MEIN 50010: Python Data Structures

Transcription

1 : Python Data Structures Fabian Sievers Higgins Lab, Conway Institute University College Dublin Wednesday,

2 Data Structures Stacks, Queues & Deques Structures

3 Data structures are a way of storing & organising data Data structures help with managing large amounts of data Different kinds of data structures are suited to different kinds of application/problem Data structures should help in retrieving data efficiently Data structures may be customised for data being stored in main (cache, RAM, disk) or secondary memory (tape, DVD)

4 1/7 Motivation A list stores a number of elements in a specific order Elements are accessed using an integer (index) can be of fixed length or expandable/shrinkable Elements can be of almost any type (integer, double/float, character/string, record/structure, lists,...) and a mixture thereof (bad idea) Most other data structures are based on lists Everyone else (non-python) calls them arrays

5 2/7 Basics The easiest list is empty >>> emptylist=[] are declared with square brackets >>> num=[2,4,6,8,10,12] The type of this construct is >>> type(num) <type list > >>> type(num[0]) <type int > Indexing begins with 0, >>> num[0] 2 Negative indexing from the back >>> num[-1] 12 Range (slice) of elements accessed using colon >>> num[0:3] [2, 4, 6] not including num[3]! Loop through the elements >>> for a in num: print(a,) 2,4,6,8,10,12

6 List 3/7 Common Methods len(list) number of elements in the list >>> L=[1,2,3] >>> len(l) 3.append(value) appends element to end of list ( += ) >>> L=[1,2,3] >>> L.append(4) >>> L [1,2,3,4] >>> L=[1,2,3] >>> L.append([4,5]) >>> L [1,2,3,[4,5]] >>> len(l) 4.extend(list) concatenates lists (or + ) >>> L=[1,2,3] >>> L.extend([4,5]) >>> L [1,2,3,4,5] >>> len(l) 5.insert( y, x ) inserts x at location y >>> L=[1,2,3] >>> L.insert(1,4) >>> L [1,4,2,3].pop() - returns last element then removes it from list >>> L=[1,2,3]; >>> L.pop() >>> L 3; [1,2]

7 4/7 Common Methods.index( x ) returns index of first x in list >>> L=[1,2,3] >>> L.index(2) 1.remove( x ) finds and removes first x from list >>> L=[1,2,3] >>> L.remove(2) >>> L [1,3].reverse() - reverses the elements in the list >>> L=[1,2,3] >>> L.reverse() >>> L [3,2,1].sort() sorts the list alphabetically in ascending order, or numerical in ascending order >>> L=[3,2,1] >>> L.sort() >>> L [1,2,3] Cannot sort mixed lists like [ 1,2,3] in Python3 (used to work in Python2) Used to be able in Python2 to.replace( a, b ), in Python3 can use List Comprehension

8 5/7 Definitions A Stack is a data structure where elements are added and subtracted at the same end (LIFO - last in first out) adding and subtracting elements at the end is fast A Queue is a data structure where elements are added at one end and removed at the other end (FIFO - first in first out) adding elements at the end and subtracting at the head is slow because all elements have to be shifted that s why queues are usually implemented as deques A Deque is a double ended queue where elements can be added and/or subtracted at either end, preserving the order of the elements in the deque to implement a queue use collections.deque package from collections import deque.pop() to remove elements from end.popleft() to remove elements from head

9 6/7 Function Tools filter returns elements for which a condition is true >>> def even(x): return x % 2 == 0 >>> L=[1,2,3,4] >>> list(filter(even, L)) [2, 4] A function can be mapped onto each element of a list >>> def square(x): return x*x >>> list(map(square, L)) [1, 4, 9, 16] Map n-argument function onto n sequences >>> def f(x,y): return x + 1.0/y >>> list(map(f,l,l)) [2.0, 2.5, 3.333, 4.25] reduce a sequence by calling binary function >>> def add(x,y): return x+y >>> functools.reduce(add, list(range(1,11))) 55

10 7/7 List Comprehensions List comprehensions are a compact way to create lists Could build up list with append >>> Squares = [] >>> for x in range(1,5):... Squares.append(x*x) [1,4,9,16] Or could map a function >>> L=range(1,5) >>> Squares = map(square, L) Instead do everything in one step >>> Squares = [x*x for x in range(1,5)] List comprehensions can be nested >>> [[x,y] for x in L for y in L] [1,1], [1,2], [1,3], [1,4], [2,1],... [4,4] List comprehensions can have conditionals >>> [(x,y) for x in L for y in L if x<y] [(1,2), (1,3), (1,4), (2,3), (2,4), (3,4)]

11 are unordered collections of unique elements >>>mylist = [1,1,3,4,5,1,3,4,2] >>>mylist[2] # access by index 3 >>>myset=set(mylist) >>>myset {1, 2, 3, 4, 5} When turning list into set, duplicates are removed Order is not preserved (there is none) Cannot access elements by index (no order) >>>myset[1] TypeError: set object does not support indexing

12 really useful for comparing gene sets, e.g., all kinases found in nucleus but not cytoplasm or genes upregulated in condition A and condition B >>>myset1 = set([1,2,3,4]) >>>myset2 = set([3,4,5,6]) >>>myset1.union(myset2) [1, 2, 3, 4, 5, 6] >>>myset1.intersection(myset2) [3, 4] >>>myset1.difference(myset2) [1, 2]

13 1/2 are like lists, declared with (parentheses) instead of [square brackets], however, parentheses are optional but useful for clarity, comma is not optional! empty=(), singleton=1, are immutable (cannot be changed), but may contain mutable objects >>> T=(1,2,3) >>> T.append(4) Traceback (most recent call last): AttributeError: tuple object has no attribute append May be desirable if quantities are not meant to change can be cast as lists if they have to change >>> L=list(T) Conversely, lists can be tupled t=tuple(l) can be used to assign multiple values at once >>> t1=(1,2,3) >>> (a,b,c)=t1

14 2/2 Common Methods None

15 1/2 1-to-1 relationship between keys and values D={key1 : val1, key2 : val2... } >>> GerEng={ Hund : dog, Katze : cat } Can get values by key but cannot get key by value There is only one value per key, assigning a value to an existing key over-writes the old value Adding a new key/value extends the dictionary, >>> GerEng[ Kuh ] = cow Entries can be deleted with >>> del GerEng[ Hund ], the entire dictionary can be wiped with.clear() The order in which key/value pairs appear in the dictionary is random and likely to change as the dictionary is changed

16 2/2 Common Methods.keys() list all the keys in the dictionary.values() list all the values in the dictionary.items list both keys and values of the dictionary <key> in <dict> find if a key exists in the dictionary dict Example: if Kuh in GerEng:... print( yes ).has key(k) deprecated, Python2 only.update(d2) merge dictionary with dictionary D2

17 Data Structures (important) types of data structures, not covered here Trees Binary Multiway Trie Hashes Graphs

18 Picture Venn (mathematics)