Variables and Expressions

• Duck-Typing : Programming Style which avoids checking an object's type to figure out what it can do.
• Duck Typing is the fundamental approach of Python.
• Assignment of a value automatically declares a variable.

a = 7
b = 'Marbles'
print(a)         # => 7
print(b)         # => Marbles

• You can chain variable assignments to give multiple var names the same value.

Use with caution as this is highly unreadable

count = max = min = 0
print(count)           # => 0
print(max)             # => 0
print(min)             # => 0

The value and type of a variable can be re-assigned at any time.

a = 17
print(a)         # => 17
a = 'seventeen'
print(a)         # => seventeen

• NaN _does not exist in Python, but you can 'create' it like so:
  _print(float("nan"))
• Python replaces null with none.
• none is an object and can be directly assigned to a variable.

Using none is a convenient way to check to see why an action may not be operating correctly in your program.

Boolean Data Type

• One of the biggest benefits of Python is that it reads more like English than JS does.

# Logical OR
print(True or True)     # => True
print(True or False)    # => True
print(False or False)   # => False

# Logical NOT
print(not True)             # => False
print(not False and True)   # => True
print(not True or False)    # => False

• By default, Python considers an object to be true UNLESS it is one of the following:
• Constant None or False
• Zero of any numeric type.
• Empty Sequence or Collection.
• True and False must be capitalized

Comparison Operators

• Python uses all the same equality operators as JS.
• In Python, equality operators are processed from left to right.
• Logical operators are processed in this order:
• NOT
• AND
• OR

Just like in JS, you can use parentheses to change the inherent order of operations.

Short Circuit : Stopping a program when a true or false has been reached.

Identity vs Equality

print (2 == '2')    # => False
print (2 is '2')    # => False

print ("2" == '2')    # => True
print ("2" is '2')    # => True

# There is a distinction between the number types.
print (2 == 2.0)    # => True
print (2 is 2.0)    # => False

• In the Python community it is better to use is and is not over == or !=

If Statements

if name == 'Monica':
    print('Hi, Monica.')

if name == 'Monica':
    print('Hi, Monica.')
else:
    print('Hello, stranger.')

if name == 'Monica':
    print('Hi, Monica.')
elif age < 12:
    print('You are not Monica, kiddo.')
elif age > 2000:
   print('Unlike you, Monica is not an undead, immortal vampire.')
elif age > 100:
   print('You are not Monica, grannie.')

Remember the order of elif statements matter.

While Statements

spam = 0
while spam < 5:
  print('Hello, world.')
  spam = spam + 1

• Break statement also exists in Python.

spam = 0
while True:
  print('Hello, world.')
  spam = spam + 1
  if spam >= 5:
    break

• As are continue statements

spam = 0
while True:
  print('Hello, world.')
  spam = spam + 1
  if spam < 5:
    continue
  break

Try/Except Statements

• Python equivalent to try/catch

a = 321
try:
    print(len(a))
except:
    print('Silently handle error here')

    # Optionally include a correction to the issue
    a = str(a)
    print(len(a)

a = '321'
try:
    print(len(a))
except:
    print('Silently handle error here')

    # Optionally include a correction to the issue
    a = str(a)
    print(len(a))

• You can name an error to give the output more specificity.

a = 100
b = 0
try:
    c = a / b
except ZeroDivisionError:
    c = None
print(c)

• You can also use the pass commmand to by pass a certain error.

a = 100
b = 0
try:
    print(a / b)
except ZeroDivisionError:
    pass

• The pass method won't allow you to bypass every single error so you can chain an exception series like so:

a = 100
# b = "5"
try:
    print(a / b)
except ZeroDivisionError:
    pass
except (TypeError, NameError):
    print("ERROR!")

• You can use an else statement to end a chain of except statements.

