This article records my practices on python data structures.

0. References

See:

1. https://realpython.com/python-data-structures/

2. https://www.geeksforgeeks.org/python-collections-module/#ordereddict

3. https://docs.python.org/3/library/collections.html#collections.OrderedDict

The following code snippets are initially run in jupyter notebook.

%autosave 10

Autosaving every 10 seconds

Part 1: Dictionaries, Maps, HashTables

dict: Your Go-To Dictionary

Because dictionaries are so important, Python features a robust dictionary implementation that’s built directly into the core language: the dict data type.

Python also provides some useful syntactic sugar for working with dictionaries in your programs. For example, the curly-brace ({ }) dictionary expression syntax and dictionary comprehensions allow you to conveniently define new dictionary objects:

phonebook = {'bob':7387, 'alice':3719, 'jack':7052}squres = {x: x*x for x in range(6)}
print(phonebook['alice'])print(squres)

3719{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Notes:

1. Python dict's keys: hashable objects which have hash values that never change during its life time.

2. Dictionaries are highly optimized and underlie many parts of the language. For example, class attributes and variables in a stack frame are both stored internally in dictionaries.

3. Performance: O(1) time complexity for lookup, insert, update, and delete operations in the average case

4. Hash table implementation

Besides plain dict objects, Python’s standard library also includes a number of specialized dictionary implementations. These specialized dictionaries are all based on the built-in dictionary class (and share its performance characteristics) but also include some additional convenience features.

collections.OrderedDict: Remember the Insertion Order of Keys

import collections
d = collections.OrderedDict(one=1, two=2, three=3)d

OrderedDict([('one', 1), ('two', 2), ('three', 3)])

d['four'] = 4d.keys()

odict_keys(['one', 'two', 'three', 'four'])

# remove and insertprint('Before Deleting')for key, val in d.items():    print(key, val)
# delete an elementd.pop('one')
# re-insert the samed['one'] = 1
# remove and insertprint('\nAfter Re-inserting')for key, val in d.items():    print(key, val)

Before Deletingone 1two 2three 3four 4
After Re-insertingtwo 2three 3four 4one 1

## popitem(): popitem(last=True)
# pop lastp = d.popitem()p

('one', 1)

# pop firstp = d.popitem(last=False)p

('two', 2)

## move_to_end(key, last=True) # Move an existing key to either end of an ordered dictionary. The item is moved to the right end if last is true (the default) or to the beginning if last is false. Raises KeyError if the key does not exist:
d['one'] = 1d['two'] = 2d

OrderedDict([('three', 3), ('four', 4), ('one', 1), ('two', 2)])

d.move_to_end('four')d

OrderedDict([('three', 3), ('one', 1), ('two', 2), ('four', 4)])

d.move_to_end('one', last=False)d

OrderedDict([('one', 1), ('three', 3), ('two', 2), ('four', 4)])

## reversed()# Python reversed() method returns an iterator that accesses the given sequence in the reverse order.# https://www.programiz.com/python-programming/methods/built-in/reversed
for key, val in d.items():    print(key, val)
print('\nreversed')for key, val in reversed(d.items()):    print(key, val)

one 1three 3two 2four 4
reversedfour 4two 2three 3one 1

collections.defaultdict: Return Default Values for Missing Keys

The defaultdict class is another dictionary subclass that accepts a callable in its constructor whose return value will be used if a requested key cannot be found.

This can save you some typing and make your intentions clearer as compared to using get() or catching a KeyError exception in regular dictionaries:

from collections import defaultdict
## use list() as default value functiondd = defaultdict(list)
# Accessing a missing key creates it and initializes it using the default factory, i.e. list() in this example.dd['dogs'].append('Rufus')dd['dogs'].append('Kathrin')dd

defaultdict(list, {'dogs': ['Rufus', 'Kathrin']})

dd['dogs']

['Rufus', 'Kathrin']

collections.ChainMap: Search Multiple Dictionaries as a Single Mapping

The collections.ChainMap data structure groups multiple dictionaries into a single mapping. Lookups search the underlying mappings one by one until a key is found. Insertions, updates, and deletions only affect the first mapping added to the chain:

from collections import ChainMap
dict1 = {'one':1, 'two':2}dict2 = {'three':3, 'four':4}
chain = ChainMap(dict1, dict2)chain

ChainMap({'one': 1, 'two': 2}, {'three': 3, 'four': 4})

print('\nchain map')for key, val in chain.items():    print(key, val)

chain mapthree 3four 4one 1two 2

# add newdict3 = {'five':5, 'six':6}chain.new_child(dict3)chain

ChainMap({'one': 1, 'two': 2}, {'three': 3, 'four': 4})

types.MappingProxyType: A Wrapper for Making Read-Only Dictionaries

MappingProxyType is a wrapper around a standard dictionary that provides a read-only view into the wrapped dictionary’s data. This class was added in Python 3.3 and can be used to create immutable proxy versions of dictionaries.

