- An abstract class is the name for any class from which you can instantiate an object.
- Abstract classes must be redefined any time an object is instantiated from them.
- Abstract classes must inherit from concrete classes.
- An abstract class exists only so that other "concrete" classes can inherit from the abstract class.
- The
any()
function will randomly return any item from the list. - The
any()
function returns True if any item in the list evaluates to True. Otherwise, it returns False. - The
any()
function takes as arguments the list to check inside, and the item to check for. If "any" of the items in the list match the item to check for, the function returns True. - The
any()
function returns a Boolean value that answers the question "Are there any items in this list?"
example
if any([True, False, False, False]) == True:
print('Yes, there is True')
>>> 'Yes, there is True'
- linked list
- queue
- set
- OrderedDict
- Static methods are called static because they always return
None
. - Static methods can be bound to either a class or an instance of a class.
- Static methods serve mostly as utility methods or helper methods, since they can't access or modify a class's state.
- Static methods can access and modify the state of a class or an instance of a class.
- Attributes are long-form version of an
if/else
statement, used when testing for equality between objects. - Attributes are a way to hold data or describe a state for a class or an instance of a class.
- Attributes are strings that describe characteristics of a class.
- Function arguments are called "attributes" in the context of class methods and instance methods.
Explanation Attributes defined under the class, arguments goes under the functions. arguments usually refer as parameter, whereas attributes are the constructor of the class or an instance of a class.
count, fruit, price = (2, 'apple', 3.5)
-
tuple assignment
-
tuple unpacking
-
tuple matching
-
tuple duplication
-
.delete()
method -
pop(my_list)
-
del(my_list)
-
.pop()
method
example
my_list = [1,2,3]
my_list.pop(0)
my_list
>>>[2,3]
- to capture command-line arguments given at a file's runtime
- to connect various systems, such as connecting a web front end, an API service, a database, and a mobile app
- to take a snapshot of all the packages and libraries in your virtual environment
- to scan the health of your Python ecosystem while inside a virtual environment
- O(n), also called linear time.
- O(log n), also called logarithmic time.
- O(n^2), also called quadratic time.
- O(1), also called constant time.
Q10. What is the correct syntax for defining a class called Game, if it inherits from a parent class called LogicGame?
-
class Game(LogicGame): pass
-
def Game(LogicGame): pass
-
def Game.LogicGame(): pass
-
class Game.LogicGame(): pass
Explanation: The parent class which is inherited is passed as an argument to the child class. Therefore, here the first option is the right answer.
- A
def sum(a, b):
"""
sum(4, 3)
7
sum(-4, 5)
1
"""
return a + b
- B
def sum(a, b):
"""
>>> sum(4, 3)
7
>>> sum(-4, 5)
1
"""
return a + b
- C
def sum(a, b):
"""
# >>> sum(4, 3)
# 7
# >>> sum(-4, 5)
# 1
"""
return a + b
- D
def sum(a, b):
###
>>> sum(4, 3)
7
>>> sum(-4, 5)
1
###
return a + b
Explanation - use '''
to start the doc and add output of the cell after >>>
-
set
-
list
-
None
-
dictionary
-
You can only build a stack from scratch.
college_years = ['Freshman', 'Sophomore', 'Junior', 'Senior']
return list(enumerate(college_years, 2019))
-
[('Freshman', 2019), ('Sophomore', 2020), ('Junior', 2021), ('Senior', 2022)]
-
[(2019, 2020, 2021, 2022), ('Freshman', 'Sophomore', 'Junior', 'Senior')]
-
[('Freshman', 'Sophomore', 'Junior', 'Senior'), (2019, 2020, 2021, 2022)]
-
[(2019, 'Freshman'), (2020, 'Sophomore'), (2021, 'Junior'), (2022, 'Senior')]
-
self
means that no other arguments are required to be passed into the method. - There is no real purpose for the
self
method; it's just historic computer science jargon that Python keeps to stay consistent with other programming languages. -
self
refers to the instance whose method was called. -
self
refers to the class that was inherited from to create the object usingself
.
Simple example
class my_secrets:
def __init__(self, password):
self.password = password
pass
instance = my_secrets('1234')
instance.password
>>>'1234'
- You can assign a name to each of the
namedtuple
members and refer to them that way, similarly to how you would access keys indictionary
. - Each member of a namedtuple object can be indexed to directly, just like in a regular
tuple
. -
namedtuples
are just as memory efficient as regulartuples
. - No import is needed to use
namedtuples
because they are available in the standard library.
We need to import it using:from collections import namedtuple
- Instance methods can modify the state of an instance or the state of its parent class.
- Instance methods hold data related to the instance.
- An instance method is any class method that doesn't take any arguments.
- An instance method is a regular function that belongs to a class, but it must return
None
.
- It protects the data from outside interference.
- A parent class is encapsulated and no data from the parent class passes on to the child class.
- It keeps data and the methods that can manipulate that data in one place.
- It only allows the data to be changed by methods.
- It tells the computer which chunk of code to run if the instructions you coded are incorrect.
- It runs one chunk of code if all the imports were successful, and another chunk of code if the imports were not successful.
- It executes one chunk of code if a condition is true, but a different chunk of code if the condition is false.
- It tells the computer which chunk of code to run if the is enough memory to handle it, and which chunk of code to run if there is not enough memory to handle it.
- dictionary
- set
- None. You can only build a queue from scratch.
- list
-
my_game = class.Game()
-
my_game = class(Game)
-
my_game = Game()
-
my_game = Game.create()
- It creates a path from multiple values in an iterable to a single value.
- It applies a function to each item in an iterable and returns the value of that function.
- It converts a complex value type into simpler value types.
- It creates a mapping between two different elements of different iterables.
Explanation: - The synax for map()
function is list(map(function,iterable))
. The simple area finder using map would be like this
import math
radius = [1,2,3]
area = list(map(lambda x: round(math.pi*(x**2), 2), radius))
area
>>> [3.14, 12.57, 28.27]
- The function will return a RuntimeError if you don't return a value.
- If the return keyword is absent, the function will return
None
. - If the return keyword is absent, the function will return
True
. - The function will enter an infinite loop because it won't know when to stop executing its code.
- It is used to skip the
yield
statement of a generator and return a value of None. - It is a null operation used mainly as a placeholder in functions, classes, etc.
