Each notebook is a self-contained document, and they can be explored in any order. But their real power is as a set of resources you can adjust and adapt. Once you understand them, you can learn to mix-and-match the examples demonstrated here to construct your own notebooks.
A few suggestions for possible user approaches are offered here. A complete listing can be found at the bottom.
A good place to start is on Jupyter itself, from the Jupyter_Notebooks folder.
- Jupyter's own Help menu is excellent. Be sure to notice its features.
- For Python code, notice the power of
- tab key for autocomplete suggestions after a period . showing an object's attributes and methods
- shift + tab keys for documentation on any object whose name the cursor is placed within
Primer notebooks are oriented to beginners.
Pythonic_Data_Analysis and Time_Series_Analysis are good early lessons on code-to-figures workflow.
Bonus/What to do when things go wrong.ipynb can help users throughout their journey.
For meteorology work, get oriented with Metpy_Introduction/Introduction to MetPy.ipynb
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Basic text data
- Pythonic_Data_Analysis
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NetCDF files
- netCDF/netCDF-Reading.ipynb
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Meteorology grids and streams
- Siphon/Siphon Overview.ipynb
- Bonus/Downloading GFS with Siphon.ipynb
- Bonus/Siphon_XARRAY_Cartopy_HRRR.ipynb
- Model_Output/Downloading model fields with NCSS.ipynb
- Satellite_Data/Working with Satellite Data.ipynb
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Weather observations
- Skew_T/Upper Air and the Skew-T Log-P.ipynb
- Surface_Data/Surface Data with Siphon and MetPy.ipynb
- NumPy/Numpy Basics.ipynb and NumPy/Intermediate Numpy.ipynb
- Primer/Numpy and Matplotlib Basics.ipynb
- Animation/Creating Animations.ipynb
- CartoPy/CartoPy.ipynb
- GOES_RGB_Demo/GOES_RGB_Image.ipynb
- Matplotlib/Matplotlib Basics.ipynb
- Satellite_Data/GOES_Interactive_Plot.ipynb
- Skew_T/Upper Air and the Skew-T Log-P.ipynb
- netCDF/netCDF-Writing.ipynb