- 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/elsestatement, 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 + bexplanation - 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')]
-
selfmeans that no other arguments are required to be passed into the method. - There is no real purpose for the
selfmethod; it's just historic computer science jargon that Python keeps to stay consistent with other programming languages. -
selfrefers to the instance whose method was called. -
selfrefers 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
namedtuplemembers 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. -
namedtuplesare just as memory efficient as regulartuples. - No import is needed to use
namedtuplesbecause 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
yieldstatement 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
whileorfor loopand 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)]- [ ]
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- [x]
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- [ ]
groceries = zip(fruits, quantities, prices)
return groceries
>>> [
('Apples', 5, 1.50),
('Oranges', 3, 2.25),
('Bananas', 4, 0.89)
]- [ ]
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.
Explaination - 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.)
- [x]
>>> dice = Game()
>>> dice.roll()- [ ]
>>> dice = Game(self)
>>> dice.roll(self)- [ ]
>>> dice = Game()
>>> dice.roll(self)- [ ]
>>> 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])- [x]
a 1
a 2
a 3
b 1
b 2
b 3
c 1
c 2
c 3- [ ]
['a', 'b', 'c'], [1, 2, 3]- [ ]
aaa
bbb
ccc
111
222
333- [ ]
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- [x]
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 + bExplanation: 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
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]-
&& -
= -
== -
||
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
- The
__init__method makes classes aware of each other if more than one class is defined in a single code file. - The
__init__method is included to preserve backwards compatibility from Python 3 to Python 2, but no longer needs to be used in Python 3. - The
__init__method is a constructor method that is called automatically whenever a new object is created from a class. It sets the initial state of a new object. - The
__init__method 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?
-
selfrefers to the class that was inherited from to create the object usingself. - There is no real purpose for the
selfmethod. It's just legacy computer science jargon that Python keeps to stay consistent with other programming languages. -
selfmeans that no other arguments are required to be passed into the method. -
selfrefers to the instance whose method was called.
Explanation: - Try running the example of the Q45 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- [x]
def __init__(self):
passQ54. 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 child node to the left is less than the value of the given node and the child node to its right is greater than the given node.
- 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.'''- [x]
def calculate_sales_tax(subtotal):
passQ58. 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
mixinto force a function to accept an argument at runtime even if the argument wasn't included in the function's definition. - You use a
mixinto allow a decorator to accept keyword arguments. - You use a
mixinto 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
mixinto 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
withkeyword lets you choose which application to open the file in. - The
withkeyword acts like aforloop, and lets you access each line in the file one by one. - There is no benefit to using the
withkeyword for opening a file in Python. - When you open a file using the
withkeyword 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 specifc 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.
- Static methods can be bound to either a class or an instance of a class.
- Static methods can access and modify the state of a class or an instance of a class.
- Static methods serve mostly as utility or helper methods, since they cannot access or modify a class's state.
- Static methods are called static because they always return None.
- 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- [x]
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
Q76. 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.
- [x]
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.")-
defaultdictwill automatically create a dictionary for you that has keys which are the integers 0-10. -
defaultdictforces 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
defaultdictwith a nonexistent key, a new default key-value pair will be created for you instead of throwing aKeyError. -
defaultdictstores a copy of a dictionary in memory that you can default to if the original gets unintentionally modified.
Q80. 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 + 1Q82. 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- [x]
def __init__(self, attr1, attr2):
self.attr1 = attr1
self.attr2 = attr2- [ ]
def __init__(attr1, attr2):
self.attr1 = attr1
self.attr2 = attr2Explanation: 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- [x]
1
2
3Q84. 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.
Q86. 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
num_list = [21,13,19,3,11,5,18]
num_list.sort()
num_list[len(num_list)//2]- mean
- mode
- median
- average
- datetime
- dateday
- daytime
- timedate
- def Game(): pass
- def Game: pass
- class Game: pass
- class Game(): pass
- The init method makes classes aware of each other if more than one class is defined in a single code file.
- The init method is included to preserve backward compatibility from Python 3 to Python 2, but no longer needs to be used in Python 3.
- The init method is a constructor method that is called automatically whenever a new object is created from a class. It sets the initial state of a new object.
- The init method initializes any imports you may have included at the top of your file.
- 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]
Q95. 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]
- def get_next_card(): # method body goes here
- def self.get_next_card(): # method body goes here
- def get_next_card(self): # method body goes here
- def self.get_next_card(self): # method body goes here
[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)
- [x]
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')
Q105. 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?: [ANSWER NEEDED]
- matrix = matrix(vector,100,100)
- matrix = vector.to_matrix(100,100)
- matrix = (vector.shape = (100,100))
- matrix = vector.reshape(100,100)
Example
import numpy as np
vector = np.random.rand(10000)
matrix = a.reshape(100, 100)
print(matrix.shape)
(100, 100)- vectorization
- attributions
- accelaration
- functional programming
-
set -
list -
tuple -
dictionary
-
sys.exc_info() -
os.system() -
os.getcwd() -
sys.executable
Q109. 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
}
- [x]
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 letters). The rest of the options return a list of the keys.
Q110. 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/exceptblocks when you want to run some code, but need a way to execute different code if an exception is raised. - You use
try/exceptblocks inside of unit tests so that the unit testes will always pass. - You use
try/exceptblocks 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 theexceptkeyword. - You use
try/exceptblocks 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