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assignment 6 5 python data structures

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5. Data Structures ¶

This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

5.1. More on Lists ¶

The list data type has some more methods. Here are all of the methods of list objects:

Add an item to the end of the list. Equivalent to a[len(a):] = [x] .

Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable .

Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) .

Remove the first item from the list whose value is equal to x . It raises a ValueError if there is no such item.

Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)

Remove all items from the list. Equivalent to del a[:] .

Return zero-based index in the list of the first item whose value is equal to x . Raises a ValueError if there is no such item.

The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.

Return the number of times x appears in the list.

Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).

Reverse the elements of the list in place.

Return a shallow copy of the list. Equivalent to a[:] .

An example that uses most of the list methods:

You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . 1 This is a design principle for all mutable data structures in Python.

Another thing you might notice is that not all data can be sorted or compared. For instance, [None, 'hello', 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.

5.1.1. Using Lists as Stacks ¶

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

5.1.2. Using Lists as Queues ¶

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

5.1.3. List Comprehensions ¶

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:

or, equivalently:

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

and it’s equivalent to:

Note how the order of the for and if statements is the same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

List comprehensions can contain complex expressions and nested functions:

5.1.4. Nested List Comprehensions ¶

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

The following list comprehension will transpose rows and columns:

As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to:

which, in turn, is the same as:

In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The del statement ¶

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

del can also be used to delete entire variables:

Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

5.3. Tuples and Sequences ¶

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple .

A tuple consists of a number of values separated by commas, for instance:

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

The statement t = 12345, 54321, 'hello!' is an example of tuple packing : the values 12345 , 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

5.4. Sets ¶

Python also includes a data type for sets . A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not {} ; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

Similarly to list comprehensions , set comprehensions are also supported:

5.5. Dictionaries ¶

Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys , which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() .

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {} . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

5.6. Looping Techniques ¶

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.

5.7. More on Conditions ¶

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c .

Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

5.8. Comparing Sequences and Other Types ¶

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

Other languages may return the mutated object, which allows method chaining, such as d->insert("a")->remove("b")->sort(); .

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Assignment 6.5, Python for Data Structure

Home › Forums › Assignment courserra › Python for Everybody – Specialization › Python Data Structures › Assignment 6.5, Python for Data Structure

Tagged:  Bob , Python for Data Structure , Python for Everybody , University of Michigan

Abhishek Tyagi

Abhishek Tyagi

6.5 Write code using find() and string slicing (see section 6.10) to extract the number at the end of the line below. Convert the extracted value to a floating-point number and print it out.

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Common Python Data Structures (Guide)

Common Python Data Structures (Guide)

Table of Contents

Dictionaries in Python: Summary

Arrays in Python: Summary

Write a Custom Class: More Work, More Control

Records, Structs, and Data Objects in Python: Summary

Sets and Multisets in Python: Summary

Stack Implementations in Python: Summary

Queues in Python: Summary

Priority Queues in Python: Summary

Conclusion: python data structures.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Stacks and Queues: Selecting the Ideal Data Structure

Data structures are the fundamental constructs around which you build your programs. Each data structure provides a particular way of organizing data so it can be accessed efficiently, depending on your use case. Python ships with an extensive set of data structures in its standard library .

However, Python’s naming convention doesn’t provide the same level of clarity that you’ll find in other languages. In Java , a list isn’t just a list —it’s either a LinkedList or an ArrayList . Not so in Python. Even experienced Python developers sometimes wonder whether the built-in list type is implemented as a linked list or a dynamic array.

In this tutorial, you’ll learn:

Note: This tutorial is adapted from the chapter “Common Data Structures in Python” in Python Tricks: The Book . If you enjoy what you read below, then be sure to check out the rest of the book .

Free Download: Get a sample chapter from Python Tricks: The Book that shows you Python’s best practices with simple examples you can apply instantly to write more beautiful + Pythonic code.

Dictionaries, Maps, and Hash Tables

In Python, dictionaries (or dicts for short) are a central data structure. Dicts store an arbitrary number of objects, each identified by a unique dictionary key .

Dictionaries are also often called maps , hashmaps , lookup tables , or associative arrays . They allow for the efficient lookup, insertion, and deletion of any object associated with a given key.

