • Using ZoneInfo
    • Data sources
      • Configuring the data sources
        • Compile-time configuration
        • Environment configuration
        • Runtime configuration
        • String representations
        • Pickle serialization
        • ChainMap objects
          • ChainMap Examples and Recipes
          • deque Recipes
          • defaultdict Examples
          • OrderedDict Examples and Recipes
          • Collections Abstract Base Classes
          • Collections Abstract Base Classes – Detailed Descriptions
          • Examples and Recipes
          • Basic Examples
          • Priority Queue Implementation Notes
          • Theory
          • Performance Notes
          • Searching Sorted Lists
          • Examples
          • Weak Reference Objects
          • Example
          • Finalizer Objects
          • Comparing finalizers with __del__() methods
          • Dynamic Type Creation
          • Standard Interpreter Types
          • Additional Utility Classes and Functions
          • Coroutine Utility Functions
          • PrettyPrinter Objects
          • Example
          • Repr Objects
          • Subclassing Repr Objects
          • Module Contents
          • Data Types
            • Supported __dunder__ names
            • Supported _sunder_ names
            • Exceptions

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