ISA 419: Data-Driven Security

class: center, middle, inverse, title-slide

.title[
# ISA 419: Data-Driven Security
]
.subtitle[
## 03: Python Functions
]
.author[
### <br>Fadel M. Megahed, PhD <br><br> Professor <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z" /></svg> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z" /></svg> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z" /></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z" /></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Spring 2025
]

---

## Quick Refresher of Last Class

✅ Use pseudocode to map out a problem.

✅ Python syntax, data types, and data structures.

✅ Convert data types using type casting.

✅ Manipulate lists and use methods on lists.

---

## Learning Objectives for Today's Class

- Understand the anatomy of a Python function, use arguments correctly, and construct your first function.

- Utilize built-in and anonymous functions (`map`, `lambda`, `filter`).

- Analyze your second dataset.

---
class: inverse, center, middle

# The Anatomy of a Python Function

---

## One Motivation for Functions

- **Reusability**: Functions allow you to reuse code.

.footnote[
<html>
<hr>
</html>

**Source:** Sonika Baniya (2021).  [First rule of coding: DRY (Don't Repeat Yourself)](https://sonikabaniya.medium.com/first-rule-of-coding-dry-dont-repeat-yourself-cfecc19449a5).
]

---

## What is a Function?

- A function is a **block of reusable code** that only runs when called.

.footnote[
<html>
<hr>
</html>

**Image Credits:** Created by the author (Fadel M. Megahed) for our ISA 419 course.
]

???

**Modularity:** Functions allow you to reuse code in different programs.

**Abstraction:** Functions allow you to focus on the big picture.

**Organization:** Functions allow you to break down your code into smaller, more manageable pieces.

---

## Recall: Python List Functions

.panelset[

.panel[.panel-name[Task]

- In [class02](https://fmegahed.github.io/isa419/spring2024/class02/02_python_basics.html), we introduced the following functions: `len()`, `max()`, `min()`, `sum()`, `sort()`, `index()`, `append()`, `pop()`, and `remove()`.

- For each of the above functions, what do you expect the function to **return**?

- How is the name of the function related to the **action** it performs?
]

.panel[.panel-name[Q1]
1. For each of the above functions, what do you expect the function to **return**?

.font90[
.can-edit.key-activity5a[
- `len()`: Edit me.  
- `max()`: Edit me.  
- `min()`: Edit me.  
- `sum()`: Edit me.  
- `sort()`: Edit me.
- `index()`: Edit me.  
- `append()`: Edit me.  
- `pop()`: Edit me.  
- `remove()`: Edit me.  
]
]
]

.panel[.panel-name[Q2]

- How is the name of the function related to the **action** it performs?

.can-edit.key-activity5b[
- Edit me.
]
]

]

---

## The Anatomy of a Python Function

``` python
def add_numbers(a, b):                       # `A: The function definition`
    
    """ This function sums two numbers """   # `B: The docstring`
    
    result = a + b                           # `C: The body of the function`
    return result                            # `C: The body of the function`

# Using the function
add_numbers(3, 5)                            # `D: Calling the function`
```

```
## 8
```

.footnote[
<html>
<hr>
</html>

**Footnotes:**

- Everything **within the function**, post the definition line, **is indented**.  
  - Vertical spacing here is for clarity, but not required (and likely violates Python's best practies).  
]
---

## The Anatomy of a Python Function (Cont.)

.font80[
**A: The function definition:**   
  - `def` is a .bold[keyword] that tells Python you are defining a function.  
  - `add_numbers` is the .bold[name] of the function, which should always be followed by parentheses and a colon.  
  - `a` and `b` are the .bold[parameters] of the function.  
  
**B: The docstring:**  
  - Optional: A .bold[docstring] is a string that describes what the function does.   
  
**C: The body of the function:**  
  - This is where the function does its work.  
  - The body is indented.  
  - Typically includes a `return` statement at the end.  
  
**D: Calling the function:**  
  - This is where you .bold[call] the function, i.e., tell Python to execute the code inside your function.  
]

---

## Python Parameters vs Arguments

.pull-left[
- **Parameters** are the variables in the function definition.  
  + `a` and `b` are the parameters in the `add_numbers` function.  
  
- **Arguments** are the values passed to the function when it is called.  
  + `3` and `5` are the arguments in the `add_numbers` function.
]

.pull-right[

# Using the function
add_numbers(3, 5)                            # `D: Calling the function`
```

```
## 8
```
]

---

## Python Parameters and Arguments

Python allows for several methods of passing arguments to a function. These include, but are not limited to the following:

- **Positional Arguments**: The arguments are passed to the function in the order in which they are defined.

- **Keyword Arguments**: The arguments are passed to the function with the parameter name.

- **Combination of Positional and Keyword Arguments**: The arguments are passed to the function in the order in which they are defined, .bold[followed by the keyword arguments].

---

## Class Activity: Modify the `add_numbers` Function

Modify the function `add_numbers` to take a list of numbers and return the sum of the numbers.

