import warnings
warnings.filterwarnings("ignore")
# Suppress warnings to keep things tidy

# Lists: Store any group of items, like a shopping list
shopping = ["apples", "bread", "milk", "eggs"]
print(shopping)

# Tuples: Similar to lists, but cannot be changed
coordinates = (10, 20)
print(coordinates)

# Sets: Unordered, no duplicates
my_badges = set(["python", "data", "python", "study"])
print(my_badges)

# Dictionaries: Key-value pairs, like a contact list
contacts = {"Alice": "1234", "Bob": "5678"}
print(contacts)

# Accessing a list by index
print(shopping[1])  # bread

# Accessing a dictionary by key
print(contacts["Bob"])

# Safe dictionary access: get() returns None if missing (or your default)
print(contacts.get("Charlie"))        # Not found
print(contacts.get("Charlie", "N/A")) # Not found, uses your default value

# Adding and updating a list
shopping.append("butter")
shopping[0] = "green apples"
print(shopping)

# Add to a set (only unique items stay)
my_badges.add("study")
my_badges.add("cooking")
print(my_badges)

# Updating or adding to a dictionary
contacts["Charlie"] = "9012"
contacts["Alice"] = "1111"
print(contacts)

# Removing items from collections
shopping.remove("milk")
my_badges.discard("python")
contacts.pop("Bob")
print(shopping)
print(my_badges)
print(contacts)

# Quick stats with a list of numbers
temperatures = [22, 19, 25, 20, 23, 19]
print("Length:", len(temperatures))
print("Max:", max(temperatures))
print("Min:", min(temperatures))
print("Sorted:", sorted(temperatures))

# Loop through a list
for item in shopping:
    print("I need to buy", item)
    

# Loop through a dictionary
for name, number in contacts.items():
    print(name, "has number", number)
    

# List Comprehensions: Build lists quickly
squares = [x * x for x in range(1, 6)]
print(squares)

# Data setup
import pandas as pd
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv"
df = pd.read_csv(url)
print("Data shape:", df.shape)
df.head()

# Turn data into lists and a dictionary
months = df["Month"].tolist()
passengers = df["Passengers"].tolist()
monthly_dict = dict(zip(months, passengers))
print(months[:3], "...", len(months))
print(passengers[:3], "...", len(passengers))
print(list(monthly_dict.items())[:3])

# Quick line plot of data
import matplotlib.pyplot as plt
plt.figure(figsize=(10,4))
plt.plot(months, passengers, marker="o")
plt.title("Monthly Airline Passengers")
plt.xlabel("Month")
plt.ylabel("Passengers")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Mini-project: Find the month with the most passengers
max_month = months[passengers.index(max(passengers))]
print("Busiest month:", max_month)
print("Passengers:", max(passengers))

# Mini-project: Count months above average
average = sum(passengers) / len(passengers)
above_avg = [m for m, p in zip(months, passengers) if p > average]
print("Months above average:", len(above_avg))
print(above_avg[:5])

# Challenge: What do you buy most?
your_list = input("Enter foods, comma separated: ").split(",")
your_set = set([item.strip() for item in your_list])
print("Unique foods:", your_set)