RussCob / SQLAIchemy-Project

Using Python and SQLAlchemy to do basic climate analysis and data exploration of our climate database. Then after initial analysis, designing a Flask API based on the queries that we just developed.

SQLAlchemy Project - Surfs Up!

Overview

• 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:

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.

• 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.

    

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Step 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 your dictionary.

• /api/v1.0/stations

  • Return 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.

  • Return a JSON list of temperature observations (TOBS) for the previous year.

• /api/v1.0/<start> and /api/v1.0/<start>/<end>

  • 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.

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 your trip in the format %m-%d. Use the daily_normals function to calculate the normals for each date string and append the results to a list.

• 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.