``` python
# Hints:
# ------
# 1. Change the function name to `sum_numbers` 
#    (so you do not have two functions with the same name).  
# 2. Change the parameters to a single parameter named `numbers_list`.  
# 3. Capitalize on the fact that the parameter input is now a list.
```

---

## Functions: Good Practices

- **Function Name:** Choose a descriptive name for your function.   
  + The name should describe what the function does.

- **Type Hints:** You can specify the type of the parameters and the return type.  
  + This is **not enforced by Python**, but it is a good practice.
  + For example:  
      * `def add_numbers(a: int, b: int) -> int:` or  
      * `def add_numbers(a: float, b: float) -> float:`.

- **Docstrings:** Always include a docstring to describe what the function does.  
  + This is a good practice and is used by Python's built-in `help()` function.

- **Return Statement:** Always include a `return` statement.  
  + If you do not include a `return` statement, the function will return `None`.

---
class: inverse, center, middle

# Built-In and Anonymous Functions in Python (`map`, `lambda`, `filter`)

---

## The `map` Function

.pull-left[
- The `map` function applies a given function to each item of an *iterable* (e.g., `list`). Its synatx is: 
  + `map(function, iterable)`.  
      * The `function` is the function we want to apply.  
      * The `iterable` is what we want to apply the function across.   
  
- The `map` function returns a **map object**, which is an iterator.

- To get the **results**, you must *type convert* the map object to a list.  
]

.pull-right[

``` python
def square(x: float) -> float:
    """This function squares a number"""
    return x ** 2
  
# Using the map function
numbers = [1, 2, 3, 4, 5]
map_operation = map(square, numbers)
squared_numbers = list(map_operation)

print('The map operation:', map_operation, '\n\n', 'The squared numbers:', squared_numbers, sep ='\n')
```

```
## The map operation:
## <map object at 0x00000236C3582A40>
## 
## 
## 
## The squared numbers:
## [1, 4, 9, 16, 25]
```
]

---

## The `lambda` Function

- The `lambda` function is an **anonymous function**, defined using the `lambda` keyword:   
    + **Anonymous:** They are **not** declared in the standard manner by using the `def` keyword.  
    + **Compact:** They allow writing functions in a concise way, often for short-term/throwaway functions.     
    + **Single Expression:** The body of a lambda is limited to just one expression. No statements or annotations are allowed; the function body is purely a single expression.
    
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/0*4eRr7IZ3sP2ZAE8H.png" alt="A schematic of the lambda function, with the keyword lambda followed by the parameters and a colon, and then the expression to be evaluated." width="50%" style="display: block; margin: auto;" />

.footnote[
<html>
<hr>
</html>

**Image Source:** John Vastola (2021). [Mastering Lambda Expressions in Python: A Hands-On Guide](https://levelup.gitconnected.com/mastering-lambda-expressions-in-python-a-hands-on-guide-e6f380701e96).
]

---

## The `lambda` Function (Cont.)

``` python
numbers = [1, 2, 3, 4, 5]

map_operation = map(lambda x: x ** 2, numbers)

squared_numbers = list(map_operation)

print(
  'The map operation:', map_operation, '\n', 
  'The squared numbers:', squared_numbers, sep ='\n'
)
```

```
## The map operation:
## <map object at 0x00000236C3532740>
## 
## 
## The squared numbers:
## [1, 4, 9, 16, 25]
```

---

## The `filter` Function

.pull-left[
- The `filter` function constructs an iterator from elements of an iterable for which a function returns `True`.  
  + Its syntax is: `filter(function, iterable)`.  
  
- The `filter` function returns a **filter object**, which is an iterator.

- To get the **results**, you must *type convert* the filter object to a list.
]

.pull-right[

``` python
def is_even(x: int) -> bool:
    """This function checks if a number is even"""
    return x % 2 == 0

# Using the filter function
numbers = [1, 2, 3, 4, 5]
filter_step = filter(is_even, numbers)
even_numbers = list(filter_step)

print('The filter operation:', filter_step, '\n', 
'The even numbers:', even_numbers, sep ='\n')
```

