import warnings
warnings.filterwarnings("ignore")

# Start by printing a simple hello message
print("Hello, welcome to visual design in Python!")

# Variables hold information
title = "Monthly Airline Passengers"
year = 1950
print(title, year)

# Lists let us store groups of items
colors = ["red", "blue", "green", "yellow"]
print("Palette:", colors)

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

# Plotting the data: first visualization
import matplotlib.pyplot as plt
plt.plot(data["Passengers"])
plt.title("Monthly Airline Passengers")
plt.xlabel("Month")
plt.ylabel("Number of Passengers")
plt.show()

# Highlighting with color
plt.figure()
plt.plot(data["Passengers"], color="purple")
plt.title("Passengers (Now in Purple)")
plt.xlabel("Month")
plt.ylabel("Number of Passengers")
plt.show()

# Safe access and error: What if column is missing?
try:
    print(data["NotAColumn"].head())
except KeyError:
    print("That column is not in the data.")
    

# Change the number in the top row
print("Before:", data.loc[0, "Passengers"])
data.loc[0, "Passengers"] = 120
print("After:", data.loc[0, "Passengers"])

# Removing extra rows - cleaning up
before_rows = len(data)
data = data.drop(data.index[-1])
after_rows = len(data)
print("Rows before:", before_rows, "Rows after:", after_rows)

# Useful summary: statistics methods
print("Mean passengers:", data["Passengers"].mean())
print("Biggest month:", data["Passengers"].max())
print("Smallest month:", data["Passengers"].min())

# Looping: print months with high passenger numbers
for i, row in data.iterrows():
    if row["Passengers"] > 400:
        print(row["Month"], "had more than 400 passengers.")
        

# List comprehension: find very busy months
busy_months = [row["Month"] for i, row in data.iterrows() if row["Passengers"] > 450]
print("Busy months:", busy_months)

# Sorting: see top months
sorted_data = data.sort_values("Passengers", ascending=False)
print(sorted_data.head(3))

# Tagging busy months for highlighting
data["Busy"] = data["Passengers"] > 400
print(data[["Month", "Passengers", "Busy"]].head())

# Real-world chart: color by busy tag
colors = ["red" if busy else "skyblue" for busy in data["Busy"]]
plt.figure(figsize=(12,5))
plt.bar(data["Month"], data["Passengers"], color=colors)
plt.xticks(rotation=90)
plt.title("Passengers per Month: Busy in Red")
plt.ylabel("Number of Passengers")
plt.tight_layout()
plt.show()

# Mini-project part 1: Ask the user for a key month to highlight
special_month = input("Which month do you think is important? ")
data["Special"] = data["Month"] == special_month
print("Special month set:", special_month)

# Mini-project part 2: Show the special month in gold
colors = ["gold" if special else ("red" if busy else "skyblue") for busy, special in zip(data["Busy"], data["Special"])]
plt.figure(figsize=(12,5))
plt.bar(data["Month"], data["Passengers"], color=colors)
plt.xticks(rotation=90)
plt.title("Passengers per Month: Highlighted")
plt.ylabel("Number of Passengers")
plt.tight_layout()
plt.show()

# Best practices: clear labels and colorblind-friendly palettes
plt.figure(figsize=(10,4))
cb_colors = ["#000000" if special else ("#0072B2" if busy else "#E69F00") for busy, special in zip(data["Busy"], data["Special"])]
plt.bar(data["Month"], data["Passengers"], color=cb_colors)
plt.title("Accessible Design: Clear and Friendly Colors")
plt.xlabel("Month")
plt.ylabel("Passengers")
plt.tight_layout()
plt.show()

# Troubleshooting: what if user picks a missing month?
try:
    if not data["Special"].any():
        raise ValueError("Month not found.")
except ValueError:
    print("That month does not exist. Please check your spelling or format.")
    

# Extra tip: Export your improved data to a new file
data.to_csv("improved_airline_design.csv", index=False)
print("CSV saved!")

# Challenge: Mark all months above a custom passenger count
threshold = int(input("Enter a number to set your own busy level: "))
data["YourBusy"] = data["Passengers"] > threshold
print(data[["Month", "YourBusy"]].head())