Python

Question 1

declare a list object

Answer 1

my_list = [1, 2, 3, 4, 5]

Question 2

Declare a dictionary object

Answer 2

my_dict = {‘key1’: ‘value1’, ‘key2’: ‘value2’}

Question 3

Pull a value from a dictionary by key

Answer 3

value_from_dict = my_dict[‘key2’]

Question 4

Pull a value from a list

Answer 4

value_from_list = my_list[2]

Question 5

What do class decorators do

Answer 5

they are functions that modify the behavior of a class or methods within a class. They allow you to add functionality to classes or methods without modifying their underlying code.

Question 6

Explain the difference between == and is in Python

Answer 6

The == operator is used to compare the values of two objects, while the is operator is used to check if two variables refer to the same object in memory.

Question 7

What is a decorator in Python?

Answer 7

A decorator in Python is a design pattern that allows you to modify or extend the behavior of functions or classes without directly modifying their code. Decorators are implemented as callable objects that take a function or class as input and return a new function or class with additional functionality.

Question 8

What is a generator in Python?

Answer 8

A generator in Python is a special type of iterator that generates values on the fly rather than storing them in memory. It allows you to iterate over a sequence of values one at a time, which is memory-efficient and suitable for handling large datasets or infinite sequences.

Question 9

What is the purpose of the __init__ method in Python classes?

Answer 9

a special method in Python classes that is called automatically when a new instance of the class is created. It is used to initialize the object’s attributes or perform any necessary setup operations.

Question 10

Explain the difference between lists and tuples in Python

Answer 10

lists are mutable (modifiable) while tuples are immutable (unchangeable). Lists are defined using square brackets [ ], and tuples are defined using parentheses ( ).

Question 11

Declare a tuple object

Answer 11

my_tuple = (1, 2, 3, 4, 5)

Question 12

What is the difference between var= (42) and var= (42,)

Answer 12

The first is the value 42 and the second is a tuple because of the comma

Question 13

of dictionaries, tuples and lists – which can not be changed

Answer 13

Tuples

Question 14

List the 3 types of “list” objects with the characters that declare them

Answer 14

1 - Dictionaries: Declared using curly braces {} with key-value pairs separated by colons : ({‘key’: ‘value’}).
2 - Tuples: Declared using parentheses () with comma-separated values ((1, 2, 3)).
3 - Lists: Declared using square brackets [] with comma-separated values ([1, 2, 3])

Question 15

of dictionaries, tuples and lists – which is not ordered

Answer 15

Dictionary

Question 16

of dictionaries, tuples and lists – which can have duplicates

Answer 16

Tuples and lists

Question 17

Show me a count of null values per column for a dataframe

Answer 17

df.isna().sum()

Question 18

Define a function that takes either an array or a PD series as an input

Answer 18

def hampel(data: Union[np.ndarray, pd.Series]) -> Result:

Question 19

Cycle through items in a list that has 30 elements

Answer 19

for num in range(29):
print(num)

Question 20

Is python 0 based or 1 based

Answer 20

0 based

Question 21

Return the top 10 rows of a dataframe named dataset

Answer 21

dataset.head(10)

Question 22

What character do you add in front of a path string to make windows \ work?

Answer 22

r
In Python, the r before a string literal indicates that the string is a “raw” string. This means that backslashes () within the string are treated as literal backslashes, and not as escape characters. This is particularly useful for Windows file paths, which often contain backslashes.

Question 23

What method on a dataframe summarizes the data with mean?

Answer 23

print(dataset.describe())

It shows:

Count: The number of non-null entries in each column.

Mean
Standard Deviation (std)

Minimum (min)

25% (1st quartile): The value below which

25% of the data falls.

50% (median): The middle value, also known as the median.

75% (3rd quartile): The value below which

75% of the data falls.
Maximum (max): The largest value in each column.