Climate Analysis - Hawaii

Climate analysis for a holiday vacation in Honolulu, Hawaii!

Climate Analysis and Exploration

Used Python and SQLAlchemy to do a basic climate analysis and data exploration of the climate database. All of the analysis have been completed using SQLAlchemy ORM queries, Pandas, and Matplotlib.

• Chose a start date and end date for the vacation.

• Used SQLAlchemy create_engine to connect to the sqlite database.

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

Precipitation Analysis

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

• Selected only the date and prcp values.

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

• Sorted the DataFrame values by date.

• Plotted the results using the DataFrame plot method.

  

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

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.

• Identified the station with 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.

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

  

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Step 2 - Climate App

• Designed a Flask API based on the queries.

• Used Flask to create your routes.

Routes

• /

• Home page.

• Listed all available routes.

• /api/v1.0/precipitation

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

• Returned a JSON representation of the 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.

• Calculated the TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.

• Calculated the TMIN, TAVG, and TMAX for dates between the start and end date inclusive.

• Used Flask jsonify to convert the API data into a valid JSON response object.

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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?

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

• Used the t-test to determine whether the difference in the means, if any, was statistically significant. Will you use a paired t-test, or an unpaired t-test? Why?

Temperature Analysis II

• Used the calc_temps function 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").

• Plotted the min, avg, and max temperature from your previous query as a bar chart.

• Used the average temperature as the bar height.

• Used the peak-to-peak (TMAX-TMIN) value as the y error bar (YERR).

  

Daily Rainfall Average

• Calculated the rainfall per weather station using the previous year's matching dates.

• Calculated the daily normals. Normals are the averages for the min, avg, and max temperatures.

• Using a function called daily_normals , calculated the daily normals for a specific date. The date string format %m-%d. Used all historic TOBS that matched that date string.

• Created a list of dates for the trip in the format %m-%d. Used the daily_normals function to calculate the normals for each date string and appended the results to a list.

• Loaded the list of daily normals into a Pandas DataFrame and set the index equal to the date.

• Used Pandas to plot an area plot (stacked=False) for the daily normals.