It is impossible to write any kind of software without first laying the groundwork using data structures. Depending on your needs, different data structures offer different approaches to storing and retrieving information. Python’s built-in standard library includes a plethora of useful data structures.
However, unlike other languages, Python’s naming approach doesn’t provide a great deal of clarity. In Java, a list is either a LinkedList or an ArrayList; there is no such thing as “simply a list.” not the case with Python. A lot of Python programmers, even veterans, aren’t sure if the developed list type is a linked list or a dynamic array. Master python by getting trained on all you need to know regarding python programming offered by SLA institute, the top Python training institute in Chennai.
Dictionaries, Maps, and Hash tables
Python’s dictionaries, or dicts for short, are among the language’s most fundamental data structures. A dictionary can hold any number of items, each of which is given a distinct “key.”
Maps, hashmaps, lookup tables, and associative arrays are all names used to describe dictionaries. They speed up operations like finding something by its key, adding it, or removing it.
Dictionary items can be thought of as the equivalent of phone books in the real world. You can use these to quickly look up a contact’s number based on their name. You don’t have to flip through a phone book cover to cover to find a number; you can just go straight to the name you’re looking for and look it up.
When talking about how the data is structured to facilitate quick lookups, this comparison starts to fall apart. However, the underlying performance characteristics remain the same. You can use a dictionary to quickly look up the definition of a word or phrase.
It’s safe to say that dictionaries are among the most ubiquitous and pivotal data structures in all of computer science. I’m curious as to how Python deals with dictionaries. Let’s have a look at all the many dictionary implementations that are built into Python and included in the standard library.
Your Go-To Dictionary
In Python, the dict data type provides a powerful dictionary implementation that is part of the language itself.
For programmatic use of dictionaries, Python additionally offers some convenient syntactic sugar. You can easily define new dictionary objects, for instance, using the curly-brace ( ) dictionary expression syntax and dictionary concepts:
Here are certain limitations on the kinds of things that can serve as keys.
Keys in Python dictionaries can be of any type that can be hashed. Objects that are hashable can be compared to one another using the __eq__ and __hash__ methods, and their hash values remain constant during the object’s lifespan. In order for two hashable objects to be considered equivalent, they must share the exact hash value.
Strings and numbers, both of which are immutable, can be hashed and so serve as good dictionary keys. Tuple objects, which contain solely hashable types, may also serve as dictionary keys.
>>> roll number
= {
… “anish”: 1345,
… “banu”: 7493,
… “jony”: 3691,
… }
>>> cubes = {x: x * x * x for x in range(6)}
>>> roll number[“anish”]
1345
>>> cubes
{0: 0, 1: 1, 2: 8, 3: 27, 4: 64, 5: 125}
Python’s default dictionary implementation is sufficient for most purposes. To a large extent, the foundation of the language rests on its dictionary. Stack frame variables and class attributes, for instance, are both internally kept in dictionaries.
Python dictionaries are built on top of a tried-and-true hash table implementation that offers the expected performance characteristics. Search, insert, update, and delete operations typically take O(1) time complexity.
The built-in Python implementation of dict is sufficient for most uses. There are, however, specialized dictionary implementations available from third parties, including skip lists and B-tree dictionaries.
Several specialized dictionaries are included in Python’s standard library in addition to the more generic dict object. All these customized dictionaries derive from the standard dictionary class (and so exhibit the same performance characteristics), but they also boast additional features for the sake of convenience.
Recollect the Insertion Order of Keys for collections.OrderedDict
There is a specific subclass of dict in Python that keeps track of the order in which keys were initially added: collections.OrderedDict.
If any of the keys are missing, use defaultdict to get their default values
The defaultdict class is another subclass of a dictionary that allows the constructor to take a callable whose return value will be used in the event that the desired key cannot be located.
When compared to using get() or catching a KeyError exception with standard dictionaries, this can reduce the amount of code you need to type while also making your intentions clearer.
Several dictionary searches in one mapping using collections.ChainMap
When using the ChainMap data structure, many dictionaries can be mapped together. When a key is entered, a lookup does a sequential search across the underlying mappings. Any changes made to the mappings after they have been chained together have no effect on the previous ones.
Wrapper for Creating Read-Only Dictionaries (MappingProxyType)
MappingProxyType encapsulates a regular dictionary and exposes its contents for reading. The addition of this class in Python made it possible to generate immutable proxies for dictionaries.
If you want to return a dictionary containing internal state from a class or module but prevent write access to this object, MappingProxyType can help. You can impose these limitations with MappingProxyType without having to make a new dictionary.
