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# How to merge two dictionaries
# in Python 3.5+

>>> x = {'a': 1, 'b': 2}
>>> y = {'b': 3, 'c': 4}

>>> z = {**x, **y}

>>> z
{'c': 4, 'a': 1, 'b': 3}

# In Python 2.x you could
# use this:
>>> z = dict(x, **y)
>>> z
{'a': 1, 'c': 4, 'b': 3}

# In these examples, Python merges dictionary keys
# in the order listed in the expression, overwriting 
# duplicates from left to right.
#
# See: https://www.youtube.com/watch?v=Duexw08KaC8

# Different ways to test multiple
# flags at once in Python
x, y, z = 0, 1, 0

if x == 1 or y == 1 or z == 1:
    print('passed')

if 1 in (x, y, z):
    print('passed')

# These only test for truthiness:
if x or y or z:
    print('passed')

if any((x, y, z)):
    print('passed')

# How to sort a Python dict by value
# (== get a representation sorted by value)

>>> xs = {'a': 4, 'b': 3, 'c': 2, 'd': 1}

>>> sorted(xs.items(), key=lambda x: x[1])
[('d', 1), ('c', 2), ('b', 3), ('a', 4)]

# Or:

>>> import operator
>>> sorted(xs.items(), key=operator.itemgetter(1))
[('d', 1), ('c', 2), ('b', 3), ('a', 4)]

# The "timeit" module lets you measure the execution
# time of small bits of Python code

>>> import timeit
>>> timeit.timeit('"-".join(str(n) for n in range(100))',
                  number=10000)

0.3412662749997253

>>> timeit.timeit('"-".join([str(n) for n in range(100)])',
                  number=10000)

0.2996307989997149

>>> timeit.timeit('"-".join(map(str, range(100)))',
                  number=10000)

0.24581470699922647

# Why Python Is Great:
# In-place value swapping

# Let's say we want to swap
# the values of a and b...
a = 23
b = 42

# The "classic" way to do it
# with a temporary variable:
tmp = a
a = b
b = tmp

# Python also lets us
# use this short-hand:
a, b = b, a

# "is" vs "=="

>>> a = [1, 2, 3]
>>> b = a

>>> a is b
True
>>> a == b
True

>>> c = list(a)

>>> a == c
True
>>> a is c
False

# • "is" expressions evaluate to True if two 
#   variables point to the same object

# • "==" evaluates to True if the objects 
#   referred to by the variables are equal

# Because Python has first-class functions they can
# be used to emulate switch/case statements

def dispatch_if(operator, x, y):
    if operator == 'add':
        return x + y
    elif operator == 'sub':
        return x - y
    elif operator == 'mul':
        return x * y
    elif operator == 'div':
        return x / y
    else:
        return None


def dispatch_dict(operator, x, y):
    return {
        'add': lambda: x + y,
        'sub': lambda: x - y,
        'mul': lambda: x * y,
        'div': lambda: x / y,
    }.get(operator, lambda: None)()


>>> dispatch_if('mul', 2, 8)
16

>>> dispatch_dict('mul', 2, 8)
16

>>> dispatch_if('unknown', 2, 8)
None

>>> dispatch_dict('unknown', 2, 8)
None

# The get() method on dicts
# and its "default" argument

name_for_userid = {
    382: "Alice",
    590: "Bob",
    951: "Dilbert",
}

def greeting(userid):
    return "Hi %s!" % name_for_userid.get(userid, "there")

>>> greeting(382)
"Hi Alice!"

>>> greeting(333333)
"Hi there!"

# Why Python is Great: Namedtuples
# Using namedtuple is way shorter than
# defining a class manually:
>>> from collections import namedtuple
>>> Car = namedtup1e('Car', 'color mileage')

# Our new "Car" class works as expected:
>>> my_car = Car('red', 3812.4)
>>> my_car.color
'red'
>>> my_car.mileage
3812.4

# We get a nice string repr for free:
>>> my_car
Car(color='red' , mileage=3812.4)

# Like tuples, namedtuples are immutable:
>>> my_car.color = 'blue'
AttributeError: "can't set attribute"

# The standard string repr for dicts is hard to read:
>>> my_mapping = {'a': 23, 'b': 42, 'c': 0xc0ffee}
>>> my_mapping
{'b': 42, 'c': 12648430. 'a': 23}  # 😞

# The "json" module can do a much better job:
>>> import json
>>> print(json.dumps(my_mapping, indent=4, sort_keys=True))
{
    "a": 23,
    "b": 42,
    "c": 12648430
}

# Note this only works with dicts containing
# primitive types (check out the "pprint" module):
>>> json.dumps({all: 'yup'})
TypeError: keys must be a string

# Why Python Is Great:
# Function argument unpacking

def myfunc(x, y, z):
    print(x, y, z)

tuple_vec = (1, 0, 1)
dict_vec = {'x': 1, 'y': 0, 'z': 1}

>>> myfunc(*tuple_vec)
1, 0, 1

>>> myfunc(**dict_vec)
1, 0, 1

# Functions are first-class citizens in Python:

# They can be passed as arguments to other functions,
# returned as values from other functions, and
# assigned to variables and stored in data structures.

>>> def myfunc(a, b):
...     return a + b
...
>>> funcs = [myfunc]
>>> funcs[0]
<function myfunc at 0x107012230>
>>> funcs[0](2, 3)
5

# Python has a HTTP server built into the
# standard library. This is super handy for
# previewing websites.

# Python 3.x
$ python3 -m http.server

# Python 2.x
$ python -m SimpleHTTPServer 8000

# (This will serve the current directory at
#  http://localhost:8000)

# Python's list comprehensions are awesome.

vals = [expression 
        for value in collection 
        if condition]

# This is equivalent to:

vals = []
for value in collection:
    if condition:
        vals.append(expression)

# Example:

>>> even_squares = [x * x for x in range(10) if not x % 2]
>>> even_squares
[0, 4, 16, 36, 64]

# Python 3.5+ supports 'type annotations' that can be
# used with tools like Mypy to write statically typed Python:

def my_add(a: int, b: int) -> int:
    return a + b

# Python's list slice syntax can be used without indices
# for a few fun and useful things:

# You can clear all elements from a list:
>>> lst = [1, 2, 3, 4, 5]
>>> del lst[:]
>>> lst
[]

# You can replace all elements of a list
# without creating a new list object:
>>> a = lst
>>> lst[:] = [7, 8, 9]
>>> lst
[7, 8, 9]
>>> a
[7, 8, 9]
>>> a is lst
True

# You can also create a (shallow) copy of a list:
>>> b = lst[:]
>>> b
[7, 8, 9]
>>> b is lst
False