- It is used to pass control from one statement block to another.
- It is used to skip the rest of a
while
orfor loop
and return to the start of the loop.
- arguments
- paradigms
- attributes
- decorators
-
slot
-
dictionary
-
queue
-
sorted list
- when it encounters an infinite loop
- when it encounters an if/else statement that contains a break keyword
- when it has assessed each item in the iterable it is working on or a break keyword is encountered
- when the runtime for the loop exceeds O(n^2)
Q27. Assuming the node is in a singly linked list, what is the runtime complexity of searching for a specific node within a singly linked list?
- The runtime is O(n) because in the worst case, the node you are searching for is the last node, and every node in the linked list must be visited.
- The runtime is O(nk), with n representing the number of nodes and k representing the amount of time it takes to access each node in memory.
- The runtime cannot be determined unless you know how many nodes are in the singly linked list.
- The runtime is O(1) because you can index directly to a node in a singly linked list.
Q28. Given the following three list, how would you create a new list that matches the desired output printed below?
fruits = ['Apples', 'Oranges', 'Bananas']
quantities = [5, 3, 4]
prices = [1.50, 2.25, 0.89]
#Desired output
[('Apples', 5, 1.50),
('Oranges', 3, 2.25),
('Bananas', 4, 0.89)]
- A
output = []
fruit_tuple_0 = (first[0], quantities[0], price[0])
output.append(fruit_tuple)
fruit_tuple_1 = (first[1], quantities[1], price[1])
output.append(fruit_tuple)
fruit_tuple_2 = (first[2], quantities[2], price[2])
output.append(fruit_tuple)
return output
- B
i = 0
output = []
for fruit in fruits:
temp_qty = quantities[i]
temp_price = prices[i]
output.append((fruit, temp_qty, temp_price))
i += 1
return output
- C
groceries = zip(fruits, quantities, prices)
return groceries
>>> [
('Apples', 5, 1.50),
('Oranges', 3, 2.25),
('Bananas', 4, 0.89)
]
- D
i = 0
output = []
for fruit in fruits:
for qty in quantities:
for price in prices:
output.append((fruit, qty, price))
i += 1
return output
- The
all()
function returns a Boolean value that answers the question "Are all the items in this list the same? - The
all()
function returns True if all the items in the list can be converted to strings. Otherwise, it returns False. - The
all()
function will return all the values in the list. - The
all()
function returns True if all items in the list evaluate to True. Otherwise, it returns False.
Explanation - all()
returns true if all in the list are True, see example below
test = [True,False,False,False]
if all(test) is True:
print('Yeah all are True')
else:
print('There is an imposter')
>>> 'There is an imposter'
(Answer format may vary. Game and roll (or dice_roll) should each be called with no parameters.)
- A
>>> dice = Game()
>>> dice.roll()
- B
>>> dice = Game(self)
>>> dice.roll(self)
- C
>>> dice = Game()
>>> dice.roll(self)
- D
>>> dice = Game(self)
>>> dice.roll()
- backtracking
- dynamic programming
- decrease and conquer
- divide and conquer
- O(1), also called constant time
- O(log n), also called logarithmic time
- O(n^2), also called quadratic time
- O(n), also called linear time
- A set is an ordered collection unique items. A list is an unordered collection of non-unique items.
- Elements can be retrieved from a list but they cannot be retrieved from a set.
- A set is an ordered collection of non-unique items. A list is an unordered collection of unique items.
- A set is an unordered collection unique items. A list is an ordered collection of non-unique items.
- Abstraction means that a different style of code can be used, since many details are already known to the program behind the scenes.
- Abstraction means the implementation is hidden from the user, and only the relevant data or information is shown.
- Abstraction means that the data and the functionality of a class are combined into one entity.
- Abstraction means that a class can inherit from more than one parent class.
def print_alpha_nums(abc_list, num_list):
for char in abc_list:
for num in num_list:
print(char, num)
return
print_alpha_nums(['a', 'b', 'c'], [1, 2, 3])
- A
a 1
a 2
a 3
b 1
b 2
b 3
c 1
c 2
c 3
- B
['a', 'b', 'c'], [1, 2, 3]
- C
aaa
bbb
ccc
111
222
333
- D
a 1 2 3
b 1 2 3
c 1 2 3
- :
def sum(a, b):
# a = 1
# b = 2
# sum(a, b) = 3
return a + b
- :
def sum(a, b):
"""
a = 1
b = 2
sum(a, b) = 3
"""
return a + b
- :
def sum(a, b):
"""
>>> a = 1
>>> b = 2
>>> sum(a, b)
3
"""
return a + b
- :
def sum(a, b):
'''
a = 1
b = 2
sum(a, b) = 3
'''
return a + b
Explanation: Use """
to start and end the docstring and use >>>
to represent the output. If you write this correctly you can also run the doctest using build-in doctest module
Q37. Suppose a Game class inherits from two parent classes: BoardGame and LogicGame. Which statement is true about the methods of an object instantiated from the Game class?
- When instantiating an object, the object doesn't inherit any of the parent class's methods.
- When instantiating an object, the object will inherit the methods of whichever parent class has more methods.
- When instantiating an object, the programmer must specify which parent class to inherit methods from.
- An instance of the Game class will inherit whatever methods the BoardGame and LogicGame classes have.
- a generic object class with iterable parameter fields
- a generic object class with non-iterable named fields
- a tuple subclass with non-iterable parameter fields
- a tuple subclass with iterable named fields
Example
# namedtuple function accepts the following arguments to generate a class
from collections import namedtuple
>>> Point = namedtuple('Point',['x','y'])
>>> point = Point(100, 200)
>>> point
Point(x=100, y=200)
# Which let you use both unpacking and iteration to access
>>> x, y = point
>>> print(f'({x}, {y})')
(100, 200)
>>> for coordinate in point:
print(coordinate)
100
200
-
&&
-
=
-
==
-
||
fruit_info = {
'fruit': 'apple',
'count': 2,
'price': 3.5
}
-
fruit_info ['price'] = 1.5
-
my_list [3.5] = 1.5
-
1.5 = fruit_info ['price]
-
my_list['price'] == 1.5
5 != 6
-
yes
-
False
-
True
-
None
Explanation - !=
is equivalent to not equal to in python
- It makes classes aware of each other if more than one class is defined in a single code file.
