Python Language Megadoc

• Python is not compiled (run directly)
• Python is statically typed (the types of the variables cannot change)
• Python is not strongly typed (do not declare types)
• Python does not use curly braces (use tabs and indentations)
• Boolean values are now True and False
• Logic operator are now and or
• Run pip install on commandline to install packages
  • In Google Colab, you can run command line arguments in the Python notebook by prefacing the commands with ! -- e.g. !pip install
• Use \ at the end of a line to indicate continue on the next line

Importing

• Importing library modules
  • import math
  • from math import sqrt
  • import numpy as np
• Importing file as a module
  • import homework1 // import homework1 file for the first time
  • import importlib; importlib.reload(homework1) // after the first time, use importlib

List

Iteration using enumerate()

Python enumerate(): Simplify Looping With Counters – Real PythonOnce you learn about for loops in Python, you know that using an index to access items in a sequence isn't very Pythonic. So what do you do when you need that index value? In this tutorial, you'll learn all about Python's built-in enumerate(), where it's used, and how you can emulate its behavior.https://realpython.com/python-enumerate/#using-pythons-enumerate

List comprehensions

• Replace loop when doing very simple operations on each element
  • only one line of code
• Creates a new list with manipulated list values
• Syntax: 

  newlist = [expression for item in iterable if condition == True]

• e.g. squares = [ x*x for x in nums if x>10] // square each element in list nums

Transform list to dictionary or sets

• List of tuples to dictionary

                

          

•  List to set

        

        

Move a list item to position

l.insert(newindex, l.pop(oldindex))

Dictionary

Dictionary Comprehension

example:

• groceries = {"milk": 2, "egg": 2}
• more_groceries = { k:v*2 for k,v in groceries.items if ....}

https://www.datacamp.com/tutorial/python-dictionary-comprehension

Priority Queue

• Defulat order: non-decreasing

import queue as Q
 
// declare a priority queue
q = Q.PriorityQueue()
 
// enqueue
q.put(10)
q.put(1)
q.put(5)
 
while not q.empty():
    print(q.get()) // dequeue

Strings

• Use single quotes ′ double quotes ”

• """ triple quotes to comment blocks of code

Access length

• len(s)

Access charAt(index)

• s[index]
• s[-1] -- counts from the end, so last char of the string

Access substring

• s[1:4] -- from 1 to 3, excluding the end
• s[1:4:2] -- from 1 to 3, but stride 2, i.e. skipping every other character
• s[2:] -- from 2 to end
• s[2:10000] -- from 2 to end
• s[:2] -- from start to 1, excluding the end
• s[:] / s[::] -- copy the entire string
• s[::-1] -- copy and reverse the string

Splitting a string

• s.split(' ') -- split by space (only one space)

Remove spaces

• s.strip() -- removes all spaces (front and back) from a string

Compare strings

• s == 'abc' -- compare values

Converting to string

• str(123) -- '123'

For loop

for ... in ... :

        print();

Specifying range

• range(5) -- 0 to 4, excluding the end
• range(0,5) -- -- 0 to 4, excluding the end

Iterate through elements

• for x in arr                   // java like for each loop
• for i in range(len(list)) // java like for loop

Iterate through dictionary

• for key, vallue in dict.items()

Iterate through pairs of things

• for (x, y) in [(a,b) , (c,d)]

Iterator

• A type of objects that have the __iter__() magic method
• Iterator objects can be iterated in for loops
• Throws StopIteration exception when ending

e.g. list, dict, etc.

Create custom iterator class

         

Usage with handling end of iteration

l = list(range(5))
 
it = l.__iter__()
 
try: 
    print(it.__next__())
except:
    print("nothing left") # prompting list end
 

** same thing happens in a python for each loop

                

Generators

A special type of function that return a lazy iterator. These are objects that you can loop over like a list.

• basically an iterator, but with less code
• stop and continue execution for each 'yield'
• stores the execution state for future consumption
• returns a generator object
  • any function with a yield in the body

How to Use Generators and yield in Python – Real PythonIn this step-by-step tutorial, you'll learn about generators and yielding in Python. You'll create generator functions and generator expressions using multiple Python yield statements. You'll also learn how to build data pipelines that take advantage of these Pythonic tools.https://realpython.com/introduction-to-python-generators/

Benefits

• Less code than basic iterator
• Memory efficient -- reduces memory usage (it doesn't store a list in memory)
• Good for one-pass summing/counting
• Good for infinite seqence
• Bad for individual values (debugging)

Define a generator function

• e.g. an infinite sequence

def f():
    num = 0
    while True:
        yield num
        num += 1

Using a generator function

• nums = f() // num is a generator object
• Get next item
  • next(nums) OR
  • nums.__next__() // using the under-the-hood iterator method
• for i in f(): // keep yielding all the items

Handling the end of an iteration