I have been experimenting with variety of the data science libraries. Though most of the time I am making sure to be using visualization modules. I have plenty of modules not covered under the Python and R. So I thought I should be making use of the tutorial approach first then I will be covering the videos too.
So What is AstroPy?
It is an interoperable astrology package. Using which you can get the pre- managed set of the data and also make sure to access them in the table or the form where you wish to access. In that context first get the astropy installed.
pip install astropy
Once installed you would have the output that is shown in the screenshot below. Some of the good features you can easily view once you start writing the code for the same.
Now what you should be using along with this astropy package? You would be most likely using the numpy, scipy, pandas, astropy and few others. Whether you choose to use the stable packages like say using the Conda environment or the current release which would be using the PIP package you can easily see the results on the same.
Once you start making use of the variety of the data with this package you would be most likely using one more package called : astroquery. This would help you make queries to the data, table and databases. So there would be something for you to review in that case. I personally love how the package works out.
python -m pip install -U --pre astroquery
Now in order for testing it out we can see the example below.
from astroquery.simbad import Simbad
result_table = Simbad.query_object("m1")
result_table.pprint()
So if we check the output for this example where we are making use of the SIMBAD, this would be something worth checking.
As you can see you have got the output. Now you can go ahead, work on your data and get the respective output as per your requirement. Some of the really good output that you are getting would be easier for you to work with. In short you have plenty of things to explore and work with and that's what we need to work around here.
I have to see the response for my attempt at working on variety of the data science libraries. If I get some good response, I would be making the video tutorials. That would help me put some good tutorials on the youtube channel named Devnami. If you like this tutorial and wish for me to continue on such libraries then you let me know about it in comments.