# tuple of file names
files = ('one.txt', 'two.txt', 'three.txt')

# simple loop
for filename in files:
    try:
        # open the file in read mode
        f = open(filename, 'r')
    except OSError:
        # handle the case where file does not exist or permission is denied
        print('cannot open file', filename)
    else:
        # do stuff with the file object (f)
        print(filename, 'opened successfully')
        print('found', len(f.readlines()), 'lines')
        f.close()

• finally is used at the end to clean up all actions under any circumstance.

def divide(x, y):
    try:
        result = x / y
    except ZeroDivisionError:
        print("Cannot divide by zero")
    else:
        print("Result is", result)
    finally:
        print("Finally...")

• Using duck typing to check to see if some value is able to use a certain method.

# Try a number - nothing will print out
a = 321
if hasattr(a, '__len__'):
    print(len(a))

# Try a string - the length will print out (4 in this case)
b = "5555"
if hasattr(b, '__len__'):
    print(len(b))

Pass

• Pass Keyword is required to write the JS equivalent of :

if (true) {
}

while (true) {}

if True:
  pass

while True:
  pass

Functions

• Function definition includes:
• The def keyword
• The name of the function
• A list of parameters enclosed in parentheses.
• A colon at the end of the line.
• One tab indentation for the code to run.
• You can use default parameters just like in JS

def greeting(name, saying="Hello"):
    print(saying, name)

greeting("Monica")
# Hello Monica

greeting("Barry", "Hey")
# Hey Barry

Keep in mind, default parameters must always come after regular parameters.

# THIS IS BAD CODE AND WILL NOT RUN
def increment(delta=1, value):
    return delta + value

• You can specify arguments by name without destructuring in Python.

def greeting(name, saying="Hello"):
    print(saying, name)

# name has no default value, so just provide the value
# saying has a default value, so use a keyword argument
greeting("Monica", saying="Hi")

• The lambda keyword is used to create anonymous functions and are supposed to be one-liners.

toUpper = lambda s: s.upper()

Notes

Formatted Strings

Remember that in Python join() is called on a string with an array/list passed in as the argument.
Python has a very powerful formatting engine.
format() is also applied directly to strings.

shopping_list = ['bread','milk','eggs']
print(','.join(shopping_list))

Comma Thousands Separator

print('{:,}'.format(1234567890))
'1,234,567,890'

Date and Time

d = datetime.datetime(2020, 7, 4, 12, 15, 58)
print('{:%Y-%m-%d %H:%M:%S}'.format(d))
'2020-07-04 12:15:58'

Percentage

points = 190
total = 220
print('Correct answers: {:.2%}'.format(points/total))
Correct answers: 86.36%

Data Tables

width=8
print(' decimal hex binary')
print('-'*27)
for num in range(1,16):
for base in 'dXb':
print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
print()
Getting Input from the Command Line
Python runs synchronously, all programs and processes will stop when listening for a user input.
The input function shows a prompt to a user and waits for them to type 'ENTER'.
Scripts vs Programs
Programming Script : A set of code that runs in a linear fashion.
The largest difference between scripts and programs is the level of complexity and purpose. Programs typically have many UI's.

Python can be used to display html, css, and JS.
It is common to use Python as an API (Application Programming Interface)

Structured Data

Sequence : The most basic data structure in Python where the index determines the order.

List
Tuple
Range
Collections : Unordered data structures, hashable values.

Dictionaries Sets

Iterable : Generic name for a sequence or collection; any object that can be iterated through.

Can be mutable or immutable. Built In Data Types

Lists are the python equivalent of arrays.

empty_list = []
departments = ['HR','Development','Sales','Finance','IT','Customer Support']

You can instantiate

specials = list()

Test if a value is in a list.

print(1 in [1, 2, 3]) #> True
print(4 in [1, 2, 3]) #> False
# Tuples : Very similar to lists, but they are immutable

Instantiated with parentheses

time_blocks = ('AM','PM')

Sometimes instantiated without

colors = 'red','blue','green'
numbers = 1, 2, 3

Tuple() built in can be used to convert other data into a tuple

tuple('abc') # returns ('a', 'b', 'c')
tuple([1,2,3]) # returns (1, 2, 3)
# Think of tuples as constant variables.

