2.4 Sequences

zip() - pairs elements from multiple iterables into tuples. Note: stops once the shortest input sequence is exhausted. Some main uses:

• loop over multiple lists using for name, age in zip(names, ages)
• dictionary creation

The dictionary creation is a useful trick when processing data from different formats to turn them into dictionaries. The general form is:

headers = ["name", "age", "city"]
row = ["Alice", 25, "New York"]
record = dict(zip(headers, row))
print(record)  # {'name': 'Alice', 'age': 25, 'city': 'New York'}

This is useful for a number of reasons:

• Allows us to work with dictionaries with offer fast key-based lookup instead of index.
• It avoids manual dictionary creation. Which allows us to work with data with different column structures.
• Can be used with CSVs, databases, APIs

2.5 collections module

from the author, “collections module is one of the most useful library modules in all of Python”. useful when there is a need to tabulate values. We briefly touched on the following objects for data handling: Counter , defaultdict and deque

Counter - useful for counting occurrences of elements in an iterable. Some examples:

• Word frequency in a text Counter(text.split())
• most common elements counter.most_common(3) lists the top 3, leaving blank lists all by occurence
• counting characters Counter("mississippi")

defaultdict provides default values for missing keys preventing KeyError

• You can provide a default value for missing keys defaultdict(lambda: "Not Found")
• Combine non-unique keys

deque - short for double-ended queue optimized for fast appends and pops from both ends, making it more efficient than a list for queue-like operations.

• Queue (first in, first out) queue.popleft()
• Stacks (last in, first out) stack.pop()
• Fixed-size buffers to automatically discards older elements when full. deque(maxlen=3)
• Effecient way to reverse a sequence e.g. deque.reverse()

2.6 List, Set and Dictionary Comprehensions

come from set-builder notation in math.

a = [ x * x for x in s if x > 0 ] # Python

is equivalent to

Some comon usecases:

• collect values of a specific dictionary fields:

stocknames = [s['name'] for s in stocks]

• perform database-like queries on sequences.

a = [s for s in stocks if s['price'] > 100 and s['shares'] > 50 ]

• combine a list comprehension with a sequence reduction:

cost = sum([s['shares']*s['price'] for s in stocks])

2.7 Objects

Finally clicked what it means that everything in Python is an object. The author goes through different examples that were really helpful. To revisit often in future.

Onto Program Organization tomorrow. Feeling great about my progress so far and exited learn more!