Lecture 2

Python Fundamentals

Byeong-Hak Choe
bchoe@geneseo.edu

SUNY Geneseo

January 23, 2026

Python Basics

Values, Variables, and Data Types

  • A value is a literal such as a number or text.

  • Examples of values:

    • 352.3 → float
    • 22 → int
    • "Hello World!" → str

Variables

a = 7
print(a)

  • A variable is a name that refers to a value.
  • Think of a variable like a label attached to a value.
    • A variable is just a name (not the value itself).

Objects

  • Sometimes you will hear variables referred to as objects.
  • Everything that is not a literal (like 10) is an object in Python.

Assignment ( = )

# Here we assign the integer value 5 to the variable x.
x = 5   

# Now we can use the variable x in the next line.
y = x + 12  
y
  • In Python, = means assignment:
    • Right side is evaluated first
    • The result is assigned to the left side

Note

✅ In math, = often means “equal.”
✅ In Python, = means “store this value in the variable.”

✍️💬 Code and comment style

  • Two guiding principles:
    • Make things easy for your future self
    • Assume you will forget details later → write it down now
  • In Python, the comment character is #
    • Anything after # is ignored by the interpreter
    • Put comments right above the code they describe
  • Use Markdown/text cells to explain:
    • What the code cell is doing,
    • Any assumptions/choices,
    • How to interpret output.

⌨️ Most Useful Google Colab Shortcuts

Windows

  • Ctrl + Enter: Run cell
  • Alt + Enter: Run cell and add new cell below
  • Ctrl + /: Comment current line
  • Ctrl + Z: Undo
  • Ctrl + Shift + Z: Redo
  • Shift + ⬅️⬆️⬇️➡️: Select text
  • Shift + Ctrl + ⬅️⬆️⬇️➡️: Select to the beginning/end of the line

Mac

  • command + return: Run cell
  • option + return: Run cell and add new cell below
  • command + /: Comment current line
  • command + Z: Undo
  • command + shift + Z: Redo
  • shift + ⬅️⬆️⬇️➡️: Select text
  • shift + command + ⬅️⬆️⬇️➡️: Select to the beginning/end of the line

Types

  • The Type column shows Python’s official type name.

  • Mutable?

    • ✅ mutable → can be changed after creation
    • ❌ immutable → cannot be changed after creation

One List, Many Types

list_example = [10, 1.23, "like this", True, None]
print(list_example)
type(list_example)
  • Common built-in types:
    • int (e.g., 10)
    • float (e.g., 1.23)
    • str (e.g., "hello")
    • bool (e.g., True)
    • NoneType (e.g., None)
  • A list can contain mixed types.

Square Brackets [] in Python

vector = ['a', 'b']
vector[0]
  • Use [] to create a list
  • Use [] to access an element by index

Curly Braces {} in Python

{'a', 'b'}  # set
{'first_letter': 'a', 'second_letter': 'b'}  # dictionary (key:value pairs)
  • {} is used to denote a set or a dictionary
  • Use {} for sets and dictionaries

Parentheses () in Python

num_tup = (1, 2, 3)
sum(num_tup)
  • Use () for tuples
  • Use () to pass arguments into functions

🧺 Data Containers in Python—List and Tuple

✅ List

  • Stores multiple values in an ordered sequence
  • 🔄 Mutable: You can change it after creation
fruits = ["apple", "banana", "orange"]
fruits.append("grape")
fruits[0] = "pear"

✅ Tuple

  • Stores multiple values in an ordered sequence
  • 🔒 Immutable: Cannot be changed after creation
geneseo_coords = (40.7158, 77.8136)
geneseo_coords[0]           # 👀 reading is OK
# geneseo_coords[0] = 100   # ❌ cannot modify

🗂️ Data Containers in Python—Dictionaries

city_to_temp = {
    "Paris": 28,
    "London": 22,
    "New York": 18,
    "Seoul": 29,
    "Rochester": 10
}

city_to_temp["Paris"]          # 🔍 look up a value by key
city_to_temp["London"] = 32    # ✏️ update a value

city_to_temp.keys()        # 🔑 all keys
city_to_temp.values()      # 🌡️ all values
city_to_temp.items()       # 🧾 (key, value) pairs
  • Stores values as key→value pairs
  • Keys are used for fast lookup
  • Useful when you want to create associations (“mapping”)

Running on Empty

lst = []
tup = ()
dic = {}

  • Being able to create empty containers is sometimes useful, especially when using loops (e.g., for, while).

  • Q. What is the type of an empty list?

