Basics

Question 1

Python

Answer 1

• popular OOP language
• used for :
  
  • web development
  • scripting (sorta)
  • software development
  • mathematical operations (such as data science)
• it can be used on lots of platforms (windows, linux, mac, raspberry, pi, etc)
• its syntax is similar to english
• uses new line to execute codes
• uses indentiation
• interpreted language (no compiler needed)

Question 2

Comments

Answer 2

• used to explain code
• use to prevent line of code from executing
• # - to comment single line of code
• ””” used to comment

bolck of codes””” - multiline string

Question 3

Variables

Answer 3

• placeholders/containers for storing data values
• no variable decleration in python
• variable is created when a value is assigned to it
• can only be alpha-numeric
• cannot start with number
• is case-sensitive
• cnoot use space or ‘-‘
• GLOBAL variables
  
  • create outside of functions and can be access anywhere in the file
  • to create a global variable inside a function, use keyword global
  • example:
    
    • def myfunc():
      global x
      x = “fantastic”
• LOCAL variables
  
  • created inside a function and can only be accessed inside that function

Question 4

Data Types

Answer 4

• Text Type: str
• Numeric Types: int, float, complex
• Sequence Types: list, tuple, range
• Mapping Type: dict
• Set Types: set, frozenset
• Boolean Type: bool
• Binary Types: bytes, bytearray, memoryview

Question 5

Numbers

Answer 5

• 3 types of number:
  
  • int
    
    • whole number without decimals, can be + or -
    • convert to interger using int(x)
  • float
    
    • ​decimal number, can be + or - or scientific (number with ‘e’ for power of 10)
    • convert to float using float(x)
  • complex
    
    • number with imaginary part written with a ‘j’
    • x = 3+5j
    • convert to complex using complex(x)
    • cannot convert from complex to other number type
• random numbers a randomely generated using functions from python random library such as random.randrange

Question 6

Casting

Answer 6

• converting one data type to another
• int() - converts variable to integer
• float() - converts variable to float
• str() - converts variable to string

Question 7

String

Answer 7

• assign string to a variable using either single quote or double quotes
• strings are arrays which mean they can be filtered via index
  
  • string[index_number]
• uses methods such as .lower(), .strip()
• can be concacted: string1 + string2
• use escape character to include insert illegal characters (")
• or block of string using triple single quotes or triple double quotes
• explame:
  
  • x = ‘string’
  • x = “string”
  • x = ‘'’block
    
    • of
    • string’’’
  • x = “"”block
    
    • of
    • string”””

Question 8

Boolean

Answer 8

• True or False values
• bool(x) to evaluate values
• most values are true except for 0 (or any empty collections such as list dict set etc)

Question 9

Operators

Answer 9

• performs operations on variables/values
• Arithmetic operators:
  
  • used on numbers to do mathematical operations:
    
    • • Addition x + y
    • • Subtraction x - y
    • * Multiplication x * y
    • / Division x / y
    • % Modulus x % y
    • ** Exponentiation x ** y
    • // Floor division x // y
• Assignment operators:
  
  • used to assign values to variables:
    
    • = x = 5 / x = 5
    • += x += 3 / x = x + 3
    • -= x -= 3 / x = x - 3
    • *= x *= 3 / x = x * 3
    • /= x /= 3 / x = x / 3
    • %= x %= 3 / x = x % 3
    • //= x //= 3 / x = x // 3
    • **= x **= 3 / x = x ** 3
    • &= x &= 3 / x = x & 3
    • |= x |= 3 / x = x | 3
    • ^= x ^= 3 / x = x ^ 3
    • >>= x >>= 3 / x = x >> 3
    • <<= x <<= 3 / x = x << 3
• Comparison operators:
  
  • used to compare 2 values/variables:
    
    • == Equal x == y
    • != Not equal x != y
    • > Greater than x > y
    • < Less than x < y
    • >= Greater than or equal to x >= y
    • <= Less than or equal to x <= y
• Logical operators:
  
  • used to combine conditional statements:
    
    • and Returns True if both statements are true - x < 5 and x < 10
    • or Returns True if one of the statements is true - x < 5 or x < 4
    • not Reverse the result, returns False if the result is true - not(x < 5 and x < 10)
• Identity operators:
  
  • used to compare object values (if they are the same or not):
    
    • is Returns True if both variables are the same object - x is y
    • is not Returns True if both variables are not the same object - x is not y
• Membership operators:
  
  • used to test if values are present or absent:
    
    • in Returns True if a sequence with the specified value is present in the object - x in y
    • not in Returns True if a sequence with the specified value is not present in the object - x not in y
• Bitwise operators:
  
  • used to compare binary numbers:
    
