# First, suppress warnings to keep things tidy
import warnings
warnings.filterwarnings("ignore")

# Now, let us import the basic libraries you will use
import pandas as pd
import matplotlib.pyplot as plt

# Data setup: Let us fetch a real dataset of monthly airline passengers
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv"
data = pd.read_csv(url)
print("Data shape:", data.shape)
data.head()

# Quick preview: Plotting the time series
plt.figure(figsize=(10,5))
plt.plot(data["Month"], data["Passengers"], marker="o")
plt.title("Monthly Airline Passengers (1949-1960)")
plt.xlabel("Month")
plt.ylabel("Number of Passengers")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Highlighting trends can help your viewers
plt.figure(figsize=(10,5))
plt.plot(data["Month"], data["Passengers"], color="skyblue", linewidth=2, label="Passengers")
plt.title("How airline passengers increased over time")
plt.xlabel("Month")
plt.ylabel("Passengers")
plt.legend()
plt.grid(True, linestyle=":", alpha=0.5)
plt.xticks(data["Month"][::12], rotation=45)
plt.tight_layout()
plt.show()

# Annotating visuals makes stories pop!
plt.figure(figsize=(10,5))
plt.plot(data["Month"], data["Passengers"], color="mediumseagreen", linewidth=2)
plt.title("Biggest jump in airline passengers")
plt.xlabel("Month")
plt.ylabel("Passengers")
max_month = data.iloc[data["Passengers"].idxmax()]["Month"]
max_passengers = data["Passengers"].max()
plt.annotate(
    "Peak: {}".format(max_passengers),
    xy=(max_month, max_passengers),
    xytext=(max_month, max_passengers+60),
    arrowprops=dict(facecolor="red", shrink=0.05),
    color="red"
)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Catching common pitfalls: Missing or wrong data
print("Are there any missing values?", data.isnull().values.any())
if data.isnull().values.any():
    print("Let us see where:")
    print(data.isnull().sum())
else:
    print("All good! No missing values to worry about.")

    

# Creating new insights: Calculate yearly totals
data["Year"] = data["Month"].str[:4]
yearly = data.groupby("Year")["Passengers"].sum().reset_index()
print(yearly)

# Visualizing yearly growth with a bar chart
plt.figure(figsize=(8,5))
plt.bar(yearly["Year"], yearly["Passengers"], color="coral")
plt.title("Yearly Airline Passengers")
plt.xlabel("Year")
plt.ylabel("Total Passengers")
plt.tight_layout()
plt.show()

# Making comparisons: Highlighting changes
plt.figure(figsize=(8,5))
bars = plt.bar(yearly["Year"], yearly["Passengers"], color="lightgrey")
bars[-1].set_color("red")
plt.title("Which year had the most passengers?")
plt.xlabel("Year")
plt.ylabel("Passengers")
plt.tight_layout()
plt.show()

# Filtering the data: Focus on recent years
recent = data[data["Year"].astype(int) >= 1955]
plt.figure(figsize=(8,5))
plt.plot(recent["Month"], recent["Passengers"], marker="o", color="navy")
plt.title("Passengers from 1955 onward")
plt.xlabel("Month")
plt.ylabel("Passengers")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Combining data: Add a moving average for smooth trends
data["MA_12"] = data["Passengers"].rolling(window=12).mean()
plt.figure(figsize=(10,5))
plt.plot(data["Month"], data["Passengers"], label="Monthly", color="lightgrey")
plt.plot(data["Month"], data["MA_12"], label="12-month Avg", color="blue", linewidth=2)
plt.title("Monthly vs. Smoothed Trend (12-month average)")
plt.xlabel("Month")
plt.ylabel("Passengers")
plt.legend()
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# Troubleshooting: Handling broken charts
try:
    plt.plot(data["WrongColumn"])
except Exception as e:
    print("Whoops! Something went wrong:", e)

    

# Quick tip: Ask for help with input()
question = input("What title would you like for your next chart? ")
print("Awesome! Your chart will use the title:", question)