- It is included to preserve backwards compatibility from Python 3 to Python 2, but no longer needs to be used in Python 3.
- It is a method that acts as a constructor and is called automatically whenever a new object is created from a class. It sets the initial state of a new object.
- It initializes any imports you may have included at the top of your file.
Example:
class test:
def __init__(self):
print('I came here without your permission lol')
pass
t1 = test()
>>> 'I came here without your permission lol'
-
How many microprocessors it would take to run your code in less than one second
-
How many lines of code are in your code file
-
The amount of space taken up in memory as a function of the input size
-
How many copies of the code file could fit in 1 GB of memory
-
fruit_info = {'fruit': 'apple', 'count': 2, 'price': 3.5}
-
fruit_info =('fruit': 'apple', 'count': 2,'price': 3.5 ).dict()
-
fruit_info = ['fruit': 'apple', 'count': 2,'price': 3.5 ].dict()
-
fruit_info = to_dict('fruit': 'apple', 'count': 2, 'price': 3.5)
Q45. What is the proper way to write a list comprehension that represents all the keys in this dictionary?
fruits = {'Apples': 5, 'Oranges': 3, 'Bananas': 4}
-
fruit_names = [x in fruits.keys() for x]
-
fruit_names = for x in fruits.keys() *
-
fruit_names = [x for x in fruits.keys()]
-
fruit_names = x for x in fruits.keys()
Q46. What is the purpose of the self
keyword when defining or calling methods on an instance of an object?
-
self
refers to the class that was inherited from to create the object usingself
. - There is no real purpose for the
self
method. It's just legacy computer science jargon that Python keeps to stay consistent with other programming languages. -
self
means that no other arguments are required to be passed into the method. -
self
refers to the instance whose method was called.
Explanation: - Try running the example of the Q42 without passing self
argument inside the __init__
, you'll understand the reason. You'll get the error like this __init__() takes 0 positional arguments but 1 was given
, this means that something is going inside even if haven't specified, which is instance itself.
- A class method is a regular function that belongs to a class, but it must return None.
- A class method can modify the state of the class, but they can't directly modify the state of an instance that inherits from that class.
- A class method is similar to a regular function, but a class method doesn't take any arguments.
- A class method hold all of the data for a particular class.
- You did not use very many advanced computer programming concepts in your code.
- The difficulty level your code is written at is not that high.
- It will take your program less than half a second to run.
- The amount of time it takes the function to complete grows linearly as the input size increases.
-
def getMaxNum(list_of_nums): # body of function goes here
-
func get_max_num(list_of_nums): # body of function goes here
-
func getMaxNum(list_of_nums): # body of function goes here
-
def get_max_num(list_of_nums): # body of function goes here
- in camel case without using underscores to separate words -- e.g.
maxValue = 255
- in lowercase with underscores to separate words -- e.g.
max_value = 255
- in all caps with underscores separating words -- e.g.
MAX_VALUE = 255
- in mixed case without using underscores to separate words -- e.g.
MaxValue = 255
- A deque adds items to one side and remove items from the other side.
- A deque adds items to either or both sides, but only removes items from the top.
- A deque adds items at either or both ends, and remove items at either or both ends.
- A deque adds items only to the top, but remove from either or both sides.
Explanation - deque
is used to create block chanin and in that there is first in first out approch, which means the last element to enter will be the first to leave.
-
my_set = {0, 'apple', 3.5}
-
my_set = to_set(0, 'apple', 3.5)
-
my_set = (0, 'apple', 3.5).to_set()
-
my_set = (0, 'apple', 3.5).set()
- :
class __init__(self):
pass
- :
def __init__():
pass
- :
class __init__():
pass
- :
def __init__(self):
pass
Q54. Which of the following is TRUE About how numeric data would be organised in a Binary Search Tree?
- For any given node in a binary search tree, the value of the node is greater than all the values in the node's left subtree and less than the ones in its right subtree.
- Binary Search Tree cannot be used to organize and search through numeric data, given the complication that arise with very deep trees.
- The top node of the binary search tree would be an arbitrary number. All the nodes to the left of the top node need to be less than the top node's number, but they don't need to ordered in any particular way.
- The smallest numeric value would go in the top most node. The next highest number would go in its left child node, the the next highest number after that would go in its right child node. This pattern would continue until all numeric values were in their own node.
- A decorator is similar to a class and should be used if you are doing functional programming instead of object oriented programming.
- A decorator is a visual indicator to someone reading your code that a portion of your code is critical and should not be changed.
- You use the decorator to alter the functionality of a function without having to modify the functions code.
- An import statement is preceded by a decorator, python knows to import the most recent version of whatever package or library is being imported.
- Only in some situations, as loops are used only for certain type of programming.
- When you need to check every element in an iterable of known length.
- When you want to minimize the use of strings in your code.
- When you want to run code in one file for a function in another file.
Q57. What is the most self-descriptive way to define a function that calculates sales tax on a purchase?
- :
def tax(my_float):
'''Calculates the sales tax of a purchase. Takes in a float representing the subtotal as an argument and returns a float representing the sales tax.'''
pass
- :
def tx(amt):
'''Gets the tax on an amount.'''
- :
def sales_tax(amount):
'''Calculates the sales tax of a purchase. Takes in a float representing the subtotal as an argument and returns a float representing the sales tax.'''
- :
def calculate_sales_tax(subtotal):
pass
Q58. What would happen if you did not alter the state of the element that an algorithm is operating on recursively?
- You do not have to alter the state of the element the algorithm is recursing on.
- You would eventually get a KeyError when the recursive portion of the code ran out of items to recurse on.
- You would get a RuntimeError: maximum recursion depth exceeded.
- The function using recursion would return None.
- The runtime for searching in a binary search tree is O(1) because each node acts as a key, similar to a dictionary.
- The runtime for searching in a binary search tree is O(n!) because every node must be compared to every other node.
- The runtime for searching in a binary search tree is generally O(h), where h is the height of the tree.
- The runtime for searching in a binary search tree is O(n) because every node in the tree must be visited.