    • & AND - Sets each bit to 1 if both bits are 1
    • | OR - Sets each bit to 1 if one of two bits is 1
    • ^ XOR - Sets each bit to 1 if only one of two bits is 1
    • ~ NOT - Inverts all the bits
    • << Zero fill left shift - Shift left by pushing zeros in from the right and let the leftmost bits fall off
    • >> Signed right shift - Shift right by pushing copies of the leftmost bit in from the left, and let the rightmost bits fall off

Question 10

Lists

Answer 10

collection that is:

• mutable (can be changed/edited)
  
  • list_name.append() - to add item
  • list_name[position_number] - to edit
  • list_name.insert(indiex, object) - add object at specified position
  • list_name.remove(item_to_remove)
  • del list_name[index]
  • list_name.clear() - remove all items
  • list_name.pop() - move last or psecified index
• allows duplicate
• written with squarish brackets []
• indexed
• to make a copy of list, use .copy() metod or list will onl be referenced

Question 11

Tuples

Answer 11

collection that is:

• unmutable (cannot be changed/edited)
• written with round brackets ()
• allows duplicate
• indexed
• to create tuple with 1 item: (item, )
• tuple() - constructor to make a tuple
• tuple methods:
  
  • count()
  • index()

Question 12

Sets

Answer 12

collection that is:

• written with curly brackets
• unordered and unindexed
• no duplicates allowed
• cannot change/edit items but can add new items
  
  • .add() - add 1 item
  • .update() - add more than 1 items
• can remove items:
  
  • .remove() - will raise error is item doesn’t exist
  • .discard() - won’t error out if item doesn’t exist
  • .pop() - to remove last item
• combine 2 sets into a 3rd one using set3 = set1.union(set2) or .update()
  
  • this will remove duplicates

Question 13

Dictionaries

Answer 13

collection that is:

• key/value pair
• mutable
• indexed and unordered
• written with curly brackets
• access values by referring to key name or call .get(key_name) method
• loop through a dict:
  
  • for x in thisdict: - loop though keys in dict
  • for x in thisdict.values(): - loop through values
  • for x, y in thisdict.items(): - loop through both keys and values
• add/edit item to dict: dict_name[“key”] = “value”
• remove item: dict_name.pop(‘key’) or del dic_name[‘key’]
• duplicate dict using .copy() method

Question 14

If Else

Answer 14

If:

• if conditions are met then execute the following codes
• uses python operators (logical, comparison, etc)

ElIf

• if previous if or elif statement is not met, try this 1

Else

• if none of the previous conditions are met then execute this

example:

if a == b:
print(‘a equal b’)

elif a < b:

print(‘a less than b’)

else:

print(‘a greater than b’)

• conditional expression:
  
  • print(“A”) if a > b else print(“B”)

Question 15

While Loops

Answer 15

• execute code(s) until looping codition is no longer true
• use break to stop loop early
• use continue to stop/skip current iteration and move on to next one
• use else to execute code when while loop is done, treats the while loop like an if else statement
• use pass to replace code(s) in loop block
• example:
  while a < b:
  # code here
  else:
  # code here

Question 16

For Loops

Answer 16

• iterator that goes through a sequence such as a list, dict, set, tuples, string
• execute code(s)/statement(s) for each item in the sequence
• use break to stop loop early
• use continue to stop/skip current iteration and move on to next one
• use pass to replace code(s) in loop block
• use else to execute code when while loop is done, treats the while loop like an if else statement
• example:
  for a in sequence/range():
  # code here
  else:
  # code here

Question 17

Functions

Answer 17

• block of code that runs only when called
• can take data as parameters
• returns data as result
• use keyword def to create a function
• use function name follwed by parenthesis to call/execute a function
• pass arguments to function speperated by commas
• exact number of arguments/parameters must be used/pass to function
• if exact number of arguments is not knowm, use *argument_name to refer to a tuple of arguments (e.i. python documentation uses *args)
• use **argument_name to pass in a dict of key/value as arguments of unknown sze and items (e.i. python documentation uses **kwargs)
• default parameters are used when function is called without passing value for a parameter
• any datatype can be passed as parameter or result
• use pass for empty functions
• functions can be recursive - call on itself

Question 18

Lambda

Answer 18

• small anonymous function taking argument(s) but only have 1 expession
• syntax: lambda argument(s): expression
• use lambda when an anonymous function is required
• example:
  x = lambda a, b, c : a + b + c
• anonymous functions are called lambda functions and defined with the keyword lambda
• lambda allows you to create “inline” functions which is useful for functional programming.Works well with functions such as Map, Filter and Reduce
• example:
  new_list = list(filter(lambda x: (x%2 == 0) , my_list))
  new_list = list(map(lambda x: x * 2 , my_list))

Question 19

Arrays

Answer 19

• Python has no arrays, uses Lists instead
• see List
• to support arrays python uses the NumPy library
• arrays are used to store multiple values in a single variable

Question 20

Classes/Objects

Answer 20

• Everything in python is an object
• class is an object constructor (blueprint to create an object)
• create a class using keyword class (class myClass(): pass)
• once created , can use it to create a class object (c1 = myClass())
• __init__() - function that is called when a class is initialized. it assignes values to proerties and execute other operatioins as defined. It is called automatically when a class is being used to create new class object.
• self is used to access variables belonging to the class wen defining the class.
  