Ranges : A list of numbers which can't be changed; often used with for loops.

Declared using one to three parameters.

Start : opt. default 0, first # in sequence.
Stop : required next number past the last number in the sequence.
Step : opt. default 1, difference between each number in the sequence.

range(5) # [0, 1, 2, 3, 4]
range(1,5) # [1, 2, 3, 4]
range(0, 25, 5) # [0, 5, 10, 15, 20]
range(0) # [ ]
for let (i = 0; i < 5; i++)
for let (i = 1; i < 5; i++)
for let (i = 0; i < 25; i+=5)
for let(i = 0; i = 0; i++)
# Keep in mind that stop is not included in the range.

Dictionaries : Mappable collection where a hashable value is used as a key to ref. an object stored in the dictionary.

Mutable.

a = {'one':1, 'two':2, 'three':3}
b = dict(one=1, two=2, three=3)
c = dict([('two', 2), ('one', 1), ('three', 3)])
# a, b, and c are all equal

Declared with curly braces of the built in dict()

Benefit of dictionaries in Python is that it doesn't matter how it is defined, if the keys and values are the same the dictionaries are considered equal.

Use the in operator to see if a key exists in a dictionary.

Sets : Unordered collection of distinct objects; objects that need to be hashable.

Always be unique, duplicate items are auto dropped from the set.

Common Uses:

Removing Duplicates
Membership Testing
Mathematical Operators: Intersection, Union, Difference, Symmetric Difference.

Standard Set is mutable, Python has a immutable version called frozenset.
Sets created by putting comma seperated values inside braces:

school_bag = {'book','paper','pencil','pencil','book','book','book','eraser'}
print(school_bag)

Also can use set constructor to automatically put it into a set.

letters = set('abracadabra')
print(letters)
#Built-In Functions
#Functions using iterables

filter(function, iterable) : creates new iterable of the same type which includes each item for which the function returns true.

map(function, iterable) : creates new iterable of the same type which includes the result of calling the function on every item of the iterable.

sorted(iterable, key=None, reverse=False) : creates a new sorted list from the items in the iterable.

Output is always a list

key: opt function which coverts and item to a value to be compared.

reverse: optional boolean.

enumerate(iterable, start=0) : starts with a sequence and converts it to a series of tuples

quarters = ['First', 'Second', 'Third', 'Fourth']
print(enumerate(quarters))
print(enumerate(quarters, start=1))

(0, 'First'), (1, 'Second'), (2, 'Third'), (3, 'Fourth')

(1, 'First'), (2, 'Second'), (3, 'Third'), (4, 'Fourth')

zip(*iterables) : creates a zip object filled with tuples that combine 1 to 1 the items in each provided iterable.
Functions that analyze iterable

len(iterable) : returns the count of the number of items.

max(*args, key=None) : returns the largest of two or more arguments.

max(iterable, key=None) : returns the largest item in the iterable.

key optional function which converts an item to a value to be compared.
min works the same way as max

sum(iterable) : used with a list of numbers to generate the total.

There is a faster way to concatenate an array of strings into one string, so do not use sum for that.

any(iterable) : returns True if any items in the iterable are true.

all(iterable) : returns True is all items in the iterable are true.

Working with dictionaries

dir(dictionary) : returns the list of keys in the dictionary.
Working with sets

Union : The pipe | operator or union(*sets) function can be used to produce a new set which is a combination of all elements in the provided set.

a = {1, 2, 3}
b = {2, 4, 6}
print(a | b) # => {1, 2, 3, 4, 6}

Intersection : The & operator ca be used to produce a new set of only the elements that appear in all sets.

a = {1, 2, 3}
b = {2, 4, 6}
print(a & b) # => {2}
Difference : The — operator can be used to produce a new set of only the elements that appear in the first set and NOT the others.