Operators ➕➖✖️➗

a = 10
b = 3

a + b
a - b
a * b
a ** b
a / b
a // b
a % b
  • All of the basic operators we see in mathematics are available to use:
    • + add
    • - subtract
    • * multiply
    • ** power
    • / divide
    • // integer divide (floor division)
    • % remainder

Operators Also Work for Lists and Strings

string_one = "This is an example "
string_two = "of string concatenation"
string_full = string_one + string_two
print(string_full)

string = "apples, "
print(string * 3)
  • + concatenates (joins) strings
  • * repeats a string multiple times
list_one = ["apples", "oranges"]
list_two = ["pears", "satsumas"]
list_full = list_one + list_two
print(list_full)
  • + concatenates (combines) lists into a longer list

Casting Variables

orig_number = 4.39898498
type(orig_number)
mod_number = int(orig_number)
mod_number
type(mod_number)
  • Casting changes type using built-in functions:
    • int(), float(), str()
    • If we try these, Python will do its best to interpret the input and convert it to the output type we’d like and, if they can’t, the code will throw a great big error.
  • Q. Classwork 2.1

✅❓ Booleans, Conditions, and if Statements

Booleans

10 == 20
10 == '10'
  • Boolean values are either:
    • True
    • False
int(True)
int(False)
  • In Python, Booleans can be converted to integers:
    • int(True) is 1
    • int(False) is 0

Boolean Operators

Here, both x and y are boolean.

  • Existing booleans can be combined by a boolean operator, which create a boolean when executed.

⚖️ Comparison Operators

Here, both x and y are variables.

🟰 The Equality Operator ==

boolean_condition1 = 10 == 20
boolean_condition2 = 10 == '10'

  • The == is an operator that compares the objects on either side and returns True if they have the same values

  • Q. What does not (not True) evaluate to?

  • Q. Classwork 2.2

Conditions → Boolean Expressions

x = 10

print(x > 5)     # True
print(x == 3)    # False
print(x != 0)    # True
  • A condition is an expression that returns True or False.

🚦🧾 Condition and if Statements

name = "Geneseo"
score = 99

if name == "Geneseo" and score > 90:
    print("Geneseo, you achieved a high score.")

if name == "Geneseo" or score > 90:
    print("You could be called Geneseo or have a high score")

if name != "Geneseo" and score > 90:
    print("You are not called Geneseo and you have a high score")
  • The real power of conditions comes when we start to use them in more complex examples, such as if statements.

The in Keyword: Membership Test

name_list = ["Lovelace", "Smith", "Hopper", "Babbage"]

"Lovelace" in name_list
"Bob" in name_list

  • in checks whether something exists inside a list, string, etc.

  • Q. Check if “a” is in the string “Wilson Ice Arena” using in. Is “a” in “Anyone”?

if-else Chain

score = 98

if score == 100:
    print("Top marks!")
elif score > 90 and score < 100:
    print("High score!")
elif score > 10 and score <= 90:
    pass
else:
    print("Better luck next time.")

if Statements with in

fruits = ["apple", "banana", "cherry"]

favorite = "banana"

if favorite in fruits:
    print(f"{favorite} is available!")
else:
    print(f"{favorite} is not in the list.")
  • The keyword in lets you check whether a value is present in a list, string, or other iterable.
  • This works seamlessly inside an if-else structure.
  • Useful for membership tests such as:
    • Validating if a company is in a stock list
    • Seeing if a word exists in a sentence

f-Strings (Formatted Strings) in Python

An f-string is a convenient way to create strings that include variable values directly inside the text.

✅ Key idea: Put an f before the quotation marks, then use {} to insert variables.

name = "Ada"
age = 20

message = f"My name is {name} and I am {age} years old."
print(message)

Indentation

x = 10

if x > 2:
    print("x is greater than 2")

  • In Python, indentation is required for code blocks, such as code inside:

    • a user-defined function (def ...),
    • a conditional (if / elif / else),
    • a loop (for / while).

  • Indentation is how Python knows which lines belong to a block.
    It tells the interpreter what should run inside the block (e.g., inside an if) and what should run after the block ends.

  • Standard Python style is 4 spaces per indentation level.

    • In Google Colab, you might see 2 spaces.

✂️ Slicing Methods with Strings and Lists

✂️ Slicing Methods

  • Slicing methods can apply for strings, lists, and DataFrames.

  • With slicing methods, we can get a subset of the data object.