- You use a
mixin
to force a function to accept an argument at runtime even if the argument wasn't included in the function's definition. - You use a
mixin
to allow a decorator to accept keyword arguments. - You use a
mixin
to make sure that a class's attributes and methods don't interfere with global variables and functions. - If you have many classes that all need to have the same functionality, you'd use a
mixin
to define that functionality.
- Add items to a stack in O(1) time and remove items from a stack on O(n) time.
- Add items to a stack in O(1) time and remove items from a stack in O(1) time.
- Add items to a stack in O(n) time and remove items from a stack on O(1) time.
- Add items to a stack in O(n) time and remove items from a stack on O(n) time.
- a stacks adds items to one side and removes items from the other side.
- a stacks adds items to the top and removes items from the top.
- a stacks adds items to the top and removes items from anywhere in the stack.
- a stacks adds items to either end and removes items from either end.
Explanation Stack uses the last in first out approach
- A base case is the condition that allows the algorithm to stop recursing. It is usually a problem that is small enough to solve directly.
- The base case is summary of the overall problem that needs to be solved.
- The base case is passed in as an argument to a function whose body makes use of recursion.
- The base case is similar to a base class, in that it can be inherited by another object.
Q64. Why is it considered good practice to open a file from within a Python script by using the with
keyword?
- The
with
keyword lets you choose which application to open the file in. - The
with
keyword acts like afor
loop, and lets you access each line in the file one by one. - There is no benefit to using the
with
keyword for opening a file in Python. - When you open a file using the
with
keyword in Python, Python will make sure the file gets closed, even if an exception or error is thrown.
- Virtual environments create a "bubble" around your project so that any libraries or packages you install within it don't affect your entire machine.
- Teams with remote employees use virtual environments so they can share code, do code reviews, and collaborate remotely.
- Virtual environments were common in Python 2 because they augmented missing features in the language. Virtual environments are not necessary in Python 3 due to advancements in the language.
- Virtual environments are tied to your GitHub or Bitbucket account, allowing you to access any of your repos virtually from any machine.
-
python3 -m doctest <_filename_>
-
python3 <_filename_>
-
python3 <_filename_> rundoctests
-
python3 doctest
- any function that makes use of scientific or mathematical constants, often represented by Greek letters in academic writing
- a function that get executed when decorators are used
- any function whose definition is contained within five lines of code or fewer
- a small, anonymous function that can take any number of arguments but has only expression to evaluate
Explanation:
The lambda notation is basically an anonymous function that can take any number of arguments with only single expression (i.e, cannot be overloaded). It has been introducted in other programming languages, such as C++ and Java. The lambda notation allows programmers to "bypass" function declaration.
- You can access a specific element in a list by indexing to its position, but you cannot access a specific element in a tuple unless you iterate through the tuple
- Lists are mutable, meaning you can change the data that is inside them at any time. Tuples are immutable, meaning you cannot change the data that is inside them once you have created the tuple.
- Lists are immutable, meaning you cannot change the data that is inside them once you have created the list. Tuples are mutable, meaning you can change the data that is inside them at any time.
- Lists can hold several data types inside them at once, but tuples can only hold the same data type if multiple elements are present.
- None
- An iterable object
- A linked list data structure from a non-empty list
- All the keys of the given dictionary
- Instance attributes can be changed, but class attributes cannot be changed
- Class attributes are shared by all instances of the class. Instance attributes may be unique to just that instance
- There is no difference between class attributes and instance attributes
- Class attributes belong just to the class, not to instance of that class. Instance attributes are shared among all instances of a class
- :
def get_next_card():
# method body goes here
- :
def get_next_card(self):
# method body goes here
- :
def self.get_next_card():
# method body goes here
- :
def self.get_next_card(self):
# method body goes here
-
get_max_num([57, 99, 31, 18])
-
call.(get_max_num)
-
def get_max_num([57, 99, 31, 18])
-
call.get_max_num([57, 99, 31, 18])
-
-- This is a comment
-
# This is a comment
-
/_ This is a comment _\
-
// This is a comment
my_list = ['kiwi', 'apple', 'banana']
-
orange = my_list[1]
-
my_list[1] = 'orange'
-
my_list['orange'] = 1
-
my_list[1] == orange
Q75. What will happen if you use a while loop and forget to include logic that eventually causes the while loop to stop?
- Nothing will happen; your computer knows when to stop running the code in the while loop.
- You will get a KeyError.
- Your code will get stuck in an infinite loop.
- You will get a WhileLoopError.
- A queue adds items to either end and removes items from either end.
- A queue adds items to the top and removes items from the top.
- A queue adds items to the top, and removes items from anywhere in, a list.
- A queue adds items to the top and removes items from anywhere in the queue.
- :
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
elif num_people > 4:
print("There are some people in the pool.")
else:
print("There is no one in the pool.")
- :
num_people = 5
if num_people > 10:
print("There is a lot of people in the pool.")
if num_people > 4:
print("There are some people in the pool.")
else:
print("There is no one in the pool.")
- :
num_people = 5
if num_people > 10;
print("There is a lot of people in the pool.")
elif num_people > 4;
print("There are some people in the pool.")
else;
print("There is no one in the pool.")
- :
if num_people > 10;
print("There is a lot of people in the pool.")
if num_people > 4;
print("There are some people in the pool.")
else;
print("There is no one in the pool.")
-
defaultdict
will automatically create a dictionary for you that has keys which are the integers 0-10. -
defaultdict
forces a dictionary to only accept keys that are of the types specified when you created thedefaultdict
(such as strings or integers). - If you try to read from a
defaultdict
with a nonexistent key, a new default key-value pair will be created for you instead of throwing aKeyError
. -
defaultdict
stores a copy of a dictionary in memory that you can default to if the original gets unintentionally modified.
Q79. What is the correct syntax for adding a key called variety
to the fruit_info
dictionary that has a value of Red Delicious
?
-
fruit_info['variety'] == 'Red Delicious'
-
fruit_info['variety'] = 'Red Delicious'
-
red_delicious = fruit_info['variety']
-
red_delicious == fruit_info['variety']
- when you want to minimize the use of strings in your code
- when you want to run code in one file while code in another file is also running
- when you want some code to continue running as long as some condition is true
- when you need to run two or more chunks of code at once within the same file
Simple Example
i = 1
while i<6:
print('Countdown:',i)
i = i + 1
Q81. What is the correct syntax for defining an __init__()
method that sets instance-specific attributes upon creation of a new class instance?
