To the Office of Transportation at The 22nd Century Sporting League,
After our inaugural 2101 season, The League is looking for ways to optimize our game scheduling process and costs. We know that transportation logistics are a major variable to consider during scheduling, and as such, we’ve got a few questions for you.
Our primary areas of focus are surrounding the number of jets that The League needs to own, and the cost of fuel for those flights. If we want The League to enjoy continued success, we'll need to make sure we manage transportation costs.
We’re sharing schedule data for the upcoming 2102 season. On each row, you’ll find information about which teams are needing to travel to their next set of games, the time the flight will likely depart (based on our estimations of gameplay durations) and the time the flight will likely land.
Additionally, we're also providing the fuel price that was paid each day during this past 2101 season. The fuel price fluctuates over time, but we're hoping you'll be able to project it to the future to help with the analysis.
The Data
team_flights.csv
| Column | Description |
|---|---|
team_name | Official team name |
departure_datetime | Date and Time (in UTC) when the flight will depart |
landing_datetime | Date and Time (in UTC) when the flight will land |
fuel_price.csv
| Column | Description |
|---|---|
date | Date when the fuel price was recorded |
fuel_price | Corresponding fuel price (in $ per gallon) |
Important Things to Know
- You can assume that the flight's average speed is 500 MPH. (So, as an example, a 2-hour flight would travel 1000 miles)
- You can assume that each team’s jet fills up with fuel equivalent to 1 gallon per mile-of-travel
- You can assume that the jet is fueled on the day the travel departs (and thus can use the fuel price corresponding to the departure date)
# Import required modules
import pandas as pd
import numpy as np
from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
# Explore the data
team_flights = pd.read_csv('./team_flights.csv')
fuel_prices = pd.read_csv('./fuel_prices_2101.csv',
index_col='date')
# Some basic data cleaning and pre-processing
team_flights['departure_datetime'] = pd.to_datetime(team_flights['departure_datetime'])
team_flights['landing_datetime'] = pd.to_datetime(team_flights['landing_datetime'])
fuel_prices.index = pd.DatetimeIndex(fuel_prices.index).to_period('D')
team_flights.head()# Plot the landing distances grouped by team
fig, ax = plt.subplots(figsize=(14, 8))
# Group by team and plot landing distances
for team_name, group in team_flights.groupby('team_name'):
ax.plot(group['landing_datetime'], group['travel_distance_miles'], label=team_name)
ax.set_xlabel('Landing Date')
ax.set_ylabel('Landing Distance (miles)')
ax.tick_params(axis='y')
# Set title and legend
fig.suptitle('Landing Distances Over Time by Team')
ax.legend(loc='upper right')
plt.show()