If you have specific needs beyond what dict can give, then one of the additional data types mentioned should be used instead. While it’s possible to get by with any of the available implementations, your code will be most straightforward and manageable if it makes extensive use of built-in Python dictionaries. Enjoy learning various data structures in Python by joining the best Python course in Chennai offered by SLA institute.
Arrays as a Data Structure
Many algorithms make use of arrays, a fundamental data format available in most computer languages.
Python array implementations that rely simply on built-in language features and the standard library will be examined below. You’ll be able to weigh the merits of each method and select the one that’s best suited to your needs.
But let’s start with the fundamentals before we dive in. So, how do arrays function, and what do they do? Arrays are collections of data in which each component may be quickly retrieved by index.
Arrays are examples of contiguous data structures since they store data in consecutive memory locations (as opposed to linked data structures like linked lists, for example).
The array data structure can be compared to a parking lot in the real world. While the entire parking lot can be considered a single entity, each individual parking space is identified by a specific number. A parking space is a container for a vehicle, and it can be vacant or include a car, motorcycle, or other vehicle.
While this is true, not all parking garages are created equal. It’s possible that certain parking lots are designated for use by just certain car types. In a parking lot reserved for recreational vehicles, for instance, bikes would not be permitted. A typed array data structure is analogous to a metered parking lot in which only elements of the same data type are permitted.
Finding a specific item in an array by its index is a relatively efficient operation. If you implement arrays correctly, you can rest assured that your access time will always be O(1) in this scenario.
The standard Python library has a number of array-like data structures, each of which has its own unique properties. Let’s check it out.
list : Mutable Dynamic Arrays
Python incorporates lists into its fundamental syntax. Even though they have the term “lists,” Python’s lists are actually dynamic arrays.
This means that a list can have its size dynamically altered by adding or removing entries, with the corresponding memory allocations and deallocations being handled automatically by the list.
Since every object in Python, including functions, can store any other object, lists in Python are extremely flexible. As a result, you can combine numerous forms of information into a single list.
Although it’s useful, the trade-off is that data is less compact because of the need to accommodate numerous data kinds at once. Thereby, the entire building needs greater room to accommodate itself.
>>> arr = [“two”, “four”, “eight”]
>>> arr[0]
‘two’
>>> # Lists have a nice repr:
>>> arr
[‘two’, ‘four’, ‘eight’]
>>> # Lists are mutable:
>>> arr[1] = “welcome”
>>> arr
[‘two’, ‘welcome’, ‘eight’]
>>> del arr[1]
>>> arr
[‘two’, ‘eight’]
>>> # Lists can hold arbitrary data types:
>>> arr.append(17)
>>> arr
[‘two’, ‘eight’, 17]
tuple : Immutable Containers
Tuples, like lists, are an integral component of Python. Tuple objects in Python are immutable, unlike lists. This means that tuple elements cannot be added or removed on the fly; instead, they must all be specified when the tuple is initially created.
Tuples are another sort of data structure that may store data points of any type. This versatility is quite useful, but it does mean that data is spread out more than it is within a typed array.
>>> arr = (“two”, “four”, “eight”)
>>> arr[0]
‘two’
>>> # Tuples have a nice repr:
>>> arr
(‘two’, ‘four’, ‘eight’)
>>> # Tuples are immutable:
>>> arr[1] = “welcome”
Traceback (most recent call last):
File “
TypeError: ‘tuple’ object does not support item assignment
>>> del arr[1]
Traceback (most recent call last):
File “
TypeError: ‘tuple’ object doesn’t support item deletion
>>> # Tuples can hold arbitrary data types:
>>> # (Adding elements creates a copy of the tuple)
>>> arr + (17,)
(‘two’, ‘four’, ‘eight’, 23)
array.array : Basic Typed Arrays
Python’s array module enables compact storage for fundamental C-style data types including bytes, 32-bit integers, floating-point numbers, and so on via the array.array function.
array classes are mutable and act in a way that is similar to lists, with one key difference: they are typed arrays that are limited to a single data type.
This limitation makes large array.array objects more compact than lists and tuples. If you need to store a lot of items of the same sort, you could find their dense storage beneficial.
It’s possible to utilize an array in place of a regular list with no further modifications to your application’s code because arrays support many of the same methods as regular lists.
