10_sqlalchemy-challenge

Deliverable(s)

Climate Analysis.ipynb

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.

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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