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├── data # Data files are CSV since that ensures easy github reability; the data files are not large, so CSV are okay for this repository- and can be easily read on github
├── images # Images saved from the notebooks to use in this README
├── notebooks # The analyses are conducted and results are shown in the notebooks
├── LICENSE
├── README.md
├── functions.py # The functions used in the notebook are stored in functions.py
├── poetry.lock # poetry file for package version management
└── pyproject.toml # poetry file for package version management
- poetry is used for package dependency management
Gardening is the inspiration for this repo. I'm planning to move my garden more than an hour away, in a different micro climate, and I want to explore the differences. Rather than ad hoc analyses, this time I'd like to build out standardized analyses that others could also reuse when importing data for different locations.
- Gather data and save the data in the data folder
- For US postal codes, go to NOAA
- Read more about this data at climate.gov
- Read my tutorial at medium's Towards Data Science archive
- Navigate to the
notebooksfolder for some prepared analyses
- Open
exploratory_data_analysis.ipynbfor some initial data exploration and wrangling. For example, explore the date ranges of each weather station in the data and display the weather stations on a map. - Compare temperature data
compare_temperature.ipynb - (WIP) Compare precipitation data
compare_rainfall_across_locations.ipynb
First let's look at the maximum temperatures between two locations for a year.
And then, let's count the number of "Great Days" where the maximum temperature is in between my favorite range. The range is a parameter so we can experiment with other ranges too.
The USDA Plant Hardiness Zones are calculated using minimum annual temperatures over a 30 year period, averaged. You may explore and download maps created with that data from 2023 at planthardiness.ars.usda.gov/.
You may also download the shapefiles and create your own maps from PRISM, as long as you provide credit to Oregon State University and the USDA like this:
The map is a good reference, but it is also helpful to see the yearly variation of minimum temperature for each year by location:
And then, we can calculate the hardiness zones over a rolling average of 30 years. PRISM updated their maps in 2023, and previously in 2012, but you may perform the most up-to-date calculations if you have the data:
