Skip to content

Latest commit

 

History

History
97 lines (69 loc) · 5.16 KB

README.md

File metadata and controls

97 lines (69 loc) · 5.16 KB

SQLAlchemy - Surfs Up!

Overview:

Decided to treat yourself to a long holiday vacation in Honolulu, Hawaii! To help with your trip planning, you need to do some climate analysis on the area.


Step 1 - Climate Analysis and Exploration:

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

  • Used the provided starter notebook and hawaii.sqlite files to complete the climate analysis and data exploration.
  • Choose a start date and end date for the trip. The vacation range is approximately 3-15 days total.
  • 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.
    • Which station has the highest number of observations?
    • Hint: Used functions such as func.min, func.max, func.avg, and func.count in the 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.

Step 2 - Climate App:

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

  • Used Flask to create your routes.

Routes:

  • /

    • Home page.
    • Listed 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 the dictionary.

  • /api/v1.0/stations

    • Returned a JSON list of stations from the dataset.

  • /api/v1.0/tobs

    • Query 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/ and /api/v1.0//

    • 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, calculated TMIN, TAVG, and TMAX for all dates greater than and equal to the start date.
    • When given the start and the end date, calculated the TMIN, TAVG, and TMAX for dates between the start and end date inclusive.

Bonus: Other Recommended Analysis:

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?
  • Used pandas's read_csv() to perform this portion.
  • Identified the average temperature in June at all stations across all available years in the dataset. Do the same for December temperature.
  • Used the t-test to determine whether the difference in the means. Will you use a paired t-test, or an unpaired t-test? Why?

Temperature Analysis II:

  • The starter notebook contained 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.
  • 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.
  • 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.


Tech Environment Used:

SQLAlchemy ORM queries, Pandas, Juputer Notebook, Matplotlib.