adv-python

Learning for advanced python concepts.

Lambda Function

An anonymous function or function without name, defined and called in the same line of code. Called with keyword lambda followed by parameter, semi-colon, function method, and output. Example:

>>> (lambda x: x**2)(5)
>>> 25

• It can only contain expressions, no statements in the body. This means no return, assert, etc.
• It is known as an IIFE (Immediately Invoked Function Expression) - pronounced 'iffy'.

---

List Filter with Lambda

A simple use of the lambda function is to filter a list. Python has a built-in function that takes a function in as a parameter: filter(function, iterable). Let's use this to get a list of integers divisible by 3 from the integers 0-100.\

nums = list(range(0,101))
print(nums)
# [0, 1, 2, ..., 99, 100]

triples = list(filter(lambda x:x%3 == 0, nums))
print(triples)
# [0, 3, 6, ..., 96, 99]

Slightly more complex - filter out integers from 0-100 which are divisible by 2 or 3.

filtered_nums = list(filter(lambda x:not(x%2 == 0 or x%3 == 0), nums))
print(filtered_nums)
# [1, 5, 7, ..., 95, 97]

---

Lambda with map()

Python's map() function is used to operate the passed lambda function on each item in an iterable list. For example, for adding the square and cube of each number in a list the lambda function will be:

lambda x:x**2 + x**3

Using this together with map:

nums = list(range(0,101))
mapped_list = list(map(lambda x:x**2 + x**3, nums))
print(mapped_list)
# [0, 2, 12, ..., 980100, 1010000]

---

As you can see, lambdas are very useful in cleaning up code and using fewer lines. Take care that you don't overcomplicate a single line though, readability is important.

Adapted from Suraj Gurav's Lambda Functions in Python: All You Need To Know, https://towardsdatascience.com/lambda-functions-with-3-practical-examples-in-python-f4ed7f266e53.

---

Hashing

We say an object is hashable if it has a hash value which never changes during its lifetime. All of Python's immutable built-in objects are hashable (e.g. ints, floats, strings) while mutable containers (e.g. lists, dictionaries) are not. Note that tuples are immutable containers and thus hashable.

Hash Tables

• A data structure where the address or index of the data element is generated from a hash function.
• This makes accessing the data faster as the hash-generated index acts as a key for the data value.
• So instead of accessing an element by going index by index through an array, elements are accessed directly by this hash-generated key.
• In Python, a simple hash table is implemented using a dictionary.
• However, you can make your own using a Linked List and a custom hashing function.
• Collisions need to be accounted for. Two different strings might hash to the same value, or two different strings might have different hashcodes but the same index.
• The time complexity for insert, find & delete is generally O(1), but can be O(n) for a terrible hash table.\

hash_table = dict()
hash_table["Damian Lillard"] = Player(team = "POR", age = 32, height = "6ft2")

Sets

• Sort of list data structure
• Forbids duplicates
• Requires items to be hashable
• Checking for membership of a value in a set is very fast, with the same speed as checking the keys in a dict (i.e. hash table speeds!). Meanwhile a list takes time proportional to the list's length (O(n) worst case).
• So, if you have hashable items and don't care about order or duplicates, use set rather than list or dict.

---

Useful Methods

Max Size

import sys

max_sz = sys.maxsize
list = range(max_sz)
print(len(list))
# 9223372036854775807