Predicting Meteor Showers Using Python and Visual Studio Code

Explore the fascinating world of meteor shower prediction using Python and Visual Studio Code in this 50-minute tutorial inspired by the Netflix Original "Over the Moon." Dive into data science techniques for forecasting celestial events, learning how to manipulate and analyze astronomical data. Set up a local environment, import necessary libraries, and work with real-world datasets to predict viewable constellations and meteor showers. Gain insights into data cleansing, datetime conversions, and creating prediction functions. Follow along as the instructor demonstrates how to determine optimal viewing dates based on moon phases and city locations. No prior coding experience is required, making this an accessible introduction to applying data science in astronomy.

- Introduction and agenda.
- Local environment setup.
- Import libraries and data.
- Explore and understand data.
- Define goal of the data exploration.
- Convert months from strings to ints.
- Convert month and day to datetime.
- Question: Why might you manipulate one column at a time?.
- Continue converting month and day to datetime.
- Question: How does Pandas datetime conversion handle cyclical data?.
- Convert hemisphere strings to ints.
- Convert moon phase strings to decimals.
- Cleanse data by dropping unneeded columns.
- Fill NaN values in moon phase column .
- Cleanse data by dropping unneeded columns.
- Review cleansed data.
- Begin creating the prediction function.
- Check to see if city is in list.
- Get the latitude of the city.
- List of constellations that are viewable.
- Return if no constellations are viewable.
- Determine which meteor showers are viewable.
- Gather moon phases for desired dates.
- Question: Why use an "&" operator and not "|".
- Determine best date based on moon phases.
- Test the new code.
- Add in Chang'e's meteor shower data.
- Recap.