Sequences and Collections are an integral part of any programming language. Python particularly handles them uniformly, i.e. any type of sequence from Strings and XML elements to arrays, lists, and Tuples are handled in a uniform way. Understanding the variety of these sequences will spare us the reinvention of the wheel as the existing common sequence interface allows us to leverage and support any new sequence types. The built-in sequences and how to group them There are 2 types of sequences: The : these hold items of different types, including nested containers. Like . container sequences list, tuple, and double-ended queues The : these hold items of simple types. Like . flat sequences str and byte A , which may be of any type, while , not as distinct Python objects. container sequence holds references to the objects it contains a flat sequence stores the value of its contents in its own memory space Another way to group sequences is Mutability VS Immutability: : sequences that , for example, list, bytearray, array.array, and collections.deque. Mutable sequences can change their values : sequences that , for example, tuple, str, and bytes. Immutable sequences can not change their values Let’s see what we can learn about Sequences! ✌️ ListComps and GenExps List Comprehensions( ) and Generator expressions( ) are Python's solutions to in a readable and clear way. ListComps GenExps build sequences The deals mainly with , the with any . first Lists latter other type of sequences List Comprehensions very concise way to , sometimes by on the existing items, using a simpler cleaner more compact syntax, all of this without having to deal with ListComp is a create a list from an existing list operating Python lambda. fruits = ["apple", "banana", "cherry", "kiwi", "mango"]
newlistUsingForLoop = []
newlistUsingListComp = []

# using traditional for loop
for x in fruits:
  if "a" in x:
    newlistUsingForLoop.append(x)

print(newlistUsingForLoop) # ['apple', 'banana', 'mango']

# using ListComp
newlistUsingListComp = [x for x in fruits if "a" in x]

print(newlistUsingListComp) # ['apple', 'banana', 'mango']

# we can notice how easily readable the list comprehension version Generator Expressions A generator expression( ) is an expression that returns a , i.e. a function that contains a statement and returns a generator object. GenExps generator object yield use the same syntax as , but are enclosed in parentheses rather than brackets. GenExps ListComps # create the generator object
squares_generator = (i * i for i in range(5))

# iterate over the generator and print the values
for i in squares_generator:
    print(i)

# this will output the square of numbers from 0 to 4 

colors = ['black', 'white']
sizes = ['S', 'M', 'L']
for tshirt in (f'{c} {s}' for c in colors for s in sizes):
    print(tshirt)

# black S
# black M
# black L
# white S
# white M
# white L Tuples are Not Just Immutable Lists Python presents Tuples as Immutable Lists, only this does not do them justice as they can be used as and also as with no field names. immutable lists records Tuples as records Tuples can serve as temporary records with no field names, this can be done only if the is and the is respected as it is number of items fixed order of items important. coordinates = [(33.9425, -118.408056), (31.9425, -178.408056)]
for lat, _ in coordinates:
    print(f 'cordinate latitude: {lat}')

# this will print the latitude each time ignoring the longitude value There are two ways of creating tuples with named fields but in this instance, they will be regarded as data classes and it is not what we want to discuss here. Tuples as immutable lists Tuples are also highly used in Python standard library for their immutability which brings and to the code. clarity performance optimization a = (10, 'alpha', [1, 2])
b = (10, 'alpha', [1, 2])
print(a == b) 
# True
b[-1].append(99)
print(a == b) 
# False
print(b)
# (10, 'alpha', [1, 2, 99]) There is one caveat to mention, as portrayed above in the code example, only contained in the tuple are the references immutable, the objects held in the references can change their values. Changing the value of an item in a tuple can lead to as tuples are it is better to use a different data structure for your specific use case. serious bugs hashable, How to Unpack and Pattern Match sequences? Unpacking Sequences and Iterables Unpacking is an that consists of an of values to a list of variables in a single assignment statement, it avoids unnecessary and error-prone use of indexes to extract elements from sequences. operation assigning iterable coordinates = (33.9425, -118.408056)
latitude, longitude = coordinates # unpacking
print(latitude) 
# 33.9425
print(longitude) 
# -118.408056 We can use the excess operator * for tuples and the excess operator ** for dictionaries when unpacking too. The target of unpacking can use nesting, i.e. something like (a, b, (c, d)). Python will do the right thing if the value has the same nesting structure. # Tuple Example
fruits = ("apple", "mango", "papaya", "pineapple", "cherry")
(green, *tropic, red) = fruits
print(green)
# apple
print(tropic)
# ['mango', 'papaya', 'pineapple']
print(red)
# cherry

