/Surfs-Up

Climate analysis for planning my holiday vacation trip in Honolulu, Hawaii using SQLAlchemy.

Primary LanguageJupyter Notebook

SQLAlchemy - Surfs Up!

Climate Analysis and Exploration

Used Python (Pandas, and Matplotlib) and SQLAlchemy to do basic climate analysis and data exploration.

  • Choosed a start date and end date for my trip. My vacation range is approximately 3-15 days total.

  • Used SQLAlchemy create_engine to connect to your sqlite database.

  • Used SQLAlchemy automap_base() to reflect my tables into classes and save a reference to those classes called Station and Measurement.

Precipitation Analysis

  • Designed a query to retrieve the last 12 months of precipitation data.

  • Loaded the query results into a Pandas DataFrame and set the index to the date column.

  • Sorted the DataFrame values by date.

  • Ploting the results using the DataFrame plot method.

    precipitation

Station Analysis

  • Designed a query to calculate the total number of stations.

  • Designed a query to find the most active stations.

    • Listed the stations and observation counts in descending order.

    • Which station has the highest number of observations?

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

    • Filtered by the station with the highest number of observations.

    • Ploting the results as a histogram with bins=12.

      station-histogram

Climate App

Designed a Flask API based on the queries that I have just developed.

  • Used Flask to create my routes.