| Step | √ | Requirement |
|---|---|---|
| 1 | √ | Create a new repository for this project called sqlalchemy-challenge |
| 2 | √ | Add your Jupyter notebook and app.py to this folder. These will be the main scripts to run for analysis. |
| 3 | √ | Use Python and SQLAlchemy to do basic climate analysis and data exploration of your climate database. |
| 4 | √ | Analysis should be completed using SQLAlchemy ORM queries, Pandas, and Matplotlib. |
| 5 | √ | Choose a start date and end date for your trip. Make sure that your vacation range is approximately 3-15 days total |
- You will need to join the station and measurement tables for some of the analysis queries.
- Use Flask jsonify to convert your API data into a valid JSON response object.
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Use SQLAlchemy create_engine to connect to your sqlite database. |
| 02 | √ | Use SQLAlchemy automap_base() to reflect your tables into classes and save a reference to those classes called Station and Measurement. |
| 03 | √ | Precipitation Data: Design a query to retrieve the last 12 months of precipitation data. |
| 04 | √ | Precipitation Data: Select only the date and prcp values. |
| 05 | √ | Precipitation Data: Load the query results into a Pandas DataFrame and set the index to the date column. |
| 06 | √ | Precipitation Data: Sort the DataFrame values by date. |
| 07 | √ | Precipitation Data: Plot the results using the DataFrame plot method. |
| 08 | √ | Precipitation Data: Use Pandas to print the summary statistics for the precipitation data. |
| 09 | √ | Station Analysis: Design a query to calculate the total number of stations. |
| 10 | √ | List the stations and observation counts in descending order. |
| 11 | √ | Station Analysis: Which station has the highest number of observations? |
| 12 | √ | Station Analysis: Get the last 12 months of temperature observation data (tobs) for the most active station |
| 13 | √ | Gets the min, max, and average temps for the most active station |
| 14 | √ | Station Analysis: Plot the results as a histogram with bins=12 |
| Step | √ | Requirement |
|---|---|---|
| 1 | √ | Route "" - home page |
| 2 | √ | List all routes available |
| 3 | √ | Route "/api/v1.0/precipitation" - Convert the query results to a Dictionary using date as the key and prcp as the value. |
| 4 | √ | Return the JSON representation of your dictionary. |
| 5 | √ | Route "/api/v1.0/stations" - Return a JSON list of stations from the dataset. |
| 6 | √ | Route "/api/v1.0/tobs" - query for the dates and temperature observations from a year from the last data point. |
| 7 | √ | Return a JSON list of Temperature Observations (tobs) for the previous year. |
| 8 | √ | Route "/api/v1.0/(start)" - Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start without an end. "When given the start only, calculate TMIN, TAVG, and TMAX for all dates greater than and equal to the start date." |
| 9 | √ | Route "/api/v1.0/(start)/(end)" - "When given the start and the end date, calculate the TMIN, TAVG, and TMAX for dates between the start and end date inclusive." |
| Step | √ | Requirement |
|---|---|---|
| 1 | √ | Gets the correct results for the last year of data (the last day in the dataset is 8/23/2017) |
| 2 | √ | Creates a pandas dataframe using the date and precipitation columns |
| 3 | √ | Sorts the dataframe by date |
| 4 | √ | Makes a plot using pandas with date as the x and precipitation as the y variables |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Correctly outputs the number of stations in the dataset (9) |
| 02 | √ | Correctly finds the most active station by using count (USC00519281) |
| 03 | √ | Gets the min, max, and average temperatures for the most active station (USC00519281) |
| 04 | √ | Correctly plots a histogram for the last year of data using tobs as the column to count. |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Correctly generates the engine to the correct sqlite file |
| 02 | √ | Uses automap_base() and reflects the database schema |
| 03 | √ | Correctly saves references to the tables in the sqlite file (measurement and station) |
| 04 | √ | Correctly creates and binds the session between the python app and database |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Returns the jsonified precipitation data for the last year in the database |
| 02 | √ | Returns json with the date as the key and the value as the precipitation |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Returns jsonified data of all of the stations in the database |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Returns jsonified data for the most active station (USC00519281) for the last year of data |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Route accepts the start date as a parameter from the URL |
| 02 | √ | Returns the min, max, and average temperatures calculated from the given start date to the end of the dataset |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Route accepts the start and end dates as parameters from the URL |
| 02 | √ | Returns the min, max, and average temperatures calculated from the given start date to the end date (inclusive) |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Uses the calc_temps function to get the min, max, and average temperatures for a date range of their choosing |
| 02 | √ | Uses the calculated temperatures to generate a bar chart with an error bar. |
| Step | √ | Requirement |
|---|---|---|
| 01 | √ | Calculates the min, max, and average temperatures for each day of their trip and appends them to a list. |
| 02 | √ | Creates a dataframe from the list and generates a stacked line chart plotting the min, max, and average temps for each day of their trip |
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