Reference no: EM132397378
SQLAlchemy Homework.
Python, SQLAlchemy, Pandas, Flask API
## Step 1 - Climate Analysis and Exploration
All of the following analysis should be completed using Python, SQLAlchemy ORM queries, Pandas, FLASK, and Matplotlib.
Use Python and SQLAlchemy to do basic climate analysis and data exploration of your climate database.
Two starter files have been provided.
1) pandas_starter.ipynb
2) app.py
Two directories with datafiles have been provided;
1) Images
2) Resources
Steps 1-11 have been completed, need help with steps 12-17.
12) Design a query to find the most active stations.
12a) List the stations and observation counts in descending order, and display top 3.
12b) Which station has the highest number of observations? Display results.
12c) Hint: You may need to use functions such as `func.min`, `func.max`, `func.avg`, and `func.count` in your queries.
13) Design a query to retrieve the last 12 months of temperature observation data (tobs).
13a) Filter by the station with the highest number of observations, and display top 3.
13b) Plot the results as a histogram with `bins=12`.
13c) Use Pandas to plot/display/save query as a bar chart.
13d) Save the plot as Images/station-histogram_myPlot.png
# Example of expected output [station-histogram](Images/station-histogram.png)
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## Step 2 - Climate App
Now that you have completed your initial analysis, design a Flask API based on the queries that you have just developed.
14) Use FLASK to create your routes in filenamed app.py.
### Routes
* `/`
* Home Webpage.
* List all routes that are available.
* `/api/v1.0/precipitation`
* Convert the query results to a Dictionary using `date` as the key and `prcp` as the value.
* Return the JSON representation of your dictionary.
* `/api/v1.0/stations`
* Return a JSON list of stations from the dataset.
* `/api/v1.0/tobs`
* query for the dates and temperature observations from a year from the last data point.
* Return a JSON list of Temperature Observations (tobs) for the previous year.
* `/api/v1.0/<start>` and `/api/v1.0/<start>/<end>`
* Return a JSON list of the minimum temperature, the average temperature, and the max temperature for a given start or start-end range.
* When given the start only, calculate `TMIN`, `TAVG`, and `TMAX` for all dates greater than and equal to the start date.
* When given the start and the end date, calculate the `TMIN`, `TAVG`, and `TMAX` for dates between the start and end date inclusive.
## Hints
* 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.
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## Step 3 - Analysis
15) Temperature Analysis I
* Use Pandas's `read_csv()` to perform this portion.
* Hawaii is reputed to enjoy mild weather all year. Is there a meaningful difference between the temperature in, for example, June and December?
* Identify the average temperature in June at all stations across all available years in the dataset. Do the same for December temperature.
* Use the t-test to determine whether the difference in the means, if any, is statistically significant. Will you use a paired t-test, or an unpaired t-test? Why?
16) Temperature Analysis II
* Use Pandas's to perform this portion.
* The starter notebook contains a function called `calc_temps` that will accept a start date and end date in the format `%Y-%m-%d` and return the minimum, average, and maximum temperatures for that range of dates.
* Use the `calc_temps` function to calculate the min, avg, and max temperatures for your trip using the matching dates from the previous year (i.e., use "2017-01-01" if your trip start date was "2018-01-01").
* Plot the min, avg, and max temperature from your previous query as a bar chart.
* Use the average temperature as the bar height.
* Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr).
* Use Pandas to plot/display/save query as a bar chart.
# Example of expected output ![temperature](Images/temperature.png)
17) Daily Rainfall Average
* Use Pandas's to perform this portion.
* Calculate the rainfall per weather station using the previous year's matching dates.
* Calculate the daily normals. Normals are the averages for the min, avg, and max temperatures.
* You are provided with a function called `daily_normals` that will calculate the daily normals for a specific date. This date string will be in the format `%m-%d`. Be sure to use all historic tobs that match that date string.
* Create a list of dates for your trip in the format `%m-%d`. Use the `daily_normals` function to calculate the normals for each date string and append the results to a list.
* Load the list of daily normals into a Pandas DataFrame and set the index equal to the date.
* Use Pandas to plot/display/save an area plot (`stacked=False`) for the daily normals.
# Example of expected output ![daily-normals](Images/daily-normals.png)