# Create correlation matrix
correlation_matrix = continous_df.corr()
plt.figure(figsize=(20, 10))

# Generate heatmap
heatmap = sns.heatmap(correlation_matrix, annot=True, linewidths=0.5)
plt.show()

# Plotting the average fare, lowest fare, and largest carrier fare by year
plt.figure(figsize=(12, 8))

# Grouping the data by year and calculating the average fares
average_fare_by_year = df.groupby('Year')[['fare', 'fare_low', 'fare_lg']].mean().reset_index()

# Creating line plots for average fare, lowest fare, and largest carrier fare
sns.lineplot(x='Year', y='fare', data=average_fare_by_year, marker='o', label='Average Fare')
sns.lineplot(x='Year', y='fare_low', data=average_fare_by_year, marker='o', label='Lowest Fare')
sns.lineplot(x='Year', y='fare_lg', data=average_fare_by_year, marker='o', label='Largest Carrier Fare')

# displaying min and max points for average fare
min_fare_year = average_fare_by_year.loc[average_fare_by_year['fare'].idxmin()]
max_fare_year = average_fare_by_year.loc[average_fare_by_year['fare'].idxmax()]

plt.scatter(min_fare_year['Year'], min_fare_year['fare'], color='red', s=100, zorder=5)
plt.scatter(max_fare_year['Year'], max_fare_year['fare'], color='green', s=100, zorder=5)
plt.text(min_fare_year['Year'], min_fare_year['fare'], f"Min: {min_fare_year['fare']:.2f}", color='red', ha='left', va='bottom')
plt.text(max_fare_year['Year'], max_fare_year['fare'], f"Max: {max_fare_year['fare']:.2f}", color='green', ha='right', va='bottom')

# Adding titles and labels
plt.title('Average Fare, Lowest Fare, and Largest Carrier Fare by Year')
plt.xlabel('Year')
plt.ylabel('Fare')
plt.legend(title='Fare Type')

# Displaying the plot
plt.show()

# Calculate the annual average fare for each year
annual_avg_fare = df.groupby('Year')['fare'].mean().reset_index()

# Rename the column to indicate this is the annual average fare
annual_avg_fare.rename(columns={'fare': 'annual_avg_fare'}, inplace=True)

# Calculate the year-over-year fare inflation rate
annual_avg_fare['fare_inflation_rate'] = annual_avg_fare[
    'annual_avg_fare'].pct_change() * 100

# Display the results
annual_avg_fare[['Year', 'annual_avg_fare', 'fare_inflation_rate']]

# Plotting
fig, ax = plt.subplots(figsize=(12, 6))

# Use different colors for positive and negative inflation rates
colors = ['green' if x >= 0 else 'red' for x in annual_avg_fare[
    'fare_inflation_rate']]

annual_avg_fare.plot(
    x='Year',
    y='fare_inflation_rate',
    kind='bar',
    ax=ax,
    color=colors,
    legend=False
)

# Add titles and labels
ax.set_title('Year-over-Year Fare Inflation Rate')
ax.set_xlabel('')
ax.set_ylabel('Fare Inflation Rate (%)')

# Display the plot
plt.show()

# Plotting market share for each airline with distinct styles
sns.lineplot(x='Year', y='large_ms', data=df_southwestern,
             label='Southwest Airlines Market Share',
             linewidth=.5, marker='o', color='blue')
sns.lineplot(x='Year', y='lf_ms', data=df_american,
             label='American Airlines Market Share',
             linestyle='--',
             linewidth=.5,
             marker='s',
             color='green')
sns.lineplot(x='Year', y='lf_ms', data=df_delta,
             label='Delta Airline Market Share',
             linestyle='-.',
             linewidth=.5,
             marker='d',
             color='orange')

# Adding a title and labels
plt.title('Market Share of Carriers with Lowest Fares Over the Years',
          fontsize=18, fontweight='bold')
plt.xlabel('', fontsize=14)
plt.ylabel('Market Share (%)', fontsize=14)

# Enhancing the legend
plt.legend(title='Airlines',
           title_fontsize='13',
           fontsize='12',
           loc='upper left')

# Customizing the grid for better readability
plt.grid(True, linestyle='--', linewidth=0.5)

# Adjusting tick parameters
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)

# Display the plot
plt.show()