MappingProxyType can be helpful if, for example, you’d like to return a dictionary carrying internal state from a class or module while discouraging write access to this object. Using MappingProxyType allows you to put these restrictions in place without first having to create a full copy of the dictionary:

from types import MappingProxyType
writable = {'one':1, 'two':2}read_only = MappingProxyType(writable)
# The proxy is read-only:read_only['one']

1

read_only['one'] = 1

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-28-11d9e8a8c63d> in <module>----> 1 read_only['one'] = 1

TypeError: 'mappingproxy' object does not support item assignment

# Updates to the original are reflected in the proxy:writable['one'] = 42read_only

mappingproxy({'one': 42, 'two': 2})

Part 2: Array Data Structures

An array is a fundamental data structure available in most programming languages, and it has a wide range of uses across different algorithms.

In this section, you’ll take a look at array implementations in Python that use only core language features or functionality that’s included in the Python standard library. You’ll see the strengths and weaknesses of each approach so you can decide which implementation is right for your use case.

But before we jump in, let’s cover some of the basics first. How do arrays work, and what are they used for? Arrays consist of fixed-size data records that allow each element to be efficiently located based on its index:

Visual representation of an arrayBecause arrays store information in adjoining blocks of memory, they’re considered contiguous data structures (as opposed to linked data structures like linked lists, for example).

A real-world analogy for an array data structure is a parking lot. You can look at the parking lot as a whole and treat it as a single object, but inside the lot there are parking spots indexed by a unique number. Parking spots are containers for vehicles—each parking spot can either be empty or have a car, a motorbike, or some other vehicle parked on it.

But not all parking lots are the same. Some parking lots may be restricted to only one type of vehicle. For example, a motor home parking lot wouldn’t allow bikes to be parked on it. A restricted parking lot corresponds to a typed array data structure that allows only elements that have the same data type stored in them.

Performance-wise, it’s very fast to look up an element contained in an array given the element’s index. A proper array implementation guarantees a constant O(1) access time for this case.

Python includes several array-like data structures in its standard library that each have slightly different characteristics. Let’s take a look.

list: Mutable Dynamic Arrays

Lists are a part of the core Python language. Despite their name, Python’s lists are implemented as dynamic arrays behind the scenes.

This means a list allows elements to be added or removed, and the list will automatically adjust the backing store that holds these elements by allocating or releasing memory.

Python lists can hold arbitrary elements—everything is an object in Python, including functions. Therefore, you can mix and match different kinds of data types and store them all in a single list.

This can be a powerful feature, but the downside is that supporting multiple data types at the same time means that data is generally less tightly packed. As a result, the whole structure takes up more space:

arr = ['one', 'two', 'three']arr[0]

'one'

# lists have a nice reprarr

['one', 'two', 'three']

# lists are mutablearr[1] = 'hello'arr 

['one', 'hello', 'three']

del arr[1]arr

['one', 'three']

# lists can hold arbitrary data typesarr.append(2333)arr

['one', 'three', 2333]

tuple: Immutable Containers

Just like lists, tuples are part of the Python core language. Unlike lists, however, Python’s tuple objects are immutable. This means elements can’t be added or removed dynamically—all elements in a tuple must be defined at creation time.

Tuples are another data structure that can hold elements of arbitrary data types. Having this flexibility is powerful, but again, it also means that data is less tightly packed than it would be in a typed array:

arr = ('one', 'two', 'three')arr[0]

'one'

arr

('one', 'two', 'three')

# Tuples are immutablearr[1] = "hello"

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-2f27284d12dc> in <module>      1 # Tuples are immutable----> 2 arr[1] = "hello"

TypeError: 'tuple' object does not support item assignment

del arr[1]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-df001b61e2e0> in <module>----> 1 del arr[1]

TypeError: 'tuple' object doesn't support item deletion

# Tuples can hold arbitrary data types:# (Adding elements creates a copy of the tuple)arr + (23,)

('one', 'two', 'three', 23)

array.array: Basic Typed Arrays

Python’s array module provides space-efficient storage of basic C-style data types like bytes, 32-bit integers, floating-point numbers, and so on.