Phone books make a decent real-world analog for dictionary objects. They allow you to quickly retrieve the information (phone number) associated with a given key (a person’s name). Instead of having to read a phone book front to back to find someone’s number, you can jump more or less directly to a name and look up the associated information.

This analogy breaks down somewhat when it comes to how the information is organized to allow for fast lookups. But the fundamental performance characteristics hold. Dictionaries allow you to quickly find the information associated with a given key.

Dictionaries are one of the most important and frequently used data structures in computer science. So, how does Python handle dictionaries? Let’s take a tour of the dictionary implementations available in core Python and the Python standard library.

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:

There are some restrictions on which objects can be used as valid keys.

Python’s dictionaries are indexed by keys that can be of any hashable type. A hashable object has a hash value that never changes during its lifetime (see __hash__ ), and it can be compared to other objects (see __eq__ ). Hashable objects that compare as equal must have the same hash value.

Immutable types like strings and numbers are hashable and work well as dictionary keys. You can also use tuple objects as dictionary keys as long as they contain only hashable types themselves.

For most use cases, Python’s built-in dictionary implementation will do everything you need. 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.

Python dictionaries are based on a well-tested and finely tuned hash table implementation that provides the performance characteristics you’d expect: O (1) time complexity for lookup, insert, update, and delete operations in the average case.

There’s little reason not to use the standard dict implementation included with Python. However, specialized third-party dictionary implementations exist, such as skip lists or B-tree–based dictionaries.

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.

Let’s take a look at them.

collections.OrderedDict : Remember the Insertion Order of Keys

Python includes a specialized dict subclass that remembers the insertion order of keys added to it: collections.OrderedDict .

Note: OrderedDict is not a built-in part of the core language and must be imported from the collections module in the standard library.

While standard dict instances preserve the insertion order of keys in CPython 3.6 and above, this was simply a side effect of the CPython implementation and was not defined in the language spec until Python 3.7. So, if key order is important for your algorithm to work, then it’s best to communicate this clearly by explicitly using the OrderedDict class:

Until Python 3.8 , you couldn’t iterate over dictionary items in reverse order using reversed() . Only OrderedDict instances offered that functionality. Even in Python 3.8, dict and OrderedDict objects aren’t exactly the same. OrderedDict instances have a .move_to_end() method that is unavailable on plain dict instance, as well as a more customizable .popitem() method than the one plain dict instances.

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:

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:

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:

All the Python dictionary implementations listed in this tutorial are valid implementations that are built into the Python standard library.

If you’re looking for a general recommendation on which mapping type to use in your programs, I’d point you to the built-in dict data type. It’s a versatile and optimized hash table implementation that’s built directly into the core language.

I would recommend that you use one of the other data types listed here only if you have special requirements that go beyond what’s provided by dict .

All the implementations are valid options, but your code will be clearer and easier to maintain if it relies on standard Python dictionaries most of the time.

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 array

Because 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:

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:

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.

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:

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:

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:

There are a number of built-in data structures you can choose from when it comes to implementing arrays in Python. In this section, you’ve focused on core language features and data structures included in the standard library.

If you’re willing to go beyond the Python standard library, then third-party packages like NumPy and pandas offer a wide range of fast array implementations for scientific computing and data science.

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.

I’ve found that this is usually much more important in the beginning than trying to squeeze out every last drop of performance right from the start.

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.

Note: This tutorial is adapted from the chapter “Common Data Structures in Python” in Python Tricks: The Book . If you enjoy what you’re reading, then be sure to check out the rest of the book .

Alright, let’s get started!

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:

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:

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:

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:

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:

Data classes are typically created using the @dataclass decorator , as you’ll see in the code example below:

To learn more about Python data classes, check out the The Ultimate Guide to Data Classes in Python 3.7 .

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:

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:

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):

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:

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:

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.

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:

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.

Python and its standard library provide several set implementations. Let’s have a look at them.

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:

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:

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:

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() :

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:

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:

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:

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:

As you’ve seen, Python ships with several implementations for a stack data structure. All of them have slightly different characteristics as well as performance and usage trade-offs.

If you’re not looking for parallel processing support (or if you don’t want to handle locking and unlocking manually), then your choice comes down to the built-in list type or collections.deque . The difference lies in the data structure used behind the scenes and overall ease of use.

list is backed by a dynamic array, which makes it great for fast random access but requires occasional resizing when elements are added or removed.

