Surfs Up!

In this project, some climate analysis is done for a trip to a long holiday vacation in Honolulu, Hawaii! And the following steps were taken:

1 - Climate Analysis and Exploration

In this analysis, Python, SQLAlchemy and Matplotlib are used for the data exploration of your climate database.

• First, use a database, hawaii.sqlite file and then
• Choose a start date and end date for the trip
• Use SQLAlchemy create_engine to connect to the sqlite database using SQLAlchemy.
• Use SQLAlchemy automap_base() to Reflect the tables into classes and save a reference to these classes

Precipitation Analysis

• Design a query to retrieve the last 12 months of precipitation data.
• Select only the date and prcp values.
• Load the query results into a Pandas DataFrame and set the index to the date column.
• Sort the DataFrame values by date.
• Plot the results using the DataFrame plot method.
• Use Pandas to print the summary statistics for the precipitation data

Station Analysis

• Design a query to calculate the total number of stations.
• Find the most active stations.
• List the stations and observation counts in descending order.
• Which station has the highest number of observations? using functions such as func.min, func.max, func.avg, and func.count.
• Retrieve the last 12 months of temperature observation data (tobs) by filter by the station with the highest number of observations.
• Plot the results as a histogram with bins=12.

2 - Climate App

Design a Flask API based on the queries.

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 the dictionary.
• /api/v1.0/stations
  • Return a JSON list of stations from the dataset.
• /api/v1.0/tobs
  • query for the dates and temperature observations from a year from the last data point.
  • Return a JSON list of Temperature Observations (tobs) for the previous year.
• /api/v1.0/ and /api/v1.0//
  • 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.

Other 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?

• Use SQLAlchemy or pandas's read_csv().
• Identify the average temperature in June at all stations across all available years in the dataset as well as for December temperature.
• Use the t-test to determine whether the difference in the means and why?, if there any statistically significant.

Temperature Analysis II

• Use 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").
• Plot the min, avg, and max temperature from your previous query as a bar chart.
• Use the average temperature as the bar height.
• Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr).

Daily Rainfall Average

• Calculate the rainfall per weather station using the previous year's matching dates.
• Calculate the daily normals. Normals are the averages for the min, avg, and max temperatures.
• Create a list of dates for the trip in the format %m-%d.
• Load the list of daily normals into a Pandas DataFrame and set the index equal to the date.
• Use Pandas to plot an area plot (stacked=False) for the daily normals.

Funke Olaleye | Data Analytics and Visualization