Arrays created with the array.array class are mutable and behave similarly to lists except for one important difference: they’re typed arrays constrained to a single data type.

Because of this constraint, array.array objects with many elements are more space efficient than lists and tuples. The elements stored in them are tightly packed, and this can be useful if you need to store many elements of the same type.

Also, arrays support many of the same methods as regular lists, and you might be able to use them as a drop-in replacement without requiring other changes to your application code.

import array
arr = array.array('f', (1.0, 1.5, 2.0, 2.5))arr[1]

1.5

arr

array('f', [1.0, 1.5, 2.0, 2.5])

# Arrays are mutablearr[1] = 23.0arr

array('f', [1.0, 23.0, 2.0, 2.5])

del arr[1]

arr

array('f', [1.0, 2.0, 2.5])

arr.append(42.0)arr

array('f', [1.0, 2.0, 2.5, 42.0])

# Arrays are "typed":arr[1] = "hello"

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-14-d4d93891e8c0> in <module>      1 # Arrays are "typed":----> 2 arr[1] = "hello"

TypeError: must be real number, not str

str: Immutable Arrays of Unicode Characters

Python 3.x uses str objects to store textual data as immutable sequences of Unicode characters. Practically speaking, that means a str is an immutable array of characters. Oddly enough, it’s also a recursive data structure—each character in a string is itself a str object of length 1.

String objects are space efficient because they’re tightly packed and they specialize in a single data type. If you’re storing Unicode text, then you should use a string.

Because strings are immutable in Python, modifying a string requires creating a modified copy. The closest equivalent to a mutable string is storing individual characters inside a list:

arr = "abcd"arr[1]

'b'

type(arr[1])

str

# Strings are immutable:arr[1] = 'e'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-2e0d5fa2424f> in <module>      1 # Strings are immutable:----> 2 arr[1] = 'e'

TypeError: 'str' object does not support item assignment

del arr[1]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-df001b61e2e0> in <module>----> 1 del arr[1]

TypeError: 'str' object doesn't support item deletion

# Strings can be unpacked into a list to get a mutable representation:arr = list('abcd')','.join(arr)

'a,b,c,d'

# Strings are recursive data structures:arr = "abcd"type('arr')

str

type(arr[0])

str

bytes: Immutable Arrays of Single Bytes

bytes objects are immutable sequences of single bytes, or integers in the range 0 ≤ x ≤ 255. Conceptually, bytes objects are similar to str objects, and you can also think of them as immutable arrays of bytes.

Like strings, bytes have their own literal syntax for creating objects and are space efficient. bytes objects are immutable, but unlike strings, there’s a dedicated mutable byte array data type called bytearray that they can be unpacked into:

arr = bytes((0, 1, 2, 3))arr[1]

1

# Bytes literals have their own syntax:arr

b'\x00\x01\x02\x03'

arr = b"\x00\x01\x02\x03"arr

b'\x00\x01\x02\x03'

# Only valid `bytes` are allowed:bytes((0, 300))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-f644e09a09b0> in <module>      1 # Only valid `bytes` are allowed:----> 2 bytes((0, 300))

ValueError: bytes must be in range(0, 256)

# Bytes are immutable:arr[1] = 23

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-15-714056f42f40> in <module>      1 # Bytes are immutable:----> 2 arr[1] = 23

TypeError: 'bytes' object does not support item assignment

del arr[1]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-df001b61e2e0> in <module>----> 1 del arr[1]

TypeError: 'bytes' object doesn't support item deletion

bytearray: Mutable Arrays of Single Bytes

The bytearray type is a mutable sequence of integers in the range 0 ≤ x ≤ 255. The bytearray object is closely related to the bytes object, with the main difference being that a bytearray can be modified freely—you can overwrite elements, remove existing elements, or add new ones. The bytearray object will grow and shrink accordingly.