  • it refers to the current instance of the class
  • doesn’t have to be called self, can be called anything but has to be 1st parameter in any function
• delete a class object using del keyword

Question 21

Inheritance

Answer 21

• allows us to define a class that inherits all the methods and properties from another class.
• Parent class is the class being inherited from, also called base class.
• Child class is the class that inherits from another class, also called derived class.
  
  • Use the pass keyword when you do not want to add any other properties or methods to the class.
  • When you add the __init__() function, the child class will no longer inherit the parent’s __init__() function and overrides it
• use the super() function to automatically inherit the methods and properties from parent:
  class Student(Person):
  def __init__(self, fname, lname):
  super().__init__(fname, lname)
  self.graduationyear = 2019

 def welcome(self):
 print("Welcome", self.firstname, self.lastname, "to the class of", self.graduationyear)
* adding a method in the child class with the same name as a function in the parent class will override the parent's function

Question 22

Iterators

Answer 22

• object you can iterate upon (list, tuples, sets, dicts, etc)
• iter() method is used to iterate an object
• use next() function to get next item in the sequence
• can use for loop to iterate through a sequence
• use StopIteration to stop iterating

Question 23

Scope

Answer 23

• local scope - can only be used iinside the function it was created
• global scope - can be used anywhere
• use global keyword to create a global variable in a local environment/function

Question 24

Modules

Answer 24

• a module is a .py file containing set of functions
• add module using import (import module_name)
• use a function from a module: module_name.function_name
• modules can contain variables
• use as keyword to rename a module
• dir() - list all functions and variables names in a module
• use from module_name import module_function/variable - to import part of a module

Question 25

Dates

Answer 25

• import datatime
• datetime module has many functions and methods for date/time manipulation
• not a datatype
• use datatime class to create a datatime object (it takes year month and day parameters)
• .strftime() - to specify datetime format
• .year - to extract year part of the date

Question 26

JSON

Answer 26

• javascript JSON object
• import json
• written in form of a dictionary { key: value, …}
• json.dumps() - convert object/datatypes to json - y = json.dumps(x)
• parse json string using json.loads()

Question 27

RegEx

Answer 27

• Regular Expression
• import re
• use to check if string conatins specific search patterns
• build-in functions:
  
  • findall - Returns a list containing all matches
  • search - Returns a Match object if there is a match anywhere in the string
  • split - Returns a list where the string has been split at each match
  • sub - Replaces one or many matches with a string

Question 28

PIP

Answer 28

• package manager for Python packages
• allows you to install python libraries
• pip install package_name - to install new package
• pip uninstall package_name - to uninstall existing package
• pip freeze - list installed packages and their versions

Question 29

Try… Except

Answer 29

• try-except-finally block is used to make make sure your next block of codes doesn’t break the program
• try - test.try to execute your block of code
• except - catches the error is any
• finally - code block to be executed whether ir errors out or not
  
  • example:
    try:
    f = open(“demofile.txt”)
    f.write(“Lorum Ipsum”)
    except:
    print(“Something went wrong when writing to the file”)
    finally:
    f.close()
• raise keyword is used to raise exception
  
  • example:
    if x < 0:
    raise Exception(“Sorry, no numbers below zero”)

Question 30

User Input

Answer 30

• python allows user input
• python 3.6 uses input()
• python 2.7 uses raw_input()
• python stops executing until user gives input
• example:
  username = input(“Enter username:”)
  print(“Username is: “ + username)

Question 31

String Formatting

Answer 31

• .format() - format selected part of a string
• {} as placeholders in the string
• can add parameters inside the brackets for further modification to the value such as floating decimal point, index if multiple entries
• example:
  myorder = “I want {0} pieces of item number {1} for {2:.2f} dollars.”
  print(myorder.format(quantity, itemno, price))

Question 32

Files

Answer 32

• can create, read, update and delete files
• open() - to hadle files, takes 2 parameters: filename and mode:
  
  • “r” - Read - Default value. Opens a file for reading, error if the file does not exist
  • “a” - Append - Opens a file for appending, creates the file if it does not exist
    • f = open(“demofile2.txt”, “a”)
      f. write(“Now the file has more content!”)
      f. close()
    • a method will write new lines at the end of file
  • “w” - Write - Opens a file for writing, creates the file if it does not exist
    • f = open(“demofile3.txt”, “w”)
      f. write(“Woops! I have deleted the content!”)
      f. close()
    • w method will overwrite the entire file
  • “x” - Create - Creates the specified file, returns an error if the file exists
    • f = open(“myfile.txt”, “x”)
      f. close()
• additional modes:
  
