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https://github.com/wrighang/10_sqlalchemy-challenge

Module 10: Advanced Data Storage and Retrieval
https://github.com/wrighang/10_sqlalchemy-challenge

apiendpoints jsonify python-classes python-objects query sqlalchemy-orm sqlalchemy-python

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Module 10: Advanced Data Storage and Retrieval

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# 10_sqlalchemy-challenge



# Deliverable(s)

[Climate Analysis.ipynb](https://github.com/wrighang/10_sqlalchemy-challenge/blob/main/surfsup/climate_analysis.ipynb)



[Climate App.py](https://github.com/wrighang/10_sqlalchemy-challenge/blob/main/surfsup/climate_app.py)



## 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 outlines the requirements for this assignment.



## Requirements



### Jupyter Notebook Database Connection

To receive full credit, you must:

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

- Use the SQLAlchemy `automap_base()` function to reflect your tables into classes.

- Save references to the classes named `station` and `measurement`.

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

- Close your session at the end of your notebook.



### Precipitation Analysis

To receive full credit, you must:

- Create a query that finds the most recent date in the dataset (`8/23/2017`).

- Create a query that collects only the date and precipitation for the last year of data without passing the date as a variable.

- Save the query results to a Pandas DataFrame to create `date` and `precipitation` columns.

- Sort the DataFrame by date.

- Plot the results by using the DataFrame plot method with `date` as the x and `precipitation` as the y variables.

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



### Station Analysis

To receive full credit, you must:

- Design a query that correctly finds the number of stations in the dataset.

- Design a query that correctly lists the stations and observation counts in descending order and finds the most active station (`USC00519281`).

- Design a query that correctly finds the min, max, and average temperatures for the most active station (`USC00519281`).

- Design a query to get the previous 12 months of temperature observation (`TOBS`) data that filters by the station that has the greatest number of observations.

- Save the query results to a Pandas DataFrame.

- Correctly plot a histogram with `bins=12` for the last year of data using `tobs` as the column to count.



### API SQLite Connection & Landing Page

To receive full credit, your Flask application must:

- Correctly generate the engine to the correct sqlite file.

- Use `automap_base()` and reflect the database schema.

- Correctly save references to the tables in the sqlite file (`measurement` and `station`).

- Correctly create and bind the session between the Python app and database.

- Display the available routes on the landing page.



### API Static Routes

To receive full credit, your Flask application must include:

- A precipitation route that:

  - Returns JSON with the date as the key and the value as the precipitation.

  - Only returns the JSONified precipitation data for the last year in the database.

- A stations route that:

  - Returns JSONified data of all of the stations in the database.

- A tobs route that:

  - Returns JSONified data for the most active station (`USC00519281`).

  - Only returns the JSONified data for the last year of data.



### API Dynamic Route

To receive full credit, your Flask application must include:

- A start route that:

  - Accepts the start date as a parameter from the URL.

  - Returns the min, max, and average temperatures calculated from the given start date to the end of the dataset.

- A start/end route that:

  - Accepts the start and end dates as parameters from the URL.

  - Returns the min, max, and average temperatures calculated from the given start date to the given end date.



### Coding Conventions and Formatting

To receive full credit, your code must:

- Place imports at the top of the file, just after any module comments and docstrings, and before module globals and constants.

- Name functions and variables with lowercase characters, with words separated by underscores.

- Follow DRY (Don't Repeat Yourself) principles, creating maintainable and reusable code.

- Use concise logic and creative engineering where possible.



### Deployment and Submission

To receive full credit, you must:

- Submit a link to a GitHub repository that’s cloned to your local machine and contains your files.

- Use the command line to add your files to the repository.

- Include appropriate commit messages in your files.



### Comments

To receive full credit, your code must:

- Be well commented with concise, relevant notes that other developers can understand.



====================================================================

## CODING_PROCESS



- Referenced activity assignments and Xpert Learning Assistant for guidance throughout the challenge, paying close attention to proper indentation in the Climate App code. Some areas of guidance listed below:



### Climate Analysis



* Looked up how to convert strings to datetime objects for date calculations, ensuring accurate implementation of code. For example, 

one_year_ago = dt.datetime.strptime(most_recent_date, "%Y-%m-%d") - dt.timedelta(days=365)



* Used SQL queries with Pandas to inspect database tables. For example: data = pd.read_sql("SELECT * FROM measurement", engine)



* Referenced activities to calculate min, max, and average temperatures and reviewed histogram plotting for visualizations.



* Looked up how to create the temperate symbol (option + shift + 8 = °)



### Climate App



* Researched how to structure 404 error messages. For example:    

    if most_recent_date is None:

                return jsonify({"error": "No data available."}), 404



* The wording for the /start and /start_end routes was initially confusing, so I clarified these requirements using Xpert Learning Assistant. I provided examples in the landing page response to guide users. For example: /api/v1.0/start_end/2010-10-10_2010-12-24