A bytearray can be converted back into immutable bytes objects, but this involves copying the stored data in full—a slow operation taking O(n) time:

%autosave 5

Autosaving every 5 seconds

arr = bytearray((0,1,2,3,4))arr[0]

0

arr

bytearray(b'\x00\x01\x02\x03\x04')

# Bytearrays are mutable:arr[1] = 23arr

bytearray(b'\x00\x17\x02\x03\x04')

del arr[1]

arr.append(42)arr

bytearray(b'\x00\x02\x03\x04*')

# Bytearrays can only hold `bytes` (integers in the range 0 <= x <= 255)arr[1] = 'Hello'

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-7-42a4439943a8> in <module>      1 # Bytearrays can only hold `bytes` (integers in the range 0 <= x <= 255)----> 2 arr[1] = 'Hello'

TypeError: 'str' object cannot be interpreted as an integer

arr[1] = 300

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-6088e2f482e7> in <module>----> 1 arr[1] = 300

ValueError: byte must be in range(0, 256)

# Bytearrays can be converted back into bytes objects:# (This will copy the data)bytes(arr)

b'\x00\x02\x03\x04*'

Arrays in Python: Summary

If you want to restrict yourself to the array data structures included with Python, then here are a few guidelines:

• If you need to store arbitrary objects, potentially with mixed data types, then use a list or a tuple, depending on whether or not you want an immutable data structure.

• If you have numeric (integer or floating-point) data and tight packing and performance is important, then try out array.array.

• If you have textual data represented as Unicode characters, then use Python’s built-in str. If you need a mutable string-like data structure, then use a list of characters.

• If you want to store a contiguous block of bytes, then use the immutable bytes type or a bytearray if you need a mutable data structure.

In most cases, I like to start out with a simple list. I’ll only specialize later on if performance or storage space becomes an issue. Most of the time, using a general-purpose array data structure like list gives you the fastest development speed and the most programming convenience.

Part 3. Records, Structs, and Data Transfer Objects

Compared to arrays, record data structures provide a fixed number of fields. Each field can have a name and may also have a different type.

In this section, you’ll see how to implement records, structs, and plain old data objects in Python using only built-in data types and classes from the standard library.

Note: I’m using the definition of a record loosely here. For example, I’m also going to discuss types like Python’s built-in tuple that may or may not be considered records in a strict sense because they don’t provide named fields.

Python offers several data types that you can use to implement records, structs, and data transfer objects. In this section, you’ll get a quick look at each implementation and its unique characteristics. At the end, you’ll find a summary and a decision-making guide that will help you make your own picks.

dict: Simple Data Objects

As mentioned previously, Python dictionaries store an arbitrary number of objects, each identified by a unique key. Dictionaries are also often called maps or associative arrays and allow for efficient lookup, insertion, and deletion of any object associated with a given key.

Using dictionaries as a record data type or data object in Python is possible. Dictionaries are easy to create in Python as they have their own syntactic sugar built into the language in the form of dictionary literals. The dictionary syntax is concise and quite convenient to type.

Data objects created using dictionaries are mutable, and there’s little protection against misspelled field names as fields can be added and removed freely at any time. Both of these properties can introduce surprising bugs, and there’s always a trade-off to be made between convenience and error resilience:

car1 = {'color':'red', 'mileage':3824, 'automatic':True}car2 = {'color':'blue', 'mileage':40231, 'automatic':False}car2

{'color': 'blue', 'mileage': 40231, 'automatic': False}

tuple: Immutable Groups of Objects

Python’s tuples are a straightforward data structure for grouping arbitrary objects. Tuples are immutable—they can’t be modified once they’ve been created.

Performance-wise, tuples take up slightly less memory than lists in CPython, and they’re also faster to construct.

As you can see in the bytecode disassembly below, constructing a tuple constant takes a single LOAD_CONST opcode, while constructing a list object with the same contents requires several more operations:

# dis — Disassembler for Python bytecode# The dis module supports the analysis of CPython bytecode by disassembling it. The CPython bytecode which this module takes as an input is defined in the file Include/opcode.h and used by the compiler and the interpreter.
import dis
dis.dis(compile("(23, 'a', 'b', 'c')", "", "eval"))

  1           0 LOAD_CONST               0 ((23, 'a', 'b', 'c'))              2 RETURN_VALUE

dis.dis(compile("[23, 'a', 'b', 'c']", "", "eval"))

  1           0 LOAD_CONST               0 (23)              2 LOAD_CONST               1 ('a')              4 LOAD_CONST               2 ('b')              6 LOAD_CONST               3 ('c')              8 BUILD_LIST               4             10 RETURN_VALUE

However, you shouldn’t place too much emphasis on these differences. In practice, the performance difference will often be negligible, and trying to squeeze extra performance out of a program by switching from lists to tuples will likely be the wrong approach.

A potential downside of plain tuples is that the data you store in them can only be pulled out by accessing it through integer indexes. You can’t give names to individual properties stored in a tuple. This can impact code readability.

Also, a tuple is always an ad-hoc structure: it’s difficult to ensure that two tuples have the same number of fields and the same properties stored in them.

