Vacation Planning with SQLAlchemy

Background

Surfs Up! I've decided to treat myself to a long holiday vacation in Honolulu, Hawaii! I'll be spending 10 sweet days in paradise and only want the best weather while I'm there. To help with my trip planning, I need to do some climate analysis on the area based off of weather data from the previous years. Here's what I did to make sure my trip was a success!

Step 1 - Climate Analysis and Exploration

To begin, I used Python and SQLAlchemy to do climate analysis and data exploration of my climate database. I then used create_engine to connect to sqlite database and automap_base() to reflect my tables into classes and save a reference to those classes called Station and Measurement.

• Libraries used: SQLAlchemy ORM, Pandas, and Matplotlib.
• Data source: hawaii.sqlite
• Tool Used: Jupyter Notebook

Precipitation Analysis

Here is what I did to get precipitation data:

• 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.

Precipitation plot over the last 12 months:

Based off of the plot below, heaviest rainfall happened around September, February, April, and July which is perfect because the plan is to go in June! The June rainfall isn't the lowest as compared to around March and December but the chances of it ruining the trip is low.

Summary Statistics:

Despite the high peeks of rainfall in the plot above and the max recorded rainfall of 6.7 inches displayed below, the average is very minimal. Even in the 75% quartile only has rainfall at 0.13 inches.

Station Analysis

Here is what I did to get station data:

• Design a query to calculate the total number of stations.
• Design a query to find the most active stations.
• List the stations and observation counts in descending order.
• Which station has the highest number of observations?
• Design a query to retrieve the last 12 months of temperature observation data (TOBS).
• Filter by the station with the highest number of observations.
• Plot the results as a histogram with bins=12.

Recorded Temperatures from Station WAIHEE 837.5(USC00519281):

This weather station had the top temperature recordings of 2,772 observations. The lowest recorded temperature was 54°F, the highest wass 85°F, and the average for teh year was 71.7°F. The highest recorded temperature had about 65 observations. Since this location recorded the most temperatures, there is a stronger reasurance in its accuracy for this trip.

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Additional Analysis

• What I've found out so far is great but I wanted to look into some more information!

Temperature Analysis II

My goals for this analysis were:

• The starter notebook contains a function called calc_temps that will accept a start date and end date in the format %Y-%m-%d. The function will return the minimum, average, and maximum temperatures for that range of dates.
• 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).

Temperature Forecast for My Trip:

I wanted to schedule the trip for June 20 to June 30 so a prepared function labelled 'calc_temps' was used to forecast what the temperature might be based off of previous year's data. Below displays what the potencial temperature might be. The lowest temperature is 71°F, average is 77°F, and highest 82°F which would be perfect!

Daily Rainfall Average

My goals for this analysis were:

• 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.
• Used the function provided called daily_normals that will calculate the daily normals for a specific date. This date string was in the format %m-%d. All the historic TOBS was used that match that date string.
• Create a list of dates was created for my trip in the format %m-%d. Used the daily_normals function to calculate the normals for each date string and append the results to a list.
• Use daily normals and save it 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.

Precipitation Forecast:

The the potentical rain for this trip confirms what was seen in the first plot above which was minimal. Based off of Station Manoa Lyon Arbo 785.2 (USC00516128), the rain fall recorded was 0.50 inches. So with all the information gathered, it looks like my 10 day trip starting June 20th would be a great time to go!

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