>>> import array
>>> arr = array.array(“f”, (2.0, 2.5, 3.0, 3.5))
>>> arr[1]
2.5
>>> # Arrays have a nice repr:
>>> arr
array(‘f’, [2.0, 2.5, 3.0, 3.5])
>>> # Arrays are mutable:
>>> arr[1] = 17.0
>>> arr
array(‘f’, [2.0, 17.0, 3.0, 3.5])
>>> del arr[1]
>>> arr
array(‘f’, [2.0, 3.0, 3.5])
>>> arr.append(21.0)
>>> arr
array(‘f’, [2.0, 3.0, 3.5, 21.0])
>>> # Arrays are “typed”:
>>> arr[1] = “welcome”
Traceback (most recent call last):
File “
TypeError : must be real number, not str
str : Immutable Arrays of Unicode Characters
When working with textual data in Python 3.x, it is stored as immutable sequences of Unicode characters in str objects. In everyday language, this signifies that a str is a fixed collection of text. A string is a recursive data structure since each character is its own str object with length 1.
Due to their compact nature and narrow focus, objects that store strings make effective use of memory. A string is the appropriate data storage mechanism for Unicode text.
Changing a string in Python entails duplicating it and then making the necessary changes to the new copy. Keeping track of individual characters in a list is most analogous to having a mutable string.
>>> arr = “pqrs”
>>> arr[1]
‘q’
>>> arr
‘pqrs’
>>> # Strings are immutable:
>>> arr[1] = “t”
Traceback (most recent call last):
File “
TypeError: ‘str’ object does not support item assignment
>>> del arr[1]
Traceback (most recent call last):
File “
TypeError: ‘str’ object doesn’t support item deletion
>>> # Strings can be unpacked into a list to
>>> # get a mutable representation:
>>> list(“pqrs”)
[‘p’, ‘q’, ‘r’, ‘s’]
>>> “”.join(list(“pqrs”))
‘abcd’
>>> # Strings are recursive data structures:
>>> type(“pqr”)
“
>>> type(“pqr”[0])
“
Summarizing Python Arrays
Python provides a number of predefined data structures, including arrays, that can be used in your programs. You have spent the previous few paragraphs learning about the fundamentals of the language and the data structures that are part of the standard library.
Third-party packages, such as NumPy and pandas, provide numerous efficient array versions for use in scientific computing and data science if you’re prepared to look further than the Python standard library.
Here are some rules to follow if you just wish to use Python’s built-in array data structures:
You can use a list or tuple, depending on whether or not you need an immutable data structure, to hold a collection of objects of arbitrary kinds.
Try utilizing array.array if you need to pack your integer or floating-point numbers tightly for optimal performance.
Use the str built into Python if your data consists of Unicode characters. When a data structure that behaves like a string but allows for modification is required, a list of characters is the best option.
You can use the immutable bytes type or, if you need a mutable data structure, a bytearray to store a sequence of bytes.
In most instances, It is preferred to begin with a basic list. If speed or storage become critical issues in the future, then It is better to consider pursuing a specialized field of study. Using a general-purpose array data structure like a list typically results in the quickest development time and the most convenient programming.
Instead of attempting to drag out every last bit of performance from the get-go, It is discovered that this is more important in general.
Structs, Records, and Data Transfer Objects
There is a consistent amount of fields in record data structures, unlike arrays. A name and a data type distinction are optional for each field.
Python’s built-in data types and classes from the standard library will be utilized in this part to demonstrate how to create records, structs, and simple data objects.
Records, structs, and data transfer objects are all possible in Python because of the language’s diverse set of data types. You’ll learn about the differences between the various implementations and how they work in this section. A summary and a checklist for selecting your own choices are provided at the end. Learn Python from the best Python training institute in Chennai and become empowered.
"dict" Means "Simple Data Objects."
As was previously established, Python dictionaries can hold any number of objects, each of which is referenced by a key. Dictionary entries can be quickly looked up, added, or deleted, and the dictionary can be thought of as a map or associative array.
Dictionaries can be used in Python as a record data type or data object. Python’s dictionary literals provide their own syntactic sugar, making it simple to generate dictionaries. Typing the dictionary syntax is a breeze because of how brief it is.
Due to the flexibility of dictionaries, data objects built with them are susceptible to changes, such as the introduction of new fields or the removal of existing ones, and there is minimal safeguarding against typos in the names of fields. There is always a compromise to be made between ease of use and robustness against unexpected errors because of these two features.
>>> bike1 = {
… “shade”: “black”,
… “speed”: 110.4,
… “automatic”: True,
… }
>>> car2 = {
… “shade”: “green”,
… “speed”: 78,
… “automatic”: False,
… }
>>> # Dicts have a nice repr:
>>> car2
{‘shade’: ‘green’, ‘automatic’: False, ‘speed’: 78}
>>> # Get shade:
>>> car2[“shade”]
78
>>> # Dicts are mutable:
>>> car2[“shade”] = 11
>>> car2[“wiper”] = “working”
>>> car2
{‘wiper’: ‘working’, ‘shade’: ‘green
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