Surf's Up an sql-alchemy-challenge

Climate analysis of Hawaii using sqlalchemy to access sqlite and Matplotlib and Pandas and setting up JSON responses via flask

Step 1 - Climate Analysis and Exploration - SQLAlchemy ORM queries, Pandas, and Matplotlib - (Hawaii_climate_analysis.ipynb)

To begin set-up the dependancies required for the task at hand. These are standard libraries used in data analysis:

matplotlib libraries

%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt

Next were numpy, pandas and date time

import numpy as np
import pandas as pd
import datetime as dt

Reflect Tables into SQLAlchemy ORM

** Python SQL toolkit and Object Relational Mapper **

import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, inspect, desc

Now to set up for a series of queries using session objects

set up engine for sqlite database stored in the Resources folder

    engine = create_engine("sqlite:///Resources/hawaii.sqlite",echo=False)

Reflect an existing database into a new model

    Base = automap_base()

Reflect the tables

   Base.prepare(engine, reflect=True)

Save references to each table in the sqlite DB that we want to access

    Meas = Base.classes.measurement
    Sta = Base.classes.station

After the set-up is created session queries were run

-Precipitation

Precipitation data for the 12 month period ending 2017-8-23
Dates and rainfall amounts selected
The values were sorted by date in a Pandas dataframe
A plot was created using Matplotlib
Pandas was used to generate summary statistics

-Weather Stations

Calculated the total number of stations
List the stations and observation counts in descending order (tobs)
Select the most active station in terms of observations made (Waikiki was the result)
Retrieve the last 12 months of temperature observation data from the most active site
Plot the results as a histogram with bins=12

Step 2 - Climate App - Sdesign a Flask API based on the queries just developed (app.py)

Import flask and jsonify libraries

    from flask import Flask, jsonify

Set-up Flask to create the APIs for the climate app

    app = Flask(__name__)

Setup routes as defined in instructions and return Jsonified data to the user

     @app.route("/")
    def welcome():
        """List all Available API routes - these routes were defined in the instructions"""
        return (
            f"Plan your trip to  Hawaii using this Climate Analysis API!<br/>\
            Note: To look at temps please specify start and end date as yyyy-mm-dd<br/>\
            Dates must be between 2010-01-01 and 2017-08-23<br/>\
            Query returns Min, Avg, Max  <br/>\
                <br/>"
            f"Available Pages:<br/>"
            f"/api/v1.0/precipitation<br/>"
            f"/api/v1.0/stations<br/>"
            f"/api/v1.0/tobs<br/>"
            f"/api/v1.0/temp/start<br/>"
            f"/api/v1.0/temp/start/end"
        )

Complete code can be found in app.py files

Bonus data can be found in the Jupyter Notebook as well

SJLimburg/sql-alchemy-challenge