# Dictionary Example
def myFish(**fish):
    for name, value in fish.items():
        print(f'I have {value} {name}')

fish = {
    'guppies': 2,
    'zebras' : 5,
    'bettas': 10
}
myFish(**fish)

# I have 2 guppies
# I have 5 zebras
# I have 10 bettas

# Nested Unpacking Example
metro_areas = [
('Tokyo', 'JP', 36.933, (35.689722, 139.691667)),
('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),
('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),
('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),
('São Paulo', 'BR', 19.649, (-23.547778, -46.635833)),
]

print(f'{"":15} | {"latitude":>9} | {"longitude":>9}')
for name, _, _, (lat, lon) in metro_areas:
    if lon <= 0:
        print(f'{name:15} | {lat:9.4f} | {lon:9.4f}')

#                     | latitude | longitude
# Mexico City         | 19.4333 | -99.1333
# New York-Newark     | 40.8086 | -74.0204
# São Paulo           | -23.5478 | -46.6358 Sequence Pattern Matching Pattern matching is done with the . It is very similar to the if-elif-else statement only cleaner more readable and wields the power of match/case statement Destructuring. is a more advanced form of , as it allows writing sequence patterns as tuples or lists or any combination of both. Destructuring unpacking Here’s a nice example I found on the that could simplify pattern matching with sequences. GUICommits website baskets = [
    ["apple", "pear", "banana"], 
    ["chocolate", "strawberry"], 
    ["chocolate", "banana"], 
    ["chocolate", "pineapple"], 
    ["apple", "pear", "banana", "chocolate"],
]
match basket:

    # Matches any 3 items
    case [i1, i2, i3]: 
        print(f"Wow, your basket is full with: '{i1}', '{i2}' and '{i3}'")
    # Matches >= 4 items
    case [_, _, _, *_] as basket_items:
        print(f"Wow, your basket has so many items: {len(basket_items)}")

    # 2 items. First should be chocolate, second should be strawberry or banana
    case ["chocolate", "strawberry" | "banana"]:
        print("This is a superb combination")

    # Any amount of items starting with chocolate
    case ["chocolate", *_]:
        print("I don't know what you plan but it looks delicious")

    # If nothing matched before
    case _:
        print("Don't be cheap, buy something else") One thing to point out, it’s that the matches any number of items, without binding them to a variable. Using extra instead of would bind the items to extra as a list with 0 or more items. *_ * *_ Time to Slice them Up A common feature of list, tuple, str, and all sequence types in Python is the support of slicing operations. We slice a list like this: , step is the number of items to skip. To evaluate the expression seq[start:stop:step], Python calls seq[start, stop, step] The Special Method . seq.getitem(slice(start, stop, step)) # On a list
l = [10, 20, 30, 40, 50, 60]
l[:2] # [10, 20]
l[2:] # [30, 40, 50, 60]
l[:3] # [10, 20, 30]

# On a str
s = 'bicycle'
s[::3] # 'bye'
s[::-1] # 'elcycib'
s[::-2] # 'eccb'

# Add, Mul, Del, assign operations on slices
l = list(range(10)) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
l[2:5] = [20, 30] # [0, 1, 20, 30, 5, 6, 7, 8, 9]
del l[5:7] # [0, 1, 20, 30, 5, 8, 9]

print(5 * 'abcd') # 'abcdabcdabcdabcdabcd' What's next? There are different uses for different types of sequences, and depending on your use case, there are better options. , M and are prime examples of that. Arrays emory views Double ended queues Memory Views The built-in class is a type that lets you handle slices of arrays . Using notation similar to the array module, the method lets you change the way multiple bytes are read or written as units without moving bits around. memoryview shared-memory sequence without copying bytes memoryview.cast The returns yet another object, always sharing the same memory. memoryview.cast memoryview octets = array('B', range(6)) # array of 6 bytes (typecode 'B')

m1 = memoryview(octets)
m1.tolist() # [0, 1, 2, 3, 4, 5]

m2 = m1.cast('B', [2, 3])
m2.tolist() # [[0, 1, 2], [3, 4, 5]]

m3 = m1.cast('B', [3, 2])
m3.tolist() # [[0, 1], [2, 3], [4, 5]]

m2[1,1] = 22
m3[1,1] = 33
print(octets) # array('B', [0, 1, 2, 33, 22, 5]) Also published here.

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