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How would I use the dataset at http://oceancolor.gsfc.nasa.gov/DOCS/DistFromCoast/ to efficiently determine the distance of a given coordinate (lat,lng) to the nearest coastline?
It's quite a large file. Is there a library that can help with processing this kind of data?
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I want to train a model using RandomForestClassifier but there are infinite values in my data
How can I handle infinite values in the pipeline before the algorithm?
Can anyone help me?
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I have column with this kind of data:
I want to count how many times valu occur in a row. It is a string, so I want to convert this '63,63,63,63,63,63,63,63,63,63,63' to this ['63','63','63'...].
I there any way to do this quickly?
Thanks
if given string is s
l=s.split(',')
l is the required list
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Is it doable to update my trained model (fbprophet) in order to surpass the dilemma of retraining the whole dataset every time?
Thanks for your help in Advance
I found a solution using the warm-start approach, here.
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When running:
mc.run_model(tmy_data)
which air mass model is used?
https://pvpmc.sandia.gov/modeling-steps/1-weather-design-inputs/irradiance-and-insolation-2/air-mass/
how can I change to other air mass model?
Moreover, where can I find that information (what mathematical models are created in python and how to change it, to run the: mc.run_model(tmy_data).
The ModelChain documentation states:
airmass_model (str, default 'kastenyoung1989') – Passed to location.get_airmass.
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How can I generate random data in google maps like format i.e. 29.299332, 52.892959?
Do you prefer any package for this purpose?
If you just want a pair of random numbers between 0 and 90 degrees, why not just use the random package?
import random
print([random.random()*90, random.random()*90]) #[34.050498339418986, 5.622759330528135]