The list over-allocates its backing storage so that not every push or pop requires resizing, and you get an amortized O (1) time complexity for these operations. But you do need to be careful to only insert and remove items using append() and pop() . Otherwise, performance slows down to O ( n ).

collections.deque is backed by a doubly-linked list, which optimizes appends and deletes at both ends and provides consistent O (1) performance for these operations. Not only is its performance more stable, the deque class is also easier to use because you don’t have to worry about adding or removing items from the wrong end.

In summary, collections.deque is an excellent choice for implementing a stack (LIFO queue) in Python.

Queues (FIFOs)

In this section, you’ll see how to implement a First-In/First-Out (FIFO) queue data structure using only built-in data types and classes from the Python standard library.

A queue is a collection of objects that supports fast FIFO semantics for inserts and deletes. The insert and delete operations are sometimes called enqueue and dequeue . Unlike lists or arrays, queues typically don’t allow for random access to the objects they contain.

Here’s a real-world analogy for a FIFO queue:

Imagine a line of Pythonistas waiting to pick up their conference badges on day one of PyCon registration. As new people enter the conference venue and queue up to receive their badges, they join the line (enqueue) at the back of the queue. Developers receive their badges and conference swag bags and then exit the line (dequeue) at the front of the queue.

Another way to memorize the characteristics of a queue data structure is to think of it as a pipe. You add ping-pong balls to one end, and they travel to the other end, where you remove them. While the balls are in the queue (a solid metal pipe) you can’t get at them. The only way to interact with the balls in the queue is to add new ones at the back of the pipe (enqueue) or to remove them at the front (dequeue).

Queues are similar to stacks. The difference between them lies in how items are removed. With a queue , you remove the item least recently added (FIFO) but with a stack , you remove the item most recently added (LIFO).

Performance-wise, a proper queue implementation is expected to take O (1) time for insert and delete operations. These are the two main operations performed on a queue, and in a correct implementation, they should be fast.

Queues have a wide range of applications in algorithms and often help solve scheduling and parallel programming problems. A short and beautiful algorithm using a queue is breadth-first search (BFS) on a tree or graph data structure.

Scheduling algorithms often use priority queues internally. These are specialized queues. Instead of retrieving the next element by insertion time, a priority queue retrieves the highest-priority element. The priority of individual elements is decided by the queue based on the ordering applied to their keys.

A regular queue, however, won’t reorder the items it carries. Just like in the pipe example, you get out what you put in, and in exactly that order.

Python ships with several queue implementations that each have slightly different characteristics. Let’s review them.

list : Terribly Sloooow Queues

It’s possible to use a regular list as a queue , but this is not ideal from a performance perspective. Lists are quite slow for this purpose because inserting or deleting an element at the beginning requires shifting all the other elements by one, requiring O ( n ) time.

Therefore, I would not recommend using a list as a makeshift queue in Python unless you’re dealing with only a small number of elements:

collections.deque : Fast and Robust Queues

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

As a result, collections.deque is a great default choice if you’re looking for a queue data structure in Python’s standard library:

queue.Queue : Locking Semantics for Parallel Computing

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

The queue module contains several other classes implementing multi-producer, multi-consumer queues that are useful for parallel computing.

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

multiprocessing.Queue : Shared Job Queues

multiprocessing.Queue is a shared job queue implementation that allows queued items to be processed in parallel by multiple concurrent workers. Process-based parallelization is popular in CPython due to the global interpreter lock (GIL) that prevents some forms of parallel execution on a single interpreter process.

As a specialized queue implementation meant for sharing data between processes, multiprocessing.Queue makes it easy to distribute work across multiple processes in order to work around the GIL limitations. This type of queue can store and transfer any pickleable object across process boundaries:

Python includes several queue implementations as part of the core language and its standard library.

list objects can be used as queues, but this is generally not recommended due to slow performance.

If you’re not looking for parallel processing support, then the implementation offered by collections.deque is an excellent default choice for implementing a FIFO queue data structure in Python. It provides the performance characteristics you’d expect from a good queue implementation and can also be used as a stack (LIFO queue).

Priority Queues

A priority queue is a container data structure that manages a set of records with totally-ordered keys to provide quick access to the record with the smallest or largest key in the set.

