WEEK5

Question 1

Pandas

Answer 1

a python library that is used to work with data in a structured way (like working with table sin excel).

Question 2

Why is pandas used

Answer 2

it makes it easier to store, manipulate and analyze data.

Question 3

pandas series

Answer 3

a column in a spreadsheet of list of numbers with labels attached to each value. ONE-DIMENSIONAL array of indexed data.

Question 4

what happens if you create a series without specifying an index?

Answer 4

pandas assigns default integer indexes (0, 1, 2, 3, ..)

Question 5

.values

Answer 5

shows the actual numbers inside the series

Question 6

.index

Answer 6

shows the index positions

Question 7

numpy

Answer 7

working with numbers and data in a super fast way and efficient way.

Question 8

whats the relationship between pandas and numpy

Answer 8

pandas is built on top of numpy

Question 9

does pandas follow the same indexing rues as python lists and NumPy arrays (data [start:end])

Answer 9

yes, you dont include the end

Question 10

does pandas allow non-sequential numeric indexes (2, 5, 3, 7, …)

Answer 10

yes

Question 11

where does index matter: pandas or numpy?

Answer 11

pandas

Question 12

whats the difference with slicing between python and pandas

Answer 12

with pandas you include the last index

Question 13

pandas dataframe

Answer 13

like a table and it is made up of multiple series aligned together. It has rows indices and column names.

Question 14

whats special about pandas and creating/changing indexes

Answer 14

you cannot change its values (its immutable)

Question 15

why can you not change the indexes in pandas?

Answer 15

1. prevents unintended changes
2. makes data structures more efficient
3. allows the same index to be used in multiple dataframes without risk of modification

Question 16

what does the ‘interaction’ do

Answer 16

finds elements that exist in both indA and indB

Question 17

what does union do? (|)

Answer 17

combines all elements from both sets, keeping only unique values.
so indA = pd.index ([1,3,5,7,9])
indB = pd.index ([2,3,5,7,11)]

union: 1,2,3,5,7,9,11

Question 18

what does ‘symmetric difference’ (^) do

Answer 18

finds elements that are only in one of the two sets (but not in both)

Question 19

Pandas provides three methods for indexing (loc, loc, ix)

Answer 19

1.Using explicit index (loc): selects based on the actual index label. Works like dictionary-style lookup.

2.Using implicit index (iloc): selects based on the position number (like a list).

3.Using ix (deprecated): ix method combined both explicit and implicit indexing but is no longer used in Pandas.

Question 20

Ufuncs

Answer 20

functions that work element-wise on arrays. they are useful for mathematical transformations in data analysis

Question 21

When performing operations between two Pandas Series, how does Pandas align the data?

Answer 21

Pandas matches data by index labels, not by position.

Question 22

What happens if an index is missing in one of the Pandas Series during an operation?

Answer 22

The result for that index will be NaN (Not a Number).

Question 23

If population has ‘Texas’ and area has ‘Texas’, what happens when calculating population / area?

Answer 23

The operation works because both Series have the ‘Texas’ index.

Question 24

What happens if population has ‘New York’ but area does not when calculating population / area?

Answer 24

The result for ‘New York’ is NaN because one value is missing.

Question 25

What does (population / area).fillna(0) do?

Answer 25

It replaces all NaN results with 0.

Question 26

rng.randint(0, 20, (2, 2))

Answer 26

Generates random numbers between 0 and 19 (20 is exclusive). (2, 2) means 2 rows and 2 columns. This creates a 2x2 NumPy array filled with random numbers.

Question 27

What are the two ways missing values can be represented in Pandas?

Answer 27

None → used for objects (like strings) NaN (Not a Number) → used for numerical data

Question 28

What happens when None appears in a numerical NumPy array or Series?

Answer 28

Pandas automatically converts None to NaN in numerical Series.

Question 29

What is the dtype of this array: np.array([1, None, 3, 4])?

Answer 29

dtype=object because None is not a number.

Question 30

Why does adding None to a NumPy array change the dtype to object?

Answer 30

Because None is not numeric, NumPy treats the array as a collection of Python objects instead of numbers.

Question 31

Objected oriented programming

Answer 31

is a way of writing computer programs using objects

Question 32

Object

Answer 32

Objects represent real-world entities (like a car, a person, or an account)

Question 33

Classes

Answer 33

Classes define the blueprint or structure for objects

Question 34

there are two alternatives of objected oriented programming (OOP): imperative programming declarative programming

Answer 34

Imperative Programming: focuses on step-by-step instructions. Declarative Programming focuses on what to do rather than how to do it.

Question 35

an example of imperative programming

Answer 35

A recipe where you tell the computer what to do in order.

Question 36

an example of declarative programming

Answer 36

Telling a restaurant, "I want a pizza" you don’t explain how to make it.

Question 37

the core principles of objected oriented programming 1. Encapsulation

Answer 37

Encapsulation: Groups related variables and functions together You don’t need to know how a car engine works — just use the steering and pedals. Everything (engine, fuel, wires) is hidden inside.

Question 38

the core principles of objected oriented programming 1. Encapsulation 2. Abstraction

Answer 38

Focuses only on important details, ignoring unnecessary ones. Simplifies complexity: You drive a car without knowing how fuel turns into motion.

Question 39

The core principles of objected oriented programming 1. Encapsulation 2. Abstraction 3. Polymorphism

Answer 39

The same function works differently for different objects One object, many forms: Different people drive the same car in their own way.

Question 40

The core principles of objected oriented programming 1. Encapsulation 2. Abstraction 3. Polymorphism 4. Inheritance

Answer 40

New things can borrow features from existing ones. Reuses features: A SportsCar inherits from Car but adds speed features.

Question 41

why use OOP? 1. modularity 2. reusability 3. scalability 4. efficiency

Answer 41

-Modularity: Code is organized into reusable objects so it is easier to understand, fix, or change one part without breaking the whole program. -Reusability: Objects and classes can be reused across programs. -Scalability: Easier to extend and maintain as programs grow. -Efficiency: Helps in structuring complex systems effectively.

Question 42

each object has 3 fundamental characteristics

Answer 42

1. identify: each object has a unique identifier that distinguishes it from other objects 2. state: this refers to the attributes or properties of an object, which can be modified, but start out based on the class initialization 3. behavior: this is defined by the methods that an object can perform or respond to

Question 43

What is the cake and what the recipe: object and class

Answer 43

Object: actual cake Class: recipe

Question 44

constructer

Answer 44

special method that initializes objects when they are created

Question 45

explain the example of a new phone with a constructor

Answer 45

When you first turn it on, it asks your name, language, and Wi-Fi, that’s the setup (constructor)

Question 46

Why do we use .groupby() when working with sales data?

Answer 46

To combine similar rows (like same store and country) and calculate total or average sales per group.

Question 47

What does df represent in Pandas?

Answer 47

df is the variable name for your DataFrame — your table of data.

Question 48

What’s the correct basic syntax to filter a DataFrame?

Answer 48

Use df[condition] — only the condition goes inside the square brackets.

Question 49

Should you ever write df(df[...]) when filtering?

Answer 49

No. You never put the whole df inside the brackets. Only the condition goes inside.