```
## The filter operation:
## <filter object at 0x00000236C35B74F0>
## 
## 
## The even numbers:
## [2, 4]
```
]

---

## Evaluating your Understanding so Far: A Kahoot

.bold[Let's evaluate your understanding of the material so far]

- Go to [Kahoot](https://kahoot.it) and enter the game pin shown on screen.

- You will be asked to answer **7 multiple choice questions**.

- You will receive **points** for answering each question **correctly** and **quickly**, i.e., your points are impacted by your speed in addition to obviously answering each question correctly.

- The winner <svg viewBox="0 0 576 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:gold;" xmlns="http://www.w3.org/2000/svg">  <path d="M552 64H448V24c0-13.3-10.7-24-24-24H152c-13.3 0-24 10.7-24 24v40H24C10.7 64 0 74.7 0 88v56c0 35.7 22.5 72.4 61.9 100.7 31.5 22.7 69.8 37.1 110 41.7C203.3 338.5 240 360 240 360v72h-48c-35.3 0-64 20.7-64 56v12c0 6.6 5.4 12 12 12h296c6.6 0 12-5.4 12-12v-12c0-35.3-28.7-56-64-56h-48v-72s36.7-21.5 68.1-73.6c40.3-4.6 78.6-19 110-41.7 39.3-28.3 61.9-65 61.9-100.7V88c0-13.3-10.7-24-24-24zM99.3 192.8C74.9 175.2 64 155.6 64 144v-16h64.2c1 32.6 5.8 61.2 12.8 86.2-15.1-5.2-29.2-12.4-41.7-21.4zM512 144c0 16.1-17.7 36.1-35.3 48.8-12.5 9-26.7 16.2-41.8 21.4 7-25 11.8-53.6 12.8-86.2H512v16z"></path></svg> (i.e., the one with the most points after the 7 questions) receives a $10 Starbucks <svg viewBox="0 0 640 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:green;" xmlns="http://www.w3.org/2000/svg">  <path d="M192 384h192c53 0 96-43 96-96h32c70.6 0 128-57.4 128-128S582.6 32 512 32H120c-13.3 0-24 10.7-24 24v232c0 53 43 96 96 96zM512 96c35.3 0 64 28.7 64 64s-28.7 64-64 64h-32V96h32zm47.7 384H48.3c-47.6 0-61-64-36-64h583.3c25 0 11.8 64-35.9 64z"></path></svg> gift card.

---
class: inverse, center, middle

# Analyzing Your Second Dataset

---

## Analyzing a Simulated Equifax Breach Dataset

.panelset[
.panel[.panel-name[Task]

- Download the `simulated_equifax_breach_data.csv` file from [Canvas](https://miamioh.instructure.com/courses/229048/files/34627330?module_item_id=5789098).

- Load the dataset into [Google Colab](https://colab.research.google.com/).

- Answer the questions in the next tabs.

- You can work in groups of 2-3 students.  
]

.panel[.panel-name[Task 1]

- Build on this code to print the first 5 rows of `ssn`.

``` python
import pandas as pd

# you will NOT need to have the same folder structure as I do
equifax_df = pd.read_csv('../../data/simulated_equifax_breach_data.csv')

# converting the pandas df into lists since we have not discussed pandas yet
name = list(equifax_df['Name'])
address = list(equifax_df['Address'])
phone_num = list(equifax_df['Phone Number'])
date_of_birth = list(equifax_df['Date of Birth'])
ssn = list(equifax_df['Social Security Number'])
driver_license = list(equifax_df['Driver License Number'])

type(phone_num) # what type of object is this?
```

```
## <class 'list'>
```
]

.panel[.panel-name[Task 2]

- Count the number of observations in our dataset, through the length of any of the lists.

``` python
# Hints:
# ------
# We have talked about this in class, in our discussion of list functions.
```

]

.panel[.panel-name[Task 3]

- How many unique names are in the dataset?

``` python
# Hints:
# ------
# 1. Use `type casting` to convert the list into a `type that only stores unique values`.  
# 2. Use the `len()` function to count the number of unique names.  
```

]

.panel[.panel-name[Task 4]

- Use `map()` and `lambda` to extract area codes from phone numbers.

``` python
# Hints:
# ------
# 1. Use the `map()` function to apply a lambda function to each phone number.
# 2. Use the `lambda` function to extract the first 3 digits of each phone number.
# 3. Convert the map object to a `list` to see the results.  
```

]

.panel[.panel-name[Task 5]

- Use `filter()` to count the number of people from Butler County (i.e., area code 513).

``` python
# No hints provided; you are a pro now :)  
```

]

---
class: inverse, center, middle

# Recap

---

## Summary of Main Points

By now, you should be able to do the following:

- Understand the anatomy of a Python function, use arguments correctly, and construct your first function.

- Utilize built-in and anonymous functions (`map`, `lambda`, `filter`).

- Analyze your second dataset.

---

## 📝 Review and Clarification 📝

1. **Class Notes**: Take some time to revisit your class notes for key insights and concepts.
2. **Zoom Recording**: The recording of today's class will be made available on Canvas approximately 3-4 hours after the end of class.
3. **Questions**: Please don't hesitate to ask for clarification on any topics discussed in class. It's crucial not to let questions accumulate.