Flight Passenger Data Analysis with NumPy

Create a Python script that analyzes flight passenger data using NumPy arrays. Use the built-in "flights" dataset to calculate statistics about airline passenger numbers over time.

Key Requirements:
- Data Loading: Load the built-in flights dataset and convert to NumPy arrays:
import seaborn as sns
import numpy as np
flights = sns.load_dataset('flights')
passengers = flights['passengers'].values

- Array Operations: Work with the passenger data using NumPy:
1. Convert the passenger column to a NumPy array
2. Reshape data if needed to work with months/years

- Statistical Analysis: Use NumPy functions to calculate:
1. Mean, median, and standard deviation of passenger numbers
2. Maximum and minimum passenger months
3. Find months with above-average passengers
4. Calculate year-over-year growth (comparing same months)

- Data Filtering: Use NumPy operations to:
1. Find all months with more than 400 passengers
2. Identify the busiest and slowest travel months
3. Compare summer (Jun–Aug) with winter (Dec–Feb) by reporting the average passengers for each season

- Output: Display results in a formatted, easy-to-read manner showing:
1. Basic statistics summary
2. Lists of high/low travel periods
3. Simple insights about travel patterns

Deliverables:
A Python (.py) file that:
- Loads the flights dataset and converts to NumPy arrays
- Uses NumPy functions for statistical calculations
- Performs array operations and filtering
- Provides clear, formatted output with travel insights
- Includes comments explaining each step

Estimate budget: $50