  • Python is:

    • zero-indexed (things start counting from 0)
    • left inclusive
    • right exclusive when we specify a range

🧩 Slicing Patterns

letters = "abcdefghij"

letters[:]      # whole string
letters[3:]     # from index 3 to end
letters[:5]     # from start to index 4
letters[:-4]    # take the last 4 characters
letters[2:7]    # index 2 to 6
letters[::2]    # step size 2
letters[::-1]   # reverse
  • Slice format: [start : end : step]
    • start is included
    • end is excluded
    • step controls how many characters to skip
  • ✅ Important (when you “skip” numbers)
    If you omit start or end, Python fills them in automatically:
    • If start is missing → slicing starts from the beginning
    • If end is missing → slicing goes to the end
    • Example: letters[::2] means “from the beginning to the end, taking every 2nd character.”

📏 Length of a String and a List

string = "cheesecake"
len(string)
list_of_numbers = [1, 2, 3, 4, 5]
len(list_of_numbers)
  • Both strings and list objects support len()
  • len() tells you how many items/characters are stored

✂️🧾 Slicing with Lists

list_example = ['one', 'two', 'three']
list_example[ 0 : 1 ]
list_example[ 1 : 3 ]
  • Python is
    • a zero-indexed language (things start counting from zero);
    • left inclusive;
    • right exclusive when we are specifying a range of values.

✂️🧾 Slicing with Lists

  • We can think of items in a list-like object as being fenced in.
    • The index represents the fence post.

🔢 Get an Item by [index]

suny = ['Geneseo', 'Brockport', 'Oswego', 'Binghamton', 
        'Stony Brook', 'New Paltz'] 
suny[0]
suny[1]
suny[2]
suny[7]
suny[-1]
suny[-2]
suny[-3]
suny[-7]

✂️ Get an Item with a Slice

suny = ['Geneseo', 'Brockport', 'Oswego', 'Binghamton', 
        'Stony Brook', 'New Paltz'] 
suny[0:2]    # A slice of a list is also a list.
suny[ : : 2]
suny[ : : -2]
suny[ : : -1]
suny[4 : ]
suny[-6 : ]
suny[-6 : -2]
suny[-6 : -4]

⚙️ Functions, Arguments, and Parameters

⚙️ Functions

int("20") 
float("14.3")
str(5)
int("xyz")
print("DANL 210")
lst = [1,2,3,4]

type(lst)
len(lst) 
max(lst)
sum(lst)

Common built-in functions you will use often:

  • type() → data type
  • len() → length
  • max() → largest value
  • sum() → total
  • A function can take inputs (called arguments) and return an output.
  • Python also lets you define your own functions with the def keyword.
  • Later, we will use such user-defined function together with pandas.

⚙️📥 Functions, Arguments, and Parameters

print("Cherry", "Strawberry", "Key Lime")
print("Cherry", "Strawberry", "Key Lime", sep = "!")
print("Cherry", "Strawberry", "Key Lime", sep=" ")

  • To call a function, write its name followed by parentheses:

    • function_name(...)

  • Inside the parentheses, you provide arguments (inputs), separated by commas.

  • A parameter is the name used in the function definition for an expected input

    • Example: sep is a parameter of print().

  • A default argument is the value used automatically if you do not specify it.

    • For print(), the default separator is a space: sep = " ".

  • Q. Classwork 2.5

🔁 Loop with while and for

➕= Updating a Variable with +=

count = 1
count += 1
print(count)
count = 1
count = count + 1
print(count)
  • += is a shortcut assignment operator
  • It means: take the current value and add something to it
  • E.g.,: count += 1 means the same thing as count = count + 1.

🔄⏳ Repeat with while

count = 1        
while count <= 5:
    print(count)
    count += 1

How this loop works

  1. Start with count = 1.
  2. Check the condition: count <= 5
    • If it is True, run the loop body.
  3. Print the current value of count.
  4. Update count using count += 1.
  5. Go back to step 2 and repeat.
  6. The loop stops when count <= 5 becomes False.

🙋⌨️ Asking the user for input

stuff = input()
# Type something and press Return/Enter on Python Console 
# before running print(stuff)
print(stuff)
  • input() pauses the program and waits for the user to type something.
  • Whatever the user types is returned as a string.
  • This is useful when you want to make your code interactive.