- :
def __init__(self, attr1, attr2):
attr1 = attr1
attr2 = attr2
- :
def __init__(attr1, attr2):
attr1 = attr1
attr2 = attr2
- :
def __init__(self, attr1, attr2):
self.attr1 = attr1
self.attr2 = attr2
- :
def __init__(attr1, attr2):
self.attr1 = attr1
self.attr2 = attr2
Explanation: When instantiating a new object from a given class, the __init__()
method will take both attr1
and attr2
, and set its values to their corresponding object attribute, that's why the need of using self.attr1 = attr1
instead of attr1 = attr1
.
def count_recursive(n=1):
if n > 3:
return
print(n)
count_recursive(n + 1)
- :
1
1
2
2
3
3
- :
3
2
1
- :
3
3
2
2
1
1
- :
1
2
3
Q83. In Python, when using sets, you use _ to calculate the intersection between two sets and _ to calculate the union.
-
Intersect
;union
-
|
;&
-
&
;|
-
&&
;||
import numpy as np
np.ones([1,2,3,4,5])
- It returns a 5x5 matric; each row will have the values 1,2,3,4,5.
- It returns an array with the values 1,2,3,4,5
- It returns five different square matrices filled with ones. The first is 1x1, the second 2x2, and so on to 5x5
- It returns a 5-dimensional array of size 1x2x3x4x5 filled with 1s.
Q85. You encounter a FileNotFoundException while using just the filename in the open
function. What might be the easiest solution?
- Make sure the file is on the system PATH
- Create a symbolic link to allow better access to the file
- Copy the file to the same directory as where the script is running from
- Add the path to the file to the PYTHONPATH environment variable
{x for x in range(100) if x%3 == 0}
- a set of all the multiples of 3 less then 100
- a set of all the number from 0 to 100 multiplied by 3
- a list of all the multiples of 3 less then 100
- a set of all the multiples of 3 less then 100 excluding 0
- Perform integer division
- Perform operations on exponents
- Find the remainder of a division operation
- Perform floating point division
- datetime
- dateday
- daytime
- timedate
-
def Game(): pass
-
def Game: pass
-
class Game: pass
-
class Game(): pass
-
my_game = Game(self) self.my_game.roll_dice()
-
my_game = Game() self.my_game.roll_dice()
-
my_game = Game() my_game.roll_dice()
-
my_game = Game(self) my_game.roll_dice(self)
a = np.array([1,2,3,4])
print(a[[False, True, False, False]])
-
{0,2}
-
[2]
-
{2}
-
[0,2,0,0]
Q92. Suppose you have a string variable defined as y="stuff;thing;junk;". What would be the output from this code?
z = y.split(';')
len(z)
- 17
- 4
- 0
- 3
Explanation:
y="stuff;thing;junk"
len(z) ==> 3
y="stuff;thing;junk;"
len(z) ==> 4
num_list = [1,2,3,4,5]
num_list.remove(2)
print(num_list)
-
[1,2,4,5]
-
[1,3,4,5]
-
[3,4,5]
-
[1,2,3]
Explanation:
num_list = [1,2,3,4,5]
num_list.pop(2)
>>> [1,2,4,5]
num_list.remove(2)
>>> [1,3,4,5]
[10,9,8,7,6,5,4,3,2,1]
-
reversed(list(range(1,11)))
-
list(reversed(range(1,10)))
-
list(range(10,1,-1))
-
list(reversed(range(1,11)))
import math
print(math.pow(2,10)) # prints 2 elevated to the 10th power
- :
print(2^10)
- :
print(2**10)
- :
y = [x*2 for x in range(1,10)]
print(y)
- :
y = 1
for i in range(1,10):
y = y * 2
print(y)
- sets only; lists or dictionaries; tuples
- lists; sets only; tuples
- tuples; sets or lists; dictionaries
- lists; dictionaries or sets; tuples
table = np.array([
[1,3],
[2,4]])
print(table.max(axis=1))
-
[2, 4]
-
[3, 4]
-
[4]
-
[1,2]
number = 3
print (f"The number is {number}")
-
The number is 3
-
the number is 3
-
THE NUMBER IS 3
- It throws a TypeError because the integer must be cast to a string.
-
my_tuple tup(2, 'apple', 3.5) %D
-
my_tuple [2, 'apple', 3.5].tuple() %D
-
my_tuple = (2, 'apple', 3.5)
-
my_tuple = [2, 'apple', 3.5]
-
write('w')
-
scan('s')
-
append('a')
-
read('r')
- vectorization
- attributions
- accelaration
- functional programming
-
set
-
list
-
tuple
-
dictionary
-
sys.exc_info()
-
os.system()
-
os.getcwd()
-
sys.executable
Q104. Suppose you have the following code snippet and want to extract a list with only the letters. Which fragment of code will _not_ achieve that goal?
my_dictionary = {
'A': 1,
'B': 2,
'C': 3,
'D': 4,
'E': 5
}
letters = []
for letter in my_dictionary.values():
letters.append(letter)
-
letters = my_dictionary.keys()
-
letters = [letter for (letter, number) in my_dictionary.items()]
-
letters4 = list(my_dictionary)
Explanation: The first one (the correct option) returns the list of the values (the numbers). The rest of the options return a list of the keys.
Q105. When an array is large, NumPy will not print the entire array when given the built-in print
function. What function can you use within NumPy to force it to print the entire array?
-
set_printparams
-
set_printoptions
-
set_fullprint
-
setp_printwhole
- You use
try/except
blocks when you want to run some code, but need a way to execute different code if an exception is raised. - You use
try/except
blocks inside of unit tests so that the unit testes will always pass. - You use
try/except
blocks so that you can demonstrate to your code reviewers that you tried a new approach, but if the new approach is not what they were looking for, they can leave comments under theexcept
keyword. - You use
try/except
blocks so that none of your functions or methods returnNone
.
-
because of the level of indentation after the for loop
-
because of the end keyword at the end of the for loop
-
because of the block is surrounded by brackets ({})
-
because of the blank space at the end of the body of the for loop
- sys.stdout
- traceback
- warnings
- exceptions
x = {1,2,3,4,5}
x.add(5)
x.add(6)
-
{1, 2, 3, 4, 5, 5, 6}
-
{5, 6, 1, 2, 3, 4, 5, 6}
-
{6, 1, 2, 3, 4, 5}
-
{1, 2, 3, 4, 5, 6}
Explanation: The .add()
method adds the element to the set only if it doesn't exist.
fruit_info = {
'fruit': 'apple',
'count': 2,
'price': 3.5
}
-
my_keys = fruit_info.to_keys()
-
my_keys = fruit_info.all_keys()
-
my_keys = fruit_info.keys
-
my_keys = fruit_info.keys()
def be_friendly(greet = "How are you!", name):
pass
-
name
is a reserved word. - Underscores are not allowed in function names.
- A non-default argument follows a default argument.
- There is nothing wrong with this function definition.
- :
a = np.zeros([3,4])
b = a.copy()
np.array_equal(a,b)
- :
a = np.empty([3,4])
b = np.empty([3,4])
np.array_equal(a,b)
- :
a = np.zeros([3,4])
b = np.zeros([4,3])
np.array_equal(a,b)
- :
a = np.array([1, np.nan])
np.array_equal(a,a)
-
// This is a comment
-
# This is a comment
-
-- This is a comment
-
/* This is a comment *\
import numpy as np
a = np.array([1,2,3])
b = np.array([4,5,6])
c = a*b
d = np.dot(a,b)
- A
c = [ a[1] * b[1], a[2] * b[2], a[3] * b[3] ]
d = sum(c)
- B
c = a[0] * b[0], a[1] * b[1], a[2] * b[2]
d = [ a[0] * b[0], a[1] * b[1], a[2] * b[2] ]
- C
c = [ a[0] * b[0], a[1] * b[1], a[2] * b[2] ]
d = sum(a) + sum(b)
- D
c = [ a[0] * b[0], a[1] * b[1], a[2] * b[2] ]
d = sum(c)
Q115. What two functions within the NumPy library could you use to solve a system of linear equations?
-
linalg.eig() and .matmul()
-
linalg.inv() and .dot()
-
linalg.det() and .dot()
-
linalg.inv() and .eye()
Explanation: Understanding this answer requires knowledge of linear algebra. Some systems of equations can be solved by the method of diagonalization, which involves finding the eigenvectors and eigenvalues of the system's matrix and multiplying related matrices.
-
my_list = (2, 'apple', 3.5)
-
my_list = [2, 'apple', 3.5]
-
my_list = [2, 'apple', 3.5].to_list()
-
my_list = to_list(2, 'apple', 3.5)
num_list = [21, 13, 19, 3, 11, 5, 18]
num_list.sort()
num_list[len(num_list) // 2]
- mode
- average
- mean
- median
Explanation: The median is the value separating the higher half from the lower half of a data sample. Here it is 13.
- Arrays and DataFrames
- Series and Matrixes
- Matrixes and DataFrames
- Series and DataFrames
Q119. Suppose you have a variable named vector
of type np.array with 10,000 elements. How can you turn vector
into a variable named matrix
with dimensions 100x100?
-
matrix = (vector.shape = (100,100))
-
matrix = vector.to_matrix(100,100)
-
matrix = matrix(vector,100,100)
-
matrix = vector.reshape(100, 100)
- dictionnary
- list
- set
- string
def myFunction(country = "France"):
print("Hello, I am from", country)
myFunction("Spain")
myFunction("")
myFunction()
- :
Hello, I am from Spain
Hello, I am from
Hello, I am from
- :
Hello, I am from France
Hello, I am from France
Hello, I am from France
- :
Hello, I am from Spain
Hello, I am from
Hello, I am from France
- :
Hello, I am from Spain
Hello, I am from France
Hello, I am from France
- Anonymous Class
- Parent Class
- Nested Class
- Abstract Class
Q123. Using Pandas, we load a data set from Kaggle, as structured in the image below. Which command will return the total number of survivors?
-
sum(titanic['Survived'])
-
[x for x in titanic['Survived'] if x == 1]
-
len(titanic["Survived"])
-
sum(titanic['Survived']==0)
Explanation: The titanic['Survived']
returns a pandas.Series
object, which contains the Survived
column of the DataFrame
.
Adding the values of this column (i.e. sum(titanic['Survived'])
) returns the total number of survivors since a survivor is represented by a 1 and a loss by 0.
characters = ["Iron Man", "Spider Man", "Captain America"]
actors = ["Downey", "Holland", "Evans"]
# example output : [("IronMan", "Downey"), ("Spider Man", "Holland"), ("Captain America", "Evans")]
-
[(x,y)] for x in characters for y in actors]
-
zip(characters, actors)
-
[ ]
d = {} for x in range(1, len(characters)): d[x] = actors[x]
-
{x:y for x in characters for y in actors}
{x : x*x for x in range(1,100)}
- a dictionary with x as a key, and x squared as its value; from 1 to 100
- a dictionary with x as a key, and x squared as its value; from 1 to 99
- a set of tuples, consisting of (x, x squared); from 1 to 99
- a list with all numbers squared from 1 to 99
Q126. Jaccard Similarity is a formula that tells you how similar two sets are. It is defined as the cardinality of the intersection divided by the cardinality of the union. Which choice is an accurate implementation in Python?
-
def jaccard(a, b): return len (a | b) / len (a & b)
-
def jaccard(a, b): return len (a & b) / len (a | b)
-
def jaccard(a, b): return len (a && b) / len (a || b)
-
def jaccard(a, b): return a.intersection(b) / a.union(b)
- Long
- Int
- Float
- Double
[1,2,3] * 3
-
[3,2,3]
-
[1, 2, 3, 1, 2, 3, 1, 2, 3]
- You will get a type error.
-
[3,6,9]
- 1
- 3
- 2
- An IndexError exception is thrown.
- Strings can be enclosed by double quotes (") or single quotes (').
- Strings can only be enclosed in single quotes (').
- Single character strings must be enclosed in single quotes ('), and the rest must be enclosed in double quotes (").
- Strings can only be enclosed in double quotes (").
- definit(self): pass
- classinit(self): pass
- classinit(): pass
- definit(): pass
() -empty parameter self -refers to all instances within a class init -a reserved method, aka a constructor init() -always executed when the class is being initiated
Q132. Suppose you need to use the sin
function from the math
library. What is the correct syntax for importing only that function?
