This project is about climate analysis on Hawaii. The following outlines what you need to do.

Used Python and SQLAlchemy to do basic climate analysis and data exploration of my climate database. All of the following analysis were completed using SQLAlchemy ORM queries, Pandas, and Matplotlib.

• Sart and end date chose for my trip (analysis).

• Used SQLAlchemy create_engine to connect to my sqlite database.

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

• 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.

• Sort the DataFrame values by date.

• Plotted the results using the DataFrame plot method.

  

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

• 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.

• Found out the station has the highest number of observations with function such as func.min, func.max, func.avg, and func.count in my queries

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

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

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

  

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

the station and measurement tables were joined.

• /

  • Home page.

  • All routes that are available.

• /api/v1.0/precipitation

  • Converted the query results to a dictionary using date as the key and prcp as the value.

  • Returned the JSON representation of my dictionary.

• /api/v1.0/stations

  • Returned a JSON list of stations from the dataset.

• /api/v1.0/tobs

  • Queried the dates and temperature observations of the most active station for the last year of data.

  • Returned a JSON list of temperature observations (TOBS) for the previous year.

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

  • Returned 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.

• Identified the average temperature in June at all stations across all available years in the dataset. Same for December temperature.

• Used the t-test to determine whether the difference in the means, if any, is statistically significant.

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