🛑🔁 Cancel an Infinite Loop with break

while True:
    user_input = input("Enter 'yes' to continue or 'no' to stop: ")
    if user_input.lower() == 'no':
        print("Exiting the loop. Goodbye!")
        break
    elif user_input.lower() == 'yes':
        print("You chose to continue.")
    else:
        print("Invalid input, please enter 'yes' or 'no'.")
  • While loop is used to execute a block of code repeatedly until given boolean condition evaluated to False.
    • while True creates an infinite loop
  • The loop runs forever unless you stop it using break
  • break exits the loop immediately

⏭️🔁 Skip Ahead with continue

while True:
    value = input("Integer, please [q to quit]: ")
    if value == 'q': # quit
        break
    number = int(value)
    if number % 2 == 0: # an even number
        continue
    print(number, "squared is", number*number)
  • continue skips the rest of the loop body for the current iteration
  • Then Python jumps back to the top of the loop

🔁🔎 Iterate with for and in

  • Use a for loop when you want to go through each item in:
    • a string
    • a list
    • a range (range())
    • or any iterable object

🔁🚶 Repeat with a for Loop

for loop syntax (the pattern)

for <item_name> in <iterable>:
    <indented code block using <item_name>>
lst_nums = [0, 1, 2, 3, 4]

for num in lst_nums:
    print(num)

How this loop works

  1. Take the first item in lst_nums → set num = 0 → run print(num)
  2. Take the next item → set num = 1 → run print(num)
  3. Repeat for 2, 3, 4
  4. Stop after the last item

Two Ways to Loop Through an Iterable

while approach

word = 'thud'
offset = 0
while offset < len(word):
    print(word[offset])
    offset += 1

for approach

word = 'thud'
for letter in word:
    print(letter)
  • Which one do you prefer?

🔢 Generate Number Sequences with range()

list( range(1, 4) )
list( range(0, 4) )
list( range(4) )
list( range(0, 4, 2) )

for x in range(0, 4):
    print(x)
  • range() creates a sequence of integers without storing a full list
  • This is memory-efficient and very common in for loops
  • Syntax is similar to slicing:
  • range( start, stop, step )
    • start defaults to 0
    • step defaults to 1
    • the sequence stops right before stop

🔢🧾 Get Index + Value with enumerate()

fruits = ["apple", "banana", "orange"]

# default: starts counting at 0
for i, fruit in enumerate(fruits):
    print(i, fruit)

# start counting at 1
for i, fruit in enumerate(fruits, start=1):
    print(i, fruit)
  • enumerate() gives you two things while looping:
    • the index (i)
    • the value (fruit)
  • Very handy when you want to label, number, or track positions.
  • Syntax: enumerate(iterable, start=0)
    • iterable can be a list, tuple, string, etc.
    • start controls the first index (default is 0)

🛑🔂 Cancel a for Loop with break

word = 'thud'
for letter in word:
    if letter == 'u':
        break
    print(letter)
  • break exits the loop immediately

⏭️🔂 Skip in a for Loop with continue

word = 'thud'
for letter in word:
    if letter == 'u':
        continue
    print(letter)
  • continue skips the current iteration and moves to the next one

🔄 Loop Control: continue, pass, break

for num in range(1, 6):

    if num == 2:
        continue   # skip printing 2

    if num == 3:
        pass       # do nothing, move on

    if num == 4:
        break      # exit the loop

    print(num)

  • continue → skips to the next iteration

  • pass → does nothing (useful as a placeholder)

  • break → exits the loop completely

  • Q. Classwork 2.7

📋📘⚡ List and Dictionary Comprehensions

❓📋 What is List Comprehension?

  • A concise way to create or modify lists.
  • Syntax: [expression for item in iterable if condition]
  1. Creating a List of Squares:
squares = [x**2 for x in range(5)]
  1. Filtering Items:
numbers = [1, 2, 3, 4, 5, 6]
evens = [x for x in numbers if x != 2]

❓📘 What is Dictionary Comprehension?

  • A concise way to create or modify dictionaries.
  • Syntax: { key_expression : value_expression for item in iterable if condition}
  1. Creating a Dictionary of Squares:
squares_dict = {x: x**2 for x in range(5)}
  1. Filtering Dictionary Items:
my_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
filtered_dict = {k: v for k, v in my_dict.items() if v != 2}
  1. Swapping Keys and Values:
original_dict = {'a': 1, 'b': 2, 'c': 3}
swapped_dict = {v: k for k, v in original_dict.items()}

🛠️ Modifying Lists and Dictionaries

The . (dot) Operator on an Object

text = "GeNeSeO"

print(text.lower())   # method on a string object
print(text.upper())   # another string method
  • In Python, the dot operator (.) means:
    “go inside this thing and access one of its members.”
  • Many data types come with built-in methods you can call using the dot operator.
  • Example: For strings, .lower() is a method that returns a lowercase version of the string.
  • ✅ Here, text is a string object, and text.lower() means:
    “use the lower() method that belongs to the string type.”