-
using math.sin
-
import math.sin
-
from math import sin
-
import sin from math
-
0
-
the count of all True values
-
a type error
-
None
print ("foo" if (256).bit_length() > 8 else "bar")
- True
- foo
- You will get an error message because constant integer values are not classes.
- bar
- If the return keyword is absent, the function will return True.
- The function will enter an infinite loop because it will not know when to stop executing its code.
- The function will return a RuntimeError if you do not return a value.
- If the return keyword is absent the function will return None.
- string; numerical
- unstructured; structured
- numerical; tabular
- tabular; numerical
- Use a C compiler like gcc or clang.
- Use a package manager like pip or conda.
- Use an IDE like Notepad++ or Idle.
- Use a package manager like NPM or NuGet.
Q138. The image below was created using Matplotlib. It is a distribution plot of a list of integers filled with numbers using the function _ and plotted with _.
-
random.uniform(0,50);plt.hist
-
random.gauss(50,20);plt.hist
-
random();plt.scatter
-
random.triangular(0,50);plt.bar
import numpy as np
a = np.arange(100)
b = a[50:60:2]
- a: all integers from 0 to 99 (inclusive) b: all even integers from 50 to 58 (inclusive)
- a: all integers from 0 to 100 (inclusive) b: all even integers from 50 to 60 (inclusive)
- a: all integers from 0 to 99 (inclusive) b: all even integers from 50 to 60 (inclusive)
- a: all integers from 0 to 99 (inclusive) b: all odd integers from 49 to 59 (inclusive)
Q140. When using NumPy in Python, how do you check the dimensionality (number and length of dimensions) of an object called my_object?
-
my_object.get_shape()
-
my_object.shape
-
my_object.dim()
-
len(my_object)
Q141. Assume you have a non-empty list named mylist and you want to search for a specific value. The minimum number of comparison will be __ and the maximum number of comparison will be _?
-
len(mylist); len(mylist)
-
1; len(mylist)
-
2; len(mylist)
-
0; len(mylist)
Explanation: Can use a break statement and the value being searched can be the first element of the list, given that it is non-empty.
- 0
- True
- None
- False
- to capture command-line arguments given at a file's runtime
- to take a snapshot of all the packages and libraries in your virtual environment
- to connect various systems, such as connecting a web front end, an API service, a database, and a mobile app
- to scan the health of your Python ecosystem while inside a virtual environment
Q144. Suppose you want to double-check if two matrices can be multipled using NumPy for debugging purposes. How would you complete this code fragment by filling in the blanks with the appropiate variables?
import numpy as np
def can_matrices_be_multiplied (matrix1, matrix2):
rowsMat1, columnsMat1 = matrix1.shape
rowsMat2, columnsMat2 = matrix2.shape
if _____ == ______ :
print('The matrices can be multipled!')
return True
else:
return False
- columnsMat1; rowsMat1;
- columnsMat1; rowsMat2;
- columnsMat1; columnsMat2;
- columnsMat2; rowsMat1;
[(x, x+1) for x in range(1,5)]
- [(1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]
- [1,2,3,4,5]
- [(1, 2), (2, 3), (3, 4)]
- [(1, 2), (2, 3), (3, 4), (4, 5)]
Q146. In Python, a class method must have __ as a function decorator, and the first parameter of the method will be a reference to __.
- @classmethod; the class
- inline; the class
- static; self
- @static; self
- :
class Father():
name = 'Robert'
class Person(Father):
def __init__(self, name):
self.fathername = super.name
self.name = name
def introduce(self):
print("My name is", self.name, "son of", self.fathername)
king = Person("Joffrey")
king.introduce()
- :
class Father():
name = 'Robert'
class Person(Father):
def __init__(self, name):
self.fathername = self.name
self.name = name
def introduce(self):
print("My name is", self.name, "son of", self.fathername)
king = Person("Joffrey")
king.introduce()
- :
class Father():
name = 'Robert'
class Person(Father):
def __init__(self, name):
self.name = name
def introduce(self):
print("My name is", self.name, "son of", super.name)
king = Person("Joffrey")
king.introduce()
- :
class Father():
name = 'Robert'
class Person(Father):
def __init__(self, name):
self.name = name
def introduce(self):
print("My name is", self.name, "son of", base.name)
king = Person("Joffrey")
king.introduce()
Explanation: In the first, super does not have .name (should be self.name), The third drops Robert, and base is not defined in the 4th.
animals = {
'a': ['ant', 'antelope', 'armadillo'],
'b': ['beetle', 'bear', 'bat'],
'c': ['cat', 'cougar', 'camel']
}
animals = defaultdict(list, animals)
print(animals['b'])
print(animals['d'])
- A
['beetle', 'bear', 'bat']
[]
- B
['beetle', 'bear', 'bat']
# an exception will be thrown
- C
['beetle', 'bear', 'bat']
None
- D
['bat', 'bear', 'beetle']
[]
Explanation: Dictionaries usually result in an exception when using the square bracket syntax. Defaultdict here returns a default value dedicated by the first parameter so instead of throwing an exception, they return the default. Note that this needs to be imported as follows: from collections import defaultdict
Q149. What will this line of code return? (Assume n is already defined as any positive integer value.)
[x*2 for x in range(1,n)]
- a list with all the even numbers less than 2*n
- a dictionary with all the even numbers less than 2*n
- a list with all the odd numbers less than 2*n
- a list with all the even numbers less than or equal to 2*n
x = 18
if x > 10:
if x > 15:
print('A')
else:
print('B')
else:
print('C')
- C
- A B
- B
- A
Q151. Suppose you have a variable named vector of type np.array with 10.000 elements. How can you turn vector into a variable named matrix with dimensions 100x100?