You can think of a priority queue as a modified queue. Instead of retrieving the next element by insertion time, it retrieves the highest-priority element. The priority of individual elements is decided by the order applied to their keys.

Priority queues are commonly used for dealing with scheduling problems. For example, you might use them to give precedence to tasks with higher urgency.

Think about the job of an operating system task scheduler:

Ideally, higher-priority tasks on the system (such as playing a real-time game) should take precedence over lower-priority tasks (such as downloading updates in the background). By organizing pending tasks in a priority queue that uses task urgency as the key, the task scheduler can quickly select the highest-priority tasks and allow them to run first.

In this section, you’ll see a few options for how you can implement priority queues in Python using built-in data structures or data structures included in Python’s standard library. Each implementation will have its own upsides and downsides, but in my mind there’s a clear winner for most common scenarios. Let’s find out which one it is.

list : Manually Sorted Queues

You can use a sorted list to quickly identify and delete the smallest or largest element. The downside is that inserting new elements into a list is a slow O ( n ) operation.

While the insertion point can be found in O (log n ) time using bisect.insort in the standard library, this is always dominated by the slow insertion step.

Maintaining the order by appending to the list and re-sorting also takes at least O ( n log n ) time. Another downside is that you must manually take care of re-sorting the list when new elements are inserted. It’s easy to introduce bugs by missing this step, and the burden is always on you, the developer.

This means sorted lists are only suitable as priority queues when there will be few insertions:

heapq : List-Based Binary Heaps

heapq is a binary heap implementation usually backed by a plain list , and it supports insertion and extraction of the smallest element in O (log n ) time.

This module is a good choice for implementing priority queues in Python . Since heapq technically provides only a min-heap implementation, extra steps must be taken to ensure sort stability and other features typically expected from a practical priority queue:

queue.PriorityQueue : Beautiful Priority Queues

queue.PriorityQueue uses heapq internally and shares the same time and space complexities. The difference is that PriorityQueue is synchronized and provides locking semantics to support multiple concurrent producers and consumers.

Depending on your use case, this might be helpful, or it might just slow your program down slightly. In any case, you might prefer the class-based interface provided by PriorityQueue over the function-based interface provided by heapq :

Python includes several priority queue implementations ready for you to use.

queue.PriorityQueue stands out from the pack with a nice object-oriented interface and a name that clearly states its intent. It should be your preferred choice.

If you’d like to avoid the locking overhead of queue.PriorityQueue , then using the heapq module directly is also a good option.

That concludes your tour of common data structures in Python. With the knowledge you’ve gained here, you’re ready to implement efficient data structures that are just right for your specific algorithm or use case.

In this tutorial, you’ve learned:

If you enjoyed what you learned in this sample from Python Tricks , then be sure to check out the rest of the book .

If you’re interested in brushing up on your general data structures knowledge, then I highly recommend Steven S. Skiena’s The Algorithm Design Manual . It strikes a great balance between teaching you fundamental (and more advanced) data structures and showing you how to implement them in your code. Steve’s book was a great help in the writing of this tutorial.

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assignment 6 5 python data structures

Chapter 6 Quiz >> Python Data Structures

1.What does the following Python Program print out?

5. What is the iteration variable in the following Python code?

8. Which of the following is not a valid string method in Python?

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Linked List

Binary tree.

Related Articles

Python Data Structures

Data Structures are a way of organizing data so that it can be accessed more efficiently depending upon the situation. Data Structures are fundamentals of any programming language around which a program is built. Python helps to learn the fundamental of these data structures in a simpler way as compared to other programming languages.

In this article, we will discuss the Data Structures in the Python Programming Language and how they are related to some specific Python Data Types . We will discuss all the in-built data structures like list tuples, dictionaries, etc. as well as some advanced data structures like trees, graphs, etc.

Python Lists are just like the arrays, declared in other languages which is an ordered collection of data. It is very flexible as the items in a list do not need to be of the same type.

The implementation of Python List is similar to Vectors in C++ or ArrayList in JAVA. The costly operation is inserting or deleting the element from the beginning of the List as all the elements are needed to be shifted. Insertion and deletion at the end of the list can also become costly in the case where the preallocated memory becomes full.

We can create a list in python as shown below.