  • “t” - Text - Default value. Text mode
  • “b” - Binary - Binary mode (e.g. images)
• Make sure the file exists, or else you will get an error.
• example:
  f = open(“demofile.txt”, “rt“)
• f.read()
  
  • returns the whole text, but you can also specify how many characters you want to return
  • f.readline() - returns 1 line
• use for to loop a file reading each line:
  f = open(“demofile.txt”, “r”)
  for x in f:
  print(x)
• f.close() - to close file when done, should always close files (sometimes changes made won’t show until file is close due to buffering, good for finally block)
• to delete a file, need to import os and use os.remove()
  
  • example:
    import os
    if os.path.exists(“demofile.txt”):
    os.remove(“demofile.txt”)
    else:
    print(“The file does not exist”)

Question 33

NumPy

Answer 33

• python library to work with arrays
• stands for Numerical Python
• arrays in numpy are called ndarray
• it is stored in 1 continuous place in memory (so it is faster than list)
• written in c/c++
• code base is in github
• import numpy as np
• np.array([1, 2, 3, 4, 5]) - defined new array
• dimensions:
  
  • 0-D - element in an array at lowest level
  • 1-D - list/array of elements
  • 2-D - array of array of elements
  • use array_name.ndim to check dimension of array or to create an array of higher dimension (np.array([1, 2, 3, 4], ndmin=5))
• can access elements in array via index
  
  • use comma seperated values for higher index:
    arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
    print(arr[0, 1, 2])
• can use index slicing array_name[start:end:step]
  
  • arr = np.array([1, 2, 3, 4, 5, 6, 7])
    print(arr[1:5:2])
  • arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
    print(arr[0:2, 1:4])
• datatypes
  
  • use array_name.dtype - returns the data type
  • can create arrays with specific data types:
    
    • arr = np.array([1, 2, 3, 4], dtype=’S’)
  • use array_name.astype() - to cast an array datatype to something else:
    
    • arr = np.array([1.1, 2.1, 3.1])
      newarr = arr.astype(‘i’)
      or newarr = arr.astype(int)
• copy() vs view()
  
  • array_name.copy() - change made to copy won’t affect original
  • array_name.view() - change made to view will affect original
  • check if an array is a view by using the array_name.base method
• array_name.shape - returns number of elements per dimension in an array
• array_name.reshape() - reshape the array into different dimension(s)
  
  • number of elements sould match the dimension:
    
    • arr.reshape(2, 3, 2) - needs to have 2*3*2 = 12 elements exactly
  • use -1 for unknown dimension, can be used no more than once
    
    • arr.reshape(2, 2, -1)
  • reshape(-1) - flattens and array into a 1D array or .flatten()
• can use for to loop through an array
  
  • for x in np.nditer(arr): - numpy build-in iter()
  • for x in arr:
  • for idx, x in np.ndenumerate(arr): - numpy build-in enumerate() which returns both index and value
• can use np.concatenate(array1, array2) - to join 2 arrays
  
  • can also use np.stack() - which is done along the axis
  • np.hstack() - along the horizontal axis (join by rows)
  • np.vstack() - along the vertical axis (join by columns)
• np.array_split() - split an array into a list of sub_arrays
  
  • arr = np.array([1, 2, 3, 4, 5, 6])
    newarr = np.array_split(arr, 3)
    result - [array([1, 2]), array([3, 4]), array([5, 6])]
  • If the array has less elements than required, it will adjust from the end accordingly.
  • alos as .hsplit .vsplit
• use np.where() to return index(es) of a given search
  
  • arr = np.array([1, 2, 3, 4, 5, 4, 4])

x = np.where(arr == 4)
result - (array([3, 5, 6]),)

• use np.sor() to sort an array
  
  • it returns a copy of the array, original is unchanged
  • arr = np.array([‘banana’, ‘cherry’, ‘apple’])
    np.sort(arr)
    result - [‘apple’ ‘banana’ ‘cherry’]
• can filter through an array using boolean indexing
  
  • usu condition to generate a list of bool based of array, returning true for element in array matching specified condition:
    
    • arr = np.array([1, 2, 3, 4, 5, 6, 7])
      filter_arr = arr % 2 == 0 <= filter condition
      newarr = arr[filter_arr]
    • results:
      filter_arr = [False True False True False True False]
      newarr = [2 4 6]
• Numpy has its own version of random module which generates random numbers in arrays of given shape
• NumPy also has ufunc“universal functions” which let you use mathematical operations on elements of array like adding 2 arrays by elements

Question 34

NumPy

Numpy_sub

Answer 34

testing