- matrix = matrix(vector,100,100)
- matrix = vector.to_matrix(100,100)
- matrix = (vector.shape = (100,100))
- matrix = vector.reshape(100,100) Exa
- 32
- 16
- 128
- No fixed length is specified
for i in range(5): pass
- 5
- the variable becomes unavailable
- 6
- 4
- Formatted string expressions
- Functional strings
- Modulo formatted strings
- Formatted string literals
- One
- All of the available CPUs
- Two
- Three
Explanation: Python threading is restricted to a single CPU at one time. The multiprocessing library will allow you to run code on different processors.
x = 5
y = 1 + (20 if x < 5 else 30)
- False
- 21
- 2
- 31
Explanation: If x < 5 ==> y = 1 + 20 Else y = 1 + 30
- conversion of a Python object hierarchy into byte stream
- conversion of a datatable into a list
- conversion of a byte stream into Python object hierarchy
- conversion of a list into a datatable
Explanation: Pickling is the process of sterilizing a Python object, that is, conversion of a byte stream into Python object hierarchy. The reverse of this process is known as unpickling.
print("codescracker".endswith("er"))
- True
- 1
- 2
- False
- True
- False
print("programming".center())
- cr
- programming
- Error says TypeError: center expected at least 1 argument, got 0
- None of the Above
- Tim Berners-Lee
- Ada Lovelace
- Guido van Rossum
- Alan Turing
- SET
- TUPLE
- DICTIONARY
- LIST
x = 1j
print(x**2 == -1)
- a run-time error telling you that the variable
j
has not been initialized - True
- 1j
- False
Explanation: The letter j
acts as the imaginary unit in Python, therefore x**2
means j**2
which is equal to -1
. The statement x**2 == -1
is evaluated as True
.
print(0xA + 0xB + 0xC)
- 33
- 63
- 0xA + 0xB + 0xC
- None
Explanation: A, B and C are hexadecimal integers with values 10, 11 and 12 respectively, so the sum of A, B and C is 33.
for i in range(5):
print(i)
else:
print("Done!")
- 1 2 3 4 5 Done!
- 0 1 2 3 4 5 Done!
- 0 1 2 3 4 Done!
- You will get a syntax error.
- Use lists instead of tuples when you have a collection of related but dissimilar objects.
- Use tuples instead of lists when you have a common collection of similar objects.
- Use tuples instead of lists for functions that need to return multiple values.
- Use lists instead of tuples when the position of elements is important.
Q167. Consider the following code snippet that uses decorators to calculate the execution time of execution_fn function:
import functools
import time
def timer(MISSING_ARG_1):
@functools.wraps(func)
def wrapper(*args, **kwargs):
start_time = time.perf_counter()
rval = func(*args, **kwargs)
end_time = time.perf_counter()
duration = end_time - start_time
print(f"Executed in {duration:.4f} seconds")
return MISSING_ARG_2
return MISSING_ARG_3
@timer
def execution_fn():
for i in range(3):
time.sleep(1)
execution_fn()
Which of the following choices are the missing arguments?
- :
MISSING_ARG_1 = wrapper
MISSING_ARG_2 = rval
MISSING_ARG_3 = func
- :
MISSING_ARG_1 = func
MISSING_ARG_2 = rval
MISSING_ARG_3 = wrapper
- :
MISSING_ARG_1 is empty
MISSING_ARG_2 = rval
MISSING_ARG_3 = wrapper
- :
MISSING_ARG_1 is empty
MISSING_ARG_2 = rval
MISSING_ARG_3 = func
- class Dog:
- Dog class:
- Dog:
- class Dog
- preprocessing
- pipeline
- filters
- pipe_filter
Q170. You should pass in a value of _ for the axis argument to the Pandas apply method to apply the function to each row.
- row
- col
- 1
- 0
- pointers
- points
- markers
- none of these
a = np.array([[1, 2], [3, 4], [5, 6]])
c = a[(a > 3) & (a < 11)]
print(c)
-
[[3, 4], [5, 6]]
-
[False, False, False, True, True, True]
-
[[0,0], [3, 4], [5, 6]]
-
[4 5 6]
my_list = [2, 'apple', 3.5]
-
orange = my_list[1]
-
my_list[1] = 'orange'
-
my_list['orange'] = 1
-
my_list[1] == orange
Q174. Assume m, n and p are positive integers. In the following comprehension, how many times will the function randint
be called?
[ [ [ randint(1,100) for i in range(m) ] for j in range(n) ] for k in range(p) ]
- m _ n _ p
- the greater value of (m,n,p)
- 1 million
- m + n + p
Q175. Suppose you have a class named MyClass which has multiple inheritance and methods with the same name in its ancestors. Which class method could you call to see which method will get priority when invoked?
-
MyClass.__mro__
-
MyClass.hierarchy()
-
callable(MyClass)
-
dir(MyClass)
Explanation: MRO stands for Method Resolution Order. It returns a list of types the class is derived from, in the order they are searched for methods.
Q176. Suppose you have a list of employees described by the code below. You want to assign alice the same salary as charlie. Which choice will accomplish that?
employees = {
'alice':{
'position':'Lead Developer',
'salary':1000
},
'bob':{
'position': 'Lead Artist',
'salary':2000
},
'charlie':{
'position':'cfo',
'salary':3000
}
}
-
employess['alice']['salary'] = employees['charlie']['salary']
-
employees.alice.salary = employees.charlie.salary
-
employees['alice'][1] = employees['charlie'][1]
-
employees['alice'].salary = employees['charlie'].salary
Explanation: This is accessing a key in a nested dictionary inside another dictionary
The command employees['alice']['salary'] = employees['charlie']['salary'] assigns the value of the 'salary' key in the dictionary of the employee 'charlie' to the 'salary' key in the dictionary of the employee 'alice'. It is the same thing as:
employees = {
'alice': {'position': 'Lead Developer', 'salary': 1000},
'bob': {'position': 'Lead Artist', 'salary': 2000},
'charlie': {'position': 'cfo', 'salary': 3000}
}
# or:
employees = {'alice': {'position': 'Lead Developer', 'salary': 1000},
'bob': {'position': 'Lead Artist', 'salary': 2000},
'charlie': {'position': 'cfo', 'salary': 3000}}
Q177. You are given a piece of code. Assume m and n are already defined as some positive integer value. When it completes, how many tuples will my list contain?
mylist = []
for i in range(m):
for j in range(n):
mylist.append((i,j))
- m
- m + n
- n
- m * n
Explanation: This code will run for m x n times, if you run this code, it will create m x n tuples.
The first loop runs for m times and the inner loop will run for n times. While the single iteration of first loop will only be completed when all of the n iterations of inner loop are completed. This is the same process for 2nd, 3rd, ... mth iterations for outer loop. Overall, both loops will run m x n times