The goal of this project was to review and analyze temperature and precipitation measurements across Hawaii. The data was provided in a sqlite database. I used sqlalchemy in python to connect to the database and query for data.
The goal of this project was to create a database shema and import data from the six .csv files provided of employee and department data. Once created, a series of queries were preformed to analyze the employee data.
The following queries were performed as part of the analysis:
- Precipitation Analysis
- Find last day of data available in database
- Gather precipitation data for all locations for the last year of data in the database
- Plot precipitation data
- Perform summary stats on the precipitation data (mean, mode, avg, etc)
- Station Analysis
- Find total number of stations with climate data
- Find most active stations (stations with most measurements)
- Gather temperature data for last years worth of measurements for stations Plot data in histogram
- Temperature Analysis I
- Gather temperature data for all stations for the Months of June and December
- Perform a paired T-test to see if the temperatures are similar for the months
- Temperature Analysis II
- Gather temperature data between 2/20/2017 - 3/02/2017
- Plot average temperature for time frame as a bar chart
- Include standard error of max and min temps
With the initial analysis performed above, I created a Flask API that performs the following:
- Home page that displays available routes
- /api/v1.0/precipitation - returns dictionary of daily precipitation values for each station
- /api/v1.0/stations - returns list of all stations in database
- /api/v1.0/tobs - returns list of temperature observations for previous year available
- /api/v1.0/ - takes a start date as input and returns list of min, max and avg temperatures between start date and last date available in database
- /api/v1.0// - takes a start and end date as input and returns a list of min, max and avg temperatues between dates provides
- climate_analysis.ipynb - jupyter notebook file for analysis functions
- app.py - python file with FLASK api
- Resources - folder containing the data for project
- Images - images of charts created during anaylsis