sqlalchemy-challenge

Purpose

The purpose of this project was to explore and analyze the weather data in Hawaii to determine when is the best time to visit.

Step 1 - Climate Analysis and Exploration

All of the following analysis was completed using SQLAlchemy ORM queries, Pandas, and Matplotlib. (Jupyter notebook link)

• The provided starter notebook and hawaii.sqlite files were used to complete the climate analysis and data exploration.
• A chosen start date and end date for the trip. The vacation range was 3 days (07/01/18 ~ 07/03/18).
• SQLAlchemy create_engine was used to connect to the sqlite database.
• SQLAlchemy automap_base() was used to reflect the tables into classes which saved a reference to those classes called Station and Measurement.

Precipitation Analysis

• A query was designed to retrieve the last 12 months of precipitation data.
• The date and prcp values were selected.
• The query results were loaded into a Pandas DataFrame and set the index to the date column.
• The DataFrame values were sorted by date.
• The results were ploted using the DataFrame plot method.
• Pandas was used to print the summary statistics for the precipitation data.

Station Analysis

• A query was design to calculate the total number of stations.
• A query was designed to find the most active stations.
• A query was designed to retrieve the last 12 months of temperature observation data (TOBS).
  • The TOBS was filtered by the station with the highest number of observations.
  • The results as a histogram was plotted with bins=12.

Step 2 - Climate App

A Flask API was designed based on the queries which havd developed above. (Flask APP link)

• Flask was used to create the routes.

Routes

• /
  • Home page.
  • List all routes that are available.

• /api/v1.0/precipitation
  • Convert the query results to a dictionary using date as the key and prcp as the value.
  • Return the JSON representation of your dictionary.

• /api/v1.0/stations
  • Return a JSON list of stations from the dataset.

• /api/v1.0/tobs
  • Query the dates and temperature observations of the most active station for the last year of data.
  • Return a JSON list of temperature observations (TOBS) for the previous year.

• /api/v1.0/<start> and /api/v1.0/<start>/<end>

  • Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.
  • When given the start only, calculate TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.

  

  • When given the start and the end date, calculate the TMIN, TAVG, and TMAX for dates between the start and end date inclusive.

  

Further Analyses

Temperature Analysis I

• Hawaii is reputed to enjoy mild weather all year. Is there a meaningful difference between the temperature in, for example, June and December?
• The average temperature in June and December were identified at all stations across all available years in the dataset. Do the same for December temperature.
• A t-test to determine whether the difference in the means.

Temperature Analysis II

• The calc_temps function was used to calculate the min, avg, and max temperatures for your trip using the matching dates from the previous year (i.e., use "2017-01-01" if your trip start date was "2018-01-01").
• The min, avg, and max temperature were ploted from the previous query as a bar chart.
  • The average temperature was set as the bar height.
  • The peak-to-peak (TMAX-TMIN) value was set as the y error bar (YERR).

Daily Rainfall Average

• The rainfall per weather station was calculated using the previous year's matching dates.
• The daily normals, the averages for the min, avg, and max temperatures, were calculated.

Disclaimer

The resources of this master branch are only for educational purposes. All reserved rights belong to UCSD Data Science and Visualization Boot Camp.