🔧 Adding an Item to a List

  • append(): Adds an item to the end of the list.
my_list = [1, 2, 3]
my_list.append(4)

🔧 Deleting Items in a List

  • remove(): Deletes the first occurrence of value in the list.
my_list = [1, 2, 3, 4, 2]
my_list.remove(2)
  • List Comprehension: Removes items based on a condition.
my_list = [1, 2, 3, 4, 2]
my_list = [x for x in my_list if x != 2]
  • del statement: Deletes an item by index or a slice of items.
my_list = [1, 2, 3, 4]
del my_list[1] 
del my_list[1:3]

🔧 Adding/Updating Items in a Dictionary

  • update(): Adds new key-value pairs or updates existing ones.
my_dict = {'a': 1, 'b': 2}
my_dict.update({'c': 3})  
my_dict.update({'a': 10})  
my_dict.update({'e': -1, 'f': 0})

🔧 Deleting Items in a Dictionary

  • Dictionary Comprehension: Removes items based on a condition.
my_dict = {'a': 1, 'b': 2, 'c': 3}
my_dict = {k: v for k, v in my_dict.items() if v != 2}
  • del statement: Deletes an item by key.
my_dict = {'a': 1, 'b': 2, 'c': 3}
del my_dict['b']

⚠️🛡️ Handle Errors with try and except

⚠️️ Errors

short_list = [1, 2, 3]
positions = [0, 1, 5, 2]   # 5 is out of range

for i in positions:
    print(short_list[i])
  • If we don’t write our own exception handler, Python will:
    • print an error message (a traceback) explaining what went wrong, and
    • stop the program.

🛡 Exception Handlers (Why we need them)

  • In Python, when something goes wrong, an exception is raised.
  • If we’re running code that might fail, we can add an exception handler so the program can respond nicely instead of crashing.
  • Common examples:
    • Using an index that’s out of range for a list/tuple
    • Looking up a key that doesn’t exist in a dictionary

⚠️🛡️ Handle Errors with try and except

short_list = [1, 2, 3]
positions = [0, 1, 5, 2]   # 5 is out of range

for i in positions:
    try:
        print(short_list[i])
    except:
        print("Index error:", i, "is not between 0 and", len(short_list) - 1)
  • Use try to run code that might fail, and except to handle the error gracefully.
    • If an error occurs, Python raises an exception and runs the except block.
    • If no error occurs, Python skips the except block.
  • Q. Classwork 2.9

📦⬇️ Importing and Installing Modules, Packages, and Libraries

📦⬇️ Importing Modules, Packages, and Libraries

  • Python is a general-purpose programming language and is not specialized for numerical or statistical computation.

  • The core libraries that enable Python to store and analyze data efficiently are:

    • pandas
    • numpy

🐼 pandas

  • pandas provides Series and DataFrames which are used to store data in an easy-to-use format.

🔢 numpy

  • numpy, numerical Python, provides the array block (np.array()) for doing fast and efficient computations;

📥🧾 import statement

  • A module is basically a bunch of related codes saved in a file with the extension .py.

  • A package is basically a directory of a collection of modules.

  • A library is a collection of packages

  • We refer to code of other module/package/library by using the Python import statement.

import LIBRARY_NAME
  • This makes the code and variables in the imported module available to our programming codes.

📥🏷️ import statement with as or from

Keyword as

  • We can use the as keyword when importing the module/package/library using its canonical names.
import LIBRARY as SOMETHING_SHORT

Keyword from

  • We can use the from keyword when specifying Python module/package/library from which we want to import something.
from LIBRARY import FUNCTION, PACKAGE, MODULE

🛠️📦 pip tool

  • To install a library LIBRARY on your Google Colab, run:
!pip install LIBRARY
  • To install a library LIBRARY on your Anaconda Python, open your Spyder IDE, Anaconda Prompt, or Terminal and run:
pip install LIBRARY

🧩 The . (dot) Operator on a Library

  • In Python, the dot operator (.) means:
    “go inside this thing and access one of its members.”
    • module.name → access something defined inside the module
    • object.attribute or object.method() → access a property or function of an object

Example 1: import sys

import sys

print(sys.version)      # attribute: Python version info
print(sys.path)         # attribute: module search paths

✅ Here, sys is a module, and sys.version and sys.path are things inside the sys module.

Example 2: import datetime

import datetime

now = datetime.datetime.now()   # module.class.method()
today = datetime.date.today()   # module.class.method()
  • datetime (left side) is the module
  • datetime.datetime is a class inside the module
  • .now() is a method you can call from that class
  • A class is a blueprint for creating objects that bundles attributes and methods together.

Common Patterns to Remember

  • module.something
  • module.class_name.method_name()
  • object.method_name()

Read it like:

“start with the thing on the left → use . to reach something inside → then (maybe) call it with ()”