https://github.com/w7negreiros/sqlalchemy_challenge_2024

SQL Alchemy Challenge - UofT Data Analytics - Bootcamp
https://github.com/w7negreiros/sqlalchemy_challenge_2024

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SQL Alchemy Challenge - UofT Data Analytics - Bootcamp

• Host: GitHub
• URL: https://github.com/w7negreiros/sqlalchemy_challenge_2024
• Owner: w7negreiros
• Created: 2024-05-06T17:09:12.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-05-07T02:14:39.000Z (9 months ago)
• Last Synced: 2024-12-04T02:13:32.354Z (2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 519 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# sqlalchemy_challenge_2024

# Instructions

Congratulations! You've decided to treat yourself to a long holiday vacation in Honolulu, Hawaii. To help with your trip planning, you decide to do a climate analysis about the area. The following sections outline the steps that you need to take to accomplish this task.

# Part 1: Analyze and Explore the Climate Data

In this section, you’ll use Python and SQLAlchemy to do a basic climate analysis and data exploration of your climate database. 

Specifically, you’ll use SQLAlchemy ORM queries, Pandas, and Matplotlib. To do so, complete the following steps:

1- Note that you’ll use the provided files (climate_starter.ipynb and hawaii.sqlite) to complete your climate analysis and data exploration.

2- Use the SQLAlchemy create_engine() function to connect to your SQLite database.

3- Use the SQLAlchemy automap_base() function to reflect your tables into classes, and then save references to the classes named station and measurement.

4- Link Python to the database by creating a SQLAlchemy session.

# IMPORTANT

Remember to close your session at the end of your notebook.

5- Perform a precipitation analysis and then a station analysis by completing the steps in the following two subsections.

# Precipitation Analysis

1- Find the most recent date in the dataset.

2- Using that date, get the previous 12 months of precipitation data by querying the previous 12 months of data.

3- Select only the "date" and "prcp" values.

4- Load the query results into a Pandas DataFrame. Explicitly set the column names.

5- Sort the DataFrame values by "date".

6- Plot the results by using the DataFrame plot method, as the following image shows:



7- Use Pandas to print the summary statistics for the precipitation data.

# Station Analysis

1- Design a query to calculate the total number of stations in the dataset.

2- Design a query to find the most-active stations (that is, the stations that have the most rows). To do so, complete the following steps:

* List the stations and observation counts in descending order.

* Answer the following question: which station id has the greatest number of observations?

3- Design a query that calculates the lowest, highest, and average temperatures that filters on the most-active station id found in the previous query.

4- Design a query to get the previous 12 months of temperature observation (TOBS) data. To do so, complete the following steps:

* Filter by the station that has the greatest number of observations.

* Query the previous 12 months of TOBS data for that station.

* Plot the results as a histogram with bins=12, as the following image shows:



5- Close your session.

# Part 2: Design Your Climate App

Now that you’ve completed your initial analysis, you’ll design a Flask API based on the queries that you just developed. To do so, use Flask to create your routes as follows:

1- /

* Start at the homepage.

* List all the available routes.

2- /api/v1.0/precipitation

* Convert the query results from your precipitation analysis (i.e. retrieve only the last 12 months of data) to a dictionary using date as the key and prcp as the value.

* Return the JSON representation of your dictionary.

3- /api/v1.0/stations

* Return a JSON list of stations from the dataset.

4- /api/v1.0/tobs

* Query the dates and temperature observations of the most-active station for the previous year of data.

* Return a JSON list of temperature observations for the previous year.

5- /api/v1.0/<'start'> and /api/v1.0/<'start'>/<'end'>

* Return a JSON list of the minimum temperature, the average temperature, and the maximum temperature for a specified start or start-end range.

* For a specified start, calculate TMIN, TAVG, and TMAX for all the dates greater than or equal to the start date.

* For a specified start date and end date, calculate TMIN, TAVG, and TMAX for the dates from the start date to the end date, inclusive.

# Hints

* Join the station and measurement tables for some of the queries.

* Use the Flask jsonify function to convert your API data to a valid JSON response object.