/SQLAlchemy-Climate-Analysis

sqlalchemy

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SQLAlchemy Climate Analysis

surfs-up.png

Step 1 - Climate Analysis and Exploration

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

  • I choose a start date and end date for my trip. I make sure that my vacation range was approximately 3-15 days total.

  • I used SQLAlchemy create_engine to connect to my sqlite database as well as, SQLAlchemy automap_base() to reflect my tables into classes and save 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.

  • Ploted the results using the DataFrame plot method.

    precipitation

  • 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.

    • Showed which station has 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.

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

      station-histogram

Step 2 - Climate App

After having completed my initial analysis, I design a Flask API based on the queries that I have just developed.

  • Used Flask to create my 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 your dictionary.

  • /api/v1.0/stations

    • Returned a JSON list of stations from the dataset.

  • /api/v1.0/tobs

    • Queryd 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, 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.

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. Did the same for December temperature.

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

Temperature Analysis II

  • The started 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.

  • 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").

  • Ploted 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).

      temperature

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.

  • I was provided with a function called daily_normals that will calculate the daily normals for a specific date. This date string will be in the format %m-%d. I was sure to use all historic TOBS that match that date string.

  • Created a list of dates 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.

  • 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.

    daily-normals