Example: Creating Python List

List elements can be accessed by the assigned index. In python starting index of the list, sequence is 0 and the ending index is (if N elements are there) N-1.

Example: Python List Operations

Python dictionary is like hash tables in any other language with the time complexity of O(1). It is an unordered collection of data values, used to store data values like a map, which, unlike other Data Types that hold only a single value as an element, Dictionary holds the key:value pair. Key-value is provided in the dictionary to make it more optimized. 

Indexing of Python Dictionary is done with the help of keys. These are of any hashable type i.e. an object whose can never change like strings, numbers, tuples, etc. We can create a dictionary by using curly braces ({}) or dictionary comprehension .

Example: Python Dictionary Operations

Python Tuple is a collection of Python objects much like a list but Tuples are immutable in nature i.e. the elements in the tuple cannot be added or removed once created. Just like a List, a Tuple can also contain elements of various types.

In Python, tuples are created by placing a sequence of values separated by ‘comma’ with or without the use of parentheses for grouping of the data sequence.

Note: Tuples can also be created with a single element, but it is a bit tricky. Having one element in the parentheses is not sufficient, there must be a trailing ‘comma’ to make it a tuple.

Example: Python Tuple Operations

Python Set is an unordered collection of data that is mutable and does not allow any duplicate element. Sets are basically used to include membership testing and eliminating duplicate entries. The data structure used in this is Hashing, a popular technique to perform insertion, deletion, and traversal in O(1) on average. 

If Multiple values are present at the same index position, then the value is appended to that index position, to form a Linked List. In, CPython Sets are implemented using a dictionary with dummy variables, where key beings the members set with greater optimizations to the time complexity.

Set Implementation:

Sets with Numerous operations on a single HashTable:

Example: Python Set Operations

Frozen sets.

Frozen sets in Python are immutable objects that only support methods and operators that produce a result without affecting the frozen set or sets to which they are applied. While elements of a set can be modified at any time, elements of the frozen set remain the same after creation.

If no parameters are passed, it returns an empty frozenset.

Python Strings are arrays of bytes representing Unicode characters. In simpler terms, a string is an immutable array of characters. Python does not have a character data type, a single character is simply a string with a length of 1.

Note: As strings are immutable, modifying a string will result in creating a new copy.

Example: Python Strings Operations

Python Bytearray gives a mutable sequence of integers in the range 0 <= x < 256.

Example: Python Bytearray Operations

Till now we have studied all the data structures that come built-in into core Python. Now let dive more deep into Python and see the collections module that provides some containers that are useful in many cases and provide more features than the above-defined functions.

Collections Module

Python collection module was introduced to improve the functionality of the built-in datatypes. It provides various containers let’s see each one of them in detail.

A counter is a sub-class of the dictionary. It is used to keep the count of the elements in an iterable in the form of an unordered dictionary where the key represents the element in the iterable and value represents the count of that element in the iterable. This is equivalent to a bag or multiset of other languages.

Example: Python Counter Operations

Ordereddict.

An OrderedDict is also a sub-class of dictionary but unlike a dictionary, it remembers the order in which the keys were inserted. 

Example: Python OrderedDict Operations

Defaultdict.

DefaultDict is used to provide some default values for the key that does not exist and never raises a KeyError. Its objects can be initialized using DefaultDict() method by passing the data type as an argument.

Note: default_factory is a function that provides the default value for the dictionary created. If this parameter is absent then the KeyError is raised.

Example: Python DefaultDict Operations

A ChainMap encapsulates many dictionaries into a single unit and returns a list of dictionaries. When a key is needed to be found then all the dictionaries are searched one by one until the key is found.

Example: Python ChainMap Operations

A NamedTuple returns a tuple object with names for each position which the ordinary tuples lack. For example, consider a tuple names student where the first element represents fname, second represents lname and the third element represents the DOB. Suppose for calling fname instead of remembering the index position you can actually call the element by using the fname argument, then it will be really easy for accessing tuples element. This functionality is provided by the NamedTuple.

Example: Python NamedTuple Operations

Deque (Doubly Ended Queue) is the optimized list for quicker append and pop operations from both sides of the container. It provides O(1) time complexity for append and pop operations as compared to the list with O(n) time complexity.

Python Deque is implemented using doubly linked lists therefore the performance for randomly accessing the elements is O(n).