This makes it easy to introduce slip-of-the-mind bugs, such as mixing up the field order. Therefore, I would recommend that you keep the number of fields stored in a tuple as low as possible:

car1 = ("red", 3812.4, True)car2 = ("blue", 40231.0, False)car1

('red', 3812.4, True)

Write a Custom Class: More Work, More Control

Classes allow you to define reusable blueprints for data objects to ensure each object provides the same set of fields.

Using regular Python classes as record data types is feasible, but it also takes manual work to get the convenience features of other implementations. For example, adding new fields to the init constructor is verbose and takes time.

Also, the default string representation for objects instantiated from custom classes isn’t very helpful. To fix that, you may have to add your own repr method, which again is usually quite verbose and must be updated each time you add a new field.

Fields stored on classes are mutable, and new fields can be added freely, which you may or may not like. It’s possible to provide more access control and to create read-only fields using the @property decorator, but once again, this requires writing more glue code.

Writing a custom class is a great option whenever you’d like to add business logic and behavior to your record objects using methods. However, this means that these objects are technically no longer plain data objects:

class Car:    def __init__(self, color, mileage, automatic):        self.color = color        self.mileage = mileage        self.automatic = automatic
car1 = Car('red', 3812.4, True)car2 = Car('blue', 40231.4, False)
car2

<__main__.Car at 0x7ff72e066ca0>

# Get the mileage:car2.mileage

40231.4

# Classes are mutable:car2.mileage = 12car2.windshield = 'broken'

dataclasses.dataclass: Python 3.7+ Data Classes

Data classes are available in Python 3.7 and above. They provide an excellent alternative to defining your own data storage classes from scratch.

By writing a data class instead of a plain Python class, your object instances get a few useful features out of the box that will save you some typing and manual implementation work:

The syntax for defining instance variables is shorter, since you don’t need to implement the .init() method.Instances of your data class automatically get nice-looking string representation via an auto-generated .repr() method.Instance variables accept type annotations, making your data class self-documenting to a degree. Keep in mind that type annotations are just hints that are not enforced without a separate type-checking tool.Data classes are typically created using the @dataclass decorator, as you’ll see in the code example below:

from dataclasses import dataclass
@dataclassclass Car:    color: str    mileage: float    automatic: bool
car1 = Car('red', 3812.4, True)car2 = Car('blue', 40231.4, False)
# Instances have a nice repr:car2

Car(color='blue', mileage=40231.4, automatic=False)

# Accessing fields:car1.mileage

3812.4

# Fields are mutable:car2.mileage = 12car2.windshield = 'broken'
car2

Car(color='blue', mileage=12, automatic=False)

# Type annotations are not enforced without a separate type checking tool like mypyCar("red", "NOT_A_FLOAT", 99)

Car(color='red', mileage='NOT_A_FLOAT', automatic=99)

collections.namedtuple: Convenient Data Objects

The namedtuple class available in Python 2.6+ provides an extension of the built-in tuple data type. Similar to defining a custom class, using namedtuple allows you to define reusable blueprints for your records that ensure the correct field names are used.

namedtuple objects are immutable, just like regular tuples. This means you can’t add new fields or modify existing fields after the namedtuple instance is created.

Besides that, namedtuple objects are, well . . . named tuples. Each object stored in them can be accessed through a unique identifier. This frees you from having to remember integer indexes or resort to workarounds like defining integer constants as mnemonics for your indexes.

namedtuple objects are implemented as regular Python classes internally. When it comes to memory usage, they’re also better than regular classes and just as memory efficient as regular tuples:

from collections import namedtuplefrom sys import getsizeof
p1 = namedtuple('Point', 'x y z')(1, 2, 3)p2 = (1, 2, 3)
p1

Point(x=1, y=2, z=3)

p1.x, p1.y, p1.z

(1, 2, 3)

getsizeof(p1)

64

getsizeof(p2)

64

namedtuple objects can be an easy way to clean up your code and make it more readable by enforcing a better structure for your data.

I find that going from ad-hoc data types like dictionaries with a fixed format to namedtuple objects helps me to express the intent of my code more clearly. Often when I apply this refactoring, I magically come up with a better solution for the problem I’m facing.