Symmetric Difference : The ^ operator can be used to produce a new set of only the elements that appear in exactly one set and not in both.

a = {1, 2, 3}
b = {2, 4, 6}
print(a — b) # => {1, 3}
print(b — a) # => {4, 6}
print(a ^ b) # => {1, 3, 4, 6}

For Statements In python, there is only one for loop.

Always Includes:

1. The for keyword
2. A variable name
3. The 'in' keyword
4. An iterable of some kid
5. A colon
6. On the next line, an indented block of code called the for clause.

You can use break and continue statements inside for loops as well.

You can use the range function as the iterable for the for loop.

print('My name is')
for i in range(5):
print('Carlita Cinco (' + str(i) + ')')

total = 0
for num in range(101):
total += num
print(total)
Looping over a list in Python
for c in ['a', 'b', 'c']:
print(c)

lst = [0, 1, 2, 3]
for i in lst:
print(i)

Common technique is to use the len() on a pre-defined list with a for loop to iterate over the indices of the list.

supplies = ['pens', 'staplers', 'flame-throwers', 'binders']
for i in range(len(supplies)):
print('Index ' + str(i) + ' in supplies is: ' + supplies[i])

You can loop and destructure at the same time.

l = 1, 2], [3, 4], [5, 6
for a, b in l:
print(a, ', ', b)

Prints 1, 2

Prints 3, 4

Prints 5, 6

You can use values() and keys() to loop over dictionaries.

spam = {'color': 'red', 'age': 42}
for v in spam.values():
print(v)

Prints red

Prints 42

for k in spam.keys():
print(k)

Prints color

Prints age

For loops can also iterate over both keys and values.

Getting tuples

for i in spam.items():
print(i)

Prints ('color', 'red')

Prints ('age', 42)

Destructuring to values

for k, v in spam.items():
print('Key: ' + k + ' Value: ' + str(v))

Prints Key: age Value: 42

Prints Key: color Value: red

Looping over string

for c in “abcdefg”:
print(c)

When you order arguments within a function or function call, the args need to occur in a particular order:

formal positional args.

*args

keyword args with default values

**kwargs

def example(arg_1, arg_2, *args, **kwargs):
pass

def example2(arg_1, arg_2, *args, kw_1=”shark”, kw_2=”blowfish”, **kwargs):
pass

Importing in Python

Modules are similar to packages in Node.js
Come in different types:

Built-In,

Third-Party,

Custom.

All loaded using import statements.

Terms

module : Python code in a separate file.
package : Path to a directory that contains modules.
init.py : Default file for a package.
submodule : Another file in a module's folder.
function : Function in a module.

A module can be any file but it is usually created by placing a special file init.py into a folder. pic

Try to avoid importing with wildcards in Python.

Use multiple lines for clarity when importing.

from urllib.request import (
HTTPDefaultErrorHandler as ErrorHandler,
HTTPRedirectHandler as RedirectHandler,
Request,
pathname2url,
url2pathname,
urlopen,
)

Watching Out for Python 2

Python 3 removed <> and only uses !=

format() was introduced with P3

All strings in P3 are unicode and encoded.
md5 was removed.

ConfigParser was renamed to configparser
sets were killed in favor of set() class.

print was a statement in P2, but is a function in P3.

Topics revisited (in python syntax)

Cheat Sheet:

If you found this guide helpful feel free to checkout my github/gists where I host similar content:

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bgoonz — Overview
Web Developer, Electrical Engineer JavaScript | CSS | Bootstrap | Python | React | Node.js | Express | Sequelize…github.com

Or Checkout my personal Resource Site:

a/A-Student-Resources
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Python Cheat Sheet:

If you found this guide helpful feel free to checkout my GitHub/gists where I host similar content:

bgoonz's gists
Instantly share code, notes, and snippets. Web Developer, Electrical Engineer JavaScript | CSS | Bootstrap | Python |…gist.github.com

bgoonz — Overview
Web Developer, Electrical Engineer JavaScript | CSS | Bootstrap | Python | React | Node.js | Express | Sequelize…github.com