Example: Python Deque Operations

UserDict is a dictionary-like container that acts as a wrapper around the dictionary objects. This container is used when someone wants to create their own dictionary with some modified or new functionality. 

Example: Python UserDict

UserList is a list-like container that acts as a wrapper around the list objects. This is useful when someone wants to create their own list with some modified or additional functionality.

Examples: Python UserList

UserString is a string-like container and just like UserDict and UserList, it acts as a wrapper around string objects. It is used when someone wants to create their own strings with some modified or additional functionality. 

Example: Python UserString

Now after studying all the data structures let’s see some advanced data structures such as stack, queue, graph, linked list, etc. that can be used in Python Language.

A linked list is a linear data structure, in which the elements are not stored at contiguous memory locations. The elements in a linked list are linked using pointers as shown in the below image:

A linked list is represented by a pointer to the first node of the linked list. The first node is called the head. If the linked list is empty, then the value of the head is NULL. Each node in a list consists of at least two parts:

Example: Defining Linked List in Python

Let us create a simple linked list with 3 nodes.

Linked List Traversal 

In the previous program, we have created a simple linked list with three nodes. Let us traverse the created list and print the data of each node. For traversal, let us write a general-purpose function printList() that prints any given list.

A stack is a linear data structure that stores items in a Last-In/First-Out (LIFO) or First-In/Last-Out (FILO) manner. In stack, a new element is added at one end and an element is removed from that end only. The insert and delete operations are often called push and pop.

The functions associated with stack are:

Python Stack Implementation

Stack in Python can be implemented using the following ways:

Implementation using List

Python’s built-in data structure list can be used as a stack. Instead of push(), append() is used to add elements to the top of the stack while pop() removes the element in LIFO order. 

Implementation using collections.deque:

Python stack can be implemented using the deque class from the collections module. Deque is preferred over the list in the cases where we need quicker append and pop operations from both the ends of the container, as deque provides an O(1) time complexity for append and pop operations as compared to list which provides O(n) time complexity. 

Implementation using queue module

The queue module also has a LIFO Queue, which is basically a Stack. Data is inserted into Queue using the put() function and get() takes data out from the Queue. 

As a stack, the queue is a linear data structure that stores items in a First In First Out (FIFO) manner. With a queue, the least recently added item is removed first. A good example of the queue is any queue of consumers for a resource where the consumer that came first is served first.

Operations associated with queue are:

Python queue Implementation

Queue in Python can be implemented in the following ways:

Implementation using list

Instead of enqueue() and dequeue(), append() and pop() function is used.

Implementation using collections.deque

Deque is preferred over the list in the cases where we need quicker append and pop operations from both the ends of the container, as deque provides an O(1) time complexity for append and pop operations as compared to list which provides O(n) time complexity.

Implementation using the queue.Queue

queue.Queue(maxsize) initializes a variable to a maximum size of maxsize. A maxsize of zero ‘0’ means an infinite queue. This Queue follows the FIFO rule. 

Priority Queue

Priority Queues are abstract data structures where each data/value in the queue has a certain priority. For example, In airlines, baggage with the title “Business” or “First-class” arrives earlier than the rest. Priority Queue is an extension of the queue with the following properties.

Heap queue (or heapq)

heapq module in Python provides the heap data structure that is mainly used to represent a priority queue. The property of this data structure in Python is that each time the smallest heap element is popped(min-heap). Whenever elements are pushed or popped, heap structure is maintained. The heap[0] element also returns the smallest element each time. 

It supports the extraction and insertion of the smallest element in the O(log n) times.

A tree is a  hierarchical data structure that looks like the below figure – 

The topmost node of the tree is called the root whereas the bottommost nodes or the nodes with no children are called the leaf nodes. The nodes that are directly under a node are called its children and the nodes that are directly above something are called its parent.

A binary tree is a tree whose elements can have almost two children. Since each element in a binary tree can have only 2 children, we typically name them the left and right children. A Binary Tree node contains the following parts.

Example: Defining Node Class

Now let’s create a tree with 4 nodes in Python. Let’s assume the tree structure looks like below – 

Example: Adding data to the tree

Tree traversal.