Using namedtuple objects over regular (unstructured) tuples and dicts can also make your coworkers’ lives easier by making the data that’s being passed around self-documenting, at least to a degree:

Car = namedtuple('Car', 'color mileage automatic')
car1 = Car('red', 3812.4, True)car2 = Car('blue', 40231.4, False)
car2

Car(color='blue', mileage=40231.4, automatic=False)

type(car1)

__main__.Car

# Fields are immtuable:car2.mileage = 12

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-39-f7f9c602e34d> in <module>      1 # Fields are immtuable:----> 2 car2.mileage = 12

AttributeError: can't set attribute

car2.windshield = "broken"

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-40-72bb797ed6af> in <module>----> 1 car2.windshield = "broken"

AttributeError: 'Car' object has no attribute 'windshield'

typing.NamedTuple: Improved Namedtuples

Added in Python 3.6, typing.NamedTuple is the younger sibling of the namedtuple class in the collections module. It’s very similar to namedtuple, with the main difference being an updated syntax for defining new record types and added support for type hints.

Please note that type annotations are not enforced without a separate type-checking tool like mypy. But even without tool support, they can provide useful hints for other programmers (or be terribly confusing if the type hints become out of date):

from typing import NamedTuple
class Car(NamedTuple):    color: str    mileage: float    automatic: bool
car1 = Car('red', 3812.4, True)car2 = Car('blue', 40231.4, False)
car2

Car(color='blue', mileage=40231.4, automatic=False)

# Fields are immutable:car1.mileage = 12

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-42-144528f7cc7a> in <module>      1 # Fields are immutable:----> 2 car1.mileage = 12

AttributeError: can't set attribute

struct.Struct: Serialized C Structs

The struct.Struct class converts between Python values and C structs serialized into Python bytes objects. For example, it can be used to handle binary data stored in files or coming in from network connections.

Structs are defined using a mini language based on format strings that allows you to define the arrangement of various C data types like char, int, and long as well as their unsigned variants.

Serialized structs are seldom used to represent data objects meant to be handled purely inside Python code. They’re intended primarily as a data exchange format rather than as a way of holding data in memory that’s only used by Python code.

In some cases, packing primitive data into structs may use less memory than keeping it in other data types. However, in most cases that would be quite an advanced (and probably unnecessary) optimization:

from struct import Struct
MyStruct = Struct('i?f')data = MyStruct.pack(23, False, 42.0)
# All you get is a blob of data:data

b'\x17\x00\x00\x00\x00\x00\x00\x00\x00\x00(B'

MyStruct.unpack(data)

(23, False, 42.0)

types.SimpleNamespace: Fancy Attribute Access

Here’s one more slightly obscure choice for implementing data objects in Python: types.SimpleNamespace. This class was added in Python 3.3 and provides attribute access to its namespace.

This means SimpleNamespace instances expose all of their keys as class attributes. You can use obj.key dotted attribute access instead of the obj['key'] square-bracket indexing syntax that’s used by regular dicts. All instances also include a meaningful repr by default.

As its name proclaims, SimpleNamespace is simple! It’s basically a dictionary that allows attribute access and prints nicely. Attributes can be added, modified, and deleted freely:

from types import SimpleNamespace
car1 = SimpleNamespace(color="red", mileage=3812.4, automatic=True)car1

namespace(color='red', mileage=3812.4, automatic=True)

# Instances support attribute access and are mutable:car1.mileage = 12car1.windshield = "broken"del car1.automatic
car1

namespace(color='red', mileage=12, windshield='broken')

Records, Structs, and Data Objects in Python: Summary

As you’ve seen, there’s quite a number of different options for implementing records or data objects. Which type should you use for data objects in Python? Generally your decision will depend on your use case:

• If you have only a few fields, then using a plain tuple object may be okay if the field order is easy to remember or field names are superfluous. For example, think of an (x, y, z) point in three-dimensional space.

• If you need immutable fields, then plain tuples, collections.namedtuple, and typing.NamedTuple are all good options.

• If you need to lock down field names to avoid typos, then collections.namedtuple and typing.NamedTuple are your friends.

• If you want to keep things simple, then a plain dictionary object might be a good choice due to the convenient syntax that closely resembles JSON.

• If you need full control over your data structure, then it’s time to write a custom class with @property setters and getters.

• If you need to add behavior (methods) to the object, then you should write a custom class, either from scratch, or using the dataclass decorator, or by extending collections.namedtuple or typing.NamedTuple.

• If you need to pack data tightly to serialize it to disk or to send it over the network, then it’s time to read up on struct.Struct because this is a great use case for it!

If you’re looking for a safe default choice, then my general recommendation for implementing a plain record, struct, or data object in Python would be to use collections.namedtuple in Python 2.x and its younger sibling, typing.NamedTuple in Python 3.

Part 4. Sets and Multisets

In this section, you’ll see how to implement mutable and immutable set and multiset (bag) data structures in Python using built-in data types and classes from the standard library.

A set is an unordered collection of objects that doesn’t allow duplicate elements. Typically, sets are used to quickly test a value for membership in the set, to insert or delete new values from a set, and to compute the union or intersection of two sets.

In a proper set implementation, membership tests are expected to run in fast O(1) time. Union, intersection, difference, and subset operations should take O(n) time on average. The set implementations included in Python’s standard library follow these performance characteristics.

Just like dictionaries, sets get special treatment in Python and have some syntactic sugar that makes them easy to create. For example, the curly-brace set expression syntax and set comprehensions allow you to conveniently define new set instances:

vowels = {'a', 'e', 'i', 'o', 'u'}squares = {x * x for x in range(10)}
print(vowels)print(squares)

{'e', 'a', 'i', 'o', 'u'}{0, 1, 64, 4, 36, 9, 16, 49, 81, 25}

But be careful: To create an empty set you’ll need to call the set() constructor. Using empty curly-braces ({}) is ambiguous and will create an empty dictionary instead.

# null set using set() instead of {}
dict_empty = {}set_empty = set()
print(type(dict_empty))print(type(set_empty))

<class 'dict'><class 'set'>

set: Your Go-To Set

The set type is the built-in set implementation in Python. It’s mutable and allows for the dynamic insertion and deletion of elements.

Python’s sets are backed by the dict data type and share the same performance characteristics. Any hashable object can be stored in a set:

vowels = {'a', 'e', 'i', 'o', 'u'}
'e' in vowels

True

letters = set('alice')letters.intersection(vowels)

{'a', 'e', 'i'}

vowels.difference(letters)

{'o', 'u'}

letters_copy = letters.copy()letters_copy

{'a', 'c', 'e', 'i', 'l'}

# The isdisjoint() method returns True if two sets are disjoint sets. If not, it returns Falseletters_copy.isdisjoint(vowels)

False

![disjoint](https://cdn.programiz.com/sites/tutorial2program/files/python-disjoint-sets_0.png)

zsh:1: unknown file attribute: h

letters_copy.issubset(letters)

True

letters_copy.issuperset(letters)

True

# add elementsvowels.add('x')vowels

{'a', 'e', 'i', 'o', 'u', 'x'}

vowels.union(letters)

{'a', 'c', 'e', 'i', 'l', 'o', 'u', 'x'}

len(vowels)

6

vowels.discard('x')vowels

{'a', 'e', 'i', 'o', 'u'}

frozenset: Immutable Sets

The frozenset class implements an immutable version of set that can’t be changed after it’s been constructed.

frozenset objects are static and allow only query operations on their elements, not inserts or deletions. Because frozenset objects are static and hashable, they can be used as dictionary keys or as elements of another set, something that isn’t possible with regular (mutable) set objects:

fs = frozenset({'a', 'e', 'i', 'o', 'u'})fs

frozenset({'a', 'e', 'i', 'o', 'u'})

fs.add('x')

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-67-cb5eaf111fad> in <module>----> 1 fs.add('x')

AttributeError: 'frozenset' object has no attribute 'add'

# Frozensets are hashable and can be used as dictionary keys:d = {frozenset({'a', 'e', 'i', 'o', 'u'}):'vowels'}d

{frozenset({'a', 'e', 'i', 'o', 'u'}): 'vowels'}

collections.Counter: Multisets

The collections.Counter class in the Python standard library implements a multiset, or bag, type that allows elements in the set to have more than one occurrence.

This is useful if you need to keep track of not only if an element is part of a set, but also how many times it’s included in the set:

from collections import Counter
inventory = Counter()loot = {'sward': 1, 'bread': 3}inventory.update(loot)inventory

Counter({'sward': 1, 'bread': 3})

more_loot = {'sward': 2, 'apple': 3}inventory.update(more_loot)inventory

Counter({'sward': 3, 'bread': 3, 'apple': 3})

One caveat for the Counter class is that you’ll want to be careful when counting the number of elements in a Counter object. Calling len() returns the number of unique elements in the multiset, whereas the total number of elements can be retrieved using sum():

len(inventory)

3

sum(inventory.values())

9

Sets and Multisets in Python: Summary

Sets are another useful and commonly used data structure included with Python and its standard library. Here are a few guidelines for deciding which one to use:

• If you need a mutable set, then use the built-in set type.

• If you need hashable objects that can be used as dictionary or set keys, then use a frozenset.

• If you need a multiset, or bag, data structure, then use collections.Counter.

Part 5. Stacks (LIFOs)

A stack is a collection of objects that supports fast Last-In/First-Out (LIFO) semantics for inserts and deletes. Unlike lists or arrays, stacks typically don’t allow for random access to the objects they contain. The insert and delete operations are also often called push and pop.

A useful real-world analogy for a stack data structure is a stack of plates. New plates are added to the top of the stack, and because the plates are precious and heavy, only the topmost plate can be moved. In other words, the last plate on the stack must be the first one removed (LIFO). To reach the plates that are lower down in the stack, the topmost plates must be removed one by one.

Performance-wise, a proper stack implementation is expected to take O(1) time for insert and delete operations.

Stacks have a wide range of uses in algorithms. For example, they’re used in language parsing as well as runtime memory management, which relies on a call stack. A short and beautiful algorithm using a stack is depth-first search (DFS) on a tree or graph data structure.

Python ships with several stack implementations that each have slightly different characteristics. Let’s take a look at them and compare their characteristics.

list: Simple, Built-In Stacks

Python’s built-in list type makes a decent stack data structure as it supports push and pop operations in amortized O(1) time.

Python’s lists are implemented as dynamic arrays internally, which means they occasionally need to resize the storage space for elements stored in them when elements are added or removed. The list over-allocates its backing storage so that not every push or pop requires resizing. As a result, you get an amortized O(1) time complexity for these operations.

The downside is that this makes their performance less consistent than the stable O(1) inserts and deletes provided by a linked list–based implementation (as you’ll see below with collections.deque). On the other hand, lists do provide fast O(1) time random access to elements on the stack, and this can be an added benefit.

There’s an important performance caveat that you should be aware of when using lists as stacks: To get the amortized O(1) performance for inserts and deletes, new items must be added to the end of the list with the append() method and removed again from the end using pop(). For optimum performance, stacks based on Python lists should grow towards higher indexes and shrink towards lower ones.

Adding and removing from the front is much slower and takes O(n) time, as the existing elements must be shifted around to make room for the new element. This is a performance antipattern that you should avoid as much as possible:

s = []s.append('eat')s.append('sleep')s.append('code')
s

['eat', 'sleep', 'code']

s.pop()

'code'

s.pop()

'sleep'

s.pop()

'eat'

s.pop()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-79-c88c8c48122b> in <module>----> 1 s.pop()

IndexError: pop from empty list

collections.deque: Fast and Robust Stacks

The deque class implements a double-ended queue that supports adding and removing elements from either end in O(1) time (non-amortized). Because deques support adding and removing elements from either end equally well, they can serve both as queues and as stacks.

Python’s deque objects are implemented as doubly-linked lists, which gives them excellent and consistent performance for inserting and deleting elements but poor O(n) performance for randomly accessing elements in the middle of a stack.

Overall, collections.deque is a great choice if you’re looking for a stack data structure in Python’s standard library that has the performance characteristics of a linked-list implementation:

from collections import deque
s = deque()s.append('eat')s.append('sleep')s.append('code')
s

deque(['eat', 'sleep', 'code'])

s.pop()

'code'

s.pop()

'sleep'

s.pop()

'eat'

s.pop()

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-84-c88c8c48122b> in <module>----> 1 s.pop()

IndexError: pop from an empty deque

s.appendleft('a')s.appendleft('b')s.appendleft('c')
s

deque(['c', 'b', 'a'])

s.popleft()

'c'

s.popleft()

'b'

s.popleft()

'a'

s.appendleft('a')s.appendleft('b')s.appendleft('c')
s

deque(['c', 'b', 'a'])

s.reverse()

s

deque(['a', 'b', 'c'])

queue.LifoQueue: Locking Semantics for Parallel Computing

The LifoQueue stack implementation in the Python standard library is synchronized and provides locking semantics to support multiple concurrent producers and consumers.

Besides LifoQueue, the queue module contains several other classes that implement multi-producer, multi-consumer queues that are useful for parallel computing.

Depending on your use case, the locking semantics might be helpful, or they might just incur unneeded overhead. In this case, you’d be better off using a list or a deque as a general-purpose stack:

from queue import LifoQueue
s = LifoQueue()s.put('eat')s.put('sleep')s.put('code')
s

<queue.LifoQueue at 0x7ff72efbcf70>

s.get()

'code'

s.get()