Climate Analysis

• I decided to treat myself to a long holiday vacation in Honolulu, Hawaii. To help with my trip planning, I decided to do a climate analysis about the area. The following sections outline the steps that I needed to take to accomplish this task.

Part 1: Analyze and Explore the Climate Data

• I used Python and SQLAlchemy to do a basic climate analysis and data exploration of your climate database. Specifically, I used SQLAlchemy ORM queries, Pandas, and Matplotlib.

For Precipitation Analysis:

• I found the most recent date in the dataset.
• Using that date, I got the previous 12 months of precipitation data by querying the previous 12 months of data.
• I selected only the "date" and "prcp" values.
• I loaded the query results into a Pandas DataFrame. Explicitly set the column names.
• I sorted the DataFrame values by "date".
• I plotted the results by using the DataFrame plot method, as the following image shows in the climate starter ipynb

• Used Pandas to print the summary statistics for the precipitation data.

For station Analysis:

• I designed a query to calculate the total number of stations in the dataset.

• Designed a query to find the most-active stations (that is, the stations that have the most rows).

• To do so, I completed the following steps:

  • Listed the stations and observation counts in descending order.
  • Answered the following question: which station id had the greatest number of observations?

• Designed a query that calculates the lowest, highest, and average temperatures that filters on the most-active station id found in the previous query.

• Designed a query to get the previous 12 months of temperature observation (TOBS) data.

• To do so, I completed the following steps:

  • Filtered by the station that had the greatest number of observations.
  • Queried the previous 12 months of TOBS data for that station.
  • Plotted the results as a histogram with bins=12, as the following image shows in climate starter ipynb

  

• Closed my session.

Part 2: Design my Climate App

I designed a Flask API based on the queries that you just developed. To do so, use Flask to create your routes as follows:

• 1. /
  • Started at the homepage.
  • Listed all the available routes.

  

• 2. /api/v1.0/precipitation
  • Converted the query results from my precipitation analysis (i.e. retrieved only the last 12 months of data) to a dictionary using date as the key and prcp as the value.
  • Returned the JSON representation of my dictionary.

  

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

• 4. /api/v1.0/tobs
  • Queried the dates and temperature observations of the most-active station for the previous year of data.
  • Returned a JSON list of temperature observations for the previous year.

  

• 5. /api/v1.0/ and /api/v1.0//
  • Returned a JSON list of the minimum temperature, the average temperature, and the maximum temperature for a specified start or start-end range.
  • For a specified start, I calculated TMIN, TAVG, and TMAX for all the dates greater than or equal to the start date.

  

  • For a specified start date and end date, I calculated TMIN, TAVG, and TMAX for the dates from the start date to the end date, inclusive.