Trees can be traversed in different ways. Following are the generally used ways for traversing trees. Let us consider the below tree – 

Depth First Traversals:

Algorithm Inorder(tree)

Algorithm Preorder(tree)

Algorithm Postorder(tree)

Time Complexity –  O(n) 

Breadth-First or Level Order Traversal

Level order traversal of a tree is breadth-first traversal for the tree. The level order traversal of the above tree is 1 2 3 4 5.

For each node, first, the node is visited and then its child nodes are put in a FIFO queue. Below is the algorithm for the same – 

Time Complexity: O(n) 

A graph is a nonlinear data structure consisting of nodes and edges. The nodes are sometimes also referred to as vertices and the edges are lines or arcs that connect any two nodes in the graph. More formally a Graph can be defined as a Graph consisting of a finite set of vertices(or nodes) and a set of edges that connect a pair of nodes.

In the above Graph, the set of vertices V = {0,1,2,3,4} and the set of edges E = {01, 12, 23, 34, 04, 14, 13}.

The following two are the most commonly used representations of a graph.

Adjacency Matrix

Adjacency list.

Adjacency Matrix is a 2D array of size V x V where V is the number of vertices in a graph. Let the 2D array be adj[][], a slot adj[i][j] = 1 indicates that there is an edge from vertex i to vertex j. The adjacency matrix for an undirected graph is always symmetric. Adjacency Matrix is also used to represent weighted graphs. If adj[i][j] = w, then there is an edge from vertex i to vertex j with weight w. 

Vertices of Graph [‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’] Edges of Graph [(‘a’, ‘c’, 20), (‘a’, ‘e’, 10), (‘b’, ‘c’, 30), (‘b’, ‘e’, 40), (‘c’, ‘a’, 20), (‘c’, ‘b’, 30), (‘d’, ‘e’, 50), (‘e’, ‘a’, 10), (‘e’, ‘b’, 40), (‘e’, ‘d’, 50), (‘e’, ‘f’, 60), (‘f’, ‘e’, 60)] Adjacency Matrix of Graph [[-1, -1, 20, -1, 10, -1], [-1, -1, 30, -1, 40, -1], [20, 30, -1, -1, -1, -1], [-1, -1, -1, -1, 50, -1], [10, 40, -1, 50, -1, 60], [-1, -1, -1, -1, 60, -1]]

An array of lists is used. The size of the array is equal to the number of vertices. Let the array be an array[]. An entry array[i] represents the list of vertices adjacent to the ith vertex. This representation can also be used to represent a weighted graph. The weights of edges can be represented as lists of pairs. Following is the adjacency list representation of the above graph. 

Graph Traversal

Breadth-First Search or BFS

Breadth-First Traversal for a graph is similar to Breadth-First Traversal of a tree. The only catch here is, unlike trees, graphs may contain cycles, so we may come to the same node again. To avoid processing a node more than once, we use a boolean visited array. For simplicity, it is assumed that all vertices are reachable from the starting vertex.

For example, in the following graph, we start traversal from vertex 2. When we come to vertex 0, we look for all adjacent vertices of it. 2 is also an adjacent vertex of 0. If we don’t mark visited vertices, then 2 will be processed again and it will become a non-terminating process. A Breadth-First Traversal of the following graph is 2, 0, 3, 1.

Time Complexity: O(V+E) where V is the number of vertices in the graph and E is the number of edges in the graph.

Depth First Search or DFS

Depth First Traversal for a graph is similar to Depth First Traversal of a tree. The only catch here is, unlike trees, graphs may contain cycles, a node may be visited twice. To avoid processing a node more than once, use a boolean visited array.

Time complexity: O(V + E), where V is the number of vertices and E is the number of edges in the graph.

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assignment 6 5 python data structures

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Python Data Structures

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About this Course

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.

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Explain the principles of data structures & how they are used

Create programs that are able to read and write data from files

Store data as key/value pairs using Python dictionaries

Accomplish multi-step tasks like sorting or looping using tuples

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Syllabus - What you will learn from this course

Chapter six: strings.

In this class, we pick up where we left off in the previous class, starting in Chapter 6 of the textbook and covering Strings and moving into data structures. The second week of this class is dedicated to getting Python installed if you want to actually run the applications on your desktop or laptop. If you choose not to install Python, you can just skip to the third week and get a head start.

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Chapter Seven: Files

Up to now, we have been working with data that is read from the user or data in constants. But real programs process much larger amounts of data by reading and writing files on the secondary storage on your computer. In this chapter we start to write our first programs that read, scan, and process real data.

Chapter Eight: Lists

As we want to solve more complex problems in Python, we need more powerful variables. Up to now we have been using simple variables to store numbers or strings where we have a single value in a variable. Starting with lists we will store many values in a single variable using an indexing scheme to store, organize, and retrieve different values from within a single variable. We call these multi-valued variables "collections" or "data structures".

Chapter Nine: Dictionaries

The Python dictionary is one of its most powerful data structures. Instead of representing values in a linear list, dictionaries store data as key / value pairs. Using key / value pairs gives us a simple in-memory "database" in a single Python variable.

Chapter Ten: Tuples

Tuples are our third and final basic Python data structure. Tuples are a simple version of lists. We often use tuples in conjunction with dictionaries to accomplish multi-step tasks like sorting or looping through all of the data in a dictionary.

To celebrate your making it to the halfway point in our Python for Everybody Specialization, we welcome you to attend our online graduation ceremony. It is not very long, and it features a Commencement speaker and very short commencement speech.

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assignment 6 5 python data structures

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4 Must-Know Python Data Structures

Practical guide with examples.

Data structures are an essential part of any programming language. How you store and manage data is one of the key factors for creating efficient programs.

Python has 4 built-in data structures:

They all have different features in terms of storing and accessing data. These differences matter because what fits best for a particular task depends on them. How you can interact with or manipulate these data structures are also different.

List is a collection of objects, represented in square brackets.

The insert method adds the new item at the specified index.

If you use the append method for this operation, the new list will be added as a new item.

Set is a collection of objects, represented in curly braces.

An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equal must have the same hash value.

For instance, we cannot use a list in a set because lists are mutable.

Tuple is a collection of objects, represented in parenthesis.

Dictionary is an unordered collection of key-value pairs.

We have done a brief introduction to the built-in data structures of Python. They play a key role in the efficiency of programs. Python also supports user-defined data structures such as arrays, stacks, queues and so on.

Thank you for reading. Please let me know if you have any feedback.

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NPTEL Data Mining Assignment Answers 2023 -Week 5

Are you a student struggling with the Data Mining NPTEL Week 5 assignment? Look no further! In this article, we have compiled a set of hints and answers to help guide you through the assignment.

Make sure to give it a try on your own first, but use these hints as a helpful resource.

NPTEL DATA MINING ASSIGNMENT ANSWERS 2023 -WEEK 5

Q1. Support vector machine is:

Answer: (B)Maximum margin classifier

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Q2. Support vectors in SVM are:

Answer:  (C) Subset of training data points

Q3. In a hard margin support vector machine:

Answer: (B) All the instances lie inside the margin

Q4. The Lagrange multipliers corresponding to the support vectors have a value:

Answer: (C) greater than zero

Q5. The primal optimization problem solved to obtain the hard margin optimal separating hyperplane is:

Answer: (A) Minimize N * W ^ T * W such that y{i}(W ^ T * x{i} + b) >= 1 for all i

Q6. The dual optimization problem solved to obtain the hard margin optimal separating hyperplane is:

Answer: (D) Maximize 1/2 * W ^ T * W + Sigma*alpha_{i} such that y{i}(W ^ T * X + b) <= 1 for all i

Q7. We are designing a SVM, W^Tx+b=0, suppose X/s are the support vectors and alpha / s the corresponding Lagrange multipliers, then which of the following statements are correct:

Answer: (D) Both A and B

Q8. If the hyperplane W^Tx + b = 0 correctly classifies all the training points (Xi, Yi) where Yi = {+1, – 1} then:

Answer: (C) W^TX+b >=0 for all i

Q9. The dual optimization problem in SVM design is usually solved using:

Answer: (D) Quadratic programming

Q10. Slack variables are used in which of the below:

Answer: (A) Soft margin SVM

Disclaimer: Please keep in mind that these answers are intended to serve as a reference for students. Our website does not guarantee the accuracy of the answers provided. We encourage all students to complete their assignments independently and use these answers as a supplement to their own understanding.

NPTEL DATA MINING WEEK 1 – 2023

NPTEL Data Mining Assignment Answers Week 3 2023

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