Surfs Up
Project Overview
Purpose
The purpose of this project is to analyze Oahu's temperature data for June and December to determine if a surf and ice cream shop is sustainable year-round. W. Avy and the investors want to open a surf shop and have requested information about temperature trends. Additionally, I have created an API to allow W. Avy and the board of directors to access the analysis easily. The endpoints show the Precipitation, Stations, Monthly Temperature, and Statistics.
Results
Summary Statistics
As expected, the December temperatures is lower when compared to June as the Winter season has colder climate conditions and the Summer season has warmer climate conditions.
Summary Statistics for June
| tobs | |
|---|---|
| count | 1700 |
| mean | 74.9441 |
| std | 3.25742 |
| min | 64 |
| 25% | 73 |
| 50% | 75 |
| 75% | 77 |
| max | 85 |
- Standard deviation: 3.26
- Minimum temperature: 64 °F
- Average temperature: 75 °F
- Maximum temperature: 85 °F
Summary Statistics for December
| tobs | |
|---|---|
| count | 1517 |
| mean | 71.0415 |
| std | 3.74592 |
| min | 56 |
| 25% | 69 |
| 50% | 71 |
| 75% | 74 |
| max | 83 |
- Standard deviation: 3.75
- Minimum temperature: 56 °F
- Average temperature: 71 °F
- Maximum temperature: 83 °F
Climate Analysis API
Available Routes
/api/v1.0/precipitation
/api/v1.0/stations
/api/v1.0/tobs
/api/v1.0/temp/{start}/{end}
Summary
- Temperature
- The minimum temperature in December is lower when compared to June
- The average temperature in June is higher when compared to December
- The maximum temperature in June is slightly high when compared to December
Additionally, a query to determine the temperature between a range of dates could provide additional insight. For example, a function like this would output the results between two specified dates:
def calc_temps(start_date, end_date):
start_date = datetime.strptime(start_date, "%Y-%m-%d")
end_date = datetime.strptime(end_date, "%Y-%m-%d")
temperature_results = (
session.query(
func.min(Measurement.tobs),
func.avg(Measurement.tobs),
func.max(Measurement.tobs),
)
.filter(Measurement.date >= start_date)
.filter(Measurement.date <= end_date)
.all()
)
# return minimum, average, and max temperature
return temperature_results[0]It would be interesting to analyze various dates daily, monthly, yearly, or by seasons and conduct further analysis. Then, a plot could be created to visualize, illustrate and determine any relationships within the data.
Resources
- Data Source:
hawaii.sqlite - Source Code:
climate_analysis.ipynb,SurfsUp_Challenge.ipynb - Software:
Python 3.6.10,Jupyter Notebook,JetBrains DataSpell - Libraries:
Pandas,Matplotlib